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Chapter Plan
• Preliminaries
• Literature Review
• Research Problem
• Significance of the Problem
Objectives
• Hypotheses
• Methodology
• Limitations
• Chapter Scheme
• References
1
C ED7
1. INTRODUCTION
1.1 PRELIMINARIES
0
One of the earliest and most enduring questions of modern theory of
finance is whether financial asset prices can be predicted. Perhaps
because of the obvious analogy between financial investments and
games of chance, mathematical models of asset prices have an
unusually rich history that predates virtually every other aspect of
economic analysis. That many prominent mathematicians and scientists
have applied their considerable skills for forecasting financial securities'
prices testifies to the fascination for and the challenges of this problem.
Indeed, modern financial economics is firmly rooted in early attempts to
beat the market - an endeavour that is still of current interest and a
matter of hot debate in publications, conferences and cocktail parties.
In general, stock pricing models provide the relationship between the not
so well defined variables for a given financial market. There have been
several attempts in this direction, but there is no unanimity in identifying
the variables, as researchers and investors are constantly bombarded es
with vast quantities of diverse information. This study, then, attempts to
2
identify the factors, which influence the stock prices more intensely than
others do.
Before attempting to identify these factors, it is advisable to take a
journey into the history of stocks. Though it is believed that recording of
financial transactions came into being as early as 9000 B.C. to 8000 B.C.,
there is no evidence to prove the existence of such a system. However,
from around 2500 B.C. to 1800 B.C., cuneiform — i.e. writing on clay
tablets with a reed similar to a stylus - came into use extensively,
especially for financial transactions [Edward Chancellor (1999)1 1 . During
this period in Mesopotamia, there was a substantial amount of economic
activity in agriculture, crafts, ranching, trading, etc. The first bond
transactions were documented in cuneiform, where silver had been lent
out to a business, and that loan had been transferred to another
individual. In addition, the earliest stock or share transactions were also
documented in cuneiform, for funding maritime trade expeditions.
Stock exchanges originally existed in the form of 'Euro-Fairs' trading in
agricultural and other commodities during the Middle Ages. Credit was
commonly given, and therefore supporting documents such as drafts,
notes and bills of exchange were created. These were the precursors to
modern stock and bond certificates.
During the Roman period, the empire contracted out many of its services
to private groups called publicani [Edward Chancellor (1999)1 1 . Shares
3
in publicani were called `socir (for large co-operatives) and 'particulae',
(for over-the-counter shares of small companies). Though the records
available of this time are incomplete, Edward Chancellor (1999) 1 states
in his book "Devil Take the Hindmost" that there is some evidence that a
speculation in these shares became increasingly widespread and that
perhaps the first ever speculative bubble in 'stocks' occurred.
During the seventeenth century, certificates of ownership of business
came into existence. The first company to issue shares of stock after the
Middle Ages was the Dutch East India Company in 1606 [Edward
Chancellor (1999)]. The innovation of joint ownership made a great
deal of Europe's economic growth possible. The technique of pooling
capital to finance the building of ships, for example, made the
Netherlands a maritime superpower. Before the adoption of the joint-
stock corporation, an expensive venture such as the building of a
merchant ship could be undertaken only by governments or by very
wealthy individuals or families.
Economic historians found the Dutch stock market of the 1600s
particularly interesting: there was clear documentation of the use of stock
futures, stock options, short selling, the use of credit to purchase shares,
a speculative bubble that crashed in 1695 and changes in trading
patterns. Edward Stringham et al (2008) 2 also noted that practices such as
short selling continued to occur during this time despite the government
passing laws against it. This was unusual because it shows individual
4
parties fulfilling contracts that were not legally enforceable and where the
parties involved could incur a loss. Stringham argues that contracts can
be created and enforced without state sanction or, in this case, in spite of
laws to the contrary.
