The Great Crash of 1929 ||

The Great Crash of 1929A Reconciliation of Theory and EvidenceAli KabiriISBN: 9781137372895DOI: 10.1057/9781137372895Palgrave Macmillan

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The Great Crash of 1929

Ali Kabiri

A Reconciliation of Theory and Evidence

The Great Crash of 1929

10.1057/9781137372895 - The Great Crash of 1929, Ali Kabiri

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Palgrave Studies in the History of Finance

Series Editors: Adrian R. Bell, D’Maris Coffman, Tony K. Moore

The study of the history of fi nancial institutions, markets, instruments and con-cepts is vital if we are to understand the role played by fi nance today. At the same time, the methodologies developed by fi nance academics can provide a new perspective for historical studies. Palgrave Studies in the History of Finance is a multi- disciplinary effort to emphasise the role played by fi nance in the past, and what lessons historical experiences have for us. It presents original research, in both authored monographs and edited collections, from historians, fi nance academics and economists, as well as fi nancial practitioners.

Titles include:

D’Maris CoffmanEXCISE TAXATION AND THE ORIGINS OF PUBLIC DEBT

Duncan NeedhamUK MONETARY POLICY FROM DEVALUATION TO THATCHER, 1967– 1982

Ali KabiriTHE GREAT CRASH OF 1929

Palgrave Studies in the History of Finance seriesSeries Standing Order ISBN: 978– 1–137– 34224–9(outside North America only)

You can receive future titles in this series as they are published by placing a stand-ing order. Please contact your bookseller or, in case of diffi culty, write to us at the address below with your name and address, the title of the series and the ISBN quoted above.Customer Services Department, Macmillan Distribution Ltd, Houndmills, Basingstoke, Hampshire RG21 6XS, England


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The Great Crash of 1929A Reconciliation of Theory and Evidence

Ali KabiriLecturer in Economics, University of Buckingham, UK


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© Ali Kabiri 2014

All rights reserved. No reproduction, copy or transmission of thispublication may be made without written permission.

No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6– 10 Kirby Street, London, EC1N 8TS.

Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages.

The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988.

First published 2014 by PALGRAVE MACMILLAN

Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS.

Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY10010.

Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world.

Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries.

ISBN 978– 1– 137– 37288– 8

This book is printed on paper suitable for recycling and made from fullymanaged and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin.

A catalogue record for this book is available from the British Library.

A catalog record for this book is available from the Library of Congress.

Typeset by MPS Limited, Chennai, India.


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To my parents Mahin and Mohammad


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vii

Contents

List of Figures ix

List of Tables xi

Preface xiii

Acknowledgements xv

Prologue 1The 1920s US stock market and the evolution of finance theory: the emergence of investment science 7The estimation of the longevity of dividend income 8New ideas on risk and uncertainty 8

1 Introduction 17

2 Literature Review and Methodology 312.1 Modern literature related to the 1920s 312.2 ‘Bubbles’ 372.3 Methodology 49

3 The US Economy and the Financial System 583.1 The boom and bust of the US stock market 583.2 The real economy 613.3 The Gold Standard 643.4 Monetary dynamics and the US stock market 713.5 The Federal Reserve in its first fifteen years of operation 85

4 The Returns to US Common Stocks from 1871 to 2010 944.1 Measuring the ‘fundamental’ value of the US

stock market 944.2 The realised return on stocks from the 1920s to 2010 1054.3 A growth model to value new technology stocks 1084.4 The formation of closed- end funds 1274.5 The 1927– 9 phase of the boom 131

5 The October Crash of 1929 and the NYSE Credit System 1455.1 Financial stability and the NYSE credit system 1455.2 Money market leverage for Common Stock trading 1495.3 The October crash of 1929 163


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viii Contents

6 The Great Contraction of 1929– 1932 and the Value of Stocks 174

7 Conclusions 187

Appendix: Results of cross- sectional tests 206

Notes 210

Bibliography 217

Index 229


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ix

List of Figures

0.1 Consumer Price Index ( 1825– 1929) 15

1.1 Prices and Dividend Index (Cowles) (Base = 100, Jan. 1913) 21

3.1 Prices and Dividend Index (Cowles) (Base = 100, Jan. 1913) 59

3.2 US Treasury/central bank gold reserves (metric tonnes) 72

3.3 US money supply ( 1899– 1931) 73

3.4 US money supply growth ( 1900– 1931) 73

3.5 Private debt levels by sector ( 1900– 1934) 75

3.6 US consumer prices ( 1900– 1952) 77

3.7 US Unskilled Labour Wage Index ( 1900– 1944) 78

3.8 Nominal GDP ( 1900– 1939) 78

3.9 Real GDP ( 1895– 1945) 79

3.10 Real and Nominal Dividend Index for US stocks ( 1900– 1933) 79

3.11 Monthly returns (Cowles Index) ( 1915– 1927) 81

3.12 US long- term Government Bond yield ( 1900– 1940) 83

3.13 Bank rate – Federal Reserve Bank of New York (1915–1937) 87

4.1 Index of airplane manufacturing stocks ( 1928– 1933) 127

4.2 Volume of shares traded on NYSE (millions/month) 140

4.3 Fundamental changes and non- fundamental component of P/D ratio changes 142

5.1 Volume of lending via ‘others’ 151

5.2 Total demand and time loans ( 1926– 1931) 152

5.3 Profits from corporate lending to NYSE 156

5.4 Profit from covered interest arbitrage £ bank rate / $ call rate 161

5.5 Bank of England and NY Fed discount rates 166

5.6 Money market rates (October 1929) 167

5.7 Daily NYSE volume (1929) 168


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x List of Figures

5.8 Ratio of Cowles (1938) Index to Credit Index 172

6.1 Index of US business activity (1899–1937) 175

6.2 Nominal dividends and prices 177

6.3 Real dividends and earnings (1929–1935) 178

6.4 Long- term bond yields (1920–1936) 179

6.5 P/D ratio ( 1926– 1935) 180

6.6 Book to market ratio of CRSP stocks ( 1926– 1946) 181

6.7 Monthly change in Consumer Price Index 184

6.8 US House Price Index ( 1900– 1935) 185


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xi

List of Tables

0.1 Smith (1924) estimates of return premium on stocks 13

4.1 Individual sector growth rates 98

4.2 Average P/D ratio for US stocks (Commercial & Financial Chronicle), September 1929 100

4.3 Annual S.D. of stock and bond indexes and actual and implied returns 101

4.4 P/D ratio for different risk premiums and growth rates for US stocks 103

4.5 P/D ratio for different risk premiums and growth rates for US stocks 104

4.6 Conversion table for Investment Fund A 106

4.7 1903 data on auto firms sales and profits (Seltzer, 1928) 119

4.8 Stages and inputs of the growth model 120

4.9 Results of sensitivity analysis 123

4.10 Automobile industry growth data 125

4.11 Auto high prices from September (Week 2) (Commercial & Financial Chronicle, 1929) 126

4.12 Results 131

4.13 Results (risk premium = 3.6 per cent) 135

5.1 Securities loans in 1929 (millions of dollars) 167

A.1 Results of regression of percentage change in P/D ratio from 1927– 9 (DEP) with earnings growth rates from 1927– 9 206

A.2 Results of regression of percentage change in P/D ratio from 1928– 9 with annual earnings growth rates from 1928– 9 206

A.3 Results of regression of percentage change in P/D ratio from 1927– 8 (DEP) and annual earnings growth rates from 1927– 8 206


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xii List of Tables

A.4 Results of regression of percentage change in P/D ratio from 1927– 8 (DEP) with annual dividend growth rates from 1927– 8 207

A.5 Results of regression of percentage change in P/D ratio from 1927– 9 on dividend growth rates from 1927– 9 207

A.6 Results of regression of percentage change in P/D ratio from 1927– 9 with dividend growth rates from 1924– 7 207

A.7 Results of regression of percentage change in P/D ratio from 1927– 9 on earnings growth rates from 1924– 7 208

A.8 Results of regression of percentage change in P/D ratio from 1927– 8 with age, net current assets, market capitalisation (size) in 1927 and percentage change in P/D ratio in 1926– 7 208

A.9 Results of regression of percentage change in market capitalisation from 1927– 9 on net current assets, age, and risk rating 208


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xiii

Preface

This book aims to produce in as detailed a way as possible a guide to the American stock market boom and crash of the 1920s and 1930s. We do this using a new approach to the study of financial history, known as historical finance, which follows the Cliometric Revolution, but aims to blend the use of historical study with more advanced financial econometric techniques, which are grounded in contemporary models. By studying the evolution of investment theory and through research on the models used at the time, we can value the stock market through the lens of the investors of the 1920s.

Using the statistical tests of the present day, but applied within the context of the models investors were known to have been using, we are able to draw clearer conclusions about what investors did and did not know about stock pricing in the 1920s. From this position, we are able to offer new ideas about how valuations were formed and make more informed judgements about whether the market was overval-ued. Investors were keenly aware of what we would consider today as advanced financial data and tools of valuation analysis, as well as data on long- term returns to stocks relative to bonds.

There are three main areas of innovation in the book. First, it contains hand- collected data from source manuals from the 1900 to 1930 period on the dividend growth rates of US Common Stock, which allows the study of the returns to investing in stocks in the USA over the long term.

The second major innovation is to reconstruct valuation models of high technology stocks in the 1920s for the aviation industry. This industry is most often associated with the boom and we calibrate a valuation model based on the growth path of earlier new technology industry. Both of these tests are based on the actual models investors were using at the time, and the discovery of new evidence for these models is a major step forward in the field. It represents an addition to the literature on the evolution of finance theory, as well as a good way of estimating how investors would have valued stocks. The approach therefore stands in diametric opposition to the use of modern tools of valuation in research of historical episodes. The new approach allows us to make more informed judgements about ‘rationality’ in the sense that we do not measure investors’ behaviour by our yardsticks alone. The existence of the models also indicates the level of sophistication of


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xiv Preface

valuation in the period, even in the event that we find a deviation from fundamentals, which we estimate.

The third innovation is to use the returns to a new type of investment vehicle, which emerged following a new study on the returns to stocks, which appeared in the 1920s. We follow the performance of an index- tracking fund, Investment Trust Fund ‘A’ from 1925 to 2010 in order to find ex- post returns to investing in a diversified basket of US stocks. This method tracks the representative investor’s returns through merger and takeover of the fund through its history and is free from survival bias. We are therefore able to assess whether the use of such a fund would have made sense to the average investor who sought to reduce volatility of returns and vitally, whether returns over the long run after the 1920s indicate that higher expectations of returns made during the 1927– 9 period were justified. Together, these approaches yield interesting and testable results about the values of stocks.

The book deals with a controversial area of finance, namely asset price ‘bubbles’ and their potential causes in a particular historical market. The 1920s US stock market is the most famous alleged period of asset over-valuation in US history. In keeping with the approach towards using historically based financial models the work also looks at the devel-opment of theories about asset bubbles and market efficiency, which occurred before and during the period in question.

Although generally the main focus of the work is on asset prices and asset pricing theory, set within the framework of the 1920s and 1930s, there are some areas which are investigated which will lead to more research about bubbles. These areas have direct relevance for the post- mortem of the 2008 Global Financial Crisis and the lessons to be learned because 1920s USA also witnessed factors common to the recent crisis. One common factor was a substantial expansion in the volume of credit to the real economy during the First World War and a major boom in property prices, which recent econometric studies suggest con-tained a bubble component (White, 2009).


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xv

The author would like to thank Professor Geoffrey E. Wood, Professor Forrest H. Capie, and Professor Ian Marsh at Cass Business School, Professor Charles A. E. Goodhart and the Financial Markets Group (FMG) at the London School of Economics and Political Science, and funding from the Economic and Social Research Council (ESRC).

A debt of gratitude must also be extended to Professor William N. Goetzmann and Professor Roger Ibbotson at the International Center for Finance at the Yale School of Management, Yale University, Professor Charles Calomiris at Columbia Business School, Columbia University, Dr David Chambers and Dr D’Maris Coffman at the Centre for Financial History, Cambridge University, and Professor Eugene White at Rutgers University. The guidance of Professor Martin Ricketts and Mr Michael J. McCrostie at the University of Buckingham is also acknowledged.

Funding for the research was provided by the ESRC as part of the PhD research which forms the basis of the book and is duly acknowledged.

Acknowledgements


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1

The research, which forms the basis of this book, was motivated by a desire to understand one of the most controversial topics in economic history. Using new data, current financial models and financial theory from the 1920s, we reconstruct fundamental estimates of the values of US stocks and test for causes of the boom and bust. Our time frame of analysis centres on the 1915– 33 period, but we use data over the very long term, from 1871 to 2010, to establish and resolve whether a ‘bubble’ – defined as a deviation from ex-post fundamental values – formed and then crashed.

We offer new insights into the nature of the boom and bust. A house price bubble, monetary expansion on a large scale, rapidly rising stock prices, new investment vehicles, new investors and a technology boom, all combined with new theories on asset returns and new models of how to value stocks. In a seemingly intractable combination, these fac-tors generated a 400 per cent rise in stock prices from 1921– 9. Equally puzzling is the 1929– 32 crash. The market crashed from 1929– 32 by the same magnitude as the boom, returning stock prices to their original levels in 1921 or lower. This was a staggering fall of over 80 per cent. All of the factors at work during the 1920s make the resolution of the ques-tion of the legitimacy of the rise in prices and the identification of the drivers of the rise in prices extremely challenging, due to the difficulty in controlling for various factors.

The symmetry of the boom and bust, however appealing at first glance, does not explain much about what drove securities prices through boom and bust. By observing the stock market’s behaviour ex-post the nature of any potential uncertainty that existed at the time due to technological shock or other factors could be overlooked and some potentially legitimate trigger for the boom missed. In

Prologue


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2 The Great Crash of 1929

order to develop a clearer understanding of the causes of boom and crash, we take a broad look at the event, examining the economic dynamics of the USA, the new financial theory that evolved in the first two decades of the twentieth century, the microstructure of the New York Stock Exchange (NYSE) market, and the behaviour of other financial assets relative to stocks. New econometric tests of the ex-ante expected returns and ex-post realised returns to investing in US Common Stocks yield additional insights using original data sources. As a result of the research, we are now able to better understand the dynamics of the boom and bust. We employ in our analysis comple-mentary research and additional tests which have been conducted by leading experts in the field of economics, finance, and historical finance, combined with our own contributions, to develop a more complete picture of the economy of the time. The work owes a huge debt to these researchers whose work is acknowledged in Chapter 2 and throughout the book.

The 1920s were a time of new theories on asset returns and asset pricing related specifically to Common Stocks. Perhaps the most prominent pre- boom book, and one which is often credited as having played a major role in the formation of the boom, was Edgar L. Smith’s Common Stocks as Long- Term Investments (1924). Smith’s work contained a long- term empirical study of the return to a series of Common Stock and corporate bond portfolios, from 1866 to 1922, which found that a large excess return on Common Stocks over corporate bonds could be earned using a passive buy- and- hold strategy. Smith (1924) identified what we now know as the Equity Risk Premium, but at the time that his book was written, no clear link was made between the volatility of stocks and the premium. His discussion does contain some reference to how downside risk was reduced by holding for periods of between 4 and 15 years, so that no capital loss would be incurred. The work refers, therefore, to a non- standard measure of the ‘Risk’ of investing in stocks. Importantly, the reason for the observed return premium for stocks was never explained.

Smith (1924) has been proven to be an accurate description of the long- term returns to Common Stocks (Goetzmann and Ibbotson, 2006). The many critics of the book, who blamed the boom and crash on the public’s interest in the book which started a fascination with Common Stocks (Graham and Dodd, 1934; Williams, 1938), can only justifiably refer to the ambiguity in the book regarding the source of the excess return of stocks over bonds, and the potential misuse of its findings by speculators.1 Fisher (1930)2 stated that the increase of Common Stock


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Prologue 3

prices in the 1920s was driven by Smith’s (1924) theory and the reduc-tion of risk by diversification, inherent in the book.

The new research helped to generate the formation of a new type of investment vehicle – the ‘ closed- end fund’ – which aimed to earn this stock return premium by holding diversified stock portfolios, over the long- term. The 1920s should be viewed as a time of financial innova-tion, which could help undiversified investors reduce their volatility of returns and earn a long- term premium, which was based on sound empirical research. Earlier quantitative research on portfolio diversifica-tion and the reduction of return volatility (Lowenfeld, 1909b), asset lifespan, and dividend discount modelling (Fisher, 1906) all illustrate the type and level of sophistication of the financial modelling going on in the years leading up to the boom of the 1920s.

For those especially interested in how investors thought in this period, we present the results of our tests of the long- run Equity Risk Premium (ERP) and our models of expected dividend income from the aggregate stock market. These tests suggest investors already demanded a premium over the risk- free asset in the 1920s. There is much to suggest that even in the 1920s, the volatility of stock returns was the reason for the ERP. The risk aversion of investors seems to be apparent and also that there was no reward for diversifiable risk, as is the expec-tation of Modern Portfolio Theory (MPT) (Markowitz, 1952). While we do not know if investors used MPT in the 1920s or desired com-pensation for stock market return volatility, there seems to have been no reward for this volatility- based ‘riskiness’ that could be reduced by holding a large portfolio of stocks.

The stock market boom of the 1920s engendered much research work to explain why the stock market was rising so much and how the technologi-cal innovations of the time were a source for optimism. This is exemplified in Dice (1926, 1929) who believed that the boom was justified on the basis of the progress of the US economy being witnessed in the 1920s.

Analysis of the economic data related to the 1920s shows that this period preceded substantial increases in productivity growth rates, ema-nating from the innovations of the time (Gordon, 2010). Patent num-bers based on these new innovations surged (Nicholas, 2008) and the entrepreneurial innovators characteristic of the USA before the 1920s moved progressively within the boundaries of the firm in organised research and development (R&D) labs (Schmookler, 1957).

In a new discovery made by our research, evidence of a two- stage industrial growth model, used to value stocks, based on a method using numerically calibrated models, was found in Moody’s Manual


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of Investments (1930). This model was intended for investors to value the aviation industry. These new ideas on modelling high technology growth illustrate the extent to which investors were capable of model-ling financial assets and high growth industries. The level of sophistica-tion, although not high by today’s standards, is nonetheless advanced enough to indicate that serious efforts were being made to value high technology industries. The model did this by showing other historical examples of high technology growth and offering numerical inputs to the growth model. The model also gave an estimate of the stage of the industrial growth cycle that the aviation industry had reached in 1929 relative to historical examples.

The US stock market rocketed by 400 per cent from 1921 to 1929. The 1920s boom also coincided with and was potentially influenced by other economic developments. The 1920s witnessed the effects of a monetary expansion of substantial scale (Friedman and Schwartz, 1963). All three factors – new theory, technology, and monetary effects – contributed to the 1920s boom.

The period before 1929 also saw advances in financial information and financial analysis of US corporations. Our tests which indicate that stocks became overvalued should be separated from the advances made during the decade in financial theory, data sources on stocks, economic and busi-ness analysis and information technology related to stock investments. Roger Babson founded the Babson Statistical Organisation in 1904, which aimed to introduce scientific analysis to forecasting economic and business trends. Moody’s Investors Service started publishing analysis of Common Stocks in 1900 (Moody’s Manual of Industrial and Miscellaneous Securities, 1900). By the 1920s these voluminous and detailed manuals gave investors rich information and data on balance sheets, profits and loss accounts, dividend history, and numerous other calculations of financial information (Moody’s Manual of Investments, 1930). The non- professional investor also had access to data on international commodity, currency and asset markets, and Common Stock price data at a weekly publication frequency for hundreds of Common Stocks and bonds (Commercial & Financial Chronicle, 1929).

The sophistication of the 1920s financial markets and of the financial literature about them, has been overshadowed by the Wall Street Crash of 1929 and the Great Depression, which together serve as a cautionary tale against financial speculation. Yet that afterlife should not distract attention from our research findings. These suggest overvaluation of stocks, intermingled with a genuine belief in a new technological or high productivity era.


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Prologue 5

Key texts related to the boom and bust of the stock market appeared in the aftermath of the Crash. New books, on how to scientifically value stocks, were penned in the years following, by both academics and prac-titioners. It is reasonable to assume that much of the technical ability for such advanced work in financial theory was already being developed during the 1920s. The most famous of these post- Crash advances in tech-nical and theoretical finance, are the now immortal works of Benjamin Graham and David Dodd, Security Analysis (1934) and J. B. Williams The Theory of Investment Value (1938). Graham and Dodd (1934) were motivated, in part, by the losses of Benjamin Graham’s investment fund, during the Crash. Following the near failure of his investment fund, he turned to devising a new way to invest in stocks. His aim was to derive a safe return from undervalued stocks based on fundamentals, through careful balance sheet analysis to seek ‘value’ in investments, rather than to predict the movements of stock prices. In this way the Crash motivated him to develop a new school of investment theory based on the ‘value investing approach’. Graham was not alone in this change to a new philosophy to try to beat the market. Another invest-ment practitioner, who went on to earn his PhD at Harvard University as a result of the boom and bust, and the desire to understand the causes of and remedies to the Great Depression was John Burr Williams. J. B. Williams is widely regarded as the first to show the full mathematical formula for the Dividend Discount Model (DDM) (Williams, 1938). The Theory of Investment Value (Williams, 1938) is an investment classic con-taining voluminous and detailed, methodical, quantitative analysis of the actual worth of a company. He devised a series of very advanced math-ematical formulae and rigorous financial analyses of balance sheets and other financial information to value stocks. He looked at industry life- cycles and growth, and different types of stocks such as utilities, which require different pricing formulae. He conducted detailed case studies of individual stocks in an attempt to determine the value of a stock from its fundamental characteristics and drew a deliberate line between the ‘investor’ who was in stocks for the long term, and the ‘speculator’. Williams (1938) also discussed the idea of how prices are set in financial markets, concluding that ‘marginal opinion’ caused prices to fluctuate. The book offers some clues as to the nature of the boom. He chided the popularity of Smith (1924) in the eyes of the investors and he lays the blame for the boom and bust on their use of the book. Throughout his analysis of actual case studies in financial valuation, such as US Steel, he compares his estimates to the actual market highs in 1929, and finds that there were large divergences between them.


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The motivation for both of these new theorists was the rejection of, and frustration with, speculative valuations and their search for the ‘intrinsic value’ of a security. Both of these post- crash classics of investing, which are substantial in length and technical complexity, refer to the work of Smith (1924) as encouraging speculation in Common Stocks. Graham and Dodd believed that investors’ irrational ideas on the source of the ERP came from Smith (1924). Graham and Dodd believed that a focus on ‘retained earnings’ as the source of the ERP drove investor sentiment to exuberant levels. They lambast investors, rightly or wrongly, for a short- term focus on high earnings growth trends as an indicator of worth and the impulse for the bubble. Smith (1924) contains a good measurement of the ERP, as modern tests of the historical data show. Therefore, blaming this work for the formation of the bubble, if in fact a cause of investors’ fascination with Common Stocks, seems to have been because the con-tents of Smith’s innovative study were used by investors in the wrong way.

Fisher’s The Stock Market Crash and After (1930) analysed the events of the boom and was written in the wake of the initial crash in October 1929. He explained why the boom was justified, based on the techno-logical progress of the era, and concluded that the outlook ‘remained bright’ for the US economy and US stocks. He also thought that risk assessments of stocks were changing in the 1920s.

By 1932 when the US market had bottomed out, the economy had already fallen into a depression. Systematic analysis of the boom and crash appeared in 1938, after a major research exercise by Alfred Cowles (Cowles, 1938), who was concerned with understanding the experience of the Great Depression and the challenge it posed to the efficiency of the stock market, and thus to the benefits of investing in stocks for the long- run. Cowles’ research (Cowles, 1938)3 involved the collation of stock prices, price and return indexes, dividends and earnings for over 60 major sectors, from 1871 to 1938. These data, which still form the historical time series used by Shiller and many other financial historians, are of high quality and reveal detail about which sectors rose and fell from the 1870s to 1938.

The most famous and the most lasting literature on the 1920s regard-ing the boom and crash, written in the 1950s, was J. K. Galbraith’s famous work The Great Crash (1954). Galbraith focused on the idea that the 1920s, although a new technological era for the USA, which the USA had previously experienced with railways and automobiles, led to an irrational over- optimism that created a bubble from 1928 to 1929, which evaporated in October 1929.

Galbraith’s main focus was the financial scandal and wild exuber-ance generated by quick profits, the novelty of the companies, public


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Prologue 7

participation in the boom, and mass media coverage. Galbraith’s book is both highly instructive and advanced in its analysis. It explains the ‘arbi-trage’ induced flow of funds to the investors and traders and the anoma-lously tight conditions in the call money markets, thereby debunking the ‘easy credit’ explanation of the boom and pointing to expectations driven by overly exuberant valuation of stocks. He acknowledged that no definitive understanding of the boom and crash had come to light since the event although he does conclude that prices were too high from 1928– 9, fraud was widespread, and investors got ‘rimmed’. The book was a clarion call to future generations that such events should not be forgotten. What we do is to add more technical rigour to the analysis of the boom, so that we can deconstruct it as far as is possible. This is to allow the reader to examine the dynamics of the boom and bust.

The 1920s US stock market and the evolution of finance theory: the emergence of investment science

In 1900 Louis Bachelier (Bachelier, 1900) was already investigating the behaviour of stock market prices on the Paris Bourse. The social sciences had begun to take notice of this area of the financial economy and although ideas about market risk and investment trust diversification were already in practical use in the UK investment trust industry, the theories that underpinned the actual strategies used by fund managers developed in the academic sphere from 1900 onwards.

Scientific studies of equity valuation and portfolio diversification first came to the fore in the early twentieth century. Lowenfeld (1909b) pro-posed new theories based on quantitative studies of international port-folios, on how diversification could reduce volatility and earn higher, and less volatile returns for an investor.

Fisher (1906) looked at how dividend discounting could be used to value stocks. The introduction of the concept of the discounting of income from Common Stocks in the form of dividends, in order to derive the value of the asset, comes from Fisher’s The Nature of Capital and Income (1906). Fisher discussed the concepts of corporate bond valuation and risk by finding the ‘ risk- free’, ‘mathematical’ and ‘com-mercial’ value of the bond. This process involved discounting income at the ‘ risk- free rate’. He also then determined two risk factors, which were applied by bond investors:

1. The ‘probability’ of these income payments. 2. A ‘caution’ factor.


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Therefore the value of the bond would be lowered by a ‘probability fac-tor’ to capture the ‘risk’ element of the income from the bond to derive the ‘mathematical’ value, and then he added a ‘caution’ factor to derive the ‘commercial value’.

Fisher then applied these principles of valuation to Common Stocks:

in the general case we have to do not simply with the risk of falling income, nor with the risk of falling below a specified income, but with both. Thus the dividends from common stock have no fixed minimum as do those from good preferred stock nor any fixed minimum as do the interest payments from bonds. They may vary and vary widely in either direction. The amount of variation may be measured with refer-ence to any specified amount selected arbitrarily as the basis of the comparison. For instance in the case of a stock which has yielded, in successive years, the following percentages 5, 5, 6, 5, 5, 4, 5, 7, 5, 3, 4, 5, we may for convenience take 5 per cent to serve as a basis for computa-tion. If these frequencies are our only guide to judging the future, they represent the probabilities of receiving the respective dividends.

On the basis of the foregoing figures it is possible to calculate the ‘risk less’ and the ‘mathematical’ value of the stock, and if we know the caution factor, it is possible to calculate the ‘commercial’ value also. (Fisher, 1906: 276– 83)

Fisher also describes the use of the ‘ risk- free’ yield curve in the discounting of income and effects of the variation of the yield curve on valuation. In addition, the use of a discount rate, which reflected the risk on Common Stock income, as well as adding a premium for ‘caution’ were defined.

The estimation of the longevity of dividend income

Another key theory detailed by Fisher (1906) was the effect of the longevity of the asset and its income on its value. Fisher’s theories demonstrate the plausibility of the idea that investors could calcu-late Common Stock value by discounting dividend income over the expected timescale of the income and that rational calculation of the lifespan of assets was also being conducted.

New ideas on risk and uncertainty

The 1920s saw a change in the academic treatment of risk and uncer-tainty. Chicago economist Frank Knight in Risk, Uncertainty, and Profit


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Prologue 9

(1921) first conceived of an explicit distinction between quantifiable and unquantifiable risk.

The practical difference between the two categories, risk and uncer-tainty, is that in the former the distribution of the outcome in the group of instances is known (either a priori or from statistics of past experience), while in the case of uncertainty this is not true, the reason being in general that it is impossible to form a group of instances, because the situation being dealt with is in a high degree unique.

The best example of uncertainty is in connection of judgement or the formation of those opinions as to the future course of events, which opinions (and not scientific knowledge) actually guide most of our conduct.

Now if the distribution of the different possible outcomes in a group of instances is known, it is possible to get rid of any real uncer-tainty by the expedient of grouping or consolidating experiences …

Uncertainty must be taken in a sense radically different from the familiar notion of risk, from which it has never properly been sepa-rated … The essential fact is that ‘risk’ means in some cases a quan-tity susceptible of measurement while at other times it is something distinctly not of this character; and there are far reaching and crucial differences in the bearings of the phenomenon depending on which of the two is really present and operating. There are other ambigui-ties in the term ‘risk’ as well but this is the most important. It will appear that a measurable uncertainty, or ‘risk’ proper as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all. We shall accordingly restrict the term ‘uncertainty’ to cases of the non quantitative type. (Knight, 1921: 19, 233– 4)

In the UK, John Maynard Keynes (1921) was also making advances towards the distinction between risk and uncertainty. He expressed the idea that probability could not be truly known in some instances. Furthermore, he discussed the non- linear nature of probability, which may change completely in new circumstances that have not been observed before, thus making guessing about probability in an unknown future state of the world impossible. It is clear from Knight (1921) that ‘uncertainty’ had been conceptualised, prior to the 1920s boom, as dis-tinct from ‘risk’, the former being incalculable and the latter conducive to the application of probability theory. The ‘Knightian/Keynesian’


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ideas on what cannot be truly known show to what extent academic theory had mastered these intricate concepts.

Our subsequent econometric tests of long- term equity returns and the Equity Risk Premium show that Price/Dividend (P/D) ratios for the stock market implied no compensation for diversifiable risk. This suggests that the 1920s market before the alleged bubble phase was efficient in the sense that there was no ‘free lunch’. The market itself would not compensate the undiversified investor and our historical volatility cal-culations suggest investors required a compensation for the volatility of the market portfolio over the risk- free asset comparable to the excess volatility of other assets.

New research on equity returns proved a major insight into the new asset class and how much an investor could earn from these assets if diversification was used. By the 1920s these ideas about investment started to come into the domain of the ordinary investor via the pub-lication of a major study on asset returns, which had started out as an investigation by a bond salesman who was aiming to show how bonds were a good investment over the long term.

Smith (1924) illustrates that by the 1920s, the financial literature had made significant advances in the methods for calculating the returns to diversified Common Stock holdings. Smith’s Common Stocks as Long- Term Investments (1924) detailed comparative tests of ten stock and bond holdings using total return calculations, over a 56- year timeframe from 1866 to 1922. He found evidence of superior total return performance of Common Stocks over corporate bonds. Over the long- term ( 20- year) frame of the analysis, Smith also noticed that investors had overestimated the ‘ long- term’ risks of stocks relative to bonds and the risks implied by prices in 1924 were lower than the prices being paid for Common Stocks.

These studies are the record of a failure – the failure of facts to sus-tain a preconceived theory. This preconceived theory may be stated as follows:

While a diversity of Common Stocks has, without doubt, proved a more profitable investment than high grade bonds in the period from 1897 to 1923, during which dollars were depreciating,4 yet with the upturn in the dollar,5 bonds may be relied upon to show better results than Common Stocks, as they did in the period from the close of the Civil War to 1896, during which the dollar was constantly increasing in purchasing power. Based upon a general understanding of the results which logically follow changes in the purchasing power of the dollar, such a theory should have been demonstrable, and the


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Prologue 11

tests of the comparative investment value of bonds and of Common Stocks covering the period from 1866 to the end of the century, which are outlined in the following pages, were undertaken in its support. But they failed, because, quite unexpectedly, they demon-strated that the premise on which the preconceived theory rested, namely that high grade bonds had proved to be better investments during the period of appreciating dollars, could not be sustained by any evidence available. (Smith, 1924: v)

Although Smith focused initially on changes in the returns to Common Stocks as a result of price level changes and the ‘purchasing power’ of the dollar, the result of his work was the identification of a need for a change in investors’ assessments of the ‘riskiness’ or per-ceived riskiness of Common Stocks, held in groups, and his advocacy of investment in Common Stocks over corporate bonds.

Smith (1924) advocated the measurement of dividend income return by adjusting for ‘stock splits’ and ‘bonus shares’, indicating the level of sophistication of the methodology for valuing returns from Common Stocks.

The usual data and charts representing market fluctuations of Common Stocks are of no value in this connection, as they disregard stock dividends, the subdivision of shares, securities of different character given in exchange, and all the other changes in the form of holdings which would come to an actual investor in Common Stocks who retained the capital distributions of his original holdings throughout an indefinite period of time. (Smith, 1924: 68– 9)

Data from Moody’s investors’ manuals provided detailed informa-tion on profits, dividends, debt levels, ‘bonus shares’, and ‘stock splits’ extending back to 1908 with less detailed but still meaningful data going back to 1900. Therefore, the calculation methods advocated by Smith were accessible to investors over this timeframe.

E. L. Smith constructed a ‘spliced’ long- term return series from his total returns over the 20- year timeframes; we base our estimates of the ERP which Smith found using the method of overlapping his samples, to find an average premium over his corporate bond returns data.

We have, then, data relating to four separate stock holdings covering various periods from 1866 to 1922. The data of no one group cover the entire period, but the several groups overlap and if we are able


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to discover that these differing groups of stock have the same invest-ment rating in those years when they do overlap, then we shall be justified in the supposition that we have made no vital change in the investment character of our holdings if we shift them at their market prices from one group of stocks into another in those years. … in pre-paring a composite series from data derived from these several groups we may be warranted in giving a wider application to our resulting conclusions than if the series were derived from a single group of stocks held throughout the entire period. (Smith, 1924: 70)

Using Smith’s original data, our analysis suggested an investor could earn a total return including capital gains and dividend income, which was on average 2.5 per cent per annum higher than corporate bonds, when holding over a long timeframe. We do not have the data to generate the corporate bond returns, and we use an estimate of 1.5 per cent over the Government Bond return for bonds. The AAA bond total return from 1870– 1929 was about 4.3 per cent, during which time the return to Government Bonds was about 3 per cent. We therefore assume a 1.3 per cent premium for high- grade bonds.

The high- grade (AAA) bond index uses the yield from Macaulay’s High Grade Rails Index from 1857 to 1918 and the yields from Moody’s AAA Corporate Bond Index from 1919 on to calculate the total returns for the index. The corporate bonds in the index are long- term with a maturity of over 20 years.

Although we cannot be sure of the bond returns from Smith, as the data are not evident, we also estimate that Baa bonds had a premium of about 1.5 per cent based on their higher volatility. The findings of a 4 per cent premium estimated from Smith (1924) seem to be reasonable, and the scale of the premium has been verified by research at the ICF at Yale, USA (Goetzmann and Ibbotson, 2006) who also find about a 4 per cent equity risk premium, over the long term before 1926 (Table 0.1).

Smith (1924) also introduced new ideas on the ‘riskiness’ of stocks. A new concept called ‘time hazard’ was demonstrated in Chapter 10 of his book. The shortest time hazard showed that an investment horizon for the 10- stock portfolio, of more than four years, reduced capital losses to zero.

In other words, there was no need for stocks to be deemed ‘risky’ when held over the longer term. Even the more conservative measure of this ‘zero capital loss’ horizon using data from the 1830s to the 1920s, was 15 years. Smith also indicated that the volatility of economic growth in the USA around its long- term trend had been falling since the 1830s.


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Prologue 13

The US economy was therefore seen as becoming more stable and hence the ‘riskiness’ of stocks was falling as the US economy developed. The ideas put forward were that ‘riskiness’ or volatility was reduced:

• Over time as the US economy became more stable.• If investors used a longer holding period of four years or more. • By using diversification to smooth out returns thereby reducing

volatility.

Smith (1924) had discovered what we now call the Equity Risk Premium, but also challenged whether short- term volatility was a meaningful concept when investors held over the long term. Smith also did not explicitly connect the volatility of stocks with excess returns to stocks.

At its core the innovation was a sound one, as it was an advantage to investors, if handled correctly, and if the level of leverage was vis-ible. How investors looked at new technology valuation was also fairly sophisticated in the 1920s. Moody’s Manual of Investments (1930) shows that two- stage industrial growth models were being used to value high technology stocks, and that both the type of model and the explicit reference it makes to a knowledge of the industry life- cycle and the sur-vival probability of an industry shows that investors, leaving aside the overvaluation period, were quite advanced in their thinking.

Science and progress have moved forward and another great indus-try has been born. Aviation has come into its own. There have

Table 0.1 Smith (1924) estimates of return premium on stocks

Timeframe Investment ($)

Total return over bonds ($)

Capital growth ($)

Income ($) Premium over bonds

1901– 1922 10 000 16 400 5420 10 980 5.2% 1901– 1922 10 000 9242 953 8289 3.0% 1901– 1922 10 000 21 954 10 590 11 364 6.0% 1880– 1899 10 000 12 002 8654 3348 4.8% 1866– 1885 10 000 2967 900 2067 2.5% 1866– 1885 10 000 – 1012 500 – 1512 1.1% 1892– 1911 10 000 11 723 7375 4348 4.7% 1902– 1922 10 000 6651 4137 2514 4.0% 1906– 1922 10 000 4938 264 4674 3.5%


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been many other new industries that now appear as commonplace. Railways in 1833, petroleum in 1865, telegraphs in 1868, auto-mobiles in 1903, radio in 1914, rayon in 1917. We look for great developments in the fields of aviation and aeronautics. We have observed from our investigations that all new industries follow similar courses of development in arriving at maturity, and that this arrival is accomplished in three distinct stages. The first is the inventive stage where the proposition is regarded as an idle dream and the public has to be slowly convinced of its feasibility. During this time the industry grows only a few percent per annum. The second is the boom stage occurring after the feasibility and worth of the industry have been demonstrated and the public mind has at last been fired with the idea. During the second stage the typical new industry in America expands at the rate of about 40 per cent per annum. In the third stage after the industry has reached maturity, the rate of expansion is reduced to an approximate equality with the rate of growth in wealth, which here is about 5 per cent per annum. Notwithstanding the certainty of losses in some stocks, the huge profits are to be made in a new industry during the second stage, and for the aircraft industry this means during the next five or ten years. However, exceedingly keen judgment in investing is neces-sary at this time, and diversification is essential. (Moody’s Manual of Investments, 1930: xvi)

Our modelling of the automobile industry, which we do when we build our model to value new technology industries, shows that Moody’s (1930) growth assumptions are quite accurate, based on this industry. Although we do not model the other industries listed as other examples such as rayon, the model they produce seems to reflect an accurate idea about the expected growth paths of successful new industries.

One of our major aims is to understand the minds of the investors in the 1920s in order to see how changes in investment theory may have influenced behaviour; the increasing number of new investors is also important for understanding this period. Industrial Common Stocks were not used to a high degree by the average investor in the USA before the 1920s. At the height of the boom, from a population of 122 million people in the USA about 30 million held some form of Common Stock and many of these holdings were via new investment trusts or closed- end funds rather than direct stock holdings. That is 25 per cent of the population (Ott, 2011) and should be contrasted


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Prologue 15

with modern- day USA where the figure for stock ownership was 52 per cent of adults in 2013, of a total population of 320 million (Saad, 2013).

Stocks were a new and exciting asset class for many investors in the 1920s and from a very low base of stock holdings in 1900, the rise to 25 per cent is significant. However, a major segment of the population, 75 per cent, did not have any direct link to them. Therefore the ubiq-uity of stock holdings is not a fair characterisation of the 1920s, but the period did witness an increase in the use of Common Stocks by the investing public, to fairly high levels (Ott, 2011).

Bonds were the traditional asset class used by investors with stocks being seen as speculative, the instrument of the business- savvy and subject to manipulation, and prone to major swings in prices not con-ducive to use by the non- business class (Ott, 2011; Fisher, 1930).

The inflation of the 1915– 20 period (Figure 0.1) reduced the real returns to fixed income assets, as coupons and the face value of the bonds, paid at maturity, are fixed. Such a deleterious effect of inflation on the real returns on fixed income securities may have influenced the behaviour of investors towards looking at stocks instead, as they were known to be protected during times of inflation (Smith, 1924). There

Figure 0.1 Consumer Price Index (1825–1929)Source: NBER Macrohistory Database.

0

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is, however, nothing to suggest that the boom was rationally created by the need to avoid the effects of anticipated inflation. We can dem-onstrate that investors were not seeking inflation protection of this kind by testing the data on long- term Government Bond yields and the growth rate of the US economy, and also by measuring the returns to investment in US Government Bonds. We perform this calculation later in Chapter 3 to show that inflation expectations in the 1920s were low.


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17

The financial crisis of 2008 has rekindled interest in topics within the diverse field of economics, ranging from understanding systemic risk within the global banking system (Acharya, 2009), to how the financial system interacts with the real economy (Bayoumi and Darius, 2011), to the neuronal activity of the human brain during asset bubbles (De Martino et al., 2013). These extremely complex and enlightening fields will drive research into many interesting questions in economics for the coming decades and promise a great insight into these areas.

What the devastating effects of the crisis have also done is to bring the study of economic history, historical finance and the history of economic thought back into the consciousness of the general public and the mainstream of economics. There are some parallels drawn between the 1920s– 1930s Great Depression and today due to the widespread and prolonged crises common to both periods, and hence a natural tendency to look back at the economic history of that period has become evident.

History matters. The reason why we should be concerned about ensur-ing that its relevance to economics and the social sciences is cultivated is that human thought, and the economic systems which we form, are in a constant state of change. Yet, many aspects of human behaviour are common to all times. Being able to grasp this subtle concept is a cornerstone of understanding economics. Although we do not seek to pre- empt the conclusions of the book, it appears that financial history contains periods of less than rational behaviour. The aim of the research was to conduct comprehensive tests to establish whether an overvalu-ation of the US stock market occurred and identify potential causes and its underlying nature. The study of long- term data, when control-ling for potential changes through time, can yield powerful insights

1Introduction


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about how the economy and economic actors behave. The research also aimed to understand asset valuation in its historical context, by basing our assessment of investors’ behaviour on the valuation models they used. This is important for the rigour of our conclusions and also because the tools of financial analysis developed in the 1920s and 1930s have shaped the development of modern finance theory.

The state of the art in research into asset bubbles comes from the new field of neuro- economics, which is likely to be important in the development of economics over the next decades. This discipline draws on two fields – psychology and economics – to see how microeconomic behaviour is influenced by human psychology. This is a complement to more classical ideas on rational behaviour, but rather than depend on the restrictive assumptions of rational human behaviour at all times, aims to investigate what occurs when rational economic behaviour does not occur, such as during asset bubbles.

A good example of the advances being made is De Martino et al.’s paper ‘In the Mind of the Market: Theory of Mind Biases Value Computation during Financial Bubbles’ (2013). The research aims to measure the human brain’s activity to understand the neuronal pro-cesses, and subsequent behavioural processes which they lead to, during asset bubbles. In a laboratory setting, using brain- imaging techniques normally used in the field of medicine, these tests reveal how economic behaviour is generated. De Martino et al. find that the formation of bubbles is linked to increased activity in an area of the brain that pro-cesses value judgements. People who had greater activity in this area of the brain were more likely to ride the bubble in a trading game, and lose money by paying more for an asset than its fundamental worth. During tests where fundamental values were no longer adhered to by traders, they also found a strong correlation between activity in the value processing part of the brain and another area that is responsible for computing social signals to infer the intentions of other people and predict their behaviour. This new and exciting work has a historical precedent, the ‘Beauty contest’ analogy (Keynes, 2007 [1936]). In this explanation of bubble formation and general price formation in financial markets, the adoption of higher- order beliefs occurs where the market price is not determined by absolute value judgements of the assets’ worth. According to this view, the best strategy is to guess what average opinion thinks the value of a security will be. What the new research direction shows is how complex and enigmatic ‘bubbles’ are, and also that answers to the major questions of economics can be aided by inno-vative techniques based in other major fields of study.


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Introduction 19

Another equally vital area of research is the use of survey data to test expectations of investors directly. In Greenwood and Shleifer’s ‘Expectations of Returns and Expected Returns’ (2013), the authors test whether expected returns and expectations of returns diverge and provide insight on why returns may exceed fundamentals using multiple sources of investor surveys.

The study of financial history and historical finance has an equally, if not more vital role to play in the resolution of whether bubbles form in the real world. This is exemplified by the work of Yale University and the International Centre for Finance, where detailed historical analysis of financial markets and financial analysis of long- term financial data have made the study of finance, and especially asset bubbles, more viable. Having a reference point for both long- term asset returns and economic growth enhances our knowledge of fair expected returns. Being able also to measure realised returns over the long term provides the reference points needed to make useful inferences about investor behaviour and represent bubbles in the light of this fundamental expec-tation and subsequent realisation. Our research draws heavily on such an approach to set the benchmark, and then we analyse whether the deviation of the benchmark displays evidence of irrationality. Seminal work by R. J. Shiller, the joint winner of the Sveriges Riksbank Prize in Economics in memory of Alfred Nobel with E. F. Fama and L. P. Hansen in 2013, published in the American Economic Review (Shiller, 1981), showed that long- term historical studies of asset prices yielded powerful tests of market efficiency. The paper showed that in the broad market index of stock prices the latter have deviated from their fundamental values in long- and short- run swings of under and overvaluation over long- term US financial market history.

Laboratory studies, where a market is created in controlled settings (a key innovation in how bubbles are studied), have the potential to resolve questions about economic behaviour and the formation of asset bubbles. Smith et al. (1988) showed, in a controlled setting with live participants, how asset prices could deviate from known fundamental values. Smith’s work went on to earn him the Nobel Prize in Economics in 2002 for work in experimental asset markets. From these seminal modern thinkers and the ability of economists to assemble and ana-lyse large amounts of data, a field in behavioural finance developed in tandem with similar research on behavioural economics. The field of behavioural economics became influenced heavily by the allied social sciences, in particular psychology, with Daniel Kahneman being awarded the Nobel Prize alongside Vernon Smith in 2002.


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We focus on a particular time period and on a specific market. Figure 1.1 represents a broad market index of US Common Stocks, and uses data from Shiller (n.d.) taken from Cowles (1938) to show the extent of the boom and crash. It is truly spectacular and terrify-ing depending on where a hypothetical investor bought and sold. Throughout the book, we use these data to look at the broad market for US Stocks. These are the data used in Shiller (2000, n.d.) to esti-mate the S&P 500 broad market index over long- term US financial history. We use this measure to represent the US stock market, but we also take direct measures from the cross- section of stocks when we conduct our econometric tests.

The Cowles (1938) index is widely acknowledged to be a good gauge of the US stock market (Goetzmann and Ibbotson, 2006). We use the Cowles index throughout the book as a proxy for the US stock market, unless otherwise stated.

The movements of the prices of Common Stocks, which rose by 400 per cent from 1921 to 1929, and collapsed from 1929 to 1932, have been the subject of many analyses in the historical, popular, and technical economic literature. The process has drawn interest due to the scale of the boom and crash and its association with the largest economic contraction in US history in the twentieth century. Perhaps the most famous and telling account of the event remains the work of Galbraith (1954) who was the first to provide a general account of the boom and bust, and it has stood the test of time. The more technical account contained in Wigmore (1985) puts numbers and much detail to the boom and bust which Galbraith (1954) did not. In many ways, this book and the research it reports owe these scholars a great debt of gratitude. The works of Eugene White have also enlightened our understanding of the period, through much econometric and historical research on the 1920s boom. The study of the period is also indebted to the work of R. Shiller, William N. Goetzmann, and Roger Ibbotson. Their work on methods to detect asset bubbles, and long- term financial data and financial history have made the insights of the future genera-tion of researchers in these fields materially richer.

We are now in a position to conduct some further tests and borrow from the insights of modern research in finance and economics, to provide a picture of the boom and bust. The book follows the tradition of a general account of the boom and crash with data on many aspects cited in Galbraith (1954).

The focus of this book is to present new data extending well before the Crash and data collected over the eighty years since, which allow


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Introduction 21

us to estimate, in a more technically rigorous way, the dynamics of the boom and crash in the 1920s– 1930s and answer the vital question as to whether a deviation from fundamental values occurred.

The econometric tests and the data and methods for the tests are also presented, so as to make the tests clear and therefore repeatable. It is hoped that some of these techniques will improve the general understanding of measuring asset bubbles. We use the term ‘bubble’ as a descriptive word, which means, only, a deviation of asset prices from their ex- post observable fundamental values. We do so with a measured degree of caution as the word has many connotations, which we do not want to invoke when using the term, such as a self- feeding increase in stock prices. One of the key findings of the research was that asset bub-bles are very hard to detect ex- ante in this period.

The main aim is to offer the reader a better understanding of the boom and bust of the 1920s US stock market from a theoretical, historical, and econometric perspective. A second aim is to introduce or at least confirm to the reader the complex and enigmatic nature of asset bubbles and hence stimulate more research on the topic of how these phenomena actually form in the real world.

