journal of technical analysis (jota). issue 20 (1985, february)

JOURNAL Issue 20 February 1985

MARKET TECHNICIANS ASSOCIATION JOURNAL

MTA Journal/February 1985 1

MARKET TECHNICIANS ASSOCIATION JOURNAL Issue 20 February, 1985

Editor: James M. Yates Bridge Data Company 10050 Manchester Road St. Louis, Missouri 63122

Contributors: John Carder John C. Edmunds C. A. Henn-Collins Thomas H. Mclnish Harlan D. Platt Marjorie A. Platt Guy Ricci Robert A. Wood

Publisher: Market Technicians Association 70 Pine Street New York, New York 10005


@Market Technicians Association 1985


MTA JOURNAL - FEBRUARY, 1985 TABLE OF CONTENTS

Title

MTA OFFICERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

MEMBERSHIP AND SUBSCRIPTION INFORMATION . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

STYLE SHEET FOR SUBMISSION OF ARTICLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

MTA LETTER FROM THE EDITOR. , . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . James M. Yates

Page

6

7

8

9

DAY-OF-THE-WEEK EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . 11 Thomas Mclnish, Robert Wood

THE ONE YEAR BAROMETER - A LEADING INDICATOR OF STOCK PRICES . . . . . . . . . . . . . . . 19 Guy Ricci

S.E.C. INDUCED DISTORTION IN STOCK PRICES . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . , . . . . . . . . 23 John Edmunds, Harlan Platt, Marjorie Platt

THE H-C COUNT - A POSSIBLE METHOD OF PREDICTING THE “HIGHS” AND “LOWS” OF SHARE PRICES . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 C. A. Henn-Collins

THE EVOLUTION OF A BREADTH INDICATOR OR TEACHING AN OLD DOG NEW TRICKS . . . . 35 John Carder


1984-85 MARKET TECHNICIANS ASSOCIATION

PRESIDENT Richard Yashewski Butcher & Singer 516/627-l 600

VICE PRESIDENT John Greeley Greeley Securities 212227-6900

VICE PRESIDENT (SEMINAR) Gail Dudack Pershing/Div. DLJ 212902-3322

PROGRAMS David Krell 212623-8533

NEWSLETTER Robert Prechter 404/536-0309

JOURNAL James Yates 314/821-5660

CERTIFICATION Charles Comer 212825-4367

MEMBERSHIP Phil Roth 212/742-6535

LIBRARY Ralph Acampora 212i747-2355

OFFICERS

SECRETARY Cheryl Stafford Wellington Management 617/227-9500

TREASURER Robert Simpkins Delafield, Harvey, Tabell 609/924-9660

COMMllTEE CHAIRPERSONS

ETHICS & STANDARDS/ PUBLIC RELATIONS Tony Tabell 609/924-9660

PLACEMENT John Brooks 404/266-6262

EDUCATION Fred Dickson 212/398-8489

COMPUTER SPECIAL INTEREST GROUP John McGinley 203/762-0229

FUTURES SPECIAL INTEREST GROUP John Murphy 212i724-6982

SAN FRANCISCO TECHNICAL SOCIETY SPECIAL INTEREST GROUP

Henry Pruden 415/459-1319


MARKET TECHNICIANS ASSOCIATION

REGULAR MEMBERSHIP - $75 per year plus $10 one-time application fee.

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

SUBSCRIBER STATUS - $75 per year.

Receives the MTA Journal and the MTA Newsletter, which contains shorter articles on technical analysis. 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) - $15

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 REGULAR MEMBERSHIP and SUBSCRIBER STATUS should be directed to:

Membership Chairman Market Technicians Association 70 Pine Street New York, New York 10005


MTA Editorial Policy

The Market Technicians Association Journal is published by the Market Technicians Associ- ation, 70 Pine Street, New York, New York 10005 to promote the investigation and analysis of price and volume activities of the world’s financial markets. The MTA Journal is distributed to individuals (both academic and practioner) and libraries in the United States, Canada, Europe and several other countries. The Journal is copyrighted by the Market Technicians Association and registered with the Library of Congress. All rights are reserved. Publication dates are Feb- ruary, May, and November.

Style for the MTA Journal

All papers submitted to the MTA Journal are requested to have the following items as prereq- uisites to consideration for publication.

Short (one paragraph) biographical presentation for inclusion at the end of the accepted article upon publication. Name and affiliation will be shown under the title.

All charts should be provided in camera ready form and be properly labeled for text reference.

All tables should be properly labeled and in camera ready form.

Paper should be submitted typewritten, double-spaced in completed form on 8% by 11 inch paper. If both sides are used, care should be taken to use sufficiently heavy paper to avoid reverse side images. Footnotes and references should be put in the end of the article.

Greek characters should be avoided in the text and in all formulae.

One submission copy is satisfactory

Manuscripts of any style will be received and examined, but upon acceptance, they should be prepared in accordance with the above policies.


MTA LETTER FROM THE EDITOR

Merger fever has even extended to the New York Stock Exchange itself, which is looking to acquire the Pacific Stock Exchange. Investors may have extended trading hours. The markets are becoming more international. Investors may not only trade twenty-four hours a day, but may do it in many different currencies.

What an opportunity for the technical analyst! What of the prior definitions? What is an opening? What is a closing? When is it? How to denominate indicators: yen or krona? Consider that a Canadian buying a stock in the United States could have sustained a thirty percent decline in the price of the stock last year and still have made money on the currency change upon sale. Can currency type be ignored in technical analysis?

Such conditions require international data. There is a wide variety of attitudes towards an availability of international data. Certainly the United States has the most advanced and complete distribution and availability of data. Canada is a close second, if not equal. Some countries are very protective of the distribution of data generally available elsewhere. The United Kingdom reports information from the exchanges but not very often, and the collection mechanism leaves a lot to be desired. Japan obtains United States data but will not provide the equivalent in return. Sounds like an export/import conference question. That is the point. Good relations depend upon reciprocity. That reciprocity does not yet exist in data exchange or marketing access. Maybe the MTA can produce a small diplomatic coup. It’s worth some conversation.

Again I am indebted to Sally Ruppert for her assistance in the production of The Journal. Pam Hollrah typed every word despite a badly (five places) broken left-wrist. G’s, H’s, T’s, and Y’s were a particular problem.

See you at Hilton Head at the Seminar in May.

James M. Yates EDITOR


This page left intentionally blank for notes, doodling, or writing articles and comments for the MTA Journal.


DAY-OF-THE-WEEK EFFECTS

Thomas H. Mclnish

Robert A. Wood

A number of scholars have reported evidence of differential behavior of security returns by day of the week. This paper examines previous work concerning day-of-the-week effects and also describes ongoing research by the authors. Before beginning the discussion of the previous work on this topic, it may be useful to explain some of the terminology.

