MARKET TECHNICIANS ASSOCIATION JOURNAL

Issue 11

May 1981

Published by : Market Technicians Association 70 Pine Street

New York, New York 10005

Copyright 1981 by Market Technicians Association

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Market Technicians Association Journal

Editor:

Anthony W. Tabell Delafield , Harvey, Tabell 909 State Road Princeton, New Jersey 08540

Associate Editor: Shary Anaya Delafield , Harvey , T abell

Thanks to the following MTA members and subscribers for their part in the creation of this issue:

Ned Davis Walter Deemer Walter L. Eckardt , Jr. Harold M. Gartley Steve Lackey , Robert J. Nurock Larry V. Unterbrink Stan Weinstein James M. Yates Martin E. Zweig

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THE MTA JOURNAL

EDITORIAL 9

OPTIONS INDEX INFORMATION AS AN INDICATOR OF SUBSEQUENT STOCK MARKET PERFORMANCE

Walter L. Eckardt, Jr. and James M. Yates

13

Exaggerated claims have been made both attacking and in defense of technical analysis. Efficient market theorists have constructed simplistic models “proving” that technical indicators have no pre- dictive value. Technicians, on the other hand, have taken refuge in the “art-form” argument, often to spare themselves the difficulties of working with rigorous standards of proof.

Both positions are taken to task in this article which illustrates, using, in this instance, option data, that technical efficiency can be demonstrated quantitatively but that such demonstration is far from simple.

OPTION PREMIUM RATIOS AND MEMBER TRADING -- TWO NEW INDICATORS OF INVESTOR SENTIMENT

Robert J. Nurock

Bob Nurock’s visibility as “resident elf” on Wall Street Week perhaps unfortunately overshadows his reputation as a serious technician. He is currently first vice president, market strategy, for Butcher & Singer, having taken over this post after spending over 12 years with Merrill Lynch. During that period, he worked closely with Bob Farrell and later became director of research marketing. He is a long-time member of the MTA.

In this article he covers the use of two market timing indicators, based on options activity, both of which he developed and which represent a unique approach to work with options.

PUT/CALL RATIOS -- THE NEW ODD-LOT SHORT INDEX? Larry V. Unterbrink

29

35

One of the most venerable of all indicators, the Odd-Lot Short Sale Index, may be approaching its demise due to lack of meaningful data. In this article, an amplification of his remarks at the last MTA seminar, Larry Unterbrink argues that put /call statistics can fulfill the same function in measuring public sentiment that the old Odd Lot figures provided.

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Issue No, 11 May, 1951

Mr. Unterbrink is the president of The Consensus of Insiders, a market letter continuously published since 1962. A graduate of John Carroll University, he has been associated with The Consensus of Insiders since 1968 and is a regular commentator on television and radio.

WHEN TO BUY AND WHEN TO SELL. . . Ned Davis and Steve Lackey.

Ned Davis, a speaker at the 1981 MTA seminar, and Steve Lackey discuss, in this article, some of the steps involved in building a stock market model. Although they do not resort to arcane mathe- matical language, they nonetheless conclusively demonstrate that standards of proof and empirical testing are necessary in the build- ing of such a model.

Mr. Davis, formerly a partner and director of technical research of J. C. Bradford & Co., set up his own research company in July, 1980. He has been deeply involved with timing models using a broad spectrum of technical indicators. Steve Lackey has worked extensively in computer programming using stock market data.

SIMPLE YET EFFECTIVE Stan Weinstein

Stan Weinstein is the editor and publisher of The Professional Tape Reader, one of the most widely-read subscription market letters. Stan has been quoted in such publications as BARRON’S, THE WALL STREET JOURNAL, and FINANCIAL WORLD, and has spoken before the NYSSA and twice before our own organization. He has personally developed a number of indicators, including Stage Analysis and the Group Intensity Trading Indicator.

He reminds us in this article that indicators can be relatively simple in concept, exploring the record of another of his inventions, the Last Hour Trading Index.

ENDING CONFUSION ON THE SHORT-TERM TRADING INDEX Martin E. Zweig, Ph.D.

Here, using the Short-term Trading Index as an example, Martin Zweig once again underscores the point that there are no magic numbers and that analysis of indicators is never simple minded. As he points out, in the case of the Arms Index, levels that are bearish for the short term may be bullish for the long term and vice versa.

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41

55

59

Index (Continued)

Mr. Zweig, an MTA member, is editor of the Zweig Forecast, an economic consultant to Avatar Associates, an investment manage- ment firm, and a professor of finance at Iona College. He has published numerous articles on technical subjects in BARRON’S and other publications. He was a speaker at the 1981 MTA seminar.

BREADTH OF THE MARKET TRENDS Harold M. Gartley

67

Harold M. Gartley can be eulogized simply as a man ahead of his time. In 1981, we spend a great deal of time talking about rigorous analysis and empirical proof. alysis and proof in the 1930’s.

Gartley was providing just such an- He was, moreover, conducting in-

depth research at a time when the most sophisticated mechanical aid to such research was an adding machine.

The following article, written in 1937, has been chosen as an ex- ample of the work that Gartley did. It is just one of many examples that can be found in his landmark three-volume course, Profits in the Stock Market, a volume long out of print but available in the MTA library. This short except, in and of itself, provides ample justification for Harold Gartley’s selection as the posthumous recipient of the seventh-annual MTA Aware for Distinguished Contribution to Technical Analysis. IN DEFENSE OF TECHNICAL ANALYSIS

Walter Deemer

75

It is a mark of human frailty that eminently rational people occasionally make totally absurd statements. A notable case is the widely-quoted pronouncement on technical analysis made by the worthy John Train in his recently-published book, THE MONEY MASTERS.

The Train Manifesto is, we think, amply refuted here by Walter Deemer, past President of the Market Technicians Association and a regular contributor to this Journal. Walter was, for many years, senior vice president in charge of technical research at the Putnam Management Company in Boston. He has recently formed his own consulting firm, Deemer Technical Research.

BOOK REVIEW Anthony W. Tabell

PAPER MONEY ‘Adam Smith’ Summit Books

79

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MARKET TECHNICIANS ASSOCIATION

MEMBERSHIP AND SUBSCRIPTION INFORMATION

REGULAR MEMBERSHIP - $50 per year plus $10 one-time application fee.

Receives the Journal, the monthly MTA Newsletter, invitations to all meetings, voting member status and a discount on the Annual Seminar Fee. Eligibility requires that the emphasis of the aplicant’s professional work involve technical analysis.

SUBSCRIBER STATUS - $50 per year plus $10 one-time application fee.

Receives the Journal and the MTA Newsletter, which contains shorter articles on technical analysis, and the subscriber receives special announcements of the MTA meetings open to The New York Society of Security Analysts and/or the public, plus a discount on the Annual Seminar Fee.

ANNUAL SUBSCRIPTION TO THE MTA JOURNAL - $35 per year.

SINGLE ISSUES OF THE MTA JOURNAL (including back issues)

are available for $15 to regular members of subscribers $15 to non-members and non-subscribers

The Market Technicians Association Journal is scheduled to be published three times each fiscal year, in approximately November, February, and May.

An Annual Seminar is held each spring.

Inquires for Regular Membership and Subscriber Status should be directed to:

Fred R. Gruber, V.P. United Jersey Bank 210 Main Street Hackensack, New Jersey 07602

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Editorial

SUPPORT AND SUPPLY FOR MICKEY MOUSE WATCHES

As has become the custom in recent years, this issue of the Journal is being published in celebration of the sixth Annual Market Technicians Association Seminar. As we go to press, the number of registrants for the Seminar is setting new records, and it is to be hoped that the MTA’s initial foray into Florida will produce the most successful seminar to date. Since this is a seminar issue, it is, therefore, fitting that every word in this volume comes from the pen of someone who will be either a speaker or award-recipient at the Sarasota festivity.

The Seminar celebrates 100 years of technical analysis, and important changes in our somewhat volatile profession take place a good deal more often than once a century. We happened to note a possible harbinger of such a major change in going over the list of panels being presented at the latest gathering. Three of the panels, or exactly half, are NOT specifically on the subject of the stock market. One is on bonds, another on commodities, and a third on options. Some of the speakers on the other three panels, moreover, plan, we are told, to speak on non-stock-market subjects as well. For this seminar issue of the Journal, the table of contents contains three out of eight articles on non-stock- market subjects.

The sudden metamorphosis of the MTA seminar into a generalized rather than a specifically-stock-market gathering may come as a surprise even to the re- doubtable Messrs. Lipstadt and Yashewski who did such an excellent job of putting together the program. Intentional or otherwise, however, it is, we think, as noted above, a harbinger. We are dealing, quite plainly, with a world in which the focus of interest in our discipline is likely to be less and less on that market which, historicaIly , has been its major concern. This provokes a few thoughts on just how to deal with this particular change.

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Last year’s seminar theme was, “Back to Basics ,I’ and indeed, we have, this year, a panel with that particular title. Our own sympathy with the “Back-to- Basics” concept is real, but also limited. It is all very well to go on repeating that two plus two equal four, and this undoubted truth will provoke very little in the way of disagreement. However, it seems to us, once a consensus has been established on such basic facts, a thriving discipline must move on, and such a moving-on involves not basics or simplicities but rather complexi- ties.

It is along these lines, we think, that the body of work, long established in the area of the stock market, can be most useful to the non-stock market technician . Much of the technical work which we have seen in non-stock areas (options are a notable exception) is fairly elementary stuff. This is not intended in any way to be a criticism. Two plus two does, indeed, equal four. It is simple recognition of the fact that, over a long period, the most intensive research on technical price behavior was applied to the stock market. There exists, in other words, more in-depth and sophisticated research in the area of equities simply because that research has been carried on longer and by more people. We think, in other words, that our colleagues, now just get- ting their feet wet in technical analysis of such market as commodities and bonds, have a great deal to draw on, in the extensive body of work already created for the stock market.

We think, on the other hand, that many of those attempting to take technical analysis into new areas have an advantage in being encumbered by less ex- cess baggage than has accumulated for years around stock analysis. They possess a particular advantage, it seems to us, in the area of data availability, where they do not find themselves chained to a communications system that was invented by Thomas Edison and which hasn’t changed that much since he invented it. The sort of information routinely available from commodity quota- tion systems, for example, should make any reasonably-intelligent stock technician turn absolutely green with envy. Those of us who have suffered the frustrations and unrealistically exhorbitant costs of available stock market data bases can only marvel at the breadth of information available instantane- ously , on line, and at a reasonable cost on other markets.

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Finally, we suspect that the wave of the future, as far as our own discipline is concerned, is not going to stop at the already-established beachheads of options, fixed-income securities, and commodities. We have witnessed, rightly or wrongly, in recent years an explosion of investment interest in areas ,as diverse as real estate, gold bullion, Louis XIV furniture, and even Mickey Mouse watches. It may be argued that such commodities defy conven- tional technical analysis. It is certainly true that it will be difficult, if not impossible to analyze them in terms of trend channels or 200-day moving averages. However, technical analysis, as we have suggested in this space in the past, ought to be able to transcend this. In its broader sense, after all, technical work is quite simply the disciplined quantitative study of the prices at which transactions take place --- in any sort of market, however organized. Such study has already begun to broaden tremendously and seems highly likely to continue to do so.

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OPTIONS INDEX INFORMATION AS AN INDICATOR OF

SUBSEQUENT STOCK MARKET PERFORMAfiCE

WALTER L. ECKARDT, JR. Southern Illinois University at Edwardsville

Associate Professor of Finance

and

JAMES M. YATES Bridge Data Company

President

Exaggerated claims have been made both attacking and in defense of technical analysis. Efficient market theorists have cons true ted simplistic models “proving” that techni- ca2 indicators have no predictive value. Technicians, on the other hand, have taken refuge in the "art-form" argument, often to spare themselves the difficulties of working with rigorous standards of proof.

Both positions are taken to task in this article which illustrates, using, in this instance, option data, that technical efficiency can be demonstrated quantitatively but that such demonstration is far from simple.

Introduction and Backqround

The profound influence of the existence of large scale, viable options markets on the process of investing today is undeniable. The technical analyst, with his emphasis on market-based information as the raw material from which ad- vantageous investment strategies can be devised, surely regards such informa- tion from the options sector as valuable incremental input. In this paper, we present a preliminary study of the employment of options trading summary information to predict the future course of the stock market.

Our effort consists of three principal components. First, we provide a brief description of the generation and structure of the AMEX Complete Option Indexes. These time series were devised by the first author under the

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FIGURE 1 SERIES SELECTION

Existing GulfOil Options Series

Exercise Expiralion Months Prices

April JU’Y October

50 expiration

45 stock price July 45’s Oct. 45’s

at-the-

40 July 40’s Oct. 40’s, money

35

30

25 v 180 days

-

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sponsorship of the American Stock Exchange in an effort to compute summary measures of market-determined option value and related characteristics. We select five option summary time series (three value indexes and two volatility indexes) for subsequent analysis.

Second, we investigate the predictive content of the five option time series plus 18%day Treasury bill price equivalents and yields and the Standard & Poor’s 500 Index (dividend adjusted). Our data consists of daily information from 1980 (262 observations per series). We search for predictability by means of correlation analysis and the direct evaluation of a technical trading rule. While the correlation study yields less than encouraging results, the trading rule based upon the predictive content of the option time series generates economically significant, “superior, ” risk-adjusted returns.

Finally, we provide a discussion of the apparently disparate positions of tech- nical analysts and EMH (efficient market hypothesis) enthusiasts with regard to the use of market-related information to achieve abnormally large, risk- adjusted returns over time. The methodology and results of the empirical portion of thi.s paper provide a good illustration of the fact that both EMH extremists and technicians who expect decision rules which lack flexibility and timeliness to yield large abnormal average returns support impractical, unten- able positions. We conclude with a short eclectic editorial on the subject of the conceptualization and testing of the predictive content of market-based information.

The AMEX Complete Option Indexes

The investment community has expressed interest in the development of a group of summary measures that describe option values in a meaningful way. Such measures are difficult to devise because option characteristics change with the passage of time and the number of existing distinct option series is a function of the price activity in the underlying stocks. Clearly, some kind of standardization procedure is required.

