journal of technical analysis (jota). issue 49 (1998, winter)

1j+111 MTAJOURNAL

d Publication of

Winter-Spring 1998 I Issue 49

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MTA Journal - Table of Contents

MTA Journal Editor and Reviewers 3

About the MTA Journal

MTA Member and Affiliate Information

MTA Board of Directors

Editorial Commentary - In This Issue

Henry 0. (Hank) Pruden, Ph.D., Editor

1 Combining Technical Analysis with Fundamental Valuation to Create a Risk Indicator for the Stock Market

Jurrien H. Timmer, CMT

9

2 Sector Rotation in an Environment of Rising Interest Rates

Gatis Roze

3 The Risk/Reward/Probability Diffusion Index 23

Larry M. Berman, CMT

4 The Bridge-CRB Bond Ratio - An Alternate View 31

Vincent Jay, CMT

5 Fibonacci Ratios 81 Time Cycles - Observation and Applications

Song Hua Huang

6 DEX Trading System

David Kirkman

7 A Comparative Study of a Technical Neural Network vs. a Fundamental Neural Network in the Forecasting of the long Bond Rate 57

Robert J. Van Eyden

MTA JOURXX l Winter - Spring 1998

2 MTA JOLRNAL * \Cinter - Spring 19%

The MTA Journal

Winter-Spring 1998 . Issue 49

Editor

Henry 0. Pruden, Ph.D. Golden Gate University

San Francisco, California

Associate Editor David L. Upshaw, CFA, CMT

Lake Quivira, Kansas

Assistant to the Editor Victor Ricciardi

Candidate fm Doctor of Business Administration Degree Golden Gate University

San Francisco, California

Connie Brown, CMT Don Dillistone, CFA, CMT Aerodynamic Investments Inc. Cormorant Buy

Gainesville, Georgia Winnepeg, illlanitoba

John A. Carder, CMT Topline Investment Graphics

Boulder; Colorado

Ann F. Cody, CFA Hilliard Lyons

Louisville, Kentucky

Robert B. Peirce Cookson, Peirce & Co., Inc.

Pittsburgh, PA

Manuscript Reviewers

Charles D. Kirkpatrick, II, CMT Kirkpatrick and Company, Inc.

Exeter; New Hampshire

John McGinley, CMT Technical Trends

Wilton, Connecticut

Robert I. mrebb, Ph.D. Associate ProfeSsor and

Paul TudorJones II Research Fellow Mclntire School of Commerce

University of Virginia Charlottesville, Virginia

Michael J. Moody, CMT Dory, Wright & Associates

Pasadena, Calijomia

Richard C. Orr, Ph.D. ROME Partners

Marblehead. Massachusetts

Kenneth G. Tower, CMT UST Securities

Princeton,, New Jersq

J. Adrian Trezise, M. App. SC. (IT) Iris Financial Engineering and

$3 terns San Francisco, CA

Publisher

Market Technicians Association, Inc. One World Trade Centq Suite 4447

New York, New York 10048

Special recognition to ad hoc reviewers: Richard Arms, Michael Baum, CMT Philip Erlanger; CMT, Herbert Labbie, CMT Theodore Loud, CM7; Bernard Schaeffer

MTA JOURNAL l \Vinter - Spting 1998

About the MTA Journal

Description of the MTA Journal

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You want your article to be published. The staff of theJourmaZwant.s to help you. Our common goal can be achieved efficiently if you will observe the following conventions. You’ll also earn the thanks of our reviewers, editors, and production people.

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Mail your manuscripts to: Dr. Henq 0. Pruden Golden Gate Universit) 536 Mission Street San Francisco, CA 94105-2968

4 MTA JOLXML l \Vinter - Spring 1998

Market Technicians Association, Inc.

Member and Affiliate Information

Member Member category is available to those “whose profes-

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MTA JOURNAL 0 \Yinter - Spring 199s 5

Market Technicians Association, Inc.

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6 MTA J0UUL-K l \\‘inter - Spring 1998

Editorial Commentary

In This Issue

Henry 0. (Hank) Pruden, Ph.D., Editor

Theoretical Model Building: The Component Parts A theoretical model starts with things or variables, or (1) units whose actions constitute the subject matter of

attention. The model then specifies the manner in which these units interact with each other, or (2) the laws of

interaction among the units of the model. Since theoretical models are generally of limited portions of the world, the limits or (3) boundaCes 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) system states in which each of the units interact differently with each other. Once these four basic features of a 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) propositions of the model.

So far, we see only the theoretical side of the theory research cycle. Should there be any 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 an empirical indicator of the term. The next operation is to substitute the appropriate empirical indicators in the propositional statement to generate a testable (6) hypothesis. The research operation consists of measuring the values on the empirical indicators of the hypothesis to determine whether the theoretically predicted values are achieved or approximated in the research test.

