Journal of Technical Analysis (JOTA). Issue 46 (1996, Summer)

MTAJOURNAL

Spring-Summer 1996 . Issue 46

.i P1161icnti0,1 of

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Market Technicians Association Journal Table of Contents

MTA Journal Editor and Reviewers

MTA Member and Affiliate Information

Style Sheet for the Submission of MTA Journal Articles 5

Market Technicians Association Board of Directors 6

Putting It Altogether

Henry 0. Pruden, Ph.D., Editor

7

The Quantification Predicament

Timothy W. Hayes, CMT

9

The winner of the third Charles H. Dow A4ward, Hayes argues that technical market analysis research should be guided

1 by objectivity and accurac?. Following rigorous quantitative methods and knowing the pitfalls of various quantitative approaches can strengthen the overall contribution of indicators while avoiding self-deception. Mr. Hares discusses the concerns of quantification. and such methods as trade-signal analysis, zone analysis, subsequent-performance analysis and re\-erse-probabilit analyis.

Seasonal@ in Canadian Equity Prices

Don Vialoux, CMT

15

2 This articles looks at the quantitative evidence in support of or in rejection of the well-knolvn assertion among equit! marketing strategists in Canada: “Buy them when it snows. sell them when it goes.” Presumably Canadian stocks are stronger between Sovember and March than at other times of the year. MIat does the evidence show? The thorough analysis and discussion in this article mar give the reader an unequivocal answer.

Patterns of Seasonal Variation in Canadian Fixed-Income Markets

R. Alain Rivet

23

Using detrended data of the Canadian fixed-income market, the hypothesis that there is no seasonal variation in price 3 , behavior was tested. Data suggest the presence of seasonal-variation patterns in Canadian fixed-income markets. The study of seasonal variation can provide trading strategies that can be tailored to long-term versus midterm bonds.

The High-low Index as a Tool to Enhance Returns

Harold 6. Parker, Jr., CMT

35

Mr. Parker obsen-ed that the 5Pweek high and low data on the AXE could provide valuable insight into the near- to

4 intermediate-term trend of the market, especial& if the signals were clear and objective. To ovel-come the subjectivitv of most interpretations of the high-low index, Mr. Parker turned to the point and figure chart first employed by Abe Cohen. Ifith modified decision rules, the Cohen point-and-figure method indicated that the high-low mdes can be very useful for timing equity entry and exit.

MTA JOU~AL/Spring-Summer 1996 i

Answering the Bell of Sentiment Indicators 39

Brent L. Leonard

The purpose of this review paper is to list. explain, and ei-aluate se\-el-al well-known stock market Sentiment Indicators over maw periods of time. These indicators include option put-call ratios. advisors letters, short interest. mutual fund cash, and other contraq, against-the-cro1j.d statistics.

Using the Z-Trend Oscillator for Long-Term Bond Market Timing

Robert T. Zukowski, CM1

49

This article xas written for the purpose of long-term bond market timing through the use of a well-know1 equity market indicator called the Coppock Curve. More specifically. it examines the concept of modif)-ing the culle for one simple reason: to better identih- major tops and bottoms lvith a shorter lead time than the curve in its original format. \Yith that in mind. Zukowki calls the modified version of the Coppock Curve the Z-Trend Oscillator because of what it can do. Most oscillators were specificalh- designed for trading period consolidation. but the Z-Trend Oscillators were specificall\. designed for trading all market conditions from accumulation. to trending, to distribution,

A Study in Volume and Price Alerts

David Bryan

57

That \‘olume precedes price is a well accepted market proposition. In this research studs Da\-id BiTan makes a further refinement in volume-price studies by offering evidence that a sample ofjust one dal- of ksual volume can predict subsequent price action. The author concludes that the predictive power of unusual I-olmne is strengthened bv the addition of evidence of a sharp price movement.

2 MTA JOUR~~~/Spring-Summer 1996

Market Technicians Association Journal Spring - Summer 1996 . Issue 46

EDITOR

Henry 0. Pruden, Ph.D. Golden Gnte I’uiueuit\;

Sun Fmrisco, Califomh

ASSOCIATE EDITOR

George A. Schade, Jr., CMT Scottsdnle, A~izonn

Manuscript Reviewers

Connie Brown, CMT Don Dillistone, CFA, CHIT Michael J. Mood!; CUT Ae~od~nnwzic Investments lm-. comom t Buy Dorsq, Tliight &+ dssorintes

Gctilzesuille, Georgia ll’innej~eg, ,I~lnnitoba Pusndenct, Cnliforaicc

Charles P. Kirkpatrick, III, CMT Richard C. Orr, Ph.D. John A. Carder, ChlT Kirkfhck md Co~njm\; 111~. Chonos Corgorrrtiol2

Tofiline Gr@ics Exete); Ah Hm@h&e Lesiugton, i\kUSSUfll usetts BOZl k/e,; co/omlo

John McGinle! David L. Upshaw, CFA, GRIT Ann F. Cod! Tech~~iccd Treds Lake QuiuircI, Kmsns

hest Finnnciul Co~porution Kilton, Cmuectirut Tc qn, Flodcc

Robert I. Webb, Ph.D. Associate Professor cod Paul TudorJones II Resenrch Fellow

,\lcl,ltire School of Commerce, liiizwsity of \i’yiin Clinrlottesuille, I’iq-inin

PUBLISHER

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MTA JO~:ILU;\L/Spring-Summer 1996

Market Technicians Association

Member and Affiliate Information

MEMBER ELIGIBILITY Member category is available to those “whose pro-

fessional efforts are spent practicing financial technical analysis that is either made available to the investing public or become a primary input into an active portfolio management process’or for whom technical analysis is the basis of their decision-making process.” Applicants for membership must be engaged in the above capacitv for five years and must be spon- sored bv three MT‘4 members familiar with the applicant’s work.

AFFILIATE ELIGIBILITY Affiliate category is available to individuals who are

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DUES Dues for Members and Affiliates are $200 per year

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a MTA JOURNAL/Spring-Summer 1996

Style Sheet for the Submission of Articles

MTA Editorial Policy

The Market Technicians Associate Journal is published bv the Market Technicians Association, Inc., One \qorld Trade Center, Suite 4447, New York, NY 10048, to promote the investigation and analysis of price and volume activities of the world’s financial markets. The MA Journal is distributed to individuals (both aca-

demic and practitioner) and libraries in the United States, Canada, Europe and several other countries. The K’A Journal is copyrighted by the Market Tech- nicians Association and registered with the Librar! of Congress. All rights are reserved.

Style for the MTA Journal

All papers submitted to the MTA Journal are re- quested to have the following items are prerequisites to consideration for publication:

1. 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.

2. All charts should be provided in camera-read! form and be properly labeled for text reference.

3. Paper should be submitted double spaced if typewritten, in completed form on 8-l/2” x 11” 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 at

the end of the article. Submission on disk is en- couraged by arrangement.

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

5. Two submission copies are necessary

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

Mail your manuscripts to: Dr. Henry Pruden P.O. Box 1348 Ross, CA 94957

MTA JOURN.L/Spring-Summer 1996 5

Market Technicians Association Board of Directors, 1996-l 997

PRESIDENT Philip Roth, ChIT Dean \litter Relnolds 2 IVorld Trade Center, 63rd Fl. New York, NI’ 10038 ?12/392-3016. Fax: 212/392-1338

VICE-PRESIDENT/LONG RANGE Andrea Seumann HSBC Futures Inc. 140 Broadway, 17th FL New York. NY 10006 ?12/825-5880. Fax: 212/823-0650

Officers & Administrator

VICE PRESIDENT/SEMINAR Mark Scott The \hlume Investor 3 Piedmont Center. #210 Atlanta. G;Z 3030.1 404!231-120i,ext. iO9 Fax: 404/231-1375

TREASURER J. Les \tilliams. CMT \Villiams Capital Management P.O. Box 120125 Arlington, TX i6102

815/X-8332. Fax: 81ii7957411

SECRETARY Dodge Dorland, ChlT LYYDOR Inr-estment Mgmt. 103 East i5th Street. #4F.‘E Neh York, SY 10021 212,'53i-12.54. Fax: 212/861-002i

MTA AMINISTRATIVE OFFICER Shellev Lebeck Market Technicians Association One \\‘orld Trade Center, #444i Nelr York, SE' 10048

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ACCREDITATION Margaret \'an Andel, CF=\. CMT Dal’inci Investments 43 \\‘aite Road Bosborough, 1l.A Oli19 308/263-9423, Fax: 508/266-2839

COMPUTER John Bollinger, CFA. GRIT Bollinger Capital Management P.O. Box 33% Manhattan Beach, CA\ 90266 310/798-8855, Fax: 310!798-8858

EDUCATION Richard Dickson Scott & Stringfellow Inc. 909 East Main Street Richmond, \‘.A 23219 804/780-3292, Fax: 8OU644-8241

ETHICS AND STANDARDS Paul Desmond Lawn’s Reports, Inc. 631 i’.S.Highwav 1. #305 North Palm Beach. FL 33408 561/842-3514, Fax: 5611842-1523

FOUNDATION Charles Comer, CMT Credit Lyonnais Securities (USA) 1301 henue of the Americas, 37th FI. New York, SE’ 10019 212/308-5630, Fax: 212/261-2512

Committee Chairpersons

IFTA LIAISON Philip Erlanger. ChlT Phil Erlanger Research P.O. Box 2680 Acton, MA Oli20 508/263-2536. Fax: 508!2661104

JOURNAL Dr. Henn Pruden P.O. Box’1348 Ross, CA 94957 4151442-6583, Fax: 313/339-1319

LIBRARY Linda Raschke LBR Group 2 Sorth Counts? Lakes Drive Slarlton, SJ 08033 609/X3-X15, Fax: 6091568-5882

MEMBERSHIP Julia Bussie AkG. Edwards & Sons 141 \Vest Jackson. #201-,c\ Chicago. IL 60604 312!%44280, Fax: 312/%-4278

NEWSLETTER Aldre\c Addison Addison Investment P.O. Box 402 Franklin. hL1 02038 508/528-86i8. Fax: 508/5?8-6914

PLACEMENT James Bohan Merrill Lynch \Yorld Financial Center. North Tower New York, S‘I’ 10281-1319 212,/449-0552. Fax: 2121439-2767

PROGRAMS \Valter Burke, CMT MC11 Monel-\\‘atch 1 Chase Manhattan Plaza. Si’th FL Sew York. ST 10005 212/908-4325. Fax: 212,‘908-4331

REGIONS James Bianco. CMT Arbor Trading Group Inc. 1000 Hart Road. #260 Barrington, IL 60010 847304-1511. Fax: 847/3041%4

PUBLIC RELATIONS Robert Zukowski, Jr.. ChIT MC\1 Xonev\Vatch 1 Chase Manhattan Plaza. 3ith Fl. Sew York. AT 10005 212,1908-4324. Fax: 212 ‘908-4331

LEGAL CONSULTANT Jerry S. Carter Alatlins Financial Consulting. Inc. 4 \V. Old State Capital Plaza, # 710 Springfield, IL 62701

21i/i88-6909. Fax: 217!;588-6903

6 MTA JOURYAL/Spring-Summer 1996

EDITOR’S COMMENTARY

Putting It Altogether

by Henry 0. Pruden, Ph.D., Editor

Philosophy, whether the thoughts of Kay1 Popper or any one else, was not supposed to be a road map for making monq in the real warld.

Ei for George Sores, philosophp would serve just that purpose. In time, he would go jiiom the abstract to theprac- tical; he would develop theories of knowledge, of how and rub\‘ peo$e think in cerfnin ways, and from those theories he wo;,ld spin new theories about the run! the financial markets functioned.

- Robert Slater, Soros: The Life, Times & Tradina Secrets of the World’s Greatest Investor

Theolies are nets cast to catch what we call “the world;” to rationalize, to explain, and to master it. 1l’e endeavor to make the mesh everfiner and finer

- Karl R. Popper, The Loaic of Scientific Discovery

Over the past twenty Tears, students of technical analvsis have frequentl! asked me: “Now that I’ve learned all of these various indicators, how do I put them altogether?” Recently the same sort of question was posed to me by an ardent yet frustrated student of technical analysis, a Mr. !vl. 11. of New Jersey who called to ask: “M”nere dan I go to learn how to put it altogether?” This man, it turned out, had read man? books on technical analysis, studied charts, acquired software and attended numerous seminars about technical analysis indicators. Along the wav he had compiled a lengthy list of assorted technical ‘indicators he wished to follow. But he had not learned these indicators in an integrated manner; rather, he had learned them two, by two, by two. That is, he picked them up bv studying one predictor indicator and one dependent indicator at a time (e.g., stochastics and the S&P 500). Mr. M. M. was perplexed and frustrated because he lacked the tool/the perspective for putting them altogether. He believed that something, somewhere must exist that would show him how to put all of the indicators together. He believed that with them altogether he could extract more, and more valuable, information from his analysis. He is still searching for some method for putting all of his individual indicators together into some meaningful whole.

Mr. 11. 51. remains perplexed and frustrated because there is an absence of places to go to learn “how to put it altogether,” and this is because there a lack of methods and guiding principles that tell the analyst how to . . . “put them altogether.” M%at is missing is a thing that sytem- atically meaningfully and exhaustivelv puts indicators to-

gether so that superior diagnosis and better prognosis result. Mr. M. M’s frustration and perplexin points out a large hole in the fabric of the technical anal!‘sis discipline.

To fill the apparent hole in technical analysis requires a shift in perspective from part to the whole and a shift in research/testing attention from the piece-meal creation and testing of indicators to a more inclusive studv of the combined contribution of several interacting indicators. In brief, this means a shift of attention to conceptual schemes, frameworks, models, systems or theories, all of which are viewed as being svno&mous with a theoretical model. In effect, filling the hole in technical analvsis in- volves the creation and testing of theoretical models of technical analysis.

Theoretical Model Building: The Component Parts

“A theoretical model starts with things or variables, or (1) zr?lits whose interactions constitute the subject matter of attention. The model then specifies the manner in which these units interact with other, or (2) the lnw of internctio?z among the units of the model. Since theoretical models are generally of limited portions of the world, the limits or (3) boundnties must be set forth within which the theory is expected to hold. Most theoretical models are presumed to represent a complex portion of the real world, part of whose complexity is revealed by the fact that there are various (4) slste,n stntes in each of which the units interact differentlv with each other. Once these four basic features of theoretical model are set forth, the theorist is in a position to derive conclusions that represent logical and true deductions about the model in operation, or the (5) ~ro~ositiolzs of the model.

So far, we see only the theoretical side of the theory research cycle. Should there be an)- desire to determine whether the model does, in fact, represent the real world, then each term in each proposition whose test is sought needs to be converted into (6) on em/irical indicntor of the term. The next operation is to substitute the appropriate empirical indicators in the propositional statement to generate a testable (7) h?otlzesis. The research operation consists of measurmg the values on the empirical indicators of the hypothesis to determine Ivhether the theoretically predicted values are achieved or ap- proximated in the research test.”

MTA JOURS;V/Spring-Summer 1996 7

Exhibit A: Some Correspondence Between Theoretical Model Building and Technical Analysis Theoretical Model Technical Analysis

lSzit of nnnlyis The four main elements of price, volume, time and sentiment; reversal and continuation patterns.

Lnua of Intemtion ~bhnle nnd pi@ vcrr~ together; selling climaws and Qclic loal points occur togethrr; uh~~n thy Dow Jones mnlks alone, look out (dizlo-ge,lcepreredrs prick tend wversnls). Some sort of a statement comiecting two or more Lmits of analysis, such as “if.... then”; ” vary (associated) together;” or if “a” is present, then “b” should be present.

Boundnries Technical analysis within its boundaries include those variables that are technical (market behavior). Fundamental analyses are outside of the boundary of technical analysis. Monetary data are on the edge of the boundar!- of technical analysis.

System States

Propositions

The four phases or svstem states of accumulation, markup, distribution and markdolvn.

(1) at turning points, volume precedes price, (2) markets progress through the four system states of accumulation, markup, distribution and markdown. (3) rising bullish sentiment and rising prices go together.

H@otheses For the above propositions, insert the following empirical indicators: (1) Granville’s on- balance volume and the Dow Jones Industrial Average. (2) trends and trading ranges, or Dow Theory Lines, or the T\‘yckoff method. (3) Investor’s Intelligence “Bull-Bear” numbers and the S&P 300.

-Robert Dubin. Theory Building

Exhibit ;\ provides some correspondences between theoretical model building and technical market analvsis.

Sources of Theoretical Models Building theoretical models that are logical, internall!

coherent and consistent, and testable in the real world is a challenging task. Thankfully the world of technical market analysis is observable, time dependent, quantitative and unambiguous, all of which help model building and testing. As for ideas, one can pursue the route followed by George Soros, who turned to first principles in human behavior and the philosophy of science to logi- cally develop theories which he then tested in the real world. Or, one can follow the hint by the anthropologist H. G. Barnett, rvho argued for borrowing and substitu- tion. Barnett argued that the “new,” that cultural change, often comes about through borrowing a thing from one field and adapting to another. Thus the horse and bugg was transformed into the automobile b? becoming the “horseless carriage” as the internal combustion engine was substituted for the horse.

Instances of “model borrowing” from science and math- ematics applied to technical analysis occur from time to time. General models in science have appeared during the past three decades. Technicians saw crrtastrophetheor! in the 70’s, chaos theory in the 80’s and now, perhaps, COM- @it! theory in the 90’s. In addition, an analyst can derive theories of technical analysis from direct personal

observation of the technical world, or by deducing from a classic writing such as from the legendary Jesse Livermore in Reminiscences of a Stock Operator. in that book, the chapters on “manipulation” by the stock market operator could easih- have given birth tb modern-day “on-balanced volume.” &all!; in my view, the new schodl of behavioral finance, which shares roots in psycholoE and sociolog! and ivith technical analysis, is a very promising source of conceptual schemes for “putting it altogether.”

References/Bibliography Barnett, H. G., Innovation: The Basis of Cultural Change. McGraw Hill, 1963

D&in, Robert. Theory Building, revised edition, Sew York. The Free Press, 19i8

Gleick, James, Chaos: Making a Sew Science. Sew York, \Xing. 198’7

Lefev6, Edwin, Reminiscenses of a Stock Operator, Burlington, IT, Fraser Publications

Popper, K. R.. The Logic of Scientific DiscoI-cry. Se\\- York, Sciences Edition, 1961

Slater. Robert, Soros: The Life. Times. 8- Trading Secrets of the \\‘orld’s Greatest Investor. New York. Irwin. 1996

I\‘aldro, 11. Mitchell, Complesitx The Emerging Science at the Edge of Order and Chaos, Sew York, Simon and Schuster, 1992

Zeeman, E.C.. Catastrophe Theory; Reading, Mass. Addison I\esley 1977

MTA JOURXWSpring-Summer 1996

The Quantification Predicament

Submitted by Timothy W. Hayes, CMT

“This indicator hns nkuqs produd huge profits! In fart, you u~oztlrl hme doubled your Mona in just six months!”

Such a claim could be a sales-pitch. It could also be an analyst’s enthusiasm about some workjust completed. But in either case, such claims appear to be meeting increasing skepticism, perhaps because enough have proven to be based more on fiction than quantifiable fact, perhaps because enough investors have been burned bv indicators that have failed to pa11 out lvhen put to real-time use, or perhaps because the combination of ever-strengthening computing power and ever-increasing program complex- itv has made excessive optimization as easier and more dangerous than ever.

