MTA Journal-57 - 2002 Winter

JOURNAL

Analysis

ofTechnical

Market Technicians Association, Inc. A Not-For-Profit Professional Organization Incorporated 1973

SM

Winter-Spring 2002

Issue 57

2JOURNAL of Technical Analysis Winter-Spring 2002

THE JOURNAL OF TECHNICAL ANALYSIS EDITOR & REVIEWERS 4

THE ORGANIZATION OF THE MARKET TECHNICIANS ASSOCIATION, INC. 5

CHARLES H. DOW AWARD WINNER MAY 1993

CHARLES DOW LOOKS AT THE LONG WAVE 6Charles D. Kirkpatrick II, CMT


INFORMATION, TIME AND RISK 9William X. Scheinman


THE QUANTIFICATION PREDICAMENT 17Timothy W. Hayes, CMT


AUTUMN PANICS: A CALENDAR PHENOMENON 22Christopher Carolan

CHARLES H. DOW AWARD WINNERS MAY 1999

CORPORATE INSIDERS BIG BLOCK TRANSACTIONS 26Eric Bjorgen and Steve Leuthold

CHARLES H. DOW AWARD CO-WINNER MAY 2001

STOCK SELECTION: A TEST OF RELATIVE STOCK VALUES REPORTED OVER 17-1/2 YEARS 30Charles D. Kirkpatrick II, CMT

CHARLES H. DOW AWARD CO-WINNER MAY 2001

SIGN OF THE BEAR 35Peter G. Eliades


IDENTIFYING BEAR MARKET BOTTOMS AND NEW BULL MARKETSPaul F. Desmond 38

JOURNAL of Technical Analysis Winter-Spring 2002 Issue 57

Table of Contents

You may have noticed the new cover and the new name Journal of Technical Analysis to our revered MTA publication. There have been other changes as well.

First, you have a new editor. Hank Pruden, your previous editor, first managed the MTA Journal in 1993, almost nine years ago. What a wonderful nine years forthe Journal. He and Dave Upshaw and many others as reviewers produced over those years a professional and useful Journal for MTA research that has flourishedamongst a group of practitioners not normally associated with research itself. This was an admirable, arduous, and unrewarded feat, one dedicated to the MTAand its professional image, and one that humbles me. Thank you Hank and Dave and all you others for your hard work over those past nine years.

Second, you may have noticed that we now have two finance professors as manuscript reviewers. We hope to entice even more. As we upgrade the articles in theJournal to satisfy more stringent criteria for content and analysis, it is imperative that we include those from the academic world to help us. We have begun to doso and welcome Professors Avner Wolf and Julie Dahlquist to our circle.

Third, we begin this editorial reign with a collection of all the Charles H. Dow award papers including this years. This may seem presumptuous at first, becauseI have twice won the award myself, but in truth, the award has never been given much notice within the MTA or elsewhere. It is an award that recognizes goodwriting and research, and as such, should be a cornerstone for this publication that attempts to do likewise. Thus, we have reproduced each winning article in itsoriginal form with updates when necessary. Only one of the winners, Bill Scheinman, is no longer with us; the remaining winners are still very much in the businessof technical analysis. We hope these papers will provide inspiration for you not only to compete for the Dow Award but also to provide us with additional researchstudies that we can share with our members and the rest of the investment world.

Charles D. Kirkpatrick II, CMT, Editor


1. Standards of Judgment

A submitted or nominated work will be judged according to the following:

a. The work is based upon the concepts of technical analysis.

b. The work is either original or is a significant extension of an estab-lished work of technical analysis.

c. The subject matter is substantive. Solid research and analysis are im-perative.

d. The work is practical and enhances the understanding of market action.A market forecast will not, by itself, be considered for the Award. Thepresentation of an analytical method or trading system is expected toinclude the results of applying the technique to specific past data ac-cording to generally accepted standards of testing.

e. The strength and clarity of writing are superior.

2. Nominations or Submissions of Published Works

Papers written especially for the Award or works published betweenJanuary 1 and December 31 of the prior year may be submitted or nomi-nated.

Nominations must be made in writing and a copy of a nominated paperor book must accompany them. There is no fee for nominations or sub-missions. Nominations and submissions are to be sent to Charles H.Dow Award, Journal of Technical Analysis, 74 Main Street, 3rd Floor,woodbridge, New Jersey 07095.

3. Style

The text must be a succinct and conclusive presentation of the subject.The charts, tables, and figures should be used to exemplify or to supple-ment the text and should not be the primary means of conveying thewriters points.

4. Papers

A submitted paper must not contain less than 1,500 or more than 4,000words. A paper shall not contain more than 10 charts, tables, or figures

CHARLES H. DOW AWARD GUIDELINESCreative market technicians are invited to submit their best papers for the annual Charles H. Dow Award for excellence in technical analysis. Editors

and publishers of technical analysis are invited to nominate for the Award an outstanding work of technical analysis published in 2000. The Charles H.Dow Award, sponsored by Market Technicians Association, Inc. (MTA), Barrons, and Dow Jones Newswires will be given to the work that breaks newground or makes innovative use of established techniques in the spirit of pioneering market technician, Charles H. Dow.

The Charles H. Dow Award is presented annually at the MTAs Annual Seminar. The winning author will receive a personal Award, will be recognizedin Barrons, and will be invited to discuss the paper at the Annual Seminar or at a monthly meeting of the MTA. The publication or a summary may bepublished in the MTA Journal, the MTA newsletter and/or the MTA website. Dow Jones Newswires will make copies of the paper available for distribu-tion to the public through various media. A perpetual plaque including the authors name with those of previous recipients of the Charles H. Dow Awardwill reside at the MTA office in New Jersey. At the discretion of the judges, the authors of runner-up papers will receive personal awards. No cash awardwill be given to any award winner or runner up.

GUIDELINES

total. Submissions must be typed on white bond paper, 8.5" by 11" size,double-spaced, in black ink.

Charts, tables, and figures should be placed in appropriate sections ofthe text. When it is not possible to do so, they must be presented onwhite paper, 8.5" by 11" size. Charts, tables, and figures must be indi-vidually labeled in numerical sequence. They shall be submitted in cam-era-ready format and may be presented in color. Seven complete copiesof the paper must accompany a nomination or submission. Each hard-copy submission or nomination must be accompanied by a least onedisc copy, preferably in Word-Excel format, that includes all graphics.Nominations or submissions of books or lengthy articles must includeat least seven copies, non-returnable. Each must include at least sevensynopses of the work, no more than three pages in length, that summa-rize intent, methodology, and conclusions.

Hardcopy submissions or nominations of lengthy articles must includeat least one disc copy as described above for papers.

5. Deadline

The last day for nominating or submitting publications is February 28.Entries received after that date may be accepted at the discretion of thejudging panel.

6. Judging Panel

The judging panel will include at least three past winners of the CharlesH. Dow Award, selection preference given to the three most recent win-ners. The past winner of longest standing will rotate out of the judgingpanel each year to be replaced by the latest Award winner.

In addition, the judging panel will include no more than one votingrepresentative from each of Barrons, Dow Jones Newswires, and theMTA. Members of the Board of Directors of the MTA, excepting theeditorial board of the Journal of Technical Analysis, shall not be eli-gible for the judging panel.


EDITOR

Charles D. Kirkpatrick II, CMTKirkpatrick & Company, Inc.

Bayfield, Colorado

ASSOCIATE EDITOR

Michael CarrCheyenne, Wyoming

Connie Brown, CMTAerodynamic Investments Inc.

Pawley's Island, South Carolina

Matthew ClaassenPrudential Financial

Vienna, Virginia

Julie Dahlquist, Ph.D.St. Mary's UniversitySan Antonio, Texas

J. Ronald Davis, CMTGolum Investors, Inc.

Portland, Oregon

Cynthia KaseKase and Company

Albuquerque, New Mexico

Cornelius LucaBridge Information Systems

New York, New York

John McGinley, CMTTechnical Trends

Wilton, Connecticut

Michael J. Moody, CMTDorsey, Wright & Associates

Pasadena, California

Jeffrey Morton, MD, CMTPRISM Trading Advisors

Missouri City, Texas

Kenneth G. Tower, CMTUST Securities

Princeton, New Jersey

Avner Wolf, Ph.D.Bernard M. Baruch College of the

City University of New YorkNew York, New York

PRODUCTION COORDINATOR

Barbara I. GompertsFinancial & Investment Graphic Design

Marblehead, Massachusetts

MANUSCRIPT REVIEWERS

JOURNAL of Technical Analysis Winter-Spring 2002 Issue 57

Journal Editor & Reviewers

The JOURNAL of Technical Analysis is published by the Market Technicians Association, Inc., (MTA) 74 Main Street, 3rd Floor, Woodbridge, NJ 07095. Itspurpose is to promote the investigation and analysis of the price and volume activities of the world's financial markets. The JOURNAL of Technical Analysisis distributed to individuals (both academic and practitioner) and libraries in the United States, Canada, Europe and several other countries. The JOURNALof Technical Analysis is copyrighted by the Market Technicians Association and registered with the Library of Congress. All rights are reserved.