Under the sponsorship of the American Stock Exchange, the first author (3) devised a group of option value indexes that use market prices to value a (usually hypothetical) standardized option for each (option) stock. Index construction is an extension of concepts developed by Galai (6) and used by the CBOE (1).

While actual index computations are somewhat involved and laborious, the basic idea is straightforward and reasonable. We present a brief intuitive discussion here and refer the reader to Eckardt (2) for the details. At the end of trading each day, a standardized call and put value is computed for each stock on which such (listed) contracts trade. A standardized option has a striking price equal to the closing stock price and has 180 days to maturity. The value of a stan- dardized contract is estimated by a weighted average of the actual market prices of the four series that possess terms most similar to the standardized terms. Figure 1 illustrates the selection process for Gulf Oil on February 15 with stock at 42. The 40 and 45 striking prices bracket the current stock price, while the July and October maturities bracket the 180-day expiration (August 14).

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LAG

1 .09 2 .08 3 -*06 4 -* 12 5 ,13 6 -.03 7 *13* 8 -,03 9 *OS

10 -,03 MEAN l OOl

SE ,010

SAF

NOTES: SAF

TABLE 1 AUTOCORROLELOGRAMS - 1980

TE TEY

-*06 -+Ol -,lO .03 -.06 l 03

.12 * 10 l 74t .12

-.07 .08 -.lO .08 -,06 -.07

* 25t .07 .58* ,19*

l 000 ,001

l 000 * 029

CI

,22* .12 ,02 *09 .19* ,18$ .13* l 09 ,149 412 ,001 ,013

= Standard 8 F’oor’s 500

F’I

-*25* .02 .08

-*09 .Ol *oo

-.Ol -,03

.09 .OY ,000 ,022

CF’I

l 06 *Ol l 03 -.08

-,08 -.09 +18 * 14f

-.Ol ,07 ,09 .04 .03 ,04 .08 -.06

-906 to7 -* 02 .ll

4001 ,001 .015 ,018

Index (dividend TB = 182~dar T-bill ecwivalent Prices TEY = 182-dar T-bill (annualized) rields CI = total call index PI = total put index WI = calls on put stocks indes cv = total call (annual X1 volatilitr PV = total Put (mnual X1 volatility

number of observations = 262 logarithm of first differences used upper value = sample autocorrelation mean = si3mPle maan of 103 first differences

cv F’V

-*04 -*17* - -,08

.ll *03

-,Ol -,oo -.04 - -*Ol

l 02 ,001 * 020

SE = standard error of 103 first differences starred values are $reeter than two standard errors

awa~ from zero

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The resulting standardized option value is divided by the current stock price and the result is multiplied by 100. The output is a standardized premium as a percentage of the current stock price. For example, suppose the standardi- zed percent premium is (4.75/42) X 100 = 11.31. The total call index is simply an unweighted average have listed call options.

qf the standardized percent premium of all stocks that The total put index is a similar average of all stocks

that possess listed put options. The call/put index is a standardized call op- tion percent permium average for all stocks that feature both listed calls and puts. 2

As an auxiliary computation for each option stock, we calculate the volatility that is implied by the market prices of the call options used to compute the standardized call value. The call volatility index is an unweighted average of the implied volatilities of the stocks in the total call index. The put vola- tility index is a similar average of call option implied volatilities for the stocks in the total put index. The procedure used to compute implied volatility is not suitable for direct application to put values, so the corresponding call- based implied volatilities are employed. 3

While other option value and volatility indexes are generated (see (2))) the five measures described above capture the flavor of the computations and will be employed in the investigation that follows.

Predictive Content of Option Indexes

The empirical analysis of this section consists of three parts. First, we per- form an autocorrelation study on the eight time series of interest (i.e. , the five option-related indexes, T-bill price equivalents and yields, and the S & P 500 Index (SAP)). This will indicate (in a restricted sense) the potential of a series to predict itself. Second, we carry out a cross-correlation study between SAP and the other time series. Can we utilize information from the option and/or T-bill series to predict the behavior of SAP? The cross-corre- lation results will provide a qualified indication in this regard. We also present some auxiliary cross-correlation results among the option and yield time series. Third, we describe and test a technical trading rule that employs option in- formation to generate buy/sell decisions for SAP. The results of these tests will shed further light on the existence and nature of the predictive capability of the option information.

Table 1 presents autocorrolelograms for the eight time series for 1980. An autocorrolelogram is simply a list of the correlations between values of a time series and other values of the same time series at specified intervals (lags). For example, lag=1 means that the correlation is computed between an element of a time series and the immediately preceeding element. Lag=2 would compare each element with the element two observations back, and so on. Correlations values range between -1 and +l. For predictive purposes, we wish to detect correlations that are significantly different from zero. Such relationships can then be used in simple (linear) forecasting models to predict the future course of events. The starred entries in Table 1 indicate statistically significant departures from zero.

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LAG THY

-10 .09 -9 .05 -8 ,05 -7 -.ll -6 -.oo -5 -,06 -4 l 04 -3 l Ol -2 -.09 -1 -+19*

0 -*09 1 -* 12 2 -,04 3 *03 4 .03 5 .02 6 -.07 7 -,03 8 -* 02 9 -.08

10 l Ol MEAN ,001

SE ,029

‘TABLE 2 CROSS-CORRELATIONS AGAINST S8P 500

CI

,03 .09

-,07 -,Ol -,08

l 02 .11 l 04 *oo

-.44* -.37* -.09

l 02 *OS

-.09 -*09 -.ll -,02 -,08 -,Ol -.07

,001 ,013

PI

-.04 -*06

,04 -.Ol

*03 -*02

.ll

.02 402 .15*

-.371 ,04 .Ol .05 *15* *05 .02 l 03

-,Ol -,oo

,08 l 000

l 022

CPI

*05 .ll

-,04 -*04 -,07 -*Ol

*15* l 05 ,06

-*30* -.42* -.09

.Ol

.04 -.03 -.04 -.04

+04 -,08 -*Ol -.08

,015 ,010

1980

cv

-,04 .08

-.oo .oo

-+ 05 -* 05

.14*

.05 *lo

-+38t -,34* -*03

.oo

.02 -*09 -.05 -,ll

+Ol -* 05 -* 05 -,08

,001 ,018

NOTES: SAP mean = ,001 SAP SE = ,010

losarithm of first differences used SE = standard error of 103 first differences starred values are greater than two standard

away from zero 13% is defined with respect to SAP (i+e+r a

PV

-* 02 .lO .Ol

-,Ol -+06 -*07

.16* *06 .15*

-,24* -.40* -*04 -*Ol

.04 -.06

+06 -+06

+08 -.04 -.08 -,lO

,001 ,020

errors

negative lasi means that SAP leads the other variable) SAP? THY? CII PII CPII CV? PV - see notes to Table 1 number of observations = 262

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It is important to note that the data used to form Table 1 is not the level of the time series, but the first difference of the logarithm. This complicated- sounding transformation really amounts to a substitution of (approximately) the percentage change in a series for series value. For example, if the first two values of SAP were 107.5 and 109, the “differenced logged” series value would be log (109 - log (107.5))= log (109/107.5) = .014. One observation is lost due to the differencing. We are not interested in predicting the level of SAP. After all, a fairly accurate prediction of future SAP would be current SAP. To make profitable investment decisions, we must predict the return of SAP. Therefore, a qualitative interpretation of the log=1 entry for SAP in Table 1 (. 09) would be that positive changes in SAP tend to be followed im- mediately by positive changes (and negative by negative) on the average, but the tendency is not very strong. For SAP, the only autocorrelation with notable strength is a positive one at log=7. On the whole, SAP offers little hope for a forecasting model based on autocorrelation.

The autocorrolelogram for 182-day T-bill price equivalents (TB) in Table 1 indicates extremely strong autocorrelations at logs 5 to 10. Such information, however, is not economically rewarding because it merely reflects the fact that returns over the (three day) weekend holding period are roughly three times as large as weekday returns. The large returns “line up” regularly and cause the observed autocorrelation pattern. If T-bill yields (TBY) are analyzed, this autocorrelation pattern disappears. We are left with a marginally signifi- cant autocorrelation at log 10 -- not very promising.

The total call index (CI) autocorrolelogram exhibits a substantial positive tendency, with significant values at logs 1, 5, 6, 7, and 9. The other option indexes show little significant autocorrelation. The mean and standard error values in Table 1 indicate the trend and variability of each time series. 4 The conclusion of the autocorrelation study is that autocorrelation-based forecast- ing techniques are likely to perform poorly for all series except CI. However, CI is an index of option value, not a price. The economic usefulness of the autocorrelation relationships observed here will have to be secured through the interaction of CI and SAP.

Table 2 displays cross-corrolelograms for TBY and the five option indexes against SAP. The concept of cross-correlation is similar to autocorrelation, but we must give added consideration to the direction of influence. Cross- correlation is the (linear) relationship between elements of two different time series. For example, the log=-10 cross-correlation of SAP and TBY means that the correlation is computed between an element of TBY and the e

9 ment of

SAP ten observations back. SAP is said to lead TBY by 10 intervals. Since we are trying to predict SAP, we are interested in positive values of log in Table 2. Unfortunately, there is only a single singificant positive log cross- correlation in Table 2, and it is marginal. The positive autocorrelation of CI we found in Table 1 did not manifest itself in an exploitable way in Table 2. If we restrict ourselves to correlation-based prediction, there is little indica- tion of the economic value of the option summary measures in forecasting future market performance.

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LAG CI/CV CI/TBY F’S/PU F'I/TBY CV/TBY F’V/TEY

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1

0 1 2 3 4 5 6 7 8 9

10 MEAN-l

SE-l MEAN-2

SE-2

,136 l ll .03 ,02 .11- ,ll .ll

-*03 -.04

l lO ,80f *l&t .O&

-,04 .OY .08 *O& ,08 ,04 .05 .08 ,001 ,013 ,001 ,018

.02 -,Ol

.Ol l 09 * 13f ,08 .1f5*

-*Ol 408 l 265

.19* *22* l 12 ,07 .02 ,02 .17* ,19* 911 l 12 .18t .OOl ,013 ,001 ,029

-*04 -.04 -* 02

412 -+04

*OS -*O&

*03

-* 02 ,000 l 022

,001 ,020

-.Ol -.02 -.07 -* OS -.03 -* 05

*13* *13* .O& . 02 .04 l 09 .08 ,oo

-to7 -,09 - +08 ,08 + 24* + 22t .21* * 14f

-.31* -+27* * 13* *OS ,05 ,05 - *04 -•04

-+O& -* 02 ,lO +03 * 12 l 09 .02 -*03 l 17* ,13* - l ll *OS l OOl ,001 ,018 ,020 ,001 ,001 ,029 * 029

NOTES: MEAN-l(2) = sample

-+ 05 * 12 .1&t

-604 -,Ol -,08

+O& .Ol

-*OS -+09

,02 *Ol ,04 l ooo l 022

,001 ,029

-.03 .12 I

-.18* ,176

-,07 l 1 l

-*O& -,O& -*03

.08 -+Ol

,oo -*09

,02 -.Ol

,04 -* 05 -,oo -0 05

.07

mean 0 f log differences for first (second) variable

SE-l(Z) = sta-dard error of 103 first differences for first (second) variable

See notes to Tables 1 and 4

TABLE 3 OTHER CROSS-CORRELATIONS - 1980

-

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Table 3 provides cross-corrolelograms for six pairs of option and T-bill series. There is a strong positive contemporaneous (log=O) relationship between CI and total call volatility (CV) and a fairly strong positive “nearly contempor- aneous” (Logs =-1, 0, +l> relationship between CI and TBY. These results are in line with the fact that volatility and the riskless rates are strong deter- minants of option value for at-the-money (stock price = striking price) con- tracts. These relationships did not hold for puts, possibly because of difficul- ties associated with volatility approximations and index construction.

Is the options information useless for the purpose of forecasting changes in SAP? The cross-correlation results are not encouraging. However, corre- lations are informative in indicating the predictive potential of a class of fore- casting models which feature linear combinations of historical observations. This class, often called Box-Jenkins (BJ) models, is certainly rich, but it is not exhaustive. Suppose we believe that large changes in CI convey predictive information about the immediate future course of SAP. We could construct a trading rule based upon this assumption. Implicit in this rule would be a pre- dictive model of SAP based upon CI , but such a model would not, in general, be a member of the B J class.

We have developed a trading rule that is based upon the belief articulated in the last paragraph. This rule was generated for a different purpose and has been used with success in the market place. Cast in the framework of this paper (and the SAP/C1 example above), the decision rule is:

1. Buy SAP when CI increases by at least .8% (buy threshold) in 1 day (buy trigger).

2. Hold for 5 trading days unless

it: SAP decreases by 1.5% or more in any day or return on the trade ever declines 2% or more from its maximum value or

C. SAP declines three consecutive days.

3. Sell and move into 182-day T-bills after 5 trading days unless SAP declines by less than .5% on day 5. In that case, hold until SAP declines .5% or more in a day (postponement return).