Since the days of the advancement of the stock market, there has been
a relentless effort to unravel the mystery of stock prices and the direction
of stock price movements. The few who could predict the direction
accurately have benefited from such predictions and created wealth. In
pursuit of this goal, several financial economists and market practitioners
have attempted to evolve methods and techniques, which would help
them to forecast stock prices accurately. However, their efforts were not
entirely fruitful and the solution to the mystery continued to elude the
players of the stock market.
The famous dramatist Oscar Wilde (1900) 3 once described a cynic as
one who "knows the price of everything, but the value of nothing". This
description holds good for some analysts and many investors who
subscribe to the theory of the 'big fool', which argues that the value of a
stock is irrelevant as long as there is a 'bigger fool' around willing to buy
the stock from them. While this may provide a basis for some profits, it
is a dangerous game to play since there is no guarantee that the latter
will still be around, when the time comes to sell.
5
Equity market professionals use a wide range of analyses to help them
make informed trading and investment decisions. Many wish to compare
current and historical market situations or review the past performance of
an instrument or index. They need tools that draw on real-time and
historical stock quotes to enable them to perform these types of
analyses.
Technical analysis charts track the historical evolution of stock quotes,
trading volumes and other indicators of activity. Technical analysts try to
identify buy and sell signals by looking at historical stock market actions.
They pay attention to recurring patterns in historical price movements, to
trends and their speed or momentum when making stock trading
recommendations.
Relative performance charts, which are also based on historical and real-
time stock quotes, enable users to compare the performance of stock
quotes against their peers or against sectors or indices over a selected
period. Other charts allow users to review how the market moved in the
past when certain fundamental levels were reached. An index-earnings
growth chart, for example, shows the relationship between earnings
growth and stock quotes for the index as a whole. These charts help
users identify buying and selling opportunities.
Institutions trading in the equity markets take data-feeds of real-time and
historical stock quotes to power their own deSktop applications, analytics •
6
and research databases. They use historical end-of-the-day stock
quotes for risk management and valuation purposes. Risk groups feed
historical end-of-day stock quotes into their systems to run their daily risk
reports. Mutual funds use historical end-of-day stock quotes to calculate
the valuation of their holdings.
Some foreign equity information products, tailored to the different needs
of different users, combine comprehensive news services, real-time
market data and powerful analysis tools. They supply real-time and
historical end-of-the-day stock quotes in flexible formats to enable
institutions to pump market information into their applications and publics
of organizations. They provide real-time equity quotes from several
exchanges over a decade or two.
Valuating common stock is a complex process, but certainly worth the
trouble for both investors and analysts. Over the years, two general
• approaches have been developed. One method called the discounted
cash flow approach estimates the stock's value based on the present
value of its future cash flows, such as dividends, operating cash flows or
free cash flows, while the other method values a stock based on its
current price relative to certain variables such as the company's
earnings, revenues or book value.
Both the discounted cash flow approach and the relative valuation
approach have certain factors in common. To start with, both techniques
7
are extensively impacted by the investors' required rate of return, because
this rate is essentially the discount rate used in many valuation models.
In addition, all asset valuation techniques are influenced by the
estimated growth rate of certain variables, such as dividends, earnings,
cash flows or sales. When one of the variables has to be estimated, the
result varies because variable inputs are likely to differ from one analyst
to another. In other words, when evaluating a stock, prices are likely to
be different because investors' required rates of return, as well as
estimates of growth rates of earnings like dividends might be different.
A postulate of sound investing is that an investor does not pay more for a
stock than its worth. This statement may seem logical and obvious, but
it is forgotten and rediscovered at some time in every generation and in
every market. There are those who are disingenuous enough to argue
that 'value is in the eye of the beholder, and that any price can be
justified if there are other investors willing to pay that price, which is
patently absurd. Perceptions may be all that matter when the asset is a
painting or a sculpture, but investors do not (and should not) buy most
assets for aesthetic or emotional reasons; stocks are acquired for the
cash flows expected on them. Consequently, perceptions of value have
to be backed by reality, which implies that the price paid for any stock
must reflect the cash flows it is expected to generate. The models of
valuation described in this study attempt to relate the stock value to the
level and the expected growth of cash flows and the risk attached to them.