Figure 1.1 Prices and Dividend Index (Cowles) (Base = 100, Jan. 1913)Source: Cowles (1938); Shiller (n.d.).

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To this end it is worth introducing the reader to the history of economic thought on ‘bubbles’ and the state of the art in the field. In Chapter 2, on the current literature on this theme, we take a deeper look at how much we know about them.

The subject of market efficiency is an area whose study has only made serious headway in the last thirty years. Another aim of the book is in part to stimulate the further study of economic and financial history and to encourage research on bubbles, especially during new technological eras.

The 1920s stock market boom – the rise in values from 1921 to 1929 shown in Figure 1.1 – was caused in the first instance by an underlying monetary expansion of immense scale. The expansion is itself of great interest to many economists as the monetary system of the 1920s was different to today’s system. The monetary base was linked to gold, and the USA was operating on a Gold Standard, hence the central bank policy of the time, and its ability to sterilise international gold flows and thus control the supply of money in the USA is of interest. Showing how the US monetary/debt system changed to produce the boom of the 1920s is a key part of the research.

The newly formed Federal Reserve, born in 1913 to stabilise the US economy and banking system, and the central bank’s policy during the 1920s are integral to understanding the boom. Furthermore, the central bank’s policy towards the overvaluation, which they perceived, is also materially important.

The dynamics of the monetary system are key to seeing how stocks, which are nominal claims on future dividends, were affected by the mon-etary expansion of the time. The research also shows how the housing market’s debt system developed through the 1920s and later in the book we look at how this may have played a role during the Great Contraction from 1929 to 1932 via a nexus between highly leveraged home owners and the banking system which provided the home loans. Key features of the 1920s private and commercial property markets, which appear to have led to a bubble in the housing market (White, 2009), include the emergence of tradable securities in commercial real estate loans which funded the rise of New York and Chicago’s skyscraper boom (Goetzmann and Newman, 2012) and contributed to the crash in house prices due to the Great Contraction in the US economy from 1929– 32.

Using methods developed by Goetzmann and Ibbotson (2006), and our own historical data set, we can estimate the level of the US stock market, which is justified based on long- term history. This approach fol-lows that of Shiller (1981) and what emerges from our long- run data and


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Introduction 23

the Equity Risk Premium (ERP) measure from Smith (1924) and using data from Goetzmann and Ibbotson (2006) is an estimate of how much stock values changed relative to expectation. The scale of the change was up to 50 per cent beyond expectation for the broad US market.

The main conclusion is that this phenomenon that we can see ex- post was driven by both ‘high expectations’ of returns for firms, thought to be creating or benefiting from a ‘new era’, as well as potentially lower risk premiums. These conclusions are based on the data we have access to, as we do not have access to expectations data from surveys of investors from the period. Thus we cannot resolve exactly to what extent each of the two components was responsible for the overvaluation relative to our models. We also show how there may have been legitimate reasons for the surge in valuation ratios for stocks by examining a technological shock occur-ring during the 1920s and the effect this may have had on valuations.

In line with our look at technological shocks and the stock market, we can measure realised returns to the 1920s stock market by looking at returns over the very long run for a proxy of the market portfolio. Using bespoke survival adjusted return data, from the 1920s to 2010 we can derive a measure of the ex-post return to the market portfolio. This allows us to test whether higher expectations of returns or changing risk premiums were justified ex- post. A major finding of the research is that the return to a diversified investor in 1925 was very similar to that expected over the long term before 1926 and was about 3.4 per cent in excess of the return to long- term Government Bonds based on data from 1925 to 2010. This level of return to stocks appears to be consistent with long- term returns prior to the 1920s. This also shows us that any high expectations of future returns in the 1920s were not forthcoming. This result does not, however, rule out that new technology could have driven the boom legitimately as high uncertainty pervaded the emer-gence of new technologies and could have legitimately increased ex- ante valuations, even if these dissipated ex- post (Pastor and Veronesi, 2009).

What we do is provide some sobriety to investors in the modern day who believe that stocks bought in the 1920s would have earned spectac-ular returns if held to the present, and also caution those who think that the Great Depression damaged long- term returns relative to historical expectation. Considering that equity instruments, or stocks, are by their very nature a claim on the dividends of a very long- lived asset, long- term investors would not have been disappointed given an accurately formed expectation if they bought in 1925.

New research from Gordon (2010) shows that the USA was on the verge of a huge deviation from its trend productivity growth path, which


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manifested from 1928 to 1950. Although the exact nature of this produc-tivity surge is still the subject of research, acknowledging such a reality of a ‘new era’ in the USA is important if the dynamics of the 1920s boom and bust are to be understood. The real shift was not large enough to jus-tify the changes seen in stock valuations. Nicholas (2008) shows patented technologies were being revalued in the 1928– 9 phase of the boom and excess returns to these stocks remained after the crash. Janeway (2012), by contrast, identifies a similar step- change, but ascribes the dynamics of the post- war period to big- state capitalism and the willingness of the US federal government to buy a range of military technologies that could be shown fit for purpose or dangerous if developed by the enemy.

Our research points heavily towards a change in investor perceptions of the value of Common Stocks, which had no foundation in the his-torical or realised experience of investing. Ex-post experience of stock returns were in line with the prediction of history, and therefore it is hard to substantiate the claims that market prices rose to reflect a bright future for investors. Additional cross- sectional tests reveal that naïve extrapolation of earnings or dividends did not occur and only very weak momentum effects can be seen in the cross- section of stock returns from 1928– 9. Furthermore, there does not appear to have been a systematic change in the ERP. These results indicate that whatever occurred in the 1920s boom was not predictable at the firm level. Our results are, how-ever, still consistent with the idea that overvaluation could be detected ex- ante, as exemplified by De Long and Shleifer (1991) who illustrate a premium on financial assets whose ex- ante valuation can be calculated. Hence the potential for a technologically driven legitimate ex- ante rise in valuations has an upper bound.

We do challenge the conclusions of academic work on the subject that claims no bubble existed. The research of Donaldson and Kamstra (1996) and McGrattan and Prescott (2001) pointed to a fundamentally justified level of the stock market at peak levels. Our results suggest something contrary.

Investor perceptions of a ‘new era’, public involvement in stocks via new financial products, and new data on the returns to investing in Common Stocks may all have influenced its formation and propa-gated the overvaluation. These factors are in addition to the very high returns, which were a general feature of the period leading up to the bubble forming, as stocks returned over 20 per cent per year before the overvaluation phase.

This period also bears the hallmarks of what modern research in the laboratory suggests are the conditions under which a ‘bubble’ is more


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Introduction 25

likely to form. However, we cannot provide conclusive proof of how the bubble formed, as this type of analysis is ‘state of the art’ and we do not want to rely on untested theories of the boom and bust when we draw conclusions. What we do know is that momentum was present and there was a technology focus. Although momentum may not neces-sarily be due to irrationality as it may also be due to the technological shock, its presence indicates potential self- feeding bubble processes.

As the research also aimed to be set within the historical context of the mind- set of investors and the theories of the time, rather than to judge them by our standards alone, the book looks in detail at the investment models, financial theory, and the evolution of equity pric-ing theory from 1900 to 1938. We also look specifically at whether investors were sophisticated in the 1920s, despite the overvaluation we detect.

As we have already seen, many respected financial economists wrote about the boom as having theoretical drivers or presented new approaches to the equity valuation behind it. Smith’s Common Stocks as Long- Term Investments (1924) stands out and is cited in Graham and Dodd (1934), J. B. Williams (1938), and Fisher (1930). Although trying to link the formation of the bubble to new ideas on valuation is diffi-cult, the literature suggests that the latter played a role large enough to warrant investigation. The fact that the research contributed to the for-mation of a new type of investment vehicle in the 1920s, the closed- end fund, and the emergence of these new funds during the 1927– 9 phase of the boom where we find the overvaluation makes the investigation of a potential theory– pricing nexus interesting and is a key innovation of the book from the perspective of historical finance.

Smith’s book (1924) offered the public investor of the 1920s an insight into how equities performed well in the preceding fifty- six years compared to bonds, providing substantial evidence for the existence of and size of the ERP. What this book also did was to leave an ambiguity as to the source of the premium. Some interpreted the book as misguiding investors into thinking that stocks were too cheap relative to their power to produce earnings which were reinvested and went on to produce more earnings (Graham and Dodd, 1934). Whether investors actually believed this is a subject for future research of the accounts of investors of the time. Our research shows that what we now know as the ERP, which Smith (1924) reported as the return of stocks over bonds, was not labelled as a premium for risk and its cause was left ambiguous. What makes this issue more complex is that Smith (1924) did devote a section to explaining to investors the nature


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of volatility and the protection of investors’ principal when investing in stocks. He also observed that the degree of volatility was changing over time and Smith’s discussion in Chapter 10 of his book indicates that he knew investors cared about the downside volatility of a stock’s price as a risk factor. Smith (1924) suggested that holding stocks for between four years and fifteen years, depending on the investor’s horizon for the volatility calculation, was enough to reduce one’s loss of principal to zero. Hence, a quasi- metric of volatility, making stocks more directly comparable to bonds, whose principal was safe, is also a key insight into the 1920s theory of equity pricing. This enriches our view of the evolution of equity theory even though the broad market appears to have priced the volatility of returns. Therefore the appearance of new ideas on the safety of stocks may have influenced investors to change their assessment of the expected returns to stocks.

The institutional and investor backgrounds to the boom are also important. The First World War, and the Liberty Bonds programme, used by the government to finance the war effort by utilising the public as a mass source of funding, brought with them an opportunity for the public to handle new financial instruments. New financial innova-tions such as closed- end funds meant that the purchase of stocks and bonds by a larger section of society was possible and banks organised new securities affiliates (Peach, 1941) to market and broker stocks and bonds to investors. The emergence of a ‘mass market’ investor relative to the pre-war period reflected rising prosperity as the economy grew and investor demand rose for securities. Furthermore, the monetary expansion in the 1915– 21 period led to new levels in the stock market due to the abnormally high growth of nominal earnings and dividends.

Returns of over 20 per cent per annum in the late 1920s, compared to the usual 6– 7 per cent were a direct result of the banking system’s expansion and may have been a factor in the surge in volume of trading and attraction to the market.

The conclusions we draw are that changes in expectations of future returns and a bias towards new technology (Nicholas, 2008) are the most obvious drivers of the boom.

A key conclusion is that the ‘overvaluation phase’ was not mono- causal. Nicholas (2008) shows that both momentum effects in monthly returns and a systematic bias towards patents were present. Extrapolations of earnings growth rates, dividend growth rates, or the effect of (Moody’s) risk ratings, net current assets, or market capitalisation, do not give any correlation to the degree of overvaluation in the cross- section of stocks.


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Introduction 27

Many factors have been suggested as having contributed to the over-valuation, and our results are an important addition to research on the topic in that we rule out many such potential causes. What we cannot do is show, directly, why investors became exuberant, as there are likely to be many reasons for this, which are beyond our ability to model clearly. Investors’ expectations of returns to US Common Stocks differed markedly during the 1927– 30 period. The subsequent reversal of the ex-post identifiable overvaluation shows that investors eventually came to a higher degree of convergence in their expectations of returns from Common Stocks. The ex-ante bases of the overvaluations, being irrational speculation or due to a technology shock, are not clearly resolvable.

As patented technology effects are found in the excess returns data from 1928– 9 (Nicholas, 2008), we take a new approach to try to model the fundamentals of a new high tech industry. In this case we can again test expectation relative to model. This exercise not only reveals the level of sophistication of investors in the 1920s but also how differing beliefs can potentially be identified. This approach is also of interest for students of the evolution of finance theory as we have direct evidence of a generic type of two- stage industrial growth model from Moody’s (1930). We are therefore able to add significant richness to the analysis of investors’ use of financial tools by modelling the aviation indus-try’s life- cycle using prices from Cowles (1938) and data from Moody’s (1930) and an industrial growth model derived from a comparison industry, suggested by Moody’s (1930), the automobile industry. Our analysis reveals that investors likely used a two- stage or three- stage model with a probability factor for industrial survival in their calcula-tions. Furthermore, we also find that the peak of the 1920s boom saw aviation valued at 300 per cent of the value expected, assuming the rep-etition of the growth path of the automobile sector. Therefore, we sug-gest that high expectations were present in the 1920s boom. On what basis these expectations were actually formed is not clear but a large step is taken towards understanding how stocks were valued and what kind of fundamental values were feasible. It remains possible that the high growth rates were ex-ante feasible due to higher growth potential than that suggested by history, or the uncertainty about new technol-ogy and which firms would win through led to stocks being priced at a high level. Despite the difficulty in answering questions we are able to provide both the theoretical context and data to support the idea that a high tech industry rose to values beyond a contemporary benchmark and to explain how investors saw new technology industry valuation.


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Another key area of interest that we also aimed to model was the NYSE’s credit system in the 1920s. This was a period in which the central bank, the US Federal Reserve, was concerned about price rises due to the credit margins available to the traders and investors active on the NYSE. Delving into the history of the Fed since its formation in 1913/14 and how it handled the monetary expansion of the 1915– 29 period, we then look at its role during the boom. The Federal Reserve Board were concerned about credit flows to the stock market and the valuation levels of the stock market. They believed that credit was being diverted from the real economy to finance speculation and actively sought a ‘bubble popping’ strategy, which failed (Bernanke, 2002). They took two approaches to halt what they saw as a bubble: the discount rate and the regulation of credit flows. The reasons for their policy fail-ure are lessons for the modern day and we highlight a major source of regulatory arbitrage as credit flowed from US companies, investment trusts, investors, and international banks to supply the brokers’ loans to a stock market being squeezed by the Fed’s policy to stop the boom. The research incorporates historical material and analysis of the monetary policy and theory used by the Federal Reserve and the US central bank’s view of the boom through the 1920s.

What we test for is whether or not a credit crisis caused the October 1929 crash, and we investigate whether a genuine new level of the mar-ket was disturbed by an ‘exogenous’ shock. We find that such a shock did occur but that the New York banks intervened in time to alleviate the credit crisis and hence the crash cannot be seen as a forced liquida-tion of stocks. The low implied expected returns at peak valuation levels in 1929, generated from our model, returned to historical expectation due to something other than a credit shock to the investors and traders.

Careful examination suggests the 1930– 2 crash (with stocks falling over 80 per cent from their peak in 1929) was not due to the preceding boom in asset prices but was instead driven largely by fundamentals during an economic shock of unprecedented scale. In short, the 1930– 2 crash appears unexpected, and should not be viewed as part of the overvaluation of stocks in the late 1920s. What we also find is that the bust from 1930 to 1932 was of a different nature entirely to the bubble. In 1930, our results suggest the market was fairly valued relative to model- based expected returns. What hit the US economy during 1929– 33 and the market between 1930 and 1932 was an economic shock, which was both unexpected and unprecedented in magnitude given the long- run performance of the economy over at least the previous thirty


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Introduction 29

years. The scale of that shock has not been repeated since. Meticulous analysis of the economics of the depression from 1929– 38 can be found in Friedman and Schwartz (1963), Temin (1976), Eichengreen (1992), and Bernanke (2000). However, the reader should be aware that the dynamics of the Great Depression are still not fully understood and hence exactly what caused the Great Depression and what remedial policy options are the best for these mega- crises is a subject still ripe for research. Recent events over the 2007– 13 period show that such research is still relevant.

The bust from 1930 to 1932 appears to have been driven by two major factors: a fall in aggregate demand interwoven with a banking system crisis. The two problems fed off one another from 1929 to 1933, with the 1931 global banking crisis also creating severe exogenous shocks to the USA. The credit boom we referred to earlier in the chapter is also of material interest to those interested in the origins of the boom and crash. The credit expansion took the form of a neutral aggregate increase in debt levels. However, the composition of debt changed markedly towards the consumer rather than business in the 1919– 29 period. Such changes may have made the depression worse.

The bankruptcy of firms, individuals, and parts of the banking system, the hoarding of cash, increases in the cost of bank lending, falls in price levels, defaults on home loans, and many other factors all contributed to the decline in output and employment of the USA (Friedman and Schwartz, 1963; Bernanke, 2000). Although a fascinating time period for students of economics, the effects should not be taken in abstraction from the human suffering endured during those years. The economic disloca-tions of those years, in the USA and Europe, led ultimately to the Second World War. We do not delve deeper into the causal flows of economic crisis during 1929– 33 except to outline how the value of the aggregate US stock market was affected. This limitation to our research is intentional; it is so complex a task to model the US economy during that time period that such analyses are better left to ongoing research efforts by others. Understanding the processes requires a level of technical mastery of the subject and large volumes of data collection which will likely require a major international group- based research effort. Our analysis is therefore directed towards aiding the reader to navigate the question of why cor-porate earnings and stock values fell so spectacularly to the low point in 1932. In doing so we address the issue of market efficiency during the crash and explain why it is likely that the market in 1930 was fairly valued and that the crash of 1930– 2 was unexpected and probably unforeseeable.


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Our research identifies three key drivers for the falls in aggregate stock market values. First, there was a fall in nominal earnings and dividends. Second, there was the implied increase in the risk premium on stocks in line with the rise on all financial asset types from 1931 to 1932. Third, the residual unexplained by our models suggests stocks became undervalued in 1932. These findings are corroborated by tests of book- to- market ratio data from 1926 to 1946. The results are consistent with Shiller (1981) suggesting extreme pessimism in 1932.


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31

2.1 Modern literature related to the 1920s

The valuation of stock markets, and specifically, instances of financial euphoria when these seem to deviate from fundamental expectations have been of perennial interest to economic and financial academia since the 1929 Crash and the Great Depression. Academic research into the boom and bust of the 1920s and 1930s was conducted in the years following, such as Cowles (1938) and Williams (1938) but has only really developed with a specific focus on the 1920s since the 1970s. This is due to advances in financial valuation theory and as the field of finance generally became more developed. The advent of computer technology has allowed the collation of data such as the CRSP database, and testing of these data has become more accurate due to the statistical power of the econometric tests we can use. The following sections discuss the modern analyses of the period and the types of tests used and inferences drawn.

Sirkin (1975) measured the growth of earnings over the 1920s for a cross- section of stocks together with an earnings growth (DCF) model. By using the dispersion of growth rates he was able to form a projec-tion of expected price/earnings ratios if investors followed a simple extrapolative rule over a five- to ten- year horizon in a two- stage model where the growth rate reverted to a much low growth rate after the high growth phase. This approach takes its justification from the potentially naïve projection of rates of the high growth of earnings in the 1920s. He concluded that the valuations in 1929 could be largely justified if investors were using a short- run projection of five to ten years forward from 1929 using short- run earnings growth from 1925– 9. He only noticed significant overvaluation in a small fraction of stocks and hence

2Literature Review and Methodology


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concluded that given the rapid growth of real earnings in the 1920s, the decade did not witness ‘an orgy of speculation’.

What this analysis (Sirkin, 1975) lacked was any solid contextual basis and hence it was only useful in showing how valuations could have risen to the extent they did. It never attempted to produce a full analysis by its construction, but did pave the way for new ways of looking at the boom and encouraged further testing of the cross- section of the stock market.

Shiller (1981) challenged the efficient markets model, which assumed that prices of stocks were the optimal forecast of the ex- post rational price. Using historical data he showed that this assumption was not consistent with these data, if the long- run trend of real dividends is assumed given. Looking at historical time series data for the Cowles/S&P 500 index which included the period of the 1920s and 1930s, an ex- post rational real Common Stock price series – the present value of subsequent de- trended real dividends – was found to be a very stable and smooth series when compared with the actual de- trended real stock price series. In other words, real dividend growth was nearly constant through US stock market history and hence prices seem to have over-reacted periodically to what was a very stable time series of dividends.

Barsky and De Long (1990) used a dividend forecasting method, which incorporated uncertainty in the ability to forecast future divi-dend growth rates, to show that the US stock market experienced fluctu-ations in its dividend growth rates, which could be used to justify actual market valuations for US stocks over the 100- year history it covered. Their conclusion was that market prices rose and fell in an explicable manner, namely in tandem with fair expectations under uncertain fore-casting. They found no bubbles in the stock market over the 100 years of data in their long- run stock price data.

De Long and Shleifer (1991) used the high valuations relative to underlying Net Asset Values (NAVs) for ‘closed-end funds’ in the late 1920s to propose that the market was overvalued relative to funda-mentals. The market values of these closed-end funds’ own stock were valued at substantial premiums to the NAV of the stocks held in the fund at market prices during the 1927– 9 period. These premiums disappeared in 1930– 3 period and as there is much long- term data to support discounts to NAV or small positive premiums through US financial history, they attributed this deviation from the expected value of the funds to irrational valuation of at least 30 per cent.

De Long and Shleifer concluded that the premium on funds was excessively high and that excessive optimism is therefore detectable in


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Literature Review and Methodology 33

1928– 9. They also cited the large numbers of closed- end funds which formed from 1927– 9 as indicators of excessive optimism and a rush to speculate on the value of stocks. Given that closed- end funds tend to sell at a discount, the positive premiums are interesting as a possible indicator of over- optimism. However, the paper was not able to resolve a critical problem, in that generalising from a sub- sample is dangerous and hence is not scientifically useful for assessing the value of stocks in general. The stringency of this reasoning must be maintained, as such a complex subject cannot be subject to ‘guilt by association’ reason-ing for the wider market. These funds were very new at the time, only forming in significant numbers in 1927, and may have been subject to unobserved variable biases specific to these stocks. What the paper did do well was introduce evidence of a deviation from fundamentals in a robust fashion for this subset of stocks and a strong result in favour of a potentially major behavioural basis for the overvaluation of the wider market.

E. N. White (1990) used a Marsh– Merton model for data from the 1920s boom and concluded that there was an overvaluation or ‘bubble component’ in prices, although this was limited in size to about 20 per cent. The component was not found to be caused by a self- feeding ‘ greater- fool process’ or an over- optimistic perception of the risk premium. Rappoport and White (1993) used the brokers’ loans market to assess whether lenders’ perceptions of an impend-ing crash caused them to raise their margin requirements, to protect against losses, which under their assumptions could be indicative of an ‘overvaluation’ in the market. The possibility of fear of a price crash due to a volatile credit situation and an informational asymmetry between brokers and investors or a desire to prevent speculation are not considered in this study. Although they acknowledged the theory and replicate the unit root tests, which indicate that no bubble occurred, as the paths of dividends and prices share a unit root, they conclude that the ‘bubble component’ which they identify is still unexplained.

Donaldson and Kamstra (1996) used a ‘Monte Carlo’ simulation to justify prices in 1929 by showing that the growth in dividends was suf-ficient, assuming dividend forecasting uncertainty, to recreate the high valuations seen by 1929. They used an out- of- sample calibration tech-nique to produce an array of 10,000 possible future paths of dividend growth based on previous dividend growth of the S&P 500. They used a non- linear ARMA- ARCH- Artificial Neural Network model to obtain out- of- sample dividend forecasts for 1920 and beyond, using only in- sample dividend data. The ‘present value’ of the forecasted dividends indicated


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prices that match the magnitude, timing, and time- series behaviour of the boom and crash in 1929 prices.

McGrattan and Prescott (2001) measured the ‘fundamental’ value of US industry by the stock of tangible and intangible capital in 1929 at the macro level using a different approach to traditional ‘discounted cash- flow’ (DCF) models. Using a 150 firm sample they found that the US Industrial and Commercial Base’s value, measured as a proportion of GDP, matched closely that produced by total stock market capitalisation in 1929, and hence did not find that US corporations were overvalued. The work used an innovative methodological approach by using non- market fundamental data to measure the value of US corporations.

Data from Shiller (2000), which formed the basis of the book Irrational Exuberance, looked at long- run aggregate Cowles and S&P 500 index data from 1871 to 2000 to show the long- run rises and falls in US mar-ket prices relative to dividends and earnings. These data showed that significant peaks and troughs occurred relative to trend based on the ex- post warranted price of the index of US stocks. The 1920s boom was an ‘overvaluation’ according to this mean- reverting tendency of asset prices relative to fundamentals. The trough of the 1930s also appears as an ‘anti bubble’ where prices were too low relative to fundamentals.

Goetzmann and Ibbotson (2006) provide key data on stock returns and the methods for estimating the ERP from 1871 to 1925 for NYSE stocks. The large- scale data collection done at the ICF and their multilevel research on this area are critical to gaining a handle on the US equity market for a timeframe which lacks coverage in the main database on US stocks, CRSP, which begins in 1926.

Recent analyses of the 1920s boom have sought new methodo-logical approaches to the problem. Nicholas (2008) measures intangible capital stock of US firms from a 128 firm sample, using a method of citation- weighted patents to determine the intangible capital of firms. The intangible capital, which was being formed by the rise in patents developed by technologically innovative firms such as chemicals and electrical manufacturing, new corporate structures, and other produc-tivity enhancing technologies, was deemed sufficient to justify the peak of the 1920s market via an adjustment for this intangible capital factor. Nicholas (2008) illustrates that intangible capital was growing significantly. The index of total firm patents registered by the USPTO rose by 80 per cent from 1920 to 1929. This fact is significant because intangible capital is thought to be responsible for the increased survival rate of firms as well as increased productivity growth in the economy and higher profit growth at an individual firm level.


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Bierman’s The Myths of 1929 and the Lessons to be Learned (1991) used Gordon’s (1962) Dividend Discount Model to illustrate that stocks were not obviously overvalued in 1929. Although this approach has been criticised as it follows the method of Sirkin (1975) in that it uses short- term growth rates, it nonetheless reinforces the idea that the boom of the 1920s was grounded in a fundamental or tangible basis for increas-ing stock valuations, whether those tangible factors were actually useful for gauging the fundamental value of stocks or not.

Bierman also focused on the political dispute between the New York banks and the Federal Reserve regarding policy towards the NYSE boom. This is a useful insight as it is known that this disagreement over policy, with the board advocating a suspension of speculative credit to banks and the New York reserve bank favouring interest rate rises, was another key dimension in the 1920s boom. Bierman illustrated that there was a definite concerted effort by the Federal Reserve board to stop market speculation by a policy of ‘direct pressure’, rather than the more passive interest rate mechanism. He also illustrated that the board, specifically Adolf Miller, a former Harvard professor of economics and friend and political ally of President Herbert Hoover, was keen on stopping specu-lation on the basis that it was detrimental to the economy, seemingly diverting funds away from the commercial centres and concentrating in the financial, to finance speculative activity.

Miller believed that the market was overvalued due to excess specula-tion, and had been on record as far back as 1925, criticising the desta-bilising influence of the margined traders on the NYSE. Bierman also details how Miller and others felt that open market operations and rate policy were ineffective and hence moved to alter the banks’ access to finance for speculative loans. Miller had been highly critical of the low interest rates in 1927 used to stabilise the recession and aid the UK with its deflationary problems. Miller was explicit in citing this low level of rates as a primary impulse to the rise of the market that he believed was a credit induced bubble, and was keen to deflate it (Miller, 1935). Bierman cites exchanges in 1931 at the US Senate committee on bank-ing and currency to illustrate these points.

Bierman dispelled some of the myths surrounding the events of the boom and crash such as the effect of short-sellers and investment pools. He concluded that the market was not obviously overpriced. Bierman also devoted a chapter to the canonical bubble stock Radio Corporation of America. In another direct reference, Bierman showed that US Steel was showing record earnings and high earnings growth and yet the price/earnings ratio fell substantially from 1927 to 1929. At a basic level


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this could indicate that the market was not responding to short- term earnings growth, but rather a much longer- term horizon, or it could mean that US Steel was not thought to be a company that would generate higher earnings over the longer term.

Wigmore (1985) is the most thorough and detailed book on the boom and crash. It provides a systematic analysis for the 1929 market peak through the worst of the market lows, to 1933. For each year he pro-vides detailed data on high/low prices, earnings, dividends, and return on equity for the prominent stocks for each major industry group such as oil, tobacco, public utilities, and chemicals. He also provides detailed information on gold reserves in the banking system, price levels and the role for major firms within each of the industry groups. He illustrates that there was a large increase in the volume of trading on both the NYSE and the unlisted curb market in 1929 and high prices for seats on the NYSE. He also details the high level of brokers’ loans which totalled 9.8 per cent of the total market value of the NYSE and, added to bank loans for speculation, totalled 18 per cent of the market cap for the whole NYSE by the peak in October 1929. However, he notes that the ratio of loans to market value has remained constant from 1926.

Wigmore (1985) cited the withdrawal of these loans as a key liqui-dation pressure in the market, as short- term traders were forced out. He also described the banking system and the sources of credit for the brokers’ loans market, stipulating that the large industrial firms such as General Motors were supplying funds to the brokers’ loans market and how a series of loan calls forced borrowers into liquidation of their stock positions. He therefore attributed the crash to a tightening of credit by both the Fed and the banking system, which called in call loans and forced brokers to pass on these tightening credit conditions to the mar-ket. Critically he detailed how the brokerage community was able to survive the initial crash as banks financed them and they reduced their exposures and raised margin requirements. He assessed that the Fed pro-vided an easy credit stance during the crash and after to 1931 by reduc-ing the acceptance rate and the discount rate in response to the crash but does not detail how or if this was effective. Hence the cause of the crash is deemed to be systemic in nature but based on the real hypoth-esis that stocks had become inflated or overvalued, effectively making the calling in of speculative loans by the banks a necessary and rational act which caused havoc in the market. He concluded that the stock mar-ket crash and slump to 1933 was due to a series of shocks to the banking and economic system and does not favour the monetary hypothesis of the Great Depression. He also concluded that an overvaluation of


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the market is irrefutable, although importantly he does not advance a hypothesis as to why or how the overvaluation came about.

Summary

The literature reveals a good deal of historical research, which has borne much fruit. These works, together with the earlier literature written in the immediate aftermath of the crash, point to some key areas which are ripe for testing, namely, whether it was a new technology bubble, whether momentum played a key role, whether new pricing theories related to the risk premium on stocks or expected growth of stocks were a major cause, and whether credit instability was a key factor in the crash.

The motivation for our research stems from the gaps in these analyses and the questions which extend from the earlier research. The aim was to try and find empirical tests that would estimate the size of the bubble and actually show whether such a bubble can be revealed in the data on an ex- ante or ex- post basis.

The modern literature on asset bubbles, which we investigate partially in the next section on ‘Bubble theory’, tends to support the idea that they form. The historical evidence of particular episodes is anecdotally and in many cases econometrically strong. However, there is also a very large body of literature that supports the Efficient Markets Hypothesis (EMH) in its various forms, which we also look at briefly in the next section. The dichotomy means that research which aims to show in an empirically testable and economically sound way that an overvalu-ation of stocks can be proven or rejected in this historical period is a tantalising prospect but it is extremely difficult to perform. The lack of coverage of these additional questions, which we described earlier, was the motivation to attempt such a complex research project. Although Goetzmann and Ibbotson (2006), Nicholas (2008), Shiller (1981, 2000), and Rappaport and White (1993) have provided much- needed depth to the level of econometric sophistication used to address the topic, there was still ample room to advance the research.

In the next section we detail the theory on asset ‘bubbles’ to inform our methodology and provide a theoretical backdrop to the issue of periods of overvaluation and what may cause them.

2.2 ‘Bubbles’

At the outset of this section we define a ‘bubble’ as a positive deviation from fundamental values of a financial asset or a group of financial assets. The term has many other connotations, which we do not imply


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when we the use the term. It is an overused phrase, which deserves deeper and more sophisticated delineation, which we hope to perform in the next section.

The evolution of thought on the behaviour of stock market prices, from an empirical and scientifically testable perspective, and the dis-covery of their random nature, stems from the early research of Jules Regnault in his Calcul des Chances et Philosophie de la Bourse (1863). In this book, he observed that the longer you hold a security, the more you can win or lose on its price variations: the price’s standard devia-tion, a measure of volatility, was thought to be directly proportional to the square root of time. The mathematician Louis Bachelier’s PhD thesis entitled ‘Théorie de la speculation’ (1900) came to the conclu-sion of a random nature in price movements. Holbrook Working (1934) introduced the econometric basis for testing whether a time series of commodity or stock prices could be generated by aggregating series of statistically generated randomly fluctuating time series. Maurice Kendall also examined the random nature of prices in ‘The Analysis of Economic Time- Series, Part I: Prices’ (1953), and M. F. M. Osborne (1959) found that the logarithm of Common Stock prices follows a ‘Brownian motion’ and provides evidence of the ‘square root of time rule’.

These empirical tests seemed to indicate that prices were un- forecastable over the short term and this body of research coalesced into a school of thought in the 1970s. It is important to remember that these were empirical observations, which grew into a much stronger theory of what the observed nature of prices actually signified about the informational efficiency of stock markets. Eugene Fama’s now famous 1965 article, ‘Random Walks in Stock Market Prices’, was based on his University of Chicago PhD thesis, and developed the findings from earlier empirical studies. Fama (1965) explained how the theory of ‘random walks’ in stock market prices challenges the proponents of technical and fundamental analysis, that is, those who thought research could offer a way to beat the market.

In 1970, Fama (1970) set out the ‘Efficient Markets Hypothesis’ (EMH) on the earlier basis of the three types of market efficiency first discussed in Roberts (1967). Asset returns were theorised to have three levels of efficiency: weak, semi- strong, and strong. The weak form meant that no historical data could help predict tomorrow’s price, the semi- strong that information quickly was incorporated into prices, and the strong form that no persons(s) held an informational advantage.

The basis for the theory were the empirical observations of ran-domness in price movements which were thought to be due to new


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information impacting the prices of stocks, and hence creating unpredictable movements of prices. Fama’s work (1970) brought a new theory on market prices, based on empirical observation, to the discussion. It also set out a testable set of hypotheses regarding the zero expected return of a speculator. Although the theory suggested that information efficiency was the reason for randomness in market prices, this was not demonstrated by the research. It was a hypothesis that followed the observed price behaviour. Fama (1970) found that there were anomalies to his own theory in the data which he tested. Firstly, evidence of serial correlation in returns, but where the sign was random, was found. Secondly, Fama found evidence of market mak-ers having informational advantage, but this finding was not deemed powerful enough to refute the general conclusion that market prices were too unpredictable for an investor to beat the market. Despite these shortcomings, the major breakthrough was the testable hypothesis that investors could not ‘beat the market’ and prices were following some type of ‘random walk’.

The EMH approach stemmed from empirical observation and assumed that because prices were not predictable, they at all times reflected all information available on asset prices. This was enhanced by event study research that illustrated that stock prices could respond effectively to new information (Fama et al., 1969). What happened subsequently in the field of market efficiency was that two major schools emerged on the issue. The first was the EMH school and the second was the ‘Behavioural Economics’ school. We delve into the Behavioural Economics school’s ideas in the following section. This new school challenged the assump-tions of the EMH.

The key difference that remained between these schools was whether the random nature of prices precluded any potential behavioural bias that caused stock prices to deviate from fundamental values. The Behavioural school moved towards laboratory studies and using innova-tive empirical techniques to resolve this issue.

The long sweep of the economic history literature is replete with evidence of episodes of alleged deviations from fundamental prices, when financial markets rose by large amounts and then crashed in value over a relatively short timeframe. An investigation of such literature would suggest that such phenomena are not common and describe a special type of situation. However, what should be noted is that such events seem to have been accepted as departures from the fundamental value of assets by many sources and lengthy and detailed research. There seems to be a clear consensus that deviations


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from ex- ante knowable fundamentals most likely did occur at these times.

The most famous of these bubbles are the South Sea Bubble (1720) and the Mississippi Bubble (1718–20), Tulip Mania (1637), British Railroads (1840s), Japanese Nikkei (1990s), and Dot com/NASDAQ ( 1998– 2000). The relative infrequency of such events in the post- war period, although they have become more frequent in recent years, should illustrate why policy- makers came to believe they had tamed the financial markets. Furthermore, the identification of bubbles is not easy and many years of careful research have allowed a deeper understanding of these phe-nomena although there is still much more to learn. The Economic History school, notably Kindleberger (1978), challenged the EMH on the basis of the identification of historical periods of boom and bust and asset price swings, which seemed to reflect behavioural elements and credit related booms. The Behavioural Economics school began to challenge the EMH based on long- term empirical testing using rich, long- term financial data (Shiller, 1981). The key insight of the emerg-ing Behavioural Finance/Economics schools was that the frequency of episodes where behavioural effects on asset prices were thought to have occurred is much higher than previously believed. Shiller (1981) pointed to almost continuous under- or overvaluation of the US stock market. Far from being only isolated historical periods of booms and crashes due to behavioural effects, periods of overvaluation and under-valuation were very common. These observations led to a major new direction of thought on how prices were formed, rather than assuming that markets always reflected all available information. The Behavioural Finance and Economics schools led to an innovation in economics by laboratory methods, using artificial asset markets, where numerous fac-tors thought to create over- and undervaluation could occur, to study the conditions of the formation of a bubble, in a controlled setting.

The testing of bubbles in the laboratory setting and the identifi-cation of conditions under which they may emerge, added weight to the growing challenge to the EMH (Smith et al., 1988). The new approaches were complemented by work in Economic History and the collation and testing of long- term data series as a bridge to the real world. They used the laboratory tests which found that bubbles did form, and set benchmarks for real- world tests with actual investment return data. Subsequent research has provided much more data and innovative methods to challenge the idea that markets are efficient and that asset markets are free from human bias when price formation occurs.


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What we focus on in this section is explaining the theory and evi-dence relating to research on bubbles in stocks and other financial assets’ prices.

As we have just discussed, there have been two opposing camps in the academic literature for a considerable length of time as to whether asset bubbles actually exist and whether prices for assets are information efficient, or biased by human behaviour. The crisis of 2008, and the rec-ognition that housing in particular in the USA had become overvalued in the earlier 2000s, coupled with the NASDAQ boom and crash of the 1998– 2000 era, have led to the ascendancy of the Behavioural school. The discussion below reviews some alternative research paths to testing whether markets are efficient, and also looks at cutting- edge research into asset bubbles. We survey the academic research on bubbles to ensure that the most recent advances are captured in our methodology, methods, and conclusions. The survey should also allow the research we conduct to be seen in context. Due to our focus on a period which has been alleged to have been subject to behavioural effects, we do not survey the large amount of literature which supports the EMH school but this bias is not intended to sway the reader towards our conclusions.

We use the term ‘bubble’ to describe a deviation of an asset’s value from its fundamental value, on the premise that its fundamentals, in a probabilistic sense, could have been determined, ex- ante, or a deviation from fundamentals ex- post. In order to be able to set the results of the research into context within the literature and to gauge the usefulness of the conclusions, there are some key taxonomic distinctions that will be useful for interpreting the possible causes of the 1920s boom and bust.

Although we can identify bubbles, ex- post, their identification ex- ante, or at least in time to enact policy to mitigate them had been regarded, in policy- making and academic circles, as a task too complex for central banks or regulatory agencies (Bernanke, 2002). Since the efficient allocation of capital is supposed to be the social function of security markets, bubbles are undesirable. Furthermore, the evidence on the tendency of bubbles to increase the cross- sectional dispersion of wealth serves as another justification for policy intervention in markets (Hirota and Sunder, 2007). The potential effect of bubbles on financial stability is also a reason to be concerned about their formation. As dis-cussed earlier, how these phenomena start and how they propagate are of major interest. The aim of research on bubbles is now to understand why they form and particularly how they start.

In the remainder of this section we look more deeply at the various types of bubble generating and propagating factors, and the research


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on them. We first look at the main four types and then discuss other alternative but not mutually exclusive types related to liquidity, credit, and monetary factors as well as the evaluation of new technology and mass psychology.

Rational bubbles

With symmetric information

Tirole (1982) uses a general equilibrium framework to argue that bub-bles cannot exist if it is commonly known that the initial allocation is interim Pareto efficient. A bubble would make the seller of the bubble asset better off, which is due to interim Pareto efficiency of the initial allocation and has to make the buyer of the asset worse off. Hence, no individual would be willing to buy the asset.

With asymmetric information

LeRoy and Porter (1981) and Shiller (1981) introduce variance bounds that indicate that the stock market is too volatile to be justified by the volatility of the discounted dividend stream. A critique of these empirical tests is that they assume that the required expected returns, r, are constant over time. In other words they cannot account for time- varying risk premiums, where investors display simultaneous desires for higher or lower reward for the volatility they assume when investing in stocks. The concept of ‘ time- varying risk premiums’ can rationalise the long- horizon predictability of stock returns where a high price– dividend ratio predicts low subsequent stock returns.

More recently, a new set of models has arisen which take a ‘real world’ approach based on the modelling of the behaviour of real agents in financial markets.

Limited liability

Allen and Gale (2000), show how limited liability – the idea that a company’s directors may be immune from losses and hence may lack ‘skin in the game’ – may induce bubbles in risky assets. The borrowers in this model obtain investment capital from banks. Because of the legal protection afforded by the limited liability status of the firm, the borrowers’ risk of sustaining direct losses is limited and their maximum losses are the Present Value (PV) of losing their future income stream conditional upon later employment or ability to borrow from banks or investors. However, a problem arises for the efficiency of financial mar-kets because the managers get to keep the upside of their investment. The pay- off structure is therefore convex, and generates a preference


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for risk and for riding bubbles. Hence, these models show that the bor-rowers’ preference for risky assets initiates a bubble and the incentives increase with the riskiness of the asset.

Rational herding

‘Herding’ in investment decisions by investors and money managers is an important mechanism for sustaining and propagating bubbles.

From a theoretical perspective, looking at corporate investment managers’ behaviour and incentives, which can be applied to asset managers if there is an elastic supply of the asset, Scharfstein and Stein (1990), in a seminal paper following the insights of Keynes (1936), show that managers’ herding behaviour is a direct outcome of the imposed incentive structure and hence they may exhibit herd behaviour in the real world based on a rational thought process, rather than an irrational one.

The ‘ sharing- the- blame effect’ modelled by Scharfstein and Stein arises because intelligent managers tend to receive correlated signals while dumb ones do not (they simply observe uncorrelated noise). Consequently if one manager mimics the behaviour of others, this sug-gests to the labour market that he has received a signal that is correlated with theirs, and is more likely to be intelligent. In contrast, a manager who takes a contrarian position is perceived as more likely to be dumb, all else being equal. Thus even when a manager’s private information tells him that an investment has a negative expected value, he may pursue it, if others do.

The authors show that in a world with two managers, there exists an equilibrium in which the second mover always mimics the investment choice of the first mover regardless of his own private signal. The herd-ing incentive of the model is relaxed if managers care about investment returns. DeMarzo et al. (2008) introduce non- standard preferences in theoretical models to explain how bubbles propagate after formation. If an agent’s utility depends on their relative wealth (envy), people will prefer to participate in the bubble so that they can maintain their rela-tive wealth level as the bubble continues.

Using empirical evidence, Frazzini and Lamont (2008) show that mutual funds can be forced by investors to invest in high- sentiment stocks and industries and thus perpetuate bubbles. The authors show that investors dynamically allocate money to funds that invest in high- sentiment stocks. As a result, managers earn higher rewards by follow-ing the pay- off incentives to investing in a bubble. The managers may know that a bubble exists but will not be rewarded if they refuse to obey the sentiments of investors.


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If pay for managers is based on relative performance another powerful reason for fund managers to mimic emerges. Therefore during a bub-ble a manager who maximises his pay over the short run would prefer to mimic the bubble in order to maximise earnings. This is similar but slightly different to the desire to maximise reputation as a good manager.

Shiller (2002) develops the idea that a money manager observing his peers investing in a particular asset may conclude that his decision is based on superior private information and may choose to buy the asset. This is based on the rational idea that betting against the herd can be very costly as markets enter a bubble. Managers who cannot keep up with their peers may suffer fund outflows as investors reallocate funds to the more successful managers who ride the bubble. An additional effect is that managers’ reputational penalties are more severe when the manager is wrong at the time when the rest of the investment community is right than when everyone is wrong. Furthermore, wages of managers who bet against the herd will suffer if performance is linked to short- term gains.

Limits to arbitrage

There is the possibility that rational agents are constrained from behaving in a way that would return prices to fundamentals. De Long et al. (1990) and Abreu and Brunnermeier (2003) show that under certain conditions rational arbitrageurs may even amplify rather than eliminate mispricing.

Fundamental shocks

If the markets’ fundamentals are changing, this makes it risky to sell into the bubble, since a subsequent positive shift in fundamentals might, ex- post, undo the initial overpricing. For example, if there were to be a rise in the value of a company which was not justified due to an overreaction to an earnings announcement, which was known to be a bubble, a trade to correct the mispricing would be subject to the risk of further upside shock to the stock which was justified based on actual fundamentals.

Noise traders

De Long et al. (1990) present a model where traders would not want to trade against the bubble, because the bubble itself was not stable due to short- term ‘noise traders’. Therefore selling against the bubble is risky even without ‘fundamental risk’, since irrational noise traders might push up the price even further and temporarily widen the mispricing. Rational traders with short horizons care about prices in the near future in addition to the long- run fundamental value and are only partially able to correct the mispricing.


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Behavioural models

Differences of opinion

These models assume a setting with investor disagreement and short sale constraints. Miller (1977) provides a simple static model for over-valuation generated by disagreement on the fundamental value and short sale constraints. Diether et al. (2002) show that stocks with high forecast dispersion (as a proxy for disagreement on fundamental value) seem to be overpriced.

Feedback or momentum trading

Lux (1995) builds a model based on a micro level mimicking action of optimistic and pessimistic investors, which under certain conditions amplifies to a large macro outcome following a shock to the real value of the asset. The model shows that a small shock can propagate into a larger one where the incentives to mimic may be determined at a short horizon and therefore a bubble may theoretically form based on a small shock, from a position of rational values. The bubble in the model Lux builds collapses as the number of optimistic investors diminishes and hence the momentum reverses and the shock then reverts to the mean following the same process as during the upswing.

De Long et al. (1990) develop a model that contains positive feedback traders, investors, and informed rational speculators. Traders base their trading on past price changes. Investors’ demand depends on an asset’s price relative to its fundamental value, and informed rational specula-tors trade in response to news about fundamentals and in anticipation of future price movements. In this model the rational speculators desta-bilise prices and cause them to overshoot the asset’s fundamental value. The reason for this overshooting is that speculators know that feedback traders will base their future demands on the magnitude of the past price change and hence increase demand.

Shiller (2000) argues that news media attention amplifies feedback- trading tendencies in the market. As more investors are interested in an asset, news media expand coverage, attracting attention from further potential investors.

Representativeness heuristic and conservatism bias

Barberis et al. (1998) formulate a model based on these two psycho-logical biases. They assume that earnings follow a random walk process, and, therefore, the best forecast for the future earnings stream is the most recent earnings realisation. Instead of using a random walk model, investors mistakenly assume that the earnings process is captured by


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either a mean- reversion model or by a trending model. When the earn-ings innovation has the same sign in consecutive periods, investors mistakenly see a pattern and extrapolate it into the future, thus over-reacting to the information contained in the past sequence of earnings.

Heterogeneous belief bubbles

In these cases, even when information asymmetry does not exist and all investors have access to the same information, the way the information is used or factored in to their models of value can differ to a significant extent.

A vital area of research is the use of survey data to test expectations of investors directly so that empirical analysis of valuation in asset markets can identify investor beliefs. Greenwood and Shleifer (2013) test whether model- based expected returns and expectations of returns diverge and hence can yield information on why returns may exceed fundamentals, using multiple sources of investor surveys linked to the behaviour of stock prices. The authors challenge a key obstacle to explaining how bubbles may form. Time- varying risk premiums, which are commonly used to explain deviations from standard models of asset pricing, are inconsistent with the results. The results suggest more than one investor group must exist as ‘expectations of returns’ derived from multiple sources of survey data are both positively correlated with past returns and negatively correlated with both actual returns and model- based expected returns. Scheinkman and Xiong (2003) show heterogeneous belief bubbles are accompanied by large trading volume and high price volatility.

Laboratory tests of bubbles

The difficulty of empirical tests for bubbles and their limited power in showing under what conditions they may form produced an innova-tion in how bubbles were studied and this new approach, laboratory testing, now represents a frontier in the analysis of these phenomena. Such freedom to conduct well- controlled tests is a major advantage to overcome the drawbacks of the other methods, empirical testing and theoretical models. Smith et al. (1988) use a trading game, where a risky asset pays a uniformly distributed random dividend and partici-pants know the fundamental value of the asset, which varies over time. Bubbles, deviations from the ex- ante known fundamental value, were seen to emerge during the trading game.

Lei et al. (2001) show, in another artificial market setting, that even when investors are forced to hold the asset until the end of the trading game, bubbles still emerge despite the fact that the buyer cannot sell it


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on to another trader. The idea that investors require a ‘greater fool’ to sell to is therefore not a necessary condition for the emergence of a bubble. This is an important result as bubbles may form due to investor irration-ality alone, and may not require one set of irrational traders to sell to.

Hirota and Sunder (2007) use laboratory experiments to explore how investors’ decision horizons affect the formation of stock prices. In their experimental markets, speculators are concerned primarily with capital gains. They find that expectations of capital gains depend on higher- order expectations, which are susceptible to cascading or mass psychology of the market. In markets populated by short- term inves-tors, prices tend to lose their dividend anchors; they can take any value depending on such expectations, and are therefore susceptible to price indeterminacy and bubbles.

Hirota and Sunder find that the allocative efficiency of markets is high in long- term horizon sessions and becomes indeterminate in short- term horizon sessions. The paper suggested several insights into the stock market environments where bubbles are likely to occur.