In any case, the need to quantify accurately and thor- oughly is greater than ever. Honest and reliable quantification methods, used in the correct wa!; are needed for increased research credibility. The!- are needed to impart objectivity. Thev are needed for effective analvsis and for the somid backing of research findings. The alternative is the purely subjective approach that uses trendlines and chart patterns alone, making no attempt to quantifv historical activity. But when the quantification process fails to deliver, instead producing misleading messages, the subjective approach is no worse an alternative - a mis- guided quantification effort can be worse than none at all. The predicament, then, is how to trulv add value through quantification.

The Concerns The major reason for quantif;\ing results is to assess the

reliabilitl\ and value of a current or potential indicator, and the major reason we have indicators is to help us interpret the historical data. The more effective the interpretation of historical market activity, the more accurate the projection about a market’s future course. An indicator can be a useful source of input for developing a market outlook if quantitative methods back its reliability.

But for several reasons, quantification must be handled with care. The initial concern is the data used to develop an indicator. If it’s inaccurate, incomplete, or subject to revision, it can do more harm than good, issuing misleading messages about the market that’s wider analysis. The data should be clean and should contain as much histor! as possible. M”nen it comes to data, more is better - the greater the data history the more numerous the like occurrences, and the greater the number of market cycles mlder study.

This lea& to the second quantification concern, and that’s sample size. The data may be extensive and clean, and the analvsis may yield an indicator that foretold the market’s direction with 100% accuracy. But if, for example,

the record was based on just three cases, the results would lack statistical significance and predictive \-alue. In contrast, there would be few questions regarding the statistical ValidiN of results based on more than 30 observations.

The third consideration is the benchmark, or the standard for comparison. The test of an indicator is not whether it would have produced a profit, but whether the profit would have been any better than a random approach, or no approach at all. \Yithout a benchmark, “random walk” suspicions may haunt the results.’

The fourth general concern is the indicator’s robustness, or fitness - the consistence of the results of indicators with similar formulas. If, for example, the analysis would lead to an indicator that used a 30-lveek moving average to produce signals with an excellent hypothetical track record, how different would the results be using moving averages of 28,29,31, or 32 weeks? If the answer was “dramaticallv worse”, then the indicator’s robustness would be thrown into question, raising the possibilitv that the historical result was an exception to the rule rather than a good example of the rule. A1n indicator can be considered “fit” if various alterations of the formula would produce similar results.

Sloreoyer, the non-robust indicator may be a symptom of the fifth concern, the optimization process. In recent years, much has been \vritten about the dangers of exces- sii-e curve-fitting and o\-er-optimization, often the result of miharnessed computing power. ;\s analytical programs have become increasingly complex and able to crunch through an ever-expanding multitude of iterations, it has become easy to over-optimize. The risk is that, armed with numerous variables to test rvith minuscule increments, a program may be able to pick out an impressive result that ma? in fact be attributable to little more than chance. The accuracy rate and gain per annum columns of Figure 1

FlGtJRE 1

SUMMARY RESULTS FRO41 HYPOTHETICAL INDICATOR TESTS I

These results contain an impresai\e-looking EXCEPTION to the rule

37 72 98 112 65 15.1 37 73 91 II3 52 10.1 36 11 96 1I.l 50 YX

These results would all be good ES-\hIPLES of the rule t..

50 10 I56 8.6 55 II s -19 II I5 n Y-l 56 I?0 4s 22 IhO x.2 56 I’ I Ai 2: I62 8 0 57 I? I J6 24 16-l 7.8 56 I20

Bu?-Hold Gain/Annum 6.3

MTA JOURXX/‘Spring-Summer 1996 9

compare results that include an impressive-looking indicator that stands in isolation (top) with indicators that look less impressive but have similar formulas (bottom). One could have far more confidence using an indicator from the latter group, even though none of them could match the results using the impressive-looking indicator from the top group.

MThat follows from these five concerns is the final general concern of lvhether the indicator will hold up on a real-time basis. One approach is to build the indicator and then let it operate for a period of time as a real-time test. At the end of the test period, its effectiveness would be assessed. To increase the chances that it will hold up

on a real-time basis, the alternatives include out-of-sample- testing and blind simulation. An out-of-sample approach might, for example, require optimization over the first half of the date range and then a real-time simulation over the second half. The results from the two halves would then be compared. X blind-simulation approach might include optimization over one period followed by several tests of the indicator over different periods.

Mlatever the approach, real-time results are likely to be less impressive than the: were during an optimization period. The reality of an! indicator developed through optimization is that, as history never repeats itself exactly, it is unlikely that any optimized indicator will do as well in the real-time future. The indicator’s creator and user must decide how much deterioration can be lived with, which will help determine ivhether to keep the indicator or go back to the drawing board.

Trade-Signal Analysis \Vith the general concerns in mind, the various quanti-

fication methods can be put to use. The first, and perhaps most widely used, is the approach that relies on by and sell signals, as shown in Figure 2.? M%en the indicator meets the condition that it deems to be bullish for the market in question, it flashes a buy signal, and that signal remains in effect until the indicator meets the condition that it deems to be bearish. X sell signal is then generated and remains in effect until the next buy signal. Since a

buy signal is always followed by a sell signal, and since a sell signal is always followed by a buy signal, the approach lends itself to quantification as though the indicator was a trading system, with a long position assumed on a buy signal and closed out on a sell signal, at which point a short position would be held until the next buv signal.

The method’s greatest benefit is that it clearly reveals the indicator’s accuracy rate, a statistic that’s appealing for its simplicity - all else being equal, an indicator that had generated hypothetical profits on 30 of 30 trades would be more appealing than an indicator that had produced hypothetical profits on 15 of 40 trades. ;Uso, the simulated trading system can be used for comparing a number of other statistics, such as the hypothetical per annum return that would have been produced bp using the indicator. The per annum return can then be compared to the gain per annum of the benchmark index.

But the method’s greatest benefit may also be its big- gest drawback. No single indicator should ever be used as a mechanical trading svstem - as stated earlier, indicators should instead be used as tools for interpreting market activity. Yet, the hypothetical and actual can be easil) confused. Although the signal-based method specifies ho\\ a market has done between the periods from one signal to the next, they are not actual records of real-time trading performance. If thev were, the results would ha\-e to account for the transaction costs per trade, with a nega-

FIGURE 3

tive effect on trading results. Figure 3 summarizes the indicator’s hypothetical trade results before and after the inclusion of a quarter-percent transaction cost, illustrating the impact that transaction costs can have on results. The more numerous the signals, the greater the impact.

Also, as noted in the results, another concern is the maximum drawdown, or the maximum loss betsveen an! consecutive signals. But again, as long as it is clear that the indicator is for perspective and not for dictating precise trading actions, indicators with trading signals can provide useful input when determining good periods for entering and exiting the market in question.

MTA JOURS,~/Spring-Summer 1996

Zone Analysis In contrast to indicators based on trading signals, indi-

cators based on zone analysis leave little room for doubt about their purpose - th& don’t even hare by and sell signals. Rather, zone analysis recognizes black, white and one or more shades of gray It quantifies the market’s performance with the indicator in various zones, which can be given such labels as “bullish”, “bearish” or “neutral,” depending upon the market’s per annum performance during all of the periods in each zone. Each period in a zone spans from the first time the indicator en- ters the zone to the next observation outside of the zone. Unlike the signal-based approach, the indicator can move from a bullish zone to a neutral zone and back to a bullish zone. An intervening move into a bearish zone is not required.

Zone analysis is therefore appealing for its ability to provide useful perspective without a simulated trading y-s- tem. The results simply indicate how the market has done with the indicator in each zone. But this type of analysis has land mines of its own. In determining the appropriate levels, the most statistically-preferable approach would be to identi$ the levels that would keep the indicator in each zone for roughly an equal amount of time. In man) cases, however, the greatest gains and losses will occur in extreme zones visited for a small percentage of time, which can be problematic for several reasons:

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

2) if the small amount of time meant that the indicator made only one sortie into the zone, or even a few, the lack of observations would lend suspicion to the indicator’s future reliability;

3) the indicator’s usefLllness must be questioned if it’s

-1

n:

neutral for the vast majority of time. A good compromise between optimal hypothetical re-

turns and statistical relevance would be an indicator that spends about 30% of its time in the high and low zones, like the indicator in Figure 4. For an indicator with more than four Tears of data, that would ensure at least a year’s worth of time in the high and low zones and would make a deficiencv of observations less likely. In effect, the time- in-zone limit prevents excessive optimization bv excluding zone-level possibilities that would look the most impressive based on per annum gain alone.

Another consideration is that in some cases, a closer examination of the zone performance reveals that the bullish-zone gains and bearish-zone losses occurred with the indicator moving in particular directions. In those cases, the bullish or bearish messages suggested by the per annum results would be misleading for a good portion of the time, as the market might actuall!- have had a consistent tendency, for example, to fall after the indicator’s first move into the bullish zone and to rise after its first move into the bearish zone.

It can therefore be useful to subdivide the zones into rising-in-zone and falling-in-zone, which can ha-\e the added benefit of making the information in the neutral

zone more useful. This requires definitions for “rising” and “falling”. One way to define those terms is through the indicator’s rate of change. In Figure 5, which applies the approach to the primarT stock market model used b\ Ned Davis Research, the indicator is “rising” in the zone if it’s higher than it was five lveeks ago and “falling” if it’s lower. Again, the time spent in the zones and the number of cases are foremost concerns when using this approach.

Alternatively, “rising” and “falling” can be defined using percentage reversals from extremes, in effect using zones and trading signals to confirm one another. In Fig- ure 6, for example, the CRB Index indicator is “rising” and on a sell signal once the indicator has risen from a trough, whereas it’s “falling” and on a buy signal after the indicator has declined from a peak. Even though the reversal requirements resulted from optimization, the indi-

MTA JOURS;\L/Spring-Summer 1996

cator includes a few poorly-timed signals and lvould be risky to use on its own. But the signals could be used

Secondly, lchat’s the benchmark? lZhile the zone approach uses relative performance to quantif) results, trade-

to provide confirmation with the indicator in its bull- “tiUHt7 ish or bearish zone, in this case the same zones as those PERCEST CH-\MGE OF DOW INDISTRIALS

ited amount of time, after which they lose their relevance. The results for a good buy-signal indicator are shown in Figure 7, which lists market performance over several periods following signals produced by a 1.91 ratio of the lo- day advance total to the IO-day decline total.

In its most basic form, the results might list performance over the next five trading day, 10 trading days, etc., sum- marizing those results with the average gain for each period. However, the results can be misleading if several other questions are not addressed. First of all, how is the average determined? If the mean and the median are close, as they are in Figure 7, then the mean is an accept- able measure. But if the mean is skelced in one direction by one or a few extreme observations, then the median is usuallv preferable. In both cases, the more observations the better.

used in Figure 4. For example, in late 19i2 and early FOLLOWSG 1.91 R4TIO OF lo-DAY ADVASCES TO lo-DAY DECLIUES

1973 the indicator would have been rising and in the upper zone, a confirmed bearish message. The indicator would then have peaked and started to lose upside momentum, generating a “falling” signal and losing the confirmation. That signal would not be confirmed mitil the indicator’s subsequent drop into its lower zone.

The chart’s box shows the negative hypothetical returns with the indicator on a sell signal while in the upper zone, and on a buy signal \\.hile in the lower zone. In contrast to the rate-of-change approach to subdividing zones, this method fails to address the market action with the indicator in the middle zone. But it does illustrate how zone analysis can be used in conjunction with trade-signal analysis to gauge the strength of an indicator’s message.

Subsequent-Performance Analysis In addition to using signals and zones, results can

be quantified by gauging market performance over various periods following a specified condition. In c

3 2 19

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I I

-0.4 -2.0 i 5

I I

-1.8 17 3 5

37

66 36

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5.x 11.2 4.0 3.5 70 152 -1.4 10.0 9.Y cl.3 83 IX2

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252 37

Oh 2s 4 I88 Xl 4 31-I

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trast to the trade-signal and zone-based quantification methods, a system based on subsequent performance cal- culates market performance after different specified time periods have elapsed. Once the longest of the time periods passes, the quantification process becomes inactive, remaining dormant until the indicator generates a new signal. In contrast, the other two approaches are alwars active, calculating market performance with every data update.

The subsequent-performance approach is thus appli- cable to indicators that are more useful for providing indications about one side of a market, indicating market advances or market declines. Ahd it’s especially useful for indicators with signals that are most effective for a lim-

:on- signal analysis includes a comparison of per annum gains with the buy-hold statistic. LikeFvise, the subsequent-performance approach can use an all-period gain statistic as a benchmark. In Figure 7, for instance, the average lo- da!- gain in the Dow Industrials has been 2% follo\+.ing a signal, nearly seven times the 0.370 mean gain for all lo- day periods. This indicates that the market has tended to perform better than normal follov+ig signals. That could not be said if the lo-da! gain was 0.4% following signals.

AA third question is how much risk has there been following a buy-signal system. or reward following a sell-signal system? Using a buv-signal svstem as an example. one \cay to address the question would be to list the percentage of cases in lshich the market was higher over the subsequent period, and to then compare that with the per-

12 MTA JOURNr\L,/Spring-Summer 1996

centage of cases in which the market was higher over an; period of the same length. Again using the IO-da! span in Figure 7 as an example, the market has been higher after 75% of the signals, yet the market has been up in only 3% of all 1Odav periods, supporting the significance of signals. Additional risk information could be provided by determining the average drawdown per signal - i.e., the mean maximum loss from high to low following signals. The mean for the lo-day period, for example, was a maximum loss of 0.7% per signal, suggesting that at some point during the lo-day span, a decline of 0.7%’ could be considered normal. The opposite approaches could be used with sell-signal indicators, with the results reflecting the chances for the market to follow sell signals by rising, and to what extent.

Along with those questions, the potential for double- counting must be recognized. If, for example, a signal is generated in January and a second signal is generated in February, the four-month performance follo\ving the Janu- ary signal would be the same as the three-month performance following the February signal. This raises the ques-

FIGURE 8

must reach its highest level in a >;ear, and the joint high must be the first in a year. The significance for the sari- ous indices can then be compared in conjunction with their benchmarks - i.e., the various all-period gains. Fig- ure 9 uses 12 of those indices to show how subsequent

‘,dV. I< 11, % / I , , / , I* iil,l,’ id,,i ,,p<i‘ml r,l,,ll,di 1 (1-I YO

tion of lvhether the three-month return reflects the impact of the first signal or the second one. Moreover, such signal clusters give heavier tveight to particular periods of market performance, making the summar? statistics more difficult to interpret. Problems related to double-counting can be reduced or eliminated bv adding a time requirement. For the signals in Figure’ 7, for instance, the condition must be met for the first time in .30 davs - if the ratio reaches 1.92, drops to 1.90, and then returns to 1.92 two days later, only the first day will have a signal. The time requirement eliminates the potential for double- counting in anv of the periods of less than 30 davs, though the longer peiiods still contain some overlap in this example.

Another application of subsequent-performance analy sis is shown in Figure 8, which is not prone to any double- counting. The signals require that three conditions are met, all for the first time in a given year - the Dow Indus- trials much reach its highest level in a year, another index

performance analysis for both buy signals anb sell signals can be used together in an indicator. For each time span, the chart’s box lists the market’s performance after buy signals, after sell signals, and for all periods.

Reversal-Probability Analysis Finally the subsequent performance approach is

useful for assessing the chances of a market reversal. In Figure 10, the “signal” is the market’s year-to-year change at the end of the year, with the siglials (years) categorized by the amount of change - )-ears with anr- amount of change, those with gains of more than S%, etc. In this case, the subsequent-perforcance analvsis is limited to the year after the various one- year gains. But the analysis takes an additional step m assessing the chances for a bull market peak tvithin

the one- and nvo-year periods after the years with market gains, or a bear market bottom Athin the one- and two- year periods after the years Gth market declines.

MTA JOURS;\L/Spring-Summer 1996 13

This analysis requires the use of tops and bottoms identified with objective criteria for bull and bear markets in the Dow Industrials. The reversal dates show that starting with 1900, there have been 30 bull market peaks and 30 bear market bottoms, with no more than a single peak and a single trough in any year. This means that for anr given year until 1995, there was a 31% chance for the yea;- to contain a bull market peak and a 31% chance for the year to contain a bear market bottom (30 years with reversals / 95 years).

Using this percentage as a benchmark, it can then be determined whether there’s been a significant increase in the chances for a peak or trough in the year after a one-year gain or loss of at least a certain amount. The chart’s boxes show the peak chances following up years and the trough chances following down years, dividing the number of cases by the number of peaks or troughs. For example, prior to 1995, there had been 31 years with gains in excess of 15% startingwith 1899. After those Tears, there was a 52% chance for a bull market peak in the subsequent year (16 following-years with peaks / 31 years with gains of more than 15%). The chances for a peak within two years increased to 74%, which can be compared to the benchmark chance for at least one peak in 61% of the two-year periods (since several two-year periods contained more than one top, this is not the exact double of the chances for a peak in any given year).

A major difference in this analysis is that in contrast to signals and zones, which depend upon the action of an indicator, this approach depends entirely on time. Each signal occurs after a fixed amount of time (one year), with the signals classified by what they show (a gain of more than 5%, etc.). Depending upon the classification, the risk of a peak or trough can then be assessed.

Conclusion Each one of these methods can help in the effort to

assess a market’s upside and downside potential, with the method selected having a lot to do with the nature of the indicator, the time frame, and the frequency of occurrences. The different analytical methods could be used to confirm one another, the confirmation building as the green lights appeared. An alternative would be a com- mon-denominator approach in which several of the approaches would be applied to an indicator using a com- mon parameter (i.e., a buy signal at 100). Although the parameter would most likely be less than optimal for an) of the individual methods, excessive optimization would be held in check. But whatever approaches are used, it needs to be stressed that each one of them has its own means of deception. By better understanding the potential pitfalls of each approach, indicator development can be enhanced, indicator attributes and drawbacks can be better assessed, and the indicator messages can be better interpreted.

The process of developing a market outlook must be based entirely on research, not sales. The goal of research

is to determine if something works. The goal of sales is to show that it does work. Yet in market analysis, the lines can blur if the analyst decides how the mark& is supposed to perform, then sells himself on this view by focusing on11 on the evidence that supports it. M’hat’s \\‘orse is the pd- tential to sell oneself on the value of an indicator by focusing only on those statistics that support one’s view, re- gardless of their statistical validity. ;Is sholvn by the various hazards associated with the methods described in this paper, such self-deception is not difficult to do.

Our goals should be objectivity accuracy and thorough- ness. Using a sound research approach, we can determine the relative value of using any particular indicator in various ways. And we can assess the indicator’s value and role relative to all the other indicators analyzed and quantified in a similar way. The indicator spectrum can then provide more useful input toward a research-based market view.

1. Reference to Burton Malkiel’s A Random itTalk Down \\Bll Street, which argues that stock prices move ran- domly and thus can& be forecasted through technical means.

2. The charts that accompany this paper were produced with the Ned Davis Research computer program.

Timothy W. Hayes, CMT Tim Hayes, CHIT, is the Senior Stock Market Xna-

lyst of Ned Davis Research, Inc., an institutional research firm in Venice, FL. For the past 10 years, Tim has been editor of NDR’s flagship publication, Stork ~UzrM Stmtegy, developing indicators, models and studies for the equitv and international services. He is also a regular author of the firm’s Institutional Hotline and Chnrt of the Dy ,services. Tim holds the Chartered Market Technician ((XT) designation, and he is a member of the Market Technicians A$,so- ciation. His research articles have appeared in the LK’A Journal, Technical Analyis of Stocks and Commodi- ties and other publications. His market commentary has been featured by The Tlirll Street Journal, Barron ‘s, Investor’s Dails, CNBC and others.

