journal of technical analysis (jota). issue 06 (1979, november)

Market Technicians Association

JOURNAL Issue 6 November 1979

MARKET TECHNICIANS ASSOCIATION JOURNAL

Issue 6

November, 1979

Published by: Market Technicians Association 70 Pine Street

New York, New York 10005

Copyright 1979 by Market Technicians Association

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Market Technicians Association Journal

Editor: William DiIanni, V.P. Wellington Management Co. 28 State Street Boston, Massachusetts 02109 (617) 227-9500

Associate Editor: Cheryl Stafford Wellington Management Co.

Editorial Advisor: William S. Doane Fidelity Management & Research

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

David Bostian, Jr. Walter R. Deemer Stan Lipstadt Henry 0. Pruden, Ph.D. David L. Upshaw

MARKET TECHNICIANS ASSOCIATION MEMBERSHIP and SUBSCRIPTION INFORMATION

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

Receives the Journal, the frequent MTA Newsletter, invitations to all meetings, voting member status and a discount on the Annual Seminar Fee. Eligibility requires that the emphasis of the applicant'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 $10 to regular members or 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.

Inquiries for Membership should be directed to:

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

INDEX

Market Technicians Association Journal - November 1979

Page

Editorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

INDICATOR ANALYSIS

Interest Rates and Stock Prices . . . . . . . . . . . . . . . . . . . . 9 by Stan Lipstadt

Public/Specialist Short Sale Ratio Revisited . . . . . . . . . . . . . 15 by Walter R. Deemer

Cycles in Consumer Sentiment - A New Clue to the 41-Month Cycle . . . . 19 by Bernard Fremerman

GENERAL TOPICS

Catastrophe Model: A Model for Technical Analysis . . . . . . . . . . . 27 by Henry 0. Pruden, Ph.D.

The March of Timing . . . . . . . . . . . . . . . . . . . . . . . . . . 35 by David L. Upshaw

Professional Opinion and Common Sense . . . . . . . . . . . . . . . . . 43 by James L. Fraser, CFA

STATISTICALLY SIGNIFICANT

A Performance Simulation of Technical Analysts . . . . . . . . . . . . 49 by Clinton M. Bidwell III

Intraday Demand/Supply Analysis - A Ground Up Approach to the Market . 63 by David Bostian, Jr. and Howard Wine11

Book Reviews

Buy Low, SellHigh . . . . . . . . . . . . . . . . . . . . . . . . . . 67 reviewed by William DiIanni

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6

EDITORIAL: “ASK GUTENBERG”

It is fair to say that the purpose of the Journal is to expand the scope of technical knowledge, not only for its members but also for those of subscriber status, subscription holders, as well as the public at large.

It is equally fair to say that the Journal is a reflection of the organization as a whole and of the members individually. It is unquestionably the most visible aspect of the Association other than perhaps its Annual Seminar. Moreover, because it is printed, it is the most durable . . . ask Gutenberg. Hence, it goes without saying, that its image should be enhanced as much as possible and its support should be universal.

There was a time when having the opportunity to write for a Journal of this nature would have been considered a tremendous honor. Only our older members recall the time when account executives had to hide their charts in their desk drawers. Using them during work-time was frowned upon by many office managers, even if they themselves were closet technicians. Now that the light has been allowed to shine from under the desk, few seem willing to bask in it.

Consequently, it is more than a bit disturbing that any editor should have to beg or cajole any person into writing for this publication. Time is precious for everyone as everyone is striving for livelihood in their particular aspects of the field. But if you have applied for membership and have been approved in due course, you have understandably accepted respon- sibilities as well as privileges. The staff should not have to ask for material for subsequent issues. You should be ready, willing and able to submit articles or book reviews at your own timely speed. And the editor's task should be relegated to gleaning the best of the brightest.

The vibrancy of any organization depends on the vibrancy of its members and supporters. And since the Market Technicians Association Journal is the most visible and enduring aspect of the organization, we all should do everything in our power to enhance its image through the impact of well- thought out, quality work . . . en masse.

Our thanks to all who made this issue possible. And, a friendly reminder, there are still plenty of empty slots for the next two issues.

William DiIanni Editor November 5, 1979

7

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8

INTEREST RATES AND STOCK PRICES

Stan Lipstadt PSM Investors, Inc.

James D. Anderson painted an excellent broad-brush analysis of interest rates and stock market behavior (MTA Journal May 1979). The analysis to follow is more specific than Anderson's, but covers a shorter time span.

Interest rates have fluctuated widely over time. Each generation of investors and consumers has learned to live with rates in some general area. Less attention is paid to generally "high" interest rates if that is all one has known. That is, the term "high interest rates" is a relative one, and can be determined only by what came before and comes after the interest rate level in question. Over a period of centuries rates have varied by many hundreds of multiples and have even been negative at certain times and under certain conditions.

It has long been my premise that interest rate levels are less informative, as they apply to stock market behavior,' than is the slope of the yield --- curve. Especially enlightening are shifts in the yield curve slope.

One measure of the slope of the yield curve is the ratio of Moody's AAA bond yields to Treasury bill yields; that is, to short-termyields.

the ratio of long-term yields tixwo time-related yields can be used, but the two

above have relatively continuous series, easily found in historical texts.

The first inquiry we can make concerns the level of the slope of the yield --- curve. The period under study is the 1,470 weeks between late 1949 and 1977. The first table below shows these computations broken into a series of "trigger points", where levels around the 100 area indicate a time when long-term rates and short-term rates are similar and readings around, say, 140 show times when long-term rates are significantly higher than short- term rates (a more normal yield curve).

Looking at that table, we find that, out of the 1,470 weeks under consideration, only 165 were found when long-term and short-term rates were within 10% of each other. That is, if long-term rates were 8%, then Treasury bill yields were 7.27% or higher. Or, if long-term rates were 92, then Treasury

9

bill yields were higher than 8.18%. This "close to inversion" condition existed for only 11% of all observations. Clearly this is a relatively rare event.

During these 165 weeks the Dow Jones Industrial Average fell at an annual rate of -21.4%.

Conversely, there were 1,305 weeks (the remainder) where the foregoing condition did not exist, where long-term yields were more than 10% greater than short-term yields. And, for those weeks the Dow Industrials rose at an average annual rate of +9.3%

Moving up the ladder a notch, there were 263 weeks where long-term and short-term yields were within 15% of each other. During these weeks (which of course includes the above case as well) the DJIA showed an annual rate of change of -11.7%. And the converse event, where long and short-term rates were more than 15% apart, corresponded to a DJIA rate of return equal to +9.5% per year.

The least restrictive trigger point covered (140%) showed the most positive DJIA results, while the most restrictive trigger point (110%) showed the largest negative results.

CONCLUSION: THE DJIA TENDS TO DO BEST WHEN THE SPREAD BETWEEN LONG-TERM AND SHORT-TERM INTEREST RATES IS LARGE, AND TENDS TO DO MOST POORLY WHEN THAT SPREAD IS VERY NARROW OR NON-EXISTENT.

The results above are not terribly surprising. Most everyone who has even cursorily looked at the relationship between interest rates and stock market performance would conclude that during periods of monetary tightness, short- term rates tend to rise vis-a-vis long-term rates, and stock prices tend to be weak. And during periods of monetary ease, short-term rates tend to drop more rapidly than long-term rates (widening the spread) and stock prices tend to react favorably.

Having looked at the static slope of the yield curve through the above analysis, we can then look at the trend of the slope, by comparing its current value with some past value, say, 52 weeks prior. That is, it might be important to have some knowledge not only of where we are as re- gards yield curve slope, but where we have been as well.

The second table shows the results of this analysis, and some of the results of this analysis, and some of the results are rather surprising. For example, we can divide those 165 weeks when AAA and T-bill rates were less than 10% apart into two separate groups: when the rates were less that 10% apart and the trend of these rates over 52 weeks was down (153 weeks) and when the rates were less than 10% apart and the trend over 52 weeks was up - (12 weeks).

A glance to the right hand side of the table shows what a difference a trend makes. When the slope was 110% or less and the trend was down the DJIA declined at an annual rate of -18.01%. But when the slope was 110% or

10

less and the trend was up the DJIA showed a rate of return of -53.88% -

Similar spreads between the various trigger points can be found throughout the table. In every case, the trend of the slope was meaningful, sometimes very much so. Even at the midpoint of the data, where the slope is 120% and the number of weeks is essentially divided in two, 543 weeks vs. 510 weeks, we find in the first case a DJIA gain of 5.59%, and in the second a DJIA gain of +16.88%.

One caveat: it would be unfair to the data to draw very strong conclusions about the 12 week performance when the spread between rates was less than 10% and the trend was up (-53.88%). Those 12 weeks constitute an extremely small sample size to the total universe, and they all occur during 1974. However, the concept is clear.

Looked at from the bullish sense first, if the current AAA/T-Bill Ratio is greater than say, 125%, the DJIA has an expected annualized rate of gain equal to almost +13% (Table I). Further, if that 125% reading is higher than that seen 52 weeks earlier (that is, the trend is up) that expected annualized rate of gain rises to almost +19% (table 2).

From the bearish side, levels below 120% on the AAA/T-Bill Yield Ratio have been accompanied by DJIA annualized rates of return of about -11% (Table II). Further, if that 120% ratio is higher than that seen 52 weeks earlier the Dow Industrials' expected annualized return drops to about -31%.

CONCLUSION: THE DJIA TENDS TO DO BEST WHEN THE RATIO OF LONG-TERM TO SHORT- TERM INTEREST RATES IS HIGH AND GREATER THAN A SIMILAR COMPUTA- TION MADE 52 WEEKS EARLIER. THE DJIA TENDS TO DO MOST POORLY WHEN THE RATIO OF LONG-TERM TO SHORT-TERM INTEREST RATES IS LOW BUT HIGHER THAN THE READING OF A YEAR PRIOR.

What we have here is a surprise, at least to me. We can agree that, from the bullish side, falling rates are positive and, the more steeply sloped the yield curve becomes, the more bullish the configuration for stocks.

But the bearish argument is fascinating. Picture, if you will, a period of tightening monetary policy resulting in a relatively flat (or inverted) yield curve. After the general peak in interest rates, and after the yield curve begins to return to a more "normal" condition, STOCKS BECOME MOST VULNERABLE TO DECLINE.

This brings us to the current situation, where we have yet to see any material improvement in the slope of the yield curve. The AAA/T-Bill Yield Ratio first dropped below 110% on September 22, 1978. It has tended to decline steadily from that point and, with only two exceptions as this is written (May 19791, has been inverted since December 22, 1978.

If interest rates do not improve materially until the fourth quarter of 1979 (and you will know that by the time you read this), the trend compar- isons will be especially negative insofar as stock price performance is concerned, until the slope of the yield curve exceeds 120%. History would -------

11

suggest that such a shift will take at least 8-10 weeks to accomplish, and -- during that shift market vulnerability will be at its greatest.

There are some technicians who maintain that interest rate studies are beyond the purview of the stock market technician. However, as professional investors we must all acknowledge, sometimes grudgingly, that a relationship between the two areas has existed and may still exist. When stock prices do not appear continually accommodating to our argument we sometimes appear all too willing to ignore the data and go with the current price trend. Too many times than we care to remember this has caused major judgment errors. As this is written (May 1979) there is no evidence that any improvement in the interest rate structure is occurring. The AAA/T-Bill Yield Ratio remains exceedingly close to its poorest reading of the cycle. Until some improvement is seen, the trend outlook for equities remains decidedly unfavorable.

Table I

MOODY'S AAAITREASURY BILL YIELD RESULTS USING D.J.I.A.

1949-1977

NUMBER OF WEEKS TRIGGER POINT ANNUALIZED DJIA RETURN

Below 110% 165 Above 110% 1305

-21.4%/yr. + 9.3%/yr.

Below 115% 263 Above 115% 1207

-11.7%/yr. + 9.5%/yr.

Below 1.20% 366 Above 120% 1104

-lO.g%/yr. +11.4%/yr.

Below 125% Above 125%

459 - 9.6%/yr. 1011 +12.9%/yr.

Below 130% Above 130%

557 913

- 7.4%/yr. +14.0%/yr.

Below 135% 616 - 5.8%/yr. Above 135% 854 +14.2%/yr.

Below 140% 659 Above 140% 811

- 4.3%/yr. +14.0%/yr.

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Table II MOODY'S AAA/TREASURY BILL YIELD

RESULTS USING D.J.I.A. 1949-1977

(INCORPORATING 52 WEEK TREND)

TRIGGER POINT NUMBER OF WEEKS ANNUALIZED DJIA RETURN

153 -18.01%/yr. 12 -53.88%/yr.