First, investors’ time horizons are critical to asset pricing. The fre-quency and impact of the failure of backward induction in a market is greater when it is populated or dominated by short- term traders; security prices in such markets are more likely to deviate from the fundamental value. Long- term investors stabilise prices near the funda-mentals through their arbitrage activity and their expectations, which are anchored to their estimates of future dividends.

Second, securities with longer maturities are more susceptible to deviations from fundamental values. As the duration of the security increases, a smaller proportion of its value in the form of dividends within their investment horizons and a greater part of their value depends on the expectations of capital gains, which in turn depends on higher- order expectations.

Third, bubbles are more likely to occur when the future dividends are more uncertain. In such cases, it is difficult for the investors to conjec-ture what others think (and what others think about what others think) about the future prospects of dividends. It becomes more likely that the investors fail to backward induct, and for the prices to be unhinged from their ‘dividend anchors’.

Recent developments in the literature on laboratory tests illustrate the advances in both the technology employed and methods used to identify bubbles and their possible causes. De Martino et al. (2013) measure the human brain’s activity to understand the neuronal processes and sub-sequent behavioural processes which they lead to. They find that parts


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of the brain responsible for predicting the behaviour of others become more active during bubbles.

Credit and financial sector liquidity

Kindleberger (1978) notes the role of central bank policies of monetary easing and tightening of bank rates as a stimulus to the formation of booms and busts in asset prices and the real economy. Laboratory tests of bubbles (Smith et al., 1988) also note the effect of the availability of credit to traders as a stimulus to the formation of bubbles. Pepper (2006) offers a theory, the ‘liquidity theory of asset prices’, and empiri-cal data to explain how credit from the financial sector is channelled to financial assets by investors and institutions. This factor is thought to cause price movements above fundamental values. Congdon (2005) stresses the effects of broad money growth, instead of the traditional monetarist focus on narrow money. Thus with a money supply shock in the form of strong broad money growth from financial institutions, portfolio choices can create asset price bubbles.

Rajan (2005, 2006) argues that low interest rates could reinforce risk- shifting, tail- risk seeking including attempts to hide tail- risk, herding, and illiquidity seeking by fund managers. Investments then concentrate in less and less liquid and more risky assets and potentially cause bubbles.

Mass psychology

Shiller (2000: 2) defines a ‘speculative bubble’ as ‘a situation in which news of price increases spurs investor enthusiasm, which spreads by psychological contagion from person to person, in the process amplify-ing stories that might justify the price increase’. This attracts ‘a larger and larger class of investors, who, despite doubts about the real value of the investment, are drawn to it partly through envy of others’ successes and partly through a gambler’s excitement’. Shiller makes a critical innovation in the study of asset bubbles, through reference to the idea of the phenomena as contagious social- psychological processes. What this definition does not provide, although can be mistaken for, is an attempt at explaining how these processes translate into asset prices. Rather, Shiller (2000) offers some key ideas on how overvaluations can be generated and propagated according to widespread beliefs. Drawing from the literature in psychology and sociology as well as economics, the empirical asset pricing research on long- term stock market prices and the dividends, which they aim to forecast, shows excess variance – wide deviations from expectations. These are unexplained by a host of advanced asset pricing models. The unexplained residuals are linked to


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the behaviour of the market participants and their biases and irration-alities, which lead to the adoption of valuations frequently detached from underlying warranted prices.

The critical point made is that under this view, the prices of stocks do not have to violate the conditions of statistical unpredictability at the firm or macro market level, as the EMH expects, to be seen as periods of behaviourally driven overvaluations.

Technological growth

A relatively new strand of the literature on bubbles contends that tech-nological innovations can legitimately cause a run up and crash in the prices of stocks and that these are only observable ex- post. Therefore the observed patterns are a legitimate reaction of investors to technological uncertainty when a new innovation or technology emerges. Nicholas (2008) examines the 1920s and finds evidence of patents being linked strongly to excess returns over 1928– 9. The effects of the changes seemed to persist after the boom and crash indicating that investors may have been revaluing new technology.

Pástor and Veronesi (2009) develop a general equilibrium model in which stock prices of innovative firms exhibit bubbles during techno-logical revolutions. In the model, the average productivity of a new technology is uncertain and subject to learning. They find empirical support for the model’s predictions in 1830– 61 and 1992– 2005 when the railroad and Internet technologies spread in the United States. According to the paper, during technological revolutions, the nature of the uncertainty changes from idiosyncratic to systematic. The resulting bubbles in stock prices are observable ex- post but unpredictable ex- ante, and they are most pronounced for technologies characterised by high uncertainty and fast adoption.

Application to the 1920s US stock market

The research draws on these studies to better understand what may have caused the formation of any deviation from fundamental asset values in the 1920s. What the preceding analysis does is prevent us from drawing fast and easy conclusions from our results about the true nature of the boom and crash, and also informs the tests we conduct.

2.3 Methodology

Our methodology is heavily empirical by design and follows previous methods from Goetzmann and Ibbotson (2006), Shiller (1981, 2000)


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on the long- term Equity Risk Premium, and stock market returns over long horizons. In order to complete this research, new data not con-tained in the standard databases such as CRSP and Cowles (1938) were gathered. Therefore the work presents data to test the results of previ-ous research and allows for additional variables not contained in these sources to be tested.

Prior research has identified some factors known to influence the formation of asset bubbles, such as:

• The presence of less well informed traders. • Fast rising prices.• Unclear fundamentals.• A lack of dividend anchors for high technology or new industries.• Credit access for leveraged investment.• Mass media interest. • Steep rises in IPO volumes and the volume of trading.• Monetary easing and high liquidity. • New investment funds.

Although we do not negate the use of such indicators, we do not use simple identification of these factors to assert the likelihood of a bub-ble, although we do use the results of one test for momentum as an auxiliary test. Our main goal was to determine the dimensions and causes of the changes of stock prices and valuations during the 1920s and 1930s using empirical tests. We also employ historical research of the financial literature to develop our models and considerable research was directed at being able to reconstruct the dimensions of the US economy during the period to avoid missing key factors, which may have driven stock market values. In this way the assump-tions made in our models can be benchmarked against economic reality.

The motivation for the research as a doctoral thesis topic was the knowledge that new data to verify or challenge previous findings would be materially important for the study of bubbles as well as the historical interest in how investors used financial models and estimated stock returns in the period. We therefore approach the question of market efficiency from a long- term asset pricing perspective using new histori-cal data for both ex- ante expectations and ex- post realisations of the returns to stocks. This allows us to quantify the bubble according to the method of Shiller (1981). From this baseline, we test for potential causes in the cross- section of the new data sources.


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The joint hypothesis problem (Fama, 1970) means that a test of market efficiency requires a model of market equilibrium, but you cannot test models of market equilibrium without assuming market efficiency, because most models of market equilibrium start with a presumption that markets are efficient. The test must use an addi-tional test(s), hence the ‘joint test’. Rejection of the test will not reveal whether the deviation from efficiency was caused by the auxiliary factor(s) or whether the model of equilibrium was mis- specified.

We resolved this potential criticism of our testing approach using a historical model of the US economy and monetary system to determine equilibrium, industry- level tests of high technology valuation, analysis of the credit system to test for exogenous shocks to stock values, and cross- sectional tests of drivers of the changes of valuations from the expectation of our models. These four additional areas of analysis allow us to have a firmer grip on whether stock prices deviated from histori-cal expectation on a fundamentally sound basis. We then employed an additional auxiliary test of ex- post returns to gauge whether ex- post returns were consistent with the presence of a fundamentally legitimate regime shift due to a technology shock. What these combined tests cannot resolve fully and which is left for future research is the possibil-ity that a bubble formed due to a technology shock, which could have created a legitimate increase in valuations due to uncertainty ex- ante, although ex- post these appear as an overvaluation. This problem aside, our methodological design establishes a reasonably robust series of testable hypotheses and we leave open the possibility that some legiti-mate change to the equilibrium level of stock values did occur, even temporary ones as information was processed by investors about new technologies or a new economic system or new modes of valuation.

To supplement our methodology, which cannot offer conclusive evidence of an ex- ante bubble, we provide results from other research on the cross- section of returns during the boom phase to test the idea that a technological shock was occurring (Nicholas, 2008) and one that could have legitimately altered the equilibrium value of stocks. This methodological approach to the vital question of ex- ante bubbles also employed tests of ‘benchmark’ assets whose ex- ante values were known (De Long and Shleifer, 1991). These are used to resolve the dichotomy of a legitimate ex- ante rise in valuations due to uncertainty and any obser-vation of a rise in valuations beyond ex- ante knowable fundamentals.

The first part of the approach was to analyse long- term growth and the fairness of long- term growth expectations based on long- term history prior to the 1920s. Further, we modelled the dynamics of the


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economy and the monetary changes of the post- 1915 period to adjust for any impact these factors may have had. We then developed a valu-ation model for the US stock market based on the long- run ERP from multiple sources; historical estimates are taken from Goetzmann and Ibbotson (2006) and Smith (1924). We then used a long- term constant growth Dividend Discount Model (DDM), the inputs for which could have been sampled by investors using available data sources. The aim was to use historical data for a broad sample of US stocks from the earli-est available time to produce a data set that was available in the 1920s, to gauge fair expectations of dividend growth and survival for US stocks forward in time from that point. The dividend growth for the DDM was estimated using survival data and dividend growth rates over the long run before 1929, using a new data set of 200 firms from 1900 to 1929 to ensure that a fair model was estimated.

To ensure the accuracy of our testing, the techniques we used to create this model needed to be evident in the 1920s. We know this was the case from our analysis of Smith (1924). We therefore built a model based on Smith (1924), which uses our 30- year sample growth rate and extends this over the long- term future. We use the realised ERP before 1926 from Goetzmann and Ibbotson (2006) and Smith (1924) as the discount rate, which reflects an expectation that historical returns were to be repeated in the future. The growth rate estimate follows the technique shown in Smith (1924) and the basis of the DDM model follows Shiller (1981).

We lack expectations data from non- market sources in any scientifi-cally testable order, such as those from investor sentiment indicators or surveys. Hence we cannot resolve what caused the bubble as clearly as would be desirable. One advantage we do have is that any realised returns can be seen ex- post as we are now far enough forward in time to calculate the actual returns relative to the expectation. The auxiliary hypothesis to our gauge of efficiency during the boom phase was to use estimates of the return to investing in stocks over the long term from the 1920s to the present. If these realised returns were different from historical expectation formed on the basis of data from before the 1920s, a change in true expected returns in the 1920s may have legitimately exceeded historical expectation. In this case the boom potentially reflected investor knowledge of a different return expecta-tion to the historical one. This would invalidate our use of the historical expectation as a legitimate test of fair values. By using new data on live returns to a market tracking closed- end fund from 1925 to 2010 which proxies for the returns to the market over the long term, we can assess


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whether the historical expectation was exceeded. This approach allows us to correct for any indexing issues or survival bias as they reflect actual investor experience.

Although our approach still leaves the question of what caused any potential overvaluation unanswered, we know it could have been due to at least two factors: a lower required return or a higher expectation of returns. This methodological approach follows Shiller (1981, 2000) who uses the ex- post rational price by valuation of the US stock market according to its realised dividends.

As we set out in the introduction to this section we aimed to further reduce the problem of whether the change in valuation from 1927 to 1929 was legitimate, using a test of the cross- section of stocks. A key component was to develop a way of looking at the cross- section to detect any systematic causes as to why individual firms rose or fell in value during the overvaluation phase. Detection of systematic biases without potential theoretical justification was used to test for the weak form of EMH. We aimed to test any common drivers causing the over-valuation, such as a systematic change in the ERP driven by the theoret-ical work of Smith (1924) using a new database collected from Moody’s Manual (1930), which was available to investors at the time.

We aimed to test for a series of other drivers of valuations, such as dividend and earnings growth and size, age, initial Price/Dividend (P/D) ratios, and net asset effects. This approach addressed the question of whether the boom was driven by simple extrapolation of fundamentals or any obvious systematic biases. These tests used new hand-collected data not present in the CRSP database using Moody’s Manual of Industrials (1930) regarding firms’ earnings growth rates and data over a longer time period. We use the results of tests of momentum and technology effects of the cross- section of stocks from previous research to complement our tests. We use Nicholas (2008) to complement our model regarding momentum effects and technology effects and Shleifer and De Long (1991) to act as a benchmark to detect any rise in valuation beyond ex- ante fundamentals.

Our cross- sectional tests aimed to resolve the key issue of ex- ante observable overvaluation and to what extent it was legitimate or con-trary to the expectation of the EMH. We therefore are able to envisage an ex- post bubble which occurred when there was a legitimate ex- ante rise in valuations as described in Pástor and Veronesi (2009) but which later breached ex- ante knowable fundamentals and also did not tech-nically violate the expected behaviour of prices according to the weak form of EMH.


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To complement these tests, a further key angle was to develop a fair value model for at least one sector of the US stock market. An innova-tive sector to choose was one associated with the ‘new era’. We focused on aviation stocks as these were most likely to have been overvalued as they were one of the newest industries in the 1920s employing a new technology and one which is cited as having been the subject of heavy speculation (Galbraith, 1954).

Integral to the validity of such an approach was the ability to calibrate other previous new technologies’ growth paths and form historical expectations of value for a new technology. For this we required evi-dence that such models and such data were available at the time rather than imposing modern industrial growth and asset valuation theory on the models we built. Although we did make one assumption in our model, which is not found in the literature, it is likely that such knowl-edge was easily available to investors.

Although this approach suffers from the problem of relying on his-tory, and may ignore issues of uncertainty of technological uptake, firm success or future growth being different from history, it offers a historically valid benchmark. The deviation from this benchmark can be explained by future research.

Key to our ability to understand the crash in October/November 1929, and whether there was an exogenous shock to expectations or required returns which had risen legitimately, was to model the mar-ket microstructure of the NYSE and identify any major new groups of investors who may have influenced prices through increased or reduced selling volume during the boom and crash. This approach looked at the interaction of Federal Reserve policy with the credit system and investi-gates their thinking regarding the overvaluation of stocks, its causes and how to control it. We also examine the effect of their policies on the credit market which funded traders on the NYSE.

Our tests examine the crash of October– November 1929 and whether a credit crisis induced a crash to leave prices below fundamentals. The question was whether a credit crisis forced investors to sell stocks and hence increase the implied ERP or expected return to stocks, or reduce potentially valid high growth estimates. Rejecting or accepting the credit crisis hypothesis allows us to make further inferences about the nature of any deviation of valuations from our model.

Finally, the research aimed to model risk premiums on comparative assets to stocks and changes in inflation and dividends and earnings, to determine the major drivers of stock market valuations during the Great Contraction from 1930 to 1932. We test whether ‘market efficiency’ was


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maintained following the October– November 1929 crash through to 1932. Due to the complex nature of the crash and the limits of being able to construct accurate methods to perform these tests, we reduced the analysis to testing our DDM using a time- varying discount factor based on other risky financial assets. To supplement this we also used CRSP data and book value data from K. French (2014) to assess Book/Market (B/M) ratios from 1926 to 1945. This approach highlights whether the market was undervalued relative to fundamentals in 1932 at the market low using two different approaches.

Limitations of the methodology

Our approach is not without flaws, as we cannot fully ensure that we have modelled the equilibrium levels of the economy accurately; how-ever, we can reduce the uncertainty as to which potential causes of the boom and crash are most likely and can provide a benchmark valuation for the stock market. In this sense changes in expectations, as opposed to changes in the required return, can be disentangled to a meaningful extent, to show that high expectations were present, in the absence of sufficiently large falls in the required returns to create the magnitude of valuation changes seen. What we cannot exclude is the presence of non- systematic time- varying risk premiums for stocks, as we do not test for them. We also cannot control fully for the effect of uncertainty during a technological shock as a justification for ex- ante feasibility of peak prices as we use previous research results, which come from other sources.

Our main model of the fair value of the market is derived from a data sample from 1900 to 1929 from which we reconstruct a historical dividend growth rate, which is used as an expected growth rate for the market over the long- run future. The construction of this growth index will have measurement error, and its use makes some assumptions about how investors would have constructed their estimates of the long- term growth potential of dividends for an investor who held the market over the long run. We use the method of Smith (1924) who used 20- year holding periods for a live index (survival adjusted) of stock returns, assuming that reinvestment occurred over a 20- year cycle, when he calcu-lated total returns. We use a 30- year period using the same method, but this difference should not affect the results to major extent, but must be acknowledged. Our measures of the ex- post returns to a proxy for the general market from 1925 to 2010 may also have some bias as we only look at one fund, but the characteristic of the fund and its market track-ing mandate, together with no debt leverage and high levels of diversifi-cation, make its use feasible to assess a general estimate of the long- run


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returns to investing in Common Stocks. Our approach to the ex- post return as an indicator of the absence of a large increase of stock mar-ket returns relative to history may be biased by the effect of the Great Depression to some extent; however, given the long- term nature of the returns and the stable trend growth rate of the US economy from before the 1870s to 2010, we assume that no major innovation in the returns to stocks was forthcoming. On this basis we infer that stocks were over-valued relative to subsequent returns in the late 1920s. This does not mean that higher levels of dividend growth rates were not feasible as an expectation relative to history and our test should not be used to infer that higher dividend growth rates were not plausible ex- ante.

One major criticism of the tests of the cross- section is that even though they may not detect a technical violation of the EMH, this should not be seen as the acceptance that markets were rational in their behaviour or processing of information. We therefore leave aside any micro- behavioural inferences from the tests. Furthermore, our investi-gation and application of the theory of Pástor and Veronesi (2009) is to show that a potential cause of a legitimate change in equilibrium values in 1927– 9 was a technological/uncertainty shock.

It remains totally plausible that another factor or series of factors trig-gered the boom, such as market- wide increases in prices leading to an illegitimate focus on the belief in a ‘new era’, or that no justification can be cited for the initiation of any bubble we detect and no changes in equilibrium values were justified in 1927– 9. Taken together these limita-tions mean that the rises in values may have been totally unjustified.

Tests at the industry level for a new technology using a historical growth- based model cannot exclude that this particular industry did not have higher fair expectations of growth than rates calibrated from historical growth. In other words, we cannot know if history was a dependable guide to the future. We also cannot discern whether the bubble was caused by genuine technological uncertainty as to which stocks would survive. We also do not test for momentum in this industry- level model due to limited data and hence we cannot identify momentum effects or reject them as an auxiliary test for this industry.

Our tests of market efficiency in 1930 from which point in time we define the bubble to have disappeared are subject to the criticism that the beginning of the recession in 1929 distorted the assumption that stock prices in 1930 did not reflect a higher risk premium. We assume that risk premiums remained the same in aggregate and time the end of the bubble on the basis of a return to the Market Price/Dividend ratio from 1927. This may overlook an increase in the market


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risk premium due to investor caution about the future course of eco-nomic growth in 1930. Such a critique is a fair one and it remains totally feasible that risk premiums for stocks may have risen to a legitimate extent from 1929 to 1930, and therefore masked a genuine change in earnings growth during the boom.

Under this alternative view, in 1930 the market was reflecting, not the collapse of optimistic growth expectations or a fall in the ERP, but instead reflected new risk premiums and new expectations due to the knowledge of the onset of the Great Contraction, which was known and factored in to prices.

Our tests of the credit crisis in 1929 and the market microstructure of the NYSE are limited by the power of our tests and the limited data. We can illustrate how the credit crisis developed and offer some evidence to suggest that a credit crisis did not cause the October crash; however, further more advanced analysis of whether the crisis was a focal point of the selling pressure or whether the crash was partially caused by the forced selling is an avenue for future research.

Our tests of the market declines from 1929–32 and our use of a time-varying risk premium may reflect our estimate of an incorrect theoretical change in the risk premium from which we infer the values of the stock market at the low in 1932. These tests are only intended as a simulation of possible valuations rather than a concrete method for identifying how prices changed during the crash.


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58

3The US Economy and the Financial System

3.1 The boom and bust of the US stock market

Perhaps one of the most famous economic time series graphs of all time is the US stock market boom and crash of the 1920s and 1930s. What can be seen is a large run- up in prices and then a large reversal. The temptation many have succumbed to is to see the boom and bust as prima facie evidence of an overvaluation. Although such an interpreta-tion would be correct to a certain extent, it does not offer explanation as to how changes in the economy and investors’ ideas may have driven the changes, which can be verified scientifically.

The tests and analysis that we conduct will enable the processes behind the data in the graph to become much clearer and allow the reader to see how the initial boom formed. We will also show that the crash from 1930 to 1932 was not directly related to the boom, and that this scale of crash was most likely unforeseen.

According to our tests, the boom in stock prices had two major components:

1. A credit/debt expansion.2. An expectation of higher future returns to Common Stocks or lower

risk premiums.

The light grey line in Figure 3.1 shows dividends from the Cowles (1938) index and their increase in nominal terms originates in the credit and debt expansion of the 1915– 29 period. The increase in Price/Dividend ratios, shown as the deviation of the Price Index from the Dividend Index from 1927 to 1930 shows that investors seem to have changed their valuations of stocks relative to their historical norm.


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The US Economy and the Financial System 59

Valuations crested in September 1929, having detached from their nor-mal levels at the end of 1927, and crashed by 45 per cent in October/November 1929. There was a subsequent rebound to a level about 20 per cent below peak values in early 1930, which quickly reversed by mid- to- late 1930. A large overvaluation, according to our tests presented in Chapter 4, had mostly dissipated by late 1930 and the subsequent fall in earnings, dividends, and stock prices were due to the onset of the ‘Great Contraction’ – an economic contraction of severe and unprece-dented magnitude. The full crash in stock values eventually culminated in the trough in June 1932.

The exact nature of the 1929– 33 ‘Great Contraction’ of the US econ-omy is still debated in the economics and finance literature, in part due to the extreme complexity of this period. We devote a special section to various theories of how and why the ‘Great Contraction’ formed, but defer that discussion until later.

What the following chapters do is set out the data and theory neces-sary to show how we can test for the presence of an overvaluation of the US stocks using long- term return data both ex- ante and ex- post. We also show how we can test for the ‘fundamental’ values of a major new technology industry. We then show how we can test the cross- section of asset valuation changes during the 1927– 9 period. Having established

Figure 3.1 Prices and Dividend Index (Cowles) (Base = 100, Jan. 1913)Source: Cowles (1938); Shiller (n.d.).

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the level of prices, which we think reflects fundamental values of the stock market (see Chapter 4), we set out our view of the microstructure of the NYSE market (Chapter 5). We examine the credit flows, volumes of shares, and the agents active in the market. During our look at the crash itself in October 1929 we test for a credit shock as a potential cause. Finally (Chapter 6) we look at the real economy, assess the funda-mental shocks during the 1929– 33 period, and examine the behaviour of stock market valuations to see whether asset values fell below funda-mental value and on what basis this may have occurred.

Although these innovative tests, which in many cases use new hand- collected data series, are the major innovation of the work we also provide data on the US economic system to illustrate how we can be assured of the strength of the tests we perform by setting them in eco-nomic context. This historical analysis of the economy establishes the basis for our assertion that we can reliably determine where equilibrium was in 1927– 9.

In essence, the research devoted to setting out the economic con-text improves our chances that the tests do not exclude factors which may negate the results. For example, if there was a step- change in the growth path of the US economy and the market was reflecting such knowledge, or there was a change in the equity risk premium due to the maturation of the stock market, or a change in the risk tolerance of the investors and traders at the time which was legitimate, we may miss a legitimate equilibrium change. This potential for omission is the major weakness of testing for bubbles, which the EMH school points out. Considering the results of our tests, there does seem to have been a fundamental change in the economy, which the stock market may have been responding to. How we measure and control for the impact of such legitimate changes is set out in our tests later in the work.

Bernanke (2002)1 highlights the difficulty of when to call a ‘bubble’ in real time. The task of an ex- post identification of an overvaluation and whether it could have been identified ex- ante in 1928– 9, means we have set ourselves the challenge of making some very accurate measurements of the drivers, size, and timing of an alleged bubble in stock market val-ues in the 1920s. Thus we must be sure that we can identify equilibrium in the market and any legitimate change in this equilibrium.

The problem of exact observation of equilibrium levels of the stock market is very important for the study of asset bubbles and deviations from fundamentals are hard to detect if the future, which is being expected, has not yet occurred. From the perspective of scientific analysis, we had a target, which was much easier to pin down, as we have the


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benefit of hindsight and long- term data on realised returns, and we can control for effects, such as a legitimate increase in stock values due to a change in productivity by measuring ex- post returns. The strength of our analysis depends ultimately on ex- post identification of an overvalu-ation. We are reasonably confident that we have identified something close enough to the real equilibrium to identify a large bubble of up to 50 per cent in stock valuations, ex- post, which is detailed in Chapter 4.

Having described the outline of the boom and crash and described the areas that we will focus on in later chapters, we turn now to focus on the historical background of the US economy. This enables us to gauge whether our models of long- term expectations of stock market returns are accurate and informs the models we build in later chapters. This helps us to judge the equilibrium level of the US market and the justification for any changes to equilibrium.

We provide a detailed look at debt levels and compositional changes in debt in various sectors of the US economy, the housing boom and housing debt held in the US banking system. We also provide some background to the Gold Standard system, the newly formed Federal Reserve System, interest rate policy, and the effects of the credit expan-sion on corporate earnings and the level of the US stock market. We also measure real and nominal GDP growth, inflation expectations, and productivity trends.

3.2 The real economy

A major component of our stock market valuation model is that we assumed the ability of investors to expect the USA would maintain its long- run historical economic growth rate over an eighty- year timescale, when looking forward from 1929.

The basis on which this assumption was made is set forth in the fol-lowing chapter. We use it to justify the use of historical dividend growth rates in our valuation models detailed in Chapter 4. The timeframe for projecting future growth from the 1920s may appear optimistic; how-ever, there were sufficient historical data available then, demonstrating that such an assumption is feasible, given the stability and longevity of the growth path of the US economy prior to the 1920s.

W. W. Rostow (1956), an early economic growth theorist, dates the emergence of the USA as being on a ‘ self- sustained’ growth path as early as the 1850s.2 This chapter demonstrates that the USA’s economic growth path over the long term prior to 1929 would have rationally been expected to continue into the long- term future from 1929 onwards.


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Following the technological advances of the second industrial revolution,3 dated from the 1870s, the USA displayed a sustained long- term economic growth path, with real GDP growing at an annualised geometric rate of 2.9 per cent per annum. The various sources of US economic growth serve as a robustness check to our long- term growth assumptions and are listed below:

Natural resources

The USA was endowed domestically with large deposits of natural resources such as minerals and oil vital to the growth of modern indus-trial enterprise both as raw material inputs and energy for transport and power. The USA was also able to utilise natural resources effectively by the application of technology. From 1900, the USA eclipsed all other major industrial powers as a source of world industrial output, rising from 23 per cent to 40 per cent of the total global industrial output by 1928 (Wright, 1990).

The institutional framework

The USA possessed institutional strength via good government com-mitted to the nation’s development.4 The USA’s institutional and legal framework facilitated economic growth via the provision of property rights, notably patent rights to entrepreneurs and innova-tors (Lamoreaux, 2010), thereby generating large incentives for the development of innovations, and via the ability to enforce property rights through the legal system with minimal threat of appropriation by government or other entities.

A highly developed railway transportation network was also in place to facilitate transportation of people and products, as well as telegraph and telephony to facilitate efficient communications across the USA. The US economy was arguably also the largest free trade zone in the world at that time and possessed a financial system that was expanding to meet the demands of the rise of the USA’s industrial base (Lamoreaux, 2010).

By 1930 the inventor, traditionally associated with the development of new technologies in the USA, had moved inside the boundaries of the firm (Schmookler, 1957) following the establishment of industrial research labs in large firms such as Du Pont, General Motors, and American Telephone & Telegraph (AT&T). This institutional capture of research and technological advancement as well as the progress of general science in the university system promoted the USA’s growth.

Much of the growth in higher education during the late nineteenth century was funded by private sources including businesses seeking to


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create local pools of expertise to meet their needs. The result was an extensive but decentralised system of colleges and universities in which research was often oriented towards the concerns of local industries, for example tires in Akron and mining in Minneapolis, which gave a larger proportion of the population access to advanced training than anywhere else in the world at that time (Lamoreaux, 2010).

The USA had developed an advanced educational system from the turn of the century to 1930, which was key in the development of research laboratories for industrial firms with a strong link between industrial research laboratories and universities. Classical examples of these linkages are the associations between Du Pont and the University of Delaware and Merck at Rutgers (Nicholas, 2003).

At the turn of the twentieth century, US industry experienced a large merger wave. The period 1865– 1920 witnessed a dramatic change in the size distribution of firms in the US economy as large- scale enter-prises emerged to dominate large segments of industry (Lamoreaux et al., 2002). This was due to the benefits, via economies of scale and rationalisation of production and distribution processes, resulting from horizontal and vertical integration (Livesay and Porter, 1969) and the adoption of new organisational structures (Chandler, 1990).

Assessments by Banerjee and Eckard (1998) concluded that these mergers realised gains in value of 12– 18 per cent, inconsistent with the theory of monopoly behaviour and hence stressing the real benefits of merger at that time. Those unconvinced of the benefits of merger cite the motives for the mergers being the need for sufficient industrial size in order to gain promotion to the NYSE which had minimum size requirements (Hannah, 2006) and the horizontal integration method of gaining market share dominance (Lamoreaux et al., 2002). US Steel’s 60 per cent market share in 1913 was considered above minimum efficient size and hence created some monopolistic distortions.

However, the growth of firm size was to a significant extent motivated by the needs of business to circumvent the obstacles to efficiency in production scale and distribution, with the dominance of forward verti-cal integration emphasised by Livesay and Porter (1969).

These larger firms which came about as a result of the merger wave, whilst possessing monopoly power to some extent, were able to devote large sums of money to the long- term commitment to conducting industrial research and development and hence contributed to the ‘pushing out’ of the technological frontier by engaging in such research (Schumpeter, 1942; Nicholas, 2004).


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US foreign direct investment (FDI)

By 1930, due to the effects of the First World War, the USA’s economic growth, and the monetary expansion in that period, the USA had become the main capital exporter, financing the reconstruction of Germany after the war and lending to other nations in South America and Europe. This large outward flow reached over $5 billion, which amounted to 60 per cent of global net capital export, with Britain and France exporting $1.3 billion each (15 per cent of the world total) and is testament to the growth of the USA into an economic superpower by this time (Feinstein et al., 2008).

Summary

As stated in the methodology (Chapter 2) we use historical analysis to help navigate the joint hypothesis problem in the identification of asset bubbles. We use long- term growth rates of GDP and dividends before the 1920s, so that we can be reasonably sure that the USA was on a stable long- term growth path. The equilibrium model we seek to establish so that we can test for an overvaluation is therefore built on these data. By 1929, continued GDP growth over a very long timescale would be the rationally expected course of the US economy, based on historical experience from the 1870s to the 1920s.

Due to the steady growth rate of the US economy over long time periods prior to 1929, although there would be a small growth component in Multi- Factor Productivity Growth (MFPG), there would be an anticipa-tion of a forward- looking GDP growth rate and hence the expectation that corporate profit growth rate would be higher than its long- term historical trend rate up to 1929, but it is not obvious whether such a calculation was being performed by investors. To accommodate this we allow our tests to simulate valuations based on a projected future growth rate of dividends of up to 3 per cent, set as an upper bound, which reflects the ex- ante long- term growth path of GDP. We are also able to accommodate expectations of dividend growth of over 3 per cent in our simulations, although these levels of expectation need to be set in the context of historical expectations, which were much lower.

3.3 The Gold Standard

Examining the international monetary system of the period and its effect on the US economy is integral to the analysis of the changes in the US stock market. We therefore show the background to the


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analysis of international gold flows into the US economy detailed in section 3.4.

The change from the Classical Gold Standard to the interwar Gold Exchange Standard system

The First World War was responsible for the disintegration of the Classical Gold Standard system and the emergence of a reconstructed international Gold Exchange Standard (GES) in the 1920s. Many coun-tries switched to free floating exchange rates during the war and slowly rejoined the new GES during the 1920s. This reconstructed system was, in key respects, different from the former system and the period of transition between the two systems, during which no international monetary standard was present, had an influential effect on monetary dynamics and inflation rates across the world.

Although the functioning of the pre- 1914 Classical Gold Standard system is still debated in the academic literature,5 there was a marked change in the structure and operation of the international Gold Standard system post- 1913, with the USA at its core. Describing the period, Mundell (2000)6 shows that US monetary policy had become much more important with the creation of the Federal Reserve System in the USA with its twelve regional Federal Reserve Banks presided over by the Federal Reserve Board, and its effect on global interest rates and price levels under the interwar Gold Standard system.

Before 1913 most countries of the world adhered to the Classical Gold Standard system, with its commitment by monetary authorities to convertibility of their currencies at fixed rates. The period was also characterised by limited central bank intervention in foreign exchange and money markets and the free mobility of capital (Bordo and Macdonald, 2003).

The classical position on the Gold Standard era is in Goschen (1892: 127):

money will be dear and scarce in the country which owes much to foreign creditors, and plentiful in that which has exported much and high interest will be attracting money to that quarter whence specie is flowing out in the payment of foreign debt. An adverse bal-ance of trade will … render the bills of a country which is most in debt difficult of sale, and tend to compel it to export specie: whereas the high rate of interest, which is generally contemporaneous of a drain … of specie, will revive a demand for bills on this same country, and enhance their value in other quarters, for there will be a general


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desire to procure the means of remitting capital to that market where it commands the highest value.

Thus

where there is a considerable efflux of specie taking place, the rate of interest will rise in the natural course of things. The abstraction caused by the bullion shipments will of itself tend to raise that rate. (Goschen, 1892: 132)

Moreover, a country can finance a temporary balance of payments deficit by borrowing abroad. Finally arbitrage in the securities market will ensure that interest rates for a similar class of bills will be equal between financial centres:

if at any time the rate of interest here falls below that which rules on the continent, it is inevitable that the whole mass of these bills will at once be sent to London, and be discounted there at the cheaper rate, so that the proceeds may be remitted in gold to the continent to be invested there in local securities at the supposed higher rate. (Goschen, 1892: 138)

Following the outbreak of the First World War and the creation of the Federal Reserve in 1913 the mechanics of the Gold Standard system as an international system were altered as Great Britain, France, and Germany suspended gold convertibility. This suspension of the com-mitment to redeem their currencies in gold allowed the creation of fiduciary money and the subsequent deterioration of the free floating exchange rates of these currencies as the high costs of war necessitated the issue of substantially increased volumes of paper currency. Austria, Hungary, Poland, Russia, and Germany experienced uncontrolled hyperinflation (Feinstein et al., 2008).

The post- war era of reconstruction saw a return to the Gold Standard reconstituted as the Gold Exchange Standard, which was deemed preferable by most major nations. Plans for reconstructing the interna-tional Gold Standard were laid at the Genoa Conference of 1922, where the financial commission, under British leadership, urged the world to return to a ‘Gold Exchange Standard’7 under which member coun-tries would make their currencies convertible to gold, but use foreign exchange – the currencies of the key reserve countries, the UK and USA – as a substitute for gold (Bordo and Macdonald, 2003). Viner (1937)


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questioned the merits of such a system, although with the benefit of hindsight following the disaster of the Great Depression also highlighted that the new system was supposed to allow poorer countries to avoid the need to hold gold reserves, which were costly.

The GES was restored worldwide in the period 1924– 7. Central bank statutes typically required a cover ratio for currencies between 30 and 40 per cent divided between gold and foreign exchange reserves. The cen-tral reserve countries, the UK and USA, were to hold reserves only in the form of gold. Many believed the GES was established based on incorrect parities. It is widely held that sterling returned to gold at an overvalued rate of between 5 and 15 per cent (Bordo and Macdonald, 2003).

The GES was in many respects different to the pre- war system, involving:

1. The transition to a higher degree of central bank management of the currency.

2. An increasingly fractional reserve system.3. The substitution of circulating gold coin with a promise to redeem

paper currency for gold at a fixed price.

Viner (1937) states that under a managed currency system the idea of the Gold Standard functioning ‘automatically’ was illusory, on the grounds that the GES system was based on a fractional reserve system and that central bank intervention had become of overriding significance:

If a currency system could be imagined under which the specie reserves of the banking system as a whole were always maintained without a central bank regulation at a constant ratio to its demand liabilities to the public, there would be only one significant differ-ence between such a currency and a simple specie currency as far as the international mechanism was concerned. Whereas under a simple specie currency, fluctuations in the quantity of specie would result in equal fluctuations, both absolutely and relatively, in the amount of means of payment, under a fixed fractional reserve, currency fluctua-tions in the quantity of specie would result in eqi- proportional but absolutely greater fluctuations in the amount of means of payment. The absolute amount of specie movement necessary for adjustment of the balance of payments to a disturbance of a given monetary size would be less under a fractional reserve currency than under a simple specie currency.

Where the ratio of the amount of currency to the amount of spe-cie is subject to the discretion of the central authority however, the


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international mechanism becomes subject to the influence of the decisions or activities of this authority and loses some, at least, of its automatic character. (Viner, 1937: 390– 1)

Viner presented his view that the managed nature of the interwar central banking system renders analysis of the mechanisms and the automatic nature of the system largely impossible:

Theorising about the nature of the international mechanism in so far as it is subject to influence by the operations of central banks cannot therefore be forthright and categorical, but must resort to analysis of the consequences for the mechanism in different types of situations of the particular choices which central bankers may conceivably make among the various species of action or inaction available to them in such situations. But whatever central banks do or refrain from doing, and for whatever reasons or absence of reason, their mere existence with discretionary power to act suffices to give some phases of the international mechanism and especially the specie- movement phase, a ‘managed’ and variable and largely unpredictable relationship to the other phases of the mechanism. (Viner, 1937: 391– 2)

With regard to the international mechanism for balance of payments adjustment, Viner stated:

Whatever form it takes, the international movement of short- term funds derives its importance for the mechanism of adjustment of international balances from the fact that these funds are highly mobile and in the absence of financial or political disturbance respond quickly, especially as between well developed money mar-kets to even moderate relative fluctuations in interest rates. (Viner, 1937: 403)

A highly incisive account of the interwar GES, which corroborates Viner (1937), comes from Gustav Cassel, a celebrated economist8 spe-cialising in monetary economics and the originator of the Purchasing Power Parity (PPP) theory. Cassel (1928) illustrates that the USA had become the centre of the GES system:

It is true that even before the War the monetary demand for gold was a factor in influencing the value of gold and therefore also the


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purchasing power of every gold currency. Nevertheless the present situation is in two aspects essentially different.

First, people now realise that a deliberate regulation of the mon-etary demand for gold is possible and may have an important influ-ence on the value of gold. … it is natural enough that people should come to recognise the stabilisation of the purchasing power of gold as against commodities to be the ultimate aim to be pursued …

Secondly, the influence on the value of gold has been concen-trated in one country, the United States. The monetary policy of this country, therefore, has acquired a first rate importance in regard to the value of gold. This circumstance has, of course, accentuated the fact that the value of gold is no independent factor of our monetary system, but a result of monetary policy. When nowadays any other country reconstructs its monetary system as a gold standard, it does not in fact link it to an independent value such as that which gold was supposed to have been before the war, but simply connects it with the value of the dollar, the immediate practical aim always being to keep the dollar exchange of the new currency at a fixed parity. …

Under such circumstances the United States are in a position to exercise an independent control over the value of their currency; the value of the dollar is simply the result of the way in which the monetary authorities of the United States chose to regulate the gen-eral supply of means of payment in the country. The Federal Reserve authorities therefore control not only the general level of prices in the United States, but also the price levels of all other gold standard countries in the world. (Cassel, 1928: 70– 3)

Keynes (1923) shared the views of Cassel (1928) and Viner (1937). He states:

In the modern world of paper currency and bank credit there is no escape from a ‘managed’ currency, whether we wish it or not; con-vertibility into gold will not alter the fact that the value of gold itself depends on the policy of the Central Banks …

But the war has effected a great change. Gold itself has become a managed currency. Gold stands at an artificial value, the future course of which almost entirely depends on the policy of the Federal Reserve Board of the United States.

[Britain rejoining the Gold Standard means] that we surrender the regulation of our price level and the handling of the credit cycle to the Federal Reserve Board. (Keynes, 1923: 170– 5)


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Keynes and Cassel illustrate the critical change the GES represented for the world monetary system. The Classical Gold Standard had become a ‘de facto’ US Dollar Standard with the US central bank having considerable scope to determine the price of gold. With its high degree of autonomy in the issue of its domestic money supply, other world countries on the GES were bound by the fixed exchange rate system to alter their money supplies to maintain parities with the US dollar.

As highlighted above, the GES allowed the Federal Reserve to pursue a significant degree of independence in its monetary policy. The idea that the Fed was totally restricted by this Gold Standard seems untenable, as there was sufficient gold in the USA to pursue to some extent all three points of the ‘ trilemma’, which existed for other countries on the GES.

As Cassel (1928: 72– 3) noted:

The United States have accumulated a very large gold reserve which has not been used for a corresponding credit expansion. Only part of this accumulated gold is actually needed as a basis of the American monetary system. The rest forms an extra reserve, from which the United States are able to supply almost any amount of gold that could practically be asked for by the outside world. And what is very important, this can be done without touching the ordinary gold cover of the currency, and therefore also without in any way affect-ing the supply of the means of payment within the United States …

The second fact is that the United States are wealthy enough to allow themselves the luxury of storing any amount of gold that may be sent to them without letting such fresh gold supplies have any effect in the way of credit expansion. Thus gold exports need not cause a fall in the American price level.

To summarise, Cassel (1928) illustrated that the USA had by then switched from a position of passivity in allowing gold inflows to expand the US monetary base to one where they continued to accumulate gold but did not allow further credit expansion. The control of the monetary base as gold flowed in to the USA was not checked during the First World War period and hence led to a large expansion of the monetary base, which allowed the credit expansion which was to become highly integral to the boom. Furthermore the work of Keynes (1923) and Cassel (1928) highlights US control over the value of gold and the credit sys-tems of other countries.

Although the long- term sufficiency of world gold production was queried by Cassel (1928), who highlighted the potential long- term


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dangers of inadequate gold supplies required to provide backing for the world’s money stock, Rockoff (1984) explains that the growth in gold discoveries, gold extraction technology, the adoption of intensive methods of production, and the tendencies of governments to relax environ-mental protection laws that restricted mining, would be expected in the long term to ensure that there was no such pressure. Investors’ fears of insufficient gold resulting in an expectation of monetary stringency do not appear to have been a factor for valuing stocks in the late 1920s.

As we go on to demonstrate, the return to long- term Government Bonds and the long- term US Government Bond yield of 3.7 per cent suggest that inflation of 0.8 per cent per annum was expected over a twenty- year horizon.

There are highly credible grounds for assuming that investors in US Common Stocks in 1929 did not expect any form of significant near or long- term price deflation, because the Federal Reserve System was rationally believed to possess the policy tools and gold reserves to main-tain the supply of money and, hence, the price level.

Summary

Research conducted at the time and more recently points to the large dislocations of the old monetary system of the world and the emer-gence of a more fragile new one. Central bank policy had come to play an increasingly large role in the USA.

3.4 Monetary dynamics and the US stock market

The origin of the 1920s boom is found in the economic dislocations of the First World War, and the expansion of the monetary base and credit system of the USA. As gold poured in from Europe to finance the war (Figure 3.2), the amount of lending which the US banking system could accommodate increased substantially (for a discussion of the theoretical foundations of how the Gold Standard system worked see Barro, 1979). Some of this lending was used to finance the holding of US government debt by the US public, via banking system credit to buyers of this debt from 1917 to 1919 for the ‘Liberty Bond’ programme (Meltzer, 2003). The new gold was also used to increase commercial lending. Gross federal debt rose from $1.2 billion in 1914 to $24 billion in 1920, as a result of the Liberty Bond programme and other wartime increases in expenditure. This outstanding debt level was about 30 per cent of GDP by the end of the war in 1918, which was soon paid down to $16 billion with a debt to GDP ratio of 16 per cent by 1929 (NBER Macrohistory Database, 2014).


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The Liberty Bond sales not only increased the public’s familiarity with securities, but also it can be argued to have been the start of the public’s desire for and comfort with both the marketing of securities and will-ingness to use them as investments, rather than rely on bank deposits (Ott, 2011). The ability to generate a large- scale investor base to absorb security issuance by the US government can be seen as a precursor to the large- scale operations of banks and their securities affiliates (Peach, 1941), who tapped into a public with the growing wealth and confi-dence to absorb the securities of commercial and industrial companies in the USA.

As a consequence of this increase in gold in the US banking system, the level of lending to the real economy via credit to households, govern-ment, and businesses expanded on a large scale (Figure 3.3). The measure of broad money in the economy (M2) rose from $20 to $48 billion from 1915 to 1929. Such a large change in the monetary dynamics of any econ-omy warrants serious consideration and the discussion below looks in detail at these dynamics, including their effect on the value of US stocks, through changes in the earnings and dividends of these companies.

Analysis of Figure 3.4 shows that the rate of growth of the M2, M3, and M4 indicators was marked around the 1915– 19 period, rising to as high as 17 per cent per annum, thus the major change in the growth of monetary aggregates occurred in this time period before settling back down to a pre- war trend rate of growth of about 5 per cent per annum.

Figure 3.2 US Treasury/central bank gold reserves (metric tonnes)Sources: Annual Report of the Director of the Mint, Washington, DC (1940); Banking and Monetary Statistics, Board of Governors of the Federal Reserve System, Washington, DC (1943).

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Figure 3.3 US money supply (1899–1931)Source: Friedman and Schwartz (1963).

Figure 3.4 US money supply growth (1900–1931)Source: Friedman and Schwartz (1963).

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As discussed later in this section the 1920s were not a time of high money growth in general. This aggregate level normality in the rate of growth of money was due to the rise of the home loan debt growth rate, being offset by the fall in the growth rate of other debt sectors.

This growth in the monetary measures and debt in the non- government economy was neutral, in real terms, at the aggregate level. The ratio of national income to total private debt remained constant and debt servicing costs remained at around 5 per cent through the period (Kuvin, 1938). However, these aggregate measures do not show that there was a key change in the debt structure of the US economy through the 1920s as inflation- adjusted commercial debt growth rates contracted but were offset by an expansion in the inflation- adjusted growth rates of lending to the home loan sector (Kuvin, 1938; Wheelock, 2008).

Figure 3.5 shows nominal levels of debt in five major categories: non- farm mortgages, farm mortgages, steam railroad companies, public utili-ties, and manufacturing and mining companies. There was one sector of the US economy which underwent significant increases of debt growth during the 1920s. This was the home loan sector listed as non- farm debt. In real terms, the total private debt burden in the USA did not increase; however, the composition of the debt changed towards home mortgages ( non- farm).

The problem of high levels of housing debt is that they can lead to prob-lems for the real economy via the impairment of banks’ balance sheets if very high levels of default occur. They also can produce severe negative wealth effects, which reduce consumption if house prices fall relative to the debt owed, and may induce behavioural changes on the part of home-owners, who may pay down loans instead of maintaining consumption if their debt burden relative to income is too high (Mishkin, 1978).

The 1920s and the 1930s seem to be prime candidates for such effects, as the high levels of debt and the fall in house prices in the 1930s may have played a significant role in the severity of the Great Depression.

The recent financial crisis of 2008 has stimulated much research on both the dynamics and parallels between the housing bubble in the USA in the 2000s and the 1920s. White (2009) finds that a bubble of about 20 per cent in real terms formed in the 1920s and peaked in 1926. Home and commercial property prices in isolated pockets such as Manhattan experienced a large boom greater than the general market in the USA due to the concentration of higher income and limited supply in those particular areas (Nicholas and Scherbina, 2013).


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As suggested by White (2009)9 there was a rise in the securitisation of mortgages, which accompanied the housing construction boom of the 1920s. Goetzmann and Newman (2012) analysed the rise of the com-mercial mortgage- backed security market (CMS), which was used, in part, to finance the construction of skyscrapers in major US cities, like New York and Chicago, and a sizeable market for commercial mortgage- backed securities emerged. Although it was much smaller than the bank- based supply of mortgages for non- farm residential loans of about $30 billion, it still formed a large segment of the mortgage market and these instruments were traded on exchanges.

The commercial real estate (CRE) bond market developed when tra-ditional lenders, including commercial banks, refused to advance funds for risky atypical CRE projects (Wiggers and Ashcraft, 2012).10 Total issuance of CRE securities was $4.1 billion between 1919 and 1931. Between 1919 and 1925, total yearly issuance grew from $57.7 million to $695.8 million, or nearly 1,106 per cent. Buildings in New York and Chicago backed 46.2 per cent and 25.9 per cent of the issuance over $1 million, respectively (Goetzmann and Newman, 2012).

Figure 3.5 Private debt levels by sector (1900–1934)Source: Kuvin (1938).

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We can therefore see that in addition to the growth in bank- based non- farm mortgage loans there was also an active market for real estate securities. However, bank- based lending was the dominant source of funding for home loans and much larger in the size of increase, at an estimated $30 billion.

The CRE sector performed poorly in the Depression. At least 80 per cent of the outstanding mortgage securities issued between 1920 and 1929 were failing to meet their contracts in 1936. The yield on these securities rose from 8 per cent in 1929 to 58 per cent in 1933. Recoverable value on those same issues ranged from approximately 80 per cent for 1920- vintage bonds to less than 40 per cent for 1928- vintage bonds (Goetzmann and Newman, 2012).