8
The proposed study is empirical in nature, aimed at studying the
relationship between corporate returns (cash returns) and stock returns
(market returns) so as to understand the relationship between earnings
(cash flows) and stock prices. Cash earnings are considered in the
place of accounting earnings (book profits) in order to avoid accounting
bias. The basic aim of this study is to convey to the participants of the
market that stock prices largely depend on fundamentals (earnings)
rather than on rumours and political or economic events in society. The
study also aims at suggesting to the participating firms that if they can
release forecast data relating to their earnings for a future period on a
continuous basis and disclose deviations thereof on completion of the
said period, the stock prices could respond to the changes in earnings
rather than to unanticipated elements. This information could make the
stock market more transparent and robust, which would put the
investors' confidence on a higher plane and hence the market would
become more vibrant.
The present work focuses on the Indian Stock Market and studies only
those stocks (large cap stocks), which are actively traded on the National
Stock Exchange, Mumbai or the Bombay Stock Exchange, Mumbai with
reference to the post liberalization period.
In India, it is generally believed that stock prices are not at all rooted in
any fundamental factors, but driven by rumours, grapevine,
manipulators, speculators, high net worth, institutional investors, etc.
9
However, in reality, although these factors do play some role in
influencing the stock prices, in the long term the fundamentals generally
influence the market price. Therefore, the proposed study is an attempt to
bring to light the significant factors that influence the stock prices.
Various economists, while trying to understand the fluctuation in stock
prices, are confronted with two major variables, viz. expected earnings
and expected rate of return (cost of capital). In developed economies,
there is a mechanism to evolve projected earnings for corporate sector.
However, in India there is no institutionalized mechanism to project
future earnings for corporate firms except their own in-house
mechanism. Therefore, such data are not available in the public domain.
If this study can establish the relationship explicitly, then the Regulating
Authorities could be convinced to include the projected earnings in the
disclosure norms. The second factor is risk free rate of return (cost of
capital), which is also critical for valuation along with earnings. However,
the cost of capital of a firm does not change as sadistically as the
earnings. Therefore, it is assumed that the cost of capital of a firm
remains stable during the short term. However, in the long term the cost
of capital should be incorporated in the valuation process. Since the
cost of capital is subject to the risk premium attached to it, it is
impossible to ascertain the cost of capital accurately and maintain it at
the same level for the entire period under study. Therefore, it is thought
prudent to take the risk free rate of return as the influencing factor
instead of the cost of capital.
10
If this study could establish that, there is a definite relationship between
accounting returns (cash basis) and stock returns and that such a
relationship could be used to establish future connection, then it would
be worthwhile to convey the findings to the regulatory authorities to bring
about changes in disclosure norms.
1.2 LITERATURE REVIEW
Eugene Fama (1991)4 in his paper discusses the various hypotheses on
efficient markets and their anomalies. The paper also redefines the
common definitions of efficient markets and investigates the joint-
hypothesis problem, the costs of information and various pricing models.
In this paper the author investigates two problems of market efficiency,
the first being information and transaction cost and the second, the joint
hypothesis problem. In another paper (1999) 6 the same author states
that stock prices fully reflect the most complete and best information
available. However, Eugene Fama himself acknowledges that his
reading of the market has been a stubborn obstacle for active investors
determined to find ways to beat the market.
Darius Palia and Jacob Thomas (1997) 5 write that a common belief
among practitioners is that unexpected changes in foreign exchange
rates shall affect the market value of certain firms. Given this common
belief, the inability to document a strong and systematic
11
contemporaneous relation between stock returns and exchange rate
changes is puzzling.
Paul Krugman (1999)7 argues that under efficient market hypothesis
(EMH), at any given time asset prices fully reflect all available
information. That seemingly straightforward proposition is one of the
most controversial ideas in all social sciences research, and its
implications continue to reverberate through investment practice. The
chief corollary to the idea that markets are efficient, that prices fully
.reflect all information, is that price movements do not follow any patterns
or trends. This means that past price movements cannot be used to
predict future price movements. Rather, prices follow what is known as
a 'random walk', an intrinsically unpredictable pattern.