(From “Putting It All Together” by Henv 0. Pruden, Ph.D., Editw’s Commenkry, MTA loumal, Spring-Summer 1996, p. 7)

n this issue appear several articles that exemplify man! of the principles that were brought out in the Spring- Summer 1996 MTA Tournal’s Editor’s Commentary “Put-

ting It All Together.” For instance, the lead article byJurrien Timmer is a multidimensional approach which incorporates a variety of indicators that tap different areas of market behavior. Jurrien uses a combination of indicators to identify the appearance and reappearance of high risk “system states” in the S&P 300.

Another example of market behavior ivithin a “system state” of high risk is effectively portrayed by Gatis Roze in his study of “Sector Rotation in an Environment of Rising Interest Rates.” In this article bp Gatis \ve observe in sharp relief the use of a “system state coordinate,” in the emplov- ment of interest-rate levels as the key indicator for defining the state-of-the-market, and interest-rate level is also the causal variable.

Still another article making effective use of a multiple- indicator approach is Larry Berman’s “The Risk/Reward/ Probability Diffusion Index: Increasing the Probability of Successful Trading by using Psychology, Support and Resis- tance Expectations, Momentum, U’ave Structure and Rever- Sal.” Here again we discover a fine piece of technical analysis work that uses a variety of indicators to tap different di- mensions of market behavior. And, these indicators are sufficiently different from one another so that they can be added together to render a stronger, comprehensive technical signal. This research by Larry captures the essential four main “units” of technical market analysis: price, Sol- ume, time and sentiment. The study also adds comprehensive reversal patterns.

Vincent Jay’s article on “The Bridge-CRB/Bond Ratio” reports that profits will probably result from trading T-Bond futures (the dependent variable) using a MACD-(M) version of Bridge-CRB/Bond Index (the independent or pre- dictor variable) to determine buy and sell signals.

implicit in the VincentJay study is a strong assertion about the direction of interplay between commodities, inflation, and interest rates, that is, the “laws of interaction” between them is that inflation causes a change in interest rate futures. This relationship or “law of interaction” between inflation and interest rates is tested bp the “hypothesis” that inflation, as measured bv the Bridge/CRB Index, accomits for turns in interest rates as measured by T-Bond futures.

Two brief, yet important, studies by Huang and Kirkman show aspects of “Fibonacci Ratio and Time Cycles” and indicators for an “OEX Trading System.”

This issue of the MTA Tournal concludes with an article emploving a “cutting edge” approach in technical analysis modellbuilding and testing. Robert Van Eyden from South Africa presents “A Comparative Study of a Technical Neural Network \‘ersus a Fundamental Neural Network in the Fore- casting of the Long Bond Rate.” In doing this article Rob ert makes an apt distinction between data that are technical and data that are fundamental. This study furnishes us with a nice case of how to set the “boundaries” of technical analysis, an activity which is vital to establishing the conditions for valid and reliable testing of the relationships among indicators of technical market behavior.

MTA JOUlKU l \Vinter - Spring 1998 7

8 MTA JOURNAL * Winter - Spring 1998

Combining Technical Analysis with Fundamental Valuation to Create a Risk Indicator for the Stock Market

Jurrien H. Timmer, CMT

Abstract

It is a widel! held assumption among stock market professionals that the tujo ~~r’ncipalf~tndameni~~l drivers of stock market performance ouer the intermediate term are earnings and interest rates, the combination of which form the diuidend discount model. The goal of this paper is to create such an indicator for the SYP 500, and, through traditional techniral analysis, dtwelop a trend/momentum-based timing indicator that s&pals periods of intermediate term risk. This timing indicator is intended for investment managers for hedp’ngpurposes.

The idea of da&ping the indicator described in this report was inspired by Paul Slacrae Montgomery’s presentation at the 19% SIT.\ Seminar.

Part I: The Fundamental Inputs The oldest and most traditional method of “fundamental” valu-

ation in the stock market is the dividend discount model. This model takes actual or expected earnings or dividends (D) and divides that number by a discount rate (I) in the following formula to arrive at a “fair value” for stocks (P):

P = D/[l+I/lOO] The numerator and denominator, earnings and interest rates,

comprise the principal inputs for this fundamentallv-driven valuation model. For the technical analyst, it may be useful to combine this kind of fundamental valuaiion with’ traditional technical analvsis in the form of trend and monmtm studies in order to identih specific time periods during which the stock market is at risk on the basis of earnings and interest rates.