This expansion in home loans created problems for the US economy in the 1929– 33 period, primarily because the term of a mortgage was only five years long and fixed in terms of repayments (Wheelock, 2008). Such problems meant that ‘ roll- over’ risk was high, and that in a deflation, fixed nominal payments on the mortgage were larger in real terms and the value of the total mortgage was also larger (Fisher, 1933; Mishkin, 1978; Wheelock, 2008). Higher levels of unemployment also made debt default more likely. In the event of a severe recession, this level of nominal fixed rate debt had the potential to cause an increase in the severity of the depression by two channels:

1. Through hazards to consumer spending via wealth effects on home values relative to debt and through a fall in nominal income relative to fixed mortgage payments.

2. Through asset impairment in the banking sector leading to credit contraction or even insolvency.

When combined, these effects could have created a feedback loop where credit frictions raised the cost of refinancing and the cost of finance for new home loans. One of the most contentious debates about how the Great Depression formed is whether the banking system was carrying a balance sheet that was, at a systemic level, too large relative to capital, given that a severe macro shock could lead to rising unemploy-ment and default (Bernanke, 2000). Another issue is that the first- order effect of a severe macro shock could lead to a feedback between the real economy and the banking sector through mortgage defaults which led to credit contraction. A second- order effect reduced demand further and increased unemployment. Exposure to such a process came into play as a result of the economic contraction that developed during 1929– 33.


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Having looked at the potentially harmful effects of the debt build- up, which we re- examine in Chapter 6, we can now inspect the impact of these changes on the US stock markets. Consumer prices rose (Figure 3.6), reflecting the growth of monetary aggregates as debt levels expanded. The magnitude of the increase in prices was 70 per cent, and 120 per cent for M2. As Figure 3.7 shows, the rise in wages for unskilled workers seems consistent with the idea that the inflation did not erode the real wages of even the least skilled workers in the US economy, and also reflected the same growth in monetary aggregates.

The consequences for the earnings of US firms from the monetary expansion were large because as nominal income increased and wages increased, we would expect the nominal earnings of companies in aggregate to increase (Figure 3.8). The real firm level effects of these types of changes (Figure 3.9) are not looked at in our work.

Figure 3.10 shows that such a theoretical prediction applied in the USA in the 1920s. Real dividends and earnings for stocks as measured by Cowles (1938) did not change in real terms following the post- 1914 increase in prices. Hence we can clearly see the first part of the boom in the 1920s was simply a reflection of nominal increases in earnings for US companies. Whether the credit boom had any direct role in any overvaluation up to 1929 is examined in Chapter 4.

Figure 3.6 US consumer prices (1900–1952)Source: Shiller (n.d.).

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Figure 3.7 US Unskilled Labour Wage Index (1900–1944)Source: NBER Macrohistory Database.

Figure 3.8 Nominal GDP (1900–1939)Source: Williamson (2014).

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Figure 3.10 Real and Nominal Dividend Index for US stocks (1900–1933)Sources: Cowles (1938); Shiller (n.d.).

Figure 3.9 Real GDP (1895–1945)Source: Williamson (2014).

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On the surface then, the stock market boom does not seem to be that spectacular given the monetary and debt dynamics, and the price level changes. The market was not irrational or exuberant up to 1927 and the index of nominal values of discounted future dividends increased passively.

In summary we can see that the 1914 increase in credit growth led to a rise in nominal GDP and prices and hence earnings and dividends.

Hence the effect of MFPG, which was documented earlier, and the expectation of its increase could have been a rational reason to increase asset prices. However, barring this expectation, which we can show to be small in magnitude and hence not able to reproduce the large jumps of valuations in the 1920s, there is no reason for investors to have believed that the USA was in a new era. What occurred during the 1914– 29 period was a credit boom, which had no real effects other than to change the type of sector of the economy that held the debt. This was the con-sumer sector, which took on debt to a large degree (Wheelock, 2008).

Having seen that a monetary expansion was the cause of the first phase of the boom we can defer measures of the expected value of US stocks to Chapter 4.

What should be clear is that the 1920s moniker ‘the roaring twenties’ is not a wholly fair or accurate one from a real economic standpoint and only the rise in MFPG can be assumed to be the true innovation to the expected returns to US stocks which stock market investors should have been concerned about.

What happened to dividends and earnings during the 1920s was a lagged response to the growth of lending in the US system, which later became reflected in rising nominal earnings and dividends for US companies.

The high returns to stock market investors in the 1920s were a con-sequence of the lending boom and the attendant rise in the price level and, with a lag, the earnings of US companies.

Stock prices are nominal, and trade at a multiple of their underlying nominal dividends. The returns on stock market investment changed dramatically as the stock market rose in tandem with increasing nomi-nal earnings and dividends. For this we use data from Shiller (n.d.).

As Figure 3.11 shows, by 1927 the average monthly return on stocks had completed a very steep rise in its 36- month moving average from 0.5 per cent per month in 1920 to 2 per cent per month in 1927, which was a deviation from trend of 300 per cent. This may have signalled to investors that expected returns to the stock market were unusually high.

This may have attracted investors to the market on the basis of the expectation of higher returns, in a general sense, and the idea that a


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new trend was emerging in the returns to be expected from stocks. What we can clearly say is that returns to investing in the latter half of the 1920s were spectacular. Thus before even the 1927– 9 phase which is cited as a bubble, the lending- induced boom had created a large return to stocks which would have been hard to ignore. In Chapter 4 we inves-tigate whether momentum, a case of self- feeding increases in returns, can be detected during the boom.

Productivity growth for the US economy

Another possible driver of the changes in valuation ratios above his-torical trend could have occurred due to a technological shock. Despite the clear changes in the US economy due to the nominal increases in earnings and dividends, there was also a period of technological change (Nicholas, 2003). Although the moniker of the ‘roaring twenties’ may be misleading as we can see that much of the ‘roaring’ was in fact dis-guised inflation, adding to the confusion, which investors may have faced during this period, is the fact that the price level from 1922– 9 was highly stable. High real earnings and dividend growth were in fact due to the changes discussed previously, but this was disguised in the 1920s

Figure 3.11 Monthly returns (Cowles Index) (1915–1927)Sources: Cowles (1938); Shiller (n.d.).

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as the price level was already elevated following the 1915– 19 inflation and was stable during the 1920s, even as the lagged effect on earnings and dividends was still catching up. As we have seen in Figure 3.10, real earnings and dividends only reached their pre- war trend levels in 1929.

The productivity advances nascent in the new generation of US firms, together with innovations in managerial techniques and technology, offered higher real growth prospects for the long- term future of the USA.

A good measure of the growth in technological advances, the key source of growth aside from the natural endowment of resources the USA possessed, is captured in the MFPG data. From an average annual growth rate of 0.39 per cent for 1870– 91, MFPG began to climb, hitting 1.14 per cent for 1890– 1913. After the First World War, it continued an upward movement, rising to 1.42 per cent for 1913– 28 before cresting at 1.9 per cent in 1928– 50 (Gordon, 2000). These data from 1928– 50 have been revised upward from 1.9 per cent to 3.7 per cent (Gordon, 2010).

Gordon argued that this peak of MFPG was attributable to a clus-ter of innovations in five areas: electricity, the internal combustion engine, petrochemicals- plastics, pharmaceuticals, and communications- entertainment (telegraph, telephone, radio, movies, recorded music, and mass- circulation newspapers and magazines). These were all well established before the Second World War, and their diffusion and improvements thus contributed to the high MFPG of 1928– 50. Advances were also witnessed in the chemicals sector with companies such as Du Pont in the fields of explosives, paints, and synthetic materi-als such as rayon. Those companies with high patent counts were likely to have performed well in the 1920s stock market and subsequently.

The mechanism for the increase in technological advances was iden-tified through the growth of research and development (R&D) labs in large industrial firms (Nicholas, 2003).11 By 1929, 75 per cent of all mechanical work was electrified (Nicholas, 2003).

Given the data on MFPG, which do show the USA improving this growth rate from the 1870s, it is likely that the 1920s witnessed the beginning of a surge in MFPG. Technological improvement was accel-erating in the 1920s and may have been one reason for the excitement about some US Common Stocks. We examine these effects in Chapter 4.

Inflation expectations

The rise in consumer prices from 1915 could have led to an increase in inflation expectations which changed investor perceptions of the returns to stocks. Smith (1924) makes reference to investor perceptions that inflationary times were good for stock returns. It is therefore sen-sible to test whether high inflation expectations were present over the


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long term, and if so, investigate whether this caused the surge in stock buying in the 1920s. What we need to know is whether investors liked stocks due to the historical experience of inflation and fear of future inflation, which may have made stocks more attractive. We can only test whether they were trying to protect against anticipated inflation by measuring inflation expectations.

We can measure the expectations of inflation in the late 1920s by two methods shown below, and see that they were low, of the order of 0.8 per cent per annum over a twenty- year forward horizon. Such low inflation expectations could not warrant a major change in stock prices. What we cannot prove directly is whether losses on bonds made inves-tors look at stocks, which were inflation- protected.

For those investors who did find interest in stocks, the novelty of the asset class or the naivety of the new investors and the unfamiliarity of investors with these types of financial instruments are definite suscepti-bility factors for the creation of asset bubbles which have been found in the laboratory setting (Smith et al., 1988).

As Figure 3.12 shows, long- term Government Bond yields rose signifi-cantly from 1919 to 1923 following the inflation of the First World War era. Long- term inflation expectations dissipated through the post- 1923

Figure 3.12 US long-term Government Bond yield (1900–1940)Source: NBER Macrohistory Database.

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era as the Classical Gold Standard was replaced by the reconstructed GES. However, the Government Bond market suggests that long- term inflation expectations, having almost fully dissipated by 1927, experi-enced a re- emergence from 1928 to 1930.

Our estimate of inflation expectations is shown below, using two different methods:

1. Fisher’s Golden Rule. 2. Inflation- adjusted long- term US Government Bond returns.

Fisher’s Golden Rule

A useful guide to gauge long- term inflation is contained in the spread between thirty- year US Government Bond yield and the long- term trend real GDP. During the inflation of the First World War period the Government Bond yield rose significantly, indicating a growth of inflation expectations. By 1929, the yield had fallen to 3.7 per cent. By 1929, realised thirty- year inflation was 2.7 per cent as measured by the Consumer Price Index.

Fisher’s Golden Rule (Fisher, 1930) stipulates that long- term inflation can be calculated as the spread between real long- term trend GDP and the long- term Government Bond yield:

Fisher’s Expected inflation (FISHER) = A – B

where,

A = L- Term Govt. Bond yield B = Real GDP growth ( 1871– 1929)

Therefore

FISHER = 3.7 per cent – 2.9 per cent = 0.8 per cent.

Long- term Government Bond returns ( 1870– 1929)

Over the 59 years from 1870 to 1929 the total real return to investment in long- term Government Bonds was 2.9 per cent.12

Long- term inflation expectations were calculated by the following formula:

Expected inflation over 20 years (EXP) = A – C


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where,

A = L- Term Govt. Bond yield (1929) C = Real L- Term Govt. Bond return ( 1871– 1929)

Therefore

EXP = 3.7 per cent – 2.9 per cent = 0.8 per cent.

Both measures show that there were minor inflation expectations of the next twenty years to about 1950. These are not large enough to produce the rise in stock market prices.

3.5 The Federal Reserve in its first fifteen years of operation

The Federal Reserve was a new institution during the 1920s boom. In this section we look at how it functioned in those early years and the problems it faced. We also examine how it functioned around its attitude towards the boom in the stock market.

The outbreak of the First World War and the 1914 crisis

In 1914, the nascent Federal Reserve System was faced with a daunting challenge. The outbreak of the First World War had large ramifications for the future of international trade and finance and 1914 saw a large demand for the sale of assets in the USA. These withdrawals could have proved hazardous for the USA’s ambitions as a reserve currency of inter-national trade as there was a potential for the USA to come off the Gold Standard if large gold withdrawals were met by the US banking system.

The situation placed enormous strain on the US banking system and was remedied by unconventional and daring moves by Treasury Secretary W. G. McAdoo. McAdoo implemented an unconventional and imaginative solution, which involved the closure of the US Stock Exchange, a measure which was deemed necessary to avoid a liquida-tion of loans and investments in financial instruments, which could have triggered a crash in values. The main aim of the action was to prevent the outflow of capital involved in a mass liquidation of securi-ties. Such a large- scale withdrawal would have threatened the solvency of the banking system as gold flowed out of the USA.

The second key response enacted by McAdoo was the coordination of the internal transfer of gold between reserve banks to make sure that


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enough gold was available to ensure depositor withdrawals, as investors withdrew currency. He also implemented the printing and distribution of $200 million of emergency currency to ensure that banks were able to supply enough currency to meet withdrawals from depositors.

The third action was the insurance of maritime exports from the USA where the marine insurance market for commercial trade had broken down in the face of the outbreak of war. By creating an insurance mar-ket McAdoo enabled the USA to recoup gold via international trade flows (Silber, 2007).

The outcome of these emergency measures was the sufficient stabi-lisation of the US financial system by maintaining gold convertibility, deemed vital to the credibility of the US dollar, and ensuring that gold outflows were stemmed and all depositor demand was satisfied.

Silber (2007) contends that the ability of the USA to remain on the Gold Standard was important in the development of the US dollar as a credible currency to be used by foreign central banks as a reserve cur-rency. The dollar therefore became as influential as sterling, which up to the outbreak of the First World War had been the main international currency for international commerce.

The actions in 1914 illustrate there was sufficient scope for the US Treasury to act outside of the traditional confines of their routines in the face of an emergency.13

Federal Reserve actions in 1919– 20

During the First World War the price level increased in the US, caused by the disruption of trade, wartime scarcity of commodities (Cassel, 1922), and increases in the money supply. Following its release from Treasury control in 1919 due to wartime measures, the Federal Reserve pursued a policy of increases in the base rate from 5 per cent to 7 per cent (Figure 3.13). The proximate aim of this increase was to counteract post- war inflation (Meltzer, 2003). The Federal Reserve succeeded in their objectives although a severe recession and deflation followed.

The resulting shock to output due to interest rate tightening is recorded by the NBER as a sharp and deep recession, which lasted from 1920 to 1921. Friedman and Schwartz (1963: 231– 2) note:

The contraction, at first mild, became extremely severe in its later stages when it was characterized by an unprecedented collapse in prices. … By June 1921 they had fallen to 56 per cent of their level in May 1920. … What was true of prices was true also of many physical magnitudes. Industrial production, employment in manufacturing,


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and similar series show a precipitous increase in the rate of decline in the autumn of 1920.

The total decline in the stock of money was 9 per cent. There was also a sharp rise in bank failures as a result of the contraction rising from 63 in 1919 to 506 in 1921 (Friedman and Schwartz, 1963). The motiva-tion for the raising of rates to such a high level was stated as due to the low level of gold reserves in the Federal Reserve System, which stood at 40 per cent of liabilities. According to Fed policy, this level was too low. The discount rate was lowered when the ratio reached 56 per cent. The Fed’s tenth annual report in 1923 indicates the switch in policy in response to criticism for the handling of the 1921 recession. They therefore stopped using the gold reserve ratio, which had prompted the raising of discount rates (Friedman and Schwartz, 1963).

Price stability and gold ‘sterilisation’ post- 1923

From 1921 and the quick recovery from a major post- war recession and deflation, prices were stabilised and economic growth was robust and consistent through the remainder of the decade. This near decade of price stability indicated that the Fed was capable of delivering a

Figure 3.13 Bank rate – Federal Reserve Bank of New York (1915–1937)Source: NBER Macrohistory Database.

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stable price level. This stability is credited in part to the leadership of the New York Central Bank governor Benjamin Strong, who is widely acknowledged to have been the dominant player in policy decisions with respect to the discount rate at the Reserve Bank of New York. The stability of the price level achieved under the Federal Reserve System from 1923 to 1929 was facilitated by the adoption of a policy of ‘gold sterilisation’. This policy was followed in order to combat the effect of increased gold inflows passively expanding the money supply and causing price level rises as had occurred during the First World War era. The Federal Reserve were keenly aware of this problem, and actively aimed to conduct ‘Open Market Operations’, selling US Treasury bonds to offset the rises in the monetary base resulting from continued gold inflows to the USA. Their desire to offset the potential expansion of the lending of the banking system through this channel demonstrates that the Federal Reserve had clear objectives and policies to maintain the prices at an even level.

Friedman and Schwartz (1963: 282– 3) stated that:

From 1923 on, gold movements were largely offset by movements in Federal Reserve credit so that there was essentially no relation between the movements in gold and in the total of high- powered money. … The sterilisation of gold movements was initially jus-tified by the System on three grounds: first, much of the gold could be regarded as only temporarily in this country pending the re-establishment of gold standards elsewhere; second, with most of the world not on the gold standard, gold movements could not serve their traditional equilibrating role; and third, with respect to sterili-zation of the inflow, the increase in short- term foreign balances in the United States made a larger gold stock more desirable. …

Once, however, other countries – in particular Britain in 1925 – returned to gold, those reasons were no longer valid, though they con-tinued to be repeated by the system. The sterilization of gold could be justified as a means of insulating internal monetary conditions from external changes.

The policy of ‘direct pressure’ 1927– 9

In order to combat a stock market that had experienced large annual increases from the early 1920s and the resultant identification of a ‘ credit- induced asset bubble’, the Federal Reserve initiated a policy of ‘direct pressure’ and discount rate increases.14 Whether the Fed had identified the overvaluation we identify is debatable, as their actions


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seem too precise to have been feasibly identifying an overvaluation, as prices relative to dividends were not excessive in 1927. They may have feared that the availability of credit had influenced stock price increases.

The raising of the discount rate from 1928 to 1929 was influenced by the belief that the stock market had been unduly inflated by the growth in ‘credit’ (loans and investments of the Reserve System banks) and hence the policy response was to contract ‘credit’ by increasing its cost or preventing it reaching speculators and investors.

The eventuality the Federal Reserve Board did not account for was a legitimate increase in ‘credit’ to the stock market reflecting the increased demand for funds based on fundamentally sound values. The policy of raising the discount rate had as its ultimate objective the subsequent lowering of the rate to below the initial position. The boom was seen as disrupting the flow of credit to the commercial or ‘real’ economy and high levels of credit were indicative of overvaluation.

The way to get lower rates was by ‘sharp, incisive action’ involving a rise in discount rates that would ‘quickly control the long- continued expansion in the total volume of credit so that we might then adopt a System policy of easing rates’.15 (Friedman and Schwartz, 1963: 257)

Writing in 1935, A. C. Miller, a key member of the Federal Reserve Board and its only academic economist (he had earned a PhD in Economics), illustrated the thinking on the Board:

[The] effects of cheap and abundant credit during the autumn of 1927 were not limited to stimulating business and production and to sustaining the price level and the European exchanges. Cheap credit gave a further great and dangerous impetus to an already over- expanded credit situation, notably to the volume of credit used on the stock exchanges, and to a further rapid upward flight of security prices. In consequence, the Federal Reserve System was confronted toward the end of the year 1927 with the problem of getting control of the fund of credit, which it had been instrumental in placing in the market and keeping it within the bounds of safety lest an uncontrollable and disastrous speculative situation should develop. In consonance with this attitude the Federal Reserve System aban-doned the policy it had been pursuing of offsetting exports of gold by the restoration of a similar volume of credit to the money market through the purchase of United States government securities, and


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allowed exportations of gold to exert their tightening effect on the money market. The effect, however, in the situation then existing was not very considerable. The stock market expansion had acquired too much momentum. It was evident that its pull was too strong to be counteracted by gold withdrawals. (Miller, 1935: 449)

The policy stance of the Federal Reserve Board in the late 1920s can be recognised in the work of Ralph Snyder, a key official at the Federal Reserve Bank of New York. He illustrates the view that dominated think-ing in the late 1920s, namely that the level of credit in the USA had exceeded the needs of trade and commerce. On 22 November 1929 he stated that:

there is a fixed and in the last half century apparently unchanging relationship between the total expansion of trade and the need of business for credit, to carry on that expansion … now we have in these computations pretty clear evidence that credit expansion must go on as least as rapidly as the growth of trade; that is, at about 4 per cent per year. Otherwise there seems to be a definite check to trade and prosperity … we seem likewise to have clear evidence that there is a sharp limitation to the beneficial effects of credit expansion, and precisely as we should expect to find it, viz., that whenever prosper-ity has reached the practical working maximum of employment for any given period, further credit expansion can only bring about undue speculative activity, and even mania, rising prices and all the familiar ills attendant upon inflation or monetary depreciation. The gambler and the speculator thrive at the expense of the rest of the community. (Snyder, 1930: 27– 9)

Currie (1934b: 57– 8) also identifies the pervasiveness of the percep-tion that ‘Credit’ had inflated the prices of Common Stocks and the specific advocacy of the discount rate as a policy tool:

Governor Harrison [New York Federal Reserve Bank] laid it down as a general rule that ‘if the total volume of credit of the country is expanding at a rate and volume faster than any normal growth of business could justify, it is incumbent upon the central bank-ing authorities, to put pressure or restraint on that growth by an increase in the discount rate’ … The important thing to the Board was the growth of speculation, ‘credit’, i.e. loans were being granted to speculators. It would appear that the attempts of various writers


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to demonstrate that ‘credit’, i.e. deposits, was not being absorbed by the stock market failed to convince the Board for the reason that its members could see that ‘credit’, i.e. loans, were being absorbed.

With specific reference to the events of 1927– 1929 he stated ‘the report for the board for 1927 remarks upon “the rapid growth of member bank credit”’ and cites an increase in the total loans and investments of reporting member banks of 8.4 per cent.

It is fair to surmise that the Federal Reserve Board’s thinking was dominated by four propositions:

1. ‘Credit’ should be directed only to the needs of commerce.2. The stock market had been overly inflated by credit.3. The dual policy of discount rate increases and direct pressure would

be effective tools in the restriction of this ‘credit’. 4. ‘Credit’ had been diverted from the real economy to the stock market

to finance speculation.

However, the consensus view overlooked a key distinction in the definition of ‘credit’. Currie’s stated observation (1934b)16 in relation to the growth of credit to fund speculation leaves open the possibility that the stock market was thought by the Federal Reserve Board to have been driven to levels above fundamental values by the growth of speculative credit. Our analysis in later chapters indicates no overvaluation was in evidence when the identification of a ‘ credit- induced inflation’ in Common Stock prices occurred.

In direct response to the Federal Reserve Board’s identification of a credit- induced asset bubble, base rates rose from 3.5 per cent to 6 per cent from January 1928 to September 1929.

The policy known as ‘direct pressure’ took the form of written official warnings to the Reserve banks to restrict loans to finance stock market equity holdings, periodically through 1928– 9. There was notable dispute as to the policy action to be taken in 1928– 9, illustrated by the disagree-ment between the Federal Reserve Board and the New York Reserve Bank as to the pursuit of direct pressure or a rate increase from 5 per cent to 6 per cent in August 1929. Broadly defined, the Federal Reserve Board favoured direct pressure and an avoidance of the use of the discount rate above the 5 per cent level due to the effect it would have on the general level of economic activity. However, the New York Reserve Bank favoured discount rate increases to 6 per cent or above on the grounds that direct pressure had been ineffective (Friedman and Schwartz, 1963).


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However, this disagreement should not detract from the consensus shared by both parties, namely, that Common Stock prices had been inflated by excessive credit growth and this credit must be reduced.

A. C. Miller described the policy of direct pressure and discount rate increases:

On February 2 the Board directed a letter to the Federal Reserve banks and on February 7 it issued a statement to the public carrying the substance of the letter previously addressed to the banks, in which, after expressing its anxiety with regard to current developments, it laid down an interpretation of the Federal Reserve act under which it was stated:

The Federal Reserve Board neither assumes the right nor has it any disposition to set itself up as an arbiter of security speculation or values. It is, however, its business to see to it that the federal reserve banks function as effectively as conditions will permit. When it finds that conditions are arising which obstruct federal reserve banks in the effective discharge of their function of so managing the credit facilities of the federal reserve system as to accommodate commerce and business, it is its duty to inquire into them and to take such measures as may be deemed suitable and effective in the circumstances to correct them; which, in the immediate situation, means to restrain the use, either directly or indirectly, of federal reserve credit facilities in aid of the growth of speculative credit.

This interpretation was the basis of what soon came to be known as the policy of ‘direct pressure’. It was, in brief, a method of exercising restraint upon the speculative credit expansion then in process by restricting the borrowings from the Federal Reserve banks by those member banks which were increasingly disposed to lend funds for speculative purposes. It should be particularly emphasized and noted that not until the Board thus declared its own attitude and the posi-tion which it deemed appropriate for the Federal Reserve System as a whole did the Federal Reserve banks come forward with propos-als for discount rate action looking to restraint of credit. It was on February 14, twelve days after the Board’s warning letter, that the Federal Reserve Bank of New York submitted to the Federal Reserve Board its recommendation that its discount rate be raised to 6 per cent. This was the first proposal for an advance in discount rates to reach the Board after the 5 per cent rate was established in July of


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the preceding year. Thereupon an acute controversy extending over a period of months developed between the Federal Reserve banks and the Federal Reserve Board. (Miller, 1935: 454– 5)

The Federal Reserve were not using monetary indicators to guide policy, although data were freely available to the governing body of the US Reserve System, as a target variable on which to base policy decisions,17 but instead using the ‘ Burgess– Reifler ( B– R) indicator’ and the level of total credit including loans and investments as a guide to policy (Humphrey, 2001). We investigate the dynamics of the market and Federal Reserve Board policy in more detail in Chapter 5.


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94

4.1 Measuring the ‘fundamental’ value of the US stock market

We have already examined whether the monetary expansion in the US economy from 1914 to 1929 was neutral from the perspective of stock market valuation ratios and real dividend growth in Chapter 3. The second part of the boom, from 1927 to 1929, whilst controlling for the effect of the increase in earnings and dividends due to the monetary changes which were occurring during this period, is a topic that is much harder to resolve. We are able to test whether a potential deviation from rational valuations occurred from 1927– 9, in three ways:

1. At the aggregate level using a DDM. 2. In the cross- section of the stock market.3. At the industry level based on industrial growth models for a new

technology industry – aviation.

In this section we focus on point 1, and following the methodology in Chapter 2 we measure the returns to Common Stock investing before and following the alleged bubble period. This allows us to establish the equilibrium level of the stock market and measure any deviation from expectation. Later in the chapter we look at points 2 and 3 and discuss how there may have been a legitimate change to this equilibrium due to a technology shock and whether there is any evidence contrary to the EMH to resolve some key questions on the 1927– 9 phase of the boom.

Comprehensive data sources were available to investors in the 1920s which enabled us to derive a total return data set for the Common Stocks of large firms. One source was the 1900– 29 data contained in Moody’s

4The Returns to US Common Stocks from 1871 to 2010


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Returns to US Common Stocks from 1871 to 2010 95

Manual of Investments (1930) and earlier editions of the manual, which have continuous data on dividends and adjustments related to the issue of bonus stock and stock splits for Common Stocks from 1900 to 1929. Smith (1924) explains how to adjust for splits and bonus stock and hence the ‘real’ or inflation- adjusted historical dividend growth rates for an equal weighted holding of large firm Common Stock over the 29 years from 1900 to 1929 could have been calculated by investors. Given that Dividend Discount Models were used at the time we use such a model to value the US market.

Commonly used data for the long- range analysis of US financial history comes from the work of Shiller (n.d.) using, in part, Alfred Cowles’s (1938) data spliced to later indexes of stock prices. In order to offer a verification of the Cowles data, which is a capitalisation weighted index, the source data for which were lost (Goetzmann and Ibbotson, 2006), we collected a new data set of all Common Stocks, which were listed in the first edition of Moody’s Manual of Industrial and Miscellaneous Securities in 1900. We therefore aimed to replicate the experience of an investor who bought one share in each company and held them from 1900 to 1929, to derive the dividend growth rate for our valuation model.

As a control for our model’s assumptions, we analysed the data in Smith (1924) of stock market returns from 1866 to 1922 to find the Equity Risk Premium – the excess return of holding the market portfolio of stocks over long- term Government Bonds. We estimated a premium of 4 per cent per annum from Smith (1924). This figure is close to the 4.2 per cent estimate in Goetzmann and Ibbotson (2006) that we use in our DDM as the discount rate.

These procedures allow us to reach a primary conclusion about the level of prices of stocks relative to ‘fundamentals’ derived from our model using the historically measured ERP and the growth rates of dividends. What we can therefore show is when the bubble formed and its size.

Data, sources, and methods

1 Measuring the income return of large Common Stocks from 1900 to 1929

By measuring the total dividend return growth rates of an equal weighted market portfolio of large stocks we can generate a feasible forward- looking expectation of growth rates, based on historical experience, to use in our valuation models for the stock market. We also construct our DDM using firm survival adjusted data from 1900 to 1929, to accurately model expected valuations, free from survivorship bias.


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We exclude capital gains from our model, as our method does not require them.

2 Data sources

We used Moody’s Manual of Industrial and Miscellaneous Securities (1900) to form a list of stocks from five main industrial classifications:

Sector 1 Industrial companies: motive power, automobile, electric power, compressed and liquid air, cycle, automatic, phonographic, pneumatic, prismatic, signal, slot machine and allied industries.

Sector 2 Manufacturing companies: iron, steel, lead, zinc, brass, brick, clay, cement, celluloid, car appliances, car manufacturers and allied industries.

Sector 3 Food products: packing, distilling, malting, brewing compa-nies, and so on.

Sector 4 Manufacturing companies: miscellaneous.Sector 5 Manufacturing companies: textile and allied industries.

The categories excluded were:

6. Water and water power. 7. Financial, trusts and banks. 8. Miscellaneous corporations. 9. Mining companies: gold, silver, lead, copper, zinc, coal, and so on.10. Guaranteed railroad stocks.

The reason for these exclusions was because our tests focus on indus-trial, commercial, and manufacturing Common Stocks, while the other categories use more complex pricing models (such as mining stocks or financials) or were regulated utilities by 1929. Hence historical returns may not be a dependable guide to valuation in 1929 and these firms may not be amenable to the use of the DDM we use.

3 Data collection

The firms from the data sources listed above were then categorised as ‘Large’: by the criterion of having full balance sheet data listings. A total of 254 firms were listed using this method from which a data set for 168 of the 254 large firms was collected. Using the available data, annual dividends from Moody’s Manual of Industrial and Miscellaneous Securities from 1900, Moody’s Manual of Railroad and Corporation


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Securities, American and Foreign from 1908 and 1919 and Moody’s Manual of Investments: Industrials from 1930 were collected for all years between 1900 and 1929.

Annual cash dividends data for each individual firm were adjusted to reflect increases in the holding of the original shareholder due to bonus stock. This method is consistent with the approach of Smith (1924) as discussed in Chapter 1.

If a bonus dividend of 50 per cent (of Par) was paid as more stock instead of cash, then all subsequent dividends were increased to reflect the additional shares of the original 1900 owner of stock. The Par value of the share was assumed as the price of the additional shares so that original shareholders were assumed to have 50 per cent more shares.

Additional irregular cash dividends were excluded from the adjust-ment of the total share holding which introduces a minor downward bias in our values for dividend return growth rates.

Where data were not found for any companies, of the 254 firms which were listed in 1900, these stocks were recorded as having no data. Where companies were taken over or merged during the 1900– 30 period, the dividend growth rate of the acquiring company or the new merged company was used.

• Missing data from before 1912 were replaced with data from Poor’s Manual of Industrials from 1912.

• Where data were not listed or not found they were excluded from the data set.

• We exclude the possibility that dividends are reinvested.

4 A composite growth rate from sector- specific data

The total cash dividends for an equal- weighted holding of large firms from each sector were tabulated for each year from 1900 to 1929. The total cash dividends in 1900 and 1929 for each sector were then used to derive five sector growth rates, denoted:

ΩLSEC,1. . . ΩLSEC,5

by the following formula:

dividends dividendsLSEC i( ) ( ),1900 1 192929

∗ +( ) =∑ ∑Ω i ,...,1 5=


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These five total dividend return growth rates were adjusted for inflation using the Consumer Price Index from 1900 to 1929 from Shiller (n.d.) to derive an inflation- adjusted geometric rate of growth per annum for each sector.

Due to the large bias arising from the high level of cash dividends of Sector 5 (the textile manufacturing industry) and the implicit high weighting in the resulting unadjusted growth rate, a composite infla-tion adjusted growth rate ΩGROWTH was calculated. We gave equal weighting to each sector’s growth rate to form a composite growth rate: ΩGROWTH.

5 Results: total dividend growth rates by sector

Table 4.1 shows the individual sector growth rates both adjusted for and unadjusted for inflation and the composite growth rate. The inflation rate was 2.7 per cent per annum for 1900– 29.

The composite real dividend growth rate for sectors 1– 5 was 1.3 per cent.

The Dividend Discount Model (DDM)

We use a constant growth DDM that will give us the P/D ratio we expect for the aggregate US market, assuming a one- dollar initial dividend grows at the rate measured from our dividend growth data set.

PVD

kt

tt

=+=

∑ ( )11

80

DtGROWTH t= +( ) ,1 Ω t = ,...,1 80

Table 4.1 Individual sector growth rates

Sector Cash dividends (1900)

Cash dividends (1929)

Nominal growth (% Ann. geo.)

Inflation- adjusted growth (% Ann. geo.)

1 42.00 181.65 5.175 2.482 122.25 195.25 1.625 − 1.083 111.15 632.91 6.2 3.504 40.00 124.00 3.98 1.285 317.85 244.78 − 0.897 − 3.60Unweighted total

633.25 1378.59 2.72 0.02

Composite 1.29


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All inputs are inflation- adjusted values.

ΩGROWTH = Dividend growth rate (Geometric ann.)

PV = Expected P/D ratiok = Rate of return on stocks (Geometric ann.)

The model can be used to solve for the rate of return or dividend growth rates.

Peak valuations for Common Stocks in 1929

Method

We use the aggregate Price/Dividend ratios for the market for large firms in 1929 as a basis for comparison to the values generated in our DDM.

We collected data from the Commercial & Financial Chronicle for the firms in our data set, which were equal to the number of firms for which we have growth data in the 1900– 29 growth data set. The firms from 1929 represent 75 per cent of the total market value of large Common Stocks and were therefore deemed to be representative of the market.

A data set was collected by hand for 700 of the 900 or so firms listed in Moody’s Manual (1930) with data for 1929. The following additional data were collected for each firm:

• Shares outstanding.• Net current assets.• Maximum prices reached during 1929.• Dividends per common share.

Using the following filter, 187 large firms’ stocks were filtered from this database:

Large firms = [Net current assets >$8m]

Price data for 146 of the 187 firms from the large firm data set were col-lected for which data were available from the Commercial & Financial Chronicle from 1929. The average Price/Dividend ratios (Table 4.2) in the second week of September 1929 for firms in our filtered data were calcu-lated as follows:

• Prices ( intra- week high) from the second week of September 1929 (Commercial & Financial Chronicle, 1929) were collected.

• The dividends (annual) of these firms were collected from Moody’s Manual (1930) for 1929 from the filtered database.


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The historical ERP and model: implied required return

The long- term historical return to stocks from 1870 to 1926 from Goetzmann and Ibbotson (2006) was estimated at 3.1 per cent from income returns and 1.1 per cent from capital gains, which gives a return premium on stocks over long- term Government Bonds of 4.2 per cent.

Our collection and modelling of data from Smith (1924) suggests an equity risk premium of 4.0 per cent based on the 2.5 per cent annu-alised geometric return premium over corporate bonds from 1866 to 1922, by adding to our estimate of the Baa corporate bond premium over Government Bonds of 1.5 per cent.

We use the P/D ratio of 19.2 from Cowles (1938) data to solve for the required return implied in our DDM and dividend growth of 1.3 per cent, which gives a required return of 6.5 per cent or an ERP of 3.6 per cent.

The volatility- based ERP for NYSE stocks

Method

We wanted to estimate the expected return to Common Stocks without using the long- term observed return for NYSE stocks from before 1927.We assumed that a larger expected return on Common Stocks than cor-porate bonds should have been demanded due to the greater ‘riskiness’ – measured as the Standard Deviation (S.D.) – of annual capital returns of Common Stocks compared to corporate or Government Bonds.

We derived the excess reward for an asset over a benchmark asset, which in this case was the US long- term Government Bond. The excess reward per unit excess volatility was denoted γ.

We used AAA corporate Bonds as the calibration asset as it was virtu-ally default- free. We also assumed Gamma was constant across assets and can be used to estimate the equity risk premium:

γ = α

β

Table 4.2 Average P/D ratio for US stocks (Commercial & Financial Chronicle), September 1929

Valuation method P/D ratio

146 Stocks average P/D ratio (1929) 30.8


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where,

α = Excess return on AAA bonds over Government Bondsβ = Excess volatility of AAA bonds over Government Bonds

Using these data, assuming that all assets have the same gamma value,

ERpremium =ωγ

where,

ω = Excess annual volatility of stocks (Goetzmann and Ibbotson, 2006)

ERpremium = Excess annual volatility based expectation of excessreturn on stocks

We used the annual S.D. of Moody’s railroad bond index (Table 4.3), which gives a 20- bond portfolio’s excess volatility over Government Bonds, to impute a risk premium for bonds.

The S.D. of annual returns was calculated as before to give the excess return to railroad bonds as:

Bondpremium railbond= ∗γ ω

where,

Bondpremium = Excess annualised geometric return on railroad bondsωrailbond = Excess annual price volatility on railroad bonds

The results suggested that a 4.1 per cent estimate for the NYSE equity risk premium was consistent with volatility (S.D.) of returns and a con-stant Gamma- value across all assets.

Table 4.3 Annual S.D. of stock and bond indexes and actual and implied returns

Name Dates S.D. (%) Ann. Geo return (%)

Gamma

AAA index 1871– 1926 4.87 3.6 0.373Baa railroad index 1871– 1926 7.54 4.4* 0.373 L- term Govt. Bond 1871– 1918 2.99 2.9NYSE stocks 1871– 1926 14.00 7.01* 0.373ER Premium 1871– 1926 – 4.1*Bond Premium 1871– 1926 – 1.5*

* Estimate from calibration.


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Estimating the fair value of the US stock market

There is no clear and easy way to try to value the US market in the 1920s, to determine what the levels of fair values were, so we restrict our modelling to the use of a DDM with our estimate of dividend growth rates. We need to use this procedure as the historical data from Cowles (1938) and Goetzmann and Ibbotson (2006) assume returns to stock market investment as a total return over the risk- free asset. Therefore our model of dividend growth has to follow the same methodology and assume the index was infinitely lived. We therefore use a constant growth DDM to reflect long- term dividends and discount them using the historically measured ERP from before 1927.

Cowles (1938) estimates real dividend growth rates of 1.25 per cent from 1871 to 1927 and a total return of 7 per cent. Our data produced a 1.3 per cent growth rate but we do not have total return data. The levels of growth we find are feasible and we use our growth rate with a DDM, consistent with Cowles’s (1938) estimate of the long- term dividend growth of the US stock market.

We use a DDM and our dividend growth estimate from 1900 to 1929 to estimate the fair value and then compare it to a 146- firm sample from the peak in September 1929 to identify the scale of the potential overvaluation.

Using a constant growth DDM to estimate the P/D ratio, we use the return premium to investing in NYSE stocks from 1871 to 1926 of 4.2 per cent from Goetzmann and Ibbotson (2006) as the discount rate; 3.1 per cent of this return premium was due to income returns and 1.1 per cent came from capital gains.

The estimates of peak prices in September 1929 are taken from a 146- firm data set, which we collected from the Commercial & Financial Chronicle (1929). Cowles (1938) finds a January 1927 value of 19.2 for the P/D ratio, which we use. We can also solve the DDM for the growth rate expected from actual P/D ratios in 1927 assuming the 4.2 per cent return to NYSE stocks.

Results

Using the DDM with a 1.3 per cent growth rate, the return expected on stocks was 7.1 per cent and the P/D ratio was 17. Peak market values in 1929 were 30.8.

The dividend growth rate in 1927 implied from the DDM using a 4.1 per cent ERP and a P/D ratio of 19.2, was 1.9 per cent. The forward- looking ERP, which we can extract from our DDM, with a growth rate of 1.3 per cent was 3.6 per cent using the P/D ratio of 19 from 1927.

We cannot know which of these values was correct and the reality of stock market pricing means these are estimates only. What we do


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find is that in 1927, we assume that a 3.6 per cent to 4.1 per cent ERP and growth rates of 1.3 to 1.9 per cent were reasonable. We therefore assume that the market was near equilibrium in valuation levels rela-tive to volatility of returns, historical returns, and historical growth rates in 1927.

Sensitivity tests

Tables 4.4 and 4.5 simulate the changes in P/D ratios under various dividend growth and risk premium assumptions. For our base, we use the level of a P/D ratio for the market of 19.2 in 1927.

The peak levels are taken from a 146- firm data set, which we collected. Cowles (1938) finds a January 1927 value of 19.2 for the P/D ratio, which we use.

The clear lesson to be drawn from these simulations using our model is that, relative to long- term historical expectation, a possible change in required return from stocks or higher future growth rates could have driven the boom. However, there need to be solid grounds for justify-ing such changes in growth expectations or decreases in the required return. We therefore also test if short- run growth expectations could have changed valuations to the degree seen in 1929.

If no risk premium changes occurred then the long- term dividend growth rates needed are 3.5 to 4.1 per cent, which are beyond the realm of plausibil-ity for a long- term growth rate of dividends compared to actual experience and the long- term growth rate of the US economy at 3 per cent. This is because the 3 per cent long-run growth rate of the economy produced a dividend growth rate of 1.3 per cent.

Conclusions

We do not know exactly where the equilibrium level of asset prices was in 1927, but we can offer two models, which estimate a position

Table 4.4 P/D ratio for different risk premiums and growth rates for US stocks


146 Stocks Average P/D ratio (1929)3.5% Risk Premium ( risk- averse model) +1.3% growth3.5% Risk Premium ( risk- averse model) + 3% growth2.0% Risk Premium ( risk- averse model) + 2% div growth2.0% Risk Premium ( risk- averse model) + 1.3% div growth1.5% Risk Premium ( risk- averse model) + 1.3% growth1.5% Risk Premium ( risk- averse model) + 2% div growth0% Risk Premium ( risk- neutral model) + 1.3% div growth

30.819.230.030.826.129.435.244.7


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Table 4.5 P/D ratio for different risk premiums and growth rates for US stocks


146 Stocks Average P/D ratio (1929)4.1% Risk Premium ( risk- averse model) + 1.9% growth4.1% Risk Premium ( risk- averse model) + 4.1% growth2.5% Risk Premium ( risk- averse model) + 2.5% div growth2.5% Risk Premium ( risk- averse model) + 1.9% div growth2.0% Risk Premium ( risk- averse model) + 1.9% div growth 1.5% Risk Premium ( risk- averse model) + 1.9% div growth2.5% Risk Premium ( risk- averse model) + 3% div growth

30.819.230.729.226.528.634.235.0

from which the changes can be simulated. The first, based on historical growth from a large data set from 1900, indicates a growth rate of 1.3 per cent per annum.

We use the method in Shiller (1981) to derive a value for the stock market based on dividend growth rates for a market index of US stocks, anticipating a continuous expected dividend growth rate from our sam-ple over the long term.

A historical ERP, before 1926, calculated as the return on stocks over the Government Bond return, of 4. 0– 4.2 per cent was taken from mod-ern research, and 1920s research from Smith (1924).

These historical returns of 4 per cent per annum imply a 1.9 per cent dividend growth rate over 80 years from our DDM, using the actual US market P/D ratios in 1927 of 19. In 1927 our model estimates an ERP ( forward- looking) of 3.7 per cent, which is lower than expected but within a feasible range.

In both models that we use to simulate the forward- looking ERP implicit in prices, we can be certain that a large fall in the ERP was needed to produce the peak values seen in 1929; in our model a fall from 3.5 per cent to 1.3 per cent, and in the model using the historical ERP from 4.1 per cent to 1.8 per cent. These are major deviations and illustrate that some major change was occurring in 1927– 9.

Alternatively, much higher levels of growth of 3. 5– 4.1 per cent also replicate the peak values of the market in 1929. Higher growth levels of dividends were feasible, given the long- term growth rate of the US econ-omy of 3 per cent over the long- term before 1929, which only managed to produce historical dividend growth rates of 1. 3– 1.9 per cent. Whether the change from ratios of 19.2 to 30.8 from 1927– 9 for the aggregate market was justified, on either of these grounds, is investigated later.


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The change in forward- looking dividend growth rates is large, and seems unusual given historical expectations and standard models.

In the next section we look at realised returns from 1925 to 2010 to test whether investors were increasing the value of stocks because of an anticipation of future growth increases, which we can now measure, ex- post.

4.2 The realised return on stocks from the 1920s to 2010

Having looked at growth and returns before the 1920s, the comple-mentary analysis was to look at returns from before the boom in the 1920s to the modern day. We use two data sources to establish the ex- post returns to the US market from 1925. The first, from Jorion and Goetzmann (1999), was a 4.1 per cent premium from 1926 to 1999 and the second from our own data set. The second was calculated using live returns to a market tracking fund from the 1920s.

The performance of an investment fund with no debt leverage, which proxies for the market portfolio, was tested to see how well the inves-tor who bought this fund in 1925 would have fared over the very long run. This approach allows us to produce a live return index, rather than rely on modern methods, which may not replicate actual investor experience.

This experiment, with live returns through the course of the 80 years, over which the fund was taken over and changed name, can inform us of two key unknowns. We need to know whether the realised future from the 1920s shows any signs of an upward shift in returns, to establish whether the future was actually better than our model of expected returns, in the previous section, and from where we establish our ‘fair value’ calculation. If the returns were much higher than historical expectation implied by valuations in the 1920s, using our model of fair values in 1927 before the alleged bubble phase the jump of P/D ratios from 19 to 30.8 could have been the product of a rational forecast of the effect of the productivity changes which were subsequently realised. The investment fund held over 40 Common Stocks and therefore acts as a proxy for the market due to the level of diversification reducing the risk in the portfolio to that of the market.

Data

Our data come from ‘Investment Trust Fund A’, a closed- end fund whose returns can be tracked through takeover and merger and which


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exists today as ‘Fundamental Investors’, a part of Capital Group LLC. The fund was established in 1925, by E. L. Smith, to hold the market over the long run (Table 4.6).

Net Asset Values (NAVs) and dividend pay- out data were gathered from the New York Times for 1925– 38 for Investment Fund ‘A’, known as ‘investment trust certificates A’ using ORBIS.

NAV and dividends data for Investors Fund ‘C’ were collected from the Commercial & Financial Chronicle at a monthly frequency for 1938– 54.

Data on

• stock- splits • dividends• prices• NAVs• merger/takeover exchange ratios of shares

were supplied by Capital Group LLC – the parent of ‘Fundamental Investors’ Fund for the entire history of the fund back to 1932.

Table 4.6 Conversion table for Investment Fund A

Date of merger

Name of entity merging

Into Conversion ratio

From To

17/10/1932 Fundamental Investors commenced operations

17/12/1934 Irving Investors Fund C, Inc. name change to

Investors Fund C, Inc.

1 0.48460

21/12/1938 Investment Trust Certificates A, Inc.

Investors Fund C, Inc.

1 1.95121

30/01/1946 Investors Fund B, Inc. Investors Fund C, Inc.

25/04/1946 Investors Fund C, Inc. name change to

Investors Manage ment Fund

31/03/1954 Investors Management Fund

Fundamental Inves tors Fund, Inc.

1 0.87583

06/03/1969 Pitmel, Inc. Fundamental Inves tors Fund, Inc.

1 1.95120

21/07/1978 Washington National Fund

Fundamental Investors Fund, Inc.

1 1.55271


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Method

We constructed a total return index for Investment Trust Fund A, assum-ing an investor had bought in 1925 and held on to those shares until 2010. The annualised geometric return from 1925 to 2010 included dividends and capital gains and was adjusted for merger, takeover, and stock splits. We can therefore mimic the return to the investor in 1925 over the very long run, whilst avoiding the potential problem of survivorship bias from using index data which may not reflect actual returns to an investor over 1925– 2010. This return was then compared to the real return on long- dated Government Bonds, adjusted for infla-tion from Goetzmann and Ibbotson (2006), to derive the realised equity premium.

We use the realised premium with a constant growth DDM to derive the dividend growth rate in 1927, which would have justified such a return, assuming perfect foresight.

Results

The results show a 3.4 per cent premium over long- term US Government Bonds, free from the effect of calculating returns from a continuously updated index constructed ex- post and any survivorship bias intro-duced by these methods. These are actual returns from 1925 to 2010 due to capital gains and income from dividends paid out, from an investment in this fund which proxies the US market. The premium Smith (1924) was expecting based on stock exchange history from 1866 to 1922 was remarkably close to one he would have earned by 2010. The nominal return was 8.9 per cent, with a 5.5 per cent nominal return to Government Bonds and inflation was 3 per cent.

The constant growth DDM, which uses actual P/D ratios from the Cowles (1938) index in 1927 of 19.2, implies dividend growth of 1.9 per cent. This growth rate is similar to historical growth rates from Cowles (1938) of 1.25 per cent from 1871 to 1927.