Jing Liu and Jacob Thomas (1999) 8 have, in their paper, attempted to
derive and test a relation between current period unexpected returns and
unexpected earnings that incorporates revisions in forecasts of future
earnings. Their motivation was to emphasize the misspecification in
returns/earnings regressions that omits information currently available
about future earnings, and to offer a solution.
Pitabas Mohanty (2001) 9 believes that there is now considerable
evidence in the US that firm specific characteristics like size, price-to-
book value, market risk premium can capture the common variation in
stock returns. However, there is no consensus among researchers on
12
whether an investor can earn risk-adjusted excess returns by investing in
small stocks.
Tuomo Vuolteenaho (2001) 10 had used a Vector Autoregressive model
(VAR) to deconstruct an individual firm's stock return into two
components: changes in cash flow (expected cash flow news) and
changes in discount rates (expected returns news). By definition', a
firm's stock returns are driven by shocks to expected cash flows (cash-
0 flow news) and/or shocks to discount rates (expected-return news). He
says that there is a substantial body of research measuring the relative
importance of cash flow and expected return news for aggregate
portfolio returns, but virtually no evidence is available on the relative
importance of these components at the firm level.
Hossein Asgharian and Bjorn Hansson (2002) 11 have investigated the
ability of factor-mimicking portfolios to explain expected returns in
I multifactor asset pricing models. In particular, the usual manner of
constructing factor-mimicking portfolios may result in estimated asset
betas (coefficient of the predictor variables) that are quite different from
the asset betas against the underlying factors, which may seriously
affect the reliability of asset pricing models.
Pastor Lubos and Pietro Veronesi (2002) 12 show that uncertainty about a
firm's average profitability increases the firm's M/B ratio as well as its
idiosyncratic return volatility. They suggest that this uncertainty is
13
especially large for the newly listed firms, but it declines over time due to
learning. Their model therefore predicts that both the M/B and the return
volatility of a typical young firm would decline as the firm ages.
Moreover, this effect is stronger for firms that pay no dividends,
confirming another prediction of the model. The model is also endorsed
by the observation that M/B declines faster for younger firms.
G. P. Samanta and Kaushik Bhattacharya (2002) 13 in their paper have
discussed the issue of whether the spread between Earning to Market
Price (E/P) ratio and interest rate contains useful information about the
movement of stock market. The results of their study reveal that though
the spread seems to have reasonably strong causal influence on returns,
the causal model helps in achieving slightly better forecasts than the
random walk model. However, they are not clear as to whether the
spread can be used as a profitable business strategy.
Andrew Ang and Jun Liu (2003) 14 have developed a model to
consistently value cash flows with changing risk-free rates, predictable
risk premiums and conditional betas in the context of a conditional
Capital Asset Pricing Model (CAPM). Practical valuation is
accomplished with an analytic term structure of discount rates, with
different discount rates applied to expected cash flows at different
horizons.
14
John Y. Campbell and Motohiro Yogo (2003) 15 in their paper argue that
tests of the predictability of stock returns may be invalid when the
predictor variable is persistent and its innovations are highly correlated
with returns. They also suggest two methods to deal with the problem.
The first one is a pretest that determines predictability of stock, when the
conventional t-test is misleading and the second, a new test of
predictability that always leads to correct inference and is more efficient
when compared to existing methods.
Francis A. Longstaff and Monika Piazzesi (2003) 16 have attempted to
quantify the risk premium attached to the standard asset-pricing theory.
They have emphasized that equilibrium asset values can be expressed
as the expected product of a pricing kernel and the cash flows from
those assets.
Burton G. Malkiel (2003) 17 in his paper presents a defence of passive
financial investment (indexing) strategies in all types of investment
markets both nationally and internationally. He justifies the case of such
strategies by relying on the theory of efficient market hypothesis and
suggests that the information generally available about individual stock
or about the market as a whole is reflected in market prices immediately.