Note that CI is used only to identify the buy threshold and the buy trigger. It is not used for the sell decision. Zero trading costs are assumed. 6 Of course, we make no claims of optimality with regard to this rule. ?’ It is, however, a totally specified action definition that can be tested empirically. Table 4 presents the results of such an experiment. A total of six tests were conducted. variable. *

In the first test (SAP/SAP), SA) was used as its own “trigger” The total return achieved over the 364 calendar day period was

28.6%. A simple buy-and-hold strategy would have netted 35.7%. Such a comparison is unfair to our strategy, however, because we were only invested in SAP 66% of the time. If we assumed that SAP has a beta coefficient of 1, the Capital Asset Pricing Model (CAPM) would assign a theoretical risk premium of (35.7 - 12.7) x 1 x .66 - 15.1%, where the riskless return was 12.7%. We could define a performance difference as the actual risk premium earned less

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TABLE 4 TRADING STRATEGY SUMMARY .- 1980

SAF/SAF SAP/C1 SAF/FI SAF/CFI SAF/CU SAF/FU

Total return (X) 28.6 34,9 39,3 50.3 52,2 29.5 Theoretical risk. premium (%I 15.1 14.9 13.2 14.8 16,8 15.8 Ferformi3nce difference (X1 0.7 7.3 13.4 22.8 22.7 1.4 Number of rurchases 26 27 27 30 33 34 Averade (calendar) dars held 9 ,2 8.7 7.7 7.8 8.1 7.4 X of time invested 66 65 57 65 73 69 Profitable trades (il) 57.7 51,9 59.3 56.7 63.6 155.5

NOTES : bur tri2der = 1 dar normal holding period = 5 trading dass bur threshold = 0,8X ~ost~onement return = -0.5X

total calendar dags = 364 total tradins dars = 262 SAP buu-and-hold return = 35.7% dars SAP UP = 56.0% riskless return = 12.7% SAFP CII FII CFIr CUV FV - see notes to Table 1 SAP/C1 = SAP performance with action sitinals

provided br CI

-22-

the theoretical risk premium. A positive perfromance difference indicates a successful risk-ad justed outcome, while a negative value indicates an unsuc- cessful outcome. For example, performance difference = (28.6 - 12.7) - 15.1= 0.7. The strategy SAP/SAP was, therefore, marginally successful even though its return was below buy-and-hold. Table 5 indicates the trade detail for the SAP /SAP test. The SAP/C1 test used CI to trade SAP. The overall return was 34.9% and the performance difference increased to ‘7.3%. Table 4 displays similar results for the trading strategy based on the other option indexes. The best perfromance difference (22.8%) was generated by the call/put index CPI) , with total call volatility (CV) (22.7%) a close second. All five option indexes outperformed SAP /SAP (which outperformed buy-and-hold) .

Our empirical investigation has generated two potentially contradictory sets of inferences. The correlation analysis save little hope of the predictive value of the option summary measures, while the trading strategy (the only one tested) indicated consistent positive economic value. We pointed out that these out- comes are not mathematically inconsistent. On the contrary, each group of conclusions is subject to its own set of limitations. The correlation computa- tions make statements about the potential value of a subset of forecasting models. Models that do not belong in this class may possibly be employed successfully with the same data. The trading results are subject to objections regarding verifiability over extended time periods and mechanical and institu- tional difficulties in executing the required transactions. Another common complaint associated with trading strategies (though not applicable in this study) is that, if enough strategies are attempted, a stellar performer will emerge by sheer chance. Put another way,

If you torture the data long enough, they confess -- even to crimes that were never committed. 9

The nature of our conclusions in this paper highlight the basic conflict be- tween technicians and believers in so-called efficient markets. We take up the subject briefly in the next section.

Technical Analysis and the Efficient Market Hypothesis

The emergence of the CAPM and the wave of associated empirical work has led some to conclude that the normative notion of an efficient market carries over into the real world essentially intact. A behavioral consequence of such an idea with respect to the economic viability of the technical analyst is devastat- ing -- he is useless. There are several versions of the EMH to be found in most current investments textbooks. Even the weakest version, where strength is defined in terms of how efficient the real market actually is, dooms the technician . Textbook authors usually cite a number of empirical efforts which claim to “disinfer” many of the technicians’ favorite predictive devices. Often, there is a single (brief) chapter devoted to technical methods, and one g&s the impression that this fare is offered only out of some regard for the evolution of modern investment theory or tradition.

-23-

DATE IN DATE OUT RETURN T-TIAY S C-DAYS

800104 800123 800201 800220 800229 800311 800318 800328 800408 800433 && 800505 800513 800530 800625 800703 800717 800729 800807 800821 800902 800930 801013 801033 8010;; 801111 801212

800122 800131 800211 800225 800305 800314 800324 800407 800415 800501 800509 800529 800619 800630 800715 800724 800801 800818 800827 800925 801010 801021 801023 801106 801121 801229

4.97 1 ,27 1.90

-2.62 -2.14 -2.13 -4,53 -0.32

1 "3 2:;2

-1.50 4.00 3.39

-2.05 1.74 0.39

-0.93 0.24

-1.46 4.51 4.01

-0.02 -2.5 4

1.22 6.15 4.77

12 18 6 8 6 10 3 5 3 5 3 3 4 6 5 10 5 7 7 9 4 4

11 16 14 20

3 5 7 12 5 7 3 3 7 11 4 6

17 23 8 10 6 8 1 1 3 6 8 10

10 17

TABLE 5 TRADING STRATEGY - TRADE DETAIL

S8F' 500 INDEX (SAF’/SAF’)

NOTES : dates in YYMilDD format return in 5: T-dars = riumber of trading dass Position held C-da,rs = rtumber of calendar daus Position held

-24-

Some technicians counter with protestations that their efforts have been unjustly mailgned. Their craft, they say, is, depsite all the mathematical computations, an art from. This reasoning is supposed to render meaning- less the result of careful empirical work that indicates the lack of predictive contents of such popular technical indicators as the advance-decline line and the short interest ratio:

We feel that both of the foregoing positions are unsupportable. Much of the EMH empirical work in the technical area has dealt with narrow applications of simple, inflexible rules. Such efforts are doomed to failure by design. Much of the highly-regarded literature (e.g. , Fama, Fisher, Jensen and Roll (5)) employs monthly observation intervals and deals principally with the common stocks of the largest firms. 10 The sparseness of the data al- most surely masks any opportunity for timely action and the highly visible, actively traded security sample probably offers the smallest possible oppor- tunity for exploitation. We are not criticizing the methodology of the (better) studies. We do question the extrapolation of these results by EMH advocates, who describe a world where any predictive activity based upon historical market-based information is a pointless economic waste. In fact, such a description offends common sense.

With regard to the technicians ’ “it’s an art” refrain, any procedure for making investment decisions is quantifiable by definition. Such a defini- tion may be quite complex and involve a great many variables, but that does not excuse its proponents from the obligation of unambiguous specifi- cation. Intuition suggests that successful technical trading rules almost certainly possess the characteristics of flexibility and timeliness. Because different market participants have varying degrees of access to information and different sets of restrictions with regard to trading activity, a degree of “technical exploitability” must surely exist at some levels. However, the technician is obliged to algorithmize (i. e . , completely structurally describe) his activity if he is to refute the claims of the EMH purists. Such algorithms must be subjected to rigorous statistical scrutiny.

The resolution of the EMA/technical analysis conflict must be effected on the field of statistical inference. This presents at least one additional problem. In general, as technical trading algorithms become increasingly complex, more data is required to perform historical validity tests. It is not difficult to specify rules that, given the available data, cannot be evaluated statis- tically with satisfactory confidence. In these cases, judgment must be suspended until sufficient data are available for testing. This may never occur. It is at this point that the technician and the EMH proponent can each claim metaphysical support. However, we feel that much rigorous work lies between the current state of empirical investigation and the region of unresolvability . We hope to participate in this important area of investigation.

-25-

Footnotes

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

We exclude some stocks in the actual computations for technical reasons such as impending delisting or merger.

This list was fixed at 25 until May, 1980. Shortly, all option stocks will have both puts and calls and this index will coincide with the total call index.

The Black-&holes theoretical call model is the basis for the implied volatility calculations. The lack of a simple, closed- form expression for puts is the large reason why the call-based implied volatilities are used. We hope to remedy this situation in the future because information is lost when we ignore the market prices of puts in this way.

Remember, the series have been transformed to differenced logarithms (or approximate percentage changes).

A negative log is a lead.

Such a strategy could be approximated by mutual fund telephone exchange. Trading costs could be introduced in a straightforward manner for other applications.

The measure of the relative worth of a trading rule is an important , interesting topic in its own right. Elsewhere (4)) we have proposed performance measures based upon realizable (rather than expected) returns.

This test is the autocorrelation analog of the correlation computations.

Attributed to Bulent Gultekin.

We cite this study despite its age because of its acknowledged importance in the literature and its meticulous experimental design.

-26-

References

1. CBOE Staff, “The CBOE Call Options Index: Methodology and Technical Considerations. ” March, 1979.

2. Eckardt , W. “The AMEX Option Index Group.” American Stock Exchange, 1980.

3. Eckardt, W. and Williams, S. “The AMEX Complete Option Indexes. ” Working Paper, American Stock Exchange - Southern Illinois University at Edwardsville, 1980.

4. Eckardt , W. and Yates, J. “Portfolio Performance Measures Based Upon Realizability and Normative Considerations. ” Presented at the Eastern Finance Association Meetings, Newport, R.I., April, 1981.

5. Fama, E., Fisher ,L., Jensen, M., and Roll, R. “The Adjustment of Stock Prices to New Information.” International Economic Review, 10 (February, 1969)) 1-21.

6. Galai, D. “A Proposal for Indexes for Traded CaB Options.” Journal of Finance, 34 (December, 19791, 1157-72.

-27-

intentionally blank

-2%

OPTION PREMIUM RATIOS AND MEMBER TRADING

TWO NEW INDICATORS OF INVESTOR SENTUlENT

ROBERT J. NUROCK First Vice President

Market Strategy Butcher & Singer

Bob Nurock’s visibility as “resident elf” on Wall Street Week perhaps unfortunately overshadows his reputa- tionas a serious technician. He is currently first vice president, market strategy, for Butcher & Singer, having taken over this post after spending over 12 years with MerrilZ Lynch. During that period, he worked closely with Bob Farrell and later became director of research marketing. He is a long-time member of the MTA.

In this article he covers the use of two market timing indicators, based on options activity, both of which he developed and which represent a unique approach to work with options.

Over the past few years, a sharp downward bias in net-purchase-minus-net- sales data indicates that there has been a significant shift in the trading activity of NYSE members. In fact, this data reveals that, on balance, NYSE members have been substantial net sellers of equities since late 1977/ early 1978.

When they perceived this trend, most analysts interpreted it as being bear- ish. We did not agree. As we saw it, three sorts of pressure, the advent of dealer markets, partially as a result of the elimination of fixed commissions; sharply increased trading volatility, adding to the risk of carrying inventor- ies ; and high interest rates, increasing the cost of carrying those inventor- ies -- were forcing members to seek ways to hedge their risks rather than expose expensive capital to the possibility of loss.

Following extensive study of this subject, in late 1979 we came to the con- clusion that an expanding and liquid market for options on a large number of heavily traded, big-capitalization stocks had enabled NYSE members to

-29

,

hedge their purchase /sale transactions, especially those in which they were principals in block trades. Some months later, in June, 1980, we introduced MOPSI, a member option purchase/sale index,we had constructed using weekly member trading data made available by the Options Clearing Corpora- tion . Subsequently , we developed a second indicator of investor sentiment which tracks the ratio of average premiums on put options to the average premiums on call options.

The Yhistorical” data available at the time we undertook this research pro- ject was very limited, dating back only to August, 1979. Recently, the Options Clearing Corporation has compiled and released sufficient back data to enable us to construct these two proprietary indicators on a weekly basis from June, 1977 (when listed put option trading began) to the present.

In constructing MOPSI, our member option purchaselsale index, we add the weekly total of member purchases of calls and sales of puts and from this sum subtract the total of member sales of calls and purchases of puts. The result of this calculation -- a positive or negaitve index number -- is record- ed as a l-week reading and also as smoothed into a 4-week moving average.

The thesis underlying MOPS1 is our belief that, when members purchase calls and /or sell puts in hedge transactions, it is usually to offset blocks of stock they have sold (or sold short) as principals to institutions or other large purchasers. When members sell calls and/or buy puts, it is usually to hedge transactions in which they have purchased stocks as principals from institu- tions or other large sellers.

Since institutions represent the largest potential source of demand and supply in the marketplace when they act in concert, their aggregate pur- chases or sales of securities tend to reflect overoptimism or overpessimism which often correspond with intermediate tops or bottoms in the stock market. When the institutions turn more bullish and increase their purchases, members, in their block trading function, are forced to sell (or sell short) securities to meet this additional demand. They are most likely to hedge these transactions by buying call or selling put options -- which results in a high reading on MOPSI. When institutions turn more bearish and increase their selling, this forces members to expand their inventories. They are most likely to hedge these transactions by selling calls or buying puts -- which results in a low reading on MOPSI.

In brief, O High readings on MOPS1 tend to reflect aggregate

institutional enthusiasm for equities.

O Low readings on MOPS1 tend to reflect aggregate institutional pessimism.

O As institutional enthusiasm or pessimism wanes, this tends to be reflected in MOPS1 as a gradual change of direction in the 4-week moving average of its most recent readings.

- 30-

Now, having reviewed the record of MOPS1 since June, 1977, we are convinced of the validity of our basic thesis: that member option activity provides technical insight into professional investor sentiment toward equities. Moreover, we have found the following decision rule to be particularly helpful in identifying a shift from pessimism to optimism on the part of institutions as the market bottoms:

When the 4-week moving average of MOPS1 is negative and the l-week reading reverses from below -15 upward through the moving average, this index gives a reliable, timely buy signal.

This is evident in Table 1, where it will be noted that MOPS1 has flashed eight buy signals since January, 1978. Of these, only the first two were off by more than 25 Dow points and came more than a week before the DJIA recorded an actual weekly closing low. The sixth signal, which came on March 21, 1980, coincided with a market low reached that week, but preceded a secondary test of that low by four weeks.

While the foregoing decision rule is applicable at market bottoms, we have thus far been unable to formulate a similar rule which reliably identified market tops. This is probably because more recent MOPS1 readings reflect a substantial increase in volatility relative to that which occurred earlier in the period for which we have history. Therefore, until we are able to analyze MOPS1 in more detail, we intend to use it only when we believe it indicates that a market bottom is being formed.*

Our second indicator, the ratio of average premiums on puts relative to calls, now includes historical data dating back to June, 1977. As will be noted in Chart 1, this indicator supports our thesis that, as investors turn pessimistic, puts are bid up to a level where the average premium is in excess of the average premium on calls, reaching extremes well in excess of 100% at market bottoms.

In using this indicator to identify a market bottom, we look for a ratio to move to an extreme above lOO%, react from that extreme, and then retest it, forming a double top. A reversal downward from such a double top formation is interpreted as a buy signal. As can be seen in Chart 1, downward reversals of this type occurred in February/March, 1978, NoV- ember, 1978, October/November, 1979, March/April, 1980, and most recently, in February, 1981, all significant market low points.