In general, the returns discussed in this paper are holding period returns calculated as follows:

R = LOG(P,) - LOG(P,.,)

where P is the price of the stock at time t, and P is the price of the stock at time t-l. The length of the interval from t-l to t may differ from study to study and may be as short as the time from one trade to the next or as long as a day, week or month. An intraday return is the return from the opening to the last trade of the day while an overnight return is the return from the previous close to the opening. Thus, the “overnight” return for Monday is the weekend return and the overnight return for Tuesday is the return for Monday night, etc. lnterday returns are the sum of successive overnight and intraday returns.

The next section discusses previous studies dealing with day-of-the-week effects and the following section describes the data and methodology Then, results are presented in three areas. The behavior of returns by day of the week are described. Next, the effect of nonsynchronous trading on observed returns by day of the week is examined. “Nonsynchronous trading” refers to the fact that securities do not all trade at the same time. Securities that trade more frequently are more synchronous. Third, the pervasiveness of day-f-the-week effects for individual stocks is investigated. The final section provides a brief summary.

BACKGROUND

For the period 1953-l 970, Cross [2] documented that the Standard and Poor’s 500 Index rose on 62% of all Fridays, but on only 39.5% of all Mondays. Also, examining the Standard and Poor’s 500, for the period 1962-l 978, Gibbons and Hess [5] found negative returns on Monday and positive returns for other days of the week as follows: Monday, - 0.134; Tuesday, 0.002; Wednesday, 0.096; Thursday, 0.028; Friday, 0.084. In addition, Gibbons and Hess found a similar pattern of day-of-the-week effects for returns on Treasury Bills.

Fama [3] found that the variance of returns for Monday was about 20% greater than for other days of the week, and on that basis concluded ihat the weekend effect was relatively unim- portant. Using a different methodology, Godfrey, Grander and Morgenstern [6] confirmed Fa- ma’s finding that the variance of weekend returns was larger than the variance of overnight returns.

Using stock returns categorized by day of the week, for the period 1953-1977, French [4] found negative returns for Monday and positive returns for the other days of the week as follows: Monday, -0.1681; Tuesday, 0.0157; Wednesday, 0.0967; Thursday, 0.0448; Friday, 0.0873. French also examined interday returns for each day of the week when the previous regular trading day was a holiday. Only the returns for Monday and Tuesday were negative. Since the returns for


Wednesday, Thursday, and Friday following holidays were positive, French concluded that negative returns on Monday reflect a weekend effect rather than a closed-market effect.

Lakonishok and Levi [7l demonstrate that day-of-the-week effects can be explained, in part, by institutional aspects of settlement. Settlement on the fifth business day came into effect on February 9, 1968. Allowing one check-clearing day, for weeks without a holiday, payment for stocks purchased on Monday through Thursday is completed on the following Monday through Thursday, respectively But payment for stock purchased on Friday is not completed until the second Monday. Hence, purchasers of stock on Friday gain two days of interest relative to purchasers on Monday Assuming returns are generated in calendar time (l), returns on Monday would be about three times as great as returns on other days of the week (less two days of interest). Thus, it is possible to ascertain how stock settlement practices and check clearing practices affect stock returns by day of the week.

Letting X represent an average daily return and Y represent an average day’s interest, for weeks without holidays, investors could expect the following pattern of returns: Monday, 3X - 2Y; Tuesday, X; Wednesday, X; Thursday, X; Friday, X + 2Y Holidays may cause the five patterns just presented to occur on other days of the week (which is referred to below as an atypical pattern). Two additional patterns may result from holidays: X + Y and 2X - Y Lakonishok and Levi [7, p. 8851 state that “If they had been available, it would have been preferable to use closing-to- opening prices” to test the effect of settlement practices on day-of-the-week returns. Lacking this data, close-to-close returns from the Center for Research in Security Prices (CRSP) tapes were used. While adjustment of returns based on the seven patterns presented above reduced Friday returns and increased Monday returns, the changes were insufficient to completely eliminate the daily effects. Further, as in the studies of Gibbons and Hess [5] and French [4], Lakonishok and Levi [7] found Wednesday returns also were unusually high, a result which could not be explained in terms of settlement practices.

Oldfield and Rogalski [9] developed and tested a model of returns over trading and nontrading periods using data which comprised every transaction from October 1974 - December 1977 for five actively-traded New York Stock Exchange (NYSE) companies. Statistical tests indicated that both intraday returns for Monday through Friday and overnight returns for Tuesday through Friday were identically distributed. Trading period returns were found to be largely in- dependent of the previous nontrading period’s returns. Weekend returns were found to have a mean five times as large and a variance twice as large as those for overnight returns. The small sample size and use of only actively traded stocks represent important limitations of this empirical work.

Oldfield and Rogalski did not provide returns by day of the week. But considering all days of the week combined, mean intraday returns were positive and, except for one of the five firms examined, mean overnight and weekend returns were also positive. Holiday-weekend returns were negative for three of the five firms considered.

Ball, Torous and Tschoegl (BlT) [l] investigated the nature of the return generating process for gold in London. Since both a morning and afternoon price were available, BTT were able to examine interday, intraday and overnight (including weekend) returns. Their results indicated systematic differences in returns across days of the week, but showed no evidence of a negative effect (i.e., the overnight return for Monday was positive rather than negative). When interday returns were regressed on dummy variables representing days of the week, the coefficient of the dummy variable for Tuesday was negative and significant, but the coefficient of the dummy variable for Wednesday was positive and significant; the coefficients for Monday and Friday were positive and insignificant, but the coefficients for Thursday were negative and insignificant.


In a similar regression using intraday returns, the coefficents for Tuesday and Thursday were negative and significant; the coefficents for Monday, Wednesday and Friday were positive and insignificant. Using overnight returns as the dependent variable, the coefficient for Wednesday was positive and significant; the coefficient for Monday, Thursday, and Friday were positive and insignificant while the coefficient for Tuesday was negative and insignificant. Variability of returns when the market was open (as measured by the standard deviation) was far greater than when the market was closed (after adjusting for the different lengths of the periods involved).

McFarland, Pettit and Sung (MPS) [8] analyzed the daily price changes of seven foreign currencies. Findings indicated higher average price changes on Monday and Wednesday than on Thursday and Friday In fact, Friday returns were negative and Monday returns were positive, exactly the opposite of the findings of Gibbons and Hess [5] and French [4] for United States stocks. The pattern of returns for foreign currencies may be partially explained in terms of settlement practices. MPS concluded that “the consecutive daily data do not conform to any simple process, but must be characterized by a more complex generating scheme....“[8, p. 7131.