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THE ORGANIZATION OF THE MARKET TECHNICIANS ASSOCIATION, INC.MEMBER AND AFFILIATE INFORMATION

MTA Member

Member category is available to those whose professional efforts arespent practicing financial technical analysis that is either made available tothe investing public or becomes a primary input into an active portfoliomanagement process or for whom technical analysis is a primary basis oftheir investment decision-making process. Applicants for Member mustbe engaged in the above capacity for five years and must be sponsored bythree MTA Members familiar with the applicant's work.

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MTA Affiliate status is available to individuals who are interested intechnical analysis and the benefits of the MTA listed below. Most impor-tantly, Affiliates are included in the vast network of MTA Members andAffiliates across the nation and the world providing you with commonground among fellow technicians.

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We want your article to be published and to be read. In the latter re-gard, we ask for active simple rather than passive sentences, minimal syl-lables per word, and brevity. Any paper longer than 20 pages, double-spaced, will be returned. Charts and graphs must be cited in the text, clearlymarked, and limited in number. All equations should be explained in simpleEnglish, and introductions and summaries should be concise and informa-tive.

1. Authors should submit, with a cover letter, their manuscript and sup-porting material on a 1.44mb diskette or through email. The coverletter should include the authors names, addresses, telephone numbers,email addresses, the article title, format of the manuscript and charts,and a brief description of the files submitted. We prefer Word for docu-ments and *.jpg for charts, graphs or illustrations.

2. As well as the manuscript, references, endnotes, tables, charts, figures,or illustrations, each in separate files on the diskette, we request thatthe authors submit a non-technical abstract of the paper as well as ashort biography of each author, including educational background andspecial designations such as Ph.D., CFA or CMT.

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Please submit your non-CMT paper to:Charles D. Kirkpatrick II, CMT7669 CR 502Bayfield, CO [email protected]

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Using the stock market principles outlined by Charles H. Dow, howcould we look at the long wave in stock prices?

Dow published The Wall Street Journal beginning in 1889 and, unfor-tunately, died in 1902. He wrote during a period of generally rising stockprices from the depression lows in the 1870s to the then all time high in1901. During that period Dow formulated his theory of the stock market.It consisted of two important components: the cyclical nature of the mar-kets and in the longer cycle, the third wave, the need for confirmationbetween economically different sectors, specifically the industrials and therailroads.

Following an earlier analogy between the stock market and ocean wavesduring the tidal cycle, Dow hypothesized in his famous Wall Street Journaleditorial of January 4, 1902:

Nothing is more certain than that the market has three well-definedmovements which fit into each other. The first is the variation due to localcauses and the balance of buying and selling at that particular time. Thesecondary movement covers a period ranging from 10 days to 60 days,averaging probably between 30 and 40 days. The third movement is thegreat swing covering from four to six years.

Some technicians, especially cycle analysts, would quibble with thesimplicity of Dows breakdown since there is evidence of other waves withperiodicity between 40 days and four years. However, cycle analysts wouldalso have to acknowledge that Dows breakdown is certainly accurate,though perhaps not inclusive, and that the periods he mentions are, re-markably, still the dominant cyclical movements today.

But Dow stopped short at the four- to six-year cycle, essentially thebusiness cycle. He assumed that stock prices had an underlying uptrendabout which these cycles oscillated. This was consistent with his experi-ence at the time. Stock prices (see chart A, Dow Jones Industrial, 1885-1902) had wild gyrations during the late 19th Century, but the underlyingtrend was generally upward. He undoubtedly would have added a fourthwave, or long wave, had he lived to see the 1929-32 crash.

Aside from recognizing that the stock market had a pattern, which isthe basis for technical analysis, Dow also recognized, in his theory of con-firmation between the Industrial Average and the Railroad Average, thatthere must be an economic rationale for any signals given by the stock

market price action. Most pure technicians conveniently overlook thisbecause it diverges from a strict price analysis. Unfortunately, investmentanalysts have evolved into three camps since Dow technicians, funda-mentalists and academics and as seems to be the way of human nature,they generally disregard the others work to reinforce their own identity.However, Dow was above all that, (or at least before it), and consideredthe economic rationale for a cyclical turn in the stock market just as impor-tant as the technical.

In the post-1929 era, we now know that the underlying long-term up-trend in stock prices can be severely interrupted. From looking at stockprices going back several hundred years we also note that the 1929-1932decline was not an anomaly. It occurs with frightening regularity, roughlyevery 40 to 60 years (see Chart B, Dow Jones Industrial, Reconstructed,1700-1940). We call this cycle the long wave and ponder on how Dowwould have analyzed it.

As an aside, there are still many analysts, especially academics, whobelieve that the long wave is imaginary. Their thesis is based on the as-sumption that markets dont have a memory. They argue that todaysprices are totally independent of yesterdays, of last weeks, of last yearsand certainly of 50 years ago prices. Furthermore, since Fourier trans-forms and other sophisticated mathematical techniques have been unableto identify with certainty such cyclicality, it probably doesnt exist. On theother hand, new experiments, especially those with non-linear mathemat-ics, are beginning to knock down the no memory thesis. Edgar Peters, inhis book Chaos and Order in the Capital Markets, suggests that the stockmarket has at least a four year memory. Professors McKinley and Lo fromWharton and MIT have demonstrated that stock price action is inconsis-tent with a no memory thesis and are now using non-linear mathematicsto study prices. Professor Zhuanxin Ding from the University of Califor-nia has shown that stock prices act as if they had long memories. Evensimple moving averages, as studied by two professors at the University ofWisconsin, Dr. William Brock and Blake LeBaron, can generate profitabletrading signals from prices alone, an inconsistency with the no memorythesis. The Economist wrote in a special section on the Frontiers of Fi-nance on October 9, 1993:

CHARLES DOW LOOKS AT THE LONG WAVE

Charles D. Kirkpatrick II, CMT


Chart ADow Jones Industrial Average

January 1885 - December 1902 (Linear Regression Trend)

Chart BDow Jones Industrial Average

1700-1940 (Reconstucted)


This was a shock for economists. Might chartists, that disreputable bandof mystics, hoodwinking innocent fund managers with their entrail-gazingtechniques and their obfuscatory waffle about double-tops and channelbreak-outs, be right more often than by chance? How could it be?

Whether we believe in price memory or not, charts of stock prices sincethe South Sea Bubble in 1720 show that there are obviously times whenthe stock market experiences enormous, speculative rises and subsequent,disastrous declines. These major events occur at periods considerably longerthan Dows four- to six-year movements. Furthermore, when we look atother economic data, such as commodity prices, GNP (even U. S. PostOffice revenue), etc., we see the same long-term periodicity.

How would Charles Dow have looked at this long wave price action forsignals? Probably he would have begun by looking only at the highs andlows of each four- to six-year cycle. Intermediate-term motion would belargely irrelevant to the long wave. Simplistically, he would likely havestated that the long wave was up when the tops and bottoms of the four-year cycles were making new highs, and conversely, when the tops andbottoms were making new lows, the long wave was declining.

In the last 60 years, this approach would have missed the 1929 crash,but the ultra long-term investor would have sold his stocks in 1930 whenthe 1929 low, a four-year cycle low, was broken. It would also have toldthe investor in 1950 that the long wave was turning upward, that it wastime to invest in the stock market. Unfortunately, there would have beenseveral false signals. For example, in the 1970s, two four-year cycle lowsbroke below previous four-year lows, wrongly suggesting that the longwave was headed down again. Also, in the 1930s, after the initial bottomin 1932, several four-year cycle lows were broken between 1937 and 1949,suggesting that the long-term cycle was turning down again after havingbottomed in 1932.

False signals also occurred in Dows original work on the four-yearcycles and are the reason for his turn to confirmation between the Indus-trial and the Railroad averages. He based his confirming signals on theeconomic assumption that expansion in industrial profits could be a tem-porary anomaly but not if the produced goods were being shipped, by rail-roads, to customers. A confirmation between the two averages in eitherdirection suggested that the new trend was real.

Unfortunately, over the long wave, the theory of industrials versus rail-roads breaks down. First, over time, railroads are not always the principalform of transportation for goods (How do you ship the service industry?and how about canals in the 1830s?), and second, the apparent cause forthe long-wave has more to due with capital formation, debt and moneythan with industrial production.

Money has a price too the interest rate. Interestingly, interest rates

over the past several hundred years have also had a long wave that hascorresponded in period, if not in turning points, with the stock market (seeChart C, U.S. Long-Term Interest Rates, Reconstructed, 1700-1940). Forthis reason, we assume Dow would have looked to the interest rate marketfor confirmation of a trend change in the long-term stock market.

Looking at interest rate trends, however, is not as simple as looking fora confirmation in trend between industrials and rails. Long wave interestrate cycles do not overlap precisely with long wave stock price cycles (seeChart D, U.S. Long-Term Interest Rates & Dow Jones Industrial Average,Reconstructed, 1700-1940). They will not confirm a move to new highsor lows as the rails will the industrials. It is important that one understandmore about the history of the long wave direction in interest rates before asignal can be confirmed for the stock market.