TREND

Below 110% Below 110%

Down UP

Above 110% Down Above 110% UP

707 545

+ 5.17%/yr. +13.97%/yr.

Below 115% Down 232 Below 115% UP 31

-10.33%/yr. -2l.l9%/yr.

Above 115% Down 628 Above 115% UP 526

+ 4.99%/yr. +14.09%/yr.

317 -_ 7.36%/yr. 47 -31.23%/yr.

Below 120% Down Below 120% UP

543 + 5.59%/yr. 510 +16.88%/yr.


Below 125% Down 377 - 6.80%/yr. Below 125% UP 81 -22.12%/yr.

Above 125% Down 483 + 6.81%/yr. Above 125% UP 476 +18.85%/yr.

Below 130% Down 446 - 5.14%/yr. Below 130% UP 110 -15.89%/yr.


414 + 7.21%/yr. 447 +19.87%lyr.

13

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14

PUBLIC/SPECIALIST SHORT SALE RATIO REVISITED

Walter R. Deemer, Sr. V.P. The Putnam Management Company, Inc.

John McGinley and I wrote an article for the May 1979 MTA Journal entitled "The Non-Member Short/Specialist Short Ratio". The ink was hardly dry on that article when one of the strategies we had analyzed in it, which employs an 8-week moving average of the ratio (and had shown impressive past results) rendered a very marginal sell signal by declining below its bearish threshold of 35%--but only to 34.9%--and promptly reversing. This sell signal was generated on May 25th at 836.28 on the DJI, and it prompt- ed me to do a lot of work at the time (including carrying the analysis back another 19 years in time) to try and determine whether the sell signal was valid or not. I reached the following conclusions:

1. The best single strategy was to use a sell threshold of 34.5% or 34.6%. This strategy, using the Dow Jones Industrial average, has generated 2251 points profit since 1946 (see Table l).*

2. A sell threshold anywhere between 32.7% and 37.1% sacrifices no more than 38 points of the 2251 points of profit generated at 34.5%; statistically, any profit in this range is "just as good" as any other point.

3. A sell threshold of 34.9% (which picks up a sell signal on May 25 of this year at 836) is statistically almost as good as using 34.6%; it generates 2226 points profit versus 2251.

*The specific strategy is as follows: Sell when (a) the 8-week moving average of the P/S ratio falls below 34.7%‘ and (b) there are two subsequent weeks (not necessarily consecutive) when the weekly P/S ratio is greater than the 8-week moving average. Buy when (a) the g-week moving average rises above 60%, and (b) there are two weeks (not necessarily consecutive) when the weekly ratio is less than the 8-week moving average. (On the buy side, the week that the 8-week moving average first rises above 60% can be counted as one of the two weeks required for a reversal if that week'sratio is less than the 8-week moving average; on the sell side, the same week that the moving average first falls below 34.7% cannot count as one of the two weeks.)

15

4. On the high side, performance suffers significantly when the threshold reaches 37.2%, and it really falls off above 38.7%.

5. On the low side, decent results are obtained with a sell threshold as far down as 25.2% Moving the threshold below that level fails to generate a sell signal before the 1970 bear market, although statistically the results are still good. Even then, any strategy which kept one in the market during a decline of that magnitude probably eliminates any lower threshold (than 25.2%) from consideration from a real-world viewpoint.

6. A sell threshold of 25.5% generates a profit of 2063 points on the Dow Jones Industrial average since 1946 (see Table 2); it also produces the best average profit: 137S, points. Unfortunately, it also kept you locked in the market during most of the 1969-1970 and 1973-1974 bear markets.

7. Conclusion: Since relatively similar results statistically are obtained using a sell threshold between 25.5% and 37.1% and very similar results are obtained between 32.7% and 37.1%, it is unlikely that any major profit or loss will be generated by the May 25 sell signal at a level of 34.9% versus the non-sell signal at a level of 34.8%. This means that the sell signal at 34.9% (at a DJI level of 836) probably will be followed by a buy signal at approximately the same level in the market or by a more severe sell signal (below 34.9%) at approximately G same level in the market. If this is not to be the case, and a major profit or loss is generated at 34.9% versus 34.8%, the statistical evidence slightly favors the non-sell signal at 34.8% versus the sell signal at 34.9%; however, the difference between the two strategies is very, very small. But the available evidence is that the recent sell signal was not valid.

8. The buy side threshold is much clearer: the ratio must be above 59.8% (in order to avoid a very premature buy signal in the 1959- 1960 bear market) and it must be below 60.3% (to generate the buy signal in December of last year at 805).

9. Selling strategies that use a different method for indicating a reversal once the 34.6% threshold is reached are less successful. If the two weeks when the weekly ratio is above the moving average can include the week the moving average first drops below 34.7% (as on the buy side) 20 points of profit are lost. If we wait for the 8-week moving average itself to turn up (by at least 1%) 74 points of profit are lost. Waiting for the second consecutive up week in the moving average costs 152 points, and finally, waiting for a third week when the weekly ratio is above the moving average reduces profits by 235 points.

10. The biggest single problem with this indicator remains its very early buy signal during the 1973-1974 bear market (in May 1974 at 817). Alas, the world's best single technical indicator is not perfect.

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TABLE I

SUMMARY OF BUY AXD SELL SIGNALS USING THRESHOLDS OF 60% AND 34.52

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

May, 1946 January, 1947 September, 1952 May, 1953 May, 1956 March, 1957 August, 1957 December, 1957 December, 1958 October, 1960 March, 1961 July, 1962 July, 1963 November, 1963 May, 1964 July, 1964 September, 1964 November, 1966 April, 1967 March, 1968 June, 1968 September, 1969 December, 1969 June, 1970 May, 1971 July, 1973 November, 1973 May, 1974 June, 1975 October, 1975 March, 1976 March, 1978 June, 1978 January, 1979

Right/Wrong 2812

DJI Points +2251

175

272

474

440

578

585

760

825

819

866

837

720

892

817

824

759

805

208

271

516

497

573

672

721

829

867

883

904

793

936

908

872

988

837

+ 33 + 96

-1 +244 i- 42 -t 23 -t 57 -t-133

-5 -+ 94 + 87 +136

-39 + 69 + 4 -t 42 + 48 + 64 + 17 + 38 + 67

-44 + 73 +216 + 44 + 16 + 91 + 55 I- 48 +164 +229 + 78 + 32

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TABLE II

SUK+!!Y OF BUY AXD SELL SIGNALS USING THRESHOLDS OF 60% AND 25.5%

Sell

Sell

Sell

Sell

Sell

Sell

Sell

Sell

BUY

BUY

BUY

BUY

BUY

BUY

BUY

BUY

August, 1946 January, 1947 Hay, 1959 October, 1960 May, 1961 July, 1962 June, 1965 November, 1966 August, 1967 April, 1968 July, 1968 August, 1969 April, 1972 June, 1973 March, 1976 Xarch, 1978

175

518

585

819

866

836

892

759

204 + 29

644 +469 + 66

706 +128 +121

901 +316 + 82

921 +102 + 55

922 + 56 + 86

963 +127 + 71

1003 +111 +244

Right/Wrong 15/O

DJI Points +2063

18

CYCLES IN CONSUM R SENTIMENT- A NEW CLUE TO THE 4 5 -MONTH CYCLE

Bernard Fremerman

Introduction:

Since 1952 the Institute for Social Research at the University of Michigan has conducted surveys of consumer sentiment. The surveys reflect consumer attitudes on a wide spectrum of economic questions. They are conducted quarterly and reflect personal financial attitudes, expected business conditions, anticipated changes in unemployment, prices,interest rates,and a number of other economic parameters.

This type of survey work has been called "psychological economics," and its purpose is to record the history of attitudes. The history should then reflect changes from optimism to pessimism and back to optimism. Such a record can be useful in determining financial plans on the individual level. This portion of the economy covers discretionary spending, and the "sentiment" index should reveal the willingness to spend, or the reluctance to spend on consumer durables. It presumably should be a leading indicator of actual expenditures such as for cars and refrigerators and houses, etc.

A study of Chart A will reveal that the major turning points in the Index of Consumer Sentiment are quite similar to the major turning points in the Dow-Jones Industrial Average, that other index of fluctuating optimism and pessimism. This chart compares the logarithms of each series on the same time scale.

I decided to investigate the Consumer Sentiment Index to determine if it contained any periodic cycles, and if so are the periodicities found in other data. As it turns out there is a great deal more work involved than I had anticipated - even with a computer. This is a preliminary report on two cycles in this index.

The Index of Consumer Sentiment used here covers the period from the third quarter of 1952 through the second quarter of 1978. It is shown as the solid line on Chart A.

19

v

. 1.

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4’ I

I I

I I I

-

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, b

- 89GO

CHART A: 1NI)k.X OF CONSUMER StNTlhlLNT (Univtnlly oi hlichiyanl VS DOW-JONES INDUSTRIAL AVtRACC

I I I I I I I I I I I ,965 1970 1975

Results:

I isolated two cycles in the Consumer Sentiment Index that appear to be statistically significant. They are 49.15 months and 40.92 months in length. If the 49.15 month (16.38 quarters) cycle continues, it has an ideal low in the third quarter of 1978 followed by an ideal peak in the third quarter of 1980. The 41-month cycle (13.64-quarters) has an ideal trough in the fourth quarter of 1977, followed by a crest in the second quarter of 1981.

These two cycles are combined and compared to the detrended data in Chart B.

Technical Details:

I first made a preliminary spectral scan of the entire series with the Foundation's Systematic Periodic Reconnaissance (SPR) Program. The span extends from the first to the thirteenth harmonic. A periodogram of this analysis is shown in Chart C. The strongest spectral peak is at the 6.3rd harmonic, corresponding to 16.34 quarters. Other peaks occur at 21.45 and 13.37.

20

1 I

1 \ \

\ , I \ :

\ : - ..I

I I 1 1965

-

/ -\

- 9Gf

(‘IIART II. INDEX Of- CONSUMER SLN TlMtNT (University ul’ Michigm) I)lil’ARl’URl~S l-ROM A 17.QUARTER TRLNO VS COhllHNLU IIWAL 16.3X QUARTERS (49.15 AION~HSI AND

I3 64 ()U,\RTkRS (41.MONTII) C‘YCLt.S

I. I I‘ ; 8’ * \ ,

v”\ \ / \ ’ I’ *’

I I I

-

I I I

(lIARI C’- SYS’ILMAIIC 1’1~1~101) Rt(‘ONN/\ISSANCL

INI)I-X 01, ~~ONSllhll~R SI.N’l IhlkNT (Ihivcnily of Michigm~)

I used the departures from a 17 term moving average of the logs of the index and arranged them in a periodic table. The best fit in terms of amplitude and regularity seemed to be in a periodic table of 16.38 terms or 49.15 months. Chart D is a display of this cycle arranged in halves and as a total for the entire array. The actual data points as well as a fitted sinusoid are shown. Because there are only enough data for the 5 repetitions of this cycle, the regularity in the various sections is not particularly consistent. However, the pattern for the average of all 5 repetitions is remarkably regular (bottom curve of Chart D) and remarkably close to sinusoidal in shape. The Bartel's test of significance shows a probability of -048 indicating that there is 1 chance in about 21 that this cycle is the result of chance. The fitted sinusoid has an amplitude of plus or minus .0318 in logs.

Chart E shows a plot of the departures from the 17 term moving average of the logs of the data. Also shown on Chart E is the ideal sine shaped curve with a period of 16.38 terms (49.15 months). On Chart F is a plot of the 17 term depart-

F

2-

u-

H-

I2 -

I" -

18 -

'2 -

0 -

x-

E,,t,,u Arrdv \ ‘\ ,’

--. ‘\ -’ /P+ \ \

/I \

\ I

/

/’

III III I III III I 5 10 I5

ures, smoothed with a 7-term moving average. It can also be compared to the idealized 16.38-term cycle. If this cycle continues, it should have reached a low in the third quarter of 1978 and will reach the next peak in the third quarter of 1980.

On the basis of the periodogram, (Chart C) the next period investigated was in the neighborhood of 13.37 terms. I followed the same procedure as before, using the logs of the departures from a 13 term moving average, and fitted the data into a 13.37 periodic table. A consistent cycle of about this length could not be found. On the assumption that I was encountering inter- ference from the 16.38 term cycle, I removed it from the logs of the data and ran another SPR. The resulting periodogram showed the strongest amplitude at 13.73 terms.