14 MTA JOU~=1L/Spring-Summer 1996

Seasonality in Canadian Equity Prices

Submitted by Don Vialoux - CMT Program, level Ill 2 December 1994

Third Revision August 1995

Introduction “Buy them when it snows, sell them when it goes!” That’s

the expression used by well known equity market strategists in Canada. The expression refers to the strategy of buying Canadian stocks when the snow starts to fall in November and taking profits when the snow melts in March. Canadian stocks tend to be stronger during this period each year than at other times of the year.

The evidence of seasonal strength provided by these strategists has been mainly anecdotal. They point to statistics measuring the low point for the Toronto Stock Ex- change 300 Composite Index (TSEC) in November to the high point in March of the following year. The statistics, when calculated this way, indeed show that the TSEC ex- hibits strong seasonahty. As indicated in Exhibit 1, the average return on investment (excluding dividends) during the twelve selected periods picked from November 1982 to November 1994 was an amazing 12.2%. In contrast, as indicated in Exhibit 2, the average annual gain by the TSEC during the same 12-year period using the end of November as a base date each year was only 6.1%. The implication is that the investor can optimize his investment returns by purchasing Canadian stocks at their lows in November and by going short when Canadian stocks reach their highs in March.

EXHIBIT 1

TSEC Returns from the Lows in November to the Higbs In March

November March Low TSE 300 High TSE 300 1982 1790.72 1983 2170.09 1983 2360.27 1984 2436.23 1984 2350.51 1985 2652.67 1985 2995.83 1986 3057.02 1986 2677.36 1987 3847.72 1987 2833.64 1988 3370.19 1988 3197.97 1989 3652.84 1989 3902.91 1990 3774.18 1990 3060.55 1991 3598.05 1991 3430.68 1992 3588.77 1992 3213.67 1993 3614.65 1993 4160.15 1994 4609.93 Mean return (excluding dividends) Totals 35,974.26 40,372.34

Source: Toronto Stock Exchange Monthly Bulletin

Percent Change t21.2 t 3.2 t12.9 t14.2 t28.4 t18.9 t14.2 - 3.3 t17.6 t 4.6 t12.5 t10.8 +12.2

EXHIBIT 2

Annual TSE 300 Composite Index Returns Using the End of November as a Base Date

End of November End of November Percent Year TSEC Year TSEC Change 1982 1838.31 1983 2540.96 t38.2 1983 2540.89 1984 2368.54 - 6.8 1984 2368.54 1985 2857.18 t20.6 1985 2857.18 1986 3046.80 t 6.6 1986 3046.80 1987 2978.34 - 2.3 1987 2978.34 1988 3294.68 t-10.6 1988 3294.68 1989 3942.77 t19.7 1989 3942.77 1990 3151.01 -20.1 1990 3151.01 1991 3448.51 t 9.4 1991 3448.51 1992 3282.83 - 5.0 1992 3282.83 1993 4180.21 t25.5 1993 4180.21 1994 4093.41 - 2.1 Totals 36,930.07 39J85.17 Average + 6.1

Source: Toronto Stock Exchange Monthly Reuiew

This anecdotal evidence may be impressive but is clearly flawed and statistically incorrect. It implies that the investor knows when the lows will be made in November and the highs will be reached in March. As indicated later in this report, the evidence also leads to a misleading strategy. ‘Yet, the evidence suggests that a statistically correct study might provide an interesting insight on the seasonality of Canadian stock prices. This report uses a simple statistical method (i.e. arithmetic or mean averages) to examine the seasonality of Canadian stock prices using the end of November and the end of March each year as a base. The period of examination was from the end of November 1982 to the end of November 1994. In addition, this report examines the fourteen industry subindexes that make up the TSEC to determine the industry groups that tend to outperform and underperform the TSEC during the November to March period. Next, the major factors causing seasonal strength during this period are examined. Finally, the report looks at the employment of investment strategies using the findings of this report.

The following study indicates that Canadian stock prices show seasonal strength from the end of November to the end of March. The Toronto Stock Exchange 300 Com- posite Index was examined during the twelve periods from the end of November to the end of March starting in No- vember 1982 and ending November 1994. The average

MTA JOURNAL/Spring-Summer 1996 15

gain each year during this period (excluding dividends) was 6.4%. Gains were realized in ten of the twelve periods.

In contrast, the TSEC displayed in Exhibit 2 rose only 6.1% per year on average during the 1 S-year period from November 1982 to November 1994. In addition, the TSEC showed gains during only seven of the twelve years.

A Study of Seasonal Price Performance of the TSEC

EXHIBIT 3 TSEC Returns from the

End of November to the End of March

End of November End of March Percent Year TSEC Year TSEC Change 1982 1838.31 1983 2156.06 t17.3 1983 2540.89 1984 2382.10 - 6.2 1984 2368.54 1985 2612.81 t10.3 1985 2857.18 1986 3047.26 t 6.7 1986 3046.80 1987 3739.47 t22.7 1987 2978.34 1988 3313.79 t11.3 1988 3294.68 1989 3578.22 t 8.6 1989 3942.77 1990 3639.54 - 7.7 1990 3151.01 1991 3495.67 t10.9 1991 3448.51 1992 3412.14 t 1.1 1992 3282.83 1993 3602.44 t 9.7 1993 4180.21 1994 4329.62 t 3.6 Totals 36,930.07 39,309.12 + 6.4

Source: Toronto Stock Exchange Monthly Review

Indeed, as the next table illustrates, investors who held Canadian stocks during the past 12 periods from the end of March to end of November lost money.

EXHIBIT 4 Annual TSE 300 Composite Returns from the

End of March to the End of November

End of March End of November Percent Year TSEC Year TSEC Change 1983 2156.06 1983 2540.89 t17.9 1984 2382.10 1984 2368.54 - 0.6 1985 2612.81 1985 2857.18 t 9.4 1986 3047.26 1986 3046.80 0.0 1987 3739.47 1987 2978.34 -20.4 1988 3313.79 1988 3294.68 - 0.6 1989 3578.22 1989 3942.77 t10.2 1990 3639.54 1990 3151.01 -13.4 1991 3495.67 1991 3448.51 - 1.3 1992 3412.14 1992 3282.83 - 3.8 1993 3602.44 1993 4180.21 t16.0 1994 4329.62 1994 4093.41 - 5.5 Totals 39,309.12 39,185.17 Average - 0.3


Higher returns from the end of November to the end of March are not a new phenomenon. They have existed since at least the start of taxation of capital gains in Canada in December 1971, as illustrated in the following table:


End of November to the End of March

End of November Year 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 Totals

TSEC 1197.7 1182.6 850.4 980.8 920.2

1017.5 1269.8 1699.6 2402.2 2012.1 1838.3 2540.9 2368.5 2857.2 3046.8 2978.3 3294.7 3942.8 3151.0 3448.5 3282.8 4180.2

50,462.g Average

from 1972 to 1994

End of March Year 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

TSEC 1239.1 1214.9

989.6 1054.1 1022.1 1063.3 1466.4 1797.6 2333.1 1587.8 2156.1 2382.1 2612.8 3047.3 3739.5 3313.8 3578.2 3639.5 3495.7 3412.1 3602.4 4329.6

53,077.l

Percent Change t 3.4 t 2.7 t16.4 t 7.5 tll.1 t 4.5

t15.5 t 5.8 - 2.9

-22.1 t17.3 - 6.2

t10.3 t 6.7

t22.7 t11.3 t 8.6 - 7.7 t10.9 t 1.1 t 9.7 t 3.6

+ 5.2


Canadian stock prices from the end of November to the end of March rose in 18 of 22 years. Average gain during each of the 22 periods was 5.2%. Median gain (identified as the return for the end of November 1985 to end of March 1986 period) was 6.7%.

In contrast, seasonal strength did not appear consistently in the end of March to the end of November periods during the past 22 years. As the next table illustrates, Canadian stocks prices recorded an average gain of only 0.5% during the 22 periods. They rose in only 7 of the 22 periods. Median return (identified as the return for the end of March to the end of November 1984 periods) was -0.6%.

16 MTA JOURIQL/Spring-Summer 1996


End of March to the End of November from 1973 to 1994

End of March End of November Year TSEC Year TSEC 1973 1239.1 1973 1182.6 1974 1214.9 1974 850.4 1975 989.6 1975 980.8 1976 1054.1 1976 920.2 1977 1022.1 1977 1017.5 1978 1063.3 1978 1269.8 1979 1466.4 1979 1699.6 1980 1797.6 1980 2402.2 1981 2333.1 1981 2012.1 1982 1587.8 1982 1838.3 1983 2156.1 1983 2540.9 1984 2382.1 1984 2368.5 1985 2612.8 1985 2857.2 1986 3047.3 1986 3046.8 1987 3739.5 1987 2978.3 1988 3313.8 1988 3294.7 1989 3578.2 1989 3942.8 1990 3639.5 1990 3151.0 1991 3495.7 1991 3448.5 1992 3412.1 1992 3282.8 1993 3602.4 1993 4180.2 1994 4329.6 1994 4093.4 Totals 53,077.l 53,358.6 Average


Percent Change

- 4.6 -30.0 - 0.9 -12.7 - 0.5

t19.4 t15.9 t33.6 -13.8 t15.8 t17.9 - 0.6 t 9.4 - 0.0 -20.4 - 0.6

t10.2 -13.4 - 1.3 - 3.8 t16.0 - 5.5

t 0.5

In conclusion, investing in Canadian stocks during the four-month period from the end of November to the end of March provides a slightly higher return with only four months of stock market risk than employing a buy/hold strategy with 12 months of stock market risk. Indeed, investing from the end of November to the end of March avoids an eight month period of stock market risk when Canadian stock prices tend to record little or no return.

Seasonal Strength in Industry Groups that Make Up the TSECS

Some industry groups exhibited more seasonal strength than others during the November to March period. A similar analysis of the 14 industry subindexes that make up the TSEC was completed from the end of November 1982 to the end of November 1994. Results of the analysis were as follows:

EXHIBIT 7

Sectoral Performance During the November to March Period of

Seasonal Strength in the TSE 300 Composite Index

Rank Industry Group Percent Change 1 Paper and Forest Products 16.1 2 3

Management Companies/Conglomerates 12.2 Metals and Minerals 11.1

4 Communications and Media 9.8 5 Transportation 8.7 6 Industrial Products 8.6 7 Consumer Products 5.6 8 Merchandising 6.4

TSE Composite Index 6.4 9 Real Estate and Construction 5.7 10 Oil and Gas 5.7 11 Pipelines 3.3 12 Gold and Precious Metals 4.6 13 Financial Services 3.5 14 Utilities 1.8


The results show that economically sensitive stock groups such as paper and forest products, base metals, communications and transportation tend to outperform the interest sensitive groups including pipelines, financial services and utilities.

Industry groups that recorded the greatest seasonal strength from November to March also tended to exhibit the greatest seasonal weakness from March to November.

EXHIBIT 8

Sectoral Performance During the March to November Periods

Rank industry Group Percent Change 1 Paper and Forest Products - 7.1 2 Real Estate and Construction - 7.0 3 Management Companies/Conglomerates - 4.9 4 Transportation - 4.8 5 Metals and Minerals - 3.0

; Industrial Products - 2.6 Oil and Gas - 2.0

8 Merchandising - 1.6 9 Communications and Media - 1.1

TSE Composite Index - 0.3 10 Consumer Products t 1.3 11 Pipelines t 1.5 12 Financial Services t 2.6 13 Utilities t 3.5 14 Gold and Precious Metals t 3.6


Results show that holding economically-sensitive stock groups from the end of March to the end of November is an inferior strategy. Indeed, holding short positions in industry groups such as Paper and Forest Products, Con- glomerates, Transportation and Metals and Minerals can be a profitable strategy.

MTA JOUKWL/Spring-Summer 1996 17

Reasons For Seasonality Seasonality occurs for at least three reasons: 1) Seasonal strength in U.S. equity markets during the

end of November to end of March period has an influence on Canadian equity prices. As the following table suggests, the S&P 500 Index from November, 1982 to November 1994 showed seasonal strength during the end of November to end of March periods in U.S. equity markets (although not as strong as seasonal strength in Cana- dian markets). From November 1982 to November 1994, the S&P 500 index rose an average of 9.4% per year (excluding dividends). During the twelve test periods, the S&P 500 index rose an average of 6.9% (i.e., 73% of the move made the by S&P 500 occurred during one third of the year).

EXHIBIT 9

Standard 8c Poor 500 Composite Index Using the End of November and the End of March

as Base Dates

End of November End of March Percent Year S&P 500 Year S&P 500 Change 1982 138.54 1983 152.96 t10.4 1983 166.40 1984 159.18 - 4.3 1984 163.58 1985 180.66 t10.4 1985 202.17 1986 249.22 1987 230.30 1988 273.70 1989 345.99 1990 322.22 1991 375.22

986 238.90 t18.2 987 291.70 t17.0 988 258.89 t12.4 989 294.87 t 7.7 990 239.94 - 1.7 991 375.22 t16.4 992 403.69 t 7.6

1992 431.35 1993 451.67 t 4.7 1993 461.79 1994 445.77 - 3.5 Totals 3,360.48 3,593.45 Average t 6.9

Standard & Poor 500 Composite Index Using the End of November as a Base Date

End of November End of November Percent Year S&P 500 Year S&P 500 Change 1982 138.54 1983 166.40 t20.1 1983 166.40 1984 163.58 - 1.7 1984 163.58 1985 202.17 t23.6 1985 202.17 1986 249.22 t23.3 1986 249.22 1987 230.30 - 7.6 1987 230.30 1988 273.70 t18.8 1988 273.70 1989 345.99 t26.4 1989 345.99 1990 322.22 - 6.9 1990 322.22 1991 375.22 t16.4 1991 375.22 1992 431.35 t15.0 1992 431.35 1993 461.79 t 7.1 1993 461.79 1994 453.69 - 1.8 Totals 3,360.48 3,675.63 Average t9.4

Source: The Wall Street Journal

Influences on Canadian equity prices occur indirectly because of the close relationship between the Canadian and American economies. They also occur directly through inter-listed trading activity in stocks that make up the TSE 300 Composite Index. A study in October 1994 indicated that 51 percent of the weighting of the TSE 300 Composite Index is based on securities inter-listed on U.S. exchanges and 18.7 percent of the value of trading in TSE 300 Composite Index stocks occurred in U.S. markets.

2) Although Canadian and American tax laws differ, surges in money flows and investment decisions occur in Canada near year end for similar reasons that they occur in the United States. Stock prices tend to rebound later when selling for tax purposes has abated.

3) Contributions to individual retirement plans in Canada, (known as Registered Retirement Savings Plans) usually concentrated in February, tend to have a similar but proportionally greater impact on Canadian equities prices because of more liberal contribution rules and “Ca- nadian content” regulations.

A Practical Method to Take Advantage of Seasonal Strength in the TSECA

According to this report, seasonality among the 14 TSEC subindexes is strongest with Canadian forest product stocks. A portfolio of forest product stocks seasonally invested beginning in November 1982 and finally liquidated in March 1994 should provide favourable results. The implication is that an investor who continues to buy Canadian forest product stocks at the end of November and sells them at the end of March greatly enhances pros- pects for an above average return on investment.

A study was completed to examine the profitability of the strategy using an initial investment of $100,000. Funds were allocated by the TSEC weighting in forest product stocks when the investment initially was made. The weightings were kept constant throughout the period of investment (despite the continuous change in the weightings over time). Eight of the nine forest product stocks in the TSEC in November 1982 were included in the study. AI1 of the eight companies remained Canadian based companies throughout the period of investment, although many were involved with mergers, takeovers and restructurings. When a company was merged or taken over, prices for the surviving company were taken. The ninth company was excluded (Consolidated Bathurst) because it did not survive throughout the investment period as a Canadian forest product company. (It was acquired by Stone Container). Other Canadian forest product stocks subsequently added to the TSE Forest Product Index also were excluded because their prices were un-

available throughout the study period. Price data were adjusted for stock splits and reverse stock splits that occurred during the period of investment. Each stock was acquired at the price on the last trade date in November and liquidated at the price on the last trade date in March.

MTA JOURNAL/Spring-Summer 1996

Each transaction was examined to determine its feasibil- ity (i.e. the ability to complete the transaction on the given date at its indicated size.)

The calculations were purposefully completed to provide a conservative return on investment.

l A commission of 1.0% of value was charged on all purchases and sales (current commission charges now are substantially less. However, in the early 1980s in Canada before commissions became negotiable, a 1 .O% rate was a fair estimate of cost).

l Dividends were not included. l Interest from cash balances held from the end of

March to the end of November each year was not included. As one would expect, knowing how well the forest prod-

ucts in general have done during the end of November to end of March periods, results from the portfolio were impressive. The $100,000 portfolio appreciated in value to $406,773 during the 12 periods of investment.

EXHIBIT 10 Value of a $100,000 Portfolio of Canadian Forest

Product Stocks Held During the November to March Periods from November 1982 - March 1994

Stock Original Original Final TSEC Investment Investment Weighting

B.C. Forest Products .11 $9,200 $38,023 Doman Industries .02 1,700 7,907 Domtar .33 27,700 44,477 Fraser Inc. .04 3,400 16,680 Great Lakes Forest .22 18,500 63,478 MacMillan Bloedel .41 34,500 202,386 Scott Paper .04 3,400 10,126 Whonnock A .02 1,700 23,696 Totals 1.19 $100,100 $406,773

Conclusion Seasonality analysis shows that Canadian stock prices

have been significantly strong during the four month period from the end of November to the end of March during the 12-year period ending November 1994. Three main factors probably influencing Canadian stock prices during this period were year end transactions for tax purposes, RRSP contributions and seasonal influences by U.S. equity markets. These three factors are expected to con-

tinue to influence the seasonality of Canadian equity prices in the future.

Investors can continue to look for opportunities to take advantage of seasonal strength in Canadian stock prices. Each year as November approaches, they can examine Canadian stock groups such as forest products and base metal stocks that tend to outperform the TSEC during the next four months. An examination of technical patterns and fundamental outlooks for individual stocks within these groups could help to identify potentially profitable investment opportunities. Investors subsequently should liquidate positions by the end of March and, when appropriate, consider short positions in these groups.