The confusing aspect between long wave interest rates and the stockmarket is that sometimes both can be moving in the same direction andsometimes each can be moving in opposite directions. This is becausestock prices have a corporate profit or growth component, as well as aninterest rate or alternative investment component. In the former, stockprices rise as a result of economic growth, industrial expansion and profit-ability along with interest rates; in the latter, stock prices rise as an alterna-tive investment to falling yields on fixed income securities. The latter, aswe shall see, is more dangerous.

When we look at the evidence over the past several hundred years wesee alternating periods of rising and falling interest rates. These are calledsecular moves and have to do with the expansion and contraction of capitaland debt.

Notice in Chart D that the peak in interest rates always precedes thelong wave peak in stock prices by many years. When interest rates and thestock market are both rising together, the industrial growth component isdominant. The period after interest rates peak is when stock prices rise asan alternative investment. During that period declining interest rates forceyield-conscious investors into alternative investments of lesser quality inorder to maintain yield. Since stocks are the most risky and least qualityinvestments, they become the final alternative, especially when their pricecontinues to appreciate as a result of increasing cash flow into the stockmarket. The recent conversion of government-guaranteed CD deposits intostock mutual funds is typical during this period. Unfortunately, it eventu-ally leads to the declining long wave in stock prices.

Each declining stock market wave has occurred only during a seculardecline in interest rates. Over the past several hundred years, you wontsee a long wave decline in stock prices while interest rates are rising. De-clining interest rates at first can cause a financial speculation and an enor-mous rise as yield is chased through lesser quality, but eventually declin-

Chart CU.S. Long-Term Interest Rates


Chart DU.S. Long-Term Interest Rates & Dow Jones Industrial Average



ing interest rates are unhealthy for the long wave in stock prices. With thisin mind, Dow would likely have developed the following confirmationrules for the long wave in stock prices:

1. When four-year stock price cycles reach new highs and business-cycleinterest rates are rising, the long wave is rising.

2. When four-year stock price cycles break below previous lows and busi-ness-cycle interest rates are rising, the long wave is rising.

3. When four-year stock price cycles break above previous highs and busi-ness-cycle interest rates are declining, the long wave has been given awarning but is still rising.

4. When four-year stock price cycles break down below previous lowsand business-cycle interest rates are declining, the long wave is declin-ing.

5. After a decline, the long wave will not turn up until business cycleinterest rates also turn up.

Using this set of rules, lets walk through the past 75 years using theaccompanying Chart E of long-term U.S. interest rates and the Dow JonesIndustrial Average since 1900.

From Dows death in 1902 both interest rates and the stock market rose.

Chart EU.S. Long-Term Interest Rates & Dow Jones Industrial Average

1900-Present

According to rule #1, the long wave was rising. Interest rates peaked in1920 and declined through 1946. Declining interest rates are a warning tobe confirmed later by a breakdown in the stock market. Thus, under rule#4, when the stock market broke to new lows in August 1930 (DJIA monthlymean = 231), it confirmed the long wave downturn.

During the 1930s and 1940s, while the initial bottom in 1932 turnedout to be the actual bottom, the gyrations were large and the stock markettrend generally flat. Interest rates declined until the end of World War II.Any upward breakout had to be taken skeptically (rule #5).

Finally, in March 1950, interest rates broke above their earlier busi-ness-cycle high (rule #5 and #1). Since rising interest rates are alwaysaccompanied by a rising stock market long wave, this was the buy signal.The DJIA was 249 at the time.

In the 1970s, the stock market broke below its prior four-year cyclelows in 1970 and in 1974. However, interest rates were still rising and thusthe long wave was still rising (rule #2).

Interest rates finally peaked in September 1981. This was a warning(rule #3), similar to the interest rate peak in 1920, that the long wave wasending. Currently, the stock market has yet to break below a previousfour-year cycle low and thereby confirm a new decline in the long wave.The last four-year low was 2340 in the DJIA in 1990*. Should it be brokenbefore a higher low is established, we will have confirmation of the down-turn in the long wave.

Would Charles Dow have looked at the long wave in this manner? Wedont know. But his principle of first observing price action simplisticallyand then confirming it with other markets, using some economic justifica-tion, gives us an excellent background for analysis of the long wave andteaches us to remain broad-minded and rational. His legacy is more thanjust a stock market theory. It is a way of thinking that transcends the nar-row confines and pettiness of much investment analysis.

March 30, 1994

*Note: 8064 in the DJIA in 2001, the NASDAQ has already begun itslong wave decline.

CDK, 2002


The nature of risk is highly sensitive to whether we act before or afterwe have all the information in hand. This is just another way of sayingthat risk and time are only opposite sides of the same coin, because theavailability of information increases with the passage of time. Thus, risk,time and information interact upon one another in complex and subtleways.

From keynote address by Peter L. Bernstein upon receiving the InauguralDistinguished Scholar Award from the Southwestern Economic Association, Dallas,

March 4, 1994.

The reader should keep in mind that any discussion of the financialmarkets is of necessity a discussion of constantly changing statistics andother data. This article was originally written in May 1994 and was sub-mitted to the MTA Journal at that time. Therefore, while the data usedherein were current as of May 20, 1994, such data applied to any specificsituation described may no longer be applicable. The same caveat appliesto the Sequel, which was written and submitted on November 11, 1994market close, and briefly discusses how each of the theories or methodsdescribed herein worked or failed to work during the period subsequent toMay 20,1994.

SYNOPSIS

This article outlines the core theories of Charles H. Dow and EdsonGould. Three of Goulds methods used to forecast stock prices, which arebased on quantifying investor psychology, are described and then illus-trated using current data. Several forecasts are then made based on howGoulds three methods and those of the author combine, in the authorsopinion, to operate in current financial markets. Future levels of interestrates, stock prices, an industry group, the technology sector, as well as twoindividual stocks, are estimated. A sequel, written six months after the origi-nal article was submitted, discusses how the forecasts turned out.

DOWS THEORIES

The granddaddy of all stock market technical studies is the Dow Theory,which was originated by Charles H. Dow around the turn of the century.According to Dow, major bull or bear trends are indicated when the DowJones Industrial and Transportation averages, one after the other, set newhighs or lows. A divergence between the indices often indicates a potentialturning point in the underlying trend of the stock market. Dow set the stagefor the later theories, still used and elaborated on by market analysts today,of what may broadly be defined as divergence analysis. That is the study ofdivergences among and between a broader universe of indices and indica-tors than were available to Dow. Dows theory was used in the context ofhis basic commandment: To know values is to know the meaning of themarket.l But Dow also said that wise investors, knowing values above allelse, buy them when there is no competition from the crowd. Indeed, theybuy them from the crowd during periods of mass pessimism, and sell themto the crowd in return for cash during late stages of advancing markets.The stock market as a whole, said Dow, represents a serious, well-consid-ered effort on the part of farsighted and well-informed men to adjust pricesto such values as exist or which are expected to exist in the not too remotefuture.2

GOULDS THEORIES

Edson Gould, who first studied the Dow Theory, was a practicing mar-ket analyst for more than fifty years between the early 1920s and late 1970s.His main focus was on forecasting the stock market. Though a student ofphysics and the harmonics of music, as well as business cycles and Greekcivilization, each of which he believed helped explain certain aspects ofhow the stock market behaved, he came to believe, after reading GustaveLeBons classic, The Crowd,3 that the action of the stock market is noth-ing more nor less than a manifestation of mass crowd psychology in ac-tion.4

The methods and techniques Gould utilized in his service, Findings &Forecasts, attempted to ...integrate the many economic, monetary and psy-chological factors that set the level and cause the changes in stock prices.5

He regarded the economic factors as important but typically late so far asthe stock market is concerned. He regarded the monetary factors as crucialfor the stock market and typically early. Whereas, he believed that, Of allthree sets of factors, the psychological factors are by far the most impor-tant in fact, the dominant factors affecting the cyclical swings of stockprices.6

THESIS

It follows from the above that one of the most important aspects of allin successfully analyzing the stock market is measuring investor sentiment.The consensus view, the most difficult factor of all to gauge accurately,can be glimpsed at times and only in part-through not only such transac-tion-based data as put/call ratios, premiums and open interests, but alsopoll-based data such as the weekly Investors Intelligence reports of whatpercentage of investment advisors are bullish or bearish. Whereas the au-thor regularly screens such data for extremes, the theories and methodswhich are derived from Gould and are discussed below are, in an of them-selves, measures of the behavior of the investment crowd and, in his opin-ion, more practically useful in making and implementing investment deci-sions. And inasmuch as they are also applied to the monetary factors, abond market opinion is derived therefrom, as well.