Then using the logs of the data with the 16.38 term cycle removed, I ex- tracted the departures from a 13 term moving average and arranged them into a periodic table. This cycle was revealed to have a period of 13.55-terms (40.65 months) with an amplitude of plus or minus -015 (in logs). It has a

probability of -0147 indicating that there is only 1 chance in 68 that it could have come about through chance.

22

205 1,. ShlOOlHEI) DLPAKTUKLS FKOhl 4 I ‘I-QUAKTER \VtKAGt A

I I CIIAKTS t. F INl)tX OF CONSUMLK SLNTIBII NT (Unirrrdy ul MichiyanI

Till: l6.H QUAKTLR CYC Lt (The Ideal Cycle is SIIOWI~ AI lhr UrcAm Liw) I I)tPAKTUKtS FKOhl h I ‘I-QUAKTLK 4Vtl<,\GL

A number of other approaches were tried to isolate this cycle. The best and most significant result was found when the 16.38 term cycle was removed from the 17 term departures. The residual was then arranged into an array of 13.64 terms or 40.92 months. As shown in Chart G, there is a fairly regular pattern through the entire set of data even through only six repetitions of the cycle available. The Bartel's test of significance indicated that a cycle of this regularity could not be the result of chance more often than once in 86 times. A sinusoid fitted to the data has an amplitude that is plus or minus .0125 in logs.

,202 -

200 .

198 -

Chart G: Indc\- of Cowumer Sentiment (University of Michigan) Ikp.lr~ures frotn a I ‘I-Quarter Trend Less the 16.3%Quarter Cycle - Awmge -- Sine Cwve

Chart H shows the 13.64 term cycle plotted along with smoothed departures. The last trough of this cycle was ideally in the fourth quarter of 1977. A peak should be reached in the second quarter of 1979 and another trough in the first quarter of 1981.

On Chart B, I have plotted the combination of the 16.38 and 13.64 term (49.15 and 40.92 months) cycles along with the departures from a 17 term

moving average. The synthesis of the two cycles is extended through the year 1984. Based on a projection of these two cycles, consumer sentiment should have reached a low in the second quarter of 1978. Happily, things may be looking up - although I sometimes wonder. We should arrive at a new peak about the second quarter of 1980, and expect the following low two years later.

23

CHART II: INDEX OF CONSUMER SENTlhlENT (llnivrtsity of Michigan)

ShlOOTIiEI~ DEPARTURES FROM A 13.QUAKTFR AVEKAGES

VS I’KE IDEAL 13.64 QIJARTER (40.92.MONTH) CYCLE

A good deal mOre analysis needs to be done on this series. Preliminary work shows that another cycle of about 21 months exists in the data and that it is very regular.

Comments:

What may be most significant about these findings is the elusive 40 month cycle. Gertrude Shirk has written recently on the status of the so called 40 month cycle in stocks. (Cycles, November 1976, Page 172 and Cycles, October 1977, Page 151). The phasing of the 40.92 cycle in the Index of Consumer Sentiment is very close to the phasing of the 40.68 cycle in stocks since it seemed to reverse itself in 1945.

The correspondence between stock prices and consumer sentiment is very interesting and opens the door to a number of questions. Does consumer sentiment influence the stock market? Or do stock price fluctuations influence consumer sentiment? Could the undulations in both be caused by something else? Perhaps the correspondence "just happened" through a fortuitous series of random circumstance. Is there something in our environment that causes patterns in mass human behavior - patterns that are predictable to a degree - but are unpredictable in individual cases? The decay of radioactive material may be a similar phenomenon where the half- life of the mass is precisely predictable, but the decay of the individual particles is indeterminate.

How important is the media in influencing consumer sentiment? In my own personal observations, I find that press reports leave me confused. Accord- ing to various reports (based largely on tests on rats) cancer has been linked to smoking, drinking, coffee consumption, depression, emotional stress, chemical additives, industrial pollution, sugar substitutes, certain anesthetics, and an improper diet (whatever that is). As I see it, the main result of all these cancer theories is that rats can get cancer from almost anything. What effect do media reports of this nature have on public sentiment?

24

For years I have read of the problems that India suffers due to food short- ages and over-population. There seemed to be no solution. In the spring of 1978 the Kansas City Times carried a news story about the "green revolu- tion" taking place in India. This article reports that India will be self- sufficient in food production and be a net exporter of food by the year 2000. This shocking revelation was followed by a Wall Street Journal editorial a month later, which discussed India's inability to solve her food and population problems.

How do these conflicting reports come about? What influence do they have on public opinion? How much harm and how much good do they do? The important thing may be to realize that many of the "facts" as relayed to us by the media are not facts at all. Much of the time they are opinions presented as facts. Sorting out which is which may be an impossible task. But, an index such as the Consumer Sentiment Index computed and recorded over time by the University of Michigan Institute for Social Research is an effort to measure and clarify what at first appears to be mass confusion.

The possible presence of cycles in these data may be of another step in bringing order into confusion. It will be interesting to see how these come out over the next few years.

CYCLES (ISSN 0011-4294) Vol. XXX, 1979, No. 3

Foundation for the Study of Cycles, Inc. 124 South Highland Avenue

Pittsburgh, Pennsylvania 15206

25

intentionally blank

26

CATASTROPHE MODEL: A MODEL FOR TECHNICAL ANALYSIS

Henry 0. Pruden, Ph.D.*

they are almost invariably wrong. (1)

That the technicians have a spotty record is not surprising if one examines the theoretical frameworks or models which these chartists use to guide the construction, interre- lation and interpretation of their indicators In fact, theoretical frameworks are conspicu- ous by their absence. Little exists beyond simple verbal anchorings to "supply and demand", "mass psychology", "fear and greed", "inertia" and similar global notions. Tech- nicians' skills and interests seem to lie outside of model building. Typically, technicians love to create indicators with which to predict the behavior of stocks or averages Give them a few positive correlations and they hatch a new system for beating the market.

Technical analysts suffer from too much data, not too little. The super- abundancy of data probably leads them to employ too many statistical indicators, many of which are redundant, conflicting, or downright confusing. As a result, technicians sub-optimize: they have too many opportunities (ex- cuses) for making buy and sell decisions. These leave technicians prey to fear and hope emanating from within themselves and to social pressure closing in on

flGURE1

them from without. No wonder there exists kiiZi%GTL~~&E~~&%Z:

that stigmatic indictment of the chartist mmt and taka action with fespact to it. CaJ.

fraternity: when advisors hold a consensus opinion which is extremely bullish or bearish,

*Dr. Pruden is a private investor, a Lecturer at Golden Gate University and a member of the Board of Directors of the Technical Securities Analysts Association of San Francisco.

27

Models are the weak link in the market (technical) decisions support system

(see Figure 1). The technicians' role is to answer money managers' questions on when optimal peaks and troughs are reached by conducting (largely statistical) analysis of data according to models of the market. Models are ideas of how the world works and are therefore guides to seeking out and evaluating what is interesting and worthwhile in the data.

What is lacking in the arena of market timing is a comprehensive, unifying, theoretical model. Indeed, the paucity of sound, unifying frameworks plagues the social sciences in general, and stock market analysis in particular. Until recently, a major, new general model was simply unavailable. But now the promise of a vast and powerful paradigm has become available with the discovery of CATASTROPHE THEORY.

CATASTROPHE THEORY

"Catastrophe theory is a new mathematical method for describing the evolution of forms in nature. It was created by Rene Thorn who wrote a revolutionary book "Structural Stability and Morpho- genesis" in 1972, expanding the philosophy behind the ideas. (3) It is particularly applicable where gradually changing forces produce sudden effects. We often call such effects catastrophes, because our intuition about the underlying continuity of the forces makes the very discontinuity of the effects so unexpected, and this has given rise to the name. The theory depends upon some new and deep theorems in the geometry of many dimensions, which classify the way that discontinuities can occur in terms of a few archetypal forms; Thorn calls these forms the elementary catastrophes. The remarkable thing about the results is that, although the proofs are sophisticated, the elementary catastrophes themselves are both surprising and relatively easy to understand, and can be profitably used by scientists who are not expert mathematicians." (4)

In a pioneering effort, Zeeman attempted to show how the elementary catastrophe, the CUSP CATASTROPHE model, could explain the unstable behavior of stock exchanges. (6) He be1 ieved a similar model could be applied to cur- rencies, property markets, or any market that admits speculators. In essence, Zeeman held that all the pertinent mathematical features of a stock exchange could be synthesized into a single concept, the Cusp Catastrophe. (Exhibit 2 shows a diagramatic rendition of a Cusp Catastrophe.)

Equilibrium Surface

The model posits two parallel surfaces. The upper behavior or equilibrium surface is represented by a price index such as the Dow-Jones Industrial Average. This behavior surface is further sub-divided into a top sheet representing bearish behavior. Each point on the behavior surface is an equilibrium juncture between supply and demand, even though incremental and transitory.

Near the center of the behavior surface lies the catastrophe model's most

28

Exhibit 2 A CUSP CATASTROPHE MODEL OF A STOCK EXCHANGE

interesting feature - a fold curve or cusp. What this suggests is that there are no equilibrium (turning points) available until the top sheet is reached after a buying stampede or the bottom sheet is reached after a selling panic. Notice that the abstract model shows the behavior surface curving over to a threshold point, after which comes the panic sell-off. One can visualize top reversal patterns, bottom reversal patterns and breakouts at the thresholds. Obviously, the thresholds are points at which to sell or buy, which is precisely how current technical theory and practice instructs us to act,

Control Surface

Since the behavior surface is the dependent variable, there must exist some independent variable(s) which account for the index or to which the index may be attributed. In Exhibit 2, the independent variables are fear and greed.

The model featured in this article (Exhibit 2) presents fear and greed as two normal but opposing factors lying on either side of the cusp. The placement of fear and greed as conflicting variables is an interpretation by this author. The approach given here to the control variables differs sharply from Zeeman's original model. Zeeman hypothesized that there are two types of investors, fundamentalists and chartists, or investors and speculators. To him it was the excessive, speculative behavior at the top which set up selling panics. However, his version did not envision buying stampedes.

29

Fear and greed are not viewed as single state variables in Exhibit 2; rather they vary along spectrums from lesser to greater degrees. Fear, for example, may be viewed as ranging from euphoria to confidence to hope to concern to worry to panic; in other words, fearlessness to fear or optimism to pessimism. Greed may be defined as ranging from low to high levels of greediness, such as when greedlessness or parsimony leads to overselling and undervaluation, and covetous drives lead to overbuying and overvalua- tion. A different perspective is to view fear as activating selling or supply and greed as activating buying or demand.

Why don't fear and greed simply cancel each other out, leaving a stable, neutral market index? The reason is because fear and greed are oppositional variables held together in dynamic tension - they are reflected in the struggle between the bears and the bulls. According to the Cusp Catastrophe Model, the conflict between fear and greed is the motor force which drives the market. The price index at any one point reflects the relative strength of these bullish and bearish forces.

Cusp Model in Operation ---

Now let us imagine Exhibit 2 in operation. The flow of the market index takes place over a smooth surface composed of equilibrium points. Changes in the control variables, fear and greed, have unique responses on the behavior surface. The dynamic process of the model causes the index to seek out local points of stable, albeit temporary, equilibrium.

Starting at a bear market low, where the market index is on the lower at- tractor sheet, the level of greed (demand) is suppressed by the level of fear (supply). Mounting greed gradually overcomes fear until the edge of the sheet is reached, at which point the market breaks out of an upside reversal pattern via a catastrophe jump to the top sheet. The index then flows along a rising channel on the top sheet until the bullish potential is exhausted. At that point, both greed and fear are high. Finally, as fear overcomes greed the market index reaches a threshold on the top sheet, then plunges to the bottom sheet via a bearish catastrophe jump.

What is dramatically different about this dynamic flow is that when both fear and greed are high and in opposition, the behavior surface solution is bimodal: within the fold curve zone a slight divergence in a control parameter can trigger either a bull catastrophe or a bear catastrophe.

Predicting which way the index will jump out of a bimodal or horizontal equilibrium trading range becomes less uncertain when the antecedent levels of fear and greed are known. Intuitively we can imagine the probable course of security prices if we frighten a group of greedy investors after there has been a substantial rise in the market index.

Salient Properties

What are the salient properties of the Catastrophe Theory model, and what are their stock market counterparts?

30

"Five properties characterize phenomena that can be described by the Cusp Catastrophe. The behavior is always bimodal in some part of its range, and sudden jumps are observed between one mode of behavior and the other. The jump from the top sheet of the behavior surface to the bottom sheet does not take place at the same position as the jump from the bottom sheet to the top one, an effect called hysteresis. Between

the top and bottom sheets, there is an inaccessible zone on the behavior axis; the middle sheet representing least likely behavior has been omitted for clarity. Finally, the Cusp Catastrophe implies the possibility of divergent behavior."(5)

1. Biomodality, 2. Catastrophic jump, 3. Hysteresis, 4. Inaccessible zone, and 5. Divergence, are squared up with their stock market equivalents in the accompanying table.