Perfect foresight of actual realised returns implied a growth rate of 1.2 per cent with a 3.4 per cent ERP. Therefore an investor in the 1920s with perfect foresight of these returns would have been expecting a dividend growth rate of 1.2 per cent.

Conclusions

The data suggest that holding this fund, which is also a useful proxy for holding the general market, could have earned an investor about 3.4 per cent per annum over long-term US government bonds from 1925 to 2010.


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The results indicate that long- term returns to the investors in the 1920s were similar to those found over the long run from 1866 to 1922 (Smith, 1924). The value is also consistent with estimates of the long- run US ERP of about 4 per cent found by Goetzmann and Ibbotson (2006) and Goetzmann and Jorion (1999). Had an investor bought stocks when levels were fair in 1925, using a closed- end fund to approxi-mate the market portfolio, they would have earned a good return over the long run and not lost out because of the Depression from 1929 to 1938, to any major extent.

Importantly this result also shows that spectacular returns were not forthcoming on stocks over the following 80 or so years when compared to historical returns prior to the 1920s. The constant growth DDM, which uses actual P/D ratios from the stock market in 1927 of 19.2, implies dividend growth of 1.9 per cent.

The resounding conclusion is that the forecasts of those investors who may have believed that the US economy had leapt to a new growth trajectory, which is possible given our simulations using the DDM, and the positive tests for patent technology in the cross- section of returns (Nicholas, 2008), were not realistic. Had this measure of realised ERP been 6 per cent from 1925 there may have been grounds for thinking that the investors had been accurately forecasting a change in the US economy and returns to stocks. In this case the realised returns would have outperformed all of our historical ERP measures. We can be sure that this was not the case assuming our return data are accurate.

The change of the MFPG rate identified in Gordon (2010) is an ex- post analysis, and as stated before, we cannot be sure whether investors understood the effect of this technologically and organisationally based growth in MFP. However, there is some positive evidence that they did value patents, which could have been used as a proxy for future growth or productivity increases (Nicholas, 2008).

Another possibility is that the changes in valuations were driven by decreased expected model- based returns; then an expected return of 4. 2– 4.8 per cent is implied at peak valuations or an ERP of 1. 3– 2 per cent but this would require a major reduction in the required return relative to actual returns from 1925 to 2010.

4.3 A growth model to value new technology stocks

Traditional accounts of the 1920s boom on the New York Stock Exchange (NYSE) indicate the cause was a speculative mania in Common Stocks (Galbraith, 1954). Aviation or ‘airplane’ Common Stocks are cited as being


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amongst the most speculative and hence most overvalued. The industry was in a nascent stage without significant dividend or earnings history.

Our investigation of this sector has two goals. One is to gauge how investors saw industrial growth models in the period, and the second is to use these models and our own to determine if prices for high technology stocks were overvalued, and the degree to which they were relative to historical models of industrial growth and, also, the general market.

The surge in prices witnessed during the boom in these stocks in the late 1920s and subsequent crash of 75– 80 per cent from the peak by late 1930, when the broad market had only fallen by 40 per cent, is cited as evidence for investor irrationality. The value of aviation stocks fell further by 1932, by a total 90 per cent, using the Cowles index series p- 66 (Cowles, 1938).

These new technology stocks of the 1920s display the hallmarks of a potential asset bubble, driven by the excitement of the age and other new technologies, such as radio, which allegedly captured the imagi-nation of the investing community (Galbraith, 1954). Whilst detailed and innovative research on patent- rich new technology firms and their stock values have been conducted by Nicholas (2008) using patents as a proxy for intangible capital, we investigate models which we have strong reason to believe were used by investors of the time for this sec-tor of the market.

We are fortunate in that we made a discovery of a two- stage industry growth model from the literature around the time of the boom and crash, which also indicates the numerical calculations and theoretical basis on which new technology industries were valued. We take on the task of trying to measure the ‘fundamentals’ of a high technology indus-try as such an industry would have been seen by investors in the 1920s.

Whilst our ability to measure the rise and crash in prices leads natu-rally to the conclusion that aviation stock prices were the subject of a large error on the part of investors, the size of the information asym-metry required to produce this effect is large. The market value of the 50 aviation firms we measure rose to approximately $1000 million by September 1929 compared with 146 large commercial and industrial Common Stocks having a value of $38,000 million.

The aggregate index of aviation stocks fell by 75 per cent, from peak levels to the low we use from 1930, implying an overvaluation of 300 per cent. The alternative scenario, which we cannot test for, is the presence of genuine uncertainty as to which firms would survive and the growth of this new industry being different from history (Pástor and Veronesi, 2009).


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Such a surge and crash in the prices of new technology stocks has been well documented following the NASDAQ, or Dot- Com boom and collapse of the late 1990s and early 2000s. Some authors argue that the NASDAQ boom was due to over- optimistic forecasts of earnings and growth from new technology stocks (Ofek and Richardson, 2003) leading to a collective overvaluation of the NASDAQ index of 100 per cent reaching a peak in the year 2000. A large amount of research on asset bubbles from economic historians finds numerous examples of asset overvaluations related to new technologies (Kindleberger, 1978). Conversely, and reflecting the potential legitimacy of such runups, Pástor and Veronesi (2009) develop a general equilibrium model in which stock prices of innovative firms exhibit bubbles dur-ing technological revolutions. In the model, the average productivity of a new technology is uncertain and subject to learning. The new framework that they offer is that during technological revolutions, the nature of this uncertainty changes from idiosyncratic to systematic. The resulting bubbles in stock prices are only observable ex- post and are unpredictable ex- ante, and they are most pronounced for technologies characterised by high uncertainty and fast adoption. The authors find empirical support for 1830– 61 and 1992– 2005 when the railroad and Internet technologies spread in the United States.

Given that high technology stocks are known to be subject to ex- post observable bubbles, whether observable ex- ante or not, leads the general need to know more about them and how they form. The modern litera-ture on asset bubbles found in the laboratory environment shows that deviations from fundamentals are more likely to be found where there is no dividend anchor (Smith et al., 1988). Studies show that assets can be mispriced due to a lack of solid basis for valuation without an underly-ing excitement about a new technology. In essence, investors are prone to forward- induct prices when unable to use backward induction from dividends (Hirota and Sunder, 2007). This may make new technologies which have low or no dividends more susceptible to bubbles, in addi-tion to or without the effect of technological uncertainty about future growth. The research in this area has much more to discover as to why new technologies are actually prone to overvaluations.

The tendency of bubbles to increase the cross- sectional dispersion of wealth serves as a reason for policy intervention to prevent such processes emerging. Given a widespread acknowledgement that such episodes are hard to predict ex- ante, and may be located in a single asset class, making the use of bank rates dangerous to stop them, even if iden-tified, led to a consensus that bubbles should not be ‘popped’ (Bernanke,


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2002). Given that new technologies drive future growth, there may also be costs for growth associated with the inhibition of technology booms.

The research therefore seeks to draw upon modern research in order to contribute to the literature on asset deviations from fundamentals but also to try and understand the valuation theories and market dynamics of this historically important era.

The aviation industry of the 1920s in the USA bears all the hallmarks of an industry likely to be at risk of being overvalued or subject to high uncertainty, from what we now know in modern finance about technol-ogy booms. This, coupled with common accounts of this industry being a speculative favourite, means that it was the test most likely to provide a good benchmark. An exciting technological area may have caused inves-tors to miscalculate the probability of success of firms in an industry that was likely to experience considerable ‘shakeout’, or an overvaluation based on the probability of survival of the industry as a whole. Equally the industry may have been subjected to high growth rate expectations which exceeded those of past high growth industries.

Modelling a new high growth industry in the 1920s

1 Defining and measuring fundamental asset values

The approach we take to estimating fundamentals rests on our ability to gauge the models used at the time and finding sufficiently robust data. Whilst such a task is notoriously difficult for any asset, we were able to gather data and evidence of modelling techniques used in the era to infer a good estimate of fundamental values for the industry as a whole. We therefore condition our models of fundamentals from data and tools specific to the time period in question and do not use econo-metric techniques or data that post- date the actual events under inves-tigation by a major timeframe. In that sense we are remodelling rational fundamental values from the 1920s investors’ view. Our approach also uses price data from the 1929 crash in values to infer actual underly-ing estimates of ex- post risk premia of market participants rather than absolute measures of the riskiness of aviation stocks.

2 Remodelling investor valuation tools for aviation stocks

Moody’s Manual of Investments (1930) offered the following advice to investors seeking to value the aviation industry as a whole. This pas-sage gives a useful insight into how contemporary investors may have viewed growth dynamics and how they may have constructed valuation models.


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Science and progress have moved forward and another great indus-try has been born. Aviation has come into its own. There have been many other new industries that now appear as commonplace. Railways in 1833, petroleum in 1865, telegraphs in 1868, automo-biles in 1903, radio in 1914, Rayon in 1917. We look for great devel-opments in the fields of aviation and aeronautics. We have observed from our investigations that all new industries follow similar courses of development in arriving at maturity, and that this arrival is accom-plished in three distinct stages. The first is the inventive stage where the proposition is regarded as an idle dream and the public has to be slowly convinced of its feasibility. During this time the industry grows only a few percent per annum. The second is the boom stage occurring after the feasibility and worth of the industry have been demonstrated and the public mind has at last been fired with the idea. During the second stage the typical new industry in America expands at the rate of about 40 per cent per annum. In the third stage after the industry has reached maturity, the rate of expansion is reduced to an approximate equality with the rate of growth in wealth, which here is about 5 per cent per annum. Notwithstanding the certainty of losses in some stocks, the huge profits are to be made in a new industry during the second stage, and for the aircraft industry this means during the next five or ten years. However, exceedingly keen judgment in investing is necessary at this time, and diversification is essential. (Moody’s Manual of Investments, 1930: xvi)

Although we do not directly transfer the parameters of the model shown above to the model we build, we aim to infer how the basic structure and theoretical basis of such a model would have been con-structed by a sophisticated investor given the data of the time and com-plementary methods of valuation which were known in the 1920s. We therefore approach aviation Common Stock valuation using a model at an industry level using a three- stage sales growth model. However, there are strong grounds to believe that our model is an accurate reflection of how stocks in growth industries were valued at the time. The only innovation we make to Moody’s model is to suggest that investors were able to measure inflation- adjusted values and use accurate historical data, which we know were available ex- ante to the boom in the 1920s.

We calibrate the growth factors in the model using one of the key his-torical industries which Moody’s advocated, automobiles, and also take Moody’s indication on which stage of development aviation was at in 1930. This date is sufficiently close to 1928– 9 to be able to make judge-ments about how this type of model could have been applied in 1928– 9.


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What is obvious from the timing of the publication date of the model is that Moody’s is not responsible for the release of an inaccurate model, which was taken up by investors and hence no connection should be made to Moody’s for any overvaluation we detect. Furthermore, the model described in Moody’s does not suggest any adjustment for the prob-ability of the new technology being any riskier than previous technologies. Their model appears to advocate an expectation of new technology firms matching a 100 per cent probability of the growth path of previous suc-cessful industries. In other words, the model does not control for the risk that stocks would not repeat the historical growth seen in other success-ful technologies even though it was then just a very young industry. The discovery of the model enables us to understand how investors may have been valuing Common Stocks and also indicates a potential flaw in their reasoning based on the probability of growth in the industry as a whole as the model assumes no probability of failure to match historical growth.

New industries such as aviation and radio are commonly cited as having displayed overvaluation (Galbraith, 1954). The reasons for new technologies being more vulnerable to overvaluation are primarily twofold: first, the lack of a ‘dividend anchor’ making accurate valua-tion more difficult using standard DDMs and second, such alternative growth- based valuations may be susceptible to over- optimistic growth assessments. A third possibility is that new technologies have high levels of uncertainty regarding which firms will succeed and this may also be a potential cause of ex- post observable bubbles which are not irrational ex- ante (Pástor and Veronesi, 2009).

The tests of aviation stock values we develop based on Moody’s Manual of Investments (1930), and our own inferences about the level of model-ling skill and data available, were derived to perform three tasks:

1. Negotiate the problem of a lack of historical data for aviation firms, as they were a new industry.

2. Value a high technology growth industry using methods and data available to investors in the 1920s.

3. Build a model which investors should have been capable of building given data and methods known in the 1920s.

Using the general ideas advocated by Moody’s Manual of Investments (1930) and using a comparable industry such as the automobile indus-try the aim was to produce a feasible growth and valuation benchmark from historical data. The automobile industry was chosen as an industry with similar characteristics as a high technology growth industry in the early 1900s, with which to calibrate our models, as it has a long- term


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history for which data were available to investors in the 1920s and because Moody’s Manual of Investments (1930) makes a direct comparison between these industries. The observation that both industries are in the transportation area also allowed a more feasible comparison to be made.

The stage of industrial development of aviation in 1929 was, accord-ing to Moody’s, directly comparable to the stage of development of automobiles in 1903. Therefore, using data on the growth of the auto industry should be a good comparison and allows us to build a growth algorithm for valuing aviation stocks.

The next section examines the assumptions that we make in the models, which are not taken from Moody’s but from our own historical an alysis. We deem these to be assumptions which could have been made by investors in the 1920s. Some of these rely on other authors’ historical research such as Klepper (2002) and Klepper and Simons (2000) relating to the evolutionary path of an oligopolistic industry. Given the historical data used by these authors and the sophistication of data readily avail-able to investors, the hypothesis of the ability of investors in the 1920s to replicate the assumptions of the survival and ‘industrial shakeout’ of firms during industrial growth and the evolution of a new industry seems fair.

3 Automobile industry growth as a template

We follow the ideas contained in Moody’s to calibrate a growth model using a readily available historical growth path from a similar new tech-nology industry. An obvious choice is the automobile industry.

Klepper (2002) illustrates that the auto industry structure evolved to form a tight oligopoly, with ‘first movers’ taking a large market share at industrial maturity.

We use data on the growth of the automobile industry as a growth template, as advocated by Moody’s Manual of Investments (1930), and base assumptions on the path of the aviation industry’s evolution on that of automobiles. It is likely that such data on industrial lifespans and market share could have been easily derived from Moody’s manuals and other sources which we detail later.

We use these data to calibrate the sales growth rate we would expect from the ‘first movers’ in the aviation industry, in essence capturing the returns to the stocks which were available for purchase in the early days of the industry, and hence adjust our expected sales growth rates to reflect a market share and profit estimate of our holding of aviation firms in 1929.

We also make an estimate of industrial maturity occurring at 25 years from the start of the high growth phase as approximated by Klepper


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(2002). We use data on the expected profit/sales ratio from Moody’s Manual of Investments (1930) for the auto industry in 1929 and the dividend/profit ratio from Cowles (1938). Both of these ratios are used in our models to determine profits and dividends at industrial maturity. We assume these data were available in the 1920s and that data could be easily accessed. Moody’s (1930) also clearly advocates a two- stage industrial growth model to value the aviation industry and demonstrates that formulaic pricing methods were used in the high technology area. These models can there-fore be used to measure the values generated by contemporary financial modelling tools by employing them with data available in the 1920s.

4 Reconstructing fundamental values for aviation Common Stocks at the industry level

Using the growth path of the automobile industry, we build a model to effectively model the expected growth level of aviation stocks using data on profits from the aviation industry. We infer the degree of over-valuation by comparing market prices at peak levels in 1929 to those predicted by the use of our models.

5 Post- crash changes in valuations

Cowles (1938) has data for a price index of airplane manufacturing stocks (series p- 66) and airplane transportation companies (series p- 67), which we use to determine the degree of overvaluation following the crash in prices. Using these data we are able to show the level of prices by late 1930, when the ex-post overvaluation we find with our model of the broad market had dissipated, but critically before the full effects of the Great Depression had made their independent effect on stock prices post- 1930. This moment was chosen as this is when Cowles’s (1938) data show when a deviation of dividends and stock price indexes ended. This devia-tion is the common identifier of the 1927– 9 bubble used in the literature.

We assume prices in late 1930 reflected a dissipation of all compo-nents of the overvaluation and hence offer us a chance to model values before the onset of the severe recession of the 1931– 3 period.

Method

1 Aviation Common Stock data sources

A data set of the 50 aviation firms for which data were available in Moody’s Manual of Investments (1930) and The Commercial & Financial Chronicle (1929) was constructed. The aim was to capture the smaller firms as well as the larger firms in order to provide a clearer and broader


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view of the industry as a whole. The firms are from the transportation and manufacturing segments of the industry.

Due to the novelty of the industry, data were taken from three dif-ferent listing sections of The Commercial & Financial Chronicle in 1929:

• the OTC securities section • the NY curb exchange section• the NYSE main listings section.

2 Data

The following data were collected for each firm for 1929 from Moody’s (1930) and from The Commercial & Financial Chronicle from September 1929:

• earnings • shares outstanding • earnings per share• price per share data (second week of September 1929).

The data were used to calculate

• Total earnings for the industry which represents the industry, includ-ing losses.

• Total market value of industry (shares outstanding × price per share for each firm).

3 Results

• Total market value of industry: $1020 million. • Total earnings (net of losses): $17.6 million.• Total earnings excluding losses: $22.9 million.

The valuation of the industry is used in later sections and compared to the value generated by our models.

Sources for other inputs used in the model

As stated in the methodology we introduce three terms listed below which are used in our models and which we think were available to investors in the 1920s.

(λ ) = 75 per cent market share at maturity of industry


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This value was taken from Klepper and Simons (2000) based on a rich data set on firm ‘shakeout’ in the auto industry and is a widely acknowl-edged source. Klepper and Simons (2000) and Klepper (2002) illustrate that the automobile and auto tyre industries evolved according to a pattern of high failure of the early movers, but that the first movers would be expected to take a 75– 80 per cent market share at maturity. They show that industrial structure evolved to form tight oligopolies with first movers taking a large share of the market at maturity. In the case of the auto tyre industry, early moving firms captured 75 per cent of the market with 80 per cent for autos. The first movers in the aviation industry, that is, the ones which we are aiming to value in our model and which investors could have bought in 1929, were assumed to reach industrial maturity over 25 years and take a 75 per cent share of the mature market. Whether investors actually knew this is debatable but it is not implausible at all, given that Moody’s Manual of Investments (1930) shows that professional investors understood industry life- cycles. The data to make such calculations were also readily available.

(α) = 12 per cent profit/sales ratio (Moody’s data for auto industry in 1929)

This value is taken from Moody’s Manual of Investments (1930) which provides data for 1929 for the industry in a table in the front section specific to autos.

(β ) = 62.5 per cent dividend/profit ratio (Cowles, 1938)

This value is taken from Cowles’s Common Stock Indices (1938) and is acknowledged as a good and reliable source but relates to the general market and not autos alone.

Building a growth algorithm from automobile data from 1904 to 1929

This section details the method of construction of the growth model using the auto industry as a template for growth. We use Moody’s (1930) tabular data on the dollar value of auto industry output of $24.6 million, in its industrial infancy in 1904, as our base year for the growth model. The data were then collected and growth rates at various stages of the model were constructed using the same data table. The data are listed in the appendix at the end of this section.


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All nominal variables were adjusted for inflation using data from Shiller (n.d.) and the NBER Macrohistory Database (2014). All values are expressed as annualised geometric rates.

1 The stages of the growth algorithm

STAGE 1Auto sales from 1904 to 1914 (adjusted for inflation)

Inflation: 2.0 per centNominal growth rate: 34 per centReal growth rate: 32 per cent

The sales growth can be described by the formula:

S S gt = = +10 0101( )

STAGE 2 Auto sales from 1914 to 1929 (adjusted for inflation)

Inflation: 3.6 per centNominal growth rate: 13.3 per centReal growth rate: 9.7 per cent

Sales growth can be described by the formula:

S S gt t= == +25 10151( )

STAGE 3 Having generated the expected sales growth rates we then used the assumptions discussed earlier to infer the dividends expected at indus-trial maturity at year 25.

Dividends at year 25 can be found by the formula:

D St t= == ∗ ∗ ∗25 25 ( ) ( ) ( )λ α β

where,

(λ) = 75 per cent market share at maturity of industry(α ) = 12 per cent profit/sales ratio (Moody’s data for auto industry

in 1929)(β ) = 62.5 per cent dividend/profit ratio (Cowles, 1938)


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STAGE 4Under the assumption that aviation Common Stocks 25 years ahead from 1929 would be expected to have the same P/D ratio as the automo-bile industry in 1927 or 1929, which can include or exclude the effect of an overvaluation in auto stocks –

V D AUTOt t= == ∗25 25 27

– then,

VV

ktt

===

+025

251( )

where,

k = Discount rateAUTO27 = Price/dividend ratio of auto stocks in 1927

Valuation of the aviation industry using a growth model

1 Estimating an accurate level of sales in 1904 for automobile firms

Table 4.7, from Seltzer (1928), illustrates those investors had access to the data needed to make judgements about historical growth rates of the auto industry and measure the level of profits relative to sales in a new industry. Industry output was at $24.6 million in 1904 (Seltzer, 1928).

In order to check whether our sales data for autos make sense relative to the aviation industry, we cross- check these data with the actual sales data of $33 million for the whole aviation industry excluding military planes from Moody’s (1930).

2 The growth model

Table 4.8 illustrates the stages of growth, which we calibrated from the auto industry and the inputs we used to value aviation stocks as detailed in the earlier section.

Table 4.7 1903 data on auto firms sales and profits (Seltzer, 1928)

Ford Motor Car Co.

Packard Motor Car

Co.

Reo Motor Car Co.

Average (%)

Sales/$m 1.3 0.5 1Profits/$m 0.3 0.3 0.3Profit/sales ratio 0.2 0.6 0.3 39.4


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3 The valuation of aviation stocks by the sales growth model

The sales value we use in our calculation is derived from the actual net profits for the entire sample of 50 firms, thereby also accounting for losses, which were used to impute the sales level using the profit/sales ratio from Seltzer (1928).

The following tests were designed to determine what level of avia-tion stock prices would be generated under the application of a growth model to represent what we believe a sophisticated investor could have inferred about asset values for aviation firms. We use profit/sales ratios taken from historically available data from the auto industry, and an assumption not found in the literature in the 1920s, that ‘industrial shakeout’ of the auto industry could have been known and estimated by investors.

4 Method

Stage 1: high growth phase. The net profits of all 50 firms from 1929 were used with the auto industry’s ‘profit/sales’ ratio during its infant stage. This was then used to give the series of values for sales.

An alternative value for earnings, which excluded loss- making firms, was also used and listed in the results in Table 4.9.

STAGE 1

S S gt = = +10 0101( )

STAGE 2

S S gt t= == +25 10151( )

where g = growth rate taken from Moody’s 1930 auto data which we estimated in the previous section (Table 4.8).

Table 4.8 Stages and inputs of the growth model

Stage 1 Stage 2 Stage 3

Time scale (years) 10 15 80Type of input Sales Sales Dividends‘Real’ growth rate (Ann. geometric per cent)

32 9.7


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STAGE 3: STEADY STATE PHASE

D St t= == ∗ ∗ ∗25 25 ( ) ( ) ( )α β λ

where:

(α ) = Earnings/sales ratio (12 per cent)(β ) = Dividend pay- out ratio (62.5 per cent)(λ) = 75 per cent market share at maturity of industry

STAGE 4

The value V of the industry was calculated as:

VV

ktt

===

+025

251( )

V D AUTOt t= == ∗25 25 27

where AUTO27 was the P/D ratio for auto stocks of 16 (taken from cross- sectional data). This value was used to eliminate, in the first instance, any bias from a potential overvaluation of stocks in 1929.

The expected value for the P/D ratio for all stocks was estimated at 19 in 1927 and at 30.8 in 1929.

Therefore the value of aviation stocks at high valuation ratios was

VV

ktt

===

+025

251( )

where a discount rate of 9 per cent was applied (k = 0.09)

V D AUTOt t= == ∗25 25 29

where AUTO29 was the P/D ratio for auto stocks = 27.8 (taken from cross- sectional data).

All of the above equations assume probability = 1 of repeating the growth path of the auto sector which is used as a calibration model. Actual market cap was used to infer the probability of this full growth.


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Moody’s model

In order to test Moody’s model’s valuation of these stocks, we used another model, which replicates the ideas shown in Moody’s (1930).

This is a two- stage growth model with a 40 per cent stage 1 growth rate for 10 years and a steady state growth rate of 5 per cent.

The crash in aviation prices

1 Estimating 1930 post- crash values

Using the data on prices, the post- crash value of stocks was estimated by taking the average of two estimates of the fall in the index of aviation stocks from Cowles (1938) from September 1929 to July 1930.

MARKlow FALLCOWLES MARKhigh= ∗%

where

MARKhigh = actual 1929 data for aviation stock values taken from the CFC (1929).

MARKlow = implied aviation stock values taken from Cowles (1938) price data from 1929 to 1930.

%FALLCOWLES = estimated per cent fall of Cowles (1938) series p- 66 from peak prices in 1929 to mid- 1930

2 Simulation of potential causes

The first potential component of the rise in aviation values was due to a higher value for all stocks including automobiles, the results of which are shown in Table 4.9, and the second from higher expected growth than that of the automobile industry. We acknowledge the potential role of uncertainty and increases in ex- ante values for these stocks but do not simulate it. We use Moody’s model and our main model.

Growth: The high market value for aviation in 1929 could have been due to higher growth expectations and these expectations, although higher than historical growth rates, are not unfeasible. We can adjust the growth rates from our model to simulate what kind of growth expecta-tions would replicate the peak prices in 1929. One possible combination was an inflation- adjusted 40 per cent stage- 1 growth rate and 15 per cent stage- 2 growth rate if the P/D ratio of 16 for auto stocks in 1927 is used. With a P/D ratio of 27.8 from 1929 the growth rates for stage 1 and 2 were 40 per cent and 9.7 per cent. These growth rates are much


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higher than history would suggest but could be feasible if the aviation industry had the potential to grow faster than automobiles. Our results using our most accurate model indicate that the aviation industry was expected to grow to a size twice that expected using the growth path of autos when using peak market values in 1929.

Uncertainty: We do not model this effect and therefore cannot exclude the idea that a new technology could rationally cause prices to rise due to a high level of uncertainty about which firms will succeed. This effect could have legitimately raised ex- ante values of aviation firms.

3 Results

The results of our sensitivity analysis are presented in Table 4.9.

4 Industry price/earnings ratio in 1929

An important comparison, which can be made using the results, is the relative valuation of the new industry when compared to the broad stock market. The results show a price/earnings ratio in 1929 of 60 for the industry as a whole, which can be compared to the general level for all Common Stocks of approximately 19 in September 1929.

Conclusions

The results of the tests indicate that investors could have been using reasonably sophisticated models in the 1920s but at peak levels the market prices reflected an overestimate of the likely returns to invest-ing in these firms’ Common Stock. Our discovery of these models from 1930 are a great bonus to our knowledge of the period and how inves-tors looked at industrial growth and asset pricing for new technologies. Using a method of valuation drawing on the actual models likely to

Table 4.9 Results of sensitivity analysis

Method of valuation Value of aviation firms / $m

Moody’s model 557Equation (1) model – auto growth (1927 P/D)Equation (2) model – auto growth (1929 P/D)

341509

Market value (September 1929) 1029Market value (1930)*low 200Market value (1930)*high 300Equation (3) model – auto growth (1927 P/D) 447


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have been used in the 1920s, we conclude that aviation stocks were overvalued in 1929 by a large margin, of 300 per cent relative to history calibrated from another new technology.

There are three potential routes by which the values exceeded model- based values: first, increased probability of survival; second, the higher level of stocks generally which affected the terminal estimate of the aviation firms at industrial maturity using the auto industry; third, a high expectation of future growth could have occurred. Higher expecta-tions of growth from this industry are not necessarily unfeasible.

The 75– 85 per cent fall in values of these stocks from 1929 to 1930 could have reflected both a general change in the risk of all stocks back to the pre- 1927 norm as well as the probability of survival or growth expectations falling. The key conclusion we make is that the model implies that growth expectations were high relative to history. It should be noted that changing risk premiums are not modelled.

The results indicate high growth rate expectations for the new indus-try were grounded in the reality of technology growth in the thirty years prior to 1929 based on data on the automobile industry but they then overshot model- based values.

Discussion and future research

The modelling technique we develop should have application in other instances of economic history and the results should inform the approach to further research. The main contribution is to show a clearer picture of the boom and crash in 1929 and also gain a handle on how investors looked at new technologies. The results provide a benchmark for solving the complex task of knowing what fundamentals should be. We do not resolve whether the growth potential of aviation was higher than history but the scale of the deviation from model suggests very high growth rela-tive to history.

We add a level of complexity to Moody’s models, when forming our judgements on how to reconstruct a model of what we expected a sophisticated investor to know about when valuing stocks. Investors were using sensible growth estimates and possibly applying a sensible probability factor.

The conclusions we draw rest on assumptions regarding the value of λ, the market share of first movers at industrial maturity, and hence the dividend growth expected from our original holding through the process of industrial shakeout. When we simulate Lambda’s value at 38 per cent, and not 75 per cent, our models give an implied 100 per cent chance of repeating auto growth, implying less sophistication on the part of investors. However, due to the sensitivity of our tests we cannot


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make firm inferences on whether or not a probability discount was being used relative to the path of auto growth.

Questions regarding the heterogeneity of agents’ models are also important for our conclusions. We suggest that two components can be identified at the aggregate level for the industry, but it should be made clear that we do not identify how stocks were valued at the firm level or by different investor clienteles. It may be that stocks were priced by focusing on earnings trends or other selection criteria at the firm level.

Natural questions emanate from the research as to why the overvalu-ation occurred. The reasons for overvaluation are more complex than originally thought in light of our conclusions. By invoking the litera-ture on limits to arbitrage (Shleifer and Vishny, 1997) or incentives to ride asset bubbles (Brunnermeier and Nagel, 2004) we can easily offer explanations as to why we observed this level of change in valuations in the presence of some more informed and statistically aware investors.

Another possible research avenue is to look at how new technologies were viewed and priced at the firm level in this period and also the perennial question of the effect of uncertainty on the valuation of new technologies.

Information on the failure of historical new technology industries and hence the ex- ante probability of successful growth of a new industry may be available in advance. This would involve measuring the survival and growth rates of all ‘new technologies’ from the USA from the dates listed in Moody’s to determine the ‘graveyard’ and eliminate any survival bias in realised growth paths used in expectations formation. The release of this information during the advent of new technologies may offer a way of preventing bubbles or mitigating their effects on the uninformed.

Data appendix

Table 4.10 Automobile industry growth data

Year Value of output $ millions

Profits $ millions

Profit/sales ratio

1904 24.6 9.84 40.0019051906190719081909 165 25.697 15.57

(continued)


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126

Year Value of output $ millions

Profits $ millions

Profit/sales ratio

1910 225 27.518 12.231911 246 29.347 11.931912 378 39.123 10.351913 443 48.875 11.031914 458 59.205 12.931915 701 112.621 16.071916 1082 158.238 14.621917 1274 160.075 12.561918 1236 159.91 12.941919 1885 286.537 15.201920 2232 222.272 9.961921 1261 71.442 5.671922 1793 218.99 12.211923 2592 272.094 10.501924 2367 206.79 8.741925 3015 389.902 12.931926 3214 427.8 13.311927 2700 391.864 14.511928 3162 473.052 14.961929 3567 437.573 12.27

Sources: Moody’s Manual of Investments (1930) and Seltzer (1928).

Table 4.11 Auto high prices from September (Week 2) (Commercial & Financial Chronicle, 1929)

Name Div. per share ($)

Net current assets ($m)

Price per share ($)

AUBURN AUTOMOBILE 12 Nl 499BROCKWAY MOTOR TRUCK 3 Nl 43CHRYSLER CORP 3 71.385 74FEDERAL MOTOR TRUCK 1 5.112 14GENERAL MOTORS CORP 3 251.288 80GRAHAM PAIGE 0 11.099 24HH FRANKLIN 1.6 17.791 13.5HUDSON MOTOR CAR 5 26.203 86HUPP MOTOR CAR CORP 2 17.342 42MACK TRUCKS 6 37.084 105MARMON MOTOR CAR 0 4.181 82MOON MOTOR CAR 0 1.31 5NASH MOTORS 6 44.281 87PACKARD MOTOR CAR 3 24.876 162PEERLESS MOTOR CAR 0 2.379 12

Table 4.10 Continued

(continued)


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Figure 4.1 Index of airplane manufacturing stocks (1928–1933)Source: Cowles (1938) series p-66.

Name Div. per share ($)

Net current assets ($m)

Price per share ($)

REO MOTOR CAR 1.4 19.972 24STUDEBAKER CORP 5 26.04 76WHITE MOTOR CO 2 1.578 47WILLYS OVERLAND 1.2 21.688 24YELLOW TRUCK AND COACH 0 19.036 37

Total 55.2 1536.5

Price / Dividend ratio 27.8

Table 4.11 Continued

4.4 The formation of closed- end funds

Although the investors who bought closed- ends funds displayed a marked tendency to overvalue them relative to their NAVs (De Long and Shleifer, 1991) and research questions their worth as an investment vehicle due to their problems during the crash (Rutterford, 2009), we investigate whether

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they were a good financial innovation. We do this in two ways: first, by looking at the reduction in volatility for a single stock investor which these funds offered, and second, by investigating the realised return to one fund over the very long run which strips out the effect of the bubble.

‘Investment trusts’ – the generic term given to corporations involved in securities investment and trading as distinct from large traders and investment pools – were issuers of equities and to a lesser extent deben-tures (debt), especially in the 1927– 9 period. They existed in a broad spectrum of sizes, management style, sector focus, and leverage ratios (Moody’s Manual of Investments, American and Foreign, 1930). Although ‘investment trusts’ had existed in the USA well before the 1920s, there was a new type of fund with a radical new approach to investing that formed in the 1920s – the closed- end fund. What we discover is that investment returns had become a hot topic for investors following the publication of E. L. Smith’s Common Stocks as Long- Term Investments (1924) and the high returns to stocks during the 1920s.

Closed- end funds, which were designed to hold a diversified basket of stocks, were formed en masse from 1927 and using the insights of Smith (1924), aimed to hold a large portfolio of stocks to earn the 4 per cent risk premium over the long term.

‘Investment trusts’ grew quickly in both number and range of assets under management in the late 1920s and from 1928 to 1929 their assets grew by $3.1 billion, with a new trust opening every day in 1929. The closed- end funds, whose equity and leverage was gained by issuance of Common Stock as well as long- term debt securities, were not critical to the price behaviour of Common Stocks in the late 1920s.

The American style of investment trust became dominated by the closed- end fund structure with the large growth in the number and range of funds under management in the 1928– 9 period. The total amount of funds under management by investment companies as a whole, includ-ing trusts, closed- end funds, and general investment companies, was approximately $7 billion in 1929 (Bullock, 1959). Moody’s Manual of Investments, American and Foreign (1930) states that large numbers of the ‘investment companies’ described as investment trusts operated under varying degrees of covenant restrictions; $4 billion of the total of $7 billion could be described as of the ‘management type’ whereby managers decided the fund’s allocation strategy and the remaining $3 billion oper-ated as closed- end indexed fund holdings.

The level of diversification and investment horizon

The holdings we looked at show high levels of diversification across indus-tries and within industries and holdings, which follow a general style of


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equal weighting. Grayson (1928) and Moody’s Manual of Investments, American and Foreign (1930) also detail the level of holdings in other investment trusts ranging from 30 to 100 Common Stocks.

Moody’s Manual of Investments (1930) details numerous other funds using a roughly ‘equal weighted’ strategy in many cases, together with high levels of diversification of their investments in public utilities, rail-roads and industrial Common Stocks. Although not all funds disclosed their holdings, those that are listed indicate the high extent of their diversification. Other closed- end funds were known to be using this type of strategy such as ‘Fund A’, a closed- end fund run by E. L. Smith. This fund had approximately 40 Common Stocks, which is typical of the average closed- end fund in Moody’s Manual of Investments (1930).

The diversification strategy within an industry reduced the risk from failure of an individual company and diversification across industries the risk of industrial failure or adverse shocks to a single industry. This pattern suggests that diversification was an established concept for closed- end funds and has direct bearing on the returns investors would expect relative to the volatility of the fund’s assets, and by implication, the returns they would expect from their holdings of equities, which made up the stock market.

Closed- end funds were by design investment vehicles with an implicit long- term investment horizon. The Equity Risk Premium, which was earned by investors over the long- term history, of 4 per cent over the Government Bond return does suggest that funds were intended for the purpose of collecting this premium rather than any notion that they were formed to defraud investors in the first instance. We look in this section at the benefits these funds offered small and undiversified inves-tors who intended to hold over the long term. Our data and test indicate that such funds did buy in to the bubble as many formed during the period of overvaluation. Some funds may have been over- leveraged and hence did not perform well or failed subsequently (Rutterford, 2009) and thus their formation during the bubble phase may have reflected their managers’ understanding of investor ignorance. However, this is by no means a test of the usefulness of the innovation that they represented. Holding a closed- end fund that was highly diversified represented the market portfolio and would be expected to gain the Equity Risk Premium as a compensation for volatility of returns over the long term.

The stock market price impact of new funds

The total number of funds reached about 130, with a new fund form-ing each day during the 1927– 9 period. These investment vehicles offered investors a way of owning a share of the US economy with the


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promise of the 4 per cent risk premium over Government Bonds over the long term.

Modern finance theory addresses the issue of the role of large invest-ment funds and their impact on stock prices, and hence it was impor-tant to be able to examine this possibility as one cause of the boom. The total amount of funds from these new investment funds, over the 26 months, is estimated at about $3 billion. This figure was used to assess any potential price impact of new funds on market prices.

Kyle’s ‘Lambda’ (Kyle, 1985) measures the market price impact of trade volume from large buyers or sellers on market prices. Modern studies estimate a value of Kyle’s Lambda of 1 (Gompers and Metrick, 2001). We estimate that a maximum $3 billion of new funds were bought over the year, or about $8 million per day. This was about 4 per cent of average value of daily market volume during 1927– 9 of $200 million. On this basis we assess that a 4 per cent value of funds enter-ing the market per day compared to the total value of the stocks traded using NYSE data would have a 4 per cent impact on prices. Although significant, this buying pressure from new investment vehicles is not enough to have had a major effect on prices, and certainly did not cause a major part of the overvaluation we detect.

The timing of the formation of such large numbers of funds from 1927 to 1929 suggests that investors were very keen to invest in stocks at this time, and there was a surge in public interest in the returns to Common Stocks and investment by new investors in that period.

In later chapters we demonstrate that their formation cannot be linked to a major change of valuations during the alleged bubble phase.

The benefits of financial innovation to the small investor

We can test whether such funds were a useful financial innovation by assuming the role of an undiversified investor in Common Stocks and estimating the reduction in volatility from diversification. We ask and answer the question: How much better would an investor do by investing in these funds?

We used the old NYSE database (2012) to calculate the S.D. of capital returns for individual stocks from 1900 to 1915 and from 1915 to 1925. The S.D. of the total portfolio of stocks (N = 70 and N = 93) was also cal-culated from these data. We assumed a proportional linear relationship between the percentage change in the S.D. of returns from N = 1 to a portfolio with N = 70 and 93, and the percentage change in the risk pre-mium. Therefore a 20 per cent fall of excess volatility of returns as more stocks were added would equate to a 20 per cent fall in the risk premium.


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We then estimated the ‘risk premium’ (ERP) – the expected excess return from Common Stocks over risk- free Government Bonds from two portfolios from Goetzmann and Ibbotson (2006). The test simu-lated a change from N = 1 to N = 93 (Table 4.12).

A change from a single- stock to a more highly diversified portfolio implies a 55 per cent decrease in S.D. thus demonstrating the benefits to the holder of one stock who moved to a diversified basket of stocks.

4.5 The 1927– 9 phase of the boom

Tests for potential drivers of the overvaluation from 1927 to 1929

Perhaps the most elusive question in finance, economics, and economic history is how bubbles form in the real world. Because our data and previous long- range tests identify a large deviation from fundamentals, ex- post, the major challenge was to devise hypotheses and tests of how to measure what actually caused investors in the 1920s to overvalue stocks relative to historical expectation.

Attempting to resolve such a question is extremely difficult due to the data available and the joint hypothesis problem (Fama, 1970). However, what we are able to do is check for some of the ‘usual suspects’ such as dividend and earnings growth rates, size, and momentum.

We first look at aggregate data for the US stock market (Cowles, 1938), which we use throughout the book, and test some plausible theories using these data. In later sections we look at the cross- section of the stock market, to see whether any factors of potential causality can be shown at the firm level.

What we find is exciting given common ideas about the 1920s. We do not find that any of the factors we test for caused the overvaluation phase.

Table 4.12 Results

Source Timeframe Stocks (N)

Portfolio S.D. (%)

Single stock S.D. (%)

Factor of original

Goetzmann +Ibbotson (2006)

1915– 1925 93 14 32 0.44

Goetzmann +Ibbotson (2006)

1900– 1915 70 13 28 0.46

Cowles (1938) 1900– 1920 – 17 – –


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To summarise our findings, which also use prior research on the cross- section from CRSP data (Nicholas, 2008) we are able to detect momentum and technology effects. None of the common potential fac-tors we test for have any explanatory power. Given these results, there is likely to have been a series of much more complex and enigmatic reasons for the formation of the bubble, which we are not able to model directly. This could include a large degree of irrational behaviour.

Such a result does not rule out that we have omitted key drivers, which investors used at the time to re- evaluate the returns to, or the risk premiums attached to stocks. What we do find is that the likeli-hood of high expectations is confirmed from these test results based on heterogeneous beliefs about fundamentals, but the most optimistic of these assessments of fundamentals pushed valuations well beyond rational expectations. We are able, therefore, to show that simple extrapolations of dividends or earnings were not responsible for the 1927– 9 phase.

In the following sections we present and test various theories of how the overvaluation may have formed.

Methodology

A large historical excess return to stocks over bonds and a higher annual volatility of returns to stocks than bonds up to the 1920s indicates investors were rewarded for this excess volatility. In this section, we conducted three tests:

1. A test of whether Smith’s (1924) ideas on ‘zero capital loss’ prob-abilities over longer holding periods could have caused the bubble.

2. Testing a valuation model where stocks and bonds were expected to produce the same returns irrespective of return volatility.

3. Testing whether extrapolation of high earnings growth rates from short- run data, projected forward using a two- stage DDM, could have generated the valuation ratio changes for the broad market.

Test 1: Changes in investor risk preference

Smith (1924) and the new stock volatility- metrics for longer- horizon investors

Smith (1924) shows an innovation in the estimation of risk, which is different from how our Gamma value (1) is calculated. He estimated a new measure of risk – ‘time hazard’. This measured the horizon over which there was a 100 per cent probability of no capital loss. Therefore


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all returns were from dividend income. This is compatible with our DDM. Two estimates were made in Smith (1924):

1. A four- year holding period needed to eliminate capital loss from vol-atility based on 40 years of data ( 1882– 1922) which excluded major crashes or recessions.

2. A fifteen- year holding period needed to eliminate capital loss, which was based on 75 years of data ( 1847– 1922) and included crashes and recessions.

Method

We modelled what would happen to valuations if investors switched from the Gamma model (1) to a new set of models (2):

γ α

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(1)

α = Excess return on Common Stocksβ = Excess volatility of Common Stocks

γ α

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β = 0, over the time horizon of the investment.

The data on holding periods from 1920 to 1930 were then compared to the horizons shown above.

Results

Investors’ actual horizons were on average only for one year, in 1928. Therefore had the use of this new volatility metric been the cause of the bubble, it would have been adopted in error and would not reflect the usual behaviour of investors’ holding periods in the 1920s. Even if the average investor held for four years, the price of stocks still should not


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have changed, as the risk/reward trade- off would have been better with other assets such as corporate bonds. However, it is still possible that such a model was adopted in error and changed valuation ratios for stocks.

A P/D ratio of over 40 was generated assuming a risk premium of 0 per cent and the growth assumptions of our main model. We later test the cross- section of returns to see whether changes in the valuation ratios of stocks were linked to the risk rating of the stock given by Moody’s. In this way we can test whether investors preferred stocks on the basis of a systematic change in the market risk premium.

The new volatility metric seems to imply that Smith (1924) was un aware of the reason for the Equity Risk Premium, either due to the risk aversion of investors or because he felt that holding for short periods eliminated risk. This is relevant because most of the gains to Common Stock in the pre- 1920s era came in the form of divi-dends. On this basis, holding for four years and gaining the dividend income suggests that Smith (1924) advocated a lower risk premium on stocks.

Test 2: ‘Earnings power’ valuation: the expectation of the same return as bonds

The second test models a method which investors could have derived from the empirical data in Smith (1924). This model assumed that stocks should be priced to give the same return as bonds, irrespective of the higher volatility of stocks (Graham and Dodd, 1934). The valuation model violates the assumption of model (1) above. By re- pricing stocks, the risk/reward ratio for stocks falls below other assets, and hence stocks are a bad bet relative to other assets.

The change investors may have made was to a new model (3) as shown below.

γ α

βstocks

bonds

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α αBonds Stocks=

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β Stocks = Excess volatility of stocks


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Results

If the return premiums were expected to be the same for stocks and bonds, the value of alpha for stocks would decrease to 1.5 per cent from 3– 4 per cent reflecting an expected valuation ratio (P/D) of 30.4 using our DDM. At the aggregate level the idea that stocks and bonds were being revalued in a relative sense appears to match the results, although the cross- sectional tests of the risk ratings of stocks are a better method for drawing any conclusions on this potential effect.

Test 3: Expected growth of earnings or dividends

The rationale for this test comes from Sirkin (1975) and from our own findings of a specific two- stage growth model from Moody’s (1930).

We tested whether high earnings growth rates, when projected for-ward in time, could replicate the change in stock market values.

We used a two- stage DDM consisting of a growth rate of five or ten years of high earnings/dividends of 12 per cent per annum from Cowles (1938) for stocks from 1925 to 1929 and a steady state growth rate of 1.3 per cent.

The results (Table 4.13) suggest that extrapolation of trends from 1925 to 1929 could have caused a rise in the stock market. We therefore tested earnings and dividend growth rates in the cross- sectional tests.

Changes in long- run dividend growth rates

We tested our DDM for a range of dividend growth rates to capture any effects of higher long- run dividend growth expectations when we con-ducted the sensitivity tests of the model in the previous section.

Optimistic real dividend growth rates of 3. 5– 4.5 per cent over the long term are plausible as drivers of the boom. We discuss the feasibility of such high growth rates in the context of the realised returns and his-torical growth rates later in the chapter. In both cases such high growth rates as an expectation are difficult to justify.

Discussion

The observed changes in aggregate US stock valuation ratios may have been caused by a change to a more risk-loving pricing model (Test 1), or

Table 4.13 Results (risk premium = 3.6 per cent)

High growth (years)

5 10

P/D ratio 20.1 23.7


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because investors formed the expectation that stocks and bonds should have the same expected returns (Test 2). Extrapolation of short run divi-dend or earnings growth rates could have justified some of the changes in aggregate stock market values (Test 3).

These aggregate- level tests were unable to resolve the question but inform tests of some potential cross- sectional drivers, which may be systematic. In the next section we use various cross- sectional tests to establish the factors or combinations of factors driving the bubble at the firm level.

Firm- level cross- sectional data

The aggregate level tests of some theories of what caused the boom using data from Cowles (1938) allowed some degree of insight into potential dimensions and drivers of the boom. We then tested for the influence of dividend growth rates and earnings growth rates, firm net assets and age, and risk ratings on changes in valuation ratios across individual firms in 1927– 9.

As noted in Cowles (1938) and Shiller (n.d.) the return rates for the stock market were averaging over 20 per cent per annum from 1924 to 1927. Investors may have used these extraordinarily high rates of growth to project future returns on stocks.

We constructed tests using a 130- firm cross- sectional data set of US stocks from a 1929 sample of large to small stocks to test primarily whether earnings or dividend growth extrapolation could explain the firm- level changes in valuation ratios from 1927 to 1929 and whether the effect of momentum in asset values or risk premium changes were impor-tant. We then added tests of the risk ratings of stocks, P/D ratios changes before the alleged bubble phase, the age of firms, and net current assets.

These tests were conducted to complement the excess returns tests for the 1928– 9 phase of the boom, which have already been carried out by others. The reason for the extensive testing, using hand- collected data, is that the CRSP database does not contain earnings data, and also only begins in 1926. Therefore, previous tests may have missed the effect of earnings, and given the ideas of Graham and Dodd (1934) there were significant grounds for testing earnings data. These authors blamed investor preoccupation with short- run earnings trends for the overvalu-ation in stocks. They asserted that this was due to a misunderstanding of the source of excess returns of stocks over bonds in Smith (1924) as being generated by retained earnings.

These tests complement previous research and allow us to discount or accept three major areas of potential mispricing that we find in


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the long- term DDM tests conducted in previous chapters. These are extrapolation of earnings and dividends, or a systematic change in the risk premium.

The tests of risk ratings, if a good proxy for the risk premium on stocks, allow us to test for a systematic change of the return on stocks in general, as low- risk premium stocks should have increased more in percentage terms than higher- risk stocks. In order to test for changes in the risk premium we assume that Moody’s risk ratings of stocks proxy for the ‘riskiness’ of stocks.

We use the change in P/D ratio as a measure of overvaluation because the standard excess returns method for cross- sectional data on stock prices would be automatically significant for dividend growth and earn-ings growth even in normal market conditions.