The index and stock charts used to illustrate this article are of:

1. Treasury Bonds Nearest Futures, Monthly

2. Treasury Bonds Nearest Futures, Weekly

3. New York Stock Exchange Financials Index, Weekly Standard & Poor's40 Utility Stock Composite, Weekly

4. Standard & Poors 400 Industrial Stocks Composite, Monthly

5. Drug Shares Index, Weekly Close (Sum of BMY, LLY, MKC, MRK,PFE, UPJ x 4.50541)

6. TXB-Hambrecht & Quist Technology Stock Index Less CBOE Bio-technology Stock Index, Weekly Average

7. Merck (MRK), Weekly

8. U.S. Robotics (USRX), Daily

GOULDS METHODS AND TECHNIQUES

Edson Gould is, perhaps, best known for his monetary rule and valua-tion barometer: His Three-Step-and-Stumble Rule states that, Wheneverany one of the three rates set by monetary authorities the rediscount rate,

INFORMATION, TIME AND RISK

William X. Scheinman



the rate for bank reserve requirements, and margin requirements on stocks increases three times in succession ... invariably ... the stock market hassubsequently ... suffered a sizable setback.7 Whereas his Senti-Meter is,the ratio of the Dow Jones Industrial Average to the average rate of an-nual cash dividends paid on that average. 8 When the Senti-Meter reads$30 per $1 of dividends or more it indicates a high and risky market. Areading of $15 or less indicates a relatively low and cheap market.

Lesser known and, perhaps, too arcane for many, the author has foundthat three of Goulds methods and techniques are more practically usefulin helping decide when and at what levels a given stock or price index istoo high, or too low and what constitutes a sentiment extreme. Withthis background in mind, lets examine Goulds theories and applicationsof Resistance Line Measurement, Unit Measurement and the Rule of Three,as well as the authors theory of the Cut-in-Half principle and its oppo-sites.

RESISTANCE LINE MEASUREMENT

According to Gould, ... the market continually reveals a quantum ofmass psychology comprising time and price. It follows that a sharp declinein a short period of time generates as much bearishness as a slow and mi-nor decline over a long period of time.9 This theory, then, is based onthree principal determinants of crowd psychology in the market place: pricechange itself, elapsed time to achieve it and the perceived amount of risk.

The resistance line theory attempts to measure these three elements of masspsychology mathematically, weighing both the vertical price change and thehorizontal elapse of time. This measure of potential risk or reward must be keyedoff whatever the investor regards as any pair of prices which consist of an im-portant high and low of the particular securitys price history. Four of the chartswhich are discussed below illustrate how the resistance lines are applied.

The theory is that a trendline rising at one-third (or two-thirds) the rateof an advance movement is likely to produce resistance to subsequent de-cline, but, if violated, the decline will accelerate from the point of penetra-tion. Similarly, a trendline declining at one-third (or two-thirds) the rate ofa decline movement may provide resistance to a subsequent advance, but,if penetrated, the advance will accelerate from that point. Sometimes theseresistance lines work, sometimes they dont; they are not foolproof. Butthe author uses resistance lines because they seem to be more accuratethan ordinary trendlines and most importantly because they can be drawnbefore the subsequent price action takes place.

LONG TREASURY BONDS

Lets see how resistance line theory may be helpful this year in gaugingwhen Treasury Bonds Nearest Futures, which have been falling mostlysince their September 1993 peak, etch a major low. Inasmuch as these Trea-suries, Monthly (Chart 1) made a major low in 1981 at 55.156 and morethan doubled it at the 1993 high of 122.313, the most important set ofresistance lines derive from that low and that high. Referring to the chart,we observe that the May 11, 1994 low of 101.125 slightly broke the rising2/3 speed line before reversing upward to close May 20 at 105.000. Impor-tant here are the facts that this same resistance line approximately definedeach of the 1987, 1990 and 1991 Treasury lows. Translated into an opinionon May 20, this means that Treasuries wont decisively break par this year.Should they do so, it might imply the onset of a renewed inflationary cycle.

Moreover, the second of Goulds methods, the unit measurement prin-ciple, also helps to determine the importance of the early-May intradaylow of 101.125.

UNIT MEASUREMENT

This technique is sometimes helpful in estimating terminal phases ofadvances and declines, of both individual stocks and market indices. In

other words, what constitutes a price which is too high or too low. Itsmeasurements are expressed in terms of bull and bear units. A bull unitconsists of the number of points of an initial advance by a stock or priceindex following the bottom of a prior important decline, succeeded by asubsequent reaction which, however, remains above that bottom and thenis followed by a second advance that goes beyond the first one. A bear unitis formed in the same manner but in the opposite direction. These mea-surements sometimes portend the length of an overall advance (or decline)and indicate levels at which a trend may meet resistance, or, at times, anextreme reversal.

Chart 1Treasury Bonds Nearest Futures, Monthly

Chart 2Treasury Bonds Nearest Futures, Weekly & Bear Unit Count


Price action with the primary trend frequently works off units threetimes (sometimes four times), in accordance with the Rule of Three, thebasis of which is discussed below. In other words, for a move with thetrend, expect three units, but be prepared for the fourth. One other impor-tant point about unit measurement is that recognition of the 2-unit level, bya sharp reaction from it, often indicates that following such a reaction thesecurity will go all the way and work off three, or four units. Whereasrecognition of that level which is defined by 2-l/3 units, without recogni-tion of the 2-unit level (by resistance from it), is grounds for caution, espe-cially for trend followers, since that is often the hallmark of a contratrendmove.

Now we are equipped to develop a second opinion about Treasury BondsNearest Futures, Weekly, which is illustrated in Chart 2. Referring to thechart, we observe that these Treasuries etched a bear unit of 4-7/8 pointsby their initial September 7-22, 1993 decline from 122.313 to 117.438.According to unit measurement theory, then, the contratrend, upward reac-tion from the 2-bear unit level of 112.563, which was reached at the No-vember 23 low of 112.031, implied that Treasuries would go down all theway to work off either 3 or 4 bear units to 107.688, or 102.813, respec-tively. With the actual intraday May 11 low of 101.125, this close lessthan 2 percent away-recognition of the theoretically maximum 4-bear unitcount, also leads to the conclusion that that was a low in Treasuries ofmajor importance.

Chart 3NYSE Financials with Resistance Lines

& S&P 40 Utilities with Unit Count

INTEREST-SENSITIVE STOCK MARKET INDICES

Because of the importance of the monetary factors, ideally the resis-tance line and unit measurement theories should also be reflected in inter-est-sensitive stock market indices. Sure enough, the New York Stock Ex-change Financials and Standard & Poors 40 Utilities indices (both on Chart3) did, so far, in 1994 faithfully reflect both resistance line measurementand unit measurement theory, respectively. Referring to the chart, we ob-

serve that the Financials week of April 8, 1994 low of 200.01 and all sub-sequent lows, which were higher (itself a positive divergence), reversedupward above the rising 2/3 speed resistance line from the 1990 low. Gouldalways said that the ability of a price index to stay above its rising 2/3speed resistance lines during reactions was the hallmark of a powerful ad-vance.

Whereas the S&P 40 Utilities, which etched a bear unit of 10.29 pointsby the September 17-October 15, 1993 decline from 189.49 to 179.20,worked off a fairly precise 4-bear unit count to 148.33, compared to theactual May 13 low of 146.85. Close enough. Moreover, these Utilities alsorespected their rising 2/3 speed resistance line from the 1981 low, whichapproximated this 4-bear unit count.

It logically follows from each of these two theories that should the afore-said risk parameters of these three interest-sensitive indices Treasuries,NYSE Financials, S&P 40 Utilities be decisively downside penetratedon a closing basis that the bear market in bonds not only had more to go onthe downside but also that stocks might have begun a bear market as well.However, the author does not believe that is the case at May 20, 1994, aswe examine next.

Chart 4S&P 400 Industrials, Monthly with Grand Bull Unit Count from 1982

A STOCK MARKET ROAD-MAP

Gould also said that over longer periods of time unit measurement wasuseful, too: A grand unit is, as the name implies, a big unit sometimestaking months to complete and years to confirm.10 We think this theoryhas been remarkably accurate since the stock markets 1982 low and that itis relevant now. Referring to Standard & Poors 400 Industrials (Chart 4)we observe that during the 14-month long 1982-1983 advance from 113.08to 195.25, a grand bull unit of 82.19 points was etched the l0-month-long1983-1984 decline to 167.64 not exceeding the 1982 low.

Thereafter, the S&P 400 steadily rose until hitting the 1986 peak of282.87, which was less than 2 percent above the 2-bull unit count at 277.46.The subsequent 12 percent reaction to that years September low of 252.07constituted recognition that the unit measurement principle was operative,and that the S&P 400 would go on to work off at least 3 or 4 bull units.


From the 1986 low, the S&P 400 gathered steam and began to acceler-ate in 1987, reaching the 3-bull unit count of 359.65 in June. That levelwas potentially an important peak level in accordance with the theory expect three units. During the next two months the S&P 400 overshot the3-bull unit count but peaked 9.7 percent higher (intraday) in August, fol-lowed by the crash.

From the 1987 crash low, stocks steadily rose until hitting the July 1990peak of 438.56 which was less than one percent below the 4-bull unit countto 441.84. That was a perfect fourth and final move, according to Gouldsunit measurement theory. During the next three months stocks fell by 21percent.

Of current relevance, in the authors opinion and experience, is thatsometimes unit counts will work off a double set of units, i.e., 6 or 8 units.This appears to be the current case for the S&P 400 Industrials, which,rising from the October 1990 low of 345.79, recognized the five-bull unitcount to 524.03 repeatedly last year by resisting further advance. How-ever, by late-1993 that level was decisively exceeded. This means to usthat the theory is saying the stock market should continue to rise until reach-ing at least the 6-bull unit count to 606.22, before the bull market whichbegan from the 1982 low is over.