Addendum I DATA FOR SECTORAL ANALYSIS

Metals and Minerals

End of November End of March Percent Year Index Year Index Change 1982 1585.62 1983 2217.04 t39.8 1983 2511.63 1984 2326.33 - 7.4 1984 1872.96 1985 2020.21 t 7.9 1985 1929.90 1986 2337.83 t21.1 1986 2085.48 1987 2566.49 t23.1 1987 2371.96 1988 2650.09 t11.8 1988 2925.38 1989 3327.71 t13.8 1989 3353.24 1990 3125.70 - 6.8 1990 2586.85 1991 3184.27 t23.1 1991 2846.05 1992 2890.57 t 1.6 1992 2528.95 1993 2938.38 tl5.9 1993 3275.99 1994 3618.55 t10.5 Totals 29,873.91 33,196.17 Average 1994 3921.40 tll.l

Gold and Precious Metals

End of November Year Index 1982 3212.94 1983 4148.45 1984 3374.50 1985 4419.10 1986 5338.40 1987 7831.84 1988 565039 1989 7426.46 1990 5293.14 1991 5068.29 1992 4967.23 1993 10,005.86 Totals 66,736.80 Average

Merchandising

End of November Year Index 1982 1621.42 1983 2274.37 1984 2033.41 1985 2961.40 1986 3510.67 1987 2882.00 1988 3603.72 1989 4480.36 1990 3665.72 1991 4060.55 1992 3611.91 1993 4407.42 Totals 39J12.95 Average

End of March Year Index 1983 3989.27 1984 4659.51 1985 3750.18 1986 4056.85 1987 8187.50 1988 6623.69 1989 4707.89 1990 6862.62 1991 5180.22 1992 4510.77 1993 6481.47 1994 10,778.81

69,788.78 1994 8757.00

End of March Year Index 1983 1954.28 1984 2150.70 1985 2188.40 1986 3659.35 1987 3898.40 1988 3486.42 1989 3957.58 1990 4059.97 1991 4421.41 1992 3997.41 1993 391O.li 1994 3921 .j7

41,605.66 1994 3442.83

Percent Change t24.2 t12.3 t10.8 - 8.2 t53.4 -15.4 -16.7 - 7.6 - 3.1

-11.0 t30.5 t 7.7

t 4.6

Percent Change t20.5 - 5.4 t 7.6

t23.6 t11.0 t21.0 t 9.8 - 9.4 t20.6 - 1.6 t 8.3 -11.0

t 6.4


Financial Services

End of November Year Index 1982 1420.80 1983 1781.82 1984 1680.81 1985 2227.70 1986 2319.59 1987 1904.70 1988 2373.90 1989 2961.07 1990 2225.20 1991 2807.70 1992 2555.46 1993 3166.80 Totals 27,425.55 Average

Utilities


End of March Year Index 1983 1766.64 1984 1590.46 1985 1761.40 1986 2246.34 1987 2580.18 1988 2101.33 1989 2550.42 1990 2588.37 1991 2701.40 1992 2658.80 1993 2654.04 1994 3183.16

28,382.54 1994 3127.42

End of March Year Index 1983 1851.26 1984 2081.38 1985 2664.54 1986 2753.43 1987 2968.43 1988 2700.15 1989 2658.88 1990 2897.07 1991 2944.65 1992 3200.52 1993 3169.78 1994 3621 .Ol

33,511.10 1994 3423.98

Communications and Media

End of November End of March Year Index Year Index 1982 2026.77 1983 2478.36 1983 3075.57 1984 3116.75 1984 3630.16 1985 4194.81 1985 4851.10 1986 6077.52 1986 6145.11 1987 6941.61 1987 5443.11 1988 6613.84 1988 7558.62 1989 8145.88 1989 8154.84 1990 6795.25 1990 5976.41 1991 6932.07 1991 6542.74 1992 7336.51 1992 7145.44 1993 7580.46 1993 8251.69 1994 9351.38 Totals 6QO1.56 75,564.44 Average 1994 7984.86

Percent Change t24.3 -10.7 t 4.8 t 0.8

t11.2 t10.3 t 7.4 -12.6 t21.4 - 5.3 t 3.9 t .5

t 3.5

Percent Chan,ge t11.0 - 8.3 t10.6 - 6.0 t12.6 t 6.5 - 2.0 - 6.4 t 4.6 - 2.6 t 2.0 t 4.5

t 1.8

Percent Change t22.3 t 1.3 t15.6 t25.3 t 8.9 t21.5 t 7.8

-16.7 tl7.0 t12.1 t 6.1

t13.3

t 9.8

Transportation and Environmental Services

End of November Year Index 1982 2335.51 1983 3458.84 1984 3316.17 1985 3766.60 1986 5601 .Ol 1987 6745.92 1988 7228.43 1989 9863.39 1990 7903.65 1991 4188.60 1992 4135.54 1993 3945.74 Totals 62,489.40 Average


67,934.83 1994 4518.56

Percent Change t26.1 -12.7 t21.2 t 8.5

t47.0 t35.8 t 3.1 - 2.7 -20.3 t17.1 t 3.4

0.0

t 8.7

Pipelines



37,654.03 1994 3825.71

Percent Change

t 2.2 t 2.1 t10.2 -15.5 t27.2 t15.8 t10.9 t 2.1 t 1.7 - 8.1 t 6.5 - 2.9

t 3.3

Real Estate and Construction



103,239.60 1994 2152.44

Percent Change

t 2.6 t 8.4 t15.6 t13.4 t22.2 t26.4 t11.8 -17.8 t18.3 -19.7 - 1.2 - 6.1

t 5.7

20 MTA JOURNAL/Spring-Summer 1996

Consumer Products

End of November End of March Year Index Year Index 1982 2039.48 1983 2477.10 1983 3147.70 1984 2871.15 1984 3086.91 1985 3465.61 1985 3915.50 1986 4674.49 1986 4786.67 1987 5434.12 1987 3840.72 1988 4056.37 1988 4128.50 1989 4672.68 1989 5369.31 1990 4804.84 1990 4445.05 1991 5084.85 1991 5880.14 1992 6335.13 1992 6044.19 1993 6335.02 1993 6779.44 1994 6755.50 Totals 53,463.61 56,966.86 Average 1994 6257.30

Industrial Products


Oil and Gas


End of March Year Index 1983 1588.22 1984 1622.66 1985 1816.83 1986 2195.50 1987 2316.28 1988 2036.82 1989 2026.54 1990 1874.47 1991 1874.33 1992 2105.17 1993 20'35.30 1994 2656.82

24,248.94 1994 2635.06


43,891.23 1994 4263.03

Percent Change t21.4 - 8.8 t12.3 t19.4 t13.5 t 5.6

t13.2 -10.5 t14.4 t 7.7 t 4.8 - .4

+ 6.6

Percent Change t18.2 -15.6 t 7.0 t13.7 t19.0 t21.2 t 7.2 - 3.7 t15.3 t 9.6 t 9.8 t 8.0

t 8.6

Percent Change

- 1.1 - 1.0

t12.5 -19.8 t40.4 t22.0 t17.6 t 0.2 - 3.4 -12.1 tzo. 1 t 2.9

t 5.7

Paper and Forest Products



41,689.98 1994 4195.78

Percent Change t34.3 t 4.5 t 6.1

t50.4 t44.6 t 6.9

t13.7 t 4.1 t19.1 t 3.6 t23.0 t 3.1

+16.1

Management Companies/Conglomerates

End of November End of March Percent Year Index Year Index Change 1982 1794.05 1983 2201.00 t22.7 1983 2642.29 1984 2735.15 t 3.3 1984 2825.67 1985 3301.83 t16.9 1985 3565.90 1986 4345.36 t21.9 1986 4140.64 1987 5748.47 t38.8 1987 4278.50 1988 5230.58 t22.2 1988 4565.22 1989 5236.88 t14.7 1989 5490.89 1990 5219.81 - 4.9 1990 4047.49 1991 4353.77 t 7.6 1991 4077.00 1992 3859.17 - 5.3 1992 3405.80 1993 4193.44 t23.1 1993 5034.41 1994 5027.85 - .l Totals 45,872.86 51,453.31 Average 1994 4856.93 t12.2

Don Vialoux, CMT Don Vialoux is currently employed at Richardson,

Greenshields of Canada, Ltd. as their U.S. equity analyst and derivative expert. He has 29 years of experience in the investment business. Don is the past president of the Canadian Society of Technical Analysts (CSTA) and is currently acting as Secretary to the CSTA. He is the past chairman of the Cana- dian Derivative Action Committee. Don holds Bach- elor of Science and Bachelor of Commerce degrees from the University of Manitoba.



Patterns of Seasonal Variation in Canadian Fixed-Income Markets 3

Submitted by R. Alain Rivet, CMT Program - level Ill 3rd Revision, June 1996

A. Introduction The literature of technical analysis and investing has

over the years contained many references to “seasonal effects” and the resultant consequences on investors’ rates of return. This paper will examine apparent seasonal effects in the Canadian tixed income markets for evidence that such effects are statistically significant.

B. Background

1. CYCLE ANALYSIS Cycle analysis has been used in many different contexts

and applied to many different time frames, ranging from several decades, as in the works of Nikolai Kondratieff and his adherents, to more contemporary comments applied to day trading or weekly trading in the commodity futures and financial futures markets.’ In this paper the objects of study will be the month end closes of two Canadian fixed income indices, the Scotia McLeod Long Term Bond Price Index and the Scotia McLeod Mid Term Bond Price Index, for the period 1948 through 1994, and 1980 through 1994, respectively. Initially, patterns of month to month variation within each year will be examined; sub sequent sections will look at possible relationships between seasonal variations and multi-year trends of bond yields and bond prices.

2. CANADIAN FIXED INCOME MARKETS The beginnings of the fixed income markets in Canada

can be traced back to the early 1870’s; the first issues of Government of Canada marketable bonds came out at that time to refinance existing provincial obligations.* Debt outstanding grew rapidly during World War I, and during World War II. The next period of rapid growth in debt outstanding occurred during the 1975-1990 period.3 As at December 1994, the value of unmatured marketable bonds issued by the government of Canada had reached Cdn. $234 billion. These bonds are held by a wide variety of investors, both foreign and domestic, individuals as well as institutional investors such as banks, pension funds and mutual funds. As an example, the value of assets held by Canadian bond-oriented mutual funds was Cdn. $13.9 billion as of August, 1994.4 The fixed income markets in Canada therefore enjoy significant size, a wide variety of participants, and for most issues of government of Canada and provincial government bonds, very good liquidity.

3. MAJOR TRENDS IN CANADIAN BOND PRICES The value of the Long Term Bond Price Index has

ranged from a low of 59.34 (September 1981) to a high of 216.21 (January 1948) (Table 1 (d)). The extent of this variation is not out of line with the variations observed in the yields of U.S. treasury bonds in the years since 1960; the latter went from 4% in 1960 to the inflation-induced levels of 14% in the early eighties, dropped back to 5.9% in the fall of 1993, and returned to the 8% level in No- vember 1994.s

An examination of the yearly averages of the Long Term Bond Price Index provides a very good overview of the major trends in Canadian bond prices from 1948 to 1994. Prices declined fairly steadily from 1948 through 1953, rebounded briefly, and declined again until 1958. Prices remained relatively stable from 1959 through 1965. The price decline from 1966 through 1981 was briefly inter- rupted by short intervals of relative stability during the 1970-1972 and the 1974-1978 periods. (Graph 2)

A clear multi-year bear market trend can be identified as having existed from 1966 through 1981; similarly a clear bull market trend can be identified from the 1981-1994 period, with 1981 being the changeover year. (Table 1 (d), “Average” column and Graph 2).

C. Methodology

1. DATA SOURCES For the purposes of this study, two monthly data series

were examined: (1) the Scotia McLeod Long Term Bond Price Index for the period 1948-1994; and (2) the Scotia McLeod Mid Term Bond Price Index for the period 1980- 1994. These two indices are the best-known bond indices in Canada. Along with government of Canada bonds, these indices comprise four different groups of bonds: 10 utilities, 10 municipals, 10 provincials and 10 industrials. The first data series, was started in 1947 and has been updated monthly since then. These two series were picked for this study because they represent a source of continuous, internally consistent and easily available data. As of November 1995, the sector weighting of the Long Term Bond Index was as follows: Government of Canada, 59%; Provincials, 25%, Corporates 13%, municipals, 3%. The Mid-Term Bond Price Index had a similar weighting. Both the long term and mid-term bond price indices were re- calculated in 1985, with a new base of 1985 = 1OO.6

2. ANALYSIS OF DATA A more formal statistical method will be used to test

for seasonality, relating each monthly datum not only to the average value of the index for that year, but also to the


index value for the month immediately preceding it. Tests of statistical significance will be tabulated not only for the raw ihdex values, but also for each of the derived data series.

The null hypothesis can be stated as follows: none of the data series examined contains seasonal variations that display statistical significance. In proceeding with the statistical treatment of the monthly index values, the author is starting out with one basic premise, namely, any seasonal difference is worth examining only if the average price in one period differs significantly from its price in another period. This can be accomplished by calculating for the period under study the index average and standard deviation by month, and from it deriving the “p- value”, or confidence interval for each month.7 The criterion in the economic literature of “p” value of .lO or less will be used. This is the criterion mentioned in Au- gust 1992 issue of Technical Analvsis of Stocks and Com- modities, in an article by Dr. Lewis C. Mokrasch. The method used here is an adaptation of the algorithms developed by Dr. Mokrasch. a For this study, the month-end closes for the Scotia McLeod Long-Term Bond Price In- dex and the Scotia McLeod Mid-Term Bond Price Index were used. This study examines the average monthly values for both indices, and also includes tests of statistical significance for those values, as well as for two other sets of monthly averages derived from the raw index numbers. These are, respectively, the average index value for each month expressed as a percentage of the year’s average value, and, the month-to-month change in each index expressed as a percentage of the previous month’s datum. The sequence of the calculations can be seen by consulting Tables 1 (d) through 3(f), which are included in the body of this paper. In addition to the raw index values for each data series, two sets of detrended data were used.

First, the raw index values for a calendar year were expressed as a percentage of the 12 month average. For each calendar year, the average 12 month index value was calculated; the average index value for each month was divided by the 12 month average for the same year and expressed as a percentage. In the subsequent set, each datum was compared to the index value of the month immediately preceding, with the difference expressed as a percentage. The same series of calculations was per-

formed as in the other tables. The reader should also note that the Long Term Bond Price Index series has been broken out into one subseries, for the 1980-1994 period, in order to compare the results of Long Term index and the Mid-Term index for the same time frame.

In order to determine the statistical significance of each month’s variation, the following formulae were used:

1) 'T'STATISTIC T= absolute value (column average - matrix average)

(SzL / NC) t (S’” / N,) where: S2‘ = standard deviation for column

S’” = standard deviation for matrix NC = data count for column Nm = data count for matrix

This calculation of the “T” statistic for the two means is used for the test of the null hypothesis.

2) 'YSTATISTIC

p = 0.5/((ltc,(T, t co) t cp (T, t co) ‘t c1 (T,t CO)~ t c4 (T,tco)4 t cg (T,tco)s)2

where Tn = T statistic for any given month as computed above and c , c,, cg, cg, c, and c5 are the standard curve approximati& coefficients.”

This formula is used to calculate the area under the distribution curve.

Il. Presentation of Results

1. 1948-1994PER100

Month Jan. Feb. Mar.

Average 138.02 137.71 137.10 Std. Dev. 42.19 42.56 42.86 N= 47.00 47.00 47.00 T= 0.29 0.24 0.14 P= 0.31 0.33 0.35

Table l(a): Summary of Results from Table l(d) Long-Term Bond Index

1948-1994 Raw Index Values

Apr. May June July Aug. Sept. Oct. Nov. Dec. Average Std. Oev. N

136.59 136.49 136.11 135.46 135.31 134.69 135.47 135.45 135.51 136.16 42.88 42.67 42.66 42.89 42.34 42.30 41.63 40.94 40.5; 42.23 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 564.00 0.07 0.05 0.01 0.11 0.13 0.23 0.11 0.11 0.11 0.36 0.36 0.37 0.35 0.35 0.33 0.35 0.35 0.35


Table l(b): Summary of Results from Table l(e) Long-Term Bond Index, 1948-1994

Monthly Average Values

Month Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Average Std. Dev. N

Average 101.54 101.16 100.58 100.13 100.13 99.83 99.20 99.24 98.78 99.63 99.82 99.98 100.00 Std. Dee 4.97 4.12 3.53 2.53 2.34 2.99 2.78 2.64 3.27 3.54 3.76 4.86 3.62 N= 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 564.00 lY= 2.08 1.87 1.07 0.34 0.36 0.38 1.86 1.84 2.44 0.69 0.32 0.03 P= 0.05 0.06 0.16 0.31 0.30 0.30 0.06 0.07 0.03 0.23 0.31 0.37

Table l(c): Summary of Results from Table l(f) Long-Term Bond Index, 1948-1994

Month-To-Month Average Price Change


Average -0.02 -0.32 -0.53 -0.38 -0.02 -0.31 -0.59 -0.07 -0.46 0.90 0.21 0.13 -0.11 Std. Dev. 2.28 2.11 2.03 2.64 2.02 1.60 2.74 2.05 2.22 2.77 2.29 1.64 2.27 jy= 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 47.00 564.00 T= 0.26 0.66 1.37 0.68 0.41 0.80 1.17 0.56 1.06 2.41 0.91 0.91 P’ 0.32 0.24 0.12 0.24 0.29 0.21 0.15 0.26 0.17 0.03 0.19 0.19

Raw Index Values For the 1948-1994 period, the month displaying the

largest standard deviation was July, the smallest, Decem- ber. The high for the year occurred in January, the low for the year came in September. As none of the “p” statistics for the raw index average monthly values is near .lO, it can be concluded that there is no statistically significant variation. (Table 1 (a))

seasonal point of view, bonds tend to be more of a sale in January, and more of a buy in September.