As for the standard put /call ratio -- which reflects volume alone -- it is true that activity in puts relative to calls also increases as investors turn bearish. However, at present, we do not attach too much significance to

*Using our original research on MOPS1 as a basis, Arthur A. Merrill (Merrill Analysis, Inc. , Box 228, Chappaqua, NY 10514) has developed an option indicator for member firm activity reported weekly in his service, Technical Trends. Mr. MerriZl has graciously agreed to test some of the observations we made during our study of recently-released back data as soon as he is able to feed it into his computer.

- 31-

Table 1

Week DJIA MOPS1 READINGS CLOSING LOW Ended Close l-Week 4-Week Avg Week Ended DJIA

l/13/78 l/20/78

5/26,‘78 612178 6/g/78

6123178 6/30/78

10/27/78 11/3/78 11/10/78

10/12/79 10/19/79 10/26/79

317180 3114180 3/21/80

10/31/80 11/7/80

12/12/80 12/19/80

776.94

859.23

818.95

807.09

809.30

785.15

932.42

937.20

-26 - 9 +lO - 5

-18 +l - 5 +14 ::

-27 -10 - 2 - 9

-18 +2 - 7 - 4 +23 - 4

-53 +3 -11 - 6 - 3 -12

-57 -24 -45 -43 - 8 -40

-43 +7 +13 - 4

-61 -21 +23 -32

313178 747.31

717178 812.46

7/7/78 812.46

11/17/78 797.73

10/26/79 809.30

4/ 18180 763.40

10/31/80 924.49

12/12/80 917.15

-32-

Ill

101

101

91

91

I!

61

I!

71

CHART 1

RATIO OF AVERAGE PREMIUMS ON PUTS RELATIVE TO CALLS (Inverted)

--.-

-.-

--

ifi, /

2 ~-..--- -.

I I III III II III III-

I960 l99l

% -50

im

-150

these ratios since the large number of puts recently admitted to trading has created a discontinuity which necessitates arbitrary adjustments. In opting for the average premium ratio, we have been influenced by the fact that, of the three factors which affect premiums -- interest rates, volatility, and speculative interest -- the first two offset each other in both the numerator

. and denominator of the ratio ; thus the end result is an excellent guide to investor psychology regardless of the character or volume of the options traded.

- 34-

PUT/CALL RATIOS -- THE NEW ODD-LOT-SHORT INDEX?

LARRY V. UNTERBRINK President

The Consensus of Insiders

One of the most venerable of all indicators, the Odd-Lot Short Sale Index, may be approaching its demise due to lack of meaningful data. In this article, an amphfication of his remarks at the last MTA seminar, Larry Unterbrink argues that put/call statistics can fulfill the same func- tion in measuring public sentiment that the old Odd Lot figures provided.

Mr. Unterbrink is the president of The Consensus of Insiders, a market letter continuously published since 1962. A graduate of John Carroll University, he has been associated with The Consensus of Insiders since 1968 and is a regular commentator on television and radio.

Since 1962 our organization has worked extensively with statistics on the action of the specialists, traders, and others who contribute to the specializ- ing function on the exchanges. We have paid particular attention to the shorting activities of those on the floor of the exchange. Since statistics on these groups are released with a two-week time delay, we needed a method of “anticipating’* what these figures would be in advance. Having long used the “daily” odd-lot short figures as another timing tool, we found we were able to “dead reckon” or predict the Specialists and Public Short figures from the “odd-lot-short” statistics available daily. Odd Lot Shorts thus became a twofold tool for our research in the 1960’s and much of the 1970’s. First, they gave us advance estimates of the Specialist-shorting statistics; and second, at extreme levels, they provided a clue to major turning points in the market. The accuracy of using odd-lot shorts, especially as a “market bottom” indicator, has been widely cited. Market bottoms were called with precision in 1962, 1966, and 1970 when the figures were reported daily

-35-

by the odd lot firm of Carlisle, DeCoppet & Co. In October, 1966, over 57,000 shares were shorted in one day. In the summer of 1970 some 40,000 shares were shorted in one day. Kison g: Niederhoffer in a BARRON’S article in 1969 documented the value of using odd-lot shorts as a predictive tool, and of course Garfield A. Drew made a career of working with odd-lot statistics.

Since February, 1976 the NYSE took over the odd-lot market-making function, and a number of brokerage firms started to do their “odd-lot” trades “in house. ‘I This dual phenomenon complicated the use of the statistics. First, the actual number of shares reported shrunk to such small levels as perhaps not to be representative, and, second, in 1977, the Option Exchanges started trading in Puts in addition to the Calls made available in 1973. With the Put- and-Call market of fungible options available, the typical odd-lot short seller found a new “casino” to play in. Odd-lot short figures often slid to under 1000 per day which could have represented as few as 20 individuals. With logic dictating a possible problem with “odd-lot shorts” as an indicator, and with its partial failure in 1973-74, we decided to abandon this sentiment indicator in favor of a new one that measured the same segment of the market by other means.

In the early 1960’s, my partner, Perry F . Wysong , did considerable analysis of the puts and calls available in the over-the-counter put-and-calI market. He designed an early put-and-call indicator. For lack of a computer and an accurate data base, the project was shelved. As far back as 1970, Marty Zweig developed an Option Activity Ratio (OAR) from SEC data on option trading from 1945 to 1968. In 1971, he published his version of the Puts/Calls Ratio. Today there are as many technical indicators based on put-and-call activity as there are services. In addition to Zweig’s, Stan Weinstein has a Call/Put Ratio, Market Logic has a Put /Call Ratio, Bob Nurock, the “elf” of WALL STREET WEEK, and Market Startegist for Butcher & Singer, uses MOPSI, a Member Option Purchase /Sell Index, plus a ratio of Average Premiums on Puts relative to Calls. Merrill Lynch publishes Put /Call Ratio and an Open Buy Call percentage ratio; Value Line tracks the trend of option premiums levels. New indicators come on line each month.

In addition to the different ways of comparing Put-and-Call data, market technicians have been using different methods of regression analysis and exponential smoothing on the resultant data. One major problem in this area is that any effort to smooth or average the data tends to make the indicator coincident or lagging, rather than predictive.

We see the Put-Call Ratio as a substitute for the Odd-Lot-Short theory. Our premise is the public is almost always WRONG at EXTREMES in the market. Actually, we think, at extremes the most emotional part of the public at large participates. Odd-lot short traders are usually correct during most of the market cycle. Only when they act out of extreme fear and greed do we use them as a contrary indicator. The same holds true for the public put- and-call speculator. In connection with this, I have done some personal field research and have come to the conclusion many of the public speculators in both odd lots and in puts and calls are actually stockbrokers or retail

-36-

registered representatives. It is an open question whether this observation should reinforce one’s view of the P/C Ratio as a valid contrary indicator. In the past, the ranks odd-lot short sellers also included many so-called professional traders. Ordinarily an “uptick” was needed to short a stock in a round lot. This rule did not apply on trades made through odd-lot dealers, so, in rapidly falling markets, professional traders reputedly would enter a series of 99-share “short” orders to establish their short position. These “bears” now also have the liquid put market to gamble in.

We think we can say with confidence the odd-lot short seller of yesterday is the put-and-call speculator of today. Measurement of this group’s actions is then our goal. There were some procedural problems right from the beg- inning . We wanted to calculate weekIy figures but have the freedom to sam- ple daily figures in turbulent markets or on days when large moves took place in the averages. We surmised that the best method to gauge sentiment would be to measure PUTS versus CALLS as we had done in our original research on the OTC Options Market in the early 1960’s. In the beginning, all calculations were done by hand and were subject to human error and, more than occasionally, bad data was presented in THE WALL STREET JOURNAL and in BARRON’S. We, therefore, needed a system that would diminish the impact of an erroneous caIcuIation or bad original data. We also needed a system that would tend to give more impact to the trades of speculators and the public and less to those trades initiated by institutions or by those on the floor of the option exchanges. Our solution was to VALUE WEIGHT the option transactions. By that I mean each day we simply take the number of put options traded on each strike for each expiration date on a particular stock and multiply this number by the day’s closing price of the option. The result is the total dollars changing hands :n puts for that stock for the day. We do the same thing for all calls traded. In this manner we then have calcu- lated the total dollars changing hands in PUTS for each stock and the total dollars changing hands in CALLS for each stock’s strike and expiration month. \Ve then divide the total dollars in PUTS by the total dollars in CALLS to obtain WYSONGS VALUE-WEIGHTED PUT-TO-CALL RATIO for each option stock.

We repeat the procedure for all the 15 original stocks on which both puts and calls were available, obtaining a P/C Ratio for each stock. Now remem- ber, we are looking for an overall P/C Ratio that will be indicative of un- sophisticated action in the market. To cure problems with the occasionally bad or erroneous data, we attempted to gather the data we needed via a computer phone patch, but initial demonstrations resulted in “glitches,” that produced erroneous calculations. Even the daily and weekly financial papers have mistakes as to price or volume that would cause too large an error in final tabulations. We did find, however, if we manually inputted the data with trained personnel, using BARRON’S weekly figures, we would be able to spot most obvious errors in the data base. -4 quick check of the daily totals from the previous five days’ WALL STREET JOURNAL’s would usually clear up any questions.

-37-

Manual input can produce error problems of its own. A compromise we use to solve this dilemma is to cast out a specific number of “outliers” in the data on the 25 stocks and then work up an average P/C on the remaining issues. This not only eliminates most bad data and calculating mistakes but tends to eliminate or de-emphasize large trades by professionals or institu- tions. Remember, we are trying to measure the action of the “public” or speculator in Puts and Calls. Most often when an individual stock’s P/C rating deviates markedly from the mean of the other stocks for a specific week, this difference is due to some heavy institutional trading representing not speculation but arbitage or hedging to offset risks the block trading desks of brokerage firms have taken. It has been confirmed to us that most of the institutional volume is in options trading at very low dollar prices. Those P/C ratios or C /P ratios that are not value or dollar weighted tend to overweight this institutional segment of the market if they rely on volume of puts and calls only. Perhaps those other put and call measures are not looking for a proxy for the old odd-lot shorter, or they don’t think there is a difference between the “public” and the institutional sentiment measures. Our goal, however, is to measure the “public.” It also takes only 1 /lOth the time to figure the ratios if you don’t value-weight, and that may explain why this method is not widely used.

WYSONG’S VALUE WEIGHTED PUT -TO - CALL RATIO dUL I AU6 I . SEP I OCT I NOV I DEC I JAN 1 FEB

I February 16, 1979 920

- 900

080

)OW JONES 860

IO INDUSTRIAL

40 ..-

63 --

00 z- PUT-TO - CALL

RAT IO

loo-: _--- _.

INVEH~EO L@G4RITHHI: SCALE

I50 -- ---~ ._. _ __

200 - .-__- _ .

We have calculated Wysong’s Value-Weighted Put-to-Call Ratio since June of 1977, or weekly for almost 200 weeks. With the expansion of puts since that time, we have spot checked other stocks’ put-and-call ratios each week and find they correlate with our original calculations on the smaller population. The chart shows how we plot the weekly P/C Ratio along with the Dow-Jones for reference. Note we have the P/C plotted semi-logarithmically and in- vert ed . Notice the correlation with the Dow-Jones.

To those who are used to working with various technical indicators, this is neither surprising nor unusual. Most indicators are coincident or slightly lagging to the market. All technical analysts, of course, search for the “Holy Grail” of indicators. . .that which is PREDICTIVE rather than coinci- dent or lagging in time. We think the Value Weighted P/C Ratio, while coincident through much of the market cycle, is PREDICTIVE when it registers EXTREME readings. In this way, it is similar to the old odd-lot short indicators. The problem is to determine what constitutes an EXTREME reading on an indicator that has a total life history of only 46 months. We now believe an extreme would be a two-week average reading of over 175 which is a bear signal. A 175 reading means that $1.75 is changing hands in Puts for every $1.00 in CALLS. These readings are only for stocks on which both PUTS and CALLS are available.

Once we had the indicator empirically monitored and some broad parameters established for extremes, we detected another small problem that could af- fect our results. Suppose you are using only extreme readings on an indi- cator and you are taking the readings once a week. What if an extreme move in the market that is likely to produce these extremes occurs over a five- day period that is not all in one week? Say it comes on a Thursday, Friday and the following Monday, Tuesday, Wednesday? Wiil you then get two weeks of relatively radical readings but not an EXTREME that would fulfill your parameters. 3 To solve that problem, we propose the use of a two-week average of the final overaIl P/C to be used as a parameter to trigger a buy or sell EXCEPT when the market is moving sideways. In inactive markets, just one week’s extreme reading could be a signal. An extreme move in the market usually whips the public speculator into a frenzy and accentuates extreme readings. If we get an extreme reading during a lackluster market, the public is surely emotional despite the market action, and thus, we would not need a two-week average reading for a signal. Once a signal is given, it stays in effect until an opposite signal is established.

Using these criteria, the following are the BUY and SELL dates on our P/C signal along with the Thursday evening Dow-Jones close on the date of publication of the reading.

-39-

DATE

7/01/77 916 l/27/78 ‘163 B/18/78 900

11/03/78 817 3/30/79 867

10/26/79 805 11/28/80 990

DOW JONE!; p/c

11 178

13 208

22 12

2 WEEK - p/c

AVERAGE

12 176

11 192

22 228

13

SIGNAL % GAIN

SELL BUY 16.7 SELL 18.0 BUY 9.2 SELL 6.1 BUY 7.2 SELL 23.0

13.4 -Average Going long and shorting the equivalent of the DJIA would have yielded a total return of 111% and an annualized return of over 25%. Just buying on the past 1977 BUY signals and selling out on the SELL signals and going to CASH in the interim would have yielded 53% over the entire period as opposed to just holding the Dow yielding 29%. Finally, it should be pointed- out the 3/30/79 sell signal parameters were empirically established, after the fact, as explained in the exception rule described above. Elminiatin g that SELL, you were invested a little over a year longer, and then received another BUY at Dow 805 which still proved very profitable. In other words, eliminat- ing the 3/30/79 sell would have cost you only about 6% of the total gain of 53% from investing long. All the above do not include dividends, interest, commissions, or earnings on cash balances. Applying this market timing to puts and calls, mutual funds, or high-beta portfolios would obviously out- perform our paper use of the DJIA.