Data

We collected time series data for 130 stocks’ historical earnings growth from a sample listed in Moody’s Manual of Investments (1930). We col-lected earnings growth, shares outstanding and dividend (annual) data using Moody’s Manual of Industrial and Miscellaneous Securities (1900), Moody’s Manual of Railroad and Corporation Securities (1908, 1919), and Poor’s Manual of Industrials (1912) to measure earnings and dividend growth rates back to 1904. Data for risk ratings, size, and year of com-pany formation were collected from Moody’s Manual of Investments (1930) for data over the 1924– 9 period.

When available, the ‘net earnings before depreciation and taxes’ figures were used although these data are not standardised in Moody’s Manuals. Price data for 1923– 9 were taken from Moody’s Manual of Investments (1930). The 1929 price used was the peak annual price and all other prices the high- low average for the year.

Method

We tested for a bubble in stock prices using the percentage change in the P/D ratio from 1927– 8 and 1928– 9 as the indicator of a bubble on the assumption that we can capture the firm- level overvaluation using this method.

This method tests whether there were any abnormal drivers of these changes and the scale of these changes at the firm level. In- sample ( 1927– 9) and out- of- sample ( 1924– 7) periods were defined from earlier results.

Earnings growth rates and dividend growth rates, calculated as annualised geometric rates from data in Moody’s Manuals were tested


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against percentage changes in P/D ratios for data from 1924– 9 at annual intervals:

Y = b0 + b1 Growthrate + e

where,

Growth rate = earnings or dividend growth rate over various horizons

To test for the potential effect of firm size, net current assets and firm age were tested, with percentage changes in P/D ratios as the dependent variable with the equation:

Y age Mcap NCassets= + + + +β β β β ε0 1 2 3log log log

Risk ratings were tested with the equation:

Y Risk NCassets age= + + + +β β β β ε0 1 2 3

where,

Risk = risk ratings from Moody’s Manual of Investments (1930)Age = age of firm from date of incorporationMCap = market capitalisation NCAssets = net current assets

Results

Tests of earnings growth rates are statistically significant and positive at the 1 per cent level and have an R2 of 24 per cent (see Appendix Tables A1– A9 for results). However, this effect only occurred ‘ in- sample’ from 1927– 8. Tests of dividend growth rates as causes of the bubble all show a negative and significant correlation with P/D ratio changes for 1924– 7 data and 1927– 9 data. This suggests dividend growth rates were not driving higher valuation ratios.

Another key result is that 1924– 7 earnings growth had no relation-ship to the change in P/D ratios suggesting even short- run trends in earnings were ignored and no extrapolative effect was occurring. This result is important, as the extremely high rates of earnings growth, of an average 20 per cent for our sample, do not seem to have an effect on valuations from outside the ‘bubble’ period.

The positive test we find for the year 1927– 8 is not reliable enough to ground any firm conclusions, and because it is ‘ in- sample’, that is, the data were only available ex- post as these are year- end data, the test could be detecting some other factor correlated to earnings or that


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investors favoured high earnings stocks, but that effect is only evident ex- post.

Auxiliary tests showed no correlation between one- year earnings growth and percentage change in P/D ratio for each year over 1923– 7.

Results of tests of risk ratings of stocks using data from Moody’s, which we assume to have captured risk premiums, were insignificant in predicting the changes in stock valuation ratios. Reassessment of the riskiness of stocks, in a systematic way, does not seem to have been driv-ing the changes in valuation ratios, according to our model.

This result should not be confused with the chance of a time- varying risk premium for individual stocks which we cannot detect using our method.

The result that investors did not look at the trends in dividends or earnings or other fundamental factors when reassessing the P/D ratios out of sample from 1924– 7 indicates that stocks were not being revalued on the basis of crude extrapolation of financial indicators.

This also shows something very important about our earlier model-ling of the monetary changes in the economy. The result implies that investors were not led to think that earnings and dividends would continue at the extremely high rates of the late 1920s and were also not ‘front running’ future earnings and dividends in anticipation of future dividend increases based on trend- following behaviour.

The results should also be viewed in light of the work of Nicholas (2008) who found significant effects from technological innovation using excess returns, measured by citation- weighted patents and momentum.

Conclusions

All tests of age, size, historical earnings, and dividend growth cor-roborate the EMH. The positive test for earnings from 1927– 8 shows that investors bought stocks which had higher earnings, although the absence of this effect in 1928– 9 is hard to explain. The in- sample nature of the result also demonstrates that such a pattern was indica-tive of a passive bias towards stocks that had higher earnings growth, but the effect was ex- ante unidentifiable. It remains possible that some other factor such as new technology or patents that we do not test for was correlated with earnings or that high earnings growth stocks were those which investors also believed were part of the new economy.

Momentum

A widely held belief about the 1920s stock market is that rising prices led to further price rises, which drew in more and more investors,


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which then subsequently made the market rise even higher. We know that investors were attracted to the market, which was rising for funda-mental reasons from 1921 to 1927, from evidence of the high volumes on the NYSE and data on investor numbers. Nicholas (2008) tests these theories using panel data from a lagged 12- month return on monthly excess returns from 1928 to 1929. He finds a significant effect at the 5 per cent significance level, which is not present in monthly return data from 1925 to 1928. Hence, only weak momentum effects were present in the bubble phase according to previous research. These effects do not represent enough of the variation in the cross- section of returns to be the sole cause of the bubble. It is feasible that momentum was a partial cause of the bubble and was in operation throughout the period from 1928 to 1929. However, it should be added that such a momentum effect could reflect a change of type described in Pástor and Veronesi (2009) and hence its detection is not automatic confir-mation of ex- ante irrationality or a self- feeding process on the part of investors’ behaviour.

The cross- sectional momentum effect we refer to does not exclude or test for a wider type of momentum effect, which could have occurred as investors saw high returns of 24 per cent per annum in the late 1920s, and were drawn into speculating in stocks generally. As the graph in Figure 4.2 shows, high volume was present from 1927

Figure 4.2 Volume of shares traded on NYSE (millions/month)Source: NBER Macrohistory Database.

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to 1929, but of itself does not show that a deviation from fundamen-tals was occurring. However, these data are at least consistent with the idea of non- fundamental based momentum effects and feedback trading.

Our primary results, using very long- run data, at the aggregate level allow us to benchmark the size of the bubble at an estimated 50 per cent over fundamentals. Although we do find that a deviation from model occurred, it did not form on the basis of an easy to spot statistical anomaly using historical financial data.

As discussed before, there was a very large change in productivity from 1929 to 1950, which was unprecedented in US history (Gordon, 2010). Nicholas (2008) shows that prices rose to some extent on the basis of intangibles measured using patents. This finding suggests that we cannot exclude the ideas of Pástor and Veronesi (2009) regarding new technology shocks without further econometric tests.

Sector- level data from Cowles (1938)

To test whether there are any obvious effects at the industry level based on the idea that the overvaluation may have been due to particular industries which were experiencing fundamental changes in valuations, which overshot these fundamentals on a predictable basis, we use data from 55 individual sectors from Cowles (1938) to identify which indus-tries had the highest percentage increases in valuation ratios from 1927 to 1929 and compared these to the residual change in valuation ratios (P/D) by 1930. The test aims to isolate the actual bubble components from real fundamental changes. By 1930 the aggregate P/D ratio was the same as in 1927, at a P/D ratio of 19.2.

The graph (Figure 4.3) from Cowles (1938) shows the change in P/D ratios at the industry level during the bubble phase from 1927 to 1929. The grey bars show the change in P/D ratio as a percentage for each industry, capturing the relative size of the changes. The black bars show the change in P/D ratios from 1927 to 1930, when the collective valu-ation of the sample had returned to the levels we find are consistent with no bubble relative to our model existing in the market, or a P/D ratio of 19.

This test is limited in power as we assume that no major change in fundamentals occurred from 1929 to 1930 as a result of the October crash and recession. There may have been signs of future changes in the economy, which will affect our inferences. However, there does not appear to be an obvious exaggeration of fundamentals from these data.


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Results

These residual components showed no statistically significant correla-tion. The scale of the changes from 1927– 9 does not have any explana-tory power for the changes from 1927– 30 at the industry level. This shows that the overvaluation we detected in the aggregate data was not caused by an obvious exaggeration of the fundamental changes based on industry type.

Conclusion

The 1927– 9 phase of the boom does not seem to have been based on any of the seemingly legitimate changes that persisted to 1930, when the overvaluation we find had dissipated.

Summary and discussion

Investment returns before the 1920s are consistent with those that were actually realised from 1925 to 2010. An ERP of 3. 5– 4 per cent, which an investor could have feasibly expected in 1926, was subsequently real-ised. However, the overvaluation we find of about 50 per cent remains unexplained.

Figure 4.3 Fundamental changes and non-fundamental component of P/D ratio changesSources: Cowles (1938) and Shiller (n.d.).

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% Change in P/D ratio 1927–1930 % Change in P/D ratio 1927–1929


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Given the scale of the increase in MPFG from 1928 to 1950 recorded by Gordon (2010), which was a 150 per cent increase from about 1.3 per cent in the preceding 50 years to over 3 per cent in the latter timeframe, it is likely that investors had some idea that technology was about to increase the performance of US companies to a material extent. Thus, a ‘new era’ was feasibly on the horizon or occurring and this was, ex- post, worthy of some change in the value of US stocks as earnings and dividends were likely to grow at an increased rate, and the returns to stocks increase. The degree to which and the ways in which such a technological shock may change valuations is of interest for future research.

The scale and dimensions of this change were tested using cross- sectional tests. These tests from Nicholas (2008) and our own results illustrate that patents played a role and that extrapolation of dividends or earnings was not occurring. There was also no systematic change of the risk premium.

Momentum effects, which are present but very weak, cannot be shown to be unrelated to a technological shock. We must leave open the possibility that momentum was due to a self- feeding process for future researchers to address but the scale of the effect is not large enough to be seen as a major factor.

The changes in stock values appear to follow a pattern consistent with the weak form of EMH as none of our measures of financial data are significant in the cross- section of stock valuation ratio changes. Such a finding, together with the results of the momentum tests, is very important and challenges the common view of this period.

Although we do find a weak bias towards stocks with high earnings growth in 1927– 8, the tests cannot establish that such an effect was occurring ex- ante as the effect emerges ex- post. The test may capture a variable related to earnings, which we do not test for.

We can establish that ex- post, a bubble did form. We are able to demonstrate such an effect for airplane stocks as well as the aggregate market using long- range data.

This leaves open the question of whether stocks were overvalued on an ex- ante observable basis, namely that investors were irrational in their purchases of stocks.

The most critical point to address is whether such large changes in valuations can be justified as due to the effects of information being limited and uncertainty being high given the nature of the economic changes of the time and changes in investor perceptions of stocks due to new technology or new methods of valuation.


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The theoretical possibility that some of the changes from 1927– 9 were legitimately driven by such effects remains highly plausible. It is also feasible that no technological shock was occurring and that valuations deviated from fundamentals on the basis of the type of effect described in Shiller (2000) where rising market values lead investors to speculate in stocks on the basis of ideas about a new era, which were unfounded on an ex- ante basis. In this case investors were drawn to higher expected returns due to the aggregate extrapolation of higher returns, or the unjustified prospect of a new technological era could have created all of the ex- ante bubble. The results from De Long and Shleifer (1991) indicate exuberance beyond ex- ante known fundamentals was present on a large scale of at least 30 per cent.


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145

5The October Crash of 1929 and the NYSE Credit System

In this chapter we look at the crash in October 1929 – where stock prices fell by 45 per cent over the last weeks of October – the credit system that developed around the NYSE, and the policy of the Federal Reserve Board towards the boom. What we will see is that an unusual credit system developed to circumvent the Federal Reserve Board’s policy to stop the boom, following the fears of credit growth that they had expressed in 1927 and earlier.

We will see how ‘regulatory arbitrage’ by the banking system led to this system and how the financial stability of the NYSE was com-promised. The chapter also shows that credit grew in line with valua-tions during the boom, but our most important contribution is to test whether the crash in October 1929 was due to a credit retraction from unstable sources of finance.

This question is of great importance because being able to confirm or rule out this possibility allows inferences about the behaviour of stock prices during the crash. A key result from the modelling of expected values of stocks is that prices seem to have been too high relative to the model. Being able to discount the potential of an exogenous shock to prices strengthens the idea that investors willingly sold stocks in large volumes in that month, which indicates a reversal of beliefs about future returns to stocks.

5.1 Financial stability and the NYSE credit system

Federal Reserve policy towards the boom

The stock market boom was affected by the actions of the Federal Reserve Board on two main fronts:


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1. The raising of the discount rate to slow the large rise in equity prices, which simultaneously created a global rate tightening cycle from 1928.

2. The adherence to the ‘Real Bills’ and ‘ Burgess- Reifler’ doctrines as a guide to policy which ultimately led to a mistaken identification of a credit- induced asset overvaluation, and the policy of ‘direct pressure’ – which forced a cessation of lending to the money mar-kets and security loans.

During the pre- Fed days, in which the USA had not had a central bank since 1832 (Hetzel, 2008), the US financial system was susceptible to periodic financial crises. An example of the mechanism by which crises arose and their wide impact on the financial system was the panic of 1907.

There was a mechanism designed to provide liquidity in time of financial crisis via the New York Clearing House System prior to the establishment of the Federal Reserve System as a ‘lender of last resort’ (Gorton and Huang, 2002).1 A key problem in 1907, but which existed generally prior to the creation of the Federal Reserve System, was a weakness stemming from the existence of non- bank financial inter-mediaries (NBFIs) outside the Clearing House System or ‘outsiders’2 who were denied routine access to the liquidity provision mechanism, which the Clearing House System provided for its members. Hence, there was a higher degree of potential financial instability with the increase in the participation of ‘outsiders’ in the NYSE and money markets. The instability arose because the banking system’s reserves were linked to the NYSE through the New York call money market. Bank reserves were loaned ‘on call’3 on the New York money market in order to earn a financial return, rather than leave the reserves idle and non- interest bearing. Call loans, in turn, were used to finance equity holdings traded on the NYSE. This meant that banking reserves and, by extension, sys-temic stability, were susceptible to shocks emanating from the NYSE or the New York money market.4

In the national banking era of financial panics, the central scarcity was the lack of high- powered money – that is, there was a scramble for reserves. In October 1907, confronted with widespread withdrawal of deposits and having meagre reserve holdings (their deposits at national New York banks were considered reserves), the next source of cash for New York’s investment trusts facing panic-induced withdrawals was the liquidation of their call loans.


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The October Crash of 1929 147

New York Clearing House Banks (NYCHBs) wanted to maintain the liquidity of the call market to maintain values of related stock market collateral. Insufficient call loan market liquidity could force undesired liquidation of call loans, perhaps forcing the ‘fire sale’ pricing of assets, which were supporting the call loans.

Such outsider threat was a sufficient motivation for the NYCHBs to deal directly with the trusts to take over call loans from the trusts. However, this duty was beyond their financial capacity as the call loans market expanded outside of their control. Some of the sources of finan-cial instability prior to the establishment of the Federal Reserve System were therefore:

1. NYSE prices were susceptible to higher volatility than would be expected from fundamental values, due to the volatility in the sup-ply of call loans.5

2. The holding of reserves in the form of call loans by the banking system was potentially dangerous for the banks as their reserve posi-tions were vulnerable to an NYSE linked money market crisis.

3. The holding of banking system reserves as call loans could lead to a contraction in the reserves of the US banking system. A contraction in high- powered money as a result of a shock to the NYSE, member bank, or NBFI would thereby create a systemic crisis with effects reaching the wider economy.6

Moen and Tallman (2003)7 and Friedman and Schwartz (1963)8 explain that the desire to ensure liquidity provision in the form of an organised lender of last resort, to intervene during such panics, and prevent the conversion of deposits to currency during banking panics was a key impetus behind the formation of the Federal Reserve.

The Federal Reserve was thus formed in 1913 as a remedy to the periodic financial crises in the USA up to that date, and represented an attempt to provide a strong central bank adhering to the Gold Standard rather than the issue of a pure fiat currency (Hetzel, 2008). The Federal Reserve comprised twelve reserve banks and a Federal Reserve Board approved by the government to provide oversight and policy formulation, with member banks forming part of a network with the reserve banks. The formation of a central bank aimed to alle-viate the problems illustrated above but with a compromise of having twelve reserve banks to prevent centralisation of economic power in one location.


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Regulatory arbitrage and the credit flows to the NYSE

Moen and Tallman (2003) describe the role of the New York call money market in the crisis of 1907, when outside lenders, who were not within the emergency liquidity provision mechanism of the New York Clearing House Banks system, became the largest lenders by volume of funds to the NYSE, via the call money markets. This created latent systemic instability in the US financial system.9

Moen and Tallman argue that compositional changes in lenders to the call loan market in 1907 made the NYSE more vulnerable to a withdrawal of these call loans, which were used to finance purchases of securities. With a money market dominated by non- reserve mem-bers, who critically lacked incentives to provide stability to the NYSE, a sudden systemic shock to the New York money market was increas-ingly likely, and could have large negative effects on market prices as a ‘liquidity panic’ took effect, in the absence of a ‘lender of last resort’ to provide liquidity.

In times of panic, as in 1907 when the New York trusts – NBFIs who were non- members of the Clearing House System – were in crisis, the inability to access reserves held in form of call loans by the banks within the Clearing House System made their reserves vulnerable and hence made them subject to runs as they were technically insolvent. As already discussed, NYSE prices were acknowledged by contemporary authors such as Myers (1931) to be susceptible to shocks to the supply of call loans. Without a ‘lender of last resort’ to take over the liquidated positions of the non- members of the Clearing House System an external money market shock could propagate through the banking system. The emergence of ‘outside’ money market based lending to investors on the NYSE increased the vulnerability of prices to falls induced by liquidity panics, and increased systemic risk.

The two key processes relating to the relationship between NYSE prices and the stability of call money provision were:

1. That a systemic shock leading to a mass withdrawal of call loans could have affected prices and been part of positive feedback loop as collateral values fell. As call loans were withdrawn, the selling of equities to repay the loans would ensue, in the absence of liquid-ity injections to stabilise the market. This selling, if large enough, would trigger large price falls and impair the value of the collateral (stocks) underlying the remaining call loans in the market. If suffi-cient price declines occurred, a cascade of selling could be triggered.


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2. The presence of ‘outside’ lenders to the money markets increased the likelihood and severity of such a cascade developing, as risks outside the system were less likely to be monitored, and liquidity provision channels were not available to respond quickly to such a shock.

The Clearing House System in the era before the Federal Reserve did have the ability to maintain liquidity and hence prevent this type of self- feeding liquidation cycle from causing a crash in values on the NYSE. Numerous authors discuss this liquidation mechanism such as Haney et al. (1932) and Harris (1933).

By 1929 the US banking system had become less vulnerable to a New York money market panic leading to a contraction of the reserve base of the US banking system. This was because under the Federal Reserve System, the main reserves were no longer held in the form of call loans as was the case prior to the Federal Reserve System. However, the risk of a large- scale call loan liquidation triggered by ‘outside’ lenders still remained, and increased with the emergence of non- members of the Federal Reserve System. As a consequence of the actions of the Federal Reserve Board, financial stabil-ity was again endangered by the emergence of lenders outside the Federal Reserve System, which led to the almost complete control of the supply of funds to brokers on the NYSE by ‘other’ lenders.

5.2 Money market leverage for Common Stock trading

The main users of money market leverage to hold equity positions were the investment pools, large private investors/traders, and the less covenant- bound of the investment companies. Rappoport and White (1994) explain the mechanics of brokers’ loans; these were loans from the money market used by brokers to supply speculators with loans using the securities purchased as collateral. The amount of brokers’ loans rose substantially by 1929 as traders, keen to take advantage of the large returns available to purchase stocks as they rose to their new equilibrium values, bought heavily. The rate on brokers’ loans rose to high levels, as there were continual gains net of costs to be made by speculating on the rise of Common Stocks.

The mechanics of brokers’ loans for a small account were as follows: In order to hold a security via a broker a 40 per cent initial cash margin of the purchase price of the security with the broker giving a 60 per cent loan was necessary. Brokers could then call the loan when the stocks dropped to 60 per cent of the purchase price as the cash deposited no


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longer covered the remaining loan. There were also no maintenance margins (Rappoport and White, 1994).

If the security price declined to a level of the cash margin then additional cash would be demanded or the securities sold. Smiley and Keehn (1988) and Rappoport and White (1994) explain that from 1928 to 1929, due to fear of an impending market crash, margin rates were raised on these loans to an average 40 per cent with some stocks requir-ing a 75– 100 per cent cash margin.

The investment pools, large traders, and less strictly covenant- bound investment companies were using money market leverage to trade in and make markets in Common Stocks. The investment pools were large ($ 10– 100 million)10 syndicates of professional traders and market mak-ers operating on the NYSE who had ‘specialist’ knowledge of the stocks they were trading. The size of their accounts with brokers meant levels of margin requirement were lower than for ‘public’ investors. Wigmore (1985) states that large investment pools had ‘no margin’ credit facili-ties via their brokers. Large professional traders were similar to the investment pools in their reliance on funding and leverage ratios and also had low or no margin credit facilities.

The trading size and higher leverage ratios meant that any liquidity shock would force large volumes of stocks onto the market in the event of a money market disturbance via the New York brokers’ loan market. Furthermore, their market- making activities would also be disrupted during a liquidity shock.

The brokers’ loans market

As illustrated in Figure 5.1, the stock market boom of the 1920s co incided with the growth of money market lending. It is vital to note that increases in lending were not necessarily the driver of increased Common Stock prices (Rappoport and White, 1994), but largely reflected a demand for funds as the theoretical value of Common Stocks listed rose during the 1920s11 due to nominal dividend increases and a change in long- term investors’ valuation of Common Stock holdings. The continued demand for funds during 1928– 9, even as call money rates increased significantly under Federal Reserve policy measures designed to restrict loans to ‘traders’, suggests ‘traders’ believed that expected returns to Common Stocks were still below the level at which money market leverage to trade Common Stocks was demanded.

The ‘call loans’ used to provide leverage for securities holdings were primarily in the form of brokers’ loans – loans issued from money market lenders to brokers who in turn loaned this money to investors


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to purchase stocks on ‘margin’. These funds were in the form of loans renewable at overnight notice, which could be ‘called’ or demanded back from brokers.

The other source was in the form of call loans issued directly to inves-tors by banks and financial institutions, thus bypassing the brokers as intermediaries in the supply of leverage. The brokers’ loans market was the more prominent of the two sources with a 75 per cent share of the total. The outstanding credit in the form of brokers’ loans formed a 10 per cent share of the total value of NYSE listed stocks in 1929 (Rappoport and White, 1993).

Figure 5.2 describes the total lending to securities investors (Haney et al., 1932) during the 1926– 30 phase of the boom using data from member banks of the Federal Reserve System. This includes loans of longer horizons know as ‘time loans’.

As a direct consequence of the discount policy of the Federal Reserve and their policy of ‘direct pressure’ to stop lending via brokers’ loans and security loans, the call money rate rose sharply in 1928– 9 as in

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Figure 5.1 Volume of lending via ‘others’Source: Haney et al. (1932).


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previous times of liquidity scarcity in the New York money market, such as 1907.

As a result of Federal Reserve policy, of public opinion, and of the Senate investigation of brokers’ loans, banks were fast becoming unwilling lenders to brokers. This change was expressed not so much in attempts to contract loans already made as in refusals to extend additional loans. In other words, a supply from banks beyond the three billion mark was not forthcoming at any price.

Consequently, as the vertical demand line approached the three billion point, it encountered a supply curve rapidly becoming almost as steep as itself. The result was a rise in the call money rate which, had it not have been for a forthcoming supply from another source, might have soared as high as 20, 50, or even 100 per cent.

The new source was of course ‘others’. As a result of new security issues, corporations were coming into possession of a far greater amount of money than was needed by them for production pur-poses. This was occurring just at a time when call money rates were rising because of the growing inelasticity of supply. A yield of 6, 7, 8, or 9 per cent, combined with the high degree of liquidity for which

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Figure 5.2 Total demand and time loans (1926–1931) Source: Haney et al. (1932).


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loans to brokers are known, proved to be too strong a temptation for the corporations.

As a result the call loan supply by others began to absorb in December, 1927, the elasticity which the Federal Reserve Board was squeezing out of the bank supply so that after this date the funds contributed by others became the elastic portion of total supply that responded to changes in the market and caused some traders to sell a part of their holdings of other securities so as to secure the funds needed to purchase the new issues.

It is clear, therefore, that the Federal Reserve Board had no regu-latory powers over the forces which were really responsible for the huge increase in speculative loans. No action that they could have taken would have been of any avail under the circumstances. Stock prices were soaring at such a rate as to make the demand for funds absolutely impervious to changes in the call money rate. Had the Board not put pressure upon banks to withhold further aid, those institutions would probably have gone on supplying the funds which brokers demanded. As occurred, however, the Board’s attempt to cut down on the supply of credit available to speculators merely caused the call money rate to advance to a point where the funds demanded were forthcoming from other sources. The Board’s failure in this instance does not mean that brokers’ loans should go unregulated in the future, but only illustrates the uselessness of attempting to regu-late the supply when the demand is inelastic. (Eiteman, 1933: 461– 3)

As shown in Figure 5.1 there was a surge in lending from ‘other’ lend-ers. Had the Federal Reserve Board not embarked on this policy, ‘other’ lenders would not have entered the market as reserve bank lending would have been sufficient to supply all the funds demanded for equity purchases. The graph shows the inflow of ‘call money’ from January 1928, the time at which the Federal Reserve policy of direct pressure was enacted. The net increase from lenders outside of the Federal Reserve System from January 1928 matches the increase in total lending data from Haney (1932) over this time period, corroborating the idea that Federal Reserve policy caused a total halt to the flow of new funds to investors and traders.

Roelse (1930) indicates that lending from non- reserve member banks to ‘non NY brokers and dealers’12 and other customers amounted to $ 2– 3 billion, which had accumulated over the two years from 1928 to 1929. This places the total of ‘all’ US brokers’ loans at an estimated $10.5 billion.13


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The financial instability caused by the actions of the Federal Reserve Board’s policies drew in funds from two main sources: foreign banks/corporations and large US corporations. The next section details the profits gained from lending to the call money market from 1928 to 1929 by these lenders. These lenders formed the majority of total lend-ing from ‘others’.

It should be noted that ‘others’ were lending and continued to lend to the call money markets post- crash; however, there was a large elastic segment of the supply whose emergence coincides with the increase in the US call money market rate spread over the US discount rate. This elastic segment is that supplied mainly by corporations and foreign banks. The remaining call money, which constituted ‘others’, was lent by wealthy individuals, interior banks and investment trusts. We focus on the two main groups in the analysis in the following section.

The changing composition of lenders in the New York call money market

A ready supply of funds from lenders outside of the jurisdiction of the Federal Reserve System was to change the composition of the lenders to the money markets heavily towards outside sources. Foreign banks and US corporations were the main suppliers.

As we have seen, there was a radical change in the composition of lenders to the investors via the brokers’ loans market, in response to the higher call loan rates and the ‘arbitrage’ profits available from lend-ing in this market. The cessation of speculative lending from Federal Reserve member banks under ‘direct pressure’ from the Federal Reserve Board was the proximate cause of this change. ‘Others’ accounted for about 80 per cent of the brokers’ loans market on the eve of the crash (Haney et al., 1932) having only previously accounted for 35– 40 per cent during the 1920s. The influence of the outside banks and New York banks occurred from around January 1928.

Smiley and Keehn (1988: 136– 7) note:

the distribution of brokers loans for others as reported by the six largest member banks in New York City was as follows: corporations 56 per cent, individuals 20 per cent, investment trusts 14 per cent and foreign 10 per cent. This understates foreign participation since, apparently, much of the foreign lending was through foreign banks rather than through member banks.14

Other sources such as Roelse (1930) estimate the percentage of lending from ‘others’ at 60 per cent of the call money market. The dominance


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of ‘other’ lenders led to the emergence of a systemic vulnerability of the NYSE to a shock emanating in the call money markets as the pos-sibility that call money market lenders’ behaviour could induce a large retraction of funds if the stimulus for the lending was removed or they became fearful of a crash, became acute.

The Fed’s dual policy did not have the desired effect of causing stock market liquidation or even slowing the stock market boom, as new sources of funding had emerged. These sources had identified the ‘arbi-trage’ or ‘ risk- free’ profit to be gained from borrowing at a lower rate of interest and lending to the call market and it is likely that they lent on the basis that rewards were high. We do not calculate whether this type of lending had a risk premium embedded in it due to the risk of a crash or whether investors were simply benefiting from the squeeze on the market by the Federal Reserve. This ability of the NYSE traders to access credit is a form of regulatory arbitrage. In this sense the Fed’s actions were muted by the availability of internationally and domestically mobile funds.

By 1929 the NYSE’s stability15 had been weakened by the effect of the Federal Reserve’s policy because:

1. Lenders outside the reserve system dominated.2. These lenders were sensitive to call money rates and the cost of

funds from which their profits on lending were derived.3. A systemic shock to this lending could not be absorbed readily

by the Reserve System due to the size of the lending at over $5 billion.16

4. The outside lenders had no reason to maintain the stability of the New York money market or the NYSE.

The mechanism of a downward spiral in stock market prices as call loans were liquidated is documented prior to 1929 (Moen and Tallman, 2003; Myers, 1931) and an established issue regarding the prices of Common Stocks traded on the NYSE during such liquidity crises.

US corporate lenders

H. G. Parker’s article ‘Where the Call Money Comes From’ (1929) makes specific reference to the lending of ‘call money’ by large corporations. Parker was the Vice- President of the Standard Statistics Company and together with the analysis offered by I. Wright (1929) who also cites the entrance of large US corporations to the call money market, we can assume that some analysts were clearly aware of at least one of the main sources of funds used by investors to hold leveraged Common Stock positions on the NYSE during the boom.


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Corporate treasurers would have been expected to seek the highest return for surplus funds not used for business projects as well as to have the ability to borrow funds at the commercial funds rate. The abnor-mally high rate of return on call money and the highly liquid nature of these financial instruments meant that lending to the call money market provided high ‘ risk- free’ returns.

Having entered the business of lending to the call money markets in early 1928, by 1929 corporations were lending in large volumes to investors via the brokers’ loans markets. As non- members of the Federal Reserve System the corporations were not party to any agreement, tacit or formal, to maintain stability on the NYSE or the call money markets. Hence, from the perspective of financial stability, they were not ideal sources of funding for investors, as they were likely to retract funding when there was a higher return to be gained by investing in other instruments, or when their cost of funds rose to the call market return.

Figure 5.3 illustrates the profit potential from lending commercial funds ‘on call’. The profit level is derived from the difference between the cost of commercial funds and the call money rate including

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Figure 5.3 Profits from corporate lending to NYSESource: Haney et al. (1932).


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transaction costs of 0.5 per cent. The difference between corporations’ cost of funds and the call money rate, net of transaction costs, was positive from mid- 1928 until October 1929.

Some of the key corporate lenders who were identified in 1928 (Parker, 1929) as large lenders to the call loan market were EI, Du Pont, De Nemours, International Shoe, American Can, Goodyear Tire and Rubber, American Smelting and Refining, Corn Products, International Nickel, Standard Oil of California, Westinghouse Electric & Manufacturing, and Eastman Kodak. The volume of these funds in 1928 was estimated at $750 million (Parker, 1929); however, there is likely to have been a large increase in 1929 to an estimated $1.5 billion.

The funds from corporations were made available due to the large profits made during the 1920s, issues of new securities, commercial loans from Federal Reserve banks and funds borrowed from other banks and then loaned on call (Haney et al., 1932).

Figure 5.3 also shows that there is considerable change in October 1929 when the profit potential from lending to the call market disap-pears. As we will see later in the chapter, considering the 24- hour term of the call loans we would expect funding to be withdrawn instantan-eously, as a result of the lack of profit from lending.

Foreign banks and corporations lending to the call loan market

Another source of funds, which supplied leverage to investors were for-eign banks and corporations.17 The total volume of funds from ‘others’ totalled $5.5 billion and we estimated this to have comprised $2 billion from US corporations and $1.5 billion from trusts, individuals, and non- reserve member US banks. This leaves about $2 billion from inter-national corporations/foreign banks.

Evidence on the flow of internationally mobile funds in the 1920s is well documented by Einzig (1930) who cited the increasing efficiency of international communications via telephone and telegraph and the increasing willingness of corporations and banks to engage in interest rate arbitrage to profit from rates of return in different money centres. Einzig also emphasises the non- trivial scale of these flows.

International currency arbitrage and the Keynes– Einzig conjecture

The Keynes– Einzig conjecture (Keynes, 1923; Einzig, 1937), established in the 1920s, hypothesised that an interest rate differential of 50 basis points or 0.5 per cent on an annualised basis, was needed in order for flows to occur to exploit the ‘ risk- free’ return between differing interest rates.


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Their analysis refers specifically to ‘covered interest rate arbitrage’ between the spot and forward markets of various currencies, the 50 basis points or 0.5 per cent figure deemed necessary for these arbi-trage trades to be entered. The conjecture has been confirmed by mod-ern econometric tests (Peel and Taylor, 2002).

Although the type of covered interest arbitrage conducted by inter-national investors between 1928 and 1929 took advantage of a unique anomaly between the spot dollar, the forward dollar, and the New York call money rate, the 0.5 per cent figure from Peel and Taylor (2002) is used in our calculations as the necessary threshold for these flows to take place in both uncovered and covered interest arbitrage in our analyses. As we will see in the next section, these profits were well in excess of the Keynes– Einzig threshold and presented an opportunity for traders.

Uncovered interest rate arbitrage

The evidence from Einzig (1937) demonstrates that a significant volume of flows of funds from ‘other’ lenders was conducted on the basis of ‘covered interest rate arbitrage’.

This entailed borrowing in, or using existing funds in low yielding currencies, lending these funds to the New York money markets, and hedging the currency risk to their loans to the New York markets in the forward US dollar market.

Another form of lending on the basis of ‘uncovered interest rate arbitrage’ was feasible in theory, but Einzig (1937) suggests this type of lending was not as prevalent in the 1925– 31 era. This type of trade involved lending to the New York call money market without covering the exchange risk.

The reason for the lack of the use of uncovered interest arbitrage is given as follows:

It may be stated that, between 1919 and 1925, practically every cur-rency fluctuated widely in terms of every other currency …

Before the war the funds engaged in interest arbitrage were to a large extent uncovered, for in many instances the risk of a deprecia-tion was that the exchange within the gold points was smaller than the difference between interest rates in the two centres concerned. After the war however, since the gold points had ceased to oper-ate, there was no limit to the risk involved in uncovered interest arbitrage. Consequently, the practice of leaving arbitrage funds uncovered was discontinued entirely … There was another reason why interest arbitrageurs after the war systematically covered their


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exchange risk. In many instances it was highly profitable to do so. The unusually wide premium on forward rates at times yielded abnormally large profits on interest arbitrage with the exchange rate covered. (Einzig, 1937: 66)

During the post- war stabilisation from 1925 he goes on to explain that the experience of instability had increased the use of forward exchange in general because:

Confidence in the stability of currencies was not so well established as before the war, and gold points were not relied upon to the same extent as the extreme limits of possible exchange movements. When on various occasions sterling or the mark [German Mark] declined to gold export point, this did not necessarily remove the desire for covering the risk of further depreciation. (Einzig, 1937: 70)

This observation occurred under the theoretical stability of the inter-war Gold Exchange Standard of fixed exchange rates, as currencies were expected to fluctuate within narrow gold import and export points of between 1 and 2 per cent depending on the currencies involved.

As previously discussed, uncovered interest arbitrage was not being conducted on a significant scale due to the unreliable nature of the ‘Gold Points’ as indicators of limits to exchange movements. This restriction led to the emergence of ‘covered interest arbitrage’ as the prevalent form of currency arbitrage and is detailed in the following section (Einzig, 1937).

Covered interest arbitrage

Forward markets existed for major international currencies and were actively traded having developed during the currency crises of the First World War to 1925 and remained active. The equation below describes the interest rate parity condition, in an efficient forward market:

i i lF S

sis c c= + −⎛

⎝⎜⎞⎠⎟

+( )1

where:

is is the domestic interest rate implied by debt of a given maturityic is the interest rate in the foreign country for debt of the same maturity


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S is the spot exchange rate ($/c)F is the forward exchange rate implied by a forward contract maturing at the same time as the domestic and foreign debt underlying is and ic. F is expressed in the same units as S, namely ($/c)

Taking natural logs of both sides of the interest parity condition yields:

i iFSs c= + ⎛

⎝⎜⎞⎠⎟

ln

where ln(F/S) is the forward premium and all interest rates are now the continuously compounded equivalents.

Covered interest parity assumes that debt instruments denominated in domestic and foreign currency are freely traded internationally and have similar risk. The major flow of funds to the New York call money market from international money market investors were via ‘covered’ or ‘hedged’ positions which could be taken between lower yielding curren-cies such as sterling/French franc/Swiss franc and thereby earning a posi-tive return on the difference between the rates at which these funds were borrowed and the higher rates on New York call money (Einzig, 1937).

During 1928– 9 the ‘covered interest rate parity’ condition in the pricing of the forward contract was not violated to large extent. However, due to the existence of a large difference between call money rates in New York and US dollar bank rates, assuming that call money and bank deposits in US dollars were perfect or near perfect substitutes as liquid interest bearing dollar assets18 a ‘ risk- free’ profit could be earned. In this case the US dol-lar bank rate was substituted by traders using the call money market and hedged at a forward rate which was closely based on the IRP condition.

1 1+( ) > ⎛⎝⎜

⎞⎠⎟

+ +( )iFS

is c′

where,

is = Call money rateic = Domestic bank rate

Figure 5.4 illustrates the profit from covered interest arbitrage between US dollar call money and sterling bank rate borrowing costs, inclusive of transaction costs of 1 per cent per annum, and the horizontal bar shows the Keynes– Einzig threshold of 0.5 per cent as used in Peel and


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Taylor (2002). These data are from Einzig (1937). This estimate also includes using actual forward rate data to hedge the dollar position with 1- month forward dollars. This type of trade, which we know was of much smaller magnitude than the lending from domestic sources which did not need a currency risk hedge, appears to have been fairly lucrative.

Although we call this an arbitrage, it is not easy to see whether there is compensation for risk in the premium of the call money market over the bank rate, rather than a ‘free lunch’.

Einzig (1937) indicates that there is a noticeable effect on forward dollar/sterling rates which suggests that the flows of funds to the New York call money market forced the forward dollar/sterling rate out of line with its ‘expected’ value under the interest rate parity condition19 of the two currencies.

The period of 1928 and 1929 is very interesting and instructive from the point of view of the forward dollar. The Wall St boom resulted in an all round rise in interest rates in NY. By the middle of 1928 the bank rate parity had moved against the dollar and simultaneously the forward dollar went to a discount.

During the autumn months the forward dollar became consider-ably undervalued compared to the discount rate parity, and also with

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Figure 5.4 Profit from covered interest arbitrage £ bank rate / $ call rateSource: Einzig (1937) and author’s calculations.


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its bank rate parity. This was not an unusual state of affairs. In fact, throughout the post war period, the forward dollar, except for brief periods had always been undervalued in relation to those parities. On the other hand, it had usually been overvalued compared with parities on interest rates based on call money, time money and com-mercial paper in NY.

Consequently the theoretical interest parity, which would natu-rally take account of these rates, must generally be more adverse to the forward dollar than would appear from either the trend of the discount rate parity or the bank rate parity. During the second half of 1928 the abnormally high call money rates in NY made this situation particularly clear …

While from the middle of 1928 until the end of 1929, the forward dollar was constantly undervalued compared with its discount rate parity, it was very heavily overvalued compared with its call money parities or the parity between the NY time money rate and the London discount rate. It was highly profitable for London banks to transfer funds to NY call money or time loans. In order to cover the exchange risk, they had to relinquish part of the transaction, and since during that period the spot dollar was in the vicinity of gold import point, this measure of precaution was essential.

Throughout the second half of 1928 and the whole of 1929 there was a material profit on covered interest arbitrage with NY funds invested in call money and time money. (Einzig, 1937: 268)

The reason why this deviation of the forward rate from its theoretical value was itself not arbitraged away is also given:

At the same time … it would have been profitable for New York banks to buy sterling bills and cover the exchange. From time to time the profit on such operations was over 1 per cent per annum, but very few American banks availed themselves of it, owing to the much more attractive investment facilities in the home market where inter-est rates rose to a very high level … (Einzig, 1937: 269)

In order to demonstrate the behaviour of the actual forward rates during the 1928– 9 timeframe and the impact of the ‘covered interest arbitrage’ trade on deviations from implied forward rates, the follow-ing data were taken from Einzig (1937). The deviation from the US dollar/sterling interest rate parity condition indicates that forward


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US dollars were being sold to cover lending from sterling to the New York call money and time money markets.

From the beginning of 1928 until the end of 1929 forward sterling came strongly under the influence of the Wall St situation. The rise in money rates in NY resulted in a premium on forward sterling from the middle of 1928 until the end of 1929 …

… this was due to an inherent weakness of forward dollars brought about by the Wall St boom …

Even though the forward dollar was at a discount throughout the second half of 1928 until September 1929, this was not by any means a sign of inherent strength in forward sterling, for through that period the forward franc in London was almost incessantly at a premium, showing that the premium on forward sterling was solely due to the peculiar Wall St situation.

The discount on forward sterling in relation to the franc was fully justified on an interest basis, for throughout 1928 and more espe-cially in 1929, interest rates in Paris were considerably lower than those prevailing in London … (Einzig, 1937: 257– 8)

What is clear from the data and analysis of the behaviour of the 1- month forward dollar/sterling rate is that a significant deviation from theoretically expected values (using the bank rate parity) occurred during the 1928– 9 phase of the Wall Street boom and that these lending flows were occurring in significant volumes as they influenced the forward rate.

5.3 The October crash of 1929

The withdrawal of call loans from the New York market

There are five main points to note about the stock market in September/October 1929:

1. The composition of lenders, operating via the brokers’ loans and call loans market, was heavily dominated by lenders from outside the Federal Reserve System.

2. The stock market was at a new level based on the heterogeneous beliefs of investors.

3. Foreign centres such as London which raised its rate by 100 basis points in September 1929, were offering 6.5 per cent and Berlin 7.5 per cent on bank deposits.


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4. The New York Federal Reserve Bank had raised its discount rate to 6 per cent on 8 August.

5. The return from the call loan market fell from 8.5 to 6 per cent by the first week of October.

In addition, investors were likely aware that instability in the money market had already manifested itself during March 1929, when a money market panic had forced the call money rate to 15 per cent and triggered a sell- off in stocks as a result (The Commercial & Financial Chronicle, March 1929). The sell- off did not gain momentum as the National City Bank in New York supplied $25 million in liquidity and offered to maintain liquidity injections to the call money market, which stabilised the selling.

We have established in the previous sections that there was poten-tial financial instability caused by the changing composition of the sources of the leverage supplied to investors and traders. Critical to the full comprehension of the huge sell- off in October 1929 is the role played by the retraction of the funds which comprised the ‘invisible banking system’20 which emerged as the liquidity supply mechanism from 1928 to 1929.

The main aim of this section of the study is to identify whether the crash in 1929 reflected a reassessment of the fundamental value of US corporate stocks, as our modelling and tests in the earlier chapters would indicate, or whether the crash was a result of some other ‘ non- fundamental’ cause, namely, a ‘liquidity contraction’. R. W. Burgess, a New York Federal Reserve Bank official, illustrates a key point with respect to the sell- off in late 1929. Namely, that the call money rate reduction occurred before the stock market crash and thus could be viewed as a factor in the crash itself. This is backed by data illustrat-ing that call money rates fell from a previously high premium through 1928– 9 to an equivalent rate to the US bank rate between September and October 1929.21

The third event in the autumn series was a rapid reduction in money rates throughout the month of October. … Call loans … 8 per cent to 6 per cent. It should be noted that much of this reduction in money rates occurred before the stock market crash and was not the con-sequence of any decrease in the amount of credit employed by the stock market. … The easier position in New York had not resulted, however, in any substantial flow from New York to the interior nor in any easing in the interior money position. In fact, the situation at that time may have been one of those rare instances when money


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may be said to have been cheap but not easy. Rates were low but cer-tain kinds of money were not easy to obtain. The banks were largely out of debt at the Reserve Banks but were conservative in their lend-ing policy, particularly as related to additional security loans, and were putting considerable pressure upon some of their borrowers. (Burgess, 1930: 18)

The Commercial & Financial Chronicle (5 October 1929, p. 2199) high-lights the call money market. On 12 October 1929 (p. 2281) it notes markedly lower rates. On 12 October it cites an excess supply of funds due to security issuance by ‘Investment Trusts’ who lent the proceeds back into the call money market, and were noted as a factor in the declining money market rates. We demonstrate that call money rates fell as demand was reduced for speculative leverage as market prices for stocks reached equilibrium values in September, and which were unlikely to witness rationally based price increases from those levels.

Having established the identity of ‘other’ lenders and the reasons for their emergence as the main lenders to the call money market, used by traders and investment pools, and the effect of the change in the composition of lenders to a money market dominated by non- reserve member lenders, the following section details two potential trigger mechanisms for the crash in stock prices which ensued in October 1929.

In October 1929, sufficient demand relative to supply did not exist for call money leverage to sustain a significant premium above the bank rate, and a fall in arbitrage based gains to zero for international and US corporate call money lending. One potential cause which may have led to very high volumes of sell trades which could not be matched with buyers and the attendant sharp falls in prices in the last two weeks of October, which in turn triggered the retraction of a total of $3.5 billion call money, was a credit or liquidity crunch. In this case a fall in the returns to lending may have caused a forced selling of stocks. The sec-ond potential explanation for the crash was that an overvalued market collapsed as investors realised that stocks were overvalued or that suffi-cient information became available to reduce the uncertainty surround-ing stock values (Figure 5.5).

Lending flows to the call and time money markets from ‘outside’ lenders

As shown in the data on brokers’ loans (Haney, 1932; Roelse, 1930), there is a large contraction in volume of funds coincident with the fall in the


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call money market rates. However, the time money market lending vol-ume showed a continued increase for the month of October (Table 5.1). This suggests that although demand for funding for stocks was falling, the supply flows of funds were generally sensitive to this arbitrage trade.

Figure 5.6 shows how the rates on money market ‘call money and time money’ fell over the month as well as whether the loans were made on the ‘street’ ( non- NYSE), for renewal of loans, new loans, or for 180- day time loans.

In the absence of more detailed data, these data indicate that a lack of profitable ‘arbitrage’ based lending could have been a driver of money market retraction. Furthermore, we also cannot rule out that lending was retracted due to fear of losses from lending to stock market traders as a crash in the stock market was ocurring. Figure 5.7 shows that credit to the traders (demand loans – call money) fell substantially during the crash and after. However, it is not possible to draw any conclusions about which way causality flowed during this fall in credit. The scale of the fall from these data is $2.7 billion but more comprehensive data suggest the fall in credit was as much as $3.5 billion. These are therefore sizeable flows of funds relative to the size of the market.

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Figure 5.5 Bank of England and NY Fed discount rates.Source: NBER Macrohistory Database.


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167

Table 5.1 Securities loans in 1929 (millions of dollars)

Date New York banks and trust companies (demand)

From others

Date New York banks and trust companies (time)

From others

Jan. 29 5043 939 Jan. 29 621 132Feb. 29 5034 914 Feb. 29 584 145.9Mar. 29 5231 979 Mar. 29 482 112.3Apr. 29 5154 1050 Apr. 29 427 144.3May 29 5061 1039 May 29 422 143.7June 29 5333 1111 June 29 464 163.3July 29 5705 1165 July 29 449 154.9Aug. 29 5962 1200 Aug. 29 530 190Sept. 29 6543 1289 Sept. 29 534 183Oct. 29 4639 599 Oct. 29 674 197.1Nov. 29 2873 424 Nov. 29 559 160.7Dec. 29 2883 494 Dec. 29 487 126

Source: Haney et al. (1932).

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Because call money rates did not increase during the crash, as we would expect if demand for loans were constant and supply was quickly retracted, the money market rates shown in Figure 5.6 imply that no forced retraction ever occurred in 1929. We see no spike in rates around the time of the crash. This indicates, on the surface, that adequate credit was reaching investors and traders during the crash. However, a deeper examination of the market’s credit supply shows that there was a forced retraction of credit on a very large scale, amounting to $1 billion, but that the New York banks stepped in to fill this void by taking over the leveraged positions of traders to avert a crisis. We discuss the entire question of whether the credit retraction caused the crash in the next section.

Did a liquidity crisis cause the October crash?

It is plausible to argue that the reason why stocks went up so much in the 1920s, beyond the level justified by the growth in nominal divi-dends, which we have documented throughout the book, was due to a fall in the required return on stocks, and by extension, a fall in the ERP. Alternatively, high expectations of future returns may have increased stock prices.

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As we already know from earlier tests, there was no risk premium based, systematic change in valuation ratios from 1927 to 1929, the period of the boom, which cannot be explained in our simulations of feasible growth and the historical ERP.