Lakshmi Narasimhan S. and H. K. Pradhan (2003) 18 find that the Indian
stock market has witnessed drastic changes during the past decade
under the broad stock market liberalization measures. In their study, the
15
authors have tested the validity of conditional CAPM for Indian stock
market and found that the risk premium changes with changing
economic conditions. The risk premium varies over time and it is
negatively correlated with the index of industrial production. They also
argue that the risk premium increases during a recessionary phase
rather than during an expansionary phase.
Ajay Pandey (2003) 19 believes that modeling and forecasting the volatility of
• capital markets are important areas of inquiry and research in financial
economics with the recognition of time-varying volatility, volatility clustering
and asymmetric response of volatility to market movements. This stream of
research has been aided by various conditional volatility (Autoregressive
Conditional Heteroskedasticity / Generalized Autoregressive Conditional
Heteroskedasticity - ARCH/GARCH type) models proposed to handle these
empirical regularities.
Jeremy J. Siegel (2003)20 defines a bubble as "a sharp rise in the price
of an asset or a range of assets in a continuous process, with the initial
rise generating expectations of further rises and attracting new buyers -
this concerns speculators interested in profits from trading in the asset
rather than its use or earnings capacity".
Eugene F. Fama and Kenneth R. French (2004) 21 argue that the capital
asset pricing model (CAPM) is still widely used in applications, such as a
estimating the cost of capital for firms and evaluating the performance of
16
managed portfolios. The attraction of the CAPM is that it offers powerful
and intuitively pleasing predictions about how to measure risk and the
relation between expected return and risk.
Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Clara Vega
(2005)22 have discussed how markets arrive at prices. There is perhaps
no question more central to economics. Their paper focuses on price
formation in financial markets where the question looms especially large.
How, if at all, is news about macroeconomic fundamentals incorporated
into stock prices, bond prices and foreign exchange rates?
Unfortunately, the process of price discovery in financial markets
remains poorly understood.
John Y. Campbell and Samuel B. Thompson (2005) 23 wrote that towards
the end of the last century, financial economists came to take the view
that aggregate stock returns are predictable. During the 1980s, a
number of papers studied valuation ratios such as the dividend-price
ratio, earnings price ratio or smoothed earnings-price ratio. Around the
same time, several papers pointed out that yields on short-term and
long-term treasury and corporate bonds were correlated with subsequent
stock returns.
Naiping Liu and Lu Zhang (2005) 24 state that recent studies have used
the value spread to predict aggregate stock returns to construct cash-
flow betas that appear to explain the size and value anomalies. Their
17
work shows that two related variables, the book-to-market spread (the
book-to-market of value stocks minus that of growth stocks) and the
market-to-book spread (the market-to-book of growth stocks minus that
of value stocks) predict returns in different directions and exhibit opposite
cyclical variations. More importantly, value spread mixes information on
the book-to-market and market-to-book spreads and appears less useful
in predicting returns.
to, Pandey I. M. (2005)25 explores the significance of profitability and growth
as drivers of shareholders wealth, measured by the market-to-book
value (M/B). The author has studied the relationship between
profitability (economic profitability) on the one hand and M/B ratio on the
other. He has used panel data, employed Generalized Method of
Moment (GMM) estimator and found that there is a strong positive
relationship between profitability and M/B ratio. Growth on the other
hand, is negatively related to M/B ratio.
Narasimhan Jegadeesh and Joshua Livnat (2006) 26 state in their paper
that there are significant positive associations between earnings
surprises and abnormal returns, around the preliminary earnings
announcements as well as in the post-earnings announcement period.
Since earnings is a summary measure of material economic events that
affect a firm in a given period, the intense focus on earnings surprises by
investors and academics is natural.
18
Lewellen Jonathan, Stefan Nagel and Jay Shanken (2006) 27 argue that
asset pricing tests are highly misleading in the sense that apparently
strong explanatory power, in fact provides exceptionally weak support for
a model. They offered a number of suggestions for improving empirical
tests and evidenced that several proposed models do not work as
satisfactorily as originally claimed.