In doing so, we first have to chose the fundamental inputs to be used for our study. The two primary options in terms of the numerator are earnings and dividends. Both are a manifestation of the same w~derlying fundamental condition of a stock or stock index, but earnings tend to fluctuate a bit more than dividends (because the latter are set quarterly bv companies). A4.s a result, earnings are probably a better gauge’for valuation purposes, as long as they are taken ol-er more than one quarter. The next decision is whether to use actual earnings or expected earnings AIc- tual earnings are conventionally looked at on a quarterly or four- quarter trailing basis (in order to smooth out quarter-to-quarter fluctuations), making it a backward-looking or /aging indicator. Since expected earnings are by definition forward-looking (making them a leading indicator), they offer a better guide for valuation purposes as long as the forecasts are reliable. Reliable in this case means reaching a critical mass in terms of earnings estimates bp taking the consensus of major research analysts. Since the early 1980’s, two firms have been providing consensus earnings expectations for the S&P 500 by polling the estimates of major 12811 Street firms for the earnings of the S&P 500 on a 12-month- forward basis. The companies are Z/B/L/S and First Call. For this paper, the data from I/B/E/S are used. Chart 1 shows both the lagging and leading earnings figures for the S&P 500. The top clip depicts a weekly bar chart of the stock index, going back to 1982. The middle clip shows the expected earnings on a 12-

month-forward basis, and the bottom clip shows actual quarter11 earnings. ‘I\‘hile Chart 1 shows both estimated earnings and a;- tual earnings, only expected earnings will used from here on in order to make it a true leading indicator. Because the earnings estimates begin in 1982. that will be the beginning of the study.

Chart1

The next step is to look at the denominator: interest rates. Be- cause equities are long-term assets, a long-term interest rate should be used, such as the 30.year Treasury or bloody’s long-term B‘L-2 corporate bond yield. For the purpose of this exercise, the Trea- sury long bond is used because data are widely available and because the bond is always about 30 years to mat&itv (whereas the bloody’s yield reflects an index which changes over time, making it unclear whether the duration has remained stable over the past 15 years). r\;ow that the interest rate vehicle has been established, we have to determine how we are going to discount the earnings numbers. The cowentional (orthodox) method divides the earnings number by [ ltI/lOO]. Another way however, was recentl! demonstrated by Paul Slacrae Montgomery at the 1996 LITA Semi- nar, and consists of simply dividing the earnings number by [I/ 1001. \Ve’ll call this the unorthodox method. Chart 2 shows both measures. The middle clip shows the orthodox method (right scale) while the bottom clip shows the unorthodox (left scale). The discount factors have been inverted to show their correlation to stock prices.

MTA JOLRN;U, l \Cinter - Spring 1998 9

Note that, while both series look exactly the same, the bottom number is much larger (e.g. 14.14 vs. 0.934 as of September 13, 1996). As a result, when both series are multiplied by the numerator (or when the inverse is divided), the latter approach will cause a much bigger impact on the product of the discounting equation. The effect of both methods is shown on Chart 3.

Chart3

The top clip shows again the weekly S&P 500. The middle clip shows expected earnings using the orthodox approach, and the bottom clip shows the effects of the unorthodox approach. It becomes immediately apparent that the second discounting

method reflects the course of interest rates in a much more ,btu- nouncd w~ay. Thus rather than just reflecting the earnings out- look, this series now truly combines the effect of both earnings expectations and the course of interest rates. Because we want to create an indicator that uses both of these drivers, the unorthodox discounting formula of D/ [I/100] is the one we will use to build our technical indicator.

Now that we have created the time series (which will be called “E/I” from now on) on which to build our trend/momentum indicator, it is useful to show what the correlation actuallv is between our computed study and the S&P 500. Chart 4 sho& both time series on a log scale.’

Chart4

The chart nicely illustrates how the stock market usually cor- relates with E/I, but that there are times when significant bearish divergences occur between the two, creating periods of risk. The 1983/84 correction, the 1987 crash, the 1990 correction, the 1994 correction and the sell-off in July 1996 all stand out as such periods. As was described earlier in this paper. the goal here is to identifv these periods through the application of technical trend and momentum studies. The product of these studies will be an indicator that gives the appropriate warnings signs when these divergences reach dangerous levels.

First, however, we should quantie the reliability of E/I by performing a linear regression (using the least squares approach) in order to determine what the correlation is between E/I and the S&P 500. Table 1 shows the output of this regression (using the computer program “Econometric \‘iews”), and below that is a graph depicting the independent variable (E/I), the dependent (fitted) variable (S&P 500)) and the residual.