The tests conducted earlier point towards an overvaluation of stocks. As set out in the methodology, we wanted to establish if the market crashed from potentially feasible values due to some other factor. If we can prove that a shock to the credit system caused the market to fall, there may be grounds for thinking the market was at a feasible level in 1929. In the opposite case, we can exclude the ‘credit shock’ hypothesis from our list of potential causes of the crash.

We therefore take an innovative look at whether an exogenous shock to credit caused the crash which would potentially have disturbed legiti-mate changes in the valuations for stocks.

Ivan Wright’s ‘Loans to Brokers and Dealers for Account of Others’ (1929) makes explicit reference to the idea that outside lenders, spe-cifically corporations would be quick to withdraw funds from the call money market when the cost of funds equalled the call money return.22 Such a threat may have been the motivation behind the raising of the margin requirements by brokers before the crash (Rappoport and White, 1994), on the understanding that there was a threat of a ‘liquidity shock’ or an overvaluation of stocks. Haney et al. (1932: 165) find that

in late 1929, when the market declined, non banking and out of town lenders hastily withdrew their funds, forcing the NY banks to expand their loans to brokers in order to prevent an utter credit debacle. Whether their withdrawals were chiefly the cause or result of the decline in the stock market cannot perhaps be determined statistically. During the market decline their withdrawals apparently coincided with the decline in the call loan rate.

On the other hand it should be observed that during the weeks end-ing Nov 4 and 12, the rate (call money) was at 6 per cent, while loans for ‘the account of others’ and total brokers loans declined steadily. This tends to indicate that at such times as this, fear, coupled with a declining stock market, is more potent in determining a withdrawal of loans by others than is a decline in the call loan rate. European lenders in our market were made very nervous by critical conditions on their own stock exchanges and other occurrences at home.

Haney et al.’s insight is valid in its injection of a note of caution into determining the flow of causality from the fall in the call loan rate to


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the withdrawal of funds and the collapse of the value of shares. The Commercial & Financial Chronicle (November, 1929), the main financial publication of its day in New York, made specific reference to the cause of the collapse in prices in October 1929:

At the end of September the market was exceedingly weak, and this weakness extended into October. The market now suffered numerous bad spells, and they came with increased frequency as the month advanced. But the great mass of the general public still held on and showed little inclination to get rid of its holdings. There was not the slightest inclination of mob selling or mob desire to sell. But after the decline had been going for several weeks there came an entirely new development, namely the calling of loans on a huge scale, not by the banks themselves but by the mongrel crowd of outside lenders. The statement of brokers’ loans issued by the federal reserve for the week ending Oct 30 furnishes absolute conclusive proof that the flood of stock which came on the market in a perfect torrent the last ten days of the month was forced out by the calling of loans on a scale which itself spelled disaster. This calling of loans, as stated, was entirely by outside lenders.

In 1931, writing in Wall Street and Lombard Street, Hirst stated:

after the crash several writers, including no less than Prof Irving Fisher of Yale sought to explain the heavy fall, which had falsified their predictions of a continued rise, by the theory of mob psychol-ogy. The price level they declared was not too high, the long bull market was justifiable and ought to have been extended. Prices fell simply because a mob of stupid and ignorant speculators all over the US suddenly took fright and began to sell. This explanation will not hold water. As a matter of fact the mob of small speculators held on till the last moment whereas many of the big speculators, being bet-ter informed and impressed by the selling movements from London and the continent, began to liquidate in September and unloaded their holdings in the market, which was consequently weakened. This weakness was intensified towards the end of September. … What brought about the final collapse was a new development – the calling in of loans by the outside lenders. The total amount called in during the last week of October was computed by The Commercial & Financial Chronicle at over 2 thousand million dollars. This forced upon the market a torrent of stocks in the last days of October and so


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ruined many thousands of speculators who, having bought on mar-gin to the limits of their credit, lost everything when their margins were swept away in this flood of liquidation. (Hirst, 1931: 12– 13)

During the large- scale liquidation that occurred in late October 1929 only $1 billion of the total liquidation of $3 billion was taken over by the large New York banks (Haney et al., 1932). This suggests that stocks were being sold voluntarily and the credit used to hold them was being repaid. The New York banks’ ‘taking over’ of leveraged stock market positions was used by investors who did not want to relinquish their positions. These traders and investors could not get their desired level of leverage from non- Federal Reserve System sources, who as we have seen were not bound by any obligation to maintain financial stability.

The large scale of the liquidity injection by New York banks denotes a liquidity crisis was occurring. Taken together, the high level of credit not required by investors shows that they may have been selling stocks which they realised were overvalued and hence no longer wished to hold at the high prices in the first two weeks of October 1929.

The offer of liquidity support by the New York banks is indicative that the banks feared the consequences for financial stability of a large- scale retraction of lending to investors and traders. The subsequent actions of the New York Federal Reserve did supply funds to the New York banks, which had taken over the call loans (Stern, 1988)23 to the extent that liquidity was needed by investors.

Our data and testing show that the market was overvalued by about 50 per cent and some $4 billion in funds entered the brokers’ loans market from ‘outside’ or ‘other’ lenders during the overvaluation phase from 1927 to 1929.

The volume data show a surge in daily trading volumes in the last two weeks of October, which reflect the crash weeks. As stock prices fell by 40 per cent during the crash, the high volumes reflect selling pressure, which could not be absorbed by buyers at the prevailing high prices before the crash began.

In order to test whether the market crash was caused by a retraction of liquidity or a fundamental reassessment of the value of stocks, we conducted some simple tests of the ratio of stock prices to credit using data from Haney et al. (1932).

The total fall in this type of credit was at least $3 billion with aggre-gate levels of money supply in the economy of $50 billion. To give a comparison of the scale of the actual fall in the level of credit, as of 2007 reconstructed M3 in the USA was $11 trillion, thus a 5 per cent


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contraction of this magnitude would equate to $550 billion in nominal 2007 dollars.24 This volume if ‘forced out’ of the market by panicking money market lenders could have triggered a crash, as the magnitude is large.

Figure 5.8 shows the ratio of stock prices to credit from the NYSE and both Reserve and non- Reserve member banks and shows both the ratio of prices to credit when we exclude the injection of call money by the New York banks for their own account in October 1929. This rescue was organised because of the potential effects of the instability of credit and their concern about the effects of a credit crisis on market values.

Adding the measure of the New York bank injection in October 1929 reduces the change in the ratio seen during the crash. This indicates that ample credit was supplied by the New York banks to investors who desired liquidity via the New York banks’ own accounts. The ratio does increase by 25– 30 per cent from October to November even with the New York banks’ injection, which indicates some forced selling could have occurred but this is within our tolerance range given the ratio’s historical levels. Investors may simply have been becoming less aggres-sive. The ratio change is 50 per cent higher when the New York bank liquidity support is not included and we therefore do not see a major credit- induced shock occurring.

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Figure 5.8 Ratio of Cowles (1938) Index to Credit IndexSources: Cowles (1938), Haney et al. (1932), and author’s calculations.


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Although the credit panic may have become a focal point for selling and the money market was a clear source of instability for the market, the New York banks were able to allow investors to maintain their desired levels of credit. Hence the crash appears to have been driven by a fundamental re- evaluation of the worth of stocks, or at least not an exogenous credit shock that forced the crash. The analysis suggests investors were, for the most part, selling overvalued stocks on a deliber-ate basis. One area that cannot be ruled out entirely is that the crisis was a focal point for sellers in a market which they thought was overvalued and may explain the scale of the sell- off, despite the overvaluation we find.

Summary

The boom occurred in tandem with a rise in the level of credit to inves-tors and traders, but this rise seems to have reflected the normal level of desired leverage by investors. The Federal Reserve Board were very concerned about the level of the stock market and believed that ‘credit’ had become excessive. We know that credit availability increases the tendency for bubbles to form in the laboratory and hence the availabil-ity of credit in general may have propagated the formation of a bubble. It is worth remembering that credit costs were high in the late 1920s, at an average of 200 basis points over the central bank rate.

The Federal Reserve restricted banks from lending to traders and sources of unstable lending developed as a type of regulatory arbitrage. The flow of credit was from investment trusts, investors, and foreign banks facilitating covered interest arbitrage, but a large percentage came from US corporations. The key question is whether the crash was caused by a credit retraction from these unregulated sources.

The data suggest that although these funds were unstable and the withdrawal of funds was based on high profits to lending, the crash was a function of the desire of investors to sell, rather than being forced to sell as the funds, which were retracted by these ‘others’, were offset by the actions of the New York banks. Such an observation leads to the conclusion that the crash was not generated by an exogenous shock and the crash is consistent with the reversal of a bubble in stock prices.


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174

6The Great Contraction of 1929– 1932 and the Value of Stocks

The Great Depression, and what caused it, was famously dubbed ‘the holy grail of macroeconomics’ by Ben S. Bernanke, the former Chairman of the Federal Reserve Board (Bernanke, 1995). This period is of great interest to economic historians and financial economists due to the scale of the crises which occurred at that time, the global dimensions, and the complexity of understanding how the Depression developed.

Although we have found that the levels of stock prices were excessive ex- post, given the data available to investors, the results of the exten-sive theoretical modelling and econometric testing we conduct show that the Crash from 1929 to 1930 was a return to the level expected in our models. The broad market appears to have been up to 50 per cent overvalued.

The subsequent crash in stock values from 1930 to 1932 is probably why the period has become infamous. In this section we look at how and why stocks fell to the lows in 1932. The fall in stock prices is linked to the severe recession known as the ‘Great Contraction’ and the scale of the collapse was over 80 per cent from 1929 to 1932.

The period from 1930 to 1933 reflected an economic shock of unprec-edented magnitude that has not been repeated in US history, and as we can illustrate about twice the size of anything the US economy had faced in the 30 years before (Figure 6.1). Our focus is on the behaviour of the US stock market during this period and we leave to one side explanations of how the Depression formed, although we provide an outline of some drivers thought to have been at work. We need to answer three major questions about the valuation of stocks:

1. Were valuations feasible in 1930, given the ex- post returns we have collected on the long- term ERP, and the ex- ante models we have built?


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The Great Contraction 1929–1932 175

2. Why did stock values fall during this period?3. Was the market undervalued at the trough of the market in 1932?

We know that by middle to late 1930, eight to twelve months post- crash, the overvaluation in the aggregate data is no longer apparent. Tests of the long- term ex- ante and ex- post risk premium imply that valuations were feasible in 1930.

From our perspective the market seems valued at levels pre- 1927 which are consistent with our model from Chapter 4. This level of valu-ation was again reached in 1930, and the valuations seem consistent with our model on the assumption that no forecast of the impending Great Contraction was lowering valuations in a major way. An implied ERP of 4 per cent using the aggregate market level suggests fair values in 1930. The events of 1930– 2 were not easy to predict and there is much to suggest investors were not irrational to have valued the market at the levels we find in 1930. Dominguez et al. (1988) illustrate that econo-mists did not forecast the Great Contraction.

The Great Contraction was of an order of magnitude twice that of other large recessions in the previous 30 years. There is much debate on whether the shock was avoidable by central bank intervention and what actually caused the shock – the credit/monetary hypothesis of

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Bernanke (2000) and Friedman and Schwartz (1963) is that credit and monetary contraction due to the failure of banks and eventually the entire banking system, which could have been saved by prompt action to provide liquidity or organised closure, and by maintaining price stability, led to credit frictions and deflation. Thus, the view offered by Friedman and Schwartz (1963) was that a recession of a common variety evolved to become a Great Depression.

The alternative view, led by Temin (1976), argues that there was a pronounced recession before the monetary/credit effects had become apparent and that causality flowed from the real economy to nominal income rather than from money to the real economy. This is a theory of a demand- led contraction, which was severe and was later exacerbated by the 1931 crisis. The 1931 crisis forced the raising of interest rates in the USA and a banking system contraction due to an exogenous shock from Europe as the UK was forced off the Gold Standard.

Eichengreen (1992) argues that the Federal Reserve was bound by the ‘fetters’ of the Gold Standard to prevent the effects of the international crisis in 1931 when the UK left the Gold Standard. This interpretation suggests the Federal Reserve was constrained by the Gold Standard and that real factors plus an inability to prevent the contraction brought about the severity of the Great Contraction.

The difference of interpretation between the demand side and money/credit arguments stems from the role of the central bank. Friedman and Schwartz (1963) argued that the central bank could have stopped the Great Contraction by liquidity support, bailouts, and timely resolution of insolvent banks, to prevent a problem for the US banking system becom-ing systemic. This analysis extends to the 1931 crisis, which was thought to have been preventable due to the existence of sufficient gold reserves and an ability to prevent the effects of the 1931 crisis reaching the USA.

Much subsequent and ongoing research on the Depression suggests that both schools of thought have merit and those interested in the mechanics of severe recessions accompanied by banking crises should consider both approaches together.

The research does not challenge any of these views directly as our aim was to focus on the valuation of the stock market. The actual causes of the Great Contraction are therefore better thought of, at this stage of our knowledge, as the interaction of both monetary, credit, and real factors. What seems to emerge as a consensus, taking the current research into account, is that high levels of debt in the consumer sector, deflation, and banking problems all played key roles in the period. The idea that the real economy contained so much debt in the hands of consumers,


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who were vulnerable to an economic downturn and the potential crea-tion of a ‘doom loop’, between consumer mortgages and the banking system, has merit. Such a feedback process between a demand shock and subsequent increases in unemployment and economic contrac-tion, which led to debt default and a banking system crisis generated internally, is worthy of further investigation. However, the scale of contraction, even under such a process, seems unusually large for this demand- led explanation alone. For both schools of thought, the global banking crisis of 1931 seems to be a key driver of the scale of the Great Contraction although they differ in how the shock was transmitted to the real economy in the USA.

In the following sections we investigate the downswing in stock prices and the underlying drivers of these changes.

Real and nominal earnings and dividends

Nominal dividends appear to have been one major driver of lower stock prices (Figure 6.2). However, the Great Contraction also produced P/D ratios that were lower than the fall of dividends would suggest. Therefore, what we must also account for is another driver of the fall in stock prices. Valuation ratios were at a level of 19.2 in 1927, where we have already

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established fair valuation according to our models. This level was again reached in mid- to- late 1930. The rise in the riskiness of stocks, which is captured in the DDM, or a lower expectation of future growth rates are potential causes, which we look at using a sensitivity test of our model.

As can be seen in Figure 6.3 the 1929 crash predates the fall in real earnings and dividends. As the economy contracted, real earnings and dividends show the market slump reflected a severe real economic con-traction in the USA. The decline was marked for the 1929– 32 period. The graph shows the behaviour of the Cowles (1938) Index adjusted for the decline in the Consumer Price Index. The Real Dividend Index fell by approximately 35 per cent by 1933. The market trough in June 1932 occurred before the real and nominal dividend indexes had reached their lows. Nominal dividends fell by approximately 60 per cent by 1933.

The implied return on stocks and growth at the market low in 1932

The rise of the Yield To Maturity (YTM) for Baa rated bonds in 1932 to 8 per cent reflected increased default risk due to the Great Contraction

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Figure 6.3 Real dividends and earnings (1929–1935)Note: The y axis shows dollar values accruing to the index of US stocks from Cowles (1938).Sources: Cowles (1938) and Shiller (n.d.).


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(Figure 6.4). The expected return on stocks could potentially have risen to reflect higher bond risk premiums during 1931–2. Although we do not perform cross-sectional tests we can model the market low in 1932 in terms of this higher risk premium on stocks.

The expected return on stocks, which we can derive from our con-stant growth DDM, and which assumes dividend growth expectations of 1.3 per cent measured in our earlier tests in Chapter 4, was 15.5 per cent in 1932 based on a P/D ratio of 7 at the trough of the market (Figure 6.5).

Although we do not test for the causes of the rises in the Government and Corporate Bond yields, they imply that financial assets were riskier across all asset groups due to the ongoing recession and the impact of

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the 1931 crisis on the US banking system. The rise in the risk of other financial assets appears to have driven the P/D ratio of the aggregate stock market to lower levels. Assuming that the expected return on stocks would have risen from between 6.5 and 7 per cent, by the same amount as the Baa bond yield from 1927 to 1932, we can estimate an expected return on stocks which was 3 per cent higher, at 9. 5– 10 per cent.

Therefore we can see one potentially rational model that would generate large falls in valuation ratios to a level of 11. 6– 12.4 in 1932. This model does imply, by design, that investors did not, or could not, forecast long- term trend levels of dividends.

Alternatively, a contraction in dividends from the level they had fallen to in 1932, implied by a P/D ratio of 7, was a dividend growth expectation in 1932 of minus 4 per cent per annum, using our DDM and an expected return on stocks of 10 per cent that reflects the rise in the yield on other assets. This is a forecast of a level of real dividends of zero per cent of the original level over an 80- year horizon. Such a pes-simistic forecast implicit in prices potentially reflected an ‘Armageddon scenario’ for the USA, which we cannot assess, ex- ante, but seems exces-sively negative.

These tests revealed that the low in 1932 was some type of ‘ anti- bubble’ or extreme pessimism about stocks, which appears to be the product

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of irrational fear but which persisted for a period of three to six months. However, it may also be the case that credit flows were disrupted during the 1931– 2 period and that stocks were sold due to a credit crunch and therefore not due to fear but rather lack of access to finance.

Using these assumptions we can show that the model we built, if used by investors, cannot replicate the fall in the market index from Cowles (1938) to a level of 7 (P/D) on the basis of the higher risk implicit in all financial asset prices. We would expect a P/D ratio of 12 in June 1932 assuming that the expected return on stocks rose in line with Baa rated bonds.

To provide an alternative perspective to questions of undervaluation of stocks relative to fundamentals we employed a method that looked at book to market ratios for a broad sample of US stocks from the CRSP database.

Figure 6.6 shows the book to market capitalisation ratio for the entire sample of CRSP data on US Common Stock prices and shares outstand-ing that have corresponding data on book values from French (2014). We derive an aggregate market value and an aggregate book value to test the time series behaviour of ‘book to market ratio’ for the US stock market. These simple time series data from 1926– 46 illustrate the degree to which the market changed before, during, and after the boom and crash. The very high values during the crash in 1932 reflect stocks in

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the USA collectively trading at well below their values at liquidation and point to an undervaluation of these firms. What these data mean theoretically is that selling these companies off would have generated a return in excess of the market value.

These data corroborate the undervaluation we find using our DDM where we used time- varying risk premiums estimates, which find a 35 per cent undervaluation. Our book to market values appear to deviate heavily from their mean values during the crash. Our data are taken using year-end values and hence do not fully capture the true book to market value in mid-1932. Nonetheless, we can see that stocks traded well below their book values during the 1931–2 period. Using our data we can see that stocks appear to have been undervalued by at least 45 per cent.

These data suggest that markets for Common Stocks appear to have been functioning partially during the 1930– 2 period based on the assumptions of a DDM and no perfect foresight of the actual trend level of dividends for stocks. Investors therefore seem to have been updat-ing their valuations on the basis of current levels of dividends, using a growth expectation from that level of dividends, rather than forecasting the long- term trend position or mean reversion of dividends to the long- term trend position. We cannot know whether assuming that dividends would revert to their mean path was feasible or not. Therefore we can only offer an explanation as to why the valuations of stocks fell to the extent they did, and also show that the low in 1932 seems unjustified.

The fall of prices of stocks most likely reflected the fall in nominal dividends, caused by the economic contraction, and a higher ERP due to risk perception increasing for all financial assets. The residual fall cannot be accounted for in our model. Whether investors should have known that stocks would ‘mean revert’ to their long run historical divi-dend growth path over the very long term is not obvious, as this would imply knowledge of the future path of dividends rather than the esti-mate of the growth of dividends from a particular point. We therefore base our model on growth of 1.3 per cent from the level of dividends in 1932. Even using this assumption our model finds a 35 per cent undervaluation of stocks in 1932 that is consistent with the apparent undervaluation shown in the book value data.

The recovery in the yields on bonds from 1932– 5 in Figure 6.4 seems to match the fall in the required return on stocks, back to levels con-sistent with our models of the long- term ERP. The book value data also indicate a return to pre- boom levels by 1935.

We cannot resolve clearly whether the final phase of the 1930– 2 crash should be seen as abject pessimism in stocks, or in the modern ter minology on ‘bubbles’ as an ‘ anti- bubble’. Using the discount factor


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linked to the yield on other financial assets, although rational, may not have been the best way to value stocks in the 1930s but this and the data on book to market ratios suggest that investors were unduly pes-simistic about stocks.

The real economy

In this section we investigate some of the salient features of the 1929– 33 period. As stated earlier, we do not aim to explain the causal flows of the Great Contraction. What we do offer is the current explanations for the processes, which led to dramatic contraction over such a short timeframe. The Federal Reserve Board’s policy, the deflation, and a banking system crisis in 1931 are all interesting features of this period and are relevant to understanding why the contraction was so severe (Fisher, 1933; Bernanke, 2000). We suggest that a large recession which was already underway in the USA turned into a mega crisis due to an exogenous shock in 1931.

The real economy from 1929 to 1933 was affected to some degree by the stock market’s fall in value. This is likely from two major channels. These direct factors were:

1. The October crash’s impact on demand in 1929– 30 where investors delayed purchases of consumer durables due to a shock factor and the signal of impending recession.

2. Losses to investors which suppressed consumption through a wealth effect over 1929– 32.

We do not delve deeper into these areas but these are important areas for future research. Our focus has been the fall of stock prices and in the discussion below we show the fall in real earnings and dividends and extract some estimates of an increase in the ERP at the market low in 1932. We attempt to offer the reader some important key data on the Great Contraction from 1929 to 1932.

During the 1929– 30 recession real GDP fell by 8 per cent. The cause of the fall in prices was initially due to a fall in aggregate demand. In total, real GDP fell by 24 per cent by 1933. Romer (1993) suggests that the crash itself played a major role in the initial downturn in demand due to high levels of uncertainty generated by stock market volatility. This is a pattern noted through the pre- war era and relates the stock market shock to real economic declines. Unemployment rose dramatically from 2 per cent in October 1929 to 11 per cent in December 1930, reflecting a real economic downturn that was severe. In total, unemployment reached 25 per cent in 1933 (Federal Reserve Bank of St. Louis, 2014).


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Figure 6.7 shows the monthly change in the Consumer Price Index from 1929 to 1937. It is worth noting that deflation of 1 per cent per month is large enough to have major effects on real activity. The deflation can clearly be seen and was of the order of 25 per cent from 1929 to 1933.

Price falls can have real effects. As the price level fell there were three real effects:

1. Debt deflation – existing debt grows in real terms during a deflation.2. Real interest rates are positive at the zero nominal rate bound and

borrowing is more difficult as the real cost of borrowing is high for companies and consumers.

3. Consumers delay purchases due to anticipated future falls in price.

Analysis of the real earnings decrease of companies must include these factors as well as the fall in demand and credit frictions. Key links between the real economy and the financial system, which affected the severity of the crisis, were the high levels of mortgage debt concentrated in the consumer sector and the concentration of these assets in the hands of the banking system. In this way the real downturn in demand lowered earnings, and also created problems in the banking system, as distressed borrowers had problems servicing the interest on their loans,

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which increased credit frictions due to bank stress and the outright fail-ure of many banks (Bernanke, 2000).

White (2009) uses an improved House Price Index to show a bubble of 20 per cent in the 1920s which peaked before the stock market, which is significant for the effects of the downturn on aggregate demand. Figure 6.8, which uses older data than White (2009), indicates that prices, in aggregate, were generally stable through the 1920s but fell during the crash.

These data are corroborated by the House Price Indexes from Wheelock (2008). Non- farm residential mortgage debt increased sharply relative to non- farm residential wealth during the 1920s and continued to rise until 1932. The Great Contraction from 1929 to 1933 resulted in very high levels of default by 1934. One estimate places arrears at over 40 per cent of all residential home loans in 1934 (Wheelock, 2008).

Indebtedness of consumers and a deflation, which lowered collat-eral to loan ratios for borrowers, known more commonly as ‘negative equity’, may have created a strong negative effect on demand through a wealth effect and a desire to reduce debt by using income. What we do know from previous research (Mishkin, 1978) is that the scale of the ‘real’ debt burden on households rose to very high levels during the period, via increased real debt servicing costs, as mortgage repayments

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Figure 6.8 US House Price Index (1900–1935)Source: NBER Macrohistory Database.


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were fixed in nominal terms, and the total real debt liabilities of US households increased.

For our research we are purely concerned with looking at what hap-pened to the value of US stocks and whether the shock was expected, and hence whether the market was rationally priced. What we can see from the data is that the fall in prices of Common Stocks had two major components:

1. The fall of nominal dividends. 2. An increase in the riskiness of stocks (ERP) or fall of the expected

dividend growth rate.

Conclusions

The Great Contraction phase produced a large fall in stock prices and valuation ratios. It is not clear whether these changes were justified. One plausible cause was the rise in the risk premiums on other financial assets. Another was the fall in nominal dividends.

Assuming that our assumptions of fundamentals are correct, this still leaves a residual component that cannot be explained by the factors in our tests. There appears to have been an ‘anti-bubble’ – or undervaluation of stocks relative to fundamentals. This can be seen using both the DDM we build and Book to Market data from CRSP and French (2014). In both cases an undervaluation of 35–40 per cent appears as a feasible conclusion regarding the market trough in June 1932. Whether the observed under-valuation was due to pessimism, and hence a behavioural phenomenon cannot be firmly established but similarly the data do strengthen the possibility that such a behavioural effect was present.


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187

The research was designed to answer an enduring question in eco-nomic and financial history and was motivated by a desire to provide additional data to Cowles (1938), the raw data for which were stored on Hollerith cards, which were lost (Goetzmann and Ibbotson, 2006). We also wanted to test theories not amenable to the use of the CRSP database, which lacks earnings data and begins in 1926 and find new measures of Common Stock returns from the 1920s over the long run.

We relied heavily on the approaches of Shiller (1981) and Goetzmann and Ibbotson (2006) to build our valuation models but we use new data, which act as a confirmation of the results of these approaches to testing for market efficiency in the 1920s.

The 1920s should be seen in the first instance as a boom generated by the credit expansion of the post- 1915 period, which caused large and legitimate rises in nominal earnings and dividends. This change occurred at a time before the USA leapt into a new high productivity phase from 1928 to 1950 (Gordon, 2010). There cannot be much doubt that there was an underlying productivity surge in the making in the 1920s and this was a potential source of legitimate enthusiasm for the expected returns to stocks. Another question the research aimed to resolve was whether this productivity change and the new innovations of the 1920s actually increased the returns to Common Stock investing from 1925 to 2010. We conclude that an obvious change did not occur and further, that an investor in 1925 would have earned an Equity Risk Premium which was very similar to that expected based on long- term history from the 1870s to 1926. Our major conclusion is that an ex- post bubble can be seen according to long- run dividend growth rates and long- run returns to Common Stocks. Furthermore, there appears to be no systematic bias in the bubble phase. Whether an ex- ante observable

7Conclusions


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bubble formed relies on the results of Shleifer and De Long (1991) who find evidence that valuations were too high. We also find evidence from Nicholas (2008) which combined with our additional tests indicate that a legitimate ex- ante change in stock values could have occurred. We also do not reject Shiller’s (2000) idea that rises in prices led to an ex- ante observable bubble as investors came to expect higher future returns at the aggregate level.

The first stage of the research was to look at the effects of the monetary and debt dynamics of the 1900– 29 period on the earnings, dividends, and prices of US stocks. We explain the reasons for the underlying boom as a debt expansion of a large magnitude, as a result of the gold flows into the US banking system during the First World War, which created a significant expansion of the monetary base and hence the amount of lending possible. The banking system was required by the Federal Reserve to have a certain level of gold relative to its deposits and in this period of passive monetary base expansion, the Federal Reserve Board did not sterilise the flows of gold.

This expansion in the monetary system was, overall, neutral in terms of the debt to income ratio of the private sector; however, this aggregate identity hides a definite shift in the composition of private debt towards the household sector and the commercial real estate sector, due to a nationwide house building, and commercial real estate construction boom. There was a net rural– urban migration of 8 million people from the farming areas to urban centres following the First World War, from a total population of 120 million. A localized bubble in major centres such as New York (Nicholas and Scherbina, 2013) and a general bubble in real house prices of 20 per cent (White, 2009) seem to have formed during a large increase in commercial and residential construction across the USA (Kuvin, 1938; Goetzmann and Newman, 2012).

The concentration of debt in the consumer sector led to problems from 1929 to 1934 as nominal house prices fell substantially and unem-ployment rose, which led to a high rate of default. Wheelock (2008) estimates 40 per cent of non- farm mortgages were in arrears in 1934. Fixed nominal debt relative to falling nominal income created problems of debt servicing and wealth effects.

In addition to the effects on the housing market, the credit and price level increases raised the nominal earnings of companies. The economic dynamics of the time meant that price level increases raised nominal profits of firms, although this full effect had a time lag of about ten years due to a severe deflationary recession in 1920– 1. The 1920s were a time of low and stable prices following the price level surge of the


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Conclusions 189

1915– 20 period. During the 1920s the delayed effect of this wartime inflation on nominal earnings appeared as a surge in the growth of earnings and dividends of stocks.

The increased earnings of US companies led to a perfectly normal increase in the prices of stocks from 1921 to 1927. In inflation- adjusted terms the earnings and dividends of US stocks did not show any ten-dency to deviate from their expected real growth path and can be seen as a reaction of stock prices to nominal changes in earnings and dividends. The large rise in nominal stock values during the 1920s, where returns were 24 per cent per annum before the 1927– 9 phase, reflected the genuine underlying changes of the economy, which were monetary in nature. Such large increases in values may have encour-aged investors.

Our methodological approach consisted of the use of long- term equity return data to calibrate a DDM to value the stock market in the 1920s. A major part of the research involved testing the long- run ERP both before and after the 1920s. We find that investors seem to have demanded compensation for the excess volatility of returns over the risk- free asset for stocks of about 4 per cent, which is close to the esti-mates for the long- run realized ERP of 4.2 per cent over the long run before 1926, and the 4 per cent ERP we calculated from Smith (1924) for his data from 1866 to 1922.

In order to replicate the dividend growth rate expectation of inves-tors over the long term before 1929 we measured dividend growth rates over 1900– 29 for a large cross- section of commercial and indus-trial firms using data that were available to investors in the 1920s, and methods of valuation known to be in use at the time (Smith, 1924). We used the new database of dividend growth rates in the constant growth DDM to estimate fair values in 1927– 32. This model used the historical ERP before 1926 and the dividend growth rate from our database. The growth rate we found was 1.3 per cent per annum over the 30- year sample and we assume this rate can be expected over the long term as a dividend growth rate for the market index of US Common Stocks.

This assumption is derived from the methods used in Smith (1924) and follows the method of Shiller (1981). We used the DDM to find the expected level of the stock market via its Price/Dividend ratio generated from the DDM.

We compared the expected level of stock valuations to the actual P/D ratios at the market peak to quantify the scale of the overvaluation, using a 200- firm cross- section from The Commercial & Financial Chronicle and


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using Cowles’s (1938) data. Having established fair values for the aggre-gate market of US Common Stocks, and the levels reached in 1929, we find an overvaluation of 50 per cent.

We were also able to measure the growth rates of dividends expected in 1927, from before the alleged bubble, by taking the P/D ratio of the market and solving for the growth levels implied from the DDM. We needed to test whether the changes from 1927 to 1929 were due to a rational forecast of the future growth of dividends due to some factor unobserved in our model. We collected new data on live returns to an investment fund, which mimics the market portfolio from 1925 to 2010. We constructed a total return rate, which was 3.4 per cent on a geometric annualised growth rate basis. Such a return is close to the expected level of the historical ERP of about 4 per cent. The 3.4 per cent realised return for the market, which we measured from the returns from 1925 to 2010 suggests that investors were savvy in the 1920s before the boom, but in 1929 they seem to have over- predicted the growth of dividends in the future.

Any fundamental changes to the returns to stocks that may have occurred must be seen in the light of the realised returns data which showed no such exceptional growth was forthcoming that could justify the high valuation ratios in 1929. The sensitivity analysis of the DDM showed that peak market valuations implied dividend growth rates of 3– 4 per cent, which were not feasible given both measures of return expectation and realised returns.

The ‘perfect foresight’ valuation test of our DDM, assuming the realised returns were used to price the market in 1927, required a 1.2 per cent dividend growth rate. Our estimate based on dividend growth history was 1.3 per cent from 1900 to 1929. Cowles’s (1938) data measure a 1.25 per cent growth rate over the long run from 1871 to 1927. Therefore by all our measures the growth expectation implied by 1929 valuation ratios of 3– 4 per cent was very high relative to ex- ante and ex- post meas-ures using a variety of assumptions.

Previous research, which has tested for a momentum effect (Nicholas, 2008), seems to indicate the possibility of a weak self- feeding bubble in the cross- section of stocks, which does not appear consistent with rational valuation methods. However, this view cannot exclude the possibility of a technological shock as described in Pástor and Veronesi (2009) where momentum effects would occur during such a shock.

The data on stock market volumes show trading volumes surged in the 1927– 9 phase and fell back sharply after the crash. However, these data in themselves, although consistent with ex- post overvaluation,


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Conclusions 191

could technically reflect a rush to participate in potentially high yield-ing growth stocks.

The aggregate market was up to 50 per cent overvalued based on our historical model and subsequent long- term returns to Common Stock investment. It is very hard to suggest that a deviation from model did not form in the 1927– 9 period, based on long- term ex- ante expecta-tions, and feasible simulations of expected growth and both the ERP before the 1927– 9 phase and the realised returns. Our results are there-fore consistent with Shiller (1981, 2000).

The 1920s did precede a new technological era for the USA, and MPFG did show a marked change in the 20 years following the late 1920s (Gordon, 2010). This was not a large enough fundamental shock to justify the full amount of the price changes seen based on an ex- post returns basis. Realised returns do not reflect any major change from long- term expectation.

Nicholas (2008) finds strong evidence of technological basis for the excess returns from 1928– 9 using patent data. Therefore, there may have been a legitimate focus on new technology and uncertainty sur-rounding it, but in terms of realised returns to stocks, does not justify the prices reached. There is a real possibility that Nicholas (2008) identi-fies a reason for the boom being initiated as a shock to the equilibrium value of the stock market as new technologies emerged in the period.

This shock would have had two possible effects. The first effect could induce a change in the growth potential of the aggregate market, which was feasible ex- ante. The second was to introduce uncertainty as to which firms would benefit the most and hence create dispersion in fore-casts of expected returns to individual stocks. Such effects could have driven some part of the boom.

To complement the long- run DDM, we also tested one sector which had potential to be overvalued ex- post but was also a new technology. The aviation industry was new in the 1920s and has a direct histori-cal precedent in the auto industry, as indicated by Moody’s Manual of Investments (1930). We used the models from Moody’s to construct our own innovative tests of the expected value of aviation stocks using a large sample of price and earnings data from The Commercial & Financial Chronicle and Moody’s Manual of Investments (1930). We calibrated an industrial growth model using the path of the auto industry as a template for the future growth of aviation stocks, and compared fundamentals to actual peak market capitalisations. Moody’s Manual of Investments (1930) showed that investors knew how to value high- growth new technologies using a two- stage growth model.


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Moody’s Manual of Investments (1930) also gave indications as to how to calibrate the models numerically and estimated the relative stage of the industry life- cycle, which the new technology aviation industry was at in 1929.

Peak values were $1 billion in 1929, and our model suggested inves-tors should have valued the industry at $350 million using our model. Actual post- crash values in 1930, from which we estimate the rational valuation of investors, were $ 200– 300 million. We therefore concluded that there was a large overvaluation of aviation stocks relative to histori-cal growth and the scale of the overvaluation was 300 per cent. Such a large deviation indicated that very high expectations could have been driving valuations.

Our conclusions can be challenged by the idea that this new tech-nology may have been different from historical expectations, making our model mis- specified, as an ex- ante test of fundamentals. We have no substantial basis to reject the idea that aviation stocks could have had higher growth than history or faced high uncertainty (Pástor and Veronesi, 2009), and that this caused the boom and crash. For the pur-pose of our conclusions, individual firms in this industry would have been difficult to assess and their success uncertain. Without a certain basis for valuation, it is likely that an ex- ante bubble could form. A key question emerging from tests of this industry and the aggregate market is to what degree can uncertainty at the firm, industry, or economy level, feasibly generate the high levels of valuations seen?

The results of these first two major tests of the broad market and one industry are new and draw on data not featured in CRSP. To complement these we also designed a test to look for drivers of the potential overvalu-ations we found. We tested the cross- section of the market, taken from a 130- firm sample for drivers of the change in P/D ratios using age, net current assets, 1927 P/D ratios, risk ratings, and market capitalisation. We also used earnings and dividend growth rates from 1924 to 1929. We found one anomaly from 1927– 8 where there was a 24 per cent R2 and 1 per cent statistical significance for one- year earnings growth from 1927– 8. From 1928– 9 there is no detectable effect. Dividend growth rates were negatively related to the changes in P/D ratio. Although the more powerful econometric approach to testing for deviations from funda-mentals is to use the cross- sectional excess returns approach, exempli-fied by Nicholas (2008), our questions required a non- standard test. The analysis of the cross- sectional data shows that high dividend growth or high earnings growth stocks did not achieve the highest changes in P/D ratios, which excludes naïve extrapolation of these values as a cause of


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Conclusions 193

the changes of stocks’ valuations. The conclusion we come to is that whatever the cause of the overvaluations, they were not due to simple extrapolation of financial data on stocks. This is a vital result for simplis-tic interpretation of the overvaluation to be rejected.

We can also exclude the idea that fundamental based investors were making rational forecasts on the basis of dividends or earnings caused by the uncertainty of when the underlying expansion would stop. Therefore, investors were not projecting higher earnings or dividends on the basis of the trend of earnings or dividends.

Our cross- sectional tests also show that a systematic change in the ERP was not driving the overvaluation, which contradicts parts of the accounts of Graham and Dodd (1934).

The sector- level tests showed that the change in P/D ratio during the overvaluation relative to the change by 1930, when the aggregate over-valuation had dissipated, showed no correlation. The bubble was not then a systematic exaggeration of fundamental changes in the value of stocks assessed by industry type. This illustrates the non- simplistic nature of the overvaluation.

It appears that our tests of the cross- section of the US stock market reveal that the weak form of the EMH cannot be rejected and that the formation of the bubble that we observe ex- post did not follow any systematic bias that can be clearly seen. The overvaluations we see may have been the product of a genuine change in the US economy. However, previous studies such as De Long and Shleifer (1991) seem to demonstrate that valuations became exuberant when tested against benchmarks that appear to be highly robust ex- ante.

Following our extensive look at the literature around the 1920s and 1930s and before, relating to asset valuations, we can trace the emer-gence of a new idea in the 1920s on the returns to stocks over the long term. Smith (1924) was a revelation to the public about the long- term return to stocks and may have influenced investors to hold stocks (Graham and Dodd, 1934; Williams, 1938).

A new asset class based on the ideas of Smith (1924) emerged. Closed- end funds themselves became subject to overvaluations (De Long and Shleifer, 1991). They formed rapidly in 1927– 9 to satisfy the demand for holding stocks in a way that reduced the risk profile, or compensation for volatility, for smaller investors.

Our models indicate these funds were a useful, risk mitigating, theoreti-cal and practical innovation. We tested whether the emergence of this new investor group during the boom phase caused the overvaluation. These funds and other investment trusts did form contemporaneously to the


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bubble, increasing in number and funds under management exponen-tially over the bubble phase to about $7 billion in 1929.

The scale of the funds invested relative to the NYSE volumes in 1929 was about 4 per cent of the total value of shares traded and were not enough to cause a major impact on prices and therefore we do not see this source as a major contributor to the boom.

These new funds were a financial innovation, which were potentially beneficial for investors in spreading risk and earning a long- run ERP that was estimated to be 4 per cent, a value that has been corroborated by subsequent studies (Cowles, 1938; Goetzmann and Ibbotson, 2006). Our estimate of ex- ante ERP was derived from the actual market values and constant expectation of return per unit excess volatility over Government Bonds. Closed- end funds were not all good investments and many performed poorly due to leverage having a severe effect on them during the crash. Therefore some funds led to large losses for some investors.

In order to test whether an exogenous shock caused valuations to fall from 1929 to 1930, assuming the counterfactual scenario of fair expectations or lower required returns in 1929, we conducted a major analysis of the credit system in the 1920s. We found some interesting facts about the emergence of a ‘shadow banking system’ that supplied funds to traders as Fed policy squeezed liquidity from the market, due to fears of a credit- induced bubble. This was a good example of regulatory arbitrage and we find that financial stability was compromised, neces-sitating the injection of $1 billion during the crash week. By analysis of the ratio of credit to prices in the 1920s, we set a level of expected ratios and tested whether the ratio deviated from expectation. We did this to ensure that a potential source of an exogenous shock was not the cause of the reversal of a potentially genuine change in valuations. We found that credit was unlikely to have been the cause of the crash in prices due to a credit crisis where investors and traders were forced to liquidate their holdings.

We reject the idea that the crash in 1929 was induced by credit con-traction by analysis of the Cowles (1938) Index price to credit ratio when accounting for the injections by the New York banks during the crash.

The crash in October 1929 was not the direct result of a credit crisis although the New York banks were forced to provide liquidity on a large scale and the financial stability of the NYSE was therefore compromised by the flow of credit from unstable ‘outside’ lenders. We therefore


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Conclusions 195

concluded that regulatory arbitrage occurred and was a source of insta-bility to the NYSE, which required intervention.

The final part of our approach to determining the dynamics of the boom and bust was to investigate the 1929– 32 crash. By 1930, the over-valuation we found had deflated and 1927 levels of valuation ratios for the aggregate market had been restored. The 1930– 2 crash was a phe-nomenon which does not directly follow from the overvaluation. The market appears to have been fairly valued in mid- 1930.

The ‘Great Contraction’ from 1929 to 1933 affected the value of stocks in two ways – falls in nominal earnings and increases in the riski-ness of all financial assets – which seems to coincide with the increase in the required return on stocks. We used the Cowles (1938) Index to measure the fall in earnings and dividends and measure the valuation ratios of the aggregate stock market.

The value of the aggregate market remained logical to a partial degree, as dividends fell and P/D ratios fell to reflect higher risk premiums on financial assets. This fall in the P/D ratio to a level of 7 in 1932 could not be justified on the basis of our expected real dividend growth rate of 1.3 per cent or the increase in the ERP due to the increased riskiness of all financial assets. The ERP could have risen to a level of 7 per cent, but valuation in 1932 indicates this ERP was 12 per cent. Whether the fall was actually rational given the long- term return to stocks and trend levels of dividends is not clear. Shiller (1981, 2000) suggests that there was an undervaluation of stocks in 1932 based on the ex- post warranted price. Our models, which assume very little could be actually known about the future of the stock market and the US economy, estimate a level P/D ratio of 12 due to higher risk premiums across other financial assets. Our data on book to market ratios during the trough in 1932 suggest that stocks traded well below their book values at around 35 per cent.

The difference between the actual level of P/D ratios of 7 and the expected level of 12 in our models cannot replicate the lows in 1932 even when we assume that dividend growth rate expectations also fell. The expectation derived from our DDM was a minus 4 per cent per annum fall in dividends. This would mean no dividends would be paid on the US stock market over an 80- year horizon, which is unfeasible. This indicates that the market low in 1932 cannot be explained using rational forecasts and implies that irrational pessimism may have been present.

A major factor, which needs to be emphasised, is that the scale of the crash exaggerates what the fair expectation of stock prices were. The


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degree of the economic collapse during the 1929– 32 period was unprec-edented, and hence an investor in 1926 was not acting irrationally in buying stocks, given the subsequent returns they would have enjoyed through 2010.

The conclusion that we reach regarding the boom has already been established to a certain degree by academic researchers who are experts in the field, notably Robert J. Shiller, Eugene N. White, William Goetzmann, and Roger Ibbotson. Their trail- blazing work demonstrated that the 1920s stock market was overvalued by a significant margin using historical and financial econometric methods of substantial power. Our results build on their insights that such phenomena did occur. These analyses rightly encourage the further study of what are perhaps the least understood areas of economics and areas which present future academics with many interesting questions regarding the true nature of asset bubbles.

Our conclusion is not to condemn the investors of the time by label-ling them as irrational, as we do not offer enough systematic evidence to draw that conclusion. We also do not rule out that investors were irrational. Some non- fundamental beliefs about very high future returns were potentially driving market prices higher although uncertainty about which firms would perform well in the new economic era may have made legitimate ex- ante changes to valuations of stocks.

What we offer at the aggregate stock market level are both rigorous and long- term tests of the large deviations of stocks from their ex- post ‘fundamentals’. Expectations of returns at the market level were higher than those observed over the long run before the 1920s and those that were realized over the long run from the 1920s to 2010. The scale of the overvaluation relative to these measures was 50 per cent for the broad market and our simulations of potential growth suggest a bubble formed on an ex- post basis.

The stock market boom bears all the hallmarks which have been iden-tified by the modern literature as indicative of ‘bubbles’:

• the presence of less well informed traders• fast rising prices• unclear fundamentals• a lack of dividend anchors (aviation industry)• credit access for leveraged investment• mass media interest• increases in the volume of trading• credit expansions on a large scale.


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Conclusions 197

These can ex- post lend some credence to the idea that a deviation from fundamentals occurred in 1927– 9 based on the findings of other research on the conditions under which bubbles may form and general factors which indicate their formation. Full resolution of whether an ex- ante ‘bubble’ formed, its magnitude, and its causal factors is dependent on future research.

Discussion: policy implications

There are two key findings, one related to financial stability and the other to asset bubbles, which have potential implications for current policy- makers.

Regulatory arbitrage and the shadow banking system

Alternative credit systems, which are outside of the regulatory purview, may also need to be investigated more deeply in light of this research and also in light of their role in the 2008 crisis during a freeze in lending from money markets (Gorton and Metrick, 2012) which forced the guar-antee of all money market funds during the crisis by the Federal Reserve. In the 1920s, the Federal Reserve became, via the New York banks, the liquidity provider of last resort, which is the original conception of the function of a central bank in a liquidity crisis (Bagehot, 1873). Such systems posed a threat to financial stability in 1929, and required large- scale liquidity support by the New York banks who were later supported by the Federal Reserve’s open market operations.

Regulatory arbitrage is a process that occurs as regulators are either ‘captured’ or entirely circumnavigated by those whom they seek to reg-ulate, which in the pursuit of a legitimate desire to generate profit may lead to excessive risk for the financial system in the event of a panic, as the transactions conducted are not within the remit of the regulators or central bank to correct, except in the last resort.

As we have seen, credit flows could not be stopped by the Federal Reserve Board. The Federal Reserve sought to squeeze the investors’ and traders’ funding from the call money markets. Their policy failed and new lenders in the international money markets and from domestic corporations and investment trusts stepped in to fill the void. This is what is known now as regulatory arbitrage and occurs when regulations are seen by investors to be obstructing their business, and channels are found to escape the regulation. In 1929, this led to the need for liquid-ity support from the Federal Reserve System during the crash as lenders withdrew funds and investors repaid loans to the outside lenders during


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the collapse of what in retrospect was an overvaluation. In the 1920s, the credit system that was built up around the NYSE was known as the ‘invisible banking system’ (Ayres, 1929), but today it exists by a differ-ent name – the ‘shadow banking system’.

The recent crisis of 2008 has shown that regulatory arbitrage is dangerous as it places the control of the financial system, and the risk within it, outside of the purview of financial regulators or at least out-side of their professed ability to take direct remedial action. In the case of subprime mortgage instruments, many banks took their risks ‘off balance sheet’ to escape regulation (Milne, 2009). They funded their investments through the money markets, which comprised a complex network of funding sources (Adrian and Ashcraft, 2012). These sources suffer from the potential to become unstable and lead to panics and runs on those institutions which rely on the funds (Gorton and Metrick, 2012). The research therefore suggests that modern policy- makers should be interested in regulatory arbitrage and the attendant increase in systemic risk that this brings. The level of credit and its sources and their stability, as well as the overall effect on systemic stability should be considered at all times.

Asset booms and busts

The research does not possess high enough statistical power to make any inferences about asset bubbles in general, other than the fact that we detect a major deviation from fundamentals. Given that bubbles are relatively infrequent on the scale that we find in 1927– 9, what the research does do is add another historical example to other instances, with a higher degree of certainty.

However, there are some obvious positive results of the research, in particular the growth of the monetary system and the price level, and the research shows that monetary expansions can be harmful to financial market efficiency. Bordo and Jeanne (2002a) used mechani-cal rules to identify booms in stock and residential property prices since 1970 in fifteen industrial countries. They defined a ‘boom’ as a situation in which asset- price growth over a three- year period lies sig-nificantly above its long- run average and a ‘bust’ to be a situation in which the three- year asset- price growth is correspondingly lower than normal. Out of 24 boom episodes that they identified for stock prices, busts followed only three. Bordo and Jeanne found more evidence for boom– bust cycles in residential property: busts followed ten of nineteen property booms. However, none of these instances was in the United States.


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Conclusions 199

As we have seen, there are many common factors between the 1929 boom and those factors known to influence asset bubble formation either via laboratory experiments or other ex- post analysis of these situations.

The paradox remains that stocks cannot be said to always form bub-bles given the presence of the criteria such as:

• less well- informed traders• fast rising prices• unclear fundamentals• a lack of dividend anchors• credit access for leveraged investment• mass media interest• steep rises in IPO volumes• increases in the volume of trading• credit expansions on a large scale• low interest rates and deviations from the Taylor rule• new economic or technological eras.