METHODOLOGY AND DATA

The data for this study comprised opening and closing prices for 978 common stocks listed on the NYSE for the period September 1971 - February 1972. The measure of nonsynchronous trading used is average time in minutes from last trade to market close (LTIME). The data were obtained from a tape provided by the NYSE.

All returns are measured as natural logarithms of price changes. lntraday and overnight returns are not computed for days on which a security did not trade (about 1 .l% of the sample). If a security has only one trade during a day, then only the overnight returns are calculated (about 2.9% of sample days). Prior to use, returns were adjusted for dividends and changes in capitalization. All returns are transactions prices.

As described above, Lakonishok and Levi [A have shown that settlement practices may alter the pattern of returns by day of the week. The appropriate Lakonishok-Levi pattern for each day in the sample was determined. Days which had an atypical Lakonishok-Levi pattern for that day of the week (as explained above) were eliminated. To examine the effect of nonsynchronous trading on observed returns, firms are classified into quintiles by level of nonsynchronous trading. To examine the pervasiveness of the-day-of-the-week effects, the (958 x 108 =) 103,464 daily intraday returns are classified as positive, zero or negative. Then, the percentage of positive, zero and negative returns compared to the total are presented. Also, the percentage of positive and negative returns compared to the total number of non-zero returns are presented. The analysis is replicated for overnight returns.

RESULTS

The mean (average) return across stocks for each day of the week are presented in Table 1. Monday and Thursday intraday returns are negative while returns for Tuesday, Wednesday and Friday are positive. The largest positive return occurs on Friday and the largest negative return occurs on Monday The weekend return (overnight Monday) and the overnight return for Wednesday-Friday are positive. Only the overnight return for Tuesday is negative. These results show that returns differ by day of the week both within and between trading and nontrading periods.

Table 1 also presents returns by day of the week categorized by level of nonsynchronous trading. In almost every case, day-of-the-week effects are more pronounced for the more synchronous stocks (category 1). Day-of-the-week effects are probably the same for synchronous


and nonsynchronous stocks. But nonsynchronous trading masks these effects. For example, if a stock trades at noon on Friday and again at noon on Monday, the Monday overnight return will include half of the return for Friday, the weekend return and half of the return for Monday Table 2 presents results which are useful in determining the pervasiveness of day-of-the-week effects. First, note that over 15% of intraday returns and 24% of overnight returns are zero.

Table 2, part (b), shows the percentage of positive and negative non-zero returns. For intraday returns, negative returns predominate on Monday and Thursday while positive returns predominate on Friday. For overnight returns, positive returns predominate on Monday, Wednes- day and Thursday and negative returns predominate on Tuesday. In general, the majority of individual returns have the same sign as the average return. Thus, similar day-of-the-week effects are exhibited by the majority of firms. Nevertheless, in every case, a substantial minority of firms experience day-of-the-week effects contrary to those of the majority of firms.

SUMMARY

A number of researchers have reported significant differences in returns by day of the week. This paper confirms that returns differ by day of the week both within and between trading and nontrading periods. Nonsynchronous trading is shown to’mask day-of-the-week effects so that for the market as a whole day-of-the-week effects are even more pronounced than they appear to be from examination of average returns for all stocks. The majority of firms experience day- of-the-week effects similar to those for the market as a whole.

While this study documents the existence of day-of-the-week effects, their cause is not yet known. Hence, it is uncertain whether knowledge of day-of-the-week effects can be used to develop superior trading strategies. The reader should also keep in mind that the specific results presented in this paper are for the period September 1971 - February 1972 (though day-of-the- week effects have been documented for additional periods by others) and that the numbers of each weekday in the sample are small (22 Mondays, 23 Tuesdays, 24 Wednesdays, 20 Thursdays, and 19 Fridays).

FOOTNOTES

1. Academics have proposed two models of the way returns accumulate through time: the calendar time model and the transactions time model. According to the calendar time model, returns for a security accrue at a constant rate through time. According to the transaction time model, returns for a security are zero except when a transaction occurs; in other words, returns arise only during moments when trading takes place. Since the length of time between transactions may differ, the calendar time and transactions time models are inconsistent.


REFERENCES

1. Ball, Clifford A.; Torous, Walter N.; and Tschoegl, Adrian E., “Gold and the ‘Weekend Ef- fect’ ” The Journal of the Futures Marketi 2 (Summer, 1982): 175-82.

2. Cross, Frank, “The Behavior of Stock Prices on Fridays and Mondays,” financial Analysts Journal 29 (November/December, 1973): 67-9.

3. Fama, Eugene F., “The Behavior of Stock-Market Prices,” Journal of Business 3 (January, 1965): 34-105.

4. French, Kenneth R., “Stock Returns and the Weekend Effect,” Journal of Financial Eco- nomics 8 (March, 1980): 55-69.

5. Gibbons, Michael R., and Hess, Patrick, “Day of the Week Effects and Asset Returns,” Journal of Business 54 (October, 1981): 579-96.

6. Godfrey, M.; Granger, C.; and Morgenstern, O., “The Random Walk Hypothesis of Stock Market Behavior,” KY/OS 17 (1964): l-30.

7. Lakonishok, Josef, and Levi, Maurice, “Weekend Effects on Stock Returns: A Note,” Jour- nal of Finance 37 (June, 1982): 883-9.

8. McFarland, James W.; Pettit, R. Richardson; and Sung, Sam K., “The Distribution of For- eign Exchange Price Changes: Trading Day Effects and Risk Measurement,” Journal of Fi- nance 37 (June, 1982): 693-715.

9. Oldfield, George S., Jr. and Rogalski, Richard J., ‘A Theory of Common Stock Returns Over Trading and Non-Trading Periods,” Journal of Finance 35 (June, 1980): 729-51.


TABLE 1

lntraday and Overnight Returns by Weekday and Nonsynchronous Quintiles

(i = Most’Synchronous)

lntraday Returns

Weekday Average 1 2 3

Monday - 2.89 -5.44 - 3.99 - 2.80 Tuesday 1.13 2.83 1.16 0.55 Wednesday 0.69 0.26 0.51 0.24 Thursday -1.30 - 2.91 -1.72 - 1.32 Friday 2.09 2.78 2.11 2.44

Overnight Returns

Weekday Average 1 2 3

Monday 1.46 2.64 1.92 1.66 Tuesday -0.43 - 0.48 - 0.53 0.01 Wednesday 0.95 1.44 1.11 1.10 Thursday 0.72 1.09 0.93 0.93 Friday 0.37 0.63 0.54 0.38

Notes: 1) All returns are multiplied by 1,000.

2) Returns are means of logarithms of price relatives.