Jacob K. Thomas and Huai Zhang (2006) 28 state that their study is
motivated by the apparent gap between predictions regarding the
determinants of market price to earning ratios (P/E ratio) and empirical
evidence. While P/E ratio should be positively related to expected
growth rate and negatively related to risk and the level of interest
rates, prior evidence suggests weak relations at the portfolio level.
1.3 RESEARCH PROBLEM
The above studies illustrate that various attempts have been made to
ascertain the value of stocks by identifying the unexpected earnings,
dividend/price relationship, book value/market value relationship,
discounted value of dividends, earning/market value relationship etc.
However, no attempts have been made to relate accounting returns
(cash flows) to stock returns and use this relationship as a benchmark to
predict the stock prices. This relationship could also be collated with the
cost of capital as the latter has undergone a radical change vis-à-vis the
integration of Indian economy with the global economy.
19
1.4 SIGNIFICANCE OF THE PROBLEM
If the research community identifies the relevant variables that influence
the stock returns and communicates the same to the investing
community in specific, and market participants in general, it will benefit
them all in arriving at a fair market value of stocks. It will also enable the
market participants to bring about transparency in market operations and
help to build confidence in the investing community. This will lead to
create stability in the market and make markets less volatile.
1.5 OBJECTIVES
1.5.1 To establish the relationship between accounting returns (cash
basis) and risk free rate of return (as independent variables) with
market returns (as dependent variable).
1.5.2 To determine the expected stock price based on the relationship
established under 1.5.1.
1.6 HYPOTHESES
1.6.1 There is a significant relationship between the earnings and risk-
free rate of return of the firm on the one hand and stock price on
the other.
1.6.2 Earnings and risk free rate of return influence the stock price.
20
1.7 METHODOLOGY
For the purpose of this study, the following three different techniques
have been used. A brief description of these techniques is given
hereunder. At the same time, a detailed explanation for all the three
techniques is given in Chapters 2, 3 and 4.
4IL
1.7.1 Multivariate Regression Model:
The Multivariate Regression Analysis (MRA) technique is an
extension of simple regression analysis. The regression that
measures the relationship between two variables becomes a
multiple regression when it is extended to include more than one
independent (predictor) variable such as X1, X2, X3, X4, etc, in
trying to explain the dependent variable Y. In the case of simple
regression analysis, the R 2 measures the strength of the
relationship, but an additional R 2 statistic called the adjusted R2
is computed to counter the basis that will induce the R2 to keep
increasing as more independent variables are added to the
regression. Like simple regression, multivariate regression is a
powerful tool that allows the examination of the determinants of
any response variable.
21
1.7.2 Probabilistic Growth Model:
This tool is newly developed and is used to forecast the price of
stocks. In this model, it is assumed that stock price is a function
of growth rate, subject to occurrence of such a growth rate. To
capture non-linear behaviour of stocks, it is necessary to
ascertain the lognormal growth rate instead of the simple growth
rate. Therefore, the lognormal growth rate is derived for all the
observations. Another important part of this model is that it lays
emphasis on the probability of occurrence of such a growth rate,
which is calculated by using the cumulative probability for
standard normal distribution.
1.7.3 Artificial Neural Network Model:
The third tool is the Artificial Neural Network. An artificial
neural network is an information-processing model that is
inspired by the way human nervous systems process
information. The key element of this model is the new
structure of the information processing system. It comprises
of a large number of interconnected processing elements
(neurons) working in harmony to decipher a particular
problem. An artificial neural network is configured for a
specific application, such as pattern recognition or data
classification, through a learning process. Neural networks,
with their remarkable ability are able to derive meaning from
complicated or imprecise data. These can be used to mine
22
patterns and discover trends that are too complex to be
noticed by either humans or other computer software
programs.
1.8 LIMITATIONS
The main limitation of the study is timely availability of data. These
models cannot be used as a black box but should be used judiciously.
These are user-specific techniques; therefore, the user should have a
thorough knowledge of the techniques used in this study.