In 1994 the S&P 400 Industrials advanced further to reach the 560.88level in February, before reacting to the April 20th low of 507.36, a drop of9.5 percent. Referring again to Chart 4, we further observe that during theFebruary-April reaction the rising 2/3 speed resistance line from the 1990low, which during April was at the 500 level, was effective in defining thatmonth's low. This means we believe current risk from the May 20th closeof 530.88 approximates four percent, say 510, whereas potential reward to 606.22 would be a gain of 14 percent. Those seem like good odds.

THE RULE OF THREE

Now, we examine the third of Edson Gould's theories, the Rule of Three.For reasons about which people have speculated for thousands of years,the number three and four have a meaning of finality about them. Forexample, Aristotle said, the Triad is the number of the whole, inasmuchas it contains a beginning, a middle and an end. This concept may bedeeply rooted in the natural family unit of father, mother and child, whichis given religious expression in the concept of the Holy Trinity. The finan-cial markets, which, after all, reflect human emotions, also frequently actin the same way. Sometimes there is a fourth movement, which usually ischaracterized as a now and never action, climactic in nature. (Threestrikes you're out; four balls take a walk). That financial markets and indi-vidual stocks typically but not always move in a series of three or foursteps is apparent in both very short-term moves as well as those encom-passing months and even years.

Chart 5Drug Shares Index, Weekly (Sum of BMY, LLY, MKC, PFE, UPJ x 4.5841)

DRUG SHARES INDEX

However, to simply illustrate the Rule of Three we next examine theDrug Shares Index (Chart 5), a composite of Bristol Meyers, Lilly, MarionMerrill Dow, Merck, Pfizer and Upjohn. Between January 8, 1992 andAugust 13, 1993 the Drugs dropped 42.8 percent in a classic bear market,which consisted of four steps down. Also, helping define the fourth step asthe final one was the fact that the August 13, 1993 low of 1011.46 closelyapproximated the 3-bear unit count from the 1992 peak, at 1012.93.

After rallying 20 percent from the 1993 low, to 1217.04 on January 14,1994, the Drugs came down again to etch a successful test of last yearslow, at the April 15, 1994 low of 1014.84. In other words, we are confidentthat a classic double bottom has been put in place for this group. Addition-ally, as illustrated later, another yardstick of extreme investor behavior tar-geted both Lilly and Merck as having etched final lows last year and thisyear.

TECHNOLOGY AND GROWTH STOCKS

No discussion of the stock market would be complete without address-ing the role of the technology and growth sectors. They are important notonly because they often represent the fastest growing companies, butalso, as I stated in my book which was first published in 1970, ...glamour/growth stocks which, because they are highly volatile ordinarily two-and-a-half times more so than those in the DJIA are favorite vehicles ofsophisticated investors.11 This volatility provides greater time opportu-nity than is available in the behavior of most other stocks.

Edson Gould, put together the first glamour average back in 1960,12

though, surprisingly, the pamphlet, A Vital Anatomy, from which wevealso earlier quoted various Gould statements about his theories and meth-ods, says nothing whatsoever about glamour stocks. Having originallygotten this idea from Gould in the late 1960s I created my own GlamourPrice Index, which consisted of the stocks of eleven highly-regarded, well-known, technology-oriented companies.l3 However, in the most recent


edition of my book, I noted that in recent years Ive scrapped my originalGlamour and several other technology- or growth-based indices in favorof the more representative Hambrecht & Quist Technology Stock Index14

and its sub-index of even more rapidly growing, smaller companies, theH&Q Growth Stocks Index. But inasmuch as the H&Q indices includestocks in the biotechnology sector, which I believe march to a differenttune than other growth and technology types, I also have created two otherindices which consist of the numerical values of each of the respectiveH&Q indices less the CBOE Biotechnology Stock Index. Hence, in thetechnology and growth sectors, we examine these five different indices:

1. H&Q Technology Stock Index, which is comprised of the publiclytraded stocks of 200 technology companies, broadly defined in fivebasic groups: Computer Hardware, Computer Software, Communica-tions, Semiconductors, Health Care (within which is a Biotechnologysub-index). The index was originally conceived in the 1970s as a price-weighted index. In 1985 it was reconstructed and market capitalizationweighted. Changes in the index occur as mergers, acquisitions and fail-ures dictate not infrequently.

2. H&Q Growth Stock Index is a subset of the Technology Index and iscomprised of all companies in the Technology Index which have an-nual revenues of less than $300 million. Companies are removed everyJanuary if they have passed $300 million in revenues.

3. CBOE Biotechnology Stock Index

4. TXB Index, which is the H&Q Technologies Excluding Biotechs

5. GXB Index, which is H&Q Growth Stocks Excluding Biotechs

THE TXB INDEX

Of these five indices, the author thinks the TXB Index is both the mostrepresentative of the overall technology sector as well as being the mostorthodox in reflecting investor psychology We examine it next. Referringto Chart 6, we observe that between their respective 1990 lows and 1994highs (through May 20, 1994), whereas the DJIA gained almost 71 percentand the Dow Transportations rose more than 131 percent, the TXB Indexmore than tripled. So much for volatility!

We also observe that at its March 18, 1994 peak, the TXB Index com-pleted a third step up from its 1991 low. In accordance with the Rule ofThree, this allows for either the possibility that that was a final step, orallows for the emergence of a fourth and final higher high, after the currentreaction is over. We favor the latter possibility and believe Goulds twoother theories provide well-defined potential risk and reward parametersfor the outcome we envisage.

Chart 6TXB Index, Weekly Average &

Dow Jones 30 Industrials and 20 Transportations

As to risk in the TXB Index, which at May 20, 1994 was down 14-l/2percent from its March 18 peak, it must not break below the rising 2/3speed resistance line from the 1991 low, in order to maintain its bullishuptrend, in accordance with Resistance Line Theory. Inasmuch as TXBclosed at 303.15 on May 20, and the aforesaid 2/3 speed line was nearingthe 287 level, that means we think that risk of this date approximates 5percent.

Whereas potential reward of a possible fourth and final rise of the TXBIndex we think may be estimated through the Unit Measurement method.Referring again to Chart 6, we further observe that the TXB Index etched abull unit of 99.96 points by its initial advance from the September 20, 1991low of 112.29 to the April 24, 1992 high of 212.25. The 2-bull unit level of312.21 was briefly recognized by its one-week reaction from near that levelin early 1994, before advancing to the higher March 18 all-time high. As-suming then, that the aforesaid resistance line risk parameter holds on thecurrent reaction, we believe that potential reward from the May 20 level isabout 35 percent to the 3-bull unit count at 412.17.

These sound like favorable odds of 7-to-1 between possible risk andreward, in the authors opinion. I note, too, that an overhead trend, whichis projected through the 1992, 1993 and March-1994 peaks and which alsoparallels the rising 2/3 speed resistance line, approximates the 400 level byyear-end 1994, as well. In other words, the author believes that the TXBIndex will rise by about one-third before this sector is vulnerable to a bearmarket.

BEAR MARKET

After the reward area is approximated, thats from where I think a bearmarket in technology and growth, as well as one for the stock market, over-all, may begin. That there will likely be a bear market between now and1995 is suggested by the facts that every single 5 year in this century hasbeen an up year, which means that there should be an intervening bearmarket before the 1995 bull market begins. However, an alternative sce-


nario is simply that it will take between now and year-end 1994 for thereward area to be reached.

If that proves to be the case and the stock market rises to record levelsand nears the potential reward areas we have outlined herein, by year-end1994 (possibly narrowly extending into early 1995), that would be the fourthconsecutive up year a possible final up year, according to the Rule ofThree. In that event, 1995, especially if perceived by too many as alwaysan up year since it is a 5 year, would then set the stage for it to becomethe first down 5 year during the past century, in the authors opinion, i.e.,1995 happens in second-half 1994.

Chart 7Merck, Weekly: 4-Step 1992-1993 Decline & 1994 Test at Cut-In-Half Level

THE CUT-IN-HALF RULE AND ITS OPPOSITE

The fourth gauge for measuring investor extremes is conceptually thesimplest of all the Cut-In-Half Rule and its opposite. Briefly stated, whenan important stock or price index loses 50 percent of its value, a rally oreven major reversal often originates from near that level. Keep in mindthat the Cut-In-Half Rule and a 50 Percent Retracement are quite different.For example, two stocks each base at the level of 50 and both rise to 100. Ifone declines to 75, before advancing once again, it has retraced 50 percentof its advance from 50 to 100. However, if the other one drops back to 50from 100, it has been cut-in-half. A textbook example of the Cut-in-HalfRule is shown in Chart 7 of Merck, which we discuss below.

Why the Cut-In-Half Rule and its related spinoffs often work is prob-ably because the investor crowd quantifies 50 percent off the top as toocheap. Whereas the opposite is that after an important stock or index doublesit often runs into trouble. At that point, investors tend to take at least someprofits. But since some indices, individual stocks, commodities and inter-est-bearing securities are more volatile than others, this same yardstick issometimes extended on the way up to a triple, quadruple, quintuple, oreven a sextuple, (with low price stocks sometimes squaring their lows).Whereas, on the way down there is sometimes a double cut-in-half (off 75percent), or more rarely a triple cut-in-half (87-l/2 percent off the top).Keep in mind, however, that in applying these Cut-In-Half yardsticks aspotential long entry points, one should be satisfied that the companys bal-

ance sheet is not in serious question.