SUMMARY AND IMPLICATIONS

Since applications of Catastrophe Theory are new and few, they tend to be more pregnant with promise than overflowing with fully ripened fruit. Indeed, it is critical for the reader to bear in mind the "newness" of the field in evaluating the methods and promise of this suggested stock market application. Despite its relative youth, I believe there are several characteristics of the Cusp Catastrophe Model which make it, perhaps the framework for explaining a securities exchange.

31

Cusp Catastrophe Features Stock Market Counterparts

1. Biomodality 1. Horizontal trading ranges.

2. Catastrophic jump

W h,

3. Hysteresis

4. Inaccessible zone

5. Divergence

TABLE

FEATURES OF THE CUSP CATASTROPHE AND THEIR STOCK MARKET COUNTERPARTS

2. Buying stampede; selling panic.

3. The cyclic pattern of ac- cumulation, markup, distribution and markdown.

4. Rapid moves through price areas to top attractors called Resistance zones and bottom attractors called Support zones.

5. Marginal shifts in demand by investors and price ex- pectations by speculators cause major swings in the market index.

Comments

1.

2.

3.

4,

5.

From a trading range or equilibrium zone there is the potential of either a bull breakout or a bear breakout.

For examples, look at April 1978 and October 1978 on the New York Stock Exchange.

Stock market behavior is a cyclical pattern in space and time. It does not -- fluctuate in a single area like a pen in a groove.

Prices tend to move horizon- tally ne~ar tops and bottoms, while moving vertically and rapidly through intervening zones. Simple supply/demand equilibrium models suggest the opposite.

Technical studies of trend divergence and sentiment attempt to capture part of this feature.

1. Catastrophe Theory is a mathematical logic for explaining things that change suddenly, by fits and starts. Heretofore, no reliable method existed for modeling discontinuous events, such as they exist in psychological, social, political and economic phenomena. It analyzes equilibrium and its breakdown. As such, it is ideally suited for understanding the stock market where price movements result from the balances and imbalances between buying power and selling pressure.

2. Applications of Catastrophe Theory tend to be highly qualitative in nature. It does not pretend to render pinpoint or unalterable pre- dictions far in advance. The theory does not negate the art of interpretation.

3. The prior history of behavior states is required to predict the future. This undercuts the assumptions of the "random walk" or efficient market hypothesis. Catastrophe Theory underscores the relevance of the historical, chart approach to analyzing the market.

4. The Cusp model encompasses duality and opposition. There is room for a greed axis and a fear axis. It brings the opposition between bullish and bearish forces into clear relief.

5. Catastrophe Theory offers unique three-dimensional graphic model for structuring two independent and one dependent variable. It furnishes a basis for classifying and interrelating trend and sentiment variables, thereby enhancing logical clarity and empirical predictability. There is also a fourth or temporal dimension.

"The-non mathematician is seized by a mysterious shuddering when he hears of 'four-dimensional' things, by a feeling not unlike that awakened by thoughts of the occult. And yet there is no more commonplace statement than that the world in which we live is a four-dimensional space-time continuum."

Albert Einstein

33

References

(1) Dreman,

(2) Little, Journal

David, "Don't Go With the Pros", Barron's (May 8, 1978), 11.

John D.C., "Decision Support Systems for Marketing Managersll, of Marketing (Summer, 1979), 9-26. -

(3) Thorn, Rene, Stabilite Structurelle et Morphogenese, New York: Benjamin Press (1972) -

(4) Zeeman, E.C., Catastrophe Theory: Selected Papers, 1972-1977, Reading, Massachusetts: Addison-Wesley Publishing Company (1977), 1.

(5) Zeeman, E.C., "Catastrophe Theory," Scientific American (April, 1976), (April, 1976), 65-83.

(6) Zeeman, E.C., "On the Unstable Behavior of Stock Exchanges", Journal of Mathematical Economics (1974), 39-49. -

34

THE MARCH OF TIMING

David L. Upshaw Drexel Burnham Lambert, Inc.

I WHY TIMERS TRY TO TIME: THE DIFFERENCE BETWEEN $196 and $3

The lure of timing and the reason so many people are trying to figure out how to do it are the subjects of this selection. Tables 1 and 2 take the Dow Jones Industrials through their gyrations of the past quarter century and show the results of perfect timing compared with buying and holding. It turns out that the perfect timer, who went long and short beginning in 1953, turned $1.00 into $196.34 by 1979. A buy-and-hold investor saw his $1.00 grow into $3.29 over the same period. (All examples exclude taxes, dividends, and commissions.)

Our history of timing begins with Table 1, in which only long positions are assumed. If the results of Table 1 impress you, wait until you read Table 2.

The Dow Jones Industrial Average Since 1953 Perfect Timing versus a Buy-and-Hold Strategy Table 1

At Buy Sell At

1953 Account Opened

1953 low 1956 high

1957 low 1960 high

1960 low 1961 high

1962 low 1966 high

1966 low 1968 high

1970 low 1973 high

1974 iow 1976 high

1378 low 7/25/79 (1)

% Gain

103.9%

63.3

29.8

85.7

32.4

66.6

75.7

13.1 (1)

$1.00 Invested In: Perfect

Timing Buy 6 Hold Account Account

s 1.00 $1.00

2.04 2.04

3.33 2.68

4.32 2.88

8.02 3.90

10.62 3.86

17.69 4.12

31.08 3.97

35.15(l) 3.29 (1)

(1) Value of account based on recent price.

35

The perfect timer made eight buys and seven sells in the 26-year period shown. A total of 15 timing decisions in 26 years can hardly be described as wild, in-and-out trading.

The advantage of perfect timing compared with a buy-and-hold strategy over this period was 10.68% to 1, derived from dividing the ending value of the timing account, $35.15, by the ending value of the buy-and-hold account, $3.29.

Because this is an illustration of perfection, let's now assume that our timer is bold enough to go short during the major declines of this period. Table 2 shows perfect timing applied to the long and short rides of the market. As in the first example, the action starts with a buy at the 1953 low.

The Dow Jones Industrial Average Since 1953 Perfect Timing of Purchases and Short Sales

Dates-Action 8 Gain

1953 Account Opened 1953 BUY 1956 Sell 103.9% 1956 Short 1957 Cover 19.4 1957 BUY 1960 Sell 63.3 1960 Short 1960 Cover 17.4 1960 BUY 1961 Sell 29.8 1961 Short 1962 Cover 27.1 1962 Buy 1966 Sell 85.7 1966 Short 1966 Cover 25.2 1966 BUY 1968 Sell 32.4 1968 Short 1970 Cover 35.9 1970 BUY 1973 Sell 66.6 1973 Short 1974 Cover 45.1 1974 BUY 1976 Sell 75.7 1976 Short 1978 Cover 26.9 1978 BUY 7/25/79 (1) 13.1 (1)

(1) Value of account based on recent price.

Table 2

$1.00 Invested In: Perfect

Timing Buy 6 Hold Account Account

$ 1.00 $1.00 2.04 2.04 2.44 1.64 3.98 2.68 4.67 2.22 6.06 2.88 1.70 2.10

14.30 3.90 17.90 2.91 23.70 3.86 32.21 2.47 53.66 4.12 77.86 2.26

136.80 3.97 173.60 2.90 196.34 (1) 3.29 (1)

The value of the timinq account, $196.34, is 59.68 times the value of the buy-and-hold account $3.29.

Of course, the results shown in Tables 1 and 2 are 100% theoretical, and, of course, no investor can time the swings of the market perfectly. The results of perfect timing are shown to illustrate why some investors and their advisors believe that the attempt to time the market is worthwhile. The prize, in terms of performance is so potentially valuable that those who try to time the market can say to those who say it can't be done, "yes, we agree with you that timing can't be done. Except that we add one word to the end of the sentence, which then reads, 'timing can't be done perfectly.' Nothing can be done perfectly. That's not to say it can't be done at all, or that it's not worthwhile to try."

36

II BACK TO REALITY: THE TIMING RESULTS OF ONE MODEL OF THE MARKET

The theoretical perfection shown in Section I explains why I believe that the effort to time the market is worthwhile. This section shows the actual results of one timing device, my market model. The model was published in 1974. Its first real time signal came in January 1975. In the tables that follow, signals before 1975 are the results of back-testing.

The record of this model, which goes back to 1965, shows better results than a buy-and-hold strategy would have produced. The results are expressec in terms of two averages, the Dow Industrials and the Indicator Digest Average, which is an unweighted average that includes all NYSE commons. I comment on my use of this model in a section following the market model tables.

The buy and sell signals generated by the model are marked with up (Buy) and down (Sell) arrows on the chart on the next page.

The Upshaw Market Model, 1965-1969, With Dow Industrials The Model's Signals versus a Buy-and-Hold Strategy Table 3

Sell

October 1965 Account Opened October 1965 March 1966 January 1967 !4arch 1968 June 1968 February 1969 October 1970 October 1971 January 1972 October 1972 January 1975 October 1977 Aprl: 1978 3ctober 1978 July 1979: Wairlng for next Buy

- 0.6% - 0.7 + 0.3 + 9.5 + 2.2 +19.0 + 4.i

signal.

% Change

Summary: Seven buy transactions and seven sale transactions in 13 years. Five of the buys produced gains; two produced losses. The value of the model account, $1.36, is 1.58 times that of the buy-and-hold account, $0.86.

37

VALUL LI~C bumru

50

2 . . 42 40 31 3‘

I I -NiSE CbMMOic $TOthi IND I l/I/ .I

-.

38

Long and short results are shown in Table 4

The Upshaw Market Model, 1965-1979, with Dow Industrials The Model's Buy-and-Sell Signals versus a Buy-and-Hold Strategy

Jan. 1965 Account Opened Jan. 1965 Short Oct. 1965 Cover Oct. 1965 Buy Mar. 1966 Sell Mar. 1966 Short Jan. 1967 Cover Jan. 1967 Buy Mar. 1968 Sell Mar. 1968 Short Jun. 1968 Cover Jun. 1968 Buy Feb. 1969 Sell Feb. 1969 Short Oct. 1970 Cover Oct. i970 BUY act. 1971 Sell Oct. 1971 Short Jan. 1972 Cover Jan. 1972 Buy Oct. 1972 Sell Oct. 1972 Short Jan. 1975 Cover Jan. 1975 Buy Oct. 1977 Sell Oct. 1977 Short Apr. 1978 Cover Apr. 1978 Buy Oct. 1978 Sell Oct. 1978 Short 7/25/79 (1)

Dates-Action

Table 4

Result (0)

- 9.8% - 0.6 + 9.1 - 0.7 - 8.7 + 0.3 +16.5 + 9.5 - 8.5 + 2.2 +24.1 +19.0 + 8.3 + 4.7 - 4.2(l)

(1) Still waiting for Buy (Cover) Signal. Value of account based on recent price.

Summary: In 14 years, seven buy signals resulted in five gains and two losses. Of seven short positions, four resulted in gains and three were covered at a loss. The current position, a short, is held at a 4.2% loss as of July 25, 1979. The value of the model account, $1.69, as of July 1979, is 1.72 times the value of the buy-and-hold account, $0.98. (The $0.98 value assumes that the Dow was purchased in June 1965 and is still held.)

Tables 5 and 6 show the model's signals applied to the Indicator Digest Average. In Table 5, only long positions are assumed, Table 6 shows the effects of going long and short the IDA.

39

The Upshaw Market Model, 1965-1979, with Indicator Digest Average The Model's Signals versus a Buy-and-Hold Strategy

Long Side Only. Taxes, Dividends, and Commissions not Included.

$1.00 Invested In: Model Timing Buy 6 Hold

Account Account

$1.00 $1.00 October 1965 Account Opened October 1965 March 1966 + 7.5% January 1967 March 1968 +12.0 June 1968 February 1969 + 0.9 October 1970 October 1971 + 6.8 January 1972 October 1972 - 7.8 January 1975 October 1977 +53.0 April 1978 October 1978 - 0.1 July 1979: Waiting for next Buy Signal.

1.08 1.08 1.21 1.12 1.22 1.35 1.30 0.88 1.20 0.89 1.84 0.71 1.84 0.77

Table 5

Summary: Seven buy signals resulted in five gains and two losses. The ending value of the model account, $1.84, is 2.39 times the ending value of the buy-and-hold account, $0.77.

The taking of long and short positions beats buying and holding by 2.40 to to 1. See Table 6 below.