10 MTA JOURYAL l \\‘inter - Spring 1998

Table 1

LS//Dependent Variable is S&P Date: 09114196 - Time: 14.48

Sample: 510711982 911311996 Included observations: 750

Variable Coefficent Std. Error t-Statistic Prob.

c -0.037291 0.020610 -1.809403 0.0708 E/I 1.035041 0.008461 122.3276 0.0000

R-Squared 0.95239 Mean dependent var 2.476477 Adjusted R-squared 0.952330 SD. dependent var 0.197685 SE. of regression 0.043162 Akaike info criterion -6.282943 Sum squared resid 1.393470 Schwarz criterion -6.270623 Loglikelihood 1293.900 F-statistic 14964.05 Durbin-Watson stat. 0.048247 Prob. (F-statistic) 0.000000

The key statistic to look at in Table 1 is “R-squared,” which measures the success of the regression in predicting the values of the dependent variable within the sample. An R‘? of 1.0 means that the regression fits perfectlv while a reading of 0 means that it fits no better than the simple mean of the dependent variable. In our regression the Rz is 0.95239, which is an excellent fit. This means that 95 pet of the behavior in the S&P 500 can be explained bp the behavior of E/I. This is encouraging, because when we build our trend/momentum indicators, we will have confidence that we are “barking up the right tree,” as it were.

Chart 5

I 20

PART II: The Technical Indicator Now that the input has been created and tested for reliabilin

using quantitative analysis, we can get to the juicy part as technicians and build a risk indicator for the S&P 500. Three traditional technical studies are calculated. TIYO are momentum studies: a 26wk rate-of-change (ROC) and a 52-wk slowed stochastic (STOCH). One is a trend study a 13-wk/26-wk Moving Average Convergence/Divergence (MACD). The time frame for these studies is the intermediate term (3-mo - 12-mo), given that the intended audience for this indicator is portfolio managers.

Table 2 shows the one year’s worth of data and formulas. Col- umn C shows the weekly closing levels for the S&P 500. Column D shows the I/B/E/S earnings estimates. Because the series is monthly and our study is weekly the same number is repeated for all the weeks in any month. Column E shows the 30- year Trea- sury yield and Column F shows our indicator E/I. Columns G through P show the output for the above stated studies. The technical studies are calculated as follows:

3 6 mo ROC: This is a simple rate of change indicator using the formula:

ROC = (E& GE/I,,) -1 * 100

For example, the ROC (column G) of E/I (column F) as of 9/ 13/96 (row 52) is [566.20 + 575.263 -1 * 100 = -1.5’75, meaning :hat E/I is 1.575 pet lower than it was 6 months ago (row 26). This is a useful measure for indicating positive or negative momentum in E/I.

1 52-wk STOCH: This is a more complicated momentum mea-

sure, and comprises the following calculations: First. we calculate Fast %‘cI( (column hI) through the following formula:

Fast %K = {E& - min(E/I,:E/I,,)} + {max(E/Itl:E/I,,) - min(E/It,:E/I,,)} * 100

Then we calculate the slow ‘%D (column S) by taking a 3-week

smoothed moving average (SMA) of the fast ‘%D (the MA is smoothed bp taking the sum of the previous three fields, subtracting the most recent hIA, adding the latest value, and dividing the result by 3). A slow %D (column 0) is calculated by taking a 9 week SMA of the Fast ‘%D. Finally Column P shows the difference between column N and column 0 in order to indicate whether STOCH is on a buy or sell signal. A buy signal is given when the fast RD is above the slow ‘%D, and vice versa.

For example, the latest week’s fast %Kvalue is (566.2 - 554.6) + (632.9 - 554.6) = 14.76. The 3 week SK1 is [ (52.15t20.67t10.99) 32.51 t 14.i6] + 3 = 22.02. The slow %D is 42.93, thus creating

a sell signal. Besides giving buy or sell signals, this study is also useful in gauging whether the absolute momentum level is high or low. For instance, a level of less than 50 combined with a sell signal would be quite bearish. 1 MACD: This is a useful trend study which consists of the spread

between two exponentially smoothed moving averages (ESMA) of E/I. The conventional approach is to take 13 weeks and 26 weeks as the two M/X’s, The 13-week exponentially smoothed M/A (column H) is calculated as follows:

EMAt,, = EMAt,, - 0.153846 * (ESMA,,, - E/It14)