STOCK SELECTIONS

Though it can be repeatedly demonstrated that these four theories ofinvestor behavior are constantly operating in all financial markets, in theauthors opinion, it does not necessarily follow that one can readily usethem in every instance. Sometimes the units are not readily discernible andthe resistance lines dont work. Moreover, sometimes there is a fifth stepin an overall advance or decline movement, which appears to contradictthe Rule of Three though a case might be made that such a fifth steprepresents an undercut (or overcut) test of the fourth step.

However, after using these theories over time to make day-to-day in-vestment decisions, I have found that they are valuable when discernibleand add confidence to a decision. That is particularly the case when morethan one theory appears to be operative in a given situation.

Chart 8US Robotics, Daily with Doubling Levels & Unit Measurement Counts

For example, referring to Chart 7 of Merck (MRK @ 30-l/4), we can ob-serve that when it closely approximated its theoretical cut-in-half level of 28-9/32 during August 1993, at the actual low of 28-5/8, on a fourth (and presumablyfinal) step down from the January 3, 1992 peak of 56-9/16, it appears to haveconstituted a classic buying juncture. Thereafter rallying to 38 by January 5,1994, Merck subsequently tested last years low at this years April 15, 18 lowsboth at 28-1/8. This makes me confident that the cut-in-half level was, or ap-proximated, a final low, especially since there is no great mystery about Mercksfundamentals and fifty percent off the top seems a reasonable if not toogreat a discount for those investors critical of the Clintons health care plans.

More volatile technology and growth stocks sometime reflect these theoreti-cal principles of how crowd behavior plays out in the financial markets in anextraordinary way. For example, referring to Chart 8 of U.S. Robotics (USRX @30-3/4), a world-wide leader in data communications, we can observe that afterdoubling the late-1991 low of 12-l/4 by the early-1992 high of 24-1/4, Roboticsdropped sharply (off 45 percent). Whereas the early-1993 high of 25-1/2 almostdoubled the summer-1992 low of 13-3/8. Then the March 1993 low of 17 was


slightly more than doubled at the October high of 35-1/4, whereas the subsequentdecline to 23 worked off an almost-perfect 4-bear unit count to 23-1/4.

Moreover, this years high of 46 (on March 8) was a perfect double,from which a reaction has begun, with a bear unit of 5-1/4 points alreadyetched and confirmed (by a lower low), and more recently appearing torecognize the 3-bear unit level of 30-1/4, by the May 10-11 lows of 29-1/4,as the new low from which to key off. That the stock of a single companycould have gone through so many extreme bull and bear moves, in such ashort period of time, shows not only that Alvin Tofflers Future Shock15

has arrived on Wall Street but also that traditional Wall Street research isincapable of dealing with it effectively. The arrival of future shock, whatsome now call the information age, also presents a challenge to stock mar-ket technicians to do their homework in order to stay ahead of the curve.

May 22, 1994

SEQUEL

At Market Close November 11,1994:

What Happened During the Subsequent Six Months

Treasury Bonds Nearest Futures (Charts 1 and 2) perfectly testedtheir May 11 low at their virtually identical July 11 low of 100.0625 compared to the May 11, 100.2500 (the numerical value of the Futures areabout one point lower than shown on the chart because the Nearest Futureshad rolled over from Junes to Septembers, and currently Decembers).Thereafter Treasuries rallied back to the August 5 high of 105.21875, thenslowly eroded until par was broken at the September 22 close of 99.40625.At that point we conceded the 13-year long uptrend in Treasuries was clearlybroken and that the major trend inference of bonds should be assumed asbeing down. By November 11, Treasuries slumped even more, closing at96.0625. Moreover, since this break of the grand resistance line of Trea-suries also took out the 4-bear unit count level, it implies to us that ulti-mately at least six bear units will be worked off. That level is 93.0625.

However, we never changed our positive stock market opinion becausethe two interest-sensitive stock market bellwethers we mostly rely on re-mained intact, notwithstanding the break in bonds.

The NYSE Financials (Chart 3), which had hit an intraday low of 199.95on April 4, closing May 20 at 214.27, slightly exceeded the 220 level dur-ing four days in June, then also slowly eroded until closing November 11exactly at 199.56. While this does constitute a break of its resistance line,and hence is clearly negative as of November 11, it seems such an obvioustest of the April 4 low, that it is conceivable to us the Financials may beable to mount at least a weak rally from here.

We draw this tentative conclusion because the third interest-sensitiveindex, Standard & Poors 40 Utility Stock Index (also on Chart 3), whichclosed November 11 at 148.51 still above its May 13 low we dontthink will take out that level. Not only has the 4-bear unit count level of148.33 been repeatedly and successfully tested during 15 trading days inOctober and November but is also defined by these Utilities rising 2/3speed resistance line from the March 1980 low. That is about as preciserecognition of a Gouldian-defined risk parameter as it ever gets! Naturally,this also means that a decisive break of it would undoubtedly require somechange in our current stock market opinion.

Our Stock Market Road-Map for Standard & Poors 400 Industri-als (Chart 4 and Chart 9), successfully tested the April 20 low (507.30) atthe June 27 low of 511.90, thereafter rising to an all time high of 564.50 onOctober 31. Closing November 11 at 550.87, potential reward has nowmoved down to only 10 percent whereas near-term risk remains about 4percent (the rising 2/3 speed resistance line moving up to about 527). Notquite as good odds as on May 20.

The Drug Shares Index (Chart 5) was up almost 18 percent at Novem-

ber 11 from May 20 and we think is headed substantially higher. Thoughgaining almost 30 percent from its April low at the November 11 close of1316.70, we think the Drugs will work off at least three bull units, a furthergain of 25 percent from here. Three bull units were worked off on the waydown, so why not three bull units on the way up?

Chart 9S&P 400 Industrials, TXB Index (Technology Less Biotechs)

The TXB Index (Technologies Less Biotechs) (Chart 6 and Chart 9), atits June 23 daily close of 293.97, never broke below its 2/3 speed resis-tance line risk parameter and subsequently rose 28.9 percent from that lowto 378.83 on November 9. Obviously the odds of further gain from herehave sharply deteriorated, potential remaining reward only a possible ad-ditional 8.8 percent, in our opinion. We have chosen to deal with this changeof the odds by building cash as specific technology and growth compo-nents reach their individual, respective potential reward zones.

Merck (MRK @ 36-3/4) (Chart 7) hit a recovery high of 37-5/8 onNovember 10 and we believe is headed into the 44-45 zone. That is de-fined by both a bull unit count and an overhead declining 1/3 speed resis-tance line.

Whereas U.S. Robotics (USRX @ 38-3/4) (Chart 8) worked off a fourthbear unit at its June 2nd low of 24, then etched a new bull unit of 5-1/2points by its subsequent initial rise to 29-1/2. USRX went on to slightlyexceed the 3-bull unit count of 40-1/2. at the November 9 high of 42-1/4.The maximum upside potential we see from here, is a 4-bull unit count to46, which would also be a prospective double top with the early-1994 peak.

CONCLUSION

I believe that this real-time experience in using the Gouldian theoriesamply demonstrates both their usefulness as well as their drawbacks, thoughonly scratching the surface of their potential applications. Their key ad-vantages are that Goulds quantifications of investor sentiment help one toboth reach and act upon specific investment conclusions on a case by casebasis, without being held hostage to an endless, self-imposed debate aboutwhat to do.


REFERENCES

1. Why Most Investors Are Mostly Wrong Most of the Time, W. X.Scheinman, 1991, Fraser Publishing Company (p. 139)

2. Scheinman, ibid

3. The Crowd, G. LeBon, 1896, Fraser Publishing Company (1982)

4. A Vital Anatomy, E. Gould, (Undated), Anametrics, Inc.

5. Gould, ibid

6. Gould, ibid

7. Gould, ibid

8. Gould, ibid

9. Gould, ibid

10. Gould, ibid

11. Scheinman, ibid

12. Gould, ibid

13. Scheinman, ibid

14. Hambrecht & Quist Technology and Growth Indices, Michael De Wittand Shiela Ennis, Hambrecht & Quist Incorporated, January 1993

15. Future Shock, A. Toftler, 1971, Bantam Doubleday

BIBLIOGRAPHY

Numbers Jung, C.G., Collected Works of C.G. Jung, General Index, (Volume 20,

pp. 485-489, Numbers), Princeton University Press, 1979

Menninger, K., Number Words and Number Symbols; A Cultural His-tory of Numbers, The M.I.T Press, 1970

Von Franz, M-L, Number and Time, Northwestern University Press,1974

Technology Veblen, T., Imperial Germany and the Industrial Revolution, Transac-

tion Publishers (1990 reprint)

William X. Scheinman, was a registered investment advisor since 1968. He movedfrom Wall Street to Reno, Nevada in December 1974 where he advised financial insti-tutions worldwide. One of the founding members of the Market Technicians Associa-tion, Bill was the founder of the African-American Students Foundation, Inc. whichbetween 1956 and 1961 brought more than 1,000 students from all over Africa to theUnited States to attend institutions of higher learning. He was the founder of theAfrican-American Leadership Foundation, Inc. Bill died on May 24, 1999.