4 5

- 1.46 - 0.78 0.39 0.70 1.41 1.02

- 0.31 - 0.24 1.75 1.39

4 5

0.54 0.52 -0.43 - 0.72

0.57 0.49 0.31 0.32 0.08 0.24


TABLE 2

(a)

Percentage of Sample Days with Negative, Zero, and Positive Returns, by Weekday

Weekday Negative Monday 51 .l Tuesday 40.9 Wednesday 42.7 Thursday 47.6 Friday 39.3

lntraday Zero 15.9 16.7 15.9 15.7 16.5

Positive 33.0 42.4 41.4 36.7 44.2

Overnight Negative Zero

33.9 24.5 38.1 28.8 33.7 27.7 35.0 26.7 36.7 26.9

Positive 41.6 33.1 38.6 38.3 36.4

03

Percentage of Sample Days with Negative and Positive Returns, as a Percentage of Nonzero Return Days, By Weekday

Weekday Monday Tuesday Wednesday Thursday Friday

lntraday Overnight Negative Positive Negative Positive

60.8 39.2 44.9 55.1 49.1 50.9 53.5 46.5 50.8 49.2 46.6 53.4 56.5 43.5 47.7 52.3 47.1 52.9 50.2 49.8

Note: Percentages based on (958 x 108 = ) 103,464 days.


BIOGRAPHY

Dr. Robert A. Wood is an Associate Professor of Finance at Pennsylvania State University. Dr. Wood has extensive professional experience with Boise Cascade and PPG Industries. He has authored and co-authored numerous articles and scholarly journals such as The Journal of finance and The Journal of financial Research.

Dr. Thomas H. Mclnish is an Associate Professor of Finance at the University of Texas at Arlington. He was previously employed by Union Camp and First Birmingham Securities Corporation. Dr. Mclnish has authored numerous articles published in journals such as The Journal of Finance and The Journal of Financial Research. He is the co-author of the book Corporate Spinoff published in 1984.


THE ONE-YEAR BAROMETER A Leading Indicator of Stock Prices

Guy Ricci

Leading indicators are standard tools used by economists to try to predict the future course of the business cycle. The stock market itself is one of the best leading indicators of economic activity

A leading indicator of stock prices must possess two properties. First, it must “forecast” the direction of the market. It does this by cresting prior to a peak in stock prices and by bottoming out prior to a low in stock prices. Second, the lead time must be as uniform as possible. Lead time is the time between a peak or trough in the indicator and a peak or trough in the market.

Several years ago, the author discovered that standard economic time series could be used as leading indicators of the stock market if one looked at them in a slightly different way. One such indicator is illustrated in this article. Many other economic time series exhibit the same behavior in varying degrees.

However, this indicator performs best and displays the most stable lead times over the time span described here.

Chart 1 shows the month-to-month annualized percentage change of corporate bank loans and commercial paper from 1973 to the present. A three-month centered moving average is used to smooth out the short-run fluctuations that mask the cyclical waves. This will be our leading indicator.

Chart 2 depicts the average of the monthly high and low prices of the Dow Jones Industrial Average (DJIA) on a logarithmic scale after removal of the trend component. The removal of the trend allows us to see more clearly the market’s cyclical fluctuations. It also makes it easier to compare it to the fluctuations of the loan growth rate depicted in Chart 1. The relationship between bank loans and the DJIA is not immediately apparent until we create Chart 3. Now we can clearly see how bank loans are related to stock prices. In Chart 3, both the de-trended DJIA and the bank loan growth rate have been plotted on a normalized scale so they can be compared directly to each other. Note that bank loans have been inversely plotted with a one year lead. For example, bank loans for 1973 are plotted upside down below the DJIA for 1974.

A possible mechanism that could account for this relationship is that when a bank loan is granted money is created. If bank loans increase, then the supply of money increases. The increase in the money supply eventually causes an increase in the rate of inflation which is bad for stock prices. This mechanism would account for both the observed inverse relationship and the observed lead time.

We can see on Chart 3, for example, that the 1982-83 rally, missed by many market watchers, was foreseen exactly one year before it began. Of course, the effective lead time shrank to about six months due to the fact that one would have had to wait to see if the trend would have reversed itself. So, by the start of 1982, one could have been fairly confident that a new bull market would begin in August 1982.

After cresting in December 1982, the inverted loan series declined precipitously until April 1984. So, by mid-1983 we should have expected that a major decline in prices would begin in De- cember 1983. As it happened, the market collapsed in January 1984. By mid-1984 we could see that the bear market would continue until at least April 1985. Taken in this context, the ex-


plosive rally we have experienced in August 1984 must be considered a bear market rally which will be followed by further sinking spells rather than the continuation of the 1982-83 bull market.

Finally, the deviations in Chart 3 are strikingly similar. A decline in inverted bank loans is ac- companied one year later by a proportional change in the DJIA. Based on this experience, we could cautiously predict that the DJIA will decline to approximately the 975-1000 level by April 1985. At that point a new bull market could begin. Of course, lead times can change so caution must be used in projecting future price moves. Even with this limitation, it still gives us an ex- cellent long-term perspective of our position in the market cycle.

CHART 1: BANK LOANS, % CHANGE 50

40

30

20

10

0

-10

-20

3 MONTH CENTERED MOVING AVERAGE

1973 1974 1976 1976 1977 197.9 1979 1990 1981 1982 1983 1984

JAN 1973 THROUGH JUL 1984


CHART 2: DJIA, DEVIATION FROM TREND LOG (AVG. OF MONTHLY HI & LO)

5 0.10 0.09 -

0.06. - 0.07 - 0.06 -

0.05 -

O-04 - 0.03 - 0.02 -

Y 0.01 0.00 - /

kj 2::: - I -0.03 - -0.04 -

-0.05 - -0.06 - -0.07 ‘-

-0.08 - -0.09 - -0-10 -

-0.11 -v

1974 1976 l<

m

76 1977 1978 1979 1 se0 1981 1982 l!


CHART 3: DJIA & BANK LOANS BANK LOANS PLOTTED WITH A 1 YR LEAD

INV

90

80 -

70 -

60 -

50 -

40 - \

30 -

2o-J

10 -

0

S INI

-0AN

DO’

\

\1

ZTED

ISTRI JON

IANK

P

n/ 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985



BIOGRAPHY

Guy Ricci holds a BS in electrical engineering from Lehigh University and an MBA from Penn- sylvania State University He worked in NASA’s satellite operations projects before entering technical analysis. He is currently a technical analyst at ISFA Corporation.