In conclusion, we think we now have a fairly reliable indicator to replace the old odd-lot short indicator. Obviously, it may not predict every major turn in the market. We do feel that, when we get a penetration of our extreme limits, the Wysong Value Weighted Put-to-Call Ratio WILL signal a move averaging at least 10% will follow shortly. When we get a buy or sell signal from the P/C, the move can be delayed, but we think it will not be fore- stalled.

We should caution you that our actual published timing in our weekly letter has not been as accurate as this empirical study, mainly because we did not have any historical data on which to base our parameters, and thus, had to make guestimates based on our old OTC P/C work from the early 1960’s. As we add data, we would expect our accuracy to improve.

We also don’t want to be dogmatic about the indicator and don’t claim it is the only ANSWER. Actually, we mean it to be the QUESTION. We hope it will stimulate both old and new technicians to do further research. There is plenty of room for improvement. There are at least 15 ways we have tried to massage the P/C figures alone, and there are at least 10 additional expectational or sentiment indicators that can be looked at.

Perhaps the best caveat to those who think they have an easy solution to market timing are the words of H. L. Mencken, “For every complicated problem there is a solution that is neat, simple, and WRONG.”

-4o-

WHEN TO BUY AND WHEN TO SELL, , ,

NED DAVIS Ned Davis Research

and

STEVE LACKEY Ned Davis Research

Ned Davis, a speaker at the 1981 MTA seminar, and Steve Lackey discuss, in this article, some of the steps involved in building a stock market model. Although they do not resort to arcane mathematical language, they nonetheless conclusively demonstrate that standards of proof and em- pirical testing are necessary in the building of such a model.

Mr. Davis, formerly a partner and director of technical research of J. C. Bradford & Co., set up his own research company in July, 1980. He has been deeply involved with timing models using a broad spectrum of technical indicators. Steve Lackey has worked extensively in computer program- ming using stock market data.

Timing the purchase and sale of investments can yield substantially better returns than a simple Buy-and-Hold Strategy. Even for long-term investors, choosing an advantageous time to buy or sell can net welcome premiums and help avoid commitment to securities whose time has not yet come.

If one knew with certainty that a market top or bottom was at hand, and that the move would be broad and substantial, and that the end of the move would be as obvious as its beginning, and that. . .

-41-

Study the table to the right. It shows how well one would have done buying and sell- ing the 30 Dow stocks using a “perfect” timing system to flash trading signals, com- pared with a strategy of buying and holding those same stocks.

3Al-E

12/06/74 577.60 09/24/76 1009.31 03/03/78 747.31 09/08/78 907.74 ll/li/78 797.73 10/05/79 897.61 n/09/79 806.48 02/08/80 895.73 04/18/80 763.40

03/27/81 334.78

IlYLIo VAI

ALL. 15% .Lm

BW/ HOLD

$10,000

$17,223

ALL 108 .PKJvEs

$10,000 17,474 22,010 26,735 29,975 33,728 37,153 41,264 47,360

$61,715

33.4;

PcrIm

BUY SHORT BUY SIQRT BUY SHORT BUY smm BUY

(OPW

$10,000 17,414 22,010

$29,299

$10,000 17,474 22,010 26,735

30,986

$40,378

9.0% 18.6% 24.E$

THERE IS NO “PERFECT” TIMING SYSTEM, BUT A “GOOD” MODEL WILL EXTRACT A HIGH PROPORTION OF EACH MAJOR MOVE IN A MARKET.

Week/v Data g/27/74 - 3/‘27/81

Dow Jones Industrial Average

981

935

707

570

Many Investors ‘SOLD EVERYTHING’

\

Watch Out For Whip-saws

This Looked Like

R Good Tfme

To Get Out I 1 Buy and Holders

-Held the Baa-

For Two Year-s I

R Good Buying Point For any Investor When to Buy and when to Se ? I’

fire things no Crystal Ball can tell.

But proper facts with proper weight

Wi II help your gains accumulate.

1975 1976 1977 1978 1979 1980 1981

-42-

Basic Model Building

An Investment Timing Model is a tool which shows when to buy and sell a security, commodity, or market. An investment should be made when the model changes from bearish to bullish, and should be liquidated when the model changes from bullish to bearish.

.-

Four Basic Steps In Building A Timing Model

1. Gather raw source information which is correlated with the In- vestment being modeled.

2. Decide on a set of rules for deriving model values from the source information.

3. Apply the rules to the source information to generate model values.

4. Interpret the model’s value to determine if a buy or sell signal has occurred.

BUII.DItGAB?SIC~~ ---

A simple model might be one which is bullish when the Rate of Inflation is falling and bearish when the Rate of Inflation is rising. The month-to-month history of such a model can be built from information readily available in most newspapers. Other models might require extensive data manipulations and calculations which can be done only with the help of a computer. Some models are derived from summing the values of other models, yielding a sort of com- posite model.

The Value of a model can be any number, with the positive number being bullishd a negative number bearish. _ -

~-YEAR-WAN --pmEL

+1

+1

+o

-1

-1

-1

-0

-0

-1

+1

+1

+1

$ 50.00

51.00

51.00

50.00

47.00

46.00

47.00

46.00

45.00

47.00

51.00

53.00

-43-

-44-

Measuring Performance

There are several dimensions to the question of whether a model is a “good” one, and numerous measures of the degree of this goodness. There is no single statistic which can be used to rate a model’s performance because one investor’s objectives will differ from another’s. A model which flashes a new signal every six weeks could be useful to a trader but worthless to anyone seeking long-term gains.

Two techniques which can be used for measuring the quality of a modei are purely statistical. The Chi-squared statistic shows the degree of “signifi- cance” of a model’s results, while the Correlation Coefficient of the source information and the market measures how closely related the two are. While both of these techniques are useful, neither addresses the objectives or strategy of any particular investor.

The diagram (opposite page) demonstrates some measures of performance which attempt to describe the most important facets of a timing model. It begins with a recap of the date and price of each trade signaled by the model, along with the gain or loss from the trade. Other features include a measure of how much time money was at risk, the size of the average gain compared to the average loss, the percentage of trades which showed gains, and the “simple interest” rate of return for the model. It shows a “Gain to Loss Ratio” which measures the model’s tendency to cut losses and let profits run. It also includes a current portfolio value assuming that $10,000 had been in- vested with the first signal then rolled over with each trade.

One good way to use this type of performance evaluation is to set ‘!standards” for each statistic which, if met, will spotlight an investment-grade model. Another way is to build numerous models for an investment, then select the one with the “best” attributes. In this manner an investor’s objectives can be weighed, yielding a customized model with a good likelihood of meeting those objectives in the future.

-45

-46-

Source information Used in Modelinq

Just as there is no limit to the number of “systems” which can be invented to trade the market, there is no limit to the variety of data which can be used as source information in modeling. Indeed, Timing Models are simply another type of system, and the secret to how well they work lies in the quality of source information and rules used in their building.

One basic set of source information is the price action of the investment itself. As described in the following pages, Trend Models and Momentum Models are derived from “the Tape” and have proved quite useful in calling major moves both up and down. Trading Volume can be used as source information to build an On-Balance Volume Model. One model which has a good record of signaling big moves is built from a ratio of Put Option Volume to Call Option Volume.

There is evidence that unexpectedly high earnings in a company are reflected in the company’s stock price. A model which trades on this evidence would have a positive value whenever earnings were higher than expected and a negative or zero value at all other times. External fundamental data such as the Consumer Price Index, Money Supply and Interest Rates can be used in models.

Information as diverse as the level of hemlines and the position of heavenly bodies has been used to make bullish and bearish statements about the mar- ket, so there is truly no restriction on the type of source information used in modeling.

The risk of getting a losing signal from a model derived from a single factor (such as a momentum alone) is fairly great. An indicator can be too early or late in changing mode, or can fail to change at all in some cycles. Research has demonstrated that the weight of evidence provided by two indicators together is generally more reliable and significant than either indicator taken spearately . The diagram (opposite page) shows one method of building a complex model which does not rely on any one factor: each sub-model has one “vote” toward determining the mode of the final model.

-47-

Rules for Building Models

Good source information which is well correlated to market cycles has the potential of making a good timing model, but good information alone by no means guarantees success. The other key ingredient in a model is the set of rules which are applied to the source information to yield a value for the model. Both the data and the rules have to work together or the model will be less than useful.

Rules, like data, are as numerous and diverse as one’s imagination and creativity allows. However., there are several common ways of transforming source information into a model. These rules can be applied to any type of data and are fairly simple to use.

The Moving Average Crossing Rule

One elementary way of deciding when to buy and sell a market is the Moving Average Crossing. It goes like this. First calculate a moving average of the raw price data of the investment, then buy whenever the price climbs above its moving average and sell when it drops below.

A moving average provides a means of determining the general direction or trend of a market by examining its recent history. A six-period moving average is computed by adding together the most recent six periods of data, then dividing by six. This average is recalculated each period by dropping the oldest data and adding the most recent, so the average “moves” with its data but does not fluctuate as much. A 1%period moving average is “smooth- er” than a 6-period moving average and measures a longer-term trend. Generally, a long-term moving average will make a model which has a rela- tively high gain per trade and a relatively low gain per annum. A short- term moving average yields lower gains .per trade (because there are more trades) and higher gains per annum (because the investment is compounded more frequently).

Variations in the method of calculating moving averages can give heavier weight to more recent data than to old data or can double-smooth the data. The Crossing Rule is applied the same, regardless of the nature of the moving average.

-4%

Shown on the chart below is the Standard & Poor’s Railroad group, along with its nine-month moving average. Notice how the moving average eliminates short-term fluctuations in price and reveals the true direction of the group. Whenever an investment makes a big move up or down, the price is certain to lead its moving average, therefore, the Moving Average Crossing Rule is guaranteed to put an investor on the right side of the move. Notice also how the price will cross its moving average frequently during a basing period such as 1971-1975. These false breakouts can net some short-term gains but, as a rule, are only marginally profitable. A final guarantee about moving averages is that the crossing will always take place after the bottom or top.

18 Ral I road Stocks

-49-

The Slope Rule

Many types of source information will tend to oscillate within a narrow range. Annual rates of growth for a particular stock might vary from +50% to -30%. Inflation normally runs from +15% to 1%. The Slope Rule builds models based upon whether the source information this period is higher or lower than it was last period.

A chart of Air Transport Stocks is shown below. The portion of the chart labeled “Price Momentum” reflects the percentage change in price over the past year. The raw momentum figures have been smoothed with a moving average. Any time this year-to-year rate of price change is falling, a negative (bearish) value goes into the model. When successive months have higher rates of growth, the model is bullish.

mnthly Oat. 14143 - 3/31/81 (Log Scale) 5 Rir Transport Stocks

I

51 A HIGH RRTE OF GROWTH

f -f-i

1 HIi-RRTE OF LOSS

Applying the Slope Rule to price momentum data can give signals very close to the top and bottom. Whip-saws can occus periods such as 1974 when the direction of the momentum was so indecisive. Those extraneous signals can sometimes be eliminated by lengthening the smoothing period.

-5o-

The Bracket Rule

Some source information has extremes in high and low values which possess good predictive capacity. These extremes can be identified as high and low cut-off points, partitioning the information into one of three brackets: Bullish, Neutral, and Bearish. utilize this Bracket Rule.

Overbought /Oversold models frequently

The chart below shows an Investors Sentiment Index which can oscillate between zero and 100. Brackets have been established at 40 and 64. When investor sentiment is extremely pessimistic (64 and above) the market is normally near a bottom so the model will be in Bullish Mode. Periods of excessive optimism can indicate a market top, therefore, readings of 40 and below are bearish. All other times give neutral values to the model.

k.kly Data 12/30/77 - 4d3/81

Dow Jones Industr i al s I

828. 991. 982. 933.

984.

a75r ,,L kh

Too Huch Psssinlsm

!8 v ERRISH Too tfuch Opttrftn

Ned Davis Research Sentiment Index

Frequently, indicators such as this one give signals in advance of an actual top or bottom. This characteristic can allow the model to be used as a “screen, ” permitting another model’s buy signals to be acted upon only when both are in Bullish Mode.

-51-

Relative Strenqth

The rules for building models can be applied to any source information. The chart below shows the Italian Stock Price Index since 1969. The middle of the graph shows the Relative Strength of this market compared to a World Stock Price Index. It began with a ratio of Italy to the World. Next momentum was calculated on the ratio, then the momentum was smoothed with a moving average. The Slope Rule was then applied to the smoothed data.

Relative Strength models give buy signals any time the investment be- haves better than other investments. This can be dangerous, since a stock which falls less than the market is defined as strong.

f,mth,y Data J/31/69 - 2/28/8J (Log Scmle>

Italian Stock Price Index

suy 1 sell Signals L?urived From Changes

In Relatfve Strength

Italy Relatfve To The World 6

I

Fine Tunina

There has been a tendency among market technicians to use the same time frames in a variety of models, i.e. , to use a 200-day moving average or a 13-week momentum for every stock in the portfolio. This consistency ig- nores the fact that different markets have different cyclical characteristics. With the assistance of a computer, a wide range of time frames can be tested to find which moving average or momentum formula has the “best” histori- cal record in calling turns, Models can then be constructed with short and long-term attributes which match the cycles of the market.

The Moving Average Crossing Rule and the Slope Rule described in this article can, at times, give signals of very short duration. One method of reducing this undesirable feature is to apply a filter to the rule, stating that the price must cross its moving average by 1% or 2%.

-52-

A Finished Model

Shown below is a model of Gold Bullion which was constructed using the techniques described here. There are seven components in the model, therefore, any time four are bullish, the model is bullish. These compon- ents include one Moving Average, two Momentums, two Relative Strengths, and two Inflation models.

The research which produced this model required more than 60 hours of computer time. Once developed, it required less than 15 minutes to reconstruct, including the chart work.

nonthly Data l/31/73 - 34’1431 ILog Scale) Gold Bull ion Price (New York)

683.

54%’

427.

338

268.

212.

-53- -

intentionally blank

-54-

SIMPLE YET EFFECTIVE

STAN WEINSTEIN The Professional Tape Reader

Stan Weinstein is the editor and publisher of The Professional Tape Reader, one of the most widely-read subscription market letters. Stan has been quoted in such publications as BARRON’s, THE WALL STREET JOURNAL, and FINANCIAL WORLD, and has spoken before the NYSSA and twice before our own organization. He has personally developed a number of indicators, including Stage Analysis and the Group Inten- sity Trading Indicator.