Month-to-Month Average Price Changes

Monthly Average Values When the index values are re-expressed as a percent-

age of the year’s average, a slightly different pattern emerges. The largest standard deviation appears for Janu- ary. There are five months that display “p” values smaller that .lO: September (.03); J anuary (.05); February (.06); July (.06) ; August (.07). These results suggest that from a

An analysis of the average monthly percentage price changes also suggest some possible trading strategies. For the data series as a whole, the average month to month price change is quite small, -.l 1. This is as could be expected, i.e. short term price fluctuations tend to cancel one another out. The largest average positive price change occurred in October (t.90); the largest negative price change was in July (-.59). The “p” value for October com- putes as .03; this suggests that in terms of short-term trading, October is a good month for taking profits. (Table l(c))

2. 1980-1994PER100

Table Z(a): Summary of Results from Table 2(d) Long-Term Bond Index, 1980-1994

Raw Index Values


Average 95.67 94.72 93.65 93.16 93.88 93.82 93.17 94.05 93.28 95.53 95.91 96.90 94.48 Std. Del: 13.79 13.64 12.99 12.64 12.64 13.47 15.07 14.92 14.23 13.32 11.74 12.49 13.50 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.32 0.07 0.24 0.39 0.18 0.18 0.32 0.11 0.32 0.29 0.45 0.72 P= 0.31 0.36 0.33 0.29 0.34 0.34 0.31 0.35 0.31 0.31 0.28 0.23


Table 2(b): Summary of Results from Table 2(e) Long-Term Bond Index, 1980-1994

Monthly Average Values

Month Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Average Std. Oev. N

,;\verage 101.42 100.30 99.19 98.63 99.39 99.24 98.24 99.25 98.54 101.16 101.82 102.83 100.00 Std. Dev. 7.39 5.60 4.81 3.21 3.27 4.77 4.19 3.72 4.78 4.92 4.74 6.02 5.12 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.73 0.20 0.63 1.51 0.66 0.59 1.53 0.73 1.13 0.88 1.42 1.77 P= 0.23 0.33 0.25 0.10 0.24 0.25 0.10 0.23 0.15 0.20 0.11 0.07

Table 2(c): Summary of Results from Table 2(f) Long-Term Bond Index, 1980-1994

Month-To-Month Average Price Change


i\verage -0.61 -0.93 -1.02 -0.42 0.81 -0.18 -0.87 1.08 -0.71 2.73 0.72 0.95 0.13 Std. Dev. 3.22 3.46 3.04 4.08 3.05 2.37 4.64 2.72 3.41 3.64 3.41 2.12 3.50 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.85 1.14 1.40 0.51 0.82 0.47 0.81 1.27 0.91 2.67 0.64 1.36

P= 0.20 0.15 0.11 0.27 0.21 0.28 0.21 0.13 0.19 0.02 0.24 0.12

RAW INDEX VALUES For this data series, the December value displayed the

average high for the year, as could be expected in a secu- lar bull market. The lows for the year occurred in April and July. None of the “p” values for the raw index values are significant. (Table 2 (a))

MONTHLY AVERAGE VALUES

For the detrended data, the largest standard deviation OccurredinJanuary. ThemonthsofApril (.lO);July (.lO); and December displayed statistically significant values. This suggests that the optimal trading strategy would be to buy at the April lows and take profits at year-end in December. It is worth nothing that the statistically significant low point for the year of the monthly average values

is April, rather than January or February. (Table 2(b))

MONTH-TO-MONTH AVERAGE PRICE CHANGES

For this data series, the average month-to-month price change is quite small, with an upward bias (t .19%). The largest positive average price change took place in Octo- ber (+2.73%); the largest negative price change occurred in March (-1.02%). The only month with a significant “p” value was October (.02). This would suggest that Oc- tober month end is an optimum time for profit-taking for short-term trading, as the average monthly value increased from 98.54 in September to 101.16 in October. (Table 2(c))

2. SCOTIA MCLEOD MID-TERM BOND INDEX

Table 3(a): Summary of Results from Table 3(d) Mid-Term Bond Index


Average 96.29 95.34 94.54 94.12 94.59 94.56 94.25 94.65 94.10 96.01 96.29 96.93 95.14

Std. Dev 8.17 8.96 9.15 9.04 9.34 10.36 11.42 11.04 10.53 10.01 8.54 9.26 9.86 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.90 0.20 0.59 1.65 0.84 0.69 1.26 0.84 1.25 0.98 1.40 1.70 P= 0.27 0.36 0.33 0.29 0.33 0.33 0.31 0.34 0.30 0.31 0.27 0.23

Table 3(b): Summary of Results from Table 3(e) Mid-Term Bond Index Monthly Average Values


Average 101.31 100.24 99.42 98.93 99.43 99.33 98.86 99.33 98.81 100.92 101.39 102.03 100.00 Std. Dee 5.55 4.44 3.68 2.26 2.40 3.57 3.32 2.88 3.52 3.45 3.68 4.49 3.75 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.90 0.20 0.59 1.65 0.84 0.69 1.26 0.84 1.25 0.98 1.40 1.70 P= 0.19 0.33 0.25 0.08 0.20 0.23 0.13 0.20 0.13 0.18 0.11 0.08


Table 3(c): Summary of Results from Table 3(f) Mid-Term Bond Index

Month-To-Month Average Price Change Month Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Average Std. Oev. N

Average -0.19 -0.98 -0.76 -0.40 0.52 -0.12 -0.41 0.51 -0.52 2.17 0.50 0.61 0.08 Std. Dev. 2.14 2.71 2.35 2.93 2.29 1.81 3.14 2.19 2.41 2.43 2.93 1.57 2.58 N= 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 180.00 T= 0.46 1.45 1.32 0.61 0.72 0.39 0.59 0.72 0.92 3.19 0.54 1.20 P= 0.28 0.11 0.12 0.25 0.23 0.30 0.25 0.23 0.19 0.01 0.26 0.14

RAW INDEX VALUES

There is only one set of data available for the Mid-Term Bond Index, for the years 1980-1994, with the general trend being a fairly steady advance in prices from 1981 through 1993, with only a brief interruption for the years 1987,1988 and 1989.

For the raw data, the average high for the year occurred in December, while the average low was registered in Sep- tember. The month with the largest standard deviation was July. None of the “p” values for the raw data aresig- n&ant. (Table 3(a))

MONTHLY AVERAGE VALUES

For the monthly values expressed as a percentage of the year’s average, the month with the smallest standard deviation was April. The months of April and December both displayed “p” values of .08. This would suggest a trading strategy of taking advantage of the April dip in prices to buy, and taking profits at year-end. (Table 3(b))

MONTH-TO-MONTH AVERAGE PRICE CHANGES Concerning the average month-to-month price

change, the value for this particular series was .OS%, i.e. a small change, but with an upward bias. The largest average positive change occurred during October, (2.17%) while the largest average negative change took place during February (-.98%). The month showing the largest standard deviation was July with a value of 3.14. The month with a statistically significant “p” value was October (.Ol ). This would suggest the following strategy for Mid-Term bonds: make use of April price weakness to buy, as noted in the previous section and take advantage of the price strength during October to sell.

Conclusion

The analysis of seasonal variations in the Canadian fixed-income markets, making use of detrended data, allows us to reject the null hypothesis that seasonal variations in the Canadian fixed-income markets are devoid of statistical significance. The study of the patterns of seasonal variations can provide trading strategies that can be tailored to long-term versus mid-term bonds. These strategies can also be customised to be better applied to month-to-month price changes versus changes in bond prices over a twelve-month trading cycle. The study of seasonal variations is best used as a confirming indicator

for those with a time frame longer than a year. Seasonal patterns do provide valuable signals for shorter-term trading.

It is also instructive to make certain comparisons between the Long Term Index and Mid-Term Index. Re- garding the raw index values, the standard deviation for the long-term index is much larger (41.23 and 13.50 vs. 9.86). This is to be expected, since longer-term bonds are more volatile than near-term bonds. This may also be partially explained by the difference in the number of observations (564 for the Long Term Index compared to 180 for the Mid-Term index). When the detrended data for the same time periods are examined (Tables 2 (b) and 3(b)), the difference in standard deviations is much smaller (5.12 versus 3.75), but it is the Long-Term index that carries the higher value. Within the calendar vear, therefore, it is the Long-Term index that has the higher variability. In terms of month-to-month price changes, the Long Term Index shows a standard deviation of 2.27 for the 1948-1994 period, and 3.50 for the 1980-1994 period, compared to 2.58 for the Mid-Term Index. Within the calendar year, the month-to-month price changes of the Long Term Index display the greater variability. For the 1980-1994 period, the April lows and December highs are statistically significant for both the Long-Term and the Mid-Term index. This clearly indicates April month- end as a favourable buy point and December as a good time for profit-taking.

The best indicator is provided by the analysis of the monthly average values of the Long Term Bond Index. It is the best indicator for the following reasons:

1) It incorporates the largest sample: forty seven years of data, or a total of five hundred and sixty-four pieces of data.

2) It covers periods of both declining and rising bond prices, thereby providing the conclusions with the greatest applicability.

If we examine the monthly average values of the Long Term Bond Index just for the years 1980-1994, the only month that shows any significance is December (p=O.O7), with the value for April at the limit (p=.lO). However, when the longer time frame 1948-1994 is examined, we find September (p=O.O3) to be a statistically significant time to look at buying, whereas January (p=O.O5) and Feb ruary (p=O.O6) can be considered statistically significant times to conduct selling.


In terms of month-to-month price changes, all three data series display both statistical significance for the month of October and a positive average price change for that same month, suggesting that it is also a good time of the year to be a seller of Canadian bonds. The study of seasonal patterns in the Canadian fixed-income markets appears to be of value to those seeking investment opportunities in this arena.

End Notes 1. John J. Murphy, Technical Analvsis of Futures Markets,

N.YI.F., 1988, pp 414455

2. J. E. Hatch, Robert F. White, Canadian Stocks, Bonds, Bills and Inflation, 1950-1987, The Research Foundation of the Institute of Chartered Financial Analysts, 1988, p. 27

3. Ibid., see also Bank of Canada Review, various issues 1988 1994

4. Albert S. Thompson, The Canadian Mutual Fund Industry, Moss, Lawson & Co. (Research Report), July 1994

5. Charles Kirkpatrick II, Charles Dow Looks at the Long w, Barron’s, June 1994

6. Note: The Scotia McLeod Mid-Term Bond Price Index and the Scotia McLeod Long Term Bond Price Index are copyright Scotia McLeod Inc. The monthly index values are courtesy Scotia McLeod Inc. The monthly index values are courtesy Scotia McLeod Inc. The figures for the raw index values are from Scotia IMcLeod’s Hand- book of Canadian Debt Market Indices, Toronto, 1994

7. Dr. Lewis C. Mokrasch, Detecting: Seasonalitv, Technical Analysis of Stocks & Commodities, August 1992

8. Ibid.; see also from the same author, Looking at lo-Year Stock Price Patterns, Technical Analvsis of Stocks & Commodities, April 1991

9. Ibid.; see also A.N. Beals, Statistics for Economics and an M. Abramowitz and E. Stagun, Handbook of Mathemati- cal Functions

The author gratefully acknowledges the assistance of Dr. L.C. Mokrasch and Mr. Harvey Chan for this study.

R. Alain Rivet, B.A. (Econ.), M.B.A. Alain Rivet is an Investment Advisor with Moss,

Lawson & Co., Ltd., a Toronto based boutique focusing on the wealth-management needs of high-net- worth individuals. Alain has been engaged in this profession for over ten years, and has concentrated his personal research efforts on the cyclical aspects of the Canadian financial markets. He has also hosted a daily commentary on the investment markets for a Toronto French-language radio station, and has made several appearances as a guest commenta- tor on local public television.

Overfor additional tables and charts


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MTA JOCRNAL/Sptinq-Summer 1996 31

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Graph 1

33

34 MTA JOU~‘;U,/Spring-Summer 1996

The High-low Index as a Tool to Enhance Returns

Submitted by Harold B. Parker, Jr., CMT 4 Introduction

The analysis of uew 52-week high and low data 011 the NkSE can provide valuable insight into the near- to intermediate-term treud of the market. These data have beer1 used in a wide variety of ways over the years, but most com- mouly they are used as a ratio (highs divided by lows) or as a difference (highs minus lows) .’ The interpretation of the data has generally fallen iuto two general categories as well. Oue method relies 011 the data to confirm or diverge from the popular indexes such as the Dow Joues ludustrials or the S&P 500. The logic behind this method is that a healthy market which is making new highs should be accompanied by a large aud/or rising number of individual stocks making new highs as well. Sew highs ill the indexes without IKW highs iu the high-low indicator are considered suspect. The reverse would be true of bottoms. The second general use of the high-low indicator has beeu as au overbought-oversold indicator. The logic behiud this method is that extremes in the indicator point to unsustainable extremes iu the market. Most forecasts usiug these two types of iuterpretatious of high-low data are rather subjective.

The shortcoming of subjectivity of interpreting the high-low index was addressed by Abe Cohen with the Chartcraft High-Low Index. He displayed his High-Low Index 011 a point aud figure chart. The index is cou- strutted by dividing the uumber of uew daily 52-week highs on the SYSE by the sum of new highs and new lows. .A simple lo-day moving average of Llie rcsultiiig percentage data (((Highs/ (Highs t LOWS) ) / 10) is theu plotted 011 a point and figure chart using a box size of 2% and a three- box reversal arid bounded on the top by 100 aud the bottom by 0. The result is a chart that is elegant in its simplicitv aud

F,GURE,

I

ohjectivitv because it filters ou; the “noise” ok small ((6%) reversals and shows reversal points clcarlv (the reversal from X’s to O’s or vice versa is un-

equivocal). Cohen considered levels below 10% to be oversold aud ihose above 90% 10 be overbought aud a reversal from those extremes to be buy (from oversold) and sell (from overbought) signals.

Cohell’s logic seems to go oue step beyond that of previous indicators. Us- ills the decision rules in the paragraph above, this indicator gives iiot only ali easilv determiued indicatioil of extreme overbought and oversold levels, but also provides au objective method for deter-

mining when these conditions are reversing. This siguifi- cantly enhances the usefulness of high-low data because it allows them to be used as a timing tool for intermediate-term moves. Unfortunately, the market rarely gets to the extreme 10% or 900/o levels before reversing, aud this can strand the investor in a reversing market without get- tiug a signal from the indicator. Cohen’s rules resulted in only two completed signals in the lo-year period studied. When invested, the Cohen method returned a respect- able compounded annualized rate of return of 18.9%. However, it was in the market only 16% of the time aud captured only a little over a quarter of the total up move. For more useful entry aud exit signals, some alteration of the decision rules seems in order.

Method Testing was done using Cohen’s calculatiou and plot-

tiug methods, described above. 0111~ long positions were taken using the following decision &les: Buy: Indicator reaches 40% or less alld reverses up

by 6 percentage points.

Exit: Indicator reaches 70%’ 01‘ greater aud reverses up by 6 percentage points.

Sell Stop: Indicator reverses prior to the sell signal above aud declines to a level below the low of the column of O’s preccdiug the buy signal. The sell stop would be triggered, for instance, if the indicator falls to 38 and reverses up to 44 or greater (crcatiug a bottom at 38) alld thcu hlls lo 36 prior 10 risiiig to 70.

The indicator is illustrated irl Figure 1.

1 !

j i

FIGURE 2

These decision rules were established empiric& based on the author’s experience. The levels of 405 and 705 looked as if thev would give a good balauce of profitable signals VS. “whipsaws” and would achieve the primary objective of developing a tool to assist an equity investor in achieving results superior to a buv and hold strategy Therefore, the investor is either long or out of the market for the purposes of the test. X secondary objective as to have drawdowns due to market fluctuations that were significarltlv less than a buy alld hold strategy.

The testing was doue over the lo-year period from 19851994. This period 1va.s chosen because Grst, the method for reporting the underlying data ivas cousis- tent, and second, it produced enough signals to give reliable results.’

Results

The results for the studv oeriod indicate that the high-low iudki can be verv useful for timing entry into the equity market. The buys and sells are listed and sum- marized in Table 1 and illustrated irl Figure 2.

The high-low index aud the decision rules described above resulted in the investor being in the market for a total of 3.76 wars out of the 9.74 years between tde start of the first signal and the end of the last signal. The compounded annual rate of return’ \vhilc illvested was 23.57%, excluding dividends. The maximum adverse excursion was 7.26%. (:Ilnximum n&r.sr: txxusion is the maximum percentage dccliue ill equity due

to market lluctuatioll of the eouit\ placed illto each ~xtc. This differs from the term drtudown, which is the I maximum percentage dccliue irl equity due to mar-

ket fluctuation from the previous peak Icvcl ofequitv.) The equity curve for using the high-low index during . the study period is conta:led in Figure 3. The starting point for the curve is the S&P 500 level at the begiuuiug of the test period.

FIGURE 3

I Equity Curve using High-Low Indicator

If one had instead bought the S&P 500 at 167.16 on December 19, 1984 and sold at 467.91 on Novem-

ber 3, 1994, OIIC ivould have achieved au 11.14%) conl-

pounded annual rate of return. excluding dividellds. for the 9.i4 year period. Using the buy a11d hold method. the maximum dra~vdo~11 ~vould have lxxm

34.23%.

Discussion and Conclusion

The High-Low indicator produced reliable signals . and performed \\~ll versus a buy and hold stratcg) during a period \\.heu INN and hold tvorked extremeI\ well. The trading signals’ for the studs period had the

36

following fworable characteristics: l 70% were profitable (20% with no ad\Terse excursion)

l High annualized rate of return while invested (23.57%)

l Low adverse excursion from entry (7.26% maximum and 2.07Y0 average)

. Ratio of % gain to M loss was very favorable (7.31% avg. gain vs. 2.78% avg. loss)

l Total Gaiu/Maximum Drawdown ratio was 9.46 vs. a ratio of 2.67 for S&P 500 buy and hold

l .A Student’s T test of the results indicates that they are highly significant, with only a 1% probability that’the) were achieved by chance

In addition, the indicator has the advantages of being objective aud easilvmaintaincd from readily available data.

The indicator gate an al’eragc of only two completed signals per year and the exit rules had an in\.estor out of the market for two-thirds of the time studied. These characteristics may be considered to be either a positive OI Ilegativc dcpcnding 011 one’s i1lvestment objectives; ho~\~- ever, thcv dicl result in lolvcr-than-market risk. The most significant disad\autaqe to the indicator rvould seem to be that it tends to bc iarly with its exit signals. Examiua- rion of Figure 2 reveals that one would haye forcgo1le sig- uificant upside movemeut iu both 1986 aud 1989 by using this indicator as an exit tool. This would suggest that other indicators might be useful adjuncts to more pre- cisely time market exits. It might also be useful to examine SOIW other decision hcnchmarks in the future. NW- ertheless, this illdicator, with the current benchmarks, resulted iI1 the investor capturing two-third of the points iu the up I~OYC iu the market during the study period ~\+ilc being inwtcd for odv one-third of the time.

‘Annualized rate ofreturn was used because the author’s decision rules resulted in being invested for nearly four)ears or a little over l/3 of the total time studied.

Bibliography Cohen, A.1V.. How to Use the Three-Point Reversal Method of Point 8: Ficrure Stock Market Trading, Sew Rochelle, Sli, Chartcraft, Inc., 1987

Colby Robert M’. and Thomas A. Sleyers, The EnclcloDe- dia of Technical hlarket Indicators, Homcwood, IL, DOW Jones-Irwin, 1988

Fosback, Sorman G., Stock Market Lozic, Fort Lauder- dale, FL, The Institute for Econometric Research Inc.. 1986

Pring, hlartin J., Technical Analvsis Esnlnincrl, McGraw Hill, New York. 1985

Harold B. Parker, Jr., CMT Harold B. Parker, Jr., Vice President alld Scnio1

Portfolio Manager, started his investment career as an account executive with E. F. Hutton L& Co. in 19i8. He left E. F. Hutton as a Portfolio Manager in 1985 to joiu Smith Barney. Mr. Parker joined Dorsey, M’right ,Y: Associates in 1994. Their management style is focused 011 using point and figure analvsis for stocks, sectors, aud the owrall equity alld fixed income markets.

MTA ,lOURSiU.,‘SprinS-Summel- l9<)(i 37

38 MTA \OPRs;\L Sprint-Summer IW

Answering the Bell of Sentiment Indicators

Submitted by Brent 1. Leonard, CMT Program - Level III 5

The put-pose of this review paper is to list, explain, attd evaluate several well-known stock market setltitnettt indicators over many periods of time. These indicators itt- elude Option put/call ratios, advisorv letters, short interest, mutual futtd cash, and other contrary against-the- crowd statistics.

The reason that this is a Review article rather than Re- search is that there has been much written ott these ittdi- caters by the experts of the industry (although very little recently, which I hope to update). Each indicator’s peaks and troughs will be juxtaposed with the appropriate itt- dex or average. I intend to first define and describe each Indicator and assess its efficacy; then, in a Discussion section, I place each on a Bell/Growth Curve model itt its appropriate place itt time.

These littdittgs should be of use to attyotte who ttceds to ascertain markel direction attd reversals for tradittg.

Much has been written over the years about cotttrar) opinion; it has becotne widely accepted and clever to go against the crowd - “When everyone looks 011e way, look the other!” Although primarily a true concept, there are a few considerations I would like to bring to light. Most serious investors arc familiar with the South Seas Bubble and Tulip Bulb Mania from Mackay’s Extraordittay Popu- lar Delusions and the Madness of Crowds. Although history repeats itself, it almost never does it exactly in the satne way. Try developing a tulip craze in Holland today, or, observe the Deutschbank’s tight stance against ittlla- lion after the wheelbarrows of DMarks decades ago.

111 his talk at the 1994 attttual TSAR cotlferettce itt Satt Francisco, John Bollinger stressed how importattt it is to know against whom to be cotttrary. Should otte take a position against the world being round, or the SUN rising tomorrow! Rather, the successful ittvestor has to estab- lish, through introspection, att ittterttal tnottitor which will wart1 hitn when he has stopped doing his own analysis and has begutl relying on peers, tnedia itetns, or a guru for opiniotts, “tips,” and timing.