S.E.C. INDUCED DISTORTION IN STOCK PRICES

John C. Edmunds Harlan D. Platt

Marjorie A. Platt

The Securities and Exchange Commission (SEC) has an important mandate, which includes protecting investors from fraud, as well as from a variety of practices that might otherwise distort securities’ prices. In controlling the many practices which have been devised over the years to manipulate securities’ prices, the SEC acts to guarantee that all transactions are bona fide, i.e., are arm’s length transactions between parties in possession of clear, accurate information. Transactions between related parties, effected with the intention of creating a false impression of rising volume and new investor participation (called wash sales), are prohibited and pros- ecuted. For many of the same reasons the SEC prohibits and prosecutes “bucketing” orders, i.e., taking orders without executing them. Both of these practices, as well as others that the SEC uncovers and prosecutes from time to time, violate the principle that securities trading is supposed to be a fair game, in which insiders have as small an advantage as possible versus the public.

This principle of fairness, which has been central to the Securities and Exchange Commis- sion’s r&on d etre since its inception, has caused the SEC to tread very lightly in the entire vicinity of stock option trading. Options are a high priority area for regulators for several reasons. Most obviously, they are riskier, and perhaps, harder to understand than a simple purchase or sale of common stock. That fact opens a broad avenue for unscrupulous or greedy investmentadvisors. It gives these advisors a well-designed and effective tool for separating clients from their money. Options are certainly one of the best brokerage account chuming- devices ever created. Stockbrokers say that one good option trading account can bring in more commission income than half a dozen ordinary accounts of the same size. In addition to the potential for violating fiduciary responsibility which options offer, regulators also worry (with justification) that option trading diverts risk-oriented capital (presumably a positive sum game, at least from society’s viewpoint) into a game which, from society’s view at least, is zero sum. And finally, regulators worry that option trading may influence the prices of the underlying securities. In other words, the option may move the stock.

We have uncovered evidence that in certain circumstances the option has indeed moved the stock. Readers who have traded options will not be surprised to hear this. Earlier studies have found that option trading has some influence on the prices of the underlying shares. What is surprising and ironic about our discovery is that the distortion we have observed appears to have been caused by an uncharacteristic combination of zeal and reluctance at the SEC. The SEC moved fairly rapidly and liberally in allowing trading in listed call options (options to buy stock). In contrast, the commission moved slowly, waiting an additional seven years, before allowing trading in listed put options (options to sell). Our findings indicate that during the period when options to buy were trading on exchanges and options to sell were not, the price movement of the underlying shares was distorted. That is, the underlying shares fluctuated asy- metrically, in a way which was markedly different from what theory and empirical studies have led us to expect.

If option trading moves the price of the underlying stock, that reverses the causality which investors expect. The stock price is supposed to be the prime mover, and the option price is supposed to follow along. The typical stock market participant presumably evaluates share prices; and presumably believes that share prices respond to events affecting the company’s prospects, and not to events or short-term trading conditions on the option exchanges.


The short-term trading conditions which we are examining occur when options expire. If an expiring option is valuable, it will be exercised, or used to buy the underlying shares. The per- son who exercises the option will most often be a floor trader, who will buy the option a few hours before its expiration date. The floor broker will obtain the underlying shares and then sell them. From the floor broker’s viewpoint, buying the option and exercising it are part of an arbitrage transaction. These transactions in the underlying shares are not motivated by spontaneous investment considerations such as expected price movement.

Investors who are following the price movements of a stock may be confused on the expiration day of the option by the flurry of stock transactions associated with options being exercised. They might misinterpret this sudden rise in trading activity; and they might be misled by the sudden price movements which can occur as option expiration approaches.

BRIEF HISTORY OF OPTION TRADING

Options to purchase or sell common stock have been traded in this country at least since the previous century. Option trading was a very small proportion of total securities trading until the advent of listed option trading. Prior to 1972, options were traded over-the-counter. Each option was created in a three-way negotiation among the option buyer, the broker, and the option seller. Each option was unique in regard to its exercise price and its expiration date. Each option covered 100 shares of the underlying stock, and most were written for six months and ten days. This life span was set in accord with the six month holding period for capital gains which prevailed at the time. The option buyer could have his profits qualify as long-term capital gains, if he held the option more than six months.

To see how this over-the-counter trading worked, let us follow a typical transaction from start to finish. A speculator thinks that the shares of Consolidated Amalgamated are going to rise over the next six months. He/she does not have enough capital to buy 100 shares outright so he/she telephones a stockbroker and asks for a quote on a call option. A call option allows the holder to buy (call away) 100 shares of Consolidated Amalgamated at a pre-established price (the exercise price) any time during the life of theoption. The broker finds someone (probably a holder of Consolidated Amalgamated shares) to sell (write) an option on the shares. The broker finds out how much the option seller wants for the option, adds on a markup, and quotes this price to the buyer. If the buyer agrees to the price, the option is written to be exercised at the price prevailing for Consolidated Amalgamated on the day the deal was struck, for example, $27 34. The option would expire six months and ten days after it was written.

To see what happens as expiration approaches, suppose that the speculator was right. Con- solidated Amalgamated shares have risen dramatically Six months and two days have passed since the deal was struck and the speculator has now held the option long enough for his gain to qualify as a long-term capital gain. He/she telephones the broker and sells the option to the broker. The broker then exercises the options, calling in the 100 share block from the seller and paying the pre-established price of $27 % The broker then sells the shares at the higher price available in the marketplace.

The problem for securities regulation arises when the broker sells the 100 shares. This sale is not spontaneous; it does not come about because some stockholder decided to sell. Instead, it is induced; it comes about because an option was exercised. It is possible that if enough such induced transactions took place during a short time span, they could outweigh the spontaneous transactions occurring during the same period, and exert downward pressure on the stock price.

During the days before the advent of listed option trading, it was not likely that induced transactions would ever distort the price of any stock. In the first place there were not very many


options in existence at any one time. And more importantly, their expiration dates were spread uniformly across trading days, so it was unlikely that more than a few options on a given stock would be exercised on a given day Matters changed considerably when the SEC in 1972 allowed the Chicago Board Options Exchange to begin trading listed options. These listed options were to be different from the earlier separately negotiated options. Listed options have standardized exercise (strike) prices and standardized expiration dates. They are traded in series, identified by the expiration month, e.g., the January series. Each new series begins trading nine months from its projected expiration date. Transaction prices are reported on quote boards and in daily newspapers.

Because of a change in the long-term capital gains holding period and a tax ruling, listed options cannot offer long-term capital gains. This means that option holders are willing to trade these options sooner than they did the six months and ten day option traded earlier.

PRICE AND TRADING IMPLICATIONS OF LISTED OPTIONS

The SEC moved cautiously in permitting listed option trading to begin April 26, 1973. To min- imize the influence which option trading would have on the prices of the underlying shares, the SEC at first permitted only call options (options to buy) on 34 of the most heavily traded, large capitalization New York Stock Exchange stocks. The SEC did not permit put options (options to sell) because they are even less understood than call options, are less frequently traded, and give another degree of latitude to would-be manipulators.