He reminds us in this article that indicators can be relatively simple in concept , exploring the record of another of his inventions, the Last Hour Trading Index.

In this day of computer studies and regression analysis, it never ceases to amaze me how poorly many complex indicators perform their job, while cer- tain relatively simple concepts (such as the Advance/Decline Line) continue to predict future market movements with a high degree of accuracy. One such indicator that continues to excite me is my Last Hour gauge.

Computation of the Last Hour Index is simple, one simply takes the net- change of the average between 3 and 4 o’clock each day and constructs a cumulative index, adding today’s figure to yesterday’s total. Despite its simplicity, it has proven highly effective. Intuitively, we’ve all known that if the market was up say 12 points at 3 o’clock and then slipped to the point where it closed up 6 points (which would be a -6 change for this indicator since the formula is the close minus 3 o’clock price), it was a disappointing day. While the public becomes excited over the day’s advance as they lis- ten to the 6 o’clock news or read their newspaper, the next morning, we would often notice that the charts of individual stocks didn’t look really terrific for that day as many issues shaded their highs and closed poorly. All this indicator does is objectively measure the market’s (I use the DJIA although it can be done,obviously, for any average or even any individual stock) strength or weakness late in the day.

-55-

The rationale behind this gauge seems obvious. I firmly believe that the last hour of trading is the most important of the day and is the truest indication of both the Specialists’ and traders’ intentions. The Specialists try to baiance themselves off and position themselves for the next day’s business, while the iraders usually make thier moves at this point since they know that early trading is filled with a lot of public orders, and they realize that there are too many cross currents at that point to get a true reading of the tape.

DJl’s Net Change For

Last Hour of Trading

CHART 1

f * : ‘78

r

DJ l’s Net Change For Last Hour

d Trading

Just a few examples should clearly demonstrate why I’ve become a believer in this tool and why I’ve added it to my Weight of the Evidence while I’ve re- jected so many other indicators. All I do is plot the Last Hour Indicator (daily) against the D. J. Industrials. Most of the time, the two lines move pretty much in tandem, which merely confirms that a trend reversal isn’t likely at that point in time. However, every once in a while, the two lines start to diverge from each other, and it’s these ‘divergences’ and ‘non- confirmations’ that I find especially useful in predicting important trend reversals. Look at Chart 1 and note that while the DJIA (top half of graph) trended lower throughout the fourth quarter of 1977 and early 1978, this gauge diverged positively as it built a base and trended higher. It was an excellent clue to the coming March, 1980-September, 1980 rally that took the DJIA from approximately 740 to near 920. Now look at Chart 2 which covers the August, 1978-November, 1978 period. While the market held up near 900 as it drifted sideways through August and September of 1978 -- actually hitting a new peak over 900 in September of that year -- our indi- cator wasn’t just diverging, It was crashing! It correctly foreshadowed the October, 1978 Massacre beautifully. Now let’s take a look at another time frame (Chart 3). In mid-November, 1978, the D. J. Industrial Average dropped to a new reaction low (point B below point A). However, our Last Hour gauge once again did its thing by holding above its comparable low. This was the other side of the coin, namely that better tidings were on the way and sure enough a new intermediate uptrend was soon underway with the DJIA moving up over 100 points in the coming months.

-56-

00 DJl’s Net Change for

- -_ Last Hour of Trading

PJ

DJl’s Net Change for Last Hour of Trading

Now look at Chart 4 (May, 1979August, 1979) and note that, while the averages hit a lower low in early June, our indicator showed far more strength and was trending higher which correctly tipped off the fine rally that took place over the next few months. Next look at Chart 5 (March, 1980-June, 1980) and see how, during the March-April period, as the DJIA was scraping bottom in the mid-700’s, this indicator rose throughout late March and all of April, despite the new marginal closing low in late April. This was a bullish positive divergence, and the market was soon rolling up one of its best 8-month advances in years as the DJIA took off from the 760 level and rallied to 1000.

Now take a look at Chart 6 (September, 1980-March, 1981) which brings us up to the present. While so many market advisors became gloomy in January of 1981 (almost 50% were bearish, according to Investors Intelli- gence) , none of my gauges was more clearly favorable than the Last Hour Indicator. Note how, throughout December and January, this indicator not only diverged positively, but actually rocketed higher and hit a new multi-year high while the DJIA struggled near 1000. And then, through- out January and February, as the averages corrected from 1000 to 930, this indicator once again flashed another bullish signal by not only holding well above its comparable low, but by again showing unbelievable strength and moving to yet another new high. Once again, this technical tool proved its worth as a few weeks later, the DJIA closed above 1000 at the best closing level in 8 years. In addition, even that feat understated the true strength of the rally as unweighted NYSE and ASE averages reached new 12-year highs. While obviously no indicator will always be ‘right on,’ and this gauge, too, will probably have its share of occasional whipsaws, based on the above evidence, I feel that this simple, yet effective, market tool should become an important part of the technical analyst’s arsenal.

-57-

intentionally blank

-58-

ENDING CONFUSIOPJ or! THE SI-I~FU-TERPI TRADING INDEX

MARTIN E. ZWEIG, PH.D. Zweig Forecast

Here, using the Short-term Trading Index as an example, Martin Zweig once again underscores the point that there are no magic numbers and that analysis of indicators is never simple minded. As he points out, in the case of the Arms Index, levels that are bearish for the short term may be bullish for the long term and vice versa.

Mr. Zweig, an MTA member, is editor of the Zweig Fore- cast, an economic consultant to Avatar Associates, an investment management firm, and a professor of finance at Iona College. He has published numerous articles on technical subjects in BARRON’S and other publications. He was a speaker at the 1981 MTA seminar.

In 19’74 one well-meaning writer reasoned that certain values on the Short- Term Trading Index gave BUY and SELL signals during bull markets but that only higher values were valid during bear markets. It’s incredible how many technicians have borrowed those proposed parameters. Did it ever occur to them that, in setting the “proper” boundaries, one needed prior assurance as to whether the major trend was bull or bear? If one could merely answer that question, the Short-Term Trading Index becomes academic. If the trend were bullish, just buy on any minor correction and forget about it. . .or vice-versa in a bear market. In fact, the real test is whether the Short-Term Trading Index can forecast bull or bear markets . . .or at the least, the direction of prices several weeks or months ahead.

-59-

Table 1 ? TRIN VS. S&P 500; DAILY VALUE, UNSMOOTHED:

1975 TO 1980

TRIN Range -S&P Performance- Probability of

Period %Change Up Market

Above 1.0 Under 1.0

1 Day Later II

- .14% + .16%

44% 58%

Above 2.0 Under .5

1 Day Later I.

- .07% + .39%

45% 65%

Above 2.0 Under .5

5 Days Later 1,

- .38% 39% + .67% 62%

Above 2.0 Under .5

10 Days Later + .05% 48% II + .65% 62%

Above 2.0

Under .5 Under .4

20 Days Later + .65% 58% 11 +1.47% 69% II +3.12% 92%

Table 2 TRIN VS. S&P 500; 2-DAY SMOOTHED:

1964 !ro 1980

TRIN Range -S&P Performance Probability of

Period % Change Up Market

Above 2.25 2.00 to 2.25 Under 2.00

Above 2.25%

2.00 to 2.25 Under 2.00

Above 2.25 2.00 to 2.25 Under 2.00

Above 2.25 2.00 to 2.25 Under 2.00

Above 2.25

2.00 to 2.25

10 Days Later 9s I.

30 Days Later II I.

3 Months Later 1, ,I

6 Months Later a‘ II

1 Year Later II

+ 2.03% + 1.13%

+ .16%

+ 4.13%

+ 1.77% + .48%

+ 8.03%

+ 5.68% + .92%

+16.26%

+11.06%

+ 1.82%

+23.57%

+17.54%

75%

63%

55%

83%

69% 57%

83%

75%

56%

92% 81%

57%

100% 75%

Under 2.00 1, + 3.00% 63%

-6O-

A second source of confusion is the belief that values less than 1.0 are bullish while values greater than 1.0 are bearish. The truth is that they are. . .and then, again, they are not. So, let’s end the confusion.

The Short-Term Trading Index equals the Daily Advance/Decline Ratio divided by the Up/Down Volume Ratio. It’s often smoothed over various periods, the most common being a simple lo-day average. The indicator is also known as the Arms Index (after its inventor), or either the MKDS or TRIN , the symbols on various quote machines. To keep it simple, call it TRIN.

Daily TRIN

First, let’s see TRIN’s forecasting properties on just a daily basis (no smoothing). The test period is 1975 to 1980. When daily TRIN was greater than 1.0, the S & P 500 dropped -. 14% the following day, rising only 44% of the time. When daily TRIN was less than 1.0, the market rose +. 16% the next day, rising 58% of the time.

Now, look at Table 1 to see how the S & P behaved after more extreme daily TRIN readings. When TRIN was greater than 2.0, the market drop- ped -. 07% the next day; but it rose an average of +. 39% when TRIN was less than .5. Five days later, the S & P was -. 38% lower when TRIN was above 2.0; but up +.67% when TRIN was under .5. Twenty days later stocks were up +.658 when TRIN was above 2.0; but they rose +l. 47% when TRIN was under .5%. Moreover, when TRIN was less than .4, the S & P was a healthy +3.12% higher 20 days later. Clearly, on a short-term basis, TRIN (unsmoothed) does forecast in accordance with popular belief; that high values are bearish while low ones are bullish.

Two Day Smoothing

Now, let’s try smoothing TRIN a bit as well as expanding the test period back to May, 1964, up through the end of 1980. I usually prefer exponen- tial smoothing to simple moving averages. . .so this test uses a two-period smoothing where the latest day gets two-thirds of the weight and the prior average gets one-third.

Highlighting some of the details in Table 2, it’s seen that after a two-day TRIN of 2.25 or more -- an extremely high score -- the S & P was up +2.03% 10 days later, advancing fully 75% of the time. But for scores of less than 2.0 on TRIN , the market rose a scant +. 16% in 10 days, advancing only 55% of the time. Moreover, very low TRINS of less than .5 (not shown in the table) failed to approach the gains made after very high TRINS, although for spans of up to 3 months they did do better than average. So, by smoothing even for two days, a modest reversal of form begins to show.

-61-

Table 3 TRIN VS. S&P 500; lo-DAY SMOOTHED

1964 TO 1980

TRIN Range -S&P Performance- Probability of

Period % Change Up Market

Above 1.5 1 Month Later + 3.83% 63% .7 to 1.5 ,t + .29% 55%

Under .7 " + 1.26% 88%

Above 1.5

.7 to 1.5

Under .7

3 Months Later + 6.90% II + .90%

11 + 1.86%

84%

57%

88%

Above 1.5

.7 to 1.5

Under .7

6 Months Later +18.71% ,I + 1.70% n - 1.27%

97%

56% 33%

Above 1.5 1 Year Later +28.81% 97%

.7 to 1.5 81 + 2.87% 63%

Under .7 II - 8.72% 17%

Table 4 TRIN VS. S&P 500; SO-DAY SMOOTHED

1964 TO 1980

TRIN Range -S&P Performance- Probability of

Period % Change Up Market

Above 1.20

.85 to 1.2 Under .85

3 Months Later + 4.44% 68% ,I + 1.07% 57% II - .75% 47%

Above 1.20

.85 to 1.2 Under .85

6 Months Later +16.46% 87% II + 1.61% 57% II - 1.66% 46%

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Six weeks after two-day TRIN was greater than 2.25%, the S & P was up +4.13, far better than the +.48% showing for values less than 2.0. Three months later, the market had risen +8.03% for TRINS better than 2.25 versus only +. 92% for TRINS under 2.0. Six months later, the highest TRIN range showed S & P gains of +16.20 . “9, against +l. 82% for TRINS under 2.0. Finally, one year after a two-day TRIN of greater than 2.25, the S & P was up a staggering +23.57%, having gained ground 100% of the time. But a year after all TRINS of less than 2.0 (99% of the cases>, the market was up 63% of the time, having advanced just +3.00% on average. Clearly, a very high TRIN is bullish on a smoothed basis. . .and its effect lingers for quite a long time.

Ten-Day TRIN

A lo-period smoothing gives 18% of the weighting to the current day’s TRIN, and 82% to the prior average. Table 3 shows how the market behaved after these values since 1964. It’s seen in the short run that both very high and very low TRINS are bullish vis-a-vis average market performance. However, by 6 months after very low TRIN scores, the tone becomes quite bearish, whereas it gets increasingly bullish for very high readings.

Approximately 1% of all observations on lo-day TRIN are greater than 1.5, another 1% are lower than .7, and the remaining 98% are between those scores. When TRIN was above 1.5, the S & P was +3.83% higher a month later. When TRIN was under .7%, the market was +l. 26% higher versus +. 29% the other 98% of the time. Three months later, high TRINS produced market gains of +6.90%; low TRINS +1.86%, and all other cases just +.90%.

Six months later the picture changes. High TRINS led to S & P advances of +18.71%, gaining ground 97% of the time. But low TRINS showed losses of -1.27%, with the market advancing a meager 33% of the time. Finally, one year later, high TRINS of 1.5 or better on a lo-day smoothed basis had led to gains of +28.81%, with stocks up 97% of the time. Conversely, for TRINS under .7, the S & P had plunged -8.72%, rising a scant 17% of the time. That’s quite a difference.

SO-Day TRIN

Now, let’s smooth over 50 periods (Table 4)) giving roughly 4% of the weighting to the current day and 96% to the previous average. Three months later the S & P was +4.44% higher when TRIN was above 1.2. . . but it was -. 75% lower when TRIN was less than .85. Six months later the market had gained +16.46% for the higher TRINS (up 87% of the time) versus losses of -1.66% for the lower TRINS (up only 46% of the time). Clearly, a pattern has emerged.

For unsmoothed TRINS over very brief intervals (akin to what the average trader sees on his quote machine), low is bullish and high is bearish. But as TRIN is smoothed and the interval for market performance is length- ened, low is bearish and high is bullish.