where ESMA,, is the first RlA in the series and consists of a simple

Chart 6

1

MTA JOURN.% l \\inter - Spring 1998 11

Table 2

4 6 SEP

D E F G H I J K L M N 0 P Exp. 30yr 6mo 13wk 26wk 9wk up/ Fast Fast Slow %SD<

500 Earn. TSY E/l ROC ESMA ESMA MACD ESMA down %K %D %D %FD

I g/21/95 587.3 36.7 6.53 562.3 24.5 538.62 515.49 23.12 22.51 0.61 99.07 99.97 93.62 6.35 ! g/28/95 588.2 37.2 6.57 566.1 24.8 542.85 519.39 23.46 22.72 0.74 100.00 99.70 94.23 5.47 i 10/5/95 586.8 37.2 6.45 576.6 24.9 548.05 523.79 24.25 23.06 1.19 100.00 99.79 95.17 4.62 i 10/12/95 588.2 37.2 6.40 581.1 25.7 553.14 528.20 24.93 23.48 1.46 100.00 99.76 96.16 3.60 i 10/19/95 589.7 37.2 6.32 566.5 27.3 558.58 532.84 25.74 23.98 1.76 100.00 100.08 97.44 2.64 j 10/26/95 578.7 37.2 6.35 585.7 26.0 562.75 536.91 25.84 24.39 1.45 98.67 99.53 98.35 1.18 7 1112195 582.5 37.5 6.30 594.6 24.4 567.66 541.35 26.31 24.82 1.49 100.00 99.71 98.99 0.73 3 1119195 594.5 37.5 6.29 595.6 20.5 571.96 545.52 26.43 25.18 1.26 100.00 99.65 99.49 0.17 3 11/16/95 601.2 37.5 6.27 597.5 19.5 575.88 549.52 26.36 25.44 0.92 100.00 99.67 99.73 -0.05 IO 1 l/23/95 601.6 37.5 6.26 598.4 18.1 579.36 553.28 26.07 25.58 0.49 100.00 100.11 99.81 0.30 I1 11130195 608.3 37.9 6.19 612.4 15.7 584.45 557.83 26.61 25.81 0.80 100.00 99.96 99.80 0.17 12 12/7/95 618.3 37.9 6.04 627.6 17.7 591.09 563.20 27.89 26.27 1.62 100.00 100.01 99.83 0.18 13 12/14/95 622.8 , 37.9 6.07 624.5 17.8 596.24 567.92 28.32 26.73 1.59 98.64 99.54 99.8 -0.26 14 12/21/95 618.4 ~ 37.9 6.12 619.4 15.5 599.81 571.89 27.92 26.99 0.93 96.39 98.50 99.66 -1.17 15 12/28/95 615.9 38.0 6.00 632.9 18.7 604.91 576.58 28.32 27.29 1.04 100.00 98.85 99.54 -0.70 16 l/4/96 616.7 38.0 6.00 632.9, 18.5 609.22 580.92 28.30 27.51 0.79 100.00 98.73 99.47 -0.74 17 l/11/96 601.8 38.0 6.12 620.5 16.0 610.96 583.97 27.00 27.40 -0.40 94.44 97.37 99.21 -1.85 18 l/18/96 611.5 38.0 6.01 631.9 ~ 22.8 614.18 587.65 26.53 27.20 -0.68 99.53 98.87 99.15 -0.29 19 1125196 621.6 38.2 6.06 631.1 23.8 616.79 591.00 25.79 26.89 -1.10 99.19 98.10 98.99 -0.89 20 2/l/96 635.8 38.2 6.08 629.1 19.9 618.68 593.92 24.75 26.42 -1.66 98.16 97.74 98.74 -1.00 21 218196 656.4 38.2 6.13 623.9 19.4 619.48 596.23 23.25 25.71 -2.46 95.65 98.26 98.58 -0.32 22 2/15/96 650.4 38.2 6.11 626.0 19.8 620.48 598.52 21.96 24.88 -2.92 96.58 97.11 98.27 -1.17 23 2/22/96 659.1 38.2 6.39 598.5 13.4 617.11 598.52 18.58 23.48 -4.90 82.96 92.08 97.48 -5.40 24 2129196 644.4 38.4 6.45 595.8 8.4 613.82 598.31 15.51 21.71 -6.20 80.32 87.81 96.38 -8.57 25 3/7/96 633.6 38.4 6.47 593.9 6.6 610.76 597.97 12.79 19.73 -6.94 79.35 83.80 94.83 -11.03 26 3/14/96 641.4 38.4 6.68 575.3 2.0 605.30 596.23 9.08 17.36 -8.28 68.15 75.66 92.44 -16.78 27 3/21/96 650.6 38.4 6.67 576.1 2.5 600.81 594.68 6.13 14.87 -8.73 68.63 73.60 90.07 -16.47 28 3128196 645.5 38.5 6.65 579.6 2.4 597.56 593.52 4.03 12.46 -8.43 70.28 70.94 87.23 -16.29 29 414196 655.9 38.5 6.68 577.0 0.1 594.40 592.26 2.15 10.17 -8.02 67.35 67.82 84.18 -16.35 30 4/11/96 636.7 38.5 6.88 560.3 -3.6 589.15 589.80 -0.65 7.76 -8.41 57.40 65.28 80.9: -15.63 31 4/18/96 645.1 38.5 6.80 566.9 -3.7 585.72 588.03 -2.31 5.53 -7.84 61.26 63.67 77.43 -13.76 32 4125196 653.5 38.5 6.79 567.7 -3.1 582.95 586.47 -3.52 3.52 -7.03 61.17 61.17 73.82 -12.65 33 512196 643.5 38.9 ' 6.96 559.6 -5.9 579.35 584.40 -5.05 1.61 -6.66 52.67 57.11 70.34 -13.23 34 519196 654.9 38.9 7.02 554.8 -6.9 575.57 582.12 -6.55 -0.20 -6.35 43.61 53.87 66.95 -13.09 35 5/16/96 671.5 38.9 6.87 566.9 -5.1 574.24 580.95 -6.71 -1.65 -5.06 50.29 51.29 63.72 -12.42 36 5123196 680.6 38.9 6.84 569.4 -4.9 573.49 580.06 -6.57 -2.74 -3.83 49.66 48.3: 61.04 -12.72 37 5/30/96 667.0 39.4 6.93 568.3 -7.2 572.69 579.15 -6.47 -3.57 -2.90 47.41 47.55 58.44 -10.89 38 6/6/96 673.8 ~ 39.4 7.03 560.2 -10.8 570.76 577.69 -6.93 -4.32 -2.62 40.84 46.88 56.06 -9.17 39 6/13/96 665.9 39.4 7.09 555.4 -11.1 568.40 575.98 -7.58 -5.04 -2.54 36.99 42.67 53.53 -10.86 40 6120196 666.8 39.4 7.10 554.6 -10.5 566.29 574.34 -8.05 -5.71 -2.34 36.35 39.64 50.96 -11.32 41 6127196 670.6 39.5 6.89 573.1 -9.5 567.33 574.24 -6.91 -5.98 -0.94 51.31 41.95 48.83 -6.88 42 714196 675.9 39.5 7.00 564.0 -10.9 566.82 573.46 -6.63 -6.12 -0.51 43.99 42.23 46.96 -4.73 43 7/11/96 651.0 39.5 7.11 555.3 -10.5 565.05 572.06 -7.01 -6.32 -0.69 36.90 42.11 45.50 -3.40 44 7118196 638.7 39.5 7.00 564.0 -10.7 564.90 571.44 -6.55 -6.37 -0.18 43.99 44.70 44.65 0.05 45 7125196 635.9 39.5 7.02 562.4 -10.9 564.52 570.75 -6.23 -6.34 0.11 42.69 40.96 43.59 -2.64 46 8/l/96 662.5 39.9 6.94 574.3 ~ -8.7 566.02 571.02 -5.00 -6.04 1.04 46.86 43.16 43.14 0.02 47 818196 664.1 39.9 6.75 590.4 -5.4 569.77 572.51 -2.74 -5.31 2.57 61.50 50.63 43.53 7.09 48 8115196 663.1 39.9 6.77 588.7 -6.0 572.68 573.76 -1.08 -4.37 3.29 59.92 53.45 44.22 9.23 49 8122196 667.0 39.9 6.84 582.7 -2.7 574.21 574.44 -0.23 -3.45 3.22 52.15 55.66 45.58 10.08 50 8/29/96 660.9 39.9 7.03 566.9 -4.8 573.09 573.86 -0.77 -2.85 2.08 20.67 46.19 46.16 0.03 51 g/5/96 653.9 40.0 7.11 563.3 -5.2 571.58 573.05 -1.47 -2.55 1.08 10.99 32.51 45.05 -12.53 52 g/12/96 671.1 40.0 7.07 566.2 -1.6 570.75 572.52 -1.77 -2.37 0.60 14.76 22.02 42.93 -20.91