Listed options were a huge success. Trading volume rose steeply, and soon other exchanges applied for permission to trade options. As evidence accumulated that option trading did not wildly distort the prices of the underlying shares, nor engender a series of frauds or manipu- lations, the SEC permitted more exchanges to trade options, and permitted options on additional stocks. At this writing four exchanges trade listed options covering the shares of approximately 225 companies.

The SEC deliberated much longer before permitting put options to trade on exchanges. Put options are merely options to sell shares. The option holder has the right to sell 100 shares of the underlying stock at a pre-established price anytime during the life of the option. A speculator who thinks that Consolidated Amalgamated shares are going down during the next few months buys a put option entitling him/her to sell the shares at one of the standardized exercise prices, for example $25 or $30 per share. Then, if the share price actually does fall, say to $15 per share, the put option holder buys 100 shares of Consolidated Amalgamated at $15 per share and, exercising the option, sells these to the option writer for $25 or $30 per share de- pending on the exercise price of the option. The option writer is obliged to buy the shares at the exercise price. The SEC finally permitted listed put option trading in 1979 on a few stocks and in mid-1980 allowed put options to trade on all stocks so that by the fourth quarter of 1980, puts and calls were traded on all stocks.

It is difficult to explain why the SEC moved with such relative caution in permitting put trading. The distortion in securities’ pricing which we have discovered appears to have occurred because call trading was allowed and put trading was not. Thus, perhaps through an excess of caution, the SEC may inadvertently have caused one of the outcomes it was seeking to prevent. The commission may have hesitated because on balance the rationale for permitting put trading was weaker than that for call trading. In any event, it was necessary to allow both call and put trading at the same time in order to prevent securities’ prices from being distorted.


THE FINDINGS

In the absence of option trading, spontaneous stock transactions lead to swings in stock price. Efficient market theory, one of the major contributions from financial literature, holds that buy and sell orders generally balance out in the short run so that price fluctuations appear to follow a stationary random walk. The efficient market concept implies that the current market price fairly reflects all the information that exists about a company at that time. Prices should there- fore fluctuate symmetrically about their current value randomly with a mean change of zero.

Among the ways that distortions from the efficient market sought by investors and the SEC could arise would be if insiders possessed information before the average investor, if trades were not executed on a first-come, first-serve basis, or if larger numbers of shares of certain companies were sold systematically on selected days upsetting the otherwise orderly market.

Consider the possible distortion when call options are exercised. Each series of options has a pre-established expiration date. On the expiration date, options that are valuable will be exercised. The exercise of a call option, if it is done as an arbitrage transaction, will involve selling 100 shares of the underlying stock. The sale will take place on the expiration date itself. The expiration date is always a Friday, and the floor broker exercising the option will not want to take the risk of holding the shares over the weekend. Consequently, there are induced sales of stock on the Friday when an option series expires. On the following Monday, the stock price would be expected to return to its unaffected level.

Trading in puts creates a countervailing force to offset the impact of call trades. Exercising a put involves buying 100 shares of the underlying stock. It is clear that if equal numbers of calls and puts are being exercised on a particular Friday, the induced sales and the induced pur- chases will offset, and will have little or no net influence on the price of the stock. Spontaneous transactions will move the stock, on that day as they do on others.

Three distinct time periods exist, each characterized by different SEC regulations on listed options. First, prior to 1973, the SEC forbade listed option trading. Second, from 1973 through the third quarter of 1980, only listed calls could be traded. Third, since the fourth quarter of 1980, both listed puts and calls have been traded on four active securities markets. Whether the securities market has been adversely effected by SEC regulation of options can be tested by in- vestigating the behavior of common stock prices in each of these periods.

Numerous research studies by people such as Fischer Black and Myron Scholes have re- peatedly shown that overall and during the period prior to 1973 the stock market was efficient. We have taken this conclusion for granted and have not conducted further research into the pre-1973 period before listed options were traded.

To investigate the second and third periods, 1973-1980 and 1980 to the present, we gathered data on the closing price of 32 companies on the day options being traded on those stocks expired. The first period was condensed to 1977-1980 because so few options traded prior to 1977. The results are very clear. By performing chi-square goodness-of-fit tests, it was dem- onstrated that stocks with trading options behaved in a manner inconsistent with the efficient market hypothesis prior to the fourth quarter of 1980. That is, the stock in these companies tended to change perversely on the option expiration date, falling far more often than rising. However, once put trading was instituted, after the fourth quarter of 1980, the chi-square test strongly showed conformity of price changes with what would occur in an efficient market.

The conclusion is straightforward. Prior to 1973 the stock market was efficient. Then when the SEC cautiously only allowed call trading from 1973-1980, the market in stocks with listed op-


tions became inefficient on the options expiration day. Finally, once the SEC corrected its own error, the market returned to efficiency

Overall the SEC has helped to guarantee the equal likelihood for gains and losses by investors, market makers and insiders. In the dynamic 1980’s, many more changes are likely in the financial arena as banks seek to become brokerage firms, brokers become bankers, and futures contracts and options become available on currencies, commodities, and financial in- struments While the SEC’s responsibilities are great, it is nonetheless essential for the SEC to confront these innovations more directly than was done with option trading in the previous decade.


BIOGRAPHY

The authors are all professors in the College of Business Administration at Northeastern Uni- versity Professor Harlan Platt’s book, W?y Companies Fail, will be issued shortly by Lexington Books. Professor Marjorie Platt is currently engaged in a bank failure study for the Federal Re- serve Bank of Boston. Professor John Edmunds has been teaching for two years at IMADE School Costa Rica. But he is now returning to Boston.


THE H - C COUNT A Possible Method of Predicting the “Highs” and “Lows” of

Share Prices

C. A. Henn-Collins

The search for a reliable system to predict when to buy or sell shares or commodities has pre- occupied many people for a long time, and the author is sure it will continue to do so, for there is much evidence that the price of indices and of many individual shares is of a cyclic nature. Broadly speaking, it is felt that this sort of history does repeat itself, but the accuracy of prediction based solely on historical data may be distorted, or even made false by major economic changes. An index could move sharply in value due to political changes, wars or rumors of wars. In the case of an individual stock, the management of the company can get worse, or the trading prospects can change radically by the same forces which impact the market indices As many of these cyclical considerations get reflected in the value of the share or index it seems there should be an opportunity for a good prediction system.