1.9 CHAPTER SCHEME
The chapter scheme given below has been followed in presenting the
details of the study conducted:
Chapter 1: This chapter covers introduction encompassing the
preliminary background of the study, a literature review,
the research problem and its significance, research
objectives, hypotheses, methodology, limitations and the
chapter scheme.
Chapter 2: In this chapter, the Multivariate Regression model along
with sources of data, type of data used, sampling design,
sample size, data analysis, results and interpretations are
23
discussed. Relevant references made in the chapter are
stated at the end.
Chapter 3: This, chapter covers the explanation of the Probabilistic
Growth model, including sources of data and type of data
used, sampling design, sample size, data analysis and
finally results and interpretations. References made in the
chapter are stated at the end.
Chapter 4: This part discusses the various aspects of the Artificial
Neural Network model, including sources of data, type of
data used, sampling design, sample size, data analysis
and results and interpretations. References made during
the discussion are given at the end of the chapter.
Chapter 5: Finally, in this chapter, all the observations made during
the entire study are summarized, conclusions are drawn,
recommendations are made and scope for further
research is suggested.
24
1.10 REFERENCES
1 Chancellor, E. (1999), "Devil Take the Hindmost: A history of financial
speculation", Penguin Books.
2. Stringham, E. Boettke P. and Clark J. R. (2008), "Are regulations
the answer for emerging stock markets", Evidence from the Czech
Republic and Poland" Quarterly Review of Economics & Finance,
Elsevier, Vol. 48, pp. 541 - 566.
3. Wilde, 0. (1900), "Nothing ... except my genius" Alastair Rolfe
(Compiler), Stephen Fry (Introduction) 1997, Penguin Books.
4. Fama, E. (1991), "Efficient capital market II", Journal of Finance, Vol.
46, pp. 1575 - 1617.
5. Palia, D. and Thomas J. (1997), "Exchange rate exposure and
firm valuation: New evidence for market efficiency", Harvard
(Financial decisions and control workshop) and Stanford
(Accounting Summer Camp), USA, http://www.som.yale.edu/Faculty/
jkt7/papers/fx.pdf.
6. Fama, E. (1999), "Think you can beat the market? Eugene Fama
still says you can't", Capital Ideas, Vol. 2, pp. 57 - 58.
7 Krugman Paul (1999), "Market Efficiency", http://www.deanlebaron .
com/book/ultimate/chapters/mkt eff.html, www.web.mit.edu/krugman/,
Paul Krugman's website www.ssrn.com .
25
8. Liu, J. and Thomas J. (1999), "Stock returns and accounting
• earnings", The Journal of Accounting Research, Vol. 38, pp. 71 - 101.
9. Mohanty, P. (2001), "Efficiency of the market for small stocks"
presented for NSE Research Initiative, Research Paper No. 1,
Mumbai, http://www.nseindia.com/content/research/res_papers.htm.
10. Vuolteenaho, T. (2001), "What drives firm level stock returns",
Working Paper No. W-8240, NBER, MA, USA.
4
11. Hossein, A. and Bjorn H. (2002), "'A critical investigation of the
explanatory role of factor mimicking portfolios in multifactor asset
pricing models", EFA Berlin Meetings Discussion Papers, SSRN:
http://ssm.com/abstract=302338.
12. Lubos, P. and Veronesi P. (2002), "Stock valuation and learning
about profitability", The Journal of Finance, Vol. 58, pp. 1749 - 1789.
13. Samanta, G. P. and Bhattacharya, K. (2002), "Is the spread
between E/P ratio and interest rate informative for future
movement of Indian stock market"? NSE Research Initiative,
Research Paper No. 7, NSE, Mumbai, India.
14. Ang, A. and Liu J. (2003), "How to discount cash flows with time-
varying expected returns", Working Paper No. 10042, NBER, MA, USA.
• 15.
Campbell, J. Y. and Motohiro Y. (2003), "Efficient tests of stock return
predictability" Working Paper No.10026, NBER, MA, USA.
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