13 week MA. The number 0.153846 is the product of a smoothing constant 2 divided by the M/X period of 13. The 26 week ESMA is calculated in similar fashion (column I). The 5LXD is a simple spread between the 13-wk ESMA and 26wk ESM.1 (column J). To create buy and sell signals, a 9-wk ESMA is taken of the spread (column I(). Finally column L shows the difference between columns J and I(, indicating whether IUCD is on a buy or sell signal. Also, the absolute level of ,ZUCD is important to identify the magnitude of rising and falling trends.

For example, the latest value for the 13-wk ES!vL1 is 571.58 - (0.153846 * (571.58 - 566.2) = 570.75. The 26vk ES!&\ is 551.58 - (0.076923 * (573.05 - 566.2) = 572.52. The RMCD is the spread: 570.75 - 572.52 = -1.77. The EShU is -2.37. creating a sell signal.

Chart 6 depicts these studies. Chart 6 nicely shows what happens to these indicators when E/I gets into the danger zone as a v&ation model for the S&P 500. The major periods of correction/consolidation in the stock market all were signaled by nega- tire readings in the ROC, MACD and STOCH. However,

MTA JOLXUL l \\-inter - Spring 1998

eyeballing these studies to determine risk in the S&P 500 is not v&v scientific, and a more systematic approach is needed. \Ve accomplish this by establishing certain conditions on the three technical studies. The most straightforward approach is to define an “if-then” condition for each stud!; and then combine the results into a composite trend/momentum signal.