In his book, Humphrey Neil1 explains that cotltrar~ opiniott is not necessarily cynical or negative, but sees both sides of att issue using ottc’s experiettce and logic to see reality. Just as some oscillators can be useful in the middle of a trend but wrong at the extremes, so are the majorit) often correct during a Bull or Bear market but mauicall! wrong when it reverses, especially when they are required to act, like buy or sell, rather than just observe. Examples of herd logic at these junctures are “This titne it is different,” or “What cat1 possibly go wrong.” Or at the nadir,

“This company is doing cvervthing tvrotlg - it’s hopeless.” In the following pages I would like to illustrate which ittdicators are the most effective in forecasting markets, itt- dividually and in combination.

One category of setitimettt measuremettt is the surveys found in Barron’s and elsewhere ott advisors, letter writers and investors. Although the majority of these surve\rs only go back a few years, their roots can be found (a;- cording to Keill) itt an SEC poll before the Crash of ‘46 where advice from Brokers and Advisors showed a bearish per cent of only 4.1%.

Earl Hadady of the Bullish Consensus feels so strongl) about this ittdicaror that he feels (itt his excellent article itt the 1986 11T.A lourttal) that Polling is a third and most importattt tnethod of attalvsis, above Technical attd Futt- dametttal. The basic qu&ott of why investors bought or sold (the public tteeds attswers, the media attempts to fill that need, either in honest attctnpts or in some cases itt- tetttiottally misleading) is ttot important; rather what the public is really doing, as tnanifest itt the Technical signals of Price and l’olume over Time. Cttfortunately, just as the media and economists range widelv in their beliefs and advice, so do technically oriented ‘gurus and letter writers. As Hadady points out, extreme examples (70% or more) occur less than ottcc a year. If 80% are of otte tnittd, ottlv 1 of 5 traders (especially in zero-sutn Futures markets) hold a cotttra positiott - therefore they are the strong hands of Richard \\‘yckoff’s Composite Operator, or the Big Money that controls markets), itnpervious to tnargitt calls or scared motley and itt tto hurry to get out without a large profit when the majority is sated - as indicated whet1 favorable ttcws now has tto effect. It is at this point that shorts are covered, tnargitts arc full, and com- placency is rampant.

In sumtnatiott then, by way of paraphrasing into an anagram, Edwin Lefevrt? in Reminiscences of a Stock Oo- erator, the tnotto F.I.G.H.T. could represent Fear, Igno- rance, Creed, and Hope over Titne exetnplifyittg the emo- tions which we need to control to be the ultimate, dispas- sionate Cotnposite Operator. or ideal Lradcr.

One way to analyze tnarkets by the notion that there is a “cotttrollittg factor” or IVyckoffian Composite Operator behind tnarket movements l\.as portrayed in a white paper lvritten by Dr. Henry “Hank” Prudett for a class at Golden Gate University He likens the market to a clothing Fashion Cycle wherein otte or tnore top designers in the haute couture world decides a new dress length, style, color is needed, it is thett created and diffused throughout the fashion elite, adopted and itnitated by the general public, until the last housel\ife in a fartn community itt

MTA JOUILUr-\LISprin~-Summer 199~ 39

the Midwest has given in to the new look. Magazines. stores, media shows have “told” the public what to wear, driving existiug dresses, ties, and other clothing into pre- mature obsolescence. Indeed, if print and television media cau “hype” or market athletic events, songs aud mov- ies, why uot glamor stocks, mutual funds and other securities?

The Indicators The odd-lot short ratio is derived by taking odd-lot

purchases added to odd-lot sales, dividing by two (much like open interest in Futures is obtained), aud dividing that into odd-lot short sales. I did not find this iudicatot au effective contrary tool, especially in relatiori to its suc-

CHART1

I I ,

I

j (Q.1, - - . - . - - I - . . - . . - . . - . . - . - . - . - . - . - . _ . _ _ . ” . . - . . - . - . - . - - . . - . - . - - . - . - - - . - - - . - . - . - . _ . _ , . , . . . . -

, n . , , - - . - - - - . I . - . - - . - - .

, “ . j ( - - . - . - - . - - . - - . - - . - .

, *o ,a , - - . - . - - - . - - - - - - - . - .

,# ,@I - - . - . - . - - . - . - . . - . - . - . - . - . - . . - . -

*go,&, - - . . - - . - . . - . “ . - . . - . - - . - . . . . . -

*w,j, - . 1 - . - - . - . - . . - . - . - . - - . - . - I - - .

,$0.00 -.-.-.---.-.--.--.-.--.--.

IoI.01

ID.001

cess before the current bull market, for the following reasons: only 2 major spikes above 13 occurred in this 12. year time frame (see chart 1). Although both preceded large upmoves, they were the result of a sideways trading range (1986) and a sharp selloff (1990). However, seven other smaller spikes above 10 did not render bull markets. Conversely low readings did riot indicate down moves in the market wtth three exceptions- 1987, 1990. and mid- 1991 - versus several that preceded upmoves. Other reasons might include these: smart money was shorting in small odd-lots to avoid the uptick rule, now extant in over- the-counter stocks; some shorting was used in a derivative fashion to hedge arid box positions, more than in the past; mauv odd-lotters with scarce mouey moved to index and

equity options over the past fifteen to twentv years.

NYSE SHORT RATIO; S&P 500

Merrill Lynrh data 1965-94

Looking at monthly data ou NYSE short interest ratio aud its effect on the S&P 500 Index, historically this was an accurate measure of contrar\ opiniou. where the earlv adopters of trend xvere correct and profitable, and those at the manic end (see arrows 011 the left side of the bottom part of Chart 2) were 180% wrong. Sharp rallies, abetted by short covering, ensued in cyclic fashion. Once we euded the 17-18 year trading rauge cycle and started the curreut bull market of 1982, things noticeably changed: shorting became and remained excessive, again mostly due to derivative hedging wherein shorts do uot have to be covered aud strong hands do not have to meet margin calls. Another factor to cousider is that currently over 10% of the NOSE is Closed End funds, mostly bond and country types. Still, as the arrows continue to show, rising spikes seem to jibe with up moves 011 the S&P 500, with the one exceptiou.

cedes either substantial declines, or at least long, sidewavs trading rarlges. Obversely from the 1987 Crash until wdll into 1989, Mutual Fund redemptions exceeded sales throughout that up market, just in time to buy (A] into the next decline (BJ.

SPECIALIST SHORT SALES VS. PUBLIC

l&nill Lych data 1975-94

What appears to be a better indicator of shorting sentiment, although far from perfect, is the Specialist versus Public ratio, shower below (Chart 3). Specialists are the closest persons to buyers’ and sellers’ decisions, although there is a oue to two-week delav in finding their actions. We can observe that not only are the Buy aud Sell signals mostly accurate (B & S not mine), with an occasional mis- fire (O), but over the long haul, timing market trades would afford you better than 50% gain over buy-and-hold. The “middle clip” iu Charts 4 & 5 refers to the areas between the dotted lines, lower half.

CHART 3

MUTUAL FUND CASH RATIO

ICI Nrd Davis Resenrrh 1978-93

Chart 4 right illustrates how excessive cash can power markets upward while, at least iu a major Bull market, too little doesn’t always correlate to a major decline. One reason for this is that the pressure of short term performance, especiallv with “Money Man- agement Consultants” de- manding low cash ratios for clieuts, poses the threat of moving them to another monev manager who will “ro- tate” the cash iuto auother sector.

III addition to the fact that excess mutual fund cash does precede rallies, the reciprocal occurrence of mutual fuud buying climax (as depicted in the X’cd Davis Chart 5) pre-

CHART 4

L

I rrio, Stock Mutual Funds Cash/Assets Ratio SOL”<.. Inr~,lm.nt C.rPP.“” ln*“t”t.


CHART5 cur at or near the bottoms, when the call buyers ivould benefit, especially in the mid-198586 span.

Cur-iously, from August 17, 1987 to October 16, 1987. the OEX put/call ratio was locked in a 60-100 range. actually risirig iuto the last few davs before the crash (theoreticalh bullish). The highest reading ever ~va.s in late 1983 - 9.28 - iu-

MARGIN DEBT 1967-93;

Mend1 Lynch dntn

As the long term chart iudicates (Chart 6 with the op- posiug arrows) Margin Debt has historically beeu a correct indicator of major tops, especially iu 1973, just before 1982 and dramatically iu 1987. After the 1990 cor- rcctiotr caused the last Margin debt reconciliation or covering, the chart shows a straight up trend, reflectiug the investiug consumer’s, gownmcnt’s and even global ap- petite for spcudirrg 011 credit. Although accurate, like maw oscillators the trerrd cm stay iu its extreme mode seemirrgly iudefirritclv 0111~ warning of its imminent burst- 1 , itig.

As I mentioned earlier iu the odd-lot short paragraph, wheu the option market got popular, especially iu March 1983 with the advent of the OEX (S&P 100 Index), the least accurate of traders, the uuderfiuauced public, switched from odd lots of stock to optious OH stocks aud indices. At the prcseut time, more than 1500 stocks, OI 7.5% of the stock market capitalization, has equitv options. The uumbcr of sector indices has also burgeoued dramatically It has been commonl!; thought that when put volume heavily outnumbers call volume, this is a contrarv irrdicator that the market OI- uuderlriug entitv will rise. This is true for the short-term day trader; however, looking at the history of the OEX on a weekly basis (Chart 7)) the opposite seems to be true. Over the 12 years, usiug Reuters parameters of below .Z OEX put/call ratio as bearish arid over 1.50 being bullish, Ive cm see the high slumbers are almost alwavs at the top, proving the put buv- crs correct. Similarly the lower numbers cousistentlv oc-

terestiugly just before the big decline of January 1984 of some 30 OEX points.

Being a veteran Option Specialist for the OEX’s largest trading firm and author of an article iu the 1993 MTX ,Journal (#41) 011 L’.O.I.C.E., a treatmerit of OEX Ii>lume aud Ooen In-

:I 1

CHART6

42 MTA ,JOURVAL/Spring-Summer 19%

CHART 7 Intermediate, position-trading Master Indicator, for which I am currently collecting data and fine-tuning, possibly for a future paper.

terest input into a TRIN formula with excellent results (as did,Jim M- . LI tm, Ray Hines, John Bollingcr, aud others in slightly different ways), I was quite surprised bv thcsc lindiugs. Obviously further study using moving averages, ;uld daily data abstracts arc uccessarv to \-crib this COIIULI-

drum. Looking at current dailv data in the next chart. we do see a more positive correla;iou bctlveeu high put volume, both in the OEX and all-equity CBOE charts, and upward price movement. This is line for dav and short term trading, but I cannot use a high cocffi~icut in In\

43

CHART 8

Option PutfCall Ratios

- ------

Market Action

VIX INDEX

CBOE 1983-91

Just a brief word about the L?X Index, Lvhich measures the Volatility of the OEX Iudcx (S&P 100) from mid 1985 011, as shown iu Chart 8 above. It actuallv depicts the Implied I’olatility of 8 OEX optiolk, iu aud out of the moue): uear mouths. Since it CHART 9

has only bceu around in a Bull market, its onl!

OPTION PREMIUM RATIO BY CHRISTOPHER CADBURY, 1986-94

Stocks L? Commodities Jlagazine

A rather recent indicator that has established many valid instances, primarily due to extensive research aud several aiticles by Christopher Cadbury (to whom I owe much gratitude for endless data), is the Option Premium Ratio. This cau only he found in the Sentimcut \\‘indoiv, Chart Page of Investor’s Business Dailv, item #.5, and essentially combines Put/ Call Option seutimeut with Implied Volatility of the 11X, only it includes all equity options, not just the OEX Index. Based on data from 10 years, (although listed options have been around over trventy) di\idiug put premiums by call premiums has ranged from .03 to a hqh of 1.74. Cadbury established that values below .29 and above 1 .I8 indicate a continuation of the trends down and up respectivclv - like extreme levels of other oscillators. Couverscly, OPR’s from 30 to mid-60s generate buy signals and levels to 1.18, sell signals iu about 200 differ- cut combinations of occurrcnccs.

Most of these abstracts are proveu almost unanimouslv bv 10 to 20 test examples, such as. “F&k co~lseculive davs of gaius or uuchaugcd values for the 6PR starting from .32 to 51 have always produced siguificaut rallies in the stock market”. X fewz however, such as “Ideutical values for the OPR iI1 the range between .SO to .88 separated bv .i to 7 tlavs have alwavs prod&l siguificaut declines in the stock market” have iusufficicut testing aud bor-

der 011 the “\\,hencver I wear a red tie 011 Friday the market goes up for 3 days” categorv. Below is a data table of oue of the most hcavilv tested’“pattern recognition” ex-

-we --- s :I

consistent behavior seems to be: down or non- \,olatile during moves of the major uptreud, [bit11 sharp up spike when the market declines, and Ilat or coiliug during trading ranges. Chart 11 shows the historical high of 150 in the Crash of 1987, and siuglc digit loins during 1993 and 1994, possiblv ai1 harbinger of things to come. Al- though the VIX is verv good for trading strategies (busing or selling options depending 011 the volatility), I find it less useful than the Option Premium Ratio, which combiues put/call senti- meut with volatility (see next page).

TABLE1

Date

Jan 28, 1986 Sep 23. 1986 Sep 30, 1986 Kov 24,1986 Sov 25. 1986 Jml 01. 1988

I Nov 1988 28, ~ YOY 29, 1988 ~ Nov 30, 1988

Mar 29, 1989 Mar 30, 1989 Mar 31,1989 Apr 03, 1989 Dee 12. 1990 Dee 13. 1990 May 04. 1990 Dee 23. 1991

~ Juu 22. 1991

Significant Rallies in the Stock Market

DJIA points and Values days before rally

(W (Days)

.51, .51, .51, .56, .78 4 2

.34, .37, .38, .40, .40 43 4

.36, .37, .38, .40, .40 0 0

.32, .47, .51, .57, .62 10 25

.4’i, .51, .57, .62, .65 16 24

.36, .37, .42, .61, .iO 12 1

.36, .38, .42, .42, .45 0 0

.38, .42, .42, .45, .54 9 3 .42, .42, .45, 34, .% 22 2 .39, .41, .42, .43, .43 0 0 .41, .42, .43, .43, .43 0 0 .42, .43, .43, .43, .53 2 4 .43, .43, .43, 33, .5j 13 3 .4i, .49, .51, .52, .57 64 6 .49, .51, .52, .57, .59 73 :‘, .42, 52, .54, ..54, .57 0 0 .50, 33, 36, .57, .81 0 0 .33, .34, 38, .42, .44 0 S&P 8

DJIA points and time of rally

(Pts) (Days) (Wks)

303 12 144 6 133 5 510 14 510 14 106 22 266 10 255 9 255 9 250 13 250 13 239 11 239 11 122 6 122 6 289 10 268 10 22 S&P 6

~ Four consecutive days of gains or unchanged values for the opGon premium ratio starting from .32 to .jl have ahvavs produced significant rallies in the stock market.

amples: it includes the date the 5-7 day series began and the OPR values; the next four columns list the number of Do~~~Joucs points aud days just before the event, and the uumbcr of points in the subsequeut rally with the uumbcr of day or weeks to complete it. More will be heard from 011 this excellent iudica-

CHART10

tor - I intend to include it in mv \laster Indicator.

SENTIMENT INDICATORS - OPINIONS

opinions of over 100 advisory letters everv week on CKBC arid later in Barron’s. Since 1966, this has beeu ;1u excellent coutrarv indicator with its “trading range” giving its best signals from high 30s (Yo of Bulls) as a Buy sig- ual and mid-iOs as a Sell. Although the Buy signals have proven verv consistent, the Sell indications, which before 1989 were quite consistent although very early (sometimes several mouths), have been effective in signaling trading ranges as our strong market ellsues.

Standard & Poor’s 500 Stock Index Y.“dd,D.I. II/Jim. 1,2,/94 IL., 2-r .I,,

469 416 L .,,cc)”

h7on ‘s Polling Sw7qs

The follolvillg section discusses the derivation of the 4 major Scutimeut survevs from Se~vslctlcm along \\?th’Charts l\lhich show Buy and Sell points. and their rcspectivc ef- fectiveuess, as showrl 1)~

c BULLS I BULLS + BEARS Enrem, Optlmlrm -- “n‘a”o”bl, !

’ bGailis - again, this paper is . .

lo review. ilot to research the gathcrilig details. .\lost effcc- live. 1 fouled. \cere the \larket \‘aue alld .\UI Sw.sletters.

1. INVESTOR’S INTELLIGENCE 1966-95

Iuvestor’s Ilitelligelice is published by \lichael Burke’s Chartcraft. aid cxprcsses the

The. MOnIn .aueraoe

Advisory Sarwce Sentiment

45

i

2. CONSENSUS,INC.1984-94, KANSAS CITY,MO The Bullish Consensus, from Consensus, 111~. in Kan-

sas City, X10, also uses opinions from advisory services, mostly investment advisors from major brokerages using house organs versus newsletters. These figures also appear on a 900 liuc arid Barron’s 011 Saturday. As Chart 12

CHART12

shows, there were a few very minor price reversals on

major Sell signals, especiall) in the coiling action of both the S&P 500 and the indicator the last 3 years. Still, profits would have bested the market as measured by the Buy-Hold strategy (see upper left corner of chart). As I write this paper, this indicator has reached a four year high of 67 (twice), versus a 71 in the first quarter of 1991.

3. MARKETVANECORP., 198&94,PASAOENA,CA

An even better sentiment indicator is found in the Market Vane of Market Vane Corp., Pasadena, CA. Com- prised of 100 of the top In- vestment Advisors from Bro- kers, aud obtained on Mon- day each week, information appears on a 900 phone

number and in Barron’s 011 Saturday of that week. Chart 12 indicates a more precise correlation between reversals, although again the sell signals in a strong Bull market tend to be more of a reaccumulation trading range than SAR (stop aud reverse). Once more, the last several vears re-

I

semble coiling action (ex- tension waves of lesser degrees) with lower highs and higher lows in the Indicator. The chart ends with the spring of 1994 correctiou as the O/P lille portends a large upmove in the uear future. During the writing of this paper, it rallied up to 62 for the iirst time since 1987. At this time, March 25, 1995, it is curiously near midrange, or 47 to 53 area, uot forecasthig the selloffs of the previous 3 indicators.

46 MTA JOUR~?LL/SprinS-Summer IVN

CHART13

4. AMERICAN ASSOCIATION OFlNDlVlDUALlNVESTORS SURVEY -1987-95 The final Iudicator of the Barron’s group is the AAII,

or American Association of Individual Investors of Chi- cago, IL, the true retail trade. With 25 postcards mailed out each day of each week, uearly 100 come back with each investor’s opinion of the market for the next six months. As might be expected, this indicator has an almost perfect correlation exemplifying the aforementioned “crowd” svndrome. Gains Per Atmum show more than 3 to 1 improvement over buy aud hold.

Discussion Section

In assembling and aualyziug all of the above data, what becomes iucreasingly evident is the difference in the time factor of each. After working uearly a year on coustruct- ing a Master Indicator from the most successful of these Sentiment Indicators, it is very apparent that each of them has a different time frame. For example, the timing of the Put/Call OEX ratio is much more short term thau Margin Debt or Mutual Fund Cash. Sot OI+ that, the optimum position ou the Bell/Growth Curve (taken from the work of Everett in 1970) 011 the uext page is quite different. It is only through a corroborating “nesting” of several Iudicators that we cau hope to validate the Master Indicator, which would be a great topic for a future paper. Using Table 2 as a guide, with help from data by Yale Hirsch in his book Don’t Sell Stocks 011 Moudav, I will try to place each Indicator OII the Curve 011 Chart 14 somewhere between A aud E. The graph is a \lodel illustrating a homo- geneous population of Investors and sentiment indicators,

and uot an actual frequency distribution. The Growth line represents a Price line and an accumulation of the aggre- gate Indicators, while the Bell Curve depicts Volume as well as the timing phases. Beneath the Bell and Growth Curves I have listed the indicators un-

der studv Odd-lot shorting would be

the highest early in A, with Public entering in the C seg- ment - they would have to cover by C, with the Special- ists startmg to short at E.