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Table 5 TRIN lo-DAY SIMPLE AVERAGE 1.5 Od GREATER:

1964 to 1980

-3 Months Later- - 6 Months Later- -1 Year Later- Date Dow ZUPI Dow ZUPI Dow ZUPI

09/07/66 + 4.0% + 6.3% + 8.4% +24.4% +16.9% +43.6%

10/06/66* + .l% + 4.3% + 2.3% + 2.6% + 2.3% + 3.0%

05/04/70 + 1.5% -11.3% + 7.8% - 1.9% +31.2% +29.2%

09/30/74 - .8% - 3.6% +26.3% +33.5% +38.2% +32.1%

11/19/74* +22.1% +31.8% + 9.1% + 5.9% + 1.0% + 5.2%

03/24/80 +14.6% +20.0% +15.8% +40.2% +31.2% +47.2%

Avg./Pd.: + 8.0% + 8.4% +15.4% +22.4% +28.2% +37.8%

$10,000= $14,667 $14,993 $19,075 $24,818 $28,733 $39,091

Annualized Return +35.9% +38.3% +33.3% +49.8% +28.2% +37.8%

Months: 15 15 27 27 51 51

*The second signals in '66 & '74 are measured out only for 1 month and 2 months respectively beyond the 3,6 & 12 month performances of the first

signals in those years.

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Maqic Numbers ?

For convenience, most technicians track a simple lo-day average of TRIN (no exponential smoothing). Many attempt to read this indicator not unlike a gypsy would read tea leaves. But, then who among us would not wish to find the “Magic Numbers?” First, let’s try the extremely low numbers, the ones which are supposed to give SELL signals (since we already have found that smoothing tends to make low values bearish). Unfortunately, there’s no magic here.

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Since 1964 there have been 14 separate clusters when simple lo-day TRIN fell under .75. I used only the “extreme” daily low reading on TRIN to track each (“cheating, ” you might say, since if you use . ‘75 as a SELL signal, you should start measuring from the day that figure is first hit). This procedure actually renders a bearish bias. Nonetheless, neither the bears nor the bulls can claim victory on “SELL” signals. Seven of 14 times the next 50-point Dow Industrial move was up after these extremely low TRINS ; the other 7 times it was down. The worst of the “SELL” signals came in April, 1968, April, 1978, and November, 1979. Each time the Dow gained roughly another 100 points before peaking. Yet, many will still use a low TRIN as a “SELL” because of the success of such signals as August 31, 1979 (Dow 888) and November 20, 1980 (Dow 1000). The Industrials quickly plunged 100 points or so in both cases. However, in the former, simple lo-day TRIN dropped to .677, while in the latter case, TRIN sank to .617, the second lowest ever. The record low was .580 in September, 1965, after which the Dow gained another 56 points before topping some 4 months later. In sum, simple lo-day TRIN gives lousy “SELL” signals.

Next, I examined “high” TRINS in the 1.333 to 1.499 range. Of 15 such cases, I rated 7 of them successful in giving “BUY” signals; November, 1978 and October, 1979, the most recent. Three others were rated “neutral” as the market zigged and zagged afterward. The remaining 5 were clear losers, especially March, 1970 and April, 1974.

Greater Than 1.5

So, it was on to the “extremely high” simple lo-day TRINS , those of 1.5 or greater. Table 4 shows the results if one had bought stocks the day TRIN first hit 1.5 (no “cheating’* here by buying on the day TRIN hit its peak). There have been only 6 such cases, including pairs of them in 1966 and in 1974. The pairs are acknowledged because in each of those years, TRIN dipped briefly under 1.0 after the first 1.5 reading; then promptly came back up to 1.5 again. I can’t call those “clusters. ” I then measured returns over the next 3, 6, and 12 months, exing out the overlap spans in the 1966 and 1974 cases. Obviously, those two moments, as well as the ones in 1974 and 1980, were bear market bottoms. . . so the returns are hefty.

Table 5 shows the results both against the Dow Industrials and versus my own Zweig Unweighted Price Index, a much broader measure of the market.

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Three months after the extremely high 1.5 TRINS, the Dow was up +8.0%; the ZUPI +8.4%. Six months later the Industrials had gained +15.4%, the ZUPI +22.4%. Finally, a year later the Dow was ahead +28.2% while the ZUPI was up a sensational +37.8%.

Even with those eye-popping results, BUY signals using simple IO-day TRINS of 1.5 are not perfect. And that’s after considering that the method gave signals only near the bottoms of each of the 4 bear markets since 1964. (Some will argue that March, 1980 was an intermediate bottom . . .but adjusting the Dow for inflation, it had fallen -46.1% in 3-l/2 years, while the ZUPI had dropped -37.0% in l-112 years. . .typical bear market declines in both time and magnitude.) For example, from the first TRIN BUY signals (excluding the second ones in ‘66 and ‘74) to the market’s final lows, the ZUPI dropped another -7.5% on average. The declines were -4.2% in 1966; -15.2% in 1970; -4.6% in 1974; and, -5.9% in 1980.

Summary

I don’t purport to have all the answers on TRIN . However, the tests per- formed here do end much confusion about the Short-Term Trading Index. First, the idea of different signal levels in bull or bear markets is so much wishful thinking . That merely begs the age-old question, “Well, are we in a bull market or a bear market?” Second, on a very short-term basis, high TRINS are bearish; low ones bullish. Third, as TRIN is smoothed over longer durations, high values become increasingly bullish; low ones bearish. . . especially for performances several months later. Finally, when a simple lo-day moving average of TRIN is taken, its most common form among technicians, extremely low values offer no meaningful forecast- ing properties. Nor, do relatively high values. Only the extremely high readings are worthy, tending to be extraordinarily bullish for periods several months later, but often at the expense of somewhat lower prices the initial few weeks.

Because TRIN (or MKDS) is available on most quote machines, it ranks among the most popular of technical indicators. Given that so many follow the index, it’s not surprising that it’s misunderstood more often than not. Actually, TRIN has truly significant meaning only a small percentage of the time. . .especially those rare cases when it gets ultra-high.

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BREADTH OF THE PlARKET TRENDS

HAROLD M. GARTLEY

Harold M. Gartley can be eulogized simply as a man ahead of his time. In 1981, we spend a great deal of time talking about rigorous analysis and empirical proof. Gartley was providing just such analysis and proof in the 1930’s. He was, moreover, conducting in-depth research at a time when the most sophisticated mechanical aid to such research was an adding machine.

The following article, written in 1937, has been chosen as an example of the work that Gartley did. It is just one of many examples that can be found in his landmark three- volume course, Profits in the Stock Market, a volume long out of print but available in the MTA library. This short excerpt, in and of itself, provides ample justification for Harold Gartley’s selection as the posthumous recipient of the seventh annual MTA Award for Distinguished Contribu- tion to Technical Analysis.

This chapter will be devoted to a study, which we have chosen to title “Breadth-of-the-Market ,‘I because it is based upon certain general sum- maries of all the daily trading which occurs on the floor of the New York Stock Exchange.

In the opinion of this author, the value of this type of study cannot be overemphasized as a timing device providing considerable aid to the technical student. It is believed that no single branch of stock market trend research will yield greater results. This contention is based on the belief that the growth and deterioration of bullish and bearish market sentiment is clearly reflected in the general market statistics, providing that adequate analysis is made.

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Basic Data Used

The facts upon which our “Breadth-of-the-Market” study is based are general statistics, including daily observations of :

1. Number of issues traded, 2. Number of advaqces, 3. Number of declines, 4. Number of prices unchanged, 5. Number of new highs, 6. Number of new lows, 7. Total volume, and 8. Ratio of trading in 15 most active stocks to total volume.

In actual use, as we will learn later, these statistics must be consider- ably refined, in order to be of any practical value.

Application to Intermediate Trend

In this discussion of Breadth-of-the-Market studies, our chief objective will be to look into their value in connection with the determination of intermediate trend reversals. Nevertheless, it will be noted that the study develops some rather consistent minor trend signals.

Evolution of Breadth of the Market Study

It is possible to designate our present subject as a kind of study of vol- ume, for it has to do largely with the observation of mostly quantitative figures. Also, it is related to volume rather directly, because a notable change in total activity will almost always cause substantial changes in many of the other Breadth-of-the-Market statistics. Possibly some read- ers will argue that the change in these statistics causes the volume. But this is only an academic hen-or-egg debate.

Not all the factors studied in Breadth-of-the-Market observations, how- ever, are exactly related to each other. Having in mind the tabulation of the eight items listed above, it is to be noted that the number of issues traded (1) will vary in almost direct relation to total volume of trading (7).

If, in the major phase of an upward intermediate trend, during a given market day a sharp general advance results in a large number of indi- vidual issues showing advances at the closing, and thus being at higher levels than they were at the previous day’s closing, the new highs for the day are likely to be in greater number.

Conversely, if in the major phase of a downward intermediate trend a sharp recession carries many stocks down, so that the declines for the day are numerous, new lows are likely to be in greater number.

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Conversely, if in the major phase of a downward intermediate trend a sharp recession carries many stocks down, so that the declines for the day are numerous, new lows are likely to be in greater number.

A dull market will result in a sharp increase in the number of unchanged issues, and at the same time, a decrease in both the number of advances and the number of declines. Also, in a dull market the number of issues traded will drop off sharply.

After an extended decline, the number of issues traded each day will fall to an average between 550 and 650 stocks, which, of course, includes both common and preferred issues. On the other hand, in the active markets which accompany intermediate tops, it is usual to see the number of issues ranging from 850 to 950. In the past several years, the total number of issues listed has averaged about 1200. (All of these statistics concern just the stocks listed on the New York Stock Exchange.)

The New York Herald-Tribune ratio of the volume of the 15 most active stocks each day compared with total volume, which is item 8 in our tabu- lation on page 2, has no direct relation to the other factors used in the study. It is included because there are times when this ratio seems to express the concentration of trading activity, which occasionally has been found to be helpful in studying the other factors.

At the time the Breadth-of-the-Market study was conceived, the primary object of the research was to determine whether a study of general market statistics, such as have been briefly discussed above, would provide any reliable means of judging intermediate reversals. When the author began the studies, in 1931, material assistance was obtained from Mr. Harry Wolf, who had been observing the data for a number of months.

In beginning the study, all of the items which seemed to be logical parts of the study were brought together, without any preconceived ideas as to what they might show. After a year or two, it was found that consider- able refinement was necessary, and even after several years of experience no arrangement of the data has been found which reliably reflects each successive intermeidate turning point.

Before observing all of the statistics enumerated above, let us first study two charts, and trace the successive steps in the procedure of refining the figures for just the number of daily advances and declines.

Chart 2 shows the period from June, 1932 through October 15, 1935. On Chart 1 the original data for the first two months is plotted. The upper line (1)) which appears in the extreme upper left-hand corner, shows the number of closings each day which were unchanged. During the period this ranged from 100 to 200 issues.

The next lower line (2) shows the number of daily advances, which range from 150 to 500. In the center of Chart is a line (3) showing the daily closing level of the Standard 90 stock index, which is used to check the

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. CHART 1

1A Ratlo of

3A . Standard

90

3

83 Standard 90

*

LJ SA

Ratio of Declines

4 No. of

Owllnes

1932

CT’, Aug Sept Ott ~OV

1932 -

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data with turning points in the market.. The bottom line (4) shows the daily number of declines, which, it will be noted, range from 125 to 325.

In each case, the minimum number of unchanged, advances and declines is about as low as is seen. Quite frequently, however, the number of advances and declines substantially exceed the maximum levels shown in these first two months.

Experience with the raw data, which has just been described, soon showed that it had to be somewhat refined if it was to be useful. So the idea was conceived to reduce all the data to some uniform basis, and instead of using just the number of advances and declines, a ratio was prepared wherein the percentage of the advances and the declines, as compared to the total issues traded each day, was computed. These appear on Chart 1. The next section of the chart from the left covering the months of August to November, inclusive, show these ratios marked 1A , 2A, and 4A.

Still it was found, however, that the data fluctuated up and down, fre- quently between wide extremes, and it was then conceived that perhaps the data could be smoothed by means of a moving average (see Chapter XII). Numerous moving averages were tried, and studied for their indi- cations at turning points, and the most satisfactory seemed to be a ‘I-day moving average. This was adopted. Soon it was found that no useful conclusions could be obtained from studying the ratio for the number of issues (1-A) which were unchanged, thus this series was dropped.

Now let us look at the final series being used, that which is shown by the upper and lower lines marked 5 and 6 in the right section of Chart 2. As soon as the study was prepared for a period of time, it was found that these ratios tended to fluctuate between rather well-defined limits.

For example, the ‘I-day moving average of the ratio of advances (5, at the top of the chart) reached an extreme of about 60 percent at its low point, the average of the high points being about 56 percent and of the Zow points about 22 percent.

In the case of the 7-day moving average of the ratio of declines (6, at the bottom of the chart), the top extreme was somewhat higher, at 64 percent of the total issues traded, while the Zow extreme was also higher, at 21 percent, the high extremes averaging about 56 percent as in the case of advances, and the low somewhat higher at about 25 percent.

Now let us briefly study the relation of these two lines on Chart 2 with the market swings as indicated by the Standard 90 stock index. First of all, it must be remembered that when one of the ratios is at a high level, the other) being its opposite, is at a low level. Experience seems to indi- cate that, if the technical student is pressed for time, just one of the ratios (for declines, plotted inverted, that is the lowest figures at the top of the plotting scale) will give very satisfactory results, within the limita- tions of the study.

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.’ .

._: . : - *. .,‘.. . .

N II

P

However, we will first consider the two ratios separately. Dotted lines have been drawn from the top series, showing the ‘I-day moving average of the ratio of advances, down to the price line; and similar lines have been drawn upward from the lower series, showing the 7-day moving average of the ratio of declines. Each vertical dotted line marks a top or bottom of either minor or intermediate trend importance on the stock price index.

By following these lines, the reader can quickly see how the ratios per- form at both minor and intermediate turning points.

A careful study of this comparison leads to the following conclusions :

1. The peaks in the ratio of declines (6) which provide the buying signals, are far more accurate and produce fewer false signals than the similar peaks in the ratio of ad- vances. (This has held true until the date of this writ- ing (August, 1937.)

2. The peaks in the ratio of declines are usually closer to the reversal point than in the case of the ratio of ad- vances.