The Upshaw Market Model, 1969-1979, with Indicator Digest Average The Model's Buy and Sell Signals versus a Buy-and-Hold Strategy

Dates-Action Result (%)

Jun. 1965 Account Opened Jun. 1965 Short Oct. 1965 Cover Oct. 1965 Buy Mar. 1966 Sell Mar. 1966 Short Jan. 1967 Cover Jan. 1967 Buy War. 1968 Sell Mar. 1968 Short Jun. 1968 Cover

- 8.3% + 7.5 + 6.7 +12.0 -18.8 + 0.9 +38.6 + 6.8 - 9.5 - 7.8 +48.2 +53.0 - 8.3 - 0.1 -11.5

Jun. 1968 Buy Feb. 1969 Sell Feb. 1969 Short Oct. 1970 Cover Oct. 1970 Buy Oct. 1971 Sell Oct. 1971 Short Jan. 1972 Cover Jan. 1972 Buy act. 1972 Sell act. 1972 Short Jan. 1975 Cover Jan. 1975 Buv Oct. 1977 Sell Oct. 1977 Short Apr. 1978 Cover Apr. 1978 Buy Oct. 1978 Sell Oct. 1978 Short 7/25/79 (1)

Table 6

$1.00 Invested In: Model Timing Buy 6 Hold

Account Account

$1.00 $1.00 0.92 1.08 0.99 1.17 1.06 1.09 1.19 1.22 0.97 1.45 0.98 1.46 1.36 0.90 1.45 0.96 1.31 1.05 1.21 0.97 1.79 0.50 2.74 0.77 2.51 0.83 2.50 0.83 2.21 0.92

(1) Still waiting for Buy (Cover) signal. Value of account based on recent price.

40

Summary: In 14 years, seven buy signals produced five gains and two losses; one of the losses was only 0.1%. On the short side, seven short positions have been covered, three at gains and four at losses. The eighth short position is still open, and on July 25, 1979, it stood at a loss of 11.5%. Including the current open position, the model has given 15 signals since June 1965, and eight of them, about half, produced gains. In spite of its roughly 50-50 record of gains and losses, the value of $1.00 invested in the model's account on the long and short side of the market, $2.21, is 2.40 times that of $1.00 invested on a buy-and-hold basis, $0.92. A dollar invested only on the long side, using the model, also did much better than its buy-and-hold counterpart, $1.84 to $0.77.

I make no claim that the future performance of this model will be as good as its past performance. I do believe that a disciplined approach to timing will probably produce better results than will be obtained by making no timing effort at all.

The theoretical results of perfect timing are very impressive. The results of my timing model, while far from theoretical perfection, still beat buying and holding by a wide margin. Everyone who has research on market timing knows that back-testing and theorizing are one thing, and the real world, on a real-time basis, is quite another. In the concluding section, I recount my own real-time use of the model, the model's present status, and how I will probably interpret the next signal that the model generates.

III PEAL-TIME EXPERIENCE WITH THE MODEL

When I published the model in October 1974, the Dow was 607, headed, as things turned out, for a low of 577, made in December. The model was negative when it was first published and had been negative on a back-tested basis since October 1972.

The model turned positive in January 1975, and I accepted that signal. I also accepted the next negative signal, which occurred in October 1977. When the next positive signal arrived in April 1978, I did not accept it because none of the financial indicators in the model were positive. (They haven't turned positive yet, by the way, and I am still troubled by this fact.) I accepted the negative signal of the model in October 1978 and have been cautious without being a super (down to 500-600) bear.

I expect that the model will have 3+ positive points, 1 neutral point, and 24 negative points by the time you read this. Thus, it is close to having the four positive points I require to grade the model bullish. The needed half-point could be added to the positive side of the model, if the Dow Industrials moving average turns up enough to reach 848. (It is now 840.) I thought that this would be possible in the near future, but a closer look at the data makes me think that this isn't apt to happen for some time. Positive points could also come from a strong rally in the bond market, a sharp improvement in the bond/bills yield ratio, or by the banks getting into a free reserve position.

41

If the model reaches positive status in the near future, with one or two of its financial components positive, I will probably accept its verdict with no reservations. If the model gives a buy signal based solely on the action of its technical components, my attitude will probably be one of acceptance with some reservations. I would expect a three-to-five month rally, but I wouldn't urge a 100% commitment to stocks. The past record of the model indicates that it is a better buyer than a seller, in that all its positive signals have been followed by rallies (see chart on page 38) but not all of its negative signals have been followed by further declines. The risk of being completely wrong in following a buy signal seems, based on the model's record, to be slight.

I believe that it is better to have timed and lost (some of the time) than never to have timed at all.

42

PROFESSIONAL OPINION AND COMMON SENSE

James L. Fraser, CFA Fraser Management Associates

Roy Neuberger, a person of tremendous Wall Street experience, realizes that investing is not a science and that we must enjoy it to do it well. More- over, we should stick to what we understand, have some guts, and never become too over-confident. Or, as Phil Carret, the founder of Pioneer Fund once said: "The moral of this tale is clear-the investor, or investment advisor, who so lacks confidence in his own judgment that he won't buy any security until is is favored by the consensus of the investment community, will buy few bargains and is unlikely to achieve results to which he could point with pride. For many years Pioneer Fund's managers have adhered to the School of Contrary Opinion, as it is some times called. If a security is a favorite among professional investors, it is probably good but it is hardly likely to be undervalued. If it is unpopular, or generally unrecog- nized, careful investigation is required to estimate its basic value. Once assured that the basic value is really there, mere unpopularity will not deter the portfolio manager who is looking for results over the long term."

A rather lengthy quote, to be sure, but one of real value if you take the time to let the words sink in. Neuberger says, "The best way to make a lot of money is to analyze a security well; to find a subtle value of a company that's quite marketable and of better value than other companies in the same industry, and where the industry outlook seems good and the future hasn't been discounted."

Just another way of saying basically the same thing. What we are all saying is that investors who have the guts to buy stocks with merit when they are under weakness do well most of the time -- especially over the long run. Furthermore, if such investors are willing to accept the profit when a stock appears fairly or fully priced, while it is popular, then those funds can be used in better alternatives.

Lucien Hooper, the Financial World columnist, has been in Wall Street for 60 years now as an active practitioner and his common sense ideas are similar to others who have been involved with securities for a long period of time. What it comes down to is that perspective and patience are the essen-

43

tial requirements for investment success. Of course, long term results de- pend upon owning shares of successful companies in areas of activity that have particularly promising future prospects which just means that we have to spend a little bit of time picking industries which are in a better position and, more specifically, choosing companies that are participating within these industries.

However, the Contrary Opinion lesson is that we all tend to be influenced by whatever feelings are sweeping over the investment community at the moment and that true investing, to be successful, requires fighting these feelings. To go against prevailing psychology is not easy but it can be profitable and it is the nub of a successful Contrarian's creed.

Contrary Opinion theory is being discussed more and more in the financial media as both professional managers and amateur individuals tend to use the expression when it fits them. Accordingly, everybody is becoming aware that to do the opposite of what most people are doing is the way to win. Simply put, you win by being contrary.

Nevertheless, it is not all that simple for the inexperienced investor to be contrary since inexperience will breed a certain contempt for long term solutions. Numerous subscribers to advisory services aim at quick results, feeling that the game is not worth the candle unless a system or technique works immediately. That is why some advisories, in utilizing our type of thinking, often try to index it or put some sort of mathematical formula to it which then requires no further thinking.

Experienced investors, both professional and amateur, know this will not work because what counts is continuous thinking and not the automatic reflex action to a mechanistic forecast. An individual, fortunate enough to have an intuitive sense of values, should be able to achieve reasonable profits with some degree of consistency. The key words here are reasonable and consistency - words not in the vocabulary of those who do not yet have market experience.

Basically, my own stock selection process depends upon fundamental values that appeal to bargain hunters who are willing to buck the crowd on a long term basis. Any one selection may fail and very few immediately rise since, by myself, I am not a marketing force on what I purchase or recommend to readers. The crowd may be right for periods of time while vogues of fashion flash over the sky as a meteor, or stay around long but sooner or later all fashions and vogues set, usually not to rise again.

Look at it another way. I was recently told, to illustrate this point, by a floor partner of a New York Stock Exchange member firm that there was good buying in international oils and other stocks which were then at or near their high prices for the past year or so. Of course there is good buying in such securities because that is what creates their high prices. But does it not make sense to mention Gulf Oil, at 16 in the last half of 1974 when it was out of favor? Of course, many stocks were then out of favor so a bargain hunter had a field day. But what about at the end of 1975 when Gulf was at 20, a few points higher than at the end of 1974 but

44

still a depressed stock on a long term picture? The publicity on Gulf political payoffs at the end of 1975 just depressed the stock a little bit more so that one had time to buy a decent quality company that is now becoming an in favor item.

Delusions and Practical Investment Procedures

Webster defines investment as "The investing of money or capital in some species of property for income or profit." Now notice that the work permanent is left out. You can no longer put it away in the safe deposit box and forget it. Our fetish has been associating investments with booms and speculations with crashes. The machinery for investing or speculating in stocks has permeated every comer of this country. Institutional buyers of securities clothe themselves as the high priests of a scientific establish- ment. Only the imagination of the public is left unprotected to bend and lean with the winds of prevailing sentiment.

The protection of the individual investor is needed against a world of "experts" that bring ever-increasing pressures for group allegiance. And how does the individual investor protect himself from the waves of mass sentiment that periodically wash over him? No storm is the image of another. No safety valve will protect against all possible contingencies. Look at the panic of 1962 again - it never should have happened economically speak- ing - but it did. What protection was there then? What protection is there now? In brevity - think for yourself.

Le Bon has said: "The majority are ruled entirely by their emotions and are, therefore, the natural prey of those who do think." Associated with this is the delusion that "playing the market" is easy (a boom time phenomenon) and that there are fixed rules of the game. Learn the rules and "go to it" appears to be the order of the day. This does not deny that there is a general philosophy that may lead to success in investments. But even when the principles are understood it is the non-conformist who will benefit.

Though we cannot forsee, predict, or prophesize, we still search for solutions. Contrary Thinking, used as an aid to identify and define a problem, is frequently far more difficult than coming up with a solution. People want solutions. But they do not want to "work-think" for them. The stock market appears a natural for solutions. But you cannot forecast the stock market accurately. Such seemingly easy success is not within most of us. When you believe yourself strong enough and clever enough to meet any danger, then will the danger overcome you. If, contrarily, you always question your position, seldom are you frightened by the unexpected.

Most of us should act as true investors - forgetting about market liquidity and the speedier ticker tape - and rely on our judgments of what an investment will bring us over longer periods of time than a trading cycle.

There are different approaches to the problem. The specific investment need of many Contrary Opinion readers is to tailor a program around undervaluation. Undervaluation means you establish that the securities being purchased

45

are worth more than they are selling. Characteristics and criteria are set

up. The following fundamental guidelines may serve as a beginning:

1. Past records give a point of departure for Contrary Opinion analysis. Average earnings, dividends, asset values and their trends should be looked over. Tangible value is the secret, either in a "turnaround" situation or in a special asset stock.

2. New and relevant facts that expect to have an influence may be present. These facts should not yet be fully realized and appreciated by a majority of the financial community. When the majority knows, we should be looking elsewhere.

3. A lower speculative component is essential. The measurement and delimitation of securities into investment and speculative areas is desirable. The method is largely to ignore popular trends and to buy ex-public participation. Other- wise, you are trading.

4. After basic principles the distinction is still one of personal imagination and ingenuity. Confidence and market level factors influence price-earnings multiples. But a strict ladder analysis, where you try to "escalate" your choice over comparative choices, is not good practice.

5. Try to purchase under favorable conditions. A clear cut demonstration of superior attractiveness is still a subjective judgment. Facts and ideas favorable to purchase are remembered, while negative factors are forgotten. Usually you come to a decision, buy, and hope you have the experience and ability to admit errors as they show up and to correct them.

Our working premise is that sentiment makes markets, with perhaps over 60% of price movements being caused by investor's emotional traits. When all seems well, we concentrate on finding individual stocks that go up. As the majority of stocks still tend to trend together, our choices will usually follow the crowd. Even Benjamin Graham and David Dodd in their Security Analysis classic reflect the tempo of our times by saying: M . . . that the market is a voting machine, whereon countless individuals register choices which are the product partly of reason and partly of emotion."

When a top is reached we are not very quick at unloading. We are equity conscious and we do not assume the necessary detachment that investment decisions require. We are captive shareholders, often being misled by the orthodox semantics of Wall Street.