Table 3

A B C D E F G H S&P 500 E/l ROC STOCH MACD COMP

1 9121195 587.3 562.3 FALSE FALSE FALSE FALSE 2 9128195 588.2 566.1 FALSE FALSE FALSE FALSE 3 1 o/5/95 586.8 576.6 FALSE FALSE FALSE FALSE

i 10/19/95 10112195 588.2 589.7 581.1 588.5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 6 10/26/95 578.7 585.7 FALSE FALSE FALSE FALSE

i 1112195 1119195 582.5 594.5 594.6 595.6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 9 11/16/95 601.2 597.5 FALSE FALSE FALSE FALSE 10 11/23/95 601.6 598.4 FALSE FALSE FALSE FALSE 11 11/30/95 608.3 612.4 FALSE FALSE FALSE FALSE 12 12l7195 618.3 627.6 FALSE FALSE FALSE FALSE 13 12/14/95 622.8 624.5 FALSE FALSE FALSE FALSE 14 12/21/95 618.4 619.4 FALSE FALSE FALSE FALSE 15 12/28/95 615.9 632.9 FALSE FALSE FALSE FALSE 16 l/4/96 616.7 632.9 FALSE FALSE FALSE FALSE 17 l/11/96 601.8 620.5 FALSE FALSE FALSE FALSE 18 1118196 611.5 631.9 FALSE FALSE FALSE FALSE 19 1125196 621.6 631 .l FALSE FALSE FALSE FALSE

;: 2/B/96 2/l 196 635.8 656.4 629.1 623.9 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 22 2115196 650.4 626.0 FALSE FALSE FALSE FALSE 23 2122196 659.1 598.5 FALSE FALSE FALSE FALSE 24 2129196 644.4 ~ 595.8 FALSE FALSE FALSE FALSE 25 317196 633.6 ~ 593.9 FALSE FALSE FALSE FALSE 26 3/14/96 641.4 575.3 FALSE FALSE FALSE FALSE 27 3121196 650.6 576.1 FALSE FALSE FALSE FALSE 28 3128196 645.5 579.6 FALSE FALSE FALSE FALSF 29 414196 655.9 577.0 FALSE FALSE FALSE FALSL 30 4/11/96 636.7 560.3 TRUE TRUE FALSE FALSL 31 4118196 645.1 566.9 TRUE TRUE FALSE FALSf 32 4125196 653.5 567.7 TRUE TRUE FALSE FALSi

ii 512196 519196 643.5 654.9 559.6 554.8 TRUE TRUE TRUE TRUE FALSE FALSE FALSF FALSL 35 5/16/96 671.5 566.9 TRUE TRUE FALSE FALSt 36 5/23/96 680.6 569.4 TRUE TRUE FALSE FALSt 37 5/30/96 667.0 568.3 TRUE TRUE FALSE FALSt 38 6/6/96 673.8 560.2 TRUE TRUE FALSE FALSf 39 6/13/96 665.9 555.4 TRUE TRUE FALSE FALSE 40 6/20/96 666.8 554.6 TRUE TRUE FALSE FALSI 41 6127196 670.6 573.1 TRUE TRUE TRUE TRUE 42 714196 675.9 564.0 TRUE TRUE TRUE TRUE

ii :;iE 651.0 555.3 TRUE TRUE TRUE TRUE 638.7 564.0 TRUE TRUE FALSE FALSI

45 7125196 635.9 562.4 TRUE FALSE TRUE FALSI 46 8/l I96 662.5 574.3 TRUE FALSE FALSE FALSI 47 B/8/96 664.1 590.4 TRUE FALSE FALSE FALSI 48 B/15/96 663.1 588.7 TRUE FALSE 49 B/22/96 667.0 582.7 TRUE FALSE 1

FALSE FALSI FALSE FALSI

50 B/29/96 660.9 566.9 TRUE FALSE ~ FALSE FALSI 51 915196 653.9 : 563.3 TRUE FALSE ' TRUE FALSI 52 g/12/96 671.1 566.2 TRUE FALSE ~ TRUE FALSI

The conditions will be very simple so we can determine if there is method that works (i.e. gives reliable signals) without having to get into back testing and optimization.

ROC SIGNAL: For ROC, we simpl! tell the computer to return “TRUE” if the latest reading is below zero, that is E/I is below its level of 6 months ago. Otherwise, return “FALSE.” The results are depicted in Table 3 in column E.

STOCH SIGNAL: Here we need to add a twist to account for the fact that this indicator can not only be on a buy or sell signal, but can also be overbought or oversold. Therefore, we tell the computer to return “TRUE” if STOCH is on a sell (i.e. the slow %D is below the fast %D) AND STOCH is below 50. indicating that momentum is below neutral (STOCH oscillates between zero and 100). I f neither of these conditions is met, the computer returns “FALSE.” Column F shows the results.