Background research has led this author to several systems which are intuitively helpful in trading securities. These include the Elliott Wave Theory, and the Coppock System, as well as the prognostications of Konratieff. All are based on historical records of price or value. The Cop- pock System, on the one hand, presupposes a cycle of change of value over a relatively short period as it is based on a system of ten-month weighted averages. Both Konratieff and Elliott produce evidence of very long cycles of ten or even a hundred years or more. In the case of the Elliott Wave Theory, one breaks down a cycle into sub-cycles in order to develop a mean- ingful forecast. The H-C Count shares with these systems the fact that the H-C Count is entirely based on historical data, but it alleges no particular pattern of cycles or sub-cycles, and its application is unrelated to any fixed time scale. The author has now applied the H-C Count to over forty indices or share prices over about a decade. This system finds cycles which appear re- petitive and are clearly indicated, with sufficient high and low differentials to be useful for trading purposes.

The H-C Count system is very simple and can be maintained from newspapers; one does not have to to rely on computerized information sources. The essence of the system is to observe the weekly closing prices for an individual share or index and count the number of weekly reversals which are observed during the duration of a full cycle. A complete cycle will contain a substantial and roughly predictable number of price reversals. One note of caution is advised: users of the H-C Count must be aware of stock splits and adjustments that occur in order to maintain the continuity of the data. For analytical purposes, the author posts the weekly price data on a chart with the numerical count (cumulative reversals) shown alongside the plotted data.

The author feels this system has, to say the least, interesting possibilities. An electrical analogy comes to mind. The H-C Count seeks to be an “el,ectrical bell” which rings at key market peaks and dips. The H-C Count is not set up to measure the current flowing through the circuit. Other tools do this better. Pursuing the analogy a little bit further, the H-C Count is more like a burglar alarm that generally does the job well enough to be used widely but is prone to a “false alarm” when badly set up. From an investment perspective, most analysts tend to measure cycles in terms of time and price magnitude but overlook the fact that cycles also have reversal char- acteristics which tend to be extremely important. We all know that during a cycle prices do not go straight up or straight down, but experience a number of minor contra-trend corrections along the way.


To use the H-C Count, a newcomer should start by making a weekly plot of the share or index based on the weekly closing value for a period of at least five years. In this plot, a major cyclical low point is noted and, arbiirarily, a count of zero is marked at this point. Each subsequent week the price or index value as defined is noted, and when the direction of the price or index value changes, a count of one is noted. Thus, if the weekly price movement makes a zigzag with three peaks, the count at the top of the third peak would be five. Note that peaks are always odd numbers and dips are even numbers and that the numbers do not depend on how much the price or value rises or falls. Sufficient accuracy is obtained by recording the figures quoted in whichever newspaper is used as the information source. If fractions are quoted, the nearest whole number suffices.

Now let us move to a practical example. In London the British government stock is widely traded and historical data readily available. The main record of its gyrations is the British Financial Times Government Securities index. Figure 1 shows a weekly record of how the figures on the index might be kept. Figure 2 shows a monthly record over the period 1975 to 1983 of this index. The figures noted on important peaks and troughs are the number of reversals from the low point chosen (in this case, January 1975). By October 1981, the H-C Count had reached 164, the author had enough experience with the system to be almost certain that the index was at a low, and anyway, the numbers were getting a little large so a restart from a new zero was made. Time has shown the zero to be correctly placed.

Starting from the zero in 1975, the counts made on Figure 1 (and later sheets not reproduced here) are summarized on Figure 2. These counts are very interesting. In 1976, there was a peak of 23 and then a low at 42 in the same year, then a high again at 63 and a low at 82. In the winter of 1978-1979, the British government was going through a sticky patch, and this was reflected in the abnormal low at 94 which at the time was disturbing to many investors. Had an investor gone into the market at 82, the potential profit to that investor was substantial when the count came right at 101 in 1979. The index was marginally at its lowest again at 114, but 164 as the system low is consistent. Low to low, or peak to peak, the counts appear valid. Table 1 summarizes the situation for the period 1975 to 1983.

TABLE 1

Application of the H-C Count to the Financial Times Government Securities Index

1975 - 1983

PERIOD UP COUNT DOWN COUNT TOTAL COUNT

75 - 78 23 19 42 78 - 80 21 19 40 80 - 81 19 21 42 81 -83 23 17 40

It should be noted that the time between dips and peaks is variable, and the H-C Count system appears not to be tricked by either fast or slow movements in value. The count is the vital thing. A reasonable assumption is that errors in the count of one or two arise because plotting to a chosen accuracy may not be optimum or the errors arise from some foible of human nature. Experience now indicates that it would be better to convert all prices or index values to a percentage of some arbitrary level. This could well lead to eliminating some small differences in cycle counts.


The British Financial Times Actuaries All Share index is perhaps the most important index of the British equity market, so a summary of it for the period 1975 to date is included as Figure 3. Here is an example of two completed cycles, with indications of being well into a third. It should be noted that the cycle is both longer‘and slower with this index than with the Govern- ment Securities Index. A quick peak at a count of nine appears (perhaps of interest to traders rather than investors), then a peak at 29 or thereabouts is reached, and at 56 a buying opportunity appears. At 79 comes the time to take a profit, and at a count of 90 the music starts again.

If we now look at a similar record for the Dow Jones Industrial Average (a similar record to Figure 1 having been kept), parallel but again interesting results come to light. Looking at Fig- ure 4, covering the period 1975 to 1983, a count of 90 is obvious from the initial low noted in January 1975 to the next low in March 1978. Again a bigger count and a longer time scale than for the British Government Securities is evident, and a strong parallel with the Actuaries All Share Index should be noted. In more detail, there is a generally rising performance to a count of 35. Next, an inspection of the cycle starting in March 1978 will reveal some similarities and some differences: 35 to 39 is again “high ground” but after 50 the index went on rising. It would be reasonable to look for a cycle of about 90. So how do things work out? Without data on the previous cycle (which the author lacks) or knowledge of other cycles, the observer would be in a state of doubt. Scrutiny of several other similar indices shows that a rise to the 30’s is pretty general as is the fall of 35 from the last major high. One might wonder how much more there is to go for having noted too that the count from the last high in October 1976 was also 90 - the same as the first major count. Actually, the high came at 47, possibly a drafting error or just the inevitable limitation of a mechanical system. One might indeed be anxious having the example of February 1979 in Figure 2 in mind. At least ten other indices often show a pattern of highs in the 30’s, 40’s and 70’s and so such a pattern might evolve here as, indeed, it did. At the count of 75, a peak-to-peak count of 116 is not unusual, but rather a jump from a count of 90 is likely for the whole cycle. After the index had fallen away a little from 75, the author thought the overall cycle would be 75 + 35 which was a good guess for the actual low at 102 some seventeen months later.