Mutual Fund Cash would be large at A, fueling the run through D, when it would drop into the single digit percentage. Conversely, Margiu would bc at its low at A, becoming manic at C and D, where the rising slope is sharpest. After au iutensive study of the history of the

OEX Index, I can onlv find it useful iu a contrary way 011

a very short term basis. Another look at Chart 7 shows that in almost all cases, except iu tops of 1986 and 1987, high numbers were found at tops, low at the bottoms, meaning traders were correct in the 101lg view. I must say that our current Bull market has had high uumbers from hedging and from those speculators trying to call the top of this market. Similarly, the VIX Index aud the Option Premium Ratio, derived from option premiums rather thau V’olmnc, are short term, a11d lvould therefore be difficult to place on the Chart.

Finally, Bearish Sentiment and gloom from Investment letters and media (magazine covers, financial newspapers and TV) respectively, would be pervasive coming into A; they would gradually mutate into complaccucy through C, aud outright euphoria aud certainty by E.

CHART 14

% t . s z 2 c

90%

80%

I r i 50%

t- : I 40% -

I’ / 30% - r/

I

,’

20% - /

: ] i’:, I ._

%/;:\-

10% - 4’

; j ;z.;i

,’ : :, -

LULY : fMlY : :;:& _

Uri‘..? - I...’ c , AooPms: UNoRlTI : KUOP~:;; I ‘W;tMOs

0 . yyx 1 133% ! 14X.’ : ,(X <I’>: 1. - 161’ : .

‘A’ ‘@I 1 ICI 1-y I Ii

l-h

Indicators Shorts: Mutual Fund Cash: Margin Debt: Put/Call Ratio: Volatility Iudcx (WX): Option Premium Ratio: Opinion Letters: (B~o~us.

.\tivisors. AAll. Sew Xledia)

Emotion:

A Odd-lot

High Low High Low Low

Gloom! Fear

B C II E Public Specialist

Low High Low High High

Creed Euphoric

Complaccnc!

Conclusion In conclusion, what I have learned in researching and

writing this paper is that although the basic concepts of Sentiment and all of Technical Analysis are eternal, some things do change as markets change. For example, sentiment indicators such as Odd-lot Shorting were rendered less effective by other inexpensive derivatives, such as options.

Also, just as some Oscillators change parameters in Bull versus Bear markets, Sentiment indicators are less reliable in cases like the present, where the stock market dots vir- tually nothing but rise. with an occasional sideways trading range. Nonetheless, the most effective of the previously reviewed categories, newsletter polling results, mutual fund cash, specialist short selling, and even option put/call ratios, should be monitored for giving reversal signals at extreme excesses, in conjunction with other technical tools such as cycles, oscillators, and support/resistance.

Sentiment is as important as an: other technical tools used by Technical Analvsts. and will continue to be so as we enter’ the area of “Behavior Finance” employing Seu- ral Networks to quantik the Psycholop of Investing.

V. Bibliography l The Crowd br Gustave Lc Bon, 1982. Cherokee

Publishing Cb.

l The Art Of Contrarv Thinking by Humphrey B. Keill, 1992, Caxton Printers

l Reminiscences Of A Stock Operator hv Edwin Lefevre, 19?3, Doran, Fraser Publish&s

l Don’t Sell Stocks On Slondav bv Yale Hirsch, 1986, Facts On File Publication;

l Sletastock Technician Odd-lot. 1982-94

l Trendlines Odd-lot Short Sales, 1991-95

l SE5E and Specialist Short Sales - XIerrili Lynch D;Gl

l Investment (:ompany Institute - .\lutual Funds

l .Ned Davis Mutual Fund Buying

l VIX Chart, CBOE (Chicago Board of Options Exchange)

l Option Premium Ratio by Christopher Cadbury

l Merrill Lynch charts on Inwstor’s Intelligence, Consensus, Inc., Market \‘ane Corp., and American Association of Individual Investors

l OEX put/call ratio data, Bloombcrg Sew

. OEX charts - Reuters/Quotron Advantage AE

the Technical Securities Analvsts Association of San Francisco, and is completing ilis Master’s ill Finance ~ and Le\rel III of the ChlT designation.

Brent has taught classes in technical analysis at Golden Gate University and Schwab University and has lectured before various groups such as X.;\.I.I. He has written several articles on technical analvsis both locally and nationally.

Brent attended Stanford Vnivcrsity and Uniter- sity of Pacific, receiving a degree in education, later completing a business curriculum with honors at Mesa College in San Diego.

I

48 MTA ,lOURN?LL/Spring-Summer 199ti

Using the Z-Trend Oscillator for Long-Term Bond Market Timing 6

Submitted by Robert T. Zukowski, CMT March 4. 1996

Overview This paper examines the concept of modifying the

Coppock Curve to better identify major tops attd bottoms in the bond futures market for lottg-term positioning. The modified versiott of the Coppock Curve is referred to as the Z-Trend Oscillator. Most oscillators are used for trading periods of price consolidation, but the Z-Trend Oscil- lator is specifically used for trading all market cottditiotts from accumulation, to trending, to distribution.

Introduction to Rate of Change and the Coppock Curve

Otte of the older, sitnpler tcchttical ittdicators to un- derstand is the rate of chattge or ROC for short. ROC catt confirm tnarket trends and forewarn of market reversals. The ROC tneasures the pace at which price is chattgittg for any titne period under study. For example, a lo-da) ROC is calculated by subtracting the price today from the price 10 days ago. The result is thett plotted as a cotttinu- ous series that oscillates above and below att equilibrium level that is usually set at 0. The closittg price is getterall! used whett calculatittg the ROC. However, the ROC cat1 be altered to isolate volume attd other ittdicators such as moving averages. Trettdlitte analysis and indicator/price divergettccs arc other aspects of the ROC that catt be used to enhance reliability. With this kind of versatility, traders can mattipulatc the ROC itt mattv useful ways.

Edwitt S. Coppock, best kttowtt for the developtnettt of the Coppock Curve, used ROC as the basis for his work. First introduced in Barrott’s itt 1962, the Coppock Curve was ettdorsed arouttd the world as a long-term ittdicator used to forecast foreign and domestic equity markets.’ The goal of Coppock’s tnometttum rvork is to smooth a price series in such a way as to make the peaks attd troughs in ROC data significant. Smoothed mometttum (referred to as the Coppock Curve) looks attd acts much like a sitte curve or an overbought/oversold oscillator as it moves from positive (overbought) territory to negative (oversold) territory attd back again. Coppock hypothesized that the market’s emotional state could be determined by addittg up the percetttage price chattges for the time period uu- der study to get a settse of tnarket tnometttutn. The result is a long-term curve that effectively measures tnarket momentum and filters out short-term attd intermediate-term tnarket swings.

‘Dudnrk, Gail S., C,IfT, SbfP Group dnnlyis ,\,Jonthly Briejng (Feb. 1996), p 4.

Because it reflects mass psychology, the Coppock Curve is labeled by tnost technicians attd traders as a setttimettt indicator. As a result, the curve siguals market tops and bottoms quite well and proves to be a valuable addition to atty trader’s tool kit. Coppock combined art 1 l- and 14 mouth ROC, smoothed over by a lo-tnottth weighted moving average, which cat1 be explaitted by the yearly titne cycles frequent in tnost tnarket indices. A buy signal occurs when the curve turns up or becotnes positively sloped while below the zero litte. A sell sigttal occurs when the curve turns down or becomes negatively sloped while above the zero line.

The Problem - Indicator Consistency When the Coppock Curve is applied to the monthly

continuation chart of U.S. Treasury Bottd future prices (UST’s) , traders are confronted with the probletn of indicator consistency. This tneatts that the cottfidettce level for each future buy/sell signal is significantly reduced because the ittdicator’s extremes or overbought/oversold levels vary frotn sigttal to signal. Notice how well chattges in the curve coincide with each major top attd bottom in UST’s (see chart 1). Again, the problem is that the ittdicator does ttot offer a high level of consistency for future buy/sell signals. In other words, traders are not sure how low or high the indicator will go before a signal is given.

MTA ~OCRML !Sprin+mmer 1996 49

L

[z-tnagsc au-aug

That could lead to the wrong position in terms of timing, (see chart 2). 111 chart 2, two examples of false signals that resulted in big losses can be seen in June 1980 and January 1992. Basically, the curve failed to keep traders in a long-term position during these periods of price action.

The Solution - Modifying the Coppock Curve By modifying the Coppock Curve, traders can isolate

overbought/oversold conditions and buy/sell signals more effectively. The added value is a high performance momentum oscillator with fixed buy/sell zones. The Z-Trend Oscillator uses the same basic calculation as the Coppock (;urve. but has a few added dimensions. (1) The indicator is optimized wily OI~C time and then back tested to lind optimum KOC and smoothing periods. Interestingly, the ROC part of the indicator when optimized coincided \\ith the 2 l-week cycle, and the weighted moving average portion coincided with the 40-week cycle, much like the yearly cycles Coppock found in most equity markets. Both the 21-week and 40-week cycles were popularized by Jim E. Tillman, CMT, of Interstate/Johnson Lane, and are fre- quently used in forecasting turning points in UST’s. (2) The indicator uses the concept behind J. Welles Wilder’s Relative Strength Index (RSI) to identify overbought/oversold conditions. III other words, the Z-trend Oscillator is a11 RSI study of the Coppock Curve. First, the raw numbers of the Coppock Curve are substituted for the usual closing price within the RSI calculation to normalize it on a scale of 0 to 100. Second, this modified version of the RSI is multiplied bv itself and then subtracted from 100 to make it oscillate above and below 0 and between defined Lanes. A&l example of defined zones would be between -70 and 70. This will identifv proxies of overbought/oversold. Once that is accompli&ed, buy/sell signals become more visible. This is where the Z-Trend Oscillator becomes

useful. It maintains a long-term position during a sideways trend by decelerating as the market’s price ranges become more narrow. (3) The buy/sell equation is written by combining the slope of the indicator with overbought/oversold extremes to determine if a profitable trade exists (see table 1 for calculations).

TABLE 1 Calculations: Z-trend Oscillator in Computrac

coef: study: coef: study: user: coef: study: s tudp: user: studv: user:

user:

user: trade: trade: trade: trade: user:

- Snap version 4.2

coef 6. rt-chg rt-chg(close, coed)-100 [see below] coef2 8. I-t-chg2 rt-chg(close, coef2)-100 [see below] sum-rot rt-chg t rt-chg 2 coef3 10. wtd-ma \vtd-ma (sum-rot, coef3) rsi rsi(wtd-ma, 10) z-trend-osc (2 * rsi)-100 mov-avg rnov-avg(z-trend-osc, 5) buy (z-trend-osc > z-trend-osc [l] &

z-trend-osc < -40) sell (z-trend-osc < z-trend-osc [l] SC

-trend-osc > 50) sold sell * .5 trade trade(buy, sell, sell, buy)

open-p1 open-pl(trade, close, j/32, 2/32) trad-pl trad-pl(trade, close, 5/32, 2/32) clos-pl clos-pl(trade, close, 0, 0) equit) open-p1 t clos-pl

[Note] - 1st step selert and add the rt-rhg study in snup. 2nd step: manually edit the rt-rhg stud) 6~ tJjb]g in -100

There are four advantages to using the Z-Trend Oscil- lator over the Coppock Curve: (1) It is smoother and less volatile, (see chart 3 for a comparison). (2) The ampli- tude is controlled through the modified version of the

CHART 3 us Boms-mmi C3rr:mT:oH

coppocx

50 MTA ~OUIWWSptin~-Summer 1996

RSI formula. (3) Major buy/sell signals become more visible. (4) Overbought/oversold zones are identified. The only disadvantage noticed is: (1) It is iueffective when used over shorter time periods such as weekly, daily arid intra-da):

Using and Customizing the Z-Trend Oscillator

A buy signal occurs when the Z-Trend oscillator (this month) is greater than it was (last month) and is less than -40. A sell signal occurs when the Z-Trend Oscillator (this month) is less thau it was (last month) arid is greater than .iO. Lead time is significantly increased over using

CHART4 3s BONDS-ncNTHLY c3h7m*~:~?l

Ius -, -n hue

Coppock’s original bu~/sell strategy (see chart 4). X more couscrvative buy/ sell approach would be to wait until the indicator crossed above or below the 0 line. However, the Lero lille trade reduces profit and iucreases risk because siguals occur well after a top or bottom has beeri complete.

A histogram is used to display the indicator for clarity but is just as accurate ~vherl displayed as a liue chart. A 5- moutil simple moving average of the indicator cau be used to help cuulirm market direction. Since this moving a~- cragc is ouly used as a coufirmation tool and not part of the buviscll equation, it was uot optimized. If the indicator is above lhc 5-month simple moviug average, there is buviug coulirmatiou. 011 the other hand, if the indicator is below the 5-mouth simple moving average, there is scll- iug co1lfirmatioIl. AII expolleutial moving average could also be used in place of the simple moving average. Trad- ers are eucouragcd to expel-iment with other moving a\- et-ages and time periods because trading styles vdrv.

Applying the Z-Trend Oscillator to Market Conditions

From late 1977 to early 1996, the Z-Treud Oscillator generated a total of 0 ollt of 9 wimling trades. (see chart

I

Iz-tnd-osc lou-aug

,/!a19 Uuill Hod3 hri% dul88 NouSE kar9 Ai?5 1

5). From September 1982 to Jlarch 1983, the UST market was considered extremely overbought, which was eli- dent by an indicator reading of greater than 70. That was a waruulg sign suggesting traders should start looking for a new sell signal. In fact, the signal was giveu in April 1983 when the indicator began to decelerate while still above the trigger level set at 50. The result was a 1Gmonth trade Ccldiug 12.69 points. From September 1987 to So- vemb& 1987, the Z-Treud Oscillator rcachcd a rcadiug of less thau -70, which warned of a potential market reversal from down to up. This oversold rcadiug bcgau to unwind in December 1987 when the indicator touched the bul trigger level set at -40. The result \va?s a 24-mouth trade for 10.25 points.

Other examples of the Z-Trend Oscillator iI1 action ca11 be sew during 1983, 198.5, 1988, 1991 alld 1994. These periods were major cougestiou zones, but uoticc that the indicator kept each position active by not getting too overbought or too oversold during each of these periods. 111 fact, the indicator hovered closer to the zero liue \\heu price rauges became more uarrolv. 01lce price ranges begau to widen and the market resumed its original di- rectiou. the iudicator accelerated. This is an important aspect \vhcu dealing rvith long-term tiuiiug iudicators because false buy/sell signals tend to occur during a uon- trending (consolidation) period. [Note: During a sidc- wavs price trend, the Z-Trend Oscillator’s maximum draw dowl (licgative open profit/loss) cau kcomc greater. This is caused by slippage/ commission aud price \olatility Hoivever. these losses are kept to a miuinlum and arc Iiltcrcd out ouce the price trend resumes iI1 its original direction.]

Testing The Z-Trend Oscillator

Since UST futures oulv started trading in late-19Ti. the

best way to show that the Z-Trend Oscillator is uot a “b! chance” indicator is to conduct a number of tests (see chart 6 for the indicator and table 2 for the results). In the first test, the Coppock Curve was applied to UST’s using Coppock’s ll- and 14month ROC, smoothed over by a lo-month weighted moving average. Also used rvas Coppock’s buy/sell strategy in an attempt to show that this indicator needs to be modified in order to work properly when applied to UST’s. Conclusion: Due to the number of false signals and poor results, the only solution was to modifv the curve. In the second test, the Z-Trend Os- cillator was used, but the ll-, 14 and lo-month parameters were maintained.

TABLE 2 Trading Results: Risk management overlay for UST

futures using Coppock Curve from 1977-1996 (1 l- and 14month ROC with a lo-month smoothing)

net profits 34.00 points 7; wins 55% wins losses i long wins 3 long losses 1 long win ‘;I; 75% short wins 2 short losses 3 short win Y’i 25% max. cons. wins 4 max. cons. losses 2 largest win largest 10s~

zS,Ljl points i.13 points

average win 10.25 points werage loss - mr 1. I L) points ;wrage wi I 3.78 points max. d2-XV down 19.09 points longest draw down 34 months slippage; comm. 7/32 months winning 74 months months losing 92 months

Buy and sell signals were also modified, (see chart 7 for the indicator and table 3 for the results). Conclusion: The results were a little better, but false signals were still present. Therefore, the onlv solution was to optimize the ROC and smoothing peiiods.


futures using Z-Trend Oscillator from 1977-1996 [ll- and 14month ROC with a lo-month smoothing]

net profits 5% wins wins losses long wins long losses long win 57 short wins short losses short win L/;.# max. cons. wins max. cons. losses largest win largest loss average win average loss average w/I max. draw down longest draw down slippageicomm. months winning months losing

69.41 points 71% 5 2 2 I 67% 3 1 i5% 3 2 25.91 points 1.91 points 13.67 points 1 .OO point 9.92 points 9.44 points 15 months T/32 102 months 36 months

In the third test, the %-Trwd Oscillator was optimized IO liud optimum ROC alld smoothing periods (see chart 8 for the indicator and table 4 for the rcsultsj. The optimization process resulted iu a fi- and H-month ROC. smoothed over bv a lOmonth lveightcd moving average. Conclusion: The’optimizatioll process enhanced the e6

52 MTA ~CILK\.U Spriny-Summer ICM

fectlveness of the Z-Trend Oscillator, which is reflected in the results. However, to show that the 6, S-and lo-month parameters were valid, the Dow Jones-20 Bond Average (DJ-20 Bond Avg), a proxy of LIST’s going back to 1915, was tested.

r Qndgsc aou_aug


futures using Z-Trend Oscillator from 1977-1996 (6- and S-month ROC with a lo-month smoothing)

net profits c/c wins wins losses long wins long losses long win 7 short wins short losses short win ‘;% max. cons. wins max. cons. losses largest win largest loss average win wcrage loss average w i I max. draw down longest draw down slippage,‘comm. months winning months losing

156.81 points 100% 9 0

R 100% 4 0 100% 9 0 31.56 points 0 points 17.42 points 0 points li.42 points 5.03 points 6 months 7/32 150 months 21 months

To make it a thorough aud fait- test, 3 separate tests from 1913 to 1946, from 1947 lo 1977 a11d from 1978 to 1996 were conducted, (see charts 9, 10 and 11 for tbc iudicators and tables .i, 6 aud 7 for the results).