The challenge that emerges is the question: Should we stop looking for them or can other indicators be used?

The inherited response to bubbles, which are known to have formed, is to leave them alone on the basis that statistically, in the cross- section of firms, they do not have detectable causes and that policy action is not timely or directed enough even once identified (Bernanke, 2002). However, this finding is paradoxical in that we can identify an asset bubble ex- post and this may offer some way of improving policy as it currently stands towards bubbles.

We now have a large body of evidence based on academic research since the 1970s to predict when a bubble is likely to be forming and the conditions under which the statistical probability of them forming is high. In the 1920s both monetary expansion and a technology shock can be detected as drivers. Ex- post, there are other anecdotal factors, which conform to the findings of laboratory tests. This is based on ex- post reasoning but nonetheless may provide an edge to their early detection or the causal mechanisms of their formation, which may be controlled or prevented.

Early warning and prevention

We leave to one side the later proposition we make regarding further research into whether some types of bubbles are desirable, or may have


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hidden benefits, such as the promotion of technological advancement. We consider how new research on bubbles and behavioural finance may aid policy.

The next step would be to design policy to prevent or limit the forma-tion using what we know about what causes them and how they work. Alternatively a good outcome would be to have early warning systems to allow prompt action via flexible regulation, once detected. For exam-ple, it may be the case that low central bank rates, which deviate from the Taylor rule, may induce excessive credit creation and risk taking and inflate asset prices in general. Thus, more serious attention would be paid to such obvious conditions of monetary easing if they can be linked to previous bubbles.

These suggestions highlight a critical point. The difference between policy- makers’ considerations and academic research is that we had to be reasonably certain that a bubble formed in 1929 and test it beyond reasonable doubt. Policy- makers are, fortunately, not constrained by these considerations to the same extent. Their concern should be the cost– benefit calculation of such an event occurring. The true cost is that which is borne by society as a whole, whether looking from a static or the more logical, dynamic perspective on welfare.

Welfare effects of bubbles

What the crisis of 2008 has done is to serve as a timely reminder that bubbles, or overvaluations of assets, may have welfare consequences, both directly to the holder and indirectly via financial institutions los-ing wealth and the creation of credit crunches. These effects need not be restricted to those actually active in financial markets, but via an increased risk of a banking crisis can lead to global effects for people totally unconnected to the assets in question.

A very important question is: who loses from bubbles? Are they detri-mental, statistically speaking, to the welfare of society? This is not very clearly defined and hence further research is needed. There is evidence to suggest that those at the lower end of the income distribution lose from asset bubbles (Hirota and Sunder, 2007). If bubbles represent a net transfer to the higher income strata, then this is not a major considera-tion unless this has an asymmetric effect on growth and the dynamic welfare of the society in question. Some may argue that such considera-tions of income distribution are important, but a much deeper, rigorous analysis is needed to determine whether those in lower income strata who buy inflated assets should be protected as they enjoy the potential upside of their gains. Care must be taken to fully analyse why people


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Conclusions 201

engage in speculative or excessive risk taking which may be detrimental to the wealth of a particular income group. To regulate them would mean restriction of freedom to transact on the most basic level and is a hard sell in a free democratic system. We therefore focus on dynamic welfare of the whole society as our yardstick for guiding policy.

It may be that some type of education on the dangers of bubbles should be offered to investors. Of course, the aim of investment is to make profit, and those with greater access to financial advice should perform better. Those at the lower end of the income distribution are likely to have less information and therefore the provision of simple guides to realistic returns through financial history and risk awareness of the volatility of investments would provide a level playing field to all investors. The conclusions we reach are that investment over the long run is a good idea, if an investor diversifies.

The communication of how to invest safely should be the function of a government seeking to prevent asymmetric welfare effects. As the case of the US housing bubble has shown, there may not be a direct link from house price bubbles to severe outcomes for welfare. It is highly probable that the Federal Reserve Board believed that the housing bub-ble of the 2000s in the USA could be contained by their actions. In the case of the USA, the housing bubble, whose effects on growth could have been reduced by decreasing bank rates to stimulate the economy when the bubble deflated, became a much larger problem due to the miscalculation of risk of certain mortgage products by banks. The problem of a bubble in housing was transmitted to areas of the global economy unrelated to the housing market. How do we therefore calcu-late the effect of the housing bubble on welfare losses? It can be argued that all bubbles create some kind of financial instability and therefore all are worthy of more detailed study to eliminate their formation.

Although generally seen as detrimental to welfare, an area which remains of potential interest and a potential reason to allow their forma-tion is that they spur technological innovation. Without investigation of whether technology bubbles lead to gains in overall dynamic welfare, as we over- invest in new technology thereby stimulating further research and the proliferation of the technology, we cannot be certain that we can apply the same analysis of their negative welfare effects. What this type of analysis may show is that we can differentiate between other types of overvaluations, which seem to serve no dynamic benefit.

Welfare considerations in the dynamic sense need to be considered rather than just the static case of observed losses or changes in the dis-tribution of income in a single time period.


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Future research

As we have seen earlier in the book, the three main approaches to asset bubble research are likely to prove fruitful. All are needed to further our understanding of these complex phenomena.

Historical cases of asset bubbles and long- range empirical research

The research has shown that history and long- range empirical studies, which follow the tradition of Goetzmann and Ibbotson (2006), Cowles (1938), and Smith (1924) and others, can yield useful knowledge about the behaviour of financial markets and asset prices. Their importance has been neglected given that such work is not easy to conduct due to the added dimension of the location and collation of data by hand, from old and sometimes incomplete sources over hundreds of years.

There are many other cases of historical asset bubbles and crises that have not been fully documented in the detail which this study has aimed to provide. One area of particular interest is technology bubbles. Studies of the NASDAQ boom and crash of the 1998– 2000 period and other international cases of potential bubbles would benefit future generations.

Laboratory tests of bubbles

These new types of tests have provided many valuable insights (Smith et al., 1988), and opened new frontiers in our understanding of asset bubbles. Furthermore these new approaches have overcome the problem faced by traditional empirical analysis, namely that some factors are extremely dif-ficult to control for in the analysis of real- world data.

In addition to the ongoing work in this area a focus on remodelling of historical episodes in the laboratory would be very interesting, most notably for the economic and financial history community, who would gain from being able to see history through the lens of laboratory behaviour. Behavioural economics would also gain from this innova-tion to their approach to see to what extent social and institutional fac-tors unrelated to finance, which are historically specific, may influence bubbles. Of course, there are limits to these approaches but they offer an interesting alternative research path for the laboratory method. The 1920s market could be remodelled to some degree and the bubbles that formed may be compared.

The laboratory method should be extended to focus more on the effect of technological shocks and uncertainty on the formation of bubbles. One key area that remains unresolved is the behaviour of investors in


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Conclusions 203

times of new technologies and whether bubbles form due to a rational reaction to not being able to identify who the winners will be.

Theoretical work

New theoretical work on technology bubbles will enhance our knowl-edge a great deal, not only about how technological shocks may affect the patterns of investment in the economy but also how we respond to them and perceive their benefits. How we respond to technological shocks due to radical uncertainty, or due to the inability to determine who will win from a new technology at the firm level, needs further work. This research should aim to resolve whether there is a way of making the distinction between effects under technological change from irrational exuberance and those based on more complex consid-erations. Being able to test this is probably the most important question to emerge from the research on the new technology we looked at.

The impact of crashes on demand and interest rate policy

The crash of 1929 may have increased the severity of the recession in 1929– 30 (Romer, 1993) and the fall in values over 1929– 32 may have had an impact on consumer spending through loss of wealth. These effects deserve to be investigated further. The NASDAQ crash has been suggested as a reason for the series of emergency rate cuts by the Federal Reserve Board and the lack of concern about the emerging housing market boom in 2001– 3, which saw US central bank rates fall to 1 per cent to mitigate the effects of the economic slowdown. This stimulus from the Federal Reserve may have caused an over- stimulus of credit in the US housing sector, stoking another boom which led to a sizeable overvaluation of property in the USA.

Therefore, the effects of crashes on wealth, investment, and spend-ing could produce useful insights as they may have knock- on effects on required monetary easing to dampen their effect on consumption.

Another area which has relevance for the crisis of 2008 and the 1920s is how property price falls, whether due to bubbles deflating or price instability, can affect consumers’ consumption behaviour and their behaviour towards paying off debt.

Credit expansions and asset bubbles

Both monetary and credit expansions have been investigated and evi-dence found to the effect that monetary factors can stoke bubbles in the housing market (Bordo and Jeanne, 2002a). The evidence is less clear for


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the stock market. Our research suggests monetary factors played a role in the 1920s, but may be specific to that era.

Credit expansion in the financial sector or financial sector liquidity is also an area of concern for stoking bubbles in the financial or housing sector. This is an area of research that has generally been neglected as monetary economics has been overlooked to a certain extent by central banks and economics generally as the assumption of money as a neu-tral variable in the real economy has gained credence. Even without challenging the idea that money grows passively as the demand for it increases, such a transmission mechanism need not exclude the pos-sibility that bubbles can be detected when money growth is very high relative to expectation. The recent financial crisis, although not prov-ing that credit growth is a danger, has rekindled the need for research to investigate both over the long sweep of history and via theoretical models how credit growth or credit availability may influence bubble formation in housing and stock markets.

Mental pathology, herd behaviour, and higher- order beliefs

One area of research where there is much room for further analysis stems from the possibility that we have missed a critical part of the reason for the formation of the asset bubble in the 1920s. The idea that investors may be irrational or at least driven by behavioural forces to start bubbles is feasible. How investors become behaviourally driven may be due to the fact that they have expectations based on illusion or fantasy about the real world. Rather than seek the explanations of ‘rational irrationality’ where growth is overestimated within normal bounds, it may be that bubbles form due to some type of collective euphoria. This could take the form of any of the types we discussed in Chapter 2 in the literature review of bubbles. It may be that bubbles are the result of herd behaviour as investors follow others in the belief that others know more or people are aiming to predict how others will behave (Keynes, 1936), or there may be a large role for chaotic and unstructured thinking based on an individual’s own thoughts, a type of pathology best described as humans being a ‘little bit crazy’. One recent area of research on this subject has been dubbed ‘Emotional Finance’ by its practitioners, David Tuckett and Richard Taffler. Tuckett’s Minding the Markets (2011) explores the role of emotions in trading. Prices in the late 1920s moved far from the fundamentals indicated in our models; new approaches to identifying how we generate fantastical thinking or via people anticipating the fantasy of others is an area ripe for research.


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Conclusions 205

Investor surveys of expectations and behaviour

Perhaps the most obvious and interesting frontier in research into bubbles and economic behaviour generally is being able to know in a scientifically rigorous way what investors’ expected returns from assets are, and the reasons for their expectations. Furthermore, how expecta-tions can be influenced by other factors is also an area ripe for further research. These studies could use data collection methods that harness the power of the Internet or instant messaging to collect large data samples at low cost.


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206

Table A.1 Results of regression of percentage change in P/D ratio from 1927– 9 (DEP) with earnings growth rates from 1927– 9

Constant 0.556(0.081)

Earnings growth 0.412**(0.203)

R- squared 0.053

No. observations 72

Sample period 1927– 1929

Table A.2 Results of regression of percentage change in P/D ratio from 1928– 9 with annual earnings growth rates from 1928– 9

Constant 0.3(0.052)

Earnings growth 0.026(0.039)

R- squared 0.006

No. observations 74


Table A.3 Results of regression of percentage change in P/D ratio from 1927– 8 (DEP) and annual earnings growth rates from 1927– 8

Constant 0.179(0.03)

Earnings growth 0.253***(0.048)

R- squared 0.274

No. observations 74


Appendix: Results of cross- sectional tests


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Appendix 207

Table A.4 Results of regression of percentage change in P/D ratio from 1927– 8 (DEP) with annual dividend growth rates from 1927– 8

Constant 0.248(0.038)

Dividend growth 0.131(0.034)

R- squared 0.017

No. observations 70


Table A.5 Results of regression of percentage change in P/D ratio from 1927– 9 on dividend growth rates from 1927– 9

Constant 0.248(0.079)

Dividend growth – 0.328**(0.164)

R- squared 0.054

No. observations 72


Table A.6 Results of regression of percentage change in P/D ratio from 1927– 9 with dividend growth rates from 1924– 7

Constant 0.211(0.041)

Dividend growth – 0.634***(0.120)

R- squared 0.286

No. observations 72



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208 Appendix

Table A.7 Results of regression of percentage change in P/D ratio from 1927– 9 on earnings growth rates from 1924– 7

Constant 0.571(0.082)

Earnings growth 0.286(0.281)

R- squared 0.013

No. observations 81


Table A.8 Results of regression of percentage change in P/D ratio from 1927– 8 with age, net current assets, market capitalisation (size) in 1927 and percentage change in P/D ratio in 1926– 7

Multivariate

Constant 0.194(0.257)

Log size (net current assets) – 0.373(0.0495)

Log age – 0.0055(0.063)

Log market CAP (27) 0.0468(0.0353)

PD 1926– 7 – 0.161(0.117)

R- squared 0.0448

No. observations 71


Table A.9 Results of regression of percentage change in market capitalisation from 1927– 9 on net current assets, age, and risk rating

Constant 0.242(0.344)

Net current assets 0.000(0.001)

Age 0.004(0.003)

(continued)


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Appendix 209

Aa – 1.127(0.413)

A – 0.168(0.316)

B – 0.088(0.350)

Ba 0.033(0.350)

Baa 0.001(0.349)

Ca – 0.581(0.484)

Caa – 0.047(0.393)

R- squared 0.113

No. of observations 72


Table A.9 Continued


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210

Prologue

1. ‘Some time ago intrinsic value was thought to be about the same thing as book value, i.e. it was equal to the net asset value of the business, fairly priced. This view of intrinsic value was quite definite, but it proved almost worthless as a practical matter because neither the average earnings nor the average market price evinced any tendency to be governed by the book value. Hence the idea was superseded by a newer view … that the intrinsic value of a busi-ness was determined by its earning power. But the phrase “earning power” must imply a fairly confident expectation of certain future results. It is not sufficient to know what the past earnings have averaged, or even that they disclose a definite line of growth or decline. There must be plausible grounds for believing this average or this trend is a dependable guide to the future. … There was however, a radical fallacy involved in the new era application of this historical fact [referring to Smith’s discovery of the Equity Return Premium]. This should be apparent from even a superficial examination of the data contained in the small and rather sketchy volume from which the new era theory may be said to have sprung. The book is entitled COMMON STOCKS AS LONG- TERM INVESTMENTS, by Edgar Lawrence Smith, pub-lished in 1924. Common stocks were shown to have a tendency to increase in value with the years, for the simple reason that they earned more than they paid out in dividends, and thus the reinvested earnings added to their worth. … The attractiveness of common stocks for the long pull thus lay essentially in the fact that they earned more than the bond interest rate upon their cost … but as soon as the price was advanced to much higher price in relation to earnings, this advantage disappeared, and with it disappeared the entire theoretical basis for investment purchases of common stocks … Hence in using the past performances of common stocks as the reason for paying prices 20 to 40 times their earnings, the new era exponents were starting with a sound premise and twisting it into a woefully unsound conclusion’ (Graham and Dodd, 1934: 64– 5, 312– 13).

2. ‘[A] potent reason for the long bull market rising to the plateau of stock prices 1923– 1930 is that there has been a material change during this period in the estimate of the public as to the risk of investing in common stock. Whether this change is justified or not, the change has occurred. … Amoung [sic] sev-eral important books which emphasise the important role of changes in the value of the dollar, none has impressed the investing public so profoundly as certain events that gave rise to investment counsel and investment trusts in America, including especially the publication of Edgar Lawrence Smith’s book [Common Stocks as Long- Term Investments].

‘The series of writers on the subject have proved, statistically that bonds are not, as compared with well selected and diversified stocks, what they have been cracked up to be; that they are especially deceptive during rising prices

Notes


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Notes 211

and that even when prices are falling they are not all that superior to stocks … they show that whatever truth there is in the “risk” carried by the stockholder as compared with the bondholder, this risk can be partly neutralised by diver-sification … both Smith and Van Strum show how this diversification does neutralise the risk and correct the unsteadiness of the stockholders income’ (Fisher, 1930: 198).

3. This is a key research database for financial historians and can be accessed via the ICF at Yale University.

4. This refers to the monetary expansion from 1915 to 1920, which eroded the ‘real returns’ to corporate and Government Bonds.

5. This refers to the effect of price stabilisation in the 1920s when near zero inflation/deflation increased the ‘real’ returns to corporate bonds.

3 The US Economy and the Financial System

1. ‘In December 1996, before my time at the Board, John Campbell of Harvard and Robert Shiller of Yale made a presentation at the Fed, in which they used dividend- price ratios and related measures to argue that the stock market was overvalued. (A version of their presentation was later published in the Journal of Portfolio Management, which is the source for all my com-ments here.) Campbell and Shiller, whom I know well and respect greatly as preeminent financial economists, rightly deserve credit for calling the possibility of a bubble to people’s attention, at a time when (lest we forget) there was significant diversity of opinion about which way the market would go. Shiller, of course, has gone on to write a best- selling book about stock market manias. Though Campbell and Shiller were among those warning of a bubble in stock prices, and deserve credit for doing so, we should not lose sight of a simple quantitative point: According to their published article, their analysis of dividend- price ratios implied that, as of the beginning of 1997, the broad stock market was priced at three times its fundamental value (Campbell and Shiller, 1998, p. 13). At that time the Standard & Poor’s 500 index was about 750, compared with a close of 842 on October 1 of this year.

‘I do not know, of course, where the stock market will go tomorrow, much less in the longer run (that’s really my whole point). But I suspect that Campbell and Shiller’s implicit estimate of the long- run value of the market was too pessimistic and that, in any case, an attempt to use this assessment to make monetary policy in early 1997 (presumably, a severe tightening would have been called for) might have done much more harm than good’ (Bernanke, 2002).

2. See Rostow (1956). 3. By 1900 the USA was the world’s main producer of natural gas, copper, petro-

leum, iron ore, zinc, phosphate, molybdenum, lead, tungsten, and many other minerals (Wright, 1990).

4. A good example of this development commitment is the provision of educa-tion for agricultural development.

5. See Bordo and Schwartz (1984). 6. ‘The importance of the United States in the international monetary system

would have been recognized much earlier had the United States possessed a


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212 Notes

central bank in the 19th century. Upon its creation in 1913, it was instantly the most powerful central bank in the world, this despite the much- vaunted prestige of the Bank of England, the acknowledged importance of sterling and the London financial market. The creation of the Federal Reserve System in 1913 was one of the most important events of the 20th century. It was the Federal Reserve System that enabled the paper dollar to become the most important currency in the world. The primacy of the dollar can be said to have begun in 1915, the second year of World War I, when the dollar took over from the pound sterling the role of most important currency in the world. The whole future of the gold standard came to depend on the policy of the US with regard to gold. During World War I, the value of gold had fallen in half as the US dollar, which remained more or less on the gold standard, experienced a doubling of its price level between 1914 and 1920. In 1921 the Federal Reserve liquidated assets and tightened credit. Prices then fell precipitously, from an index of 200 (1914 = 100) in 1920 to 140 in 1921. The Federal Reserve then shifted to a policy of stabilizing the price level and it remained more or less constant until 1929. Thus, during the 1920s, the US price level was about 40 per cent above the pre- war gold- standard equilibrium’ (Mundell, 2000).

7. The Gold Exchange Standard in the UK practically involved the withdrawal of gold coin from circulation, thus economising on its use, and the promise to buy gold bars at a fixed sterling price

8. Cassel had testified before the Senate Banking Committee in the USA, the Genoa conference in 1922, and had authored a ‘Memorandum on the world’s monetary problems’ at the invitation of the League of Nations in 1920.

9. ‘The nationwide “bubble” that appeared in the early 1920s and burst in 1926 was similar in magnitude to the recent real estate boom and bust. Fundamentals, including a post- war construction catch- up, low interest rates and a “Greenspan put”, helped to ignite the boom in the twenties, but alternative monetary policies would have only dampened not eliminated it. Both booms were accompanied by securitization, a reduction in lending standards, and weaker supervision. Yet, the bust in the twenties, which drove up foreclosures, did not induce a collapse of the banking system’ (White, 2009: abstract).

10. After the war, there was a general population migration into urban areas, mostly to the larger cities (migration of 9 million individuals to urban areas with populations over 30,000). Returning soldiers felt they had better employment opportunities in urban areas and an agricultural depres-sion caused by falling commodity prices spurred ‘millions of people from farms . . . [to] large cities’ (Simpson, 1933: 163).

11. Nicholas (2008) finds evidence of correlation between patents and excess stock returns in the 1920s. Notable examples in the data are Westinghouse, General Electric, and Du Pont, who feature prominently in the chemicals and electrical industries.

12. Our estimate uses a 4 per cent long- term return under the Refunding Act of 1870 and Consumer Prices data from the NBER Macrohistory Database. Our estimate of 2.9 per cent was found by taking the L- Term Govt. Bond yield in 1870 (4.0 per cent) and deflating by the Consumer Price Index from


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Notes 213

1871 to 1900 = 5.5 per cent return. For 1900– 29, we took the 3 per cent L- Term Govt. Bond yield in 1900, and deflating by the CPI = 0.3 per cent return.

13. This observation bears directly on our assessments of the Federal Reserve’s ability to abrogate the effects of the monetary contraction and lends support to the counterfactual scenarios tested by McCallum (1990) and Bordo et al. (2002).

14. Chapter 5 deals specifically with the operation of the policies and their effects on the NYSE and New York money market.

15. The quoted words are those of New York Federal Reserve Bank governor Harrison.

16. ‘[S]ince with leading monetary writers, credit has come to mean demand deposits, an abnormal expansion of credit has been held to cause inflation. Some writers understanding credit to mean loans and investments, have been led to maintain that it is a rapid expansion of loans which causes infla-tion, without attempting to work out a relation between loans and the value of money. It has become almost a fashion to refer to the post- war period up to 1929 as one of inflation due not to increased velocity but to excessive expansion in the volume of credit. These opinions must have been based on the movements of loans and investments; actual figures of demand deposits can harldy be said to warrant such a conclusion … since the reserve admin-istration understands “credit” to mean loans and investments it has been led to distinguish between the qualitative and quantitative aspects of “credit”, stressing the former most heavily. Unfortunately, by taking credit to mean loans and investments, the banking authorities have completely misunder-stood the theorists’ arguments and have therefore misapplied them’ (Currie, 1934b: 51– 4).

17. This was not an official Fed measure until 1941 (Humphrey, 2001).

5 The October Crash of 1929 and the NYSE Credit System

1. Gorton and Huang (2002: 1– 14) describe the method that ‘clearinghouses developed to turn illiquid loan portfolios into money, private money that could be handed out to depositors in exchange for their demand deposits during their times of panic. Clearing house loan certificates originated in the inter- bank clearing system as a way to economise on cash during a panic. During a banking panic member banks were allowed to apply to a clearing-house committee, submitting assets as collateral in exchange for certificates. If the committee approved the submitted assets offered in exchange, then certificates would be issued only up to a percentage of the face value of the assets. The bank borrowing against its illiquid assets would have to pay inter-est on the certificates to the clearinghouse. The certificates could then be used to honor inter- bank obligations where they replaced cash, which instead could be used to pay out to depositors … during the panic of 1907 about $500m was issued (4.5 per cent of the money stock).’

2. In 1907 trust companies were outside the jurisdiction of the Clearing House System.

3. Call loans were money market instruments, which could be called at one day’s notice and were deemed highly liquid when compared to real estate loans.


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214 Notes

4. ‘In the Pre Fed era, New York Clearing House Banks were the predominant source of funds for call loans on the NYSE. New York Clearing House Banks (NYCHBs) were able to maintain liquidity on the call loan market in prior panics despite disruptions to intermediation, because they monopolised the funding of the market. The growth of trust companies in New York (not members of the NYCHB system) along with the increasing sophistication of the interior banks led to an increasing proportion of call loan volume outside the control of the NYCHB. At that time NY banks still held about 40 per cent of their loans as call loans. Hence a substantial devaluation of call loan assets would seriously weaken their balance sheets and threaten their solvency. Any widespread liquidation of call loans by the trusts or interior banks might trigger a rapid loss of stock market value. As the call market share of NY National Banks fell relative to “outsiders” the probability of observing a collapsing value/price scenario increased’ (Moen and Tallman, 2003: 1– 2).

5. Myers (1931) notes that seasonal demands for reserves would contract call loans and cause price falls on the NYSE. Moen and Tallman (2003) also note that interior banks and trusts were lending directly to the call loan market making the supply of funds more erratic.

6. A contraction in reserves would be expected to contract loans to non- financial businesses and hence effect an economic contraction.

7. ‘That is why the key reformers of the banking system J. L. Laughlin and P. Warburg agreed that the development of a rediscount market comprised of self liquidating loans would be a promising alternative to the call loan market as the basis for liquid bank assets. Even if these assets had bad outcomes – e.g. claims could not be paid off – the loans are claims to inher-ently less volatile assets and the correlation across these real assets is less likely than the correlation across stocks. … perhaps most importantly, the reformers aimed at repairing the underlying flaw in the financial system – no reliable and rapid method to increase base money. One solution, a central banking institution would be willing to hold self liquidating real assets through the discount function, essentially exchanging private claims (the assets to be discounted) and public claims (currency)’ (Moen and Tallman, 2003: 9– 10).

8. ‘The Federal Reserve was created by men whose outlook on the goals of cen-tral banking were formed by their experiences during the national banking era panics. The basic problem seemed to them to be banking crises produced by or resulting in an attempted shift by the public from deposits into cur-rency. In order to prevent such shifts from producing either widespread bank failures, some means was required for converting deposits into cur-rency without a reduction in the total of the two. This in turn required the existence of some form of currency that could be rapidly expanded – to be provided by the Federal Reserve note – and some means of enabling banks to convert their assets readily to such currency – to be the role of discounting’ (Friedman and Schwartz, 1963).

9. ‘Changes in the identity of the intermediaries providing those funds help explain why the movement to establishment of a central bank in the USA took hold only after the panic of 1907. The growing significance of non clearinghouse creditors to the call money market diluted the relative


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Notes 215

financial influence of the NYC bankers and compromised the apparent coin-surance arrangement between brokers and the NY clearinghouse lenders’ (Moen and Tallman, 2003: Abstract).

10. This is an estimated figure based on Wigmore (1985).11. Rappoport and White (1994) note that the ratio of security loans to total

market value of stocks remained fairly constant through the 1920s boom and demonstrate effectively that the brokers’ loans market was not driving the stock price rises.

12. These loans are both call and time money loans to brokers and dealers in securities providing leverage to investors.

13. Assuming non- member security loans were divided between brokers and other customers in the same ratio as Reserve member banks as detailed in Roelse (1930).

14. Smiley and Keehn (1988) highlight the fact that our figures are only for the reporting member banks and the NYSE and there may have been a large pool of funds for which we have inadequate data. Nonetheless, there are sufficient data to determine that there was substantial selling pressure caused by a large reduction in the supply of call loans in October 1929.

15. The exposure to a call loan contraction.16. The New York banks’ liquidity injection totalled only $1 billion. Whether

the inadequate size of this injection was due to policy, resources, or organ-isational restrictions is unclear but indicates that the Reserve System did not have the capacity to supply liquidity to offset the external liquidity shock.

17. The identity of these banks or other entities engaged in currency arbitrage- based lending to the NYSE call money markets is beyond the scope of this study.

18. Call money, being a highly liquid instrument, was in effect the same as a US dollar bank balance paying interest and was unlikely to suffer from signifi-cant credit risk premiums as the loan could be liquidated within a 24- hour timeframe.

19. We use bank interest rate parity between dollar/sterling to determine the theoretical value of the forward rate.

20. Ayres (1929) described the suppliers of call loans the ‘invisible banking system’.

21. Global Financial Data (2009) and The Commercial & Financial Chronicle (1929).

22. ‘If “others” have lent their surplus funds to brokers on call because it is more profitable than to invest or use these funds in business … Accordingly these funds will be withdrawn and invested or used in the most profitable way when the call rates decline … The withdrawal of the funds in the market by only a few lenders will bring pressure on the market and liquidation of securities wholly out of proportion to the amount of money taken out of the market’ (Wright, 1929: 132– 3).

23. ‘The increase in required reserves, which necessarily accompanied the bulge in the money supply resulting from the surge in bank lending to securities firms, was met in part by sizable open market purchases of U.S. government securities by the New York Federal Reserve Bank and by discount window borrowing by New York commercial banks. According to a senior official of the New York Fed at the time, that bank kept its “discount window wide


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216 Notes

open and let it be known that member banks might borrow freely to estab-lish the reserves required against the large increase in deposits resulting from the taking over of loans called by others”. As a consequence, the sharp run- up in short- term interest rates that had characterized previous financial crises was avoided in this case. Money market rates generally declined in the first few months following October 1929’ (Stern, 1988).

24. This sum is equivalent to 5 per cent of the total supply of money in the entire United States at that time.


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229

Index

Numbers in bold refer to figures and tables.

Abreu, D. and M. K. Brunnermeier, 44aeronautics industry, 14Allen, F. and D. Gale, 42American Telephone & Telegraph

(AT&T), 62arbitrage, 7, 28, 125, 155, 157–63

covered/uncovered interest rate arbitrage, 158–63, 161

interest rate parity, 159–63Keynes–Einzig conjecture, 157–8,

160–1ARMA-ARCH-Artificial Neural

Network model, 33asset pricing, 2, 37, 46–8, 50, 123

see also bubbles; Common Stocks; Dividend Discount Model; dividend growth rates; equity returns; overvaluation; prices; risk; stock value collapse; valuing stocks

assetscommercial real estate bonds, 75–6fixed income, 15lifespan, 3, 8mortgage-backed securities, 75prices, 1, 2, 19, 21, 28, 34, 39,

40, 48, 80, 103–4, 181railroad bonds, 101risk-free, 3, 10, 102, 155, 156see also closed-end funds;

Common Stocks; corporate bonds; Government Bonds; high-technology stocks; investment trust funds

automobile industry, 14, 27, 113–15, 117–24, 119, 123

aviation industry, 4, 13–14, 27, 54, 84

case study, 94, 108–27, 119, 120, 123, 125–7, 131, 191–2

Babson, Roger, 4Babson Statistical Organisation, 4Bachelier, Louis, 7, 38Banerjee, A. and W. E. Eckard, 63Barberis, N., 45Barsky, R. B. and J. B. De Long, 32Behavioural Economics, 19, 39,

40–57, 23see also economic behaviour;

neuro-economicsBernanke, Ben S., 60, 174, 176, 211n1Bierman, J., 35boom and bust, 1, 3–7, 20–1, 58–61,

197–9literature review and methodology,

31–7see also bubbles; economic

behaviour; fundamental values; Great Contraction; Great Depression; overvaluation; stock market; valuing stocks; volatility

Bordo, M. D. and O. Jeanne, 198broad/narrow money growth, 48brokers’ loans market, 33, 36, 149–57,

152see also call money markets

bubblesbehavioural models, 44–6British Railroads (1840), 40credit induced, 35, 88, 91, 146, 194definition, 1, 21, 37–8Dot com/NASDAQ (1998–2000), 40,

41, 110early warning and prevention,

199–200effects on social welfare, 200–1fundamental shocks, 44herding behaviour, 43–4, 204heterogeneous belief bubbles, 46historical analysis, 19, 202impact on financial policy, 203


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230 Index

bubbles – continuedlaboratory studies, 19, 46–8, 202limited liability factors, 42–3limits to arbitrage, 44liquidity factors, 48Mississippi Bubble (1718–20), 40momentum effects, 24–6, 37, 45,

81, 140–1, 141Nikkei (1989), 40noise traders, 44over-optimism, 6–7psychological factors, 48–9rational bubbles, 42–4role of symmetric/asymmetric

information, 42self-feeding processes, 25, 81South Sea Bubble (1720), 40speculative bubbles, 48–9, 108technological growth, 49theory of, 37–57, 203time horizons, 47Tulip Mania (1637), 40see also economic behaviour;

fundamental values; Great Contraction; Great Depression; house price bubbles; neuro-economics; overvaluation; stock market; valuing stocks; volatility

Burgess, R. W., 164–5

call money market, 7, 36, 146–73, 151, 156, 161, 166, 167

Cassel, Gustav, 68–9, 70, 212n8closed-end funds, 3, 25, 26, 32–3,

105–8, 127–31, 131, 193–4Commercial & Financial Chronicle, 4,

99, 115–16, 165Common Stocks

analysis of returns, 94–144buy-and-hold strategy, 2, 26calculating returns, 10–16changes in values relative to

expectation, 23cross-sectional tests of stock values,

53, 56, 133–40, 189–97, 206–9diversified, 10dividend discount modelling, 7–8effect of asset lifespan on value, 8excess return over bonds, 2–3,

10–11, 25

excess returns and volatility, 13long-term returns, 2overestimating risk, 10–13price data, 4prices and dividend index,

20–1, 21speculative investment, 6, 35time hazard investment horizon,

12, 133–4valuation of, 7–8volatility reduction, 12–13zero capital loss estimation, 12, 26,

132, 133–4see also corporate bonds; Dividend

Discount Model; dividend growth rates; equity returns; Equity Risk Premium; fundamental values; Government Bonds; investment; overvaluation; prices; risk; stock market; valuing stocks

Congdon, T., 48corporate bonds, 2, 10–12, 134

AAA (high grade) bond index, 11, 100–1

Baa bonds, 11, 100risk factors, 7–8valuation of, 7see also government bonds

corporate lenders, 155–8, 156see also non-bank financial

intermediariesCowles, Alfred

Common Stock Indices, 1871–1937, 6, 20, 27, 95, 102, 107, 115, 141–2

credit instability and crisis, 28, 29, 37, 48, 54, 145–73

credit, loans, and debt, 58–9, 70, 71–7, 73, 80, 145–73, 151, 152, 167, 168, 176–7, 187, 188, 203–4

speculative loans/credit, 35, 36, 91, 92

stock prices to credit ratio, 172CRSP (Center for Research in Security

Prices) database, 31, 34, 50, 132, 136, 181

Currie, L., 90–1, 213n16

De Long, J. B. 44, 45De Long, J. B. and A. Shleifer, 24,

32–3, 144, 193


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Index 231

De Martino, B., 18, 47DeMarzo, P. M., 43Dice, C. A., 3Diether, K. B., 45Dividend Discount Model (DDM),

3, 5, 35, 52, 55, 94–108, 135–9, 189–90

see also dividend growth rates; valuing stocks

dividend growth rates, 27, 55–6, 64, 81, 124, 135–9, 189–90, 192–3

dividend forecasting, 32–4dividend growth index, 55, 102fair expectations, 32, 52, 56,

179–80, 194, 195see also equity returns; productivity

growth; valuing stocksDodd, David, 5–6Donaldson, R. G. and M. Kamstra,

24, 33Du Pont, 62, 63, 82

earnings growth rates, 6, 27, 35–6, 53, 57, 132, 135–40, 143, 192, 206, 208

earnings growth (discounted cash flow – DCF) model, 31, 34

econometric tests, 2, 10, 21, 31economic behaviour, 14, 17–18

herding, 43–4, 204historical context, 18irrationality, 6, 17, 19, 25, 27, 32, 47,

49, 109, 132, 140, 144, 195–6, 204laboratory studies, 19, 41–57, 202risk aversion, 3, 134see also neuro-economics

Economic History school, 40, 48Eichengreen, B. J., 176Einzig, P., 157–9, 161–2emotional finance, 204equity returns

Book/Market (B/M) ratio, 55, 181–2, 181

calculating, 10–16, 37–57efficiency, 38–9expected, 2, 3, 19Market Price/Dividend ratio, 55Price/Dividend (P/D) ratio, 10, 53,

58–9, 59, 98–9, 100, 102–4, 103, 104, 108, 141–2, 142, 180, 189–90

realised, 2, 19, 23, 55–6, 105–8, 190

Smith’s return premium estimates, 13

Standard Deviations (S.D.), 100–1, 101, 131

see also valuing stocksEquity Risk Premium (ERP), 2, 3,

10–11, 23, 25, 50, 129–30, 131, 189

calculation of, 10–16, 95, 100–1, 104

retained earnings, 6, 137volatility-based, 100–1see also fundamental values

exogenous economic shock, 28–30, 54, 145, 173, 183, 194

Fama, Eugene F., 19, 38–9Federal Reserve, 22, 65, 71, 85–93,

145–73, 197bank rate, 86–7, 87, 91‘bubble popping strategy’, 28discount rate adjustment, 28, 88–9,

91, 92, 146, 151–2interest rate mechanisms, 35price stability policy, 87–8regulation of credit flow, 28, 35, 54,

89–92, 146see also financial markets;

regulatory arbitragefinancial crisis (2008), 17, 41, 74,

197–8financial history, 17–18, 145–73financial markets

curb market, 36discount rates, 166instability, 145–73‘marginal opinion’, 5money market leverage, 149–64money market rates, 167price setting and volatility, 5securities loans, 167see also brokers’ loans market; call

money market; market efficiencyfinancial modelling, 1, 3–4, 50, 115

time series data, 6, 32, 34, 38, 58, 137, 181–2

financial theory and analysis, 2–5, 7–16, 31–57


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232 Index

First World War, 26, 65, 71, 85–6Fisher, Irving

The Nature of Capital and Income (1906), 7

The Stock Market Crash and After (1930), 2, 6, 25, 210–11n2

Fisher’s Golden Rule, 84Frazzini, A. and O. A. Lamont, 43Friedman, M. and A. J. Schwartz,

86–7, 88, 89, 147, 176, 214n8French, K., 55fund managers, see investment trust

fundsfundamental value

deviation from, 19, 21, 33, 39–40

discounted cash flow (DCF) model, 34

ex-ante observable, 40ex-post observable, 1, 21measurement of, 94–105

Galbraith, J. K.The Great Crash (1954), 6–7, 20

General Motors, 36, 62global banking system, 17, 177Goetzmann, William N. and Roger

Ibbotson, 20, 22–3, 34Goetzmann, William N. and Frank

Newman, 75gold, 22, 65, 36, 70–2, 72,

85–6, 188Gold Standard/Gold Exchange

Standard, 22, 64–71, 86, 147, 159, 176, 212n7

Gordon, M. J., 35Gordon, R. J., 23, 82, 108, 143Goschen, G. J., 65–6Government Bonds

calculating returns, 12, 16, 23, 71, 83–4, 83, 95, 100–1, 104, 107, 129–31, 194

long-term inflation expectations, 84–5

Graham, Benjamin and David DoddSecurity Analysis (1934), 5–6, 25,

137, 210n1Great Contraction (1929–32), 22,

54–5, 59, 174–86, 194

exogenous shock, 183real economy, 183–6, 184see also stock value collapse

Great Depression (1930s), 4, 6, 17, 23, 29, 36, 67, 74, 76, 174

Greenwood, R. and A. Schleifer, 19, 46

Haney, L. H., 153, 169Hansen, L. P., 19high-technology growth, 4, 113–14high-technology stocks, 27

valuation of, 13–14see also aviation industry; new

technology; technological innovation; technological shock; technology

Hirota, S. and S. Sunder, 47Hirst, F. W., 170–1Hoover, Herbert, 35horizontal and vertical

integration, 63house price bubbles, 1, 22, 41, 74,

185, 188, 203housing market debt, 22, 74–5,

184–6

industrial growth model, 3–4, 13–14, 27

industry life-cycles, 5, 13–14, 27, 117

investmentdiversification, 3, 7, 10, 13, 14, 55,

105, 112, 128–31long-term, 2search for ‘intrinsic value’, 5–6speculative, 6, 35theory, 5, 14see also closed-end funds; Dividend

Discount Model; equity returns; investment pools; investment science; investment trust funds; investors; risk; valuing stocks

investment pools, 35, 149–50, 165investment science, 7–16investment trust funds, 105–8, 106

fund managers, 7, 43–4, 48, 129portfolio diversification, 3, 7, 10,

13, 14, 55, 105, 112, 128–31see also closed-end funds


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Index 233

investors, 1, 3, 4access to financial data, 11distinguished from speculators, 5expectations, 19, 32, 52, 56,

179–80, 194, 195herding behaviour, 43–4, 204irrationality, 6, 17, 19, 25, 27,

32, 47, 49, 109, 132, 140, 144, 195–6, 204

risk aversion, 3, 134

Janeway, W. H., 24

Kahneman, Daniel, 19Kendall, Maurice, 38Keynes, John Maynard, 9–10, 43, 69Kindleberger, C. P., 40, 48Klepper, S., 114, 117Knight, Frank

Risk, Uncertainty, and Profit (1921), 8–10

Kyle, A., 130

Lei, V., 46LeRoy, S. F. and R. D. Porter, 42Liberty Bonds programme, 26, 71–2Lowenfeld, H., 7Lux, T., 45

Macaulay’s High Grade Rails Index, 11

market efficiency, 19, 22, 30, 32Behavioural Economics, 39, 40,

41–57 Efficient Markets Hypothesis

(EMH), 37, 38–9, 40, 139, 143joint hypothesis problem, 51, 64, 131testing methods, 41–57

Marsh–Merton model, 33McAdoo, W. G., 85–6McGrattan, E. R. and E. C. Prescott,

24, 34Miller, Adolf C., 35, 89–90, 92–3Miller, E. M., 45Modern Portfolio Theory (MPT), 3Moen, J. R. and E. W. Tallman, 147,

148, 214n4, 214n7, 214–15n9monetary expansion, 1, 4, 22, 26, 70,

73, 80, 188

‘Monte Carlo’ simulation, 33Moody’s AAA Corporate Bond

Index, 11Moody’s Investors Service, 4Moody’s Manual of Industrial and

Miscellaneous Securities (1900), 4, 95, 96

Moody’s Manual of Investments (1930), 3–4, 13–14, 27, 53, 94–5, 97, 99, 111–14, 129

Moody’s Manual of Investments, American and Foreign (1930), 129

Mundell, R. A., 65, 211–12n6Myers, M. G., 148

Net Asset Values (NAVs), 32, 106, 128

neuro-economicsbrain activity during asset bubbles,

17, 18, 47–8irrationality, 17, 19psychology and economic

behaviour, 14, 17–18new technology, 23, 37, 54

growth model valuation, 108–27, 119, 120

valuation of, 14see also aviation industry;

technological innovation; technological shock; technology

New York Clearing House Banks (NYCHBs), 147, 148, 214n4

New York Stock Exchange (NYSE), 2, 35, 36, 54, 108

credit system, 28, 29, 145–73microstructure, 60, 145–73volume of credit (1929), 168see also stock market

Nicholas, T., 24, 26, 34, 49, 139, 140non-bank financial intermediaries

(NBFIs), 146, 147, 148

Osborne, M. F. M., 38overvaluation, 4, 17

analysis of stock market returns, 94–144

aviation industry case study, 94, 108–27, 119, 120, 123, 125–7, 131, 191–2


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234 Index

overvaluation – continuedcross-sectional tests of stock values,

53, 56, 136–9, 189–97, 206–9high expectations, 23, 55, 122–3,

132, 135–6long-term inflation expectations,

82–5lower risk premiums, 23momentum effects, 24–6, 37, 81,

140–1, 141new financial products and

investment vehicles, 24, 25, 26over-optimism and exuberance,

6–7, 26, 32–3, 144perception of a ‘new era’, 24, 56, 143tests for potential drivers, 131–43,

135, 141, 142

Pareto efficiency, 42Parker, H. G., 155Pástor, L. and P. Veronesi, 49, 110,

140–1Pepper, G. T., 48petroleum industry, 14Poor’s Manual of Industrials (1912), 97Price/Dividend (P/D) ratio, see equity

returnsprices

drivers of price rises, 1, 53Efficient Markets Hypothesis

(EMH), 37, 38–9, 40, 53legitimacy of price rises, 1liquidity theory, 48‘marginal opinion’ as cause of

fluctuation, 5random nature of, 38–9prices and dividend index, 20–1, 21square root of time rule, 38standard deviations, 38

probability theory, 9–10see also risk; uncertainty

productivity growth, 3, 12–13, 81–2Multi-Factor Productivity Growth

(MFPG), 64, 80, 82, 108, 143, 191sector growth rates, 98

Purchasing Power Parity, 68

radio, 14Radio Corporation of America, 35railways, 14, 62

Rajan, R. G., 48Rappoport, P. and E. N. White, 33, 149rayon, 14, 82Regnault, Jules, 38regulatory arbitrage, 145, 148–9, 173,

197–8shadow banking system, 194, 197–8

returns on investment, see equity returns

riskdiversifiable, 3, 10effect of economic growth on

investment risk, 12–13measure of, 2overestimation of long-term risk,

10–11, 12quantifiable/unquantifiable, 9reduction of, by diversification, 3roll-over, 76systemic, 17time hazard investment horizon, 12time-varying risk premium, 42, 46,

55, 57uncertainty and risk, 1, 8–16see also closed-end funds; Equity

Risk Premium; Modern Portfolio Theory; uncertainty; valuing stocks; volatility

Roberts, H. V., 38Rockoff, H., 71Roelse, H. V., 153, 154Romer, C. D., 183Rostow, W. W., 61

Scharfstein, D. S. and J. C. Stein, 43Scheinkman, J. A. and W. Xiong, 46Second World War, 29Seltzer, L. H., 119Shiller, Robert J., 6, 19, 22, 32, 34, 40,

42, 44, 45, 48, 95short selling, 35Silber, W. L., 86Sirkin, G., 31–2, 35, 135skyscraper building boom, 22, 75Smiley, G. and R. H. Keehn, 154Smith, Edgar L., 106

Common Stocks as Long-Term Investments (1924), 2–3, 5, 6, 10–12, 23, 25–6, 55, 82, 95, 107, 132–4


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Index 235

Smith, Vernon L., 19, 46Snyder, Ralph, 90stock market (USA)

analysis of returns, 94–144boom (1920s), 3, 4, 6, 7–16, 20–6,

58–61, 131–44, 151crash (1929), 4, 6–7, 20–1, 54,

58–61, 145–73crash (1930–2), 28–30, 58credit system, 145–73equilibrium level 60–1, 94–105fair value, 52, 54, 55, 102, 105,

189–90liquidity crisis, 168–73,

172, 215n16microstructure, 145–73monetary dynamics, 71–81money market retraction, 163–8prices and dividend index,

20–1, 21realised returns, 2, 19, 23, 55–6,

105–8stock valuation and the real

economy, 174–86see also Great Contraction; Great

Depression; overvaluation; stock value collapse

stock value collapse, 174–86extreme pessimism (anti-bubble),

180–1, 182–3, 186fall in nominal earnings and

dividends, 30, 177–8, 177, 182, 186

fall in real dividends and earnings, 178

increase in risk premiums, 30, 178–80, 179, 180, 182, 186

stock undervaluation, 30, 180–2, 181, 186

Strong, Benjamin, 88

Taffler, Richard, 204tangible/intangible capital, 34technological innovation

asset bubbles, 49, 109, 110, 202patents, 3, 26, 34, 49, 109, 143research & development (R&D)

labs, 3, 62–3, 82technological shock, 1, 23, 27, 51, 56,

81, 143, 191

technologyaviation industry case study, 94,

108–27, 119, 120, 123, 125–7, 131, 191–2

boom and growth, 1, 4, 6growth model for new technology

stocks, 108–27, 119, 120Multi-Factor Productivity Growth

(MFPG), 64, 80, 82telegraph, 14, 62Temin, P., 176Tirole, J., 42Tuckett, David, 204

uncertainty, 1, 33, 123distinct from risk, 9–10dividend forecasting, 32–4

USAbusiness activity index (1899–1937),

175construction industry

boom, 188consumer prices, 77, 184debt levels, 71, 74–7, 75dividend index, 79economic dynamics, 2economic growth, 3, 12–13, 81–2economy and financial system,

58–93foreign direct investment

(FDI), 64gross domestic product (GDP), 78,

79, 183growth path, 23–4, 62–4higher education, 62–3inflation, 15–16, 15, 82–5, 188–9institutional framework, 62–3long-term government bond

yield, 83money supply and growth,

72–4, 73natural resources, 62recession, 183returns on stocks, 80–1, 81rural–urban migration,

188, 212n10stock ownership, 14–15stock valuation and the real

economy, 174–86unemployment, 76, 183


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236 Index

USA – continuedwages/wage index, 77, 78see also Federal Reserve; Great

Contraction; Great Depression; New York Stock Exchange; stock market

US Steel, 35–6, 63

valuing stocksbalance sheet analysis, 5Dividend Discount Model (DDM),

3, 5, 35, 52, 55, 94–108, 135–9, 189–90

dividend discount modelling, 3, 7–8, 42

industrial growth model, 3–4, 108–27, 119, 120

industry life-cycles, 5, 13–14

scientific theory, 5utilities, 5value investment approach, 5

Viner, J., 66–8volatility, 3, 7, 26, 42

compensation for, 10effect of diversification, 13measurement of, 26, 38US economic growth, 12–13

Wheelock, D., 188White, Eugene N., 20, 33, 75, 212n9Wigmore, B. A., 20, 36–7, 150Williams, John Burr

The Theory of Investment Value (1938), 5, 25

Working, Holbrook, 38Wright, Ivan, 169, 215n22


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Documents

The Great Crash of 1929 ||