Another point is that what makes stock prices is marginal demand and marginal supply. To illustrate, the Canadian market, being smaller, is dramatically influenced by the infusion of U. S. money - more so, let us say, then the recent inoculation of foreign money into U. S. stocks. However, let the

46

buyer beware. The Canadian oil market, especially concentrated in what they call "junior industrials", may become a frenzy. This market begins to feed on itself, and that is where the danger lies.

The market is high and mass media makes us buyers. We feel confident. We spend money. We create an optimistic environment for ourselves, our economy and, of course, our securities.

When stocks drop, we refuse to believe tape action. Indicators then start to instruct us. We follow them. They satisfy us for a while, until we are the slaves of mechanistic methods of forecasting. Independent reason is by-passed for group-think. We feel better suffering in conformity than re- joicing alone.

Survey of Hazards

S. A. Nelson said: "All systems at times work out well. Most or all of them break down in practice sooner or later, partly, however, through the inability or unwillingness of traders to follow them when following the rule becomes expensive or dangerous. Many systems are founded upon the tendency in the market toward action and reaction, and they are oftentimes a help, but should always be put in a secondary place, the preference being given to values."

Controversy and misrepresentation surround important questions. Great ex- pectations in Wall Street are too often followed by herd thinking and fail- ure. This business of investment is a matter of attempting to project future trends of optimism or pessimism with a large capacity of judging what the public will do under certain circumstances. May I suggest a contrarian should wait for values and not over reach. A survey of hazards brings us back to some kind of sensible, fundamental approach.

One of the important reasons why there were dark ages was because of the lack of calculable law. No one could rely upon the value of money, the accuracy of weights and measures, the stability of government, the safety of farming. Complete inadequacy of all things prevented any concerted effort toward constructive work in socio-economic fields. Perhaps we have come full circle.

Today we presume we have all problems licked. Russia presumed her agricul- ture was healthy until too late. She cut her losses after the collapse. India presumed that democracy, state socialism, and aid from both East and West would bring economic success. China presumed that 1000 years of chop- ping down trees - the land having lost 9/lOths of her forests - could be overcome in less than a decade.

Insofar as action and reaction are always present in all things, the imper- sonality of the pendulum can sweep you away.

The investor's burden of proof is to be aware that industrial advances have outrun growth in logical behavior. We still have psychological forces and

47

foibles to deal with. This includes emotional reactions to concurrent publication of news and opinion. We, as investors, are not as educated as industry, statements *to the contrary notwithstanding.

Just as many uncertainties face the investor today as faced his counterpart 10 years ago. If, as often claimed, he is more aware of the relationship of uncertainties to the stock market, are his investment decisions any better? Just because he has more intelligent questions, is he emotionally more stable? A look at daily headlines would indicate emotions running rampant in other sectors of society. Excitement comes to the top in all human endeavors. Intelligent questions cannot be equated with emotional caution. The problem, stock market wise, is when do psychological conditions predom- inate?

The balancing of uncertainties is a matter of mental attitudes. Logic is largely used to back up our own desires. We can learn through books and by experience, but our ultimate successes and failures rest largely in character and temperament. Contrary Opinion selections follow logical precepts, but your own behavior patterns decide whether you win or lose.

And it is our emotions that must be controlled. As economic needs must be related to available resources, so must the contrary investor be guided to use his own thinking ability. Any program not in sympathy with his emotional climate will not work.

Do not adhere to any formula or system. Keep no idols, but rather stoke your noggin with antidotes for the temptations of conformity. Rely not on a consensus indicator approach as a substitute for contrary good sense. Then you will not be short circuited. In fact you may even win.

48

A PERFORMANCE SIMULATION OF TECHNICAL ANALYSTS

Clinton M. Bidwell III

INTRODUCTION TO BIDWELL ARTICLE

The following article contains the results of a portfolio simulation study done in 1977-1978 by Professor Clinton Bidwell of the University of Hawaii with the cooperation of the Market Technicians Association; all of the participants were MTA members. One of the terms of the agreement between Pro- fessor Bidwell and the MTA concerning the study was that any publication of the results of the study would be at the discretion of the Hoard of Gover- nors of the MTA. The Board has decided to authorize the MTA Journal to publish it here despite widespread misgivings concerning the validity of the results, the accuracy of the data, and the methodology used in the study related to communication difficulties which arose when Professor Bidwell relocated from North Carolina to Hawaii at the beginning of the simulation. As a result, participants did not receive timely confirmation of their trades, and agreed-upon summaries of standings (which were to show how each person stood relative to the others) were unduly delayed. Conse- quently, many participants were unable to participate consistently. As Professor Bidwell observes in the article "...The results...should not be considered as indicative of the actual performance of these participants in the 'real world' endeavors"; indeed, some participants felt they were so far removed as to be rendered invalid.

The Boards'decision to authorize the MTA Journal to publish Professor Bid- well's article here was made despite our misgivings because we felt that suppression of information such as that contained in the article for any reason was not consistent with the Purposes and Principles of the Market Technicians Association. We ask that our misgivings be kept in mind when you read Professor Bidwell's article, but in the end, it is each of you who will decide the merits of his study. This is as it should be.

Walter R. Deemer MTA President

1978-1979 Year

49

Prologue

In June of 1977, 38 members of the MTA agreed to participate in a real time portfolio decision-making simulation. Due to a variety of unfore- seen circumstances, the continuity of communication from my office to each participant, which was supposed to be monthly, was limited to about six communications over the 12-month period. The limited amount of feed- back had an obvious but incalculable effect on portfolio performances, as the interest of many participants waned. Therefore the results here- in presented should not be considered as indicative of the actual performance of these participants in their "real world" endeavors.

I cannot overemphasize the importance of this caveat in appraising the simulation results. It would begrosslyunfair to the participants to generalize from any of these findings.

Introduction

A detailed search of academic research on technical analysis uncovers very few recent papers in this area. ' This is apparently due to the uni- formity of past research findings which fail to find value in technical methods and are in fact supportive of the Random Walk hypothesis.2 Studies conducted and authored by Roberts: Osborne: Alexander: Granger and Morgensternf Moore: Famaf Fama and Blume,? and Van Horne and Parker 10

have all concluded that attempts to improve stock market timing or selectivity by technical analytical techniques fail to show the benefit of any technical trading rules, especially when the commissions costs of trading are included. As an example, Van Horne and Parker concluded that even if transactions costs are ignored, "none of the mechanical trading rules produced a total closing balance as large as that produced under the buy-and-hold strategy."l'

The sole notable exception to the above described research findings was a 1967 article by Robert LevyI wherein he stated that his study shows that "stock prices follow discernable trends and patterns which have p;:- dictive significance; and the theory of Random Walk has been refuted." Levy's work was contested by Jensen 140n the basis that Levy simply employed 13 methods, one after another until he discovered one which would have produced superior returns had it been employed during the historical period under study (spurious correlation).

The net result of these studies has been to relegate technical analysis to a field unworthy of further academic inquiry. In fact, the majority of modern investment textbooks do not even contain a chapter on the subject!

Footnotes appear at the end of the article

50

Misspecification of past Research

To date virtually all research on technical analysis has been based on computer simulations of the efficacy of a single technical decision- making tool. Practicing technical analysis, on the basis of these studies, has been pronounced valueless by implication. However, the practitioners assert (rightfully) that what has been tested does not contradict their perceived ability to outperform naive buy-and-hold strategies. This is so because practitioners employ a number of different technical tools, giving altered weightings to each technical tool at different moments. "Only a naive technician or academician would argue that technicians employ only one technical indicator at a time. Instead, sophisticated users of technical indices use many technical factors in varying weights depending on the situation."" In short, it seems apparent that the work of technical analysts cannot be replicated by a computer simulation covering only one or a few technical tools. It is one thing to say that, for example, the logarithms of price change are independent of each other, but quite another to state that technical analysis as practiced is ineffectual.

Enabling Conditions for this Research

The Market Technicians Association was contacted to ascertain if they would cooperate in the initiation of a real time simulation of technical practitioner decision making. Based on their feelings that the technical field has been improperly maligned and in the spirit of academic inquiry, they agreed.

Research Design

Several self-selected members of the MTA agreed to participate in one of two portfolio decision-making simulations. Both simulations would commence 6/15/77 and end 6/15/78. The one year period is relatively short, but given the time constraints of the participants a longer test period was deemed inadvisable. During that time period they would input buy and sell orders based on their technical analysis of market forces. At the outset of the simulation, each participant was "given" a $l,OOO,OOO paper portfolio. Within the rules of the particular simulation, each participant, by market timing and/or selectivity, attempted to gain extraordinary portfolio returns. Decision making was effectuated by time stamped orders sent in by the analysts.

51

"A" Simulation (Market Timing)

The A simulation initially attracted 12 technical analysts as participants plus two late participants. In this simulation, alternative choices were confined to buying, selling (previous position) or selling short a no- load Index Fund. The Index Fund utilized in this study is the First Index Investment Trust (Vanguard Group). Funds not invested would return the average outstanding Treasury Bill rate as calculated quarterly.

Margin was not permitted, thus, for example, a short position of 50%, tied up 50% of the fund's assets. Because the First Index Fund is a no-load fund, no buy or sell commissions were charged to the analyst. Cash dividends as paid quarterly by the Fund were coverted into an increased number of shares if the participant was long, a decreased number if short.

Profile of "A" Participants

The technical market experience of the 14 technical analyst participants* ranges from four to over 40 years, averaging 16 years. These analysts are employed by brokerage firms, advisory services, and mutual fund management companies: in eight instances the individual is the only technical analyst in his or her firm. No one of the group spent more than 30% of his or her time in portfolio management. Only three analysts identified a single most important technical indicator; these analysts identified member trading, point and figure charts, and ten day total advances. Interestingly seven of the fourteen utilized fundamental variables (earnings, price/earnings, price/dividend, price/book value, bond prices) to some extent in their analysis.

"B" Simulation (Market Timing and Selectivity)

Also starting in 6/15/77, this simulation attracted 25 participants with each "given" a beginning portfolio of $1 million. Over the one year test period, each could buy, sell (previous position) or sell short any common stock listed on the New York or American Stock Exchange with up to 20% of the portfolio value committed in any one issue. Preferred stocks, bonds, warrants, options, and over-the-counter securities were excluded from the potential investment universe. As in the A Simulation, funds not invested will return the average outstanding Treasury Bill yeild for that quarter.

Margin is prohibited withshortsales handled as if they were cash transactions (i.e., a short sale of $100,000 will tie up $100,000 of the monies). Commissions for transactions are charged at ten cents per share. Dividends are not included in the return computations.

*Two participants joined the Game during the year. Their results are only included for the appropriate quarters.

52

Profile of the "B" Participants

The length of experience as technical analysts ranged from five to over 20 years, with an average experience of 12 years. Thirteen of the 23 respondents are employed with brokerage firms, the remainder with institutional investors. No one spends more than 50% of his or her time in portfolio management. This is an important consideration in this simulation as it requires a portfolio management type of decision making. Thus, as the results reflect portfolio management rather than technical acumen the research design may be somewhat unfair with results representing the confounding of two talents, only one of which we desire to test.

Roughly half of the participants pinpointed a technical indicator they considered most important in market timing. Those indicators included in descending popularity: advance-decline index, trend line analysis, divergence analysis, Elliott Wave, breadth and interest rates. Again, roughly half identified a single technical tool they considered most important to stock selection; in descending popularity: relative strength, trend line analysis, Elliott Wave, point & figure charts and volume. Five respondents also employed fundamental variables to some extent in their analysis, including P/E, company image, profit forecasts, product development, and earnings growth.