3. All of the important turning points to the upside were indicated by peaks above the 50 percent zone in the ratio of declines. This was not ture in the case of the ratio of advances. See, for example, the situtations in April and May of 1933 and January of 1934.

4. In persistent advances, such as those of July-September, 1932 and March-July of 1933, the ratio of advances is likely to suggest several false selling signals before it is time to liquidate stocks, and it occasionally misses signalling an important selling point.

5. Although the ratio of declines occasionally shows buying indications which prove to be premature, it correctly signals all the real buying points.

6. The chief value to be derived from a study of both of the ratios is that when either ratio reaches an extreme peak, such as from 58 percent upward, a turning point is sig- nailed; that is, if the ratio of advances rises so that its ‘/-day moving averages is at a level above 60 percent, it is time to consider selling stocks, particularly if an advance has been under way for some time. Conversely, when the ratio of declines rises so sharply that its 7-day moving average exceeds 60 percent. it is time to buy stocks, especially if a decline has proceeded for some time.

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Combined with other technical factors, these indications, although they recur only infrequently, can be made extremely profitable.

It should be noted that the 7-day moving averages of the ratios of ad- vances and declines at turning points do not show any patterns which recur often enough to be reliable indicatqrs of the type of reversal which is being indicated. Study, for example, the patterns of turning points in the ratios which are circled, of all the price reversals during the period shown on Chart 2. .Note that no two of them are sufficiently alike to be any means of indicating how important the succeeding move may be.

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IN DEFENSE OF TECHNICAL ANALYSIS

WALTER DEEMER Deemer Technical Research

It is a mark of human frailty that eminently rational people occasionally make totally absurd statements. A notable case is the widely-quoted pronouncement on technical analysis made by the worthy John Train in his recently-published book, THE MONEY MASTERS.

The Train manifesto is, we think, amply refuted here by Walter Deemer, past President of the Market Technicians Association and a regular contributor to this JournaZ. Walter was, for many years, senior vice president in charge of technical research at the Putnam Management Company in Boston. He has recently formed his own consulting firm, Deemer Technical Research.

There seems to be a never-ending series of salvos aimed at “technical analysis” these days. I ran across the latest one in a just-published book entitled THE MONEY MASTERS. In it, the author (of an otherwise excellent book) expresses the opinion that “technical analysis” is worthless because none of its practition- ers is willing to accept a wager that, if presented with an unidentified stock’s price chart which has been ripped in half, they can predict from the price action depicted on the first half of the chart what took place on the second half. Frank- ly, after reading this passage, I am starting to have nightmares during which someone storms into my office to administer such a test to me -- a test which I freely admit that, despite some 16 years experience, I couldn’t possibly pass. Before it is too late, I thought I would take pen in hand and, in response to this latest challenge, offer a word or two in defense of “technical analysis*’ --- especially as it is actually practiced today.

Before I begin, we should try and define what is meant by the term “technical analysis. ” To me, technical analysis is the anticipation of future price moves of individual stocks -- and of the stock market itself -- based on their past

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price movements plus various underlying factors such as supply/demand and sentiment. It exists for one very simple reason: the shares of stock of a particular company are not the same as the company itself. Stocks go up and down because -- and ofily because -- someone buys or sells them. The reason for that purchase or sale may, but then again may not, be related to what is going on at the company. Very often a transaction is made to adjust a port- folio position, because cash has just become available to invest or is suddenly needed elsewhere -- or simply in response to market conditions. In similar fashion, the stock market is not the same as the economy as a whole. It thus seems quite logical to me that investors thinking of buying or selling shares in a company should, along with all other factors, consider the technical condition of the shares themselves, plus the technical condition of the stock market, be- fore reaching their final decision, and I must say that I find it very difficult to understand why this point is lost on so many people. Indeed, technical analysis could easily be considered the most basic and necessary part of the entire investment decision-making process.

Now, with regard to our specific example, the reference in the book is to a form of technical analysis which can best be described at the “stone age” school. It has previously surfaced in such places as a prestigious professional journal, where an article described a research study in which people were asked to dif- ferentiate between actual stock price charts and charts generated from random data, and a bank in Philadelphia which tested applicants for a technician’s job on, among other things, their ability to predict future price performance of a stock after being given a weekly chart of it -- but with no other identification. (Sound familiar?) Unfortunately, none of this has the remotest connection with how technicians actually operate these days. The time when a technician attempt- ed to project future price movements of a stock by looking at a chart of its past price action -- and nothing else -- has long since passed; analysts today function on a much, much more sophisticated level than that.

For instance, at the end of February of this year, I remarked that oil stocks were vulnerable to “as much as a 25% or a 30% correction .‘I Now, at the time I said this, the price chart of Mobil, to use a specific example, was reflecting a powerful advance ; the stock price had reached a new all-time high that very week, and trading volume was contracting nicely on the subsequent reaction. The chart, according to all the traditional rules of technical analysis, was bull- ish. Despite this, Mobil, along with almost every other oil stock, did indeed have a 25% - 30% correction within the next four weeks. What was the technical evidence which made this correction predictable and which caused me to go against the bullish charts?

To begin with, the stock market had begun a decline in mid-February. By month-end the DJIA had already given up some 55 points. The decline was foreshadowed by a number of important negative technical developments: key indicators, such as the NY SE public /specialist short selling ratio and a nor- malized AMEX/NYSE volume ratio, had turned pretty clearly bearish; there was a textbook-perfect case of a negative breadth divergence in the market beginning in mid-January, and finally the bond market, which traditionally leads the stock market, had started a decline of truly historic proportions in January. Hence, by late February the oils were fighting a pretty well-estab- lished downtrend in the general market.

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But there was more. At the end of February, most stock market observers were predicting only a mild correction in the market; 840 was one widely- cited target for the Dow-Jones Industrial Average (which then stood at 862). The stock market, however, rarely does what most people expect it to. Since the majority expected only a mild correction, the contrary possibilities were either for the market not to have any correction at all (but since the Dow was already down some 55 points, that theory was already invalid), or for it to have a more serious correction than generally expected, which, of course, ultimately proved to be the case. If the decline was going to be a really ser- ious one, the chances were pretty good that even the strongest area in the market -- the oils-- were vulnerable to a significant correction.

Finally, there was yet another factor to consider. In late February there was mounting evidence that margin traders, emboldened by the general forecast of only a mild stock market setback, were continuing to buy stocks on balance (later confirmation of this came from the NYSE’s report that margin debt rose $640 million in February, reaching a. level of $12,460 million at the end of the month). Two generalizations are worth noting concerning margin traders. They have a tendency to follow strength in the market, which suggested they were probably quite heavily involved in oil stocks, and they are usually shaken out of their long positions (at least to some extent) before an intermediate stock market correction runs its course. The extremely high interest rates being charged by brokers on debit balances at the time suggested,in addition, that traders would be apt to close out positions more quickly than usual, since it was abnormally expensive to carry them. This all implied that the oil stocks were in relatively weak hands in late February and was the final link in the chain of reasoning that led to my conclusion that they were vulnerable to a significant correction.

The astute reader will note that all of the technical analysis described above was designed to verify -- or refute -- the strength that the chart of each in- dividual oil stock was reflecting in late February. The chart pattern alone-, however, could easily have led one to the opposite conclusion. If the same chart pattern shown in the illustration had existed at the very beginning of a strong upmove in the general market, while at the same time there was great skepticism on Wall Street towards the particular goup and some important long- term technical indicators were in very bullish positions, you would have a stock that is enjoying a “power move. ” It would not only be providing current mar- ket leadership, but would probably continue to do so for some considerable period of time. The whole point of this is that the price chart of an individual stock -- or of the market itself -- is only the beginning, the very beginning, point in technical analysis, and that all sorts of various other technical factors must be considered in reaching one’s final judgment.

Actually, when you come right down to it, looking at charts in a vacuum makes about as much sense as looking at fundamentals in a vacuum. Technicians do not run around asking people to reach a conclusion on the worthiness of a stock on the basis of an income statement and balance sheet from which all identifica- tion has been removed or whether they can distinguish between a real and a randomly-generated income statement or balance sheet. Does anyone really think a fundamental analyst should make a recommendation on a company without con- sidering its position in the economy, the direction of the economy, and a myriad of other considerations? Hardly.

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Technical analysis, quite obviously, is not a quick and easy short-cut to success in the stock market. When used properly, it is an important tool which requires a lot more than a quick glance at a stock’s price chart. But’ it cannot be dismissed as meaningless and irrelevant to the investment pro- cess -- not as long as stocks are bought and sold by human beings who are subject to psychological forces as well as economic ones, and who have irrational thoughts as well as rational ones. When technical analysis is used properly -- subject to both its strengths and its limitations -- then, I submit, it is both a relevant and vital part of the investment process.

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PAPER YONEY ‘Adam Smith’ Published by Summit Books New York

Reviewed by Anthony W. Tabell Delafield , Harvey, Tabell

John Brooks (of The New Yorker magazine, not the MTA) titled a book Once In Golconda and opened it with the quotation, “Golconda, now a ruin, was a city in southeastern India where, according to legend, everyone who passed through got rich .I’ He was using the phrase to describe Wall Street in the 1920’s, but it could also, as all but the most callow striplings among our membership are aware, be applied to our not-always-totally-sane industry as it flourished during the 1960’s. The unquestioned poet laureate of that particular era was George J. W . Goodman who, in 1967, under the pseudonym of ‘Adam Smith ,’ published The Money Game, Unlike more recent stock market books which have reached best-seller lists (an example was reviewed in the last issue of the Jo:urna.l), The Money Game not only made marvelously good sense, but was incredibly delightful reading. In it, Goodman created his marvelously zany cast of characters which still stand as symbols for that particular decade --- the Gnome of Zurich, Poor Grenville, Odd-Lot Robert, the Great Winfield, etc. Goodman is not a technician, but he has, unques- tionably , an appreciation for technical analysis. No one without such appre- ciation could describe falling in love as being ‘I. . .like the feeling you get when coming off the base.” Indeed, in The Money Game, he produced one of the better short definitions of technical analysis which ran as follows:

“About the time of AustraIopithecus, a caveman made a wall drawing thus:

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“You can see that the vertical bar represents the range of the stock for that day, and the little lateral line is where it closed for the day. The next day the caveman did the same thing. After a couple of weeks, the cave wall looked like this:

“Thus was born the first ‘bar’ chart, named after the vertical lines. * However, this first chart was trend- less, which is to say nobody could make anything of it.

“Then one week the cave wall looked like this:

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75

71

“The caveman drew a line connecting the tops and bottoms, thus creating a Channel, and the first Trend was born.

II . . .Once it is accepted that the patterns can represent motion, it follows that a Trend is a, Trend is a Trend until it stops being a Trend. In other words, if something is going like this:

*The figures on the right include a prehistoric buffalo, and are not related. -8O-

it will keep going like that until it goes like this :

The worst problem arrives when it goes like this:

unless it goes like this:

which would seem to indicate that it was about to go:

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but then it turns around and goes:

This is called a Trap, or the Exception That Proves the Rule. ‘I

In any case, ‘Adam Smith’ is back. Following a nine-year hiatus since 1972’s Super Money, he has just come forth with Paper Money. It is required reading.

1981, fortunately or unfortunately, depending on your point of view, is not 1968, but Goodman retains his ability to unearth charming financial lunacy wherever it exists. He has tracked it this time to the real estate market in Beverly Hills and summoned up a couple of Hollywood real estate agents who are the close kin of Winfield, Robert, and the rest. Witness the following:

“The house was on perhaps a third of an acre. It was about forty years old. . .The paint was peeling. Upstairs there was a master bedroom with bath and two children’s rooms with a bathroom between them. ‘3BR. Zbth, charm.’

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“‘The asking on this is five hundred ninety-five,’ said Irma, ‘but 1 think it wouid go for five hundred seventy-five. It needs some work. ’

“We went to look at a house six or eight blocks away, north of Wilshire, north of Santa rtlonica, in a more fashionable area. It was an English-style house, tightly shuttered, on perhaps half an acre, with other houses adjacent and an alley in the back. We couldn’t get in. No one had left a key, and there was no one home.

“‘This is your tear-downer,-’ It-ma said “‘Tear-downer?’ “‘You wouldn’t want it, it has a bad floor plan, and it needs

some work, ’ she explained. “‘Somebody will buy this and tear it down? How much is it?’ “The asking price is a miiiion, but it would go for nine fifty.’ “‘What’s the single most valuable asset in selling a house?’

I asked. I’; If you can put ‘tennis court property’ in the ad, ’ Irma said. “‘How much would that add to the value of the property?’ “‘In Beverly Hills, half a million,’ Irma said. ‘That’s if the court is actually there.’

“‘But you can’t say it’s there if it’s not, can you?’ I asked. “‘You can say ‘tennis court property’ if there’s room for a

court, but you wouldn’t get a half a million more, only a couple of hundred.’

“‘Some of these prices are crazy,’ Lee volunteered.”

There is more serious stuff included, and Goodman is interesting even when serious, He has apparently spent much of the eight years since Wall Street returned to sanity studying Saudi Arabia, and his discourse on OPEC, the Eurodollar Market, etc. , is as good a discussion as any we are aware of on a complex subject aimed at the intelligent layman. Interestingly enough, after discussing all of the currently fashionable worries, in the international financial pitcure, Goodman’s final investment conclusion, in the penultimate chapter, is : “Let’s look at the stock market. Could be a real party. I’ He quotes a French adage: “Achetez aux canons, vendez aux clarions -- ‘Buy on the cannons, sell on the trumpets.’ The cannons are the enemy artillery pounding your city; the trumpets are those that sound the defender’s charge that routes them .‘I

He goes on to note,

“A carrier pigeon took the news [of the Eattle of Waterloo] across the Channel to the man who had helped the duke finance his campaign. Nathan Rothschild gave the bird a pigeon bon bon, went to the exchange, sold a little just to create a mild panic, then threw his fortune on the buy side, after which there was a bull market that lasted for ninety-nine years. That is the real way to buy on the ‘cannons. ”

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Not only is Paper Money 100 times more literate and 1000 times better written than the typical gloom-and-doom scenarios which constitute the bulk of cur- rently-available financial reading for the layman, it is possible to venture that it will be closer to being right.

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