This indicator has always produced huge profits! In fact,

you would have doubled your money in just six months!

Such a claim could be a sales pitch. It could also be an analysts enthu-siasm about some work just completed. But in either case, such claimsappear to be meeting increasing skepticism, perhaps because enough haveproven to be based more on fiction than quantifiable fact, perhaps becauseenough investors have been burned by indicators that have failed to panout when put to real-time use, perhaps because the combination of ever-strengthening computing power and ever-increasing program complexityhave made excessive optimization as easy, and dangerous, as ever.

In any case, the need to quantify accurately and thoroughly is greaterthan ever. Honest and reliable quantification methods, used in the correctway, are needed for increased research credibility. They are needed to im-part objectivity. They are needed for effective analysis and for the soundbacking of research findings. The alternative is the purely subjective ap-proach that uses trendlines and chart patterns alone, making no attempt toquantify historical activity. But when the quantification process fails todeliver, instead producing misleading messages, the subjective approachis no worse an alternative a misguided quantification effort can be worsethan none at all. The predicament, then, is how to truly add value throughquantification.

THE CONCERNS

The major reason for quantifying results is to assess the reliability andvalue of a current or potential indicator, and the major reason we haveindicators is to help us interpret the historical data. The more effective theinterpretation of historical market activity, the more accurate the projec-tion about a markets future course. An indicator can be a useful source ofinput for developing a market outlook if quantitative methods back its re-liability.

But for several reasons, quantification must be handled with care. Theinitial concern is the data used to develop an indicator. If its inaccurate,incomplete, or subject to revision, it can do more harm than good, issuingmisleading messages about the market thats under analysis. The data shouldbe clean and contain as much history as possible. When it comes to data,more is better the greater the data history, the more numerous the likeoccurrences, and the greater the number of market cycles under study.

This leads to the second quantification concern, and thats sample size.The data may be extensive and clean, and the analysis may yield an indica-tor that foretold the markets direction with 100% accuracy. But if, forexample, the record was based on just three cases, the results would lackstatistical significance and predictive value. In contrast, there would befewer questions regarding the statistical validity of results based on morethan 30 observations.

The third consideration is the benchmark, or the standard for compari-son. The test of an indicator is not whether it would have produced a profit,but whether the profit would have been any better than a random approach,or no approach at all. Without a benchmark, random walk suspicionsmay haunt the results.1

The fourth general concern is the indicators robustness, or fitness the consistency of the results of indicators with similar formulas. If, forexample, the analysis would lead to an indicator that used a 30-week mov-ing average to produce signals with an excellent hypothetical track record,

how different would the results be using moving averages of 28, 29, 31, or32 weeks? If the answer was dramatically worse, then the indicatorsrobustness would be thrown into question, raising the possibility that thehistorical result was an exception to the rule rather than a good example ofthe rule. An indicator can be considered fit if various alterations of theformula would produce similar results.

Figure 1Summary Results From Hypothetical Indicator Tests

These results contain an impressive-looking EXCEPTION to the rule ...Number Moving

of Average Accuracy Gain/Annum

Trades (Periods) Buy Level Sell Level Rate (%) (%)

40 70 100 110 50 11.2

39 71 99 111 50 11.3

37 72 98 112 65 15.1

37 73 97 113 52 10.1

36 74 96 114 50 9.8

These results would all be good EXAMPLES of the rule ...

50 20 15.6 8.6 55 11.8

49 21 15.8 8.4 56 12.0

48 22 16.0 8.2 56 12.1

47 23 16.2 8.0 57 12.1

46 24 16.4 7.8 56 12.0

Buy-Hold Gain/Annum 6.3

Moreover, the non-robust indicator may be a symptom of the fifth con-cern, and thats the optimization process. In recent years, much has beenwritten about the dangers of excessive curvefitting and over-optimization,often the result of unharnessed computing power. As analytical programshave become increasingly complex and able to crunch through an ever-expanding multitude of iterations, it has become easy to over-optimize.The risk is that armed with numerous variables to test with minuscule in-crements, a program may be able to pick out an impressive result that mayin fact be attributable to little more than chance. The accuracy rate andgain per annum columns of Figure 1 compare results that include an im-pressive-looking indicator that stands in isolation (top) with indicators thatlook less impressive but have similar formulas (bottom). One could havefar more confidence using an indicator from the latter group even thoughnone of them could match the results using the impressive-looking indica-tor from the top group.

What follows from these five concerns is the final general concern ofwhether the indicator will hold up on a real-time basis. One approach is tobuild the indicator and then let it operate for a period of time as a real-timetest. At the end of the test period, its effectiveness would be assessed. Toincrease the chances that it will hold up on a real-time basis, the alterna-tives include out-ofsample testing and blind simulation. An out-of-sampleapproach might, for example, require optimization over the first half of thedate range and then a real-time simulation over the second half. The resultsfrom the two halves would then be compared. A blind-simulation approachmight include optimization over one period followed by several tests ofthe indicator over different periods.

Whatever the approach, real-time results are likely to be less impres-

THE QUANTIFICATION PREDICAMENT

Timothy W. Hayes, CMT



sive than results during an optimization period. The reality of any indica-tor developed through optimization is that, as history never repeats itselfexactly, it is unlikely that any optimized indicator will do as well in thereal-time future. The indicators creator and user must decide how muchdeterioration can be lived with, which will help determine whether to keepthe indicator or go back to the drawing board.

TRADE-SIGNAL ANALYSIS

With the general concerns in mind, the various quantification methodscan be put to use. The first, and perhaps most widely used, is the approachthat relies on buy and sell signals, as shown in Figure 2.2 When the indi-cator meets the condition that it deems to be bullish for the market in ques-tion, it flashes a buy signal, and that signal remains in effect until the indi-cator meets the condition that it deems to be bearish. A sell signal is thengenerated and remains in effect until the next buy signal. Since a buy sig-nal is always followed by a sell signal, and since a sell signal is alwaysfollowed by a buy signal, the approach lends itself to quantification asthough the indicator was a trading system, with a long position assumed ona buy signal and closed out on a sell signal, at which point a short positionwould be held until the next buy signal.

Figure 2

The methods greatest benefit is that it clearly reveals the indicatorsaccuracy rate, a statistic thats appealing for its simplicity all else beingequal, an indicator that had generated hypothetical profits on 30 of 40 tradeswould be more appealing than an indicator that had produced hypotheticalprofits on 15 of 40 trades. Also, the simulated trading system can be usedfor comparing a number of other statistics, such as the hypothetical perannum return that would have been produced by using the indicator. Theper annum return can then be compared to the gain per annum of the bench-mark index.

But the methods greatest benefit may also be its biggest drawback. Nosingle indicator should ever be used as a mechanical trading system asstated earlier, indicators should instead be used as tools for interpretingmarket activity. Yet, the hypothetical and actual can be easily confused.Although the signal-based method specifies how a market has done be-tween the periods from one signal to the next, they are not actual records ofreal-time trading performance. If they were, the results would have to ac-count for the transaction costs per trade, with a negative effect on tradingresults. Figure 3 summarizes the indicators hypothetical trade results be-fore and after the inclusion of a quarter-percent transaction cost, illustrat-ing the impact that transaction costs can have on results. The more numer-ous the signals, the greater the impact.

Also, as noted in the results, another concern is the maximum draw-down, or the maximum loss between any consecutive signals. But again,as long as it is clear that the indicator is for perspective and not for dictat-ing precise trading actions, indicators with trading signals can provide usefulinput when determining good periods for entering and exiting the marketin question.

ZONE ANALYSIS

In contrast to indicators based on trading signals, indicators based onzone analysis leave little room for doubt about their purpose they donteven have buy and sell signals. Rather, zone analysis recognizes black,white and one or more shades of gray. It quantifies the markets perfor-mance with the indicator in various zones, which can be given such labelsas bullish, bearish or neutral depending upon the markets per an-num performance during all of the periods in each zone. Each period in azone spans from the first time the indicator enters the zone to the nextobservation outside of the zone. Unlike the signal-based approach, the in-dicator can move from a bullish zone to a neutral zone and back to a bull-ish zone. An intervening move into a bearish zone is not required.