Results - A Simulation

This simulation (hereafter also referred to as the A Game) is a real time test of market timing ability of technical analysts, as manifested by their purchasing or short selling an index fund.

final Nes”Its Of A stimulation w

Quarterly Ret"r"s, Non-ann"allzed

No. Quarter I b/15-9/15

* return ~Nnnkinp,

1. 1.35

2. l.oe

3. 2.13

4. -2.75

5. -1.40

6. -1.94

7. - .12

e. 1.81

9.. -

10. - .oe

Il. .79

12. 4.62

13. 3.42

14. -

I 5)

f 6)

I 3)

112)

(10)

Ill)

( 9)

I 4)

( 8) - .6B

( 7) -1.19

1 1) 2.15

I 2) 2.04

Ikean .74 std. De” 2.16

T-Bill 1.35 First ,ndex rind -1.90

Quarter 2 Quartet 3 P/15-12/15 12/15-J/15

k Flcturn

1.52

.46

2.80

-2.1s

-2.18

-2.18

2.43

1.63

.39

1.96 1.52

-2.18

(Rankin9) . return IRanks)

f 6) 1.57 I 4)

t 7) - 4.02 (13)

I 1) 4.52 I 1)

(10) - 3.54 (10)

(10) - 7.30 114)

(10) - 3.54 (10)

( 2) - 2.36 I 8)

I 5) 1.35 ( 5)

.31 ( 6)

I 8) - 1.43 I 7)

I 9) - 2.95 I 9)

I 3) 4.51 I 2)

( 4) 3.18 r 3)

- 3.54 110)

-.83 3.79 1.57

-3.54

plartcr 4 3/15-6/H

Number YeaT of

6/15-C/15 Trade * Return

- 1.41

11.32

4.45

11.32

-11.78

11.32

.25

7.9,

3.9,

7.16

6.66

1.32

- 9.50

Il.,2

(Rank) etc. o.lte.9

Ill) 3.03 (7) 2

( 1) 8.50 (0 7

( 9) 14.62 (2) 11

I 1) 2.14 (9) 2

114) -21.12 (12) 2

I 11 2.99 I 8) 1

(12) .13

( 5) 13.18

(10) -

( 7) 4.85

I 8) 3.09

f 6) 19.87

(13) - 1.46 Ill) 1

I11 - 1

3.76 4.15 7.85 10.23 1.62 6.20

L1.32 2.96

IO) 8

3) 4

1

5) 2

6) 19

1) 7

53

Table I presents the quarterly and annual return performance of each participant, their relative performance ranking ( ) in each quarter, and the number of order trade dates over the test period.

The starting position of each participant is 100% Treasury Bills, changes in position are evidenced by submission of written orders. The above returns are non-annualized holding period yields. The year result is equal to a product of the four quarterly results. For example, for Participant 3A: 14.62% = (2.13% + 100%) (2.80% + 100%) (4.52% + 100%). Thus the returns are compounded to yield the annual result. The Treasury Bill return is the average of the weekly go-day Treasury Bill rates during the quarters. The mutual fund return is the quarterly relative price change in the First Index Fund, including reinvestment of cash dividends.

Because of the probable diminution of interest by some of the participants, the number of trades shows a somewhat diminishing quarterly patterns over the year period (Table II). The average number of trade dates per full- year participant is 5.5 (ranging from one to 191, with an average return produced of 4.15%. It initially appeared plausible that as a participant's interest could perhaps be measured indirectly by his or her number of trade dates that account activity and return performance may be positively corre- lated. A regression testing this hypothesis (R = .08) fails to confirm any such relation. An additional regression testing the effect of years of analytical experience on return performance disclose no significant relationship (R* = -28).

TABLE II

Quarterly Total Trade Activity - A

Quarter 1 Quarter 2 Quarter 3 Quart& 4 Year

X Trades (% of Total) 28 (42.4%) 10 (15.2%) 19 (28.8%) 9 (13.6%) 66

Note : Somewhat greater activity in the first quarter might be expected due to the establishing of initial positions.

Analysis of Returns

Although eight of the twelve participants outperformed a naive buy and hold policy of the Index Fund, the market test period is one where the Treasury Bill yield exceeded the stock market returns.

To better appraise the attained returns, a random decision-making computer program was developed. This program randomly selected dates over the test period to randomly either buy, sell, or sell short the Index Fund. The returns of a participant having "X" number of trades are directly compared to randomly generated portfolios having the same number of trades. The measurement of each participant's performance relative to a randomly generated sample portfolio universe with identical portfolio activity does not completely solve the problem of participant apathy but does somewhat

54

reduce its effect. The sample population of 1200 portfolio simulations contained the same proportional number of portfolios with number of trades as did the participant samle. (i.e., four of the 12 participants had two trades therefore 400 of the 1200 randomly generated portfolios had two trade dates.) The mean and one standard deviation away from the mean of each portfolio type, classified by number of trades, along with the annualized return of each participant are visualized in the following graph.

EXHIBIT I

Performance of 1200 Randomly Generated Portfolios, Classified by No. of Trade Dates,

Compared with Performance of Participants at that Level of Trades

A

0 A

il- - A' ' 4 7 8 11 19 Trades

2- 7

Yean + return of random portfolios at each level of trade

0 Upper 6 lower boundaries representing one standard deviation awey from mean

A Return of participant

55

The 1200 portfolios produced an average annual return over the 6/15/77 - 6/15/78 period of 2.81%. As can be noted from Exhibit I, onehalf of the sample outperformed their random counterpart. Three of the participants evidenced outstanding performance above 1 SD from their expected returns, while one did substantially worse. The average participant, however, outperformed 60% of his or her randomly generated portfolio counterparts. Although this result is not statistically significant because of the large standard deviation of rank (S.D. = 27.3 U = 60%), it does reflect better than expected performance at a given level of participation.

Risk-Adjusted Return Analysis

The risk adjusted technique appropriate to ex post portfolio returns is to standardize achieved returns by the variability of the portfolios.'6 This index of risk-adjusted performance takes each sample portfolio's mean holding period yield less the mean holding period yield of the risk- free asset, divided by the standard deviation of the portfolio's holding period yields. For our study, the reward-to-variability index, R - VAR, as based on the four quarterly return observations, for the pth portfolio, is:

R- VAR = HPY - I

P cs

P

The numerator reflects the "reward" and the denominator, "risk"17.

Table III presents R - VAR for each full year A participant and for the Index Fund.

TABLE III R-VAR -- A SimulatiOn Based on quarterly returns

b/15/77-6/15/70 Average Variability Reward to

quarterly of quarterly variability return return Ratio (R/V)*

Participants (percent) (percent)

Al .757s 1.4481 -.5266 A2 2.2100 6.4845 .1064 A3 3.4750 1.1982 1.6316 A4 .7125 7.0936 -.I138 AS -5.6650 4.8443 -1.4832 A6 .9150 6.9724 -. 0868 A7 .osoo 1.9614 -.7495 A8 3.1800 3.1723 .5233 A10 1.2475 3.9934 -.0682 All .e275 4.1777 -.1658 Al2 4.6500 2.1134 1.4810 Al3 -.2150 6.2192 -.2790

Index Fund (Buy & Hold) .9050 6.9778 -.oea

*R/V ratio=(average return-l.52 percent)/variability; 1.52% equals the mean quarterly risk-free returns.

56

The risk adjusted results as shown speak for themselves with half outperforming the market's R - VAR ratio. As can be seen, there was actually a negative expected returns premium (below risk-free rate) to the stock market over this 12 month period. The quite positive R - VAR ratios of participant A3 and Al2 bespeaks of their ex post ability to have received high returns per amount of risk (variability) accepted.

Results - B Simulation

This simulation (hereafter also referred to as the B Game) is a real time test of the timing and stock selectivity of technical analysts as manifested by their portfolio apportionment between Treasury Bills and long or short positions in listed common equities under guidelines previously stated.

TABLE IV Final Results of B Simulation Quarterly Returns, Non-annualized

NO. Quarter 1 Quarter 2 Quarter 3 b/15/77-9/15/77 g/15/77-12/15/77 12/15/77-3/15/78 9 Return

1. -1.94 2. -8.58 3. .71 4. 1.35 5. -5.51 7. 1.35 8. 3.12 9. -8.07

10. -5.18 11. -3.81 12. -2.05 13. 1.35 14. -5.31 15. -2.43 lb. -3.06 17. -2.62 18. -1.87 19. 1.35 20. 5.43 21. -3.70 22. -1.09 23. -6.21 24. .92 25. -2.45 26. -5.40

(Rank) 0 Return (P.&k) % Return

Quarter 4 Year NUllber 3/15/78-b/15/70 b/15/77 - of

(11) (25) f 8) f 3) (22) f 3) f 2) (24) (19) (18) (12) f 3) (20) (13) (16) (15) (10) ( 3) f 1) (17) f 9) (23) f 7) (14) (21

(Rank) a Return (Rank) b/15/78

l4zan -2.15 1.73 .99 14.49 15.15 Std.Dev. 3.50 4.32 4.22 11.76 14.65 T.Bill 1.35 1.52 1.57 1.62 6.20 S&P 500 -2.83 -3.36 -4.74 10.35 -1.29

-2.40 16.76

.43 1.52

-5.29 -1.64

.96 -1.87 - .18

2.61 - .88

1.52 8.97

- .73 .70 .65

- .73 3.33 5.25

- .74 1.48 5.36 4.97

- .19 3.41

(24) -1.14 f 1) -2.48 (15) -1.35 f 9) 1.46 (25) -4.78 (22) 2.62 i12; 8.99 (23) .68 (lb) -3.35 f 8) .55 (21) -3.04 f 9) 1.01 f 2) 5.01 (18) 1.36 (13) -2.27 (14) .42 (18) 2.19 ( 7) - .62 ( 4) 14.75 (26) 3.82 (11) -1.59 ( 3) 5.02 f 5) -3.09 (17) - .45 f 6) .93

(17) 4.09 (22) (21) 31.44 f 2) (18) 4.10 (21) f 8) 6.39 (20) (25) 17.22 f 8) f 7) 1.51 (25) f 2) 25.87 ( 3) (12) 18.42 ( 6) (24) 12.63 (13) (13) 9.78 (15) (22) 3.38 (23) (10) 17.19 f 9) f 4) 19.92 f 5) ( 9) 1.56 (24) (20) 57.94 f 1) (14) 9.48 (17) ( 6) 7.69 (19) (lb) 9.71 (16) ( 1) 8.47 (18) ( 5) 17.45 f 7) (19) 11.32 (14) ( 3) 19.98 ( 4) (23) 16.51 (10)

(15) 13.93 (12)

(111 16.21 (11)

-1.52 (24) 36.82 ( 4)

3.86 (19) 11.06 (12) -0.12 (22)

3.84 (20) 42.82 ( 2)

7.55 (17) 3.03 (21) 8.94 (15)

-2.68 (25) 21.80 ( 7) 29.93 f 5) -0.29 (23) 50.67 ( 1)

7.75 (lb) 7.19 (18)

14.18 (11) 38.12 ( 31 16.56 f 9)

9.95 i14j 24.52 f 6) 19.62 ( 8) 10.42 (13) 14.75 (10)

Trades

59

129 12 20

61 45 33 31 52 19 10

4 10

4 58

218 10

212 26 27

8 4

Note: For a more detailed explanation of Table IV see the paragraph following Table I. Participant 6B dropped out of the B Game September 13, 1977, having made no transactions.

57

Analysis of the raw quarterly returns for the B Game shows 23 of the 25 participants outperforming the market. If one, de novo, assumes a probability of .5 of performance better than the market, the binomial sign test (a non-parametric statistical test) finds the probability of the actual performance occuring by chance is less than .OOl.

TABLE V

B Game Account Activity

Participant

1B 6 7 2B 22 59 3B 32 129 4B 2 12 5B 6 20 7B 2 7 8B 11 61 9B 25 45

10B 2 33 11B 3 31 12B 16 52 13B 1 19 14B 2 10 15B 2 4 16~ 1 10 17B 1 7 18B 2 4 19B 3 58 20B 58 218 21B 6 10 22B 9 212 23B 7 26 24B 9 27 25B 1 8 26~ 1 4

# Trade Dates # Trades

58

The average number of trade dates is 9.2, with an average of 4.7 separate transactions submitted on each date. As in the case in the A Game, neither the account activity (R2 = .06) nor the years of participant analytical experience (R2 = .006) vears a statistically significant relationship to return performance.

The distribution of B Game trade dates over the four quarters was 69 (1st Quarter), 51 (2nd Quarter), 45 (3rd Quarter) and 65 (4th Quarter).

Risk-Adjusted Return Analysis

The risk adjustment technique is the same as that used to analyze the A Game results. The returns are normalized for the riskiness of each portfolio as measured by the ex post quarterly standard deviation of returns.