Turning to the current cycle shown on Figure 4, as of October 1st the count is 27. No high ground has been reached yet and there is every hope of such in the range 35 to 45. One can then take a view of what is likely to follow.

In conclusion, a system of prediction of share price or index value based on simple construc- tion but general application has been described. The value of this system as an investment tool appears good, but application over a longer period is called for to confirm this view. The author would like to be associated with its further exploitation.

@Copyright C. A. Henn-Collins 1984


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THE EVOLUTION OF A BREADTH INDICATOR OR

TEACHING AN OLD DOG NEW TRICKS

John Carder

THE A-D LINE:

The Advance-Decline line (A-D line) is the original market breadth indicator. It is simply the cumulative sum of the difference of each day’s advancing and declining issues. If there are 700 advancing issues and 600 declining issues today, then today’s A-D line is 100 above yesterday’s value.

THE A-D RATIO:

One of the first refinements of the A-D line was the A-D ratio. It is the cumualtive sum of the following ratio:

(Advances-Declines)/lssues Traded

Over the short-term it is almost identical to the A-D line (compare figure 1 and 2). Its advantage lies in the fact that its moves are comparable. I will illustrate with three examples:

CASE I II III

Issues traded 2000 500 4000 Advances 1000 250 2000 Declines 500 125 1000 Unchanged 500 125 1000

A-D 500 125 1000 (A-D)/1 0.25 0.25 0.25

In all three examples, half the issues advanced and one-quarter declined. Because there were differing numbers of Issues Traded in each case, the A-D line changed by a different amount in each case. However, the A-D ratio would increase by 0.25 in all three cases. Why bother? Two reasons come to mind: 1) Markets are generally growing. There are more issues traded on the NYSE today than there were in 1930. A move in today’s A-D line would dwarf a move in 1930’s A-D line caused by a move of comparable breadth (i.e. I vs. II), while the A-D Ratio would show the same sized moves in each case. 2) Not all markets are the same size. The NASDAQ currently trades around 4000 issues a day, while the New York Stock Exchange (NYSE) trades around 2000 issues per day. The NASDAQ A-D line would move about twice as fast as the NYSE A-D line. Given the same proportion of Advances, Declines, and Issues Traded on the two markets, their A-D ratio will move the same amount.

PVITI AND NVITI:

Paul Dysart created two A-D lines, the Positive Volume Issues Traded Index (PVITI) and the Negative Volume Issues Traded Index (NVITI). When today’s NYSE composite volume was greater than yesterday’s, he added the difference (A-D) to the PVITI total. When it was less, he added it to the NVITI total. When I first started charting the indices, I used the A-D Ratio instead of the difference. I just called them Positive Volume and Negative Volume. Figure 3


shows both lines. Note that the time scales are neither linear nor comparable, since volume oscillates erratically. Originally, Dysart felt that NVITI was the most valuable, since the traders on days of lower volume were supposed to be acting with cooler heads. This indicator has lost much of its usefulness. As you can see in figure 3, the market has trended up with greater volume and down with lower volume, causing the Positive Volume line to rise while the Neg- ative Volume line falls.

POSITIVE AND NEGATIVE VOLUME ON THE DJIA:

Confronted with the problem of the degeneration of these indicators, I decided to try to make a substitution. Because institutions were becoming such major factors in the market, I wanted an indicator of their action in the market. Since institutions deal in such large quantities, they are limited in the range of stocks that they can buy. I decided to substitute the volume of the Dow Jones Industrial Average (DJIA) for the NYSE composite volume, since these “Blue Chips“ were big enough to be on an institution’s menu. I must admit that I wasn’t expecting much of a change. I always assumed that DJIA volume tracked the NYSE volume pretty well. As you can see from figure 4, it made a very big difference. While Positive Volume is still rising and Negative Volume is still falling, they are now giving definite signals. I believe that Positive Vol- ume is the most useful. It seems to show what the institutions are doing and is very useful for buy signals. Negative Volume is less reliable but can be used for sell signals.

THE FUTURE:

In the fourth quarter of 1984, Positive Volume on the DJIA broke out above the consolidation range of the previous year. The subsequent advance was so strong that Negative Volume on the NYSE paused in its decline and was even able to turn up! I read this as a very bullish signal. Only time will tell.

VARIATIONS:

If you have four daily statistics (Issues Traded, Advances, Declines, and some volume figure) for any market, you can apply these tools. Figue 5 shows Positive and Negative Volume for the New York Exchange Bond market. Figure 6 shows Positive and Negative Volume of the NYSE using the volume of the Dow Jones Transportation Average. I hope that you will want to teach this new trick to some of your old dogs.


FIGURE 1

1981 1982 1983 1984 I I I I I I I I I I I I I I

ADVANCE-DECLINE LINE ON THE NYSE

L drawn dai ly from 13 Mar 1981 to 14 Feb 1985

8000

0

-8000

I I I I I I I I I I

I I I I

I

-16

-20

-24

FIGURE 2 -28

1981 1982 1983 1984 I I I I I I I I I I I I I I I I

ADVANCE-DECLINE RATIO ON THE NYSE drawn dai ly from 13 Mar 1981 to 14 Feb 1985


I-- l I I I I I I I I I

1981 1982 1983 1984

POSITIVE VOLUME

FIGURE 3

15

- NEGATIVE VOLUME 0

1981 1982 1983 , 1984 I I I I I I I I I I I I I

Positive and negative volume on the NYSE drawn from 13 Mar 1981 to 14 Feb 1985

30

I I I I I I I I I I I I I

1981 1982 1983 1984 40

I POSITIVE VOLUME

32

FIGURE 4 24

16

NEGATIVE VOLUME

1981 1982 1983 1984 I I I 1 I I I I I I I I I

Positive and negative volume on the DJIR drawn from 13 Mar 1981 to 14 Feb 1985


I I I I I I I I I I I I

1981 1982 1983 1984

POSITIVE VOLUME 50

FIGURE 5 40

30

NEGATIVE VOLUME 20

1981 1982 1983 1984 I I I I I I I I I I I I I

Positive and negative volume on the NYEB drawn from 13 Mar 1981 to 14 Feb 1985

I I I I I I

1981 1982

POSITIVE VOLUME

I I I 1 I I I

1983 1984

40

30

FIGURE 6

20

10

NEGATIVE VOLUME

1981 1982 1983 1984 1 I I I I I I I I I I I

Positive and negative volume on the DJTR drawn from 13 Mar 1981 to 14 Feb 1985


BIOGRAPHY

John Carder received a B.A. in mathematics from the University of Colorado at Boulder. He currently acts as an advisor to several of his family’s trusts. He is the author of the programs used to draw the charts appearing in this paper.


journal of technical analysis (jota). issue 20 (1985, february)

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