Figure 3Summary Results For Indicator In Figure 2 No Transaction Costs

Value Line Geometric $ 574,104 1/24/72 5/30/96Last Profit Number Days Gain Model Buy/HoldSignal Current of Per Per Batting Gain Per Gain Per $10,000"Sell" Trade Trades Trade Trade Average Annum Annum Investment

5/07/96 -2.9% 240 37 1.9% 50% 18.1% 4.8% $574,104

Maximum Drawdown: -4.68%

Summary Results For Indicator In Figure 2 Including Transaction CostsOf A Quarter Percent Per Trade

Value Line Geometric $173,271 1/24/72 5/30/96Last Profit Number Days Gain Model Buy/HoldSignal Current of Per Per Batting Gain Per Gain Per $10,000"Sell" Trade Trades Trade Trade Average Annum Annum Investment

5/07/96 -3.4% 240 37 1.4% 45% 12.4% 4.8% $173,271

Maximum Drawdown: -4.68%

Zone analysis is therefore appealing for its ability to provide usefulperspective without a simulated trading system. The results simply indi-cate how the market has done with the indicator in each zone. But this typeof analysis has land mines of its own. In determining the appropriate lev-els, the most statistically-preferable approach would be to identify the lev-els that would keep the indicator in each zone for roughly an equal amountof time. In many cases, however, the greatest gains and losses will occur inextreme zones visited for a small percentage of time, which can be prob-lematic for several reasons:

1. if the time spent in the zone is less than a year, the per annum gain canpresent an inflated picture of performance;

2. if the small amount of time meant that the indicator made only onesortie into the zone, or even a few, the lack of observations would lendsuspicion to the indicators future reliability;

3. the indicators usefulness must be questioned if its neutral for the vastmajority of time.

A good compromise between optimal hypothetical returns and statisti-cal relevance would be an indicator that spends about 30% of its time inthe high and low zones, like the indicator in Figure 4. For an indicator withmore than four years of data, that would ensure at least a years worth oftime in the high and low zones and would make a deficiency of observa-tions less likely. In effect, the time-in-zone limit prevents excessive opti-mization by excluding zone-level possibilities would look the most im-


pressive based on per annum gain alone.Another consideration is that in some cases, a closer examination of

the zone performance reveals that the bullish-zone gains and bearish-zonelosses occurred with the indicator moving in particular directions. In thosecases, the bullish or bearish messages suggested by the per annum resultswould be misleading for a good portion of the time, as the market mightactually have had a consistent tendency, for example, to fall after theindicators first move into the bullish zone and to rise after its first moveinto the bearish zone.

Figure 4

It can therefore be useful to subdivide the zones into rising-in-zone andfalling-in-zone, which can have the added benefit of making the informa-tion in the neutral zone more useful. This requires definitions for risingand falling. One way to define those terms is through the indicators rateof change. In Figure 5, which applies the approach to the primary stockmarket model used by Ned Davis Research, the indicator is rising in thezone if its higher than it was five weeks ago and falling if its lower.Again, the time spent in the zones and the number of cases are foremostconcerns when using this approach.

Figure 5

Alternatively, rising and falling can be defined using percentagereversals from extremes, in effect using zones and trading signals to con-firm one another. In Figure 6, for example, the CRB Index indicator isrising and on a sell signal once the indicator has risen from a troughwhereas its falling and on a buy signal after the indicator has declinedfrom a peak. Even though the reversal requirements resulted from optimi-

zation, the indicator includes a few poorly-timed signals and would berisky to use on its own. But the signals could be used to provide confirma-tion with the indicator in its bullish or bearish zone, in this case the samezones as those used in Figure 4. For example, in late 1972 and early 1973the indicator would have been rising and in the upper zone, a confirmedbearish message. The indicator would then have peaked and started to loseupside momentum, generating a falling signal and losing the confirma-tion. That signal would not be confirmed until the indicators subsequentdrop into its lower zone.

Figure 6

The charts box shows the negative hypothetical returns with the indi-cator on a sell signal while in the upper zone, and on a buy signal while inthe lower zone. In contrast to the rate-of-change approach to subdividingzones, this method fails to address the market action with the indicator inthe middle zone. But it does illustrate how zone analysis can be used to inconjunction with trade-signal analysis to gauge the strength of an indicatorsmessage.

SUBSEQUENT-PERFORMANCE ANALYSIS

In addition to using signals and zones, results can be quantified by gaug-ing market performance over various periods following a specified condi-tion. In contrast to the trade-signal and zone-based quantification meth-ods, a system based on subsequent performance calculates market perfor-mance after different specified time periods have elapsed. Once the long-est of the time periods passes, the quantification process becomes inactive,remaining dormant until the indicator generates a new signal. In contrast,the other two approaches are always active, calculating market performancewith every data update.

The subsequent-performance approach is thus applicable to indicatorsthat are more useful for providing indications about one side of a market,indicating market advances or market declines. And its especially usefulfor indicators with signals that are most effective for a limited amount oftime, after which they lose their relevance. The results for a good buy-signal indicator are shown in Figure 7, which lists market performanceover several periods following signals produced by a 1.91 ratio of the 10-day advance total to the 10-day decline total.

In its most basic form, the results might list performance over the nextfive trading days, 10 trading days, etc., summarizing those results with theaverage gain for each period. However, the results can be misleading ifseveral other questions are not addressed. First of all, how is the averagedetermined? If the mean and the median are close, as they are in Figure 7,then the mean is an acceptable measure. But if the mean is skewed in one


direction by one or a few extreme observations, then the median is usuallypreferable. In both cases, the more observations the better.

Secondly, whats the benchmark? While the zone approach uses rela-tive performance to quantify results, trade-signal analysis includes a com-parison of per annum gains with the buy-hold statistic. Likewise, the sub-sequent-performance approach can use an all-period gain statistic as abenchmark. In Figure 7, for instance, the average 10-day gain in the DowIndustrials has been 2% following a signal, nearly seven times the 0.3%mean gain for all 10-day periods. This indicates that the market has tendedto perform better than normal following signals. That could not be said ifthe 10-day gain was 0.4% following signals.

Figure 7

Percent Change Of Dow Industrials Following 1.91 Ratio Of 10-dayAdvances To 10-Day Declines

Trading Days LaterSignal 10-DayDate A/D 5 10 22 63 126 252

06/23/47 1.96 -0.1 2.9 5.3 0.3 0.1 3.7

03/29/48 2.05 2.2 3.2 5.8 11.2 4.0 0.6

07/13/49 2.06 1.4 1.9 3.5 7.0 15.2 28.4

11/20/50 2.01 1.5 -1.7 -1.4 10.0 9.8 18.8

01/25/54 2.00 0.5 1.1 0.3 8.3 18.2 36.4

01/24/58 2.00 -0.1 -0.4 -3.1 0.6 10.3 31.4

07/10/62 1.98 -1.4 -2.0 0.9 0.0 14.0 21.5

11/07/62 1.91 2.4 3.5 4.8 10.3 17.3 21.1

01/13/67 1.94 1.4 1.1 2.6 2.9 5.6 6.9

08/31/70 1.91 1.1 -1.8 -0.5 3.9 15.5 17.9

12/03/70 1.95 1.5 1.7 3.6 11.1 14.1 5.0

12/08/71 1.98 1.0 3.5 6.2 10.6 10.4 20.2

01/08/75 1.98 2.8 2.7 12.0 20.9 37.2 41.4

01/06/76 2.05 2.5 6.6 8.3 12.7 11.3 10.9

08/23/82 2.02 0.2 2.6 3.9 14.6 22.6 34.0

10/13/82 2.03 1.9 -0.9 2.4 6.7 13.9 24.6

01/21/85 1.93 1.3 2.3 1.4 0.4 7.6 20.1

01/14/87 2.19 2.9 6.3 7.3 10.7 22.1 -5.4

02/04/91 1.96 4.7 5.8 6.9 6.1 7.8 16.7

01/06/92 1.99 -0.5 1.7 1.8 1.5 4.3 3.4

Median 1.4 2.1 3.6 7.7 12.6 19.4

Mean 3.1 2.0 3.6 7.5 13.1 17.9

Mean All Periods 0.2 0.3 0.7 2.0 4.0 8.1

% Cases Higher 80 75 85 100 100 100

% Cases Higher All Periods 56 58 60 63 67 70

Signals based on 10-day total of NYSE advances over 10-day total of NYSE declines. Concept courtesy of

Dan Sullivan, modified by Ned Davis Research.

A third question is how much risk has there been following a buy-sig-nal system, or reward following a sell-signal system? Using a buy-signalsystem as an example, one way to address the question would be to list thepercentage of cases in which the market was higher over the subsequentperiod, and to then compare that with the percentage of cases in which themarket was higher over any period of the same length. Again using the 10-day span in Figure 7 as an example, the market has been higher after 75%of the signals, yet the market has been up in only 58% of all 10-day peri-ods, supporting the significance of signals. Additional risk information couldbe provided by determining the average drawdown per signal i.e., themean maximum loss from high to low following signals. The mean for the10-day period, for example, was a maximum loss of 0.7% per signal, sug-

gesting that at some point during the 10-day span, a decline of 0.7% couldbe considered normal. The opposite approaches could be used with sell-signal indicators, with the results reflecting the chances for the market tofollow sell signals by rising, and to what extent.

Along with those questions, the potential for double-counting must berecognized. If, for example, a signal is generated in January and a secondsignal is generated in February, the four-month performance following theJanuary signal would be the same as the three-month performance follow-ing the February signal. This raises the question of whether the three-monthreturn reflects the impact of the first signal or the second one. Moreover,such signal clusters give heavier weight to particular periods of marketperformance, making the summary statistics more difficult to interpret. Prob-lems related to double-counting can be reduced or eliminated by adding atime requireme

Documents

MTA Journal-57 - 2002 Winter