TABLE VI

R-VAR -- B Simulation Based on quarterly returns 6/15/77 - - 6/15/78

Participants

Average Variability quarterly of quarterly

return return (percent) (percent)

Reward-to- variability ratio (WV)*

Bl -.3475 3.0038 -.6217 B2 9.2850 18.2966 .4244 B3 ,9725 2.2759 -.2406 84 2.6800 2.4743 .4688 85 .4100 11.2108 -. 0990 87 .9600 1.8230 -.3072 BE 9.7350 11.2791 .7283 B9 2.2900 11.3637 .0678 BlO .9800 8.0366 -.0672 Bll 2.2825 5.6698 .1345 812 -.6475 2.8264 -.7669 813 5.2675 7.9512 .4713 B14 7.1475 10.4278 -5397 B15 -.0600 1.8891 -.8364 B16 13.3275 29.7857 .3964 B17 1.9825 5.2158 -0887 B18 1.8200 4.2705 .0702 B19 3.4425 4.4787 .4292 820 8.4750 4.4365 1.5677 821 4.2075 9.3545 .2873 B22 2.5300 6.0124 .1680 823 6.0375 10.7376 -4207 B24 4.8275 8.4549 .3912 B25 2.7100 7.5478 .1577 B26 3.7875 9.0744 .2499

s & P 500 -.1450 7.0400 -.2365

*R/V ratio=(average return -1.52 percent)/variability

59

Of the 25 B participants who completed the simulation, 20 outperformed the R- VAR of the S & P 500. If we assume an expected probability of outper- formance of .5, the binomial sign test indicates that the probability of this occurring by chance is .002. Simply stated, the B participants as a

group, as measured by market relative risk-adjusted excess returns, offered substantially and statistically significant superior results. A graphic presentation of these results is presented in Exhibit II below.

Further Research

Commencing 7/l/78, sixteen participants from the first year simulations agreed to participate in another A type parrotry. To improve communications with each "player", a confirmation will be sent on each trade, plus periodic general correspondence and, as in the first research effort, quarterly performance appraisals.

The MTA is not sanctioning this new research endeavor due, primarily, to the lack of communication during the previous year from game headquarters. This fact has resulted in three dropouts, with the remaining thirteen participating as individuals.

The results of the one-year test simulation are supportive of the timing and selectivity abilities of the group of technical analysts who parti- cipated; this is particularly true in the B simulation.

60

Footnotes

1. One notable Exception, E. L. Bishop and J. R. Rollins, "Lowry's Reports: A denial of Market Efficiency?" The Journal of Portfolio Management, Vol. 4, No. 1 (Fall, 1977, pp. 21-27.

2. The term "random walk" was first used in 1905 by a Yale professor in describing an efficient search pattern for a drunk in a field. As is true of a drunk wandering through a field, random walk as applied to security prices views future stock price changes as independent of past movements. (Karl Pearson and the Right Honor- able Lord Rayleigh, "The Problem of the Random Walk," Nature, Vol. 72, Nov. 1865, (19051, pp. 294, 318, and 342).

3. Harry V. Roberts, "Stock Market 'Patterns' and Financial Analysis: Methodological Suggestions", Journal of Finance, Vol. 14, No. 1 (March, 19591, pp. l-10.

4. M.F.M. Osborne, "Brownian Motion in the Stock Market," Operations Research, Vol. 7 (March-April, 1959), pp. 145-173.

5. Sidney S. Alexander, "Price Movements in Speculative Markets: Trends or Random Walks," Industrial Management Review, Vol. 2 (May, 19611,

6. Clive W. J. Granger and Oskar Morgenstern, "Spectral Analysis of New York Stock Market Prices," Kyklos, Vol. 16 (1963), pp. l-27.

7. Arnold B. Moore, "Some Characteristics of Changes in Common Stock Prices," in Paul H. Cootner, The Random Character of Stock Market Prices, (Cambridge, Massachusetts: The MIT Press, 19641, pp. 139- 161.

8. Eugene F. Fama, "The Behavior of Stock Market Prices," Journal of Business, Vol. 38, No. 1 (January, 19651, pp. 34-105.

9. Eugene F. Fama and Marshall E. Blume, "Filter Rules and Stock Market Trading," Journal of Business, Security Prices: A Supplement, Vol. 39, No. 1, Part 2 (January, 19661, pp. 226-241.

10. James C. Van Horne and George G. D. Parker, "The Random Walk Theory: An Empirical Test," Financial Analysts Journal, Vol. 23, No. 6 (November-December, 19671, pp. 87-92.

11. Ibid., p. 90.

12. Robert A. Levy, "Random Walk: Reality or Myth," Financial Analysts Journal Vol. 23, No. 6 (November-December, 19671, pp. 69-77.

61

13. Ibid., p. 76

14. Michael C. Jensen, "Random Walk: Reality or Myth - Comment," Financial Analysts Journal, Vol. 23, No. 6 (November-December, 1967) pp. 77-86

15. George E. Pinches, "The Random Walk Hypothesis and Technical Analysis," Financial Analysts Journal, Vol. 26, No. 2 (March-April, 19791, pp. 109

16. W. F. Sharpe, "Mutual Fund Performance," Journal of Business of the University of Chicago, 39 (January, 1966), pp. 119-138.

The author gratefully acknowledges the assistance of Mike Hirai, Graduate Student, University of Southern California.

62

INTRADAY DEMAND/SUPPLY ANALYSIS A GROUND UP APPROACH TO THE MARKET

David Bostian, Jr. and Howard Wine11 Bostian Research Associates

A BULLISH OUTLOOK BASED ON PERSISTENT POSITIVE DEMAND PATTERNS FOR THE MAJORITY OF STOCKS

Bostian Research Associates has held a positive intermediate outlook for the equity market since December, 1978 because of the persistent positive demand recorded by our daily computer studies of over 3000 listed and unlisted equities. While we believe that economic and fundamental analyses are vital in making longer term assessments, it is also our belief that nearly all relevant fundamental factors are reflected in the balance of demand and supply over the intermediate term.

ON-LINE COMPUTER INSTALLATION

Our in-office computer installation is connected to our primary data base by a private line and can produce a C.R.T. graph for any of over 3000 common stocks in approximately 60 to 90 seconds depending on the time period displayed. The plotting interval is daily. A hard copy capability exists for each graph in which there is a permanent interest.

MEASURING UNDERLYING DEMAND

The basic measurement we employ for each stock is intraday analysis. (See Understanding Intraday Intensity Index Analysis page which follows.) To help in shorter-term timing, we have added a "momentum filter". The sample graph of Control Data covering the period 3/l/78 to 8/15/79 illustrates the movement of our demand and momentum (inverted) lines for CDA during a series of volatile price swings. Other characteristics of our graphic display are also noted. While different stocks can produce a number of varied patterns, the majority of listed and unlisted common stocks have shown positive demand patterns over the past three quarters.

63

POSITIVE PATTERNS IN BELLWETHER STOCKS

Components of the Dow Jones Industrial Average have been showing patterns of consistent demand in our computer studies even though the senior average has lagged other sectors of the stock market. The enlarged graph of International Paper during the May-July period illustrates this pattern which has appeared in many key stocks during periodic price weakness over the past ten months. When the majority of bellwether stocks exhibit patterns of persistent demand, we must conclude that the stock market is on solid ground.

UNDERSTANDING INTRADAY INTENSITY INDEX ANALYSIS

CO,VCEF’T: The lntraday Intensity index approach is based on the assumption that a pattern of inuaday price relationships may show a Puer picture of the demand/supply balance in a given stock than would an analysis merely of the direction of day- to-day price changes. While intraday analysis may lead to the same demand/supply picture as drawn from conventional day-to-day price chane analysis, there are important times when intraday analysis produces a different picture of a stock’s technical tone. Specifically, price changes tiequently can result from a surge of buying or selling at the opening that does not represent the real demand/supply picture because the opposite side of the market does not reflect its strength until sometime during the uading day. Inuaday Intensity Index analysis deals only with the daily range of trading, which may be thought of as a battlefield for demand and supply within the day. Intra- day analysis, in contrast to day- to-day analysis, considers only where the daily closing price falls within the daily uading range. Demand is shown to be the stronger force if the closing price is in the top portion of the daily range and supply is considered the stronger force if the closing price is in the lower portion of the daily range. Volume also has an important role and is discussed be!ow.

Df~GR.&\f: Assume, for purpose of example, that a stock closes at 90, down thorn 92 at the prior day’s close. The high for the day it closes at 90 is 91,the low for the day it closes at 90 is 87, and the volume of Wading is 45,000 shares, up from 30,000 shares the prior day. The price action is diagramed as follows:

92 I

lprior dd I

91 down to 90 is negative 91

t

(high) intraday action 90 (dose)

FORML’LA: WhiIe the stock in the above example closed Iower than the prior day on higher volume, generally considered negative action, the positive segment of the daily range was greater than the negative segment resulting in a bullish intraday reading. This is expressed in a simple formula: (1) Ignoring the day-to-day price change (down 2 points), the distance from the daily low (87) to the close (90) is determined in terms of “eighths”- here three pointsfip or plus 24 eighths. The distance from the daily high (91) to the daily close (90) similarly is determined in terms of “eighths”- one point down or minus 8 eighths. This establishes the positive and negative inuaday range factors. (2) The positive and negative factors then are netted, keeping the sign of the greater value. Netting “+24” and “-8” results in a differential value of “+16. ” (A close m the lower part of the price range would be net minus. ) (3) The daily volume, rounded to the near- est 1000 shares, is multiplied by me differential range value in(2). Multiplying “+16” times a 45 volume factor results in a daily intensity figure of “+720. ” (4) Finally, s:nce no daily figure tells the entire story, it is important to look for patterns of ac- cumulanon or distribution over a period of time with the aid of a computer.

64

1NtERNtTTIONAL POPER CO BOSTIAN RESEARCH 41.6 7/l CM9 1 47

46

45

44

43

42

41

40

39

-

, -

I -

l-

47

0

-47

‘I I I

‘Ii I I ‘, ’

I I

II 1 1

/

/

Price kmentum /

Filter

i I I

Intraday Demand

66

Book .

revaews BUY LOW, SELL HIGH

by: John E. Mahoney Published by Pagurian Press Ltd. 1978

Reviewed by: William DiIanni, V.P. Wellington Management Company

An investor who likes a mechanistic approach to the stock market should enjoy this book. Mr. Mahoney has discovered a formula for investing which challenges various current forms of index-matching, and even shows why the discredited formula plans of the past did not work. Furthermore, he candidly and totally discredits all other investment theories in the process.

The title "Buy Low, Sell High" is more than just a commonplace title for a book concerning investments or the stock market. It is the very essence of his formula.

"Remember only the basic principle for making a profit: buy low

sell high. Translate this into market fluctuations: buy on a scale down, sell on a scale up."

It all sounds rather simple . . . much like a dollar-cost averaging, but that is pretty much where the similarity stops.

After testing many variations of his principles the writer found results that could not be ignored.

"In fact, this study calls for a reexamination of the whole field of investment management. A new field of research has opened up."

Such strong statements will naturally provoke argument and dissent. No one method ever had all the answers. Moreover, if everyone followed an identical method or theory, it simply would not work. Differences of opinion and methodology make for the proverbial horse race; they are even more essential to the workings of an auction market.

But these very differences which cause violent fluctuations, are what Mr. Mahoney exploits. He tries to make his newly defined volatility work for him. He is not interested in beta coefficient which is the common approach to volatility. His new method breaks volatility into "frequency and ampli-

67

tude of fluctuations."

To test his theory, the 30 Dow Industrials were used during the 1963-1976 period. Each of the 30 stocks had its own Frequency-Amplitude Volatility Rating (FAVR) . After extensive computer work, it was found that maximum performance by individual stocks with favorable FAVR were scattered among all the formula plans.

"There is no question in the author's mind that not only is the formula plan investing superior, but that selection of high volatility stocks, consistent with quality and safety, is essential for standout performance."

The 30 Dow stocks used during the stated 14 year period showed how the account would have performed as it mechanically sold at specified selling intervals as issues rose in value. Also, the individual graphs trace how the Sales Differential line declined during periods of market weakness as purchases were systematically made.

"The outstanding feature of formula plans is that stocks will be sold as they rise and bought as they fall . . . . Time and again it was seen that the formula-planned account did not drop as far as the stock price, and when the stock finally recovered, it greatly outperformed the stock price."

Over the tested time period the DJIA increased by only 137% with dividends reinvested. The best formula plans, with all 30 DJI stocks gained about 190%. The 10 most volatile of the 30 stocks, processed at approximately higher selling intervals would increase the return on investment to 261%.

The results are impressive. Few can argue that. The period in question witnessed wide market fluctuations and protracted long term downtrends in many of the Dow components. Some even had poor volatility ratings, an ingredient the author frowns upon. He said, however,

"the rate of return should be improved considerably by selecting stocks which have higher volatility".

An investor, following this method, need not be concerned whether the market rises or falls sharply. He will want declines to make purchases: and rises, to effect sales.

It is not a book one would expect a technical analyst to write. Reason enough to read it. Whether it will be widely accepted, depends on how much one accepts mechanical views towards investments virtually devoid of subjective judgment.

In a sense, the book is against everything the traditional market analyst stands for. It simply takes the fun out of the game . . . but it does make money.

68

intentionally blank

69

intentionally blank

70

journal of technical analysis (jota). issue 06 (1979, november)

Economy & Finance