TABLE 5 Trading Results: Risk management overlay for DJ-20 Bond Avg using Z-Trend Oscillator from 1915-1946 [ 6 and &month ROC with a 1 O-month smoothing]

net profits % wins wins losses long wins long losses long win c/c short wins short losses short win % max. cons. wins max. cons. losses largest win largest loss average win average loss average w/l max. draw down longest draw down slippage/comm. months winning months losing

59.97 points 100%

t 2 0 100% 2 0 100% 4 0 20.94 points 0 points 14.99 points 0 points 14.99 points 5.94 points 7 months 0 316 months 21 months

MTA lOURUAL./Sprin~Summer 1996 53

I

TXBLE 6 Trading Results: Risk management overlay for DJ-20 Bond Avg using Z-Trend Oscillator from 1947-1977 [6- and S-month ROC with a IO-month smoothing]

net profits 5% wins wins losses long wins long losses long win R short wins short losses short win ? ‘I max. cons. wins max. cons. losses largest win largest loss average win average loss averagc IV: I max. draw down longest draw down slippage/ comm. months winning months losing

52.67 points 71% 12 5 4 4 50% 8

AS% 3 1 13.21 points 2.21 points 4.tiO points 1.31 points 3.29 points 3.70 points 21 months 0 294 months 75 months

pct19 lFt?bsZ Uun84 Pm leb89 Nun91 KM93 WI6 I

TABLE 7 Trading Results: Risk management overlay for DJ-20 Bond Avg using Z-Trend Oscillator from 1978-1996 [6- and S-month ROC with a lo-month smoothing1

net profits % wins wins losses long wins long losses long win % short wins short losses short win % max. cons. wins max. cons. losses largest win largest loss average win average loss average w/l max. draw down longest draw down slippage/comm. months winning months losing

73.40 points 78% 7 2 4 0 100% 3

iO% 3 1 19.35 points 5.26 points 10.39 points 3.17 points 8.15 points 4.22 points 9 months 0 150 months 18 months

u-

The first test from 1915 to 1946 was a partial success. The &, S-and lO-month parameters worked extremely well. Howewr. the indicator failed to react to the different market conditions. The second test from 1947 to 1977 leas also a partial success. The 6-. 8- aud lOmouth parameters generated excellent results, but the indicator failed to react as it should have. given the different market conditions. The third test from 19% to 1996 was a complete success. The 6, 8- and lOmonth parameters worked extremely ~vell, aud the indicator operated properly given the different market conditions.

Conclusion: Though the Z-Trend Oscillator seems to

work better on UST’s than on the DJ-20 Bond Avg, the results are very encouraging. Since the Z-Trend Oscilla- tor managed to signal every major top and bottom over an 80 year period in the DJ-20 Bond Avg, the results revealed that buy/sell signals did not occur “by chance.” In an attempt to show that the Z-Trend Oscillator can significantly increase profit potential, one last test was conducted that revealed using the Z-Trend Oscillator is more profitable than using a simple buy-and-hold strategy, (see table 8 for the results).

TABLE 8

Comparison: Z-Trend Oscillator versus buy-and- hold strategy

Market Z-Trend Oscillator Buy-and-Hold

DJ-20 Bond Avg (1915 - 1946) 59.97 points 12.19 points DJ-20 Bond Avg (1947 - 1977) 52.67 points -36.50 points DJ-20 Bond Avg (1978 - 1996) 73.40 points 9.34 points UST futures (1977 - 1996) 156.81 points 25.13 points

Conclusions Based on the results of all tests, it was well worth the

time and effort to construct, customize, optimize, back test, and update the Z-Trend Oscillator. The tests show that a complete and effective indicator can be used to signal every major top and bottom in UST’s. Traders now have a long-term indicator within their technical arsenal that can (1) Consistently identify an overbought/oversold condition within the market; (2) Locate buy/sell signals with a much faster lead time; (3) Identify the long-term trend of the market. Traders who follow other markets could try this indicator on those markets. Note: The optimized parameters would most likely be different in other markets and overbought/oversold conditions could also vary. For example, instead of being overbought at 70 and oversold at -70; a market could become overbought at 50 and oversold at -50. Traders should consider isolating dominant time cycles within the market and using those cycles in place of the ROC and smoothing periods.

Long-term trend analysis is a very important aspect of technical analysis, and if done correctly, there is no reason why traders shouldn’t be on the right side of the trend. During the research, a few areas that warrant further attention were discovered: (1) Using indicator and moving average crossovers as the basis for the buy/sell equation. (2) Fitting the indicator to weekly, daily and intraday time periods. (3) Applying the indicator to commodity markets, currency markets and mutual funds.

Despite these minor troubling aspects, the Z-Trend Oscillator can do what was once thought impractical: consistently signal major tops and bottoms, and identify the trend for long-term positioning.

Bibliography Colby, Robert W. & Meyers, Thomas A., The Encvclonedia of Technical Market Indicators , 1988, Business One, Irwin, p. 414.

Faber, Bruce R., “The Rate of Change Indicator,” Techni- cal Analvsis of Stocks & Commodities, Volume 12; October 1992, p. 13.

Hayes, Tim, “The Coppock Guide,” Technical Analvsis of Stocks & Commodities, Volume 11; March 1993, p. 50.

Kemplin, Raymond, “The Coppock Curve: A Famous Indicator Flashes a Long-Term Buy Signal,” Barron’s, November 22, 1982, p. 10.

Middleton, Elliott, “The Coppock Curve,” Technical Analvsis of Stocks & Commodities, Volume 12; November 1994, p. 59.

Pring, MartinJ., Martin Prine on Market Momentum, 1993, Probus Publishing, p. 52.

Wilder, Welles J., New Concerts in Technical Trading Systems , 1978 , Trend Research, p, 112.

Robert T. Zukowski, CMT

Robert T. Zukowski, CMT, is a Senior Technical Analyst at MCM MoneyWatch, a financial advisory firm located in New York. He is also a board member of the professional Market Technicians Associa- tion in charge of public relations.


L

MTA JOURNAL/Spring-Summer 19%

A Study in Volume and Price Alerts

Submitted by David Bryan 7 From my earliest readings on technical analysis at the

genesis of my investment career, to my present day more experienced view of the markets, I have learned that the role of volume in security analysis plays an important role in predicting the future path of stocks. Despite the belief that this role of volume analysis was true, somewhere in the stubborn Missouri - like recesses of my mind persis- tent doubt existed as to the actual validity of this concept. Yes, we have all seen stocks blast off with high volume and continue to advance. But as a group did they perform any better than the averages? From my readings there existed no recollection of a specific study correlating the subsequent price action of a stock after an unusual volume occurrence. Many authors have stated that price follows volume, but is this an educated opinion or is it a fact? Joseph Granville, in his book Granville’s New Stratew of Dailv Stock Market Timing for Maximum Profit’, states that “stocks do not rise in price unless demand exceeds supply. Demand is measured in volume and thus volume must precede price.” Although Mr. Granville was writing about his technique of “on balanced volume,” also known as OBV, which is an accumulation of positive or negative volume over a certain period, accumulation and distribution volume patterns point to probable changes in price. It is not the purpose of this paper to prove or disprove the usefulness of OBV, per se. The reference to Granville’s OBV is to only lay the foundation for the basic belief that most technicians adhere to the concept that volume precedes price. However, in this paper a tack is taken that is different from Granville’s approach. The primarv goal of this paper is to offer evidence that a sample of iust one tradine dav of unusual volume can predict subseauent price action.

Martin J. Pring, in his book Technical Analvsis Ex- plained*, asserts the principle that volume goes with price. Most technicians willingly accept this principle at face value. Mr. Pring observes that a price rise accompanied by expanding volume is a normal market characteristic. He also writes that a breakout from a price pattern that occurs on heavy volume, especially on the downside, acts to confirm the price trend. Why do most technicians accept the concept that volume confirms the price trend? Or do they simply wish to believe it to be so? Indeed, what are the true probabilities of price following volume?

In this study, we make a major departure from the usual pattern of relating trend of volume over extended time periods to the study of a single occurrence of abnormal volume. Data were collected over the course of eighteen months in which stock prices, after an unusual day of volume and price behavior, were compared to the S&P 500 Index.

The data studied in this paper use either a combination of volume and price alerts or simply volume alone. The study does not incorborate any bersonal technical iudments. The study quantifies the action of stocks during the one year period of July 10, 1991 through July 9, 1992. The level of the S&P 500 was recorded along with the entry of each stock that qualified for the study.

Methodology Before delving into the results of the study, a review of

data sources and review methods shall be presented. We are cataloging securities whose shares demonstrate unusual volume characteristics and a combination of unusual volume and price characteristics. The study gathered data drawn from the stock listings in The Wall Street Tournal beginning with the letter “A” on the New York Stock Ex- change. The “A” section, chosen for our sample, represents approximately 13% of all stocks listed on the NYSE. We deem this a meaningful sample for our exploratory considerations. The stock listings in The Wall Street Tour- nal underline the issues that are among the 40 largest percentage changes in trading volume, compared with average daily trading volume over the past 65 days. These issues are labeled “volume alert” stocks. Stock quotations in boldface have experienced price moves in excess of 5%. These issues are labeled “price alert” stocks. Issues printed in boldface type and underlined are labeled “volume & price alert” stocks, which signals that the stock had both a volume and price alert. The study breaks down into two specific areas of volume activity: first, on stocks exhibiting a volume alert (underlined), and second, on stocks exhibiting a price 8c volume alert (boldfaced and underlined). These two types of occurrences are the focus of study. Prices of securities and the S&P 500 were measured on the opening price the day after an alert was noted. The study excluded stocks under $7 l/2 and preferred issues. Measurement periods were one, three, and six months. The total number of occurrences compiled during the period studied totaled 375. The collection of alerts occurred for a period of one year and then an additional six months to collect the data for the longest comparison. The best and worst performing stocks in each group were disallowed in an effort to reduce any unusual distortions.

Each study covered stocks with up and down alerts. For example, an underlined security closing up for the day qualified as an up-volume alert. The two methods are:

1) Stocks demonstrating a volume alert, both positive and negative.

2) Stocks demonstrating avolume & price alert, both positive and negative.


Each of the foregoing study methods constitutes the remainder of the composition. If volume is believed to be an indicator of subsequent price action, then the sections labeled as down alerts represent periods in which the securities should perform inferior to the market. For example, in advancing markets the down alert stocks should lag behind the market. In declining markets, they should fall further than the market decline. In the up alert study, the securities’ performance should exceed the market’s performance. The S&P 500 is the measure of the markets performance. The percentage gains or losses present a net average of all stocks, as well as the S&P 500 for each category. Periods are broken down into categories of up or down alerts, not of a rising or declining market. In real time it is obvious that one knows only the direction of the alert.

Years ago I began trading securities that broke out to new highs from any of several different types of consolidation patterns. My actions, then and now, are no different from those of many other technicians. Merrill Lynch Inc., for example, with whom I was an account executive for eleven years, publishes a report each morning called Dailv Market Observations3. One of the items covered in this report is a listing of stocks that demonstrate unusual volume characteristics, categorized by direction of price movement. The report, compiled by Philip L. Rettew, vice- president at Merrill Lynch, is an important source of volume data. In a later telephone conversation with Mr. Rettew regarding this report, I asked if a study had ever been undertaken to study the after- effects of positive and negative volume alerts. His answer was, “no, because that is not the objective of the report. It is published for the reason that its name implies. It is used for observations.” Mr. Rettew went on further to say that occurrences such as selling or buying climaxes, new offerings and programs trades might run counter to the perceived trend of the observed alert. These factors would make it all the more difficult to measure the performance. A high volume selling climax and the subsequent negative alert that it dem- onstrates should, and in most cases, actually be interpreted as a potential positive signal by the alert technician. Mr. Rettew says that “the report tries to identify or observe occurrences from a daily sea of data in which one should further explore.” Our study will attempt, despite the instances of buying and selling climaxes and other market noises, to determine if unusual activity in volume and price are worthy anticipators of subsequent price movements. In our case, we make observations without judgment.

Volume Alerts The tables that follow are arranged so that each stock

is measured against the corresponding movement in the S&P 500 index. For instance, in the first table the section for down alerts under the one month heading rose 1.2% compared to the S&P 500’s rise of .82%. We would wish, if volume is a useful indicator, to have the down alerts stocks decline more than the market. Following each pe-

riod a 20% stop loss was also measured in an attempt to counter bad signals. It is a commonly held belief by most traders that the use of stop loss orders can effectively increase one’s odds of being successful in trading. This con-

cept is utilized in this report simple as an easy addition to view the results with a small dose of money management.

TABLE #l

Volume Alerts

Sample Size - 116 Down & 115 Up

DOWN ALERTS

One Month Three Months Six Months Stocks S&P Stocks S&P Stocks S&P

+1.2% +.82% +1.86% (1.69%) +4.55% +4.1%

Use of Stop Loss

+0.72% (0.17%) t&34%

UP ALERTS

+3.02% +.42% +4.12% (1.66%) +3.94% +3.55%

Use of Stop Loss

+3.23% +5.01% +5.76%

Under the down alert category, the results were disap- pointing because the securities rose more than the market despite a negative volume alert. The three-month study was a particularly rough environment for the down alert securities because they actually increased in value during a declining market environment. The six-month period, although showing a smaller difference in percentage changes, also failed to perform as desired.

As an adjunct to the study, the 20% stop loss program was conducted, which significantly improved the desired results. Although massaging the results with a stop loss program does not necessarily add validity to the test results, it does add strength to the case for the use of sound money management principles. The employment of the stop loss narrowed the results for the one-month period to nearly neutral results. For the three-month period the results improved as the down alert stocks declined by 0.17% rather than rising by 1.86%. The six-month study showed the greatest improvement as the down alert stocks rose only 0.34% as compared to the nonstop loss gain of 4.55%. Although it might appear that the improvement of results by the use of a stop loss program might infer that the volume alert does not work, nothing could be further from the truth. The improvement of the results by the stop loss actually reinforces the significance of the results. Because a stock signals a report, perhaps in many cases a selling climax after a long decline, or even a buying climax after a long uptrend, it alerted us to a developing and changing situation. The stop loss simply prevented the continued loss of funds. The well used phrase to cut your losses and let your profits run still applies.

As illustrated in table #l, the up alerts produced desired results for all periods. The one and three-month studies managed to beat the averages by several percent-


age points. The three-month gain of 4.12% favorably compared to a market loss of 1.69%. The disparity represents a sizable difference not explained by chance. The gain from the three-month period to the end of the six-month period declined to only a 0.39% gain in favor of the studied stocks.

The use of the stop loss program again supplemented the desired results. The use of the stop loss improved the three-month results by roughly 20% from a gain of t4.12% to a gain +5.01%. The six-month results benefited by 44% as the gain increased from t3.94% to t5.76%.

VOLUME ALERTS COMMENTS From the data gathered in this study it is clear that the

use of a positive volume alert can benefit the trader of securities. As to why the positive alerts performed better than the down alerts, one can only guess. Many of the negative alerts appeared climactic in nature and could easily explain part of the difference in less than antici- pated results. As noted the use of the 20% stop loss did improve the results in most categories.

Price 81 Volume Alerts The second part of the study combines the volume alert

with the price alert. As previously noted, a price alert is demonstrated by a price move in excess of 5% in one trading session.

The following table outlines the results of the combined results.

TABLE #2

Price & Volume Alerts

Sample Size - 31 Down 8c 48 Up

DOWN ALERTS

One Month Three Months Six Months Stocks S&P Stocks S&P Stocks S&P

(4.65%) +0.67% (7.28%) (3.9%) (3.88%) +2.14%

Use of Stop Loss

(4.95%) (7.94%) (4.72%)

UP ALERTS

(0.55%) (0.42%) +2.16% (2.11%) t4.32% +3.54%

Use of Stop Loss

(0.43%) +3.08% +5.24%

In every period measured, except for the one-month up period, the alerts worked in their direction of signal. The down alerts worked in an exceptional manner by under-performing the S&P 500 by wide margins, even deQing the market’s general direction. For instance, the down alert stocks declined in the one and six-month periods despite an advancing market. During the six-month period the advantage to the trader was six percentage points. Had a trader shorted all of the stocks in the six-month study, he would have had a positive return of 3.88% during a period in which the market rose 2.14%. In each case, the

stocks declined in value even though during the one and six-month studies the market rose. Evidently securities with a sharp rise in price accompanied by a sharp increase in volume performed as expected.

The evidence is also compelling for the up alerts, as they best the market by large margins. In the three-month period the alerts rose 2.16% during a market decline of 2.11%. During the six-month study, the alerts did nearly 33% better than the market.

The use of the stop loss increased the results in all cases. For instance, in the three-month up alert study, the results increased by 50% from a +2.16% to t3.08%.

PRICE & VOLUME ALERT COMMENTS The results obtained when combining both volume

alerts and price alerts in conjunction appear to be more than just random results. When the alerted securities go counter to the market by wide margins, then indeed the proof of a one-day event effecting future stock prices is hard core evidence of the theory’s validity.

General Thoughts & Conclusion From the data shown in both categories of study, it is

clearly evident that volume does indeed precede price in the sense that high volume alerts lead to improved performance of securities in relation to the market. It is more evident that a combination of unusual volume combined with a 5% or more price movement improves the random results of equity trading even further. The trader should carefully examine each alert to determine its best use. In many of the stocks measured, a signal occurs, which to the average technician represents a selling or buying climax. In reality, most technicians would probably not trade the signal, and some would have been tempted to go in the opposite direction of the alert. These contingencies in no way weaken the case for the use of using volume alerts for trading. It is not, nor was it ever contemplated, that this study should lead to a system of trading securities based solely on volume or price & volume alerts. However, if one wished to utilize the methods presented as a system, it is certainly plausible. The odds of success appear to be favorable.

The primary intention of this study is to examine the effects that a one-day volume event might cause to future price changes. The evidence presented makes a strong case that volume alerts, either singularly or with price, should not be ignored. It is a tool that can be used to alert the technician of possible impending change. Per- haps the alert is part of a larger technical pattern that is signaling the end or beginning of a technical pattern. It is a sign to investigate.

After collecting and analyzing the data over a period of l-1/2 years, and after trading in real time some years later, I find that I am constantly searching for unusual price and volume behavior in any stock. In my own particular trading methods, I look for several types of consolidation patterns for equity purchases. A volume or volume/price


alert signals me to scrutinize the security. Often the ac tion may be signaling the commencement of a new trenc or perhaps the death of an old trend. The individual trade] must make that call. The investor will find that in addi

’ tion to the previous day’s trading results, he or she wil find a useful technical tool that, if used consistently, car increase trading profits. Earlier in the paper, we statec that the purpose of the study was to determine if the ex amination of our “alerts” would prove their utility as a tech nical tool. The data presented points to a positive conclu sion.

The Final Word A popular theory among technicians is that price fol

lows volume. The evidence pathered in this study clearlv ind; cates that it would be we abtn@niate to state that brice follow an explosion oftice and volume. The initial kickoff of largl price moves, and higher than normal volume, leads tc continued outperformance in equities. It does not aF pear to be important if the volume signal was positive o negative.

Bibliography 1 Granville, Joseph Ensign, Granville’s New Strategy of

Daily Stock Market Timing for Maximum Profit. Prentice-Hall, Englewood Cliffs, NJ, 1976

2 Pring, Martin J., Technical Analysis Explained. McCraw- Hill, Inc., New York, 1980

Services 3 Daily Market Observations, Merrill Lynch Inc., One

Liberty Plaza, New York, NY

David Bryan

David Bryan is currently a vice-president and portfolio manager with Wilmington Trust FSB in Vero Beach, Florida. Previously he was an account executive with a major national brokerage firm for eleven years, and Chief Investment Officer for trust investments for eleven years with a major regional bank in North Carolina. David is a graduate of Mississippi State University and the Stock Market Institute. His favorite equity scenario is a lengthy consolidation, extending three years or more, accompanied by im- proving fundamentals. He finds that using technical analysis hand in hand with fundamental analysis alerts him to the best opportunities in the best companies. His hobbies include collecting old investment books and material, collecting bull and bear statues, fly fishing, and shotgun target shooting. He is married and has three children.


Economy & Finance

Journal of Technical Analysis (JOTA). Issue 46 (1996, Summer)