MACD SIGNAL: For hWCD, we also set two conditions: return “TRKE” if MACD is below zero, AND it is on a sell signal, meaning that MACD is below its 9 week ESMd. Otherwise, return “FALSE.” Column G shows the output.

Finally, we set a last if-then condition to return “TRUE” when II three indiuidual conditions are true. If only some are true, or if one are true, return “FALSE.”

A “TRYE” therefore will signal those time periods when all lree technical studies tell us that the underhing trend and mo- lenturn conditions of our indicator E/I are reaching dangerous ,vels for the S&P 300.

Given that the stock market can ignore rising rates and dete- orating earnings momentum for some time without correcting 1s was the case in 1987), it is important to note again that the bjective of this signal is to flash a warning to get out of stocks (by edging) when E/I’s trend and momentum conditions get rml!~ angerous, rather than every time the slightest negative diver- ence occurs.

MTA JOURML 0 FVinter - Spring I998

Table 4

Date

6-Apr-84 13-Apr-84 20-Apr-84 27-Apr-84 4-May-84

ll-May-84 18-May-84 25-May-84

Portfolio Value Value of # Value of Initial Transaction Margin Finance Cumulative Weekly Portfolio Value -unhedged 1 contract contracts hedge Margin Commission Adjustment Charges Drawdown Pet P/L -Hedged

1,333,853 1,309,883 1,466,629 1508,444 1,666,897 1,599,331 1,424,371 1,125,815

SELL 78,135 145 1 79,150 1 79,370 1 80,395 1 79,860 1 78,585 1 76,460 1

11.333.853 ,329,575 -1.812.500 -2,320 -0.02% -0.02% 11,307.563 ,476,750 -1,812.500 -147,175 -690 -1 .33% -1 .31 % 11.316,412 ,508,650 -1.812.500 -31,900 -690 -1.61% -0.29% 11.325088 ,657,275 -1.812.500 -148,625 -690 -2.93% -1.32% 11.331.701 ,579,700 -1.812.500 77,575 -690 -2.25% 0.68% 11.342,961

1,394,825 -1,812,500 184,875 -690 -0.63% 1 .62% 11.356,053 1,086,700 -1.812,500 308,125 -690 2.08% 2.71% 11.366,717

l-Jun-84 11,004,428 75,555 10,955,475 -1,812,500 131,225 -690 3.23% 1 .l 5% 11,373,236 8-Jun-84 11,269,946 77,310 11.209,950 -1,812,500 -254,475 -690 0.98% -2.24% 11,392,487

15-Jun-84 11,042.683 75,680 CLOSE 10,973.600 -1,812,500 236,350 -690 3.05% 2.07% 11,398,413

Bringing all of this together, Chart 7 shows the S&P 500, our indicator E/I and those periods of risk as defined by our trend/ momentum signal. As has been the case with all charts. the S&P 500 and E/I are charted as a log in order to show price changes in equal proportion.

Part III: Implementing the Hedging Strategy Before evaluating the success of our hedging strategy certain

assumptions need to be set regarding its implementation. 1. The study beginning wilth a portfolio of S10,000,000 in Janu-

ary 1983. That date reflects the beginning of the various technical studies that are in use. Since thev are trend and momentum based, there is by definition a lag between the beginning of the indicator E/I and its studies STOCH. ROC, and MACD.

2. The cash portfolio is indexed to the S&P 500 index, and the timing signal is executed through the short sale of S&P index futures.

3. The timing model is updated every Friday afternoon at 3:30 pm to allow for enough time for any transactions to be executed in the market as of that week. This is not entirely scientific because the indicator historically reflects end-of-wkek values, but it will have to do for the purpose of this study. 1Vaiting for the next Monday leaves too large a gap in terms of potential price swings.

4. Mhen a signal is generated (i.e. when all three study conditions are TRCE), a transaction is immediatelv executed con- sisting of the short sale of S&-P 500 futures e&al to the total market value of the porttolio at that time. This is done as follows: The market value of the portfolio is divided by the product of $500 and the price of the S&P 500 index. An example illustrating the first signal in 1984 is shown in rhe table below. On Friday April 13th, 1984, a signal is given. At that time, the value of one S&P contract is S500 x 156.27 = $78,135. The number of contractS needed to hedge the portfolio is therefore 145 (11,309,853 + 78,135).

5. Transaction costs associated with the short sale are as follows: The commission is $16 for a round-trip, and a financing spread of 2 percentage points is applied to the cost of the initial margin. The question of margin is trick!; because if cash on hand is available, then by definition the entire portfolio is not invested in the S&P 500. Therefore, for simplification’s sake, it will be assumed that the margin is borrowed at a cost of 2 pet over and above what the margin amount will earn in the futures account. Additional margin will be applied directly to the cash account, however. Table 4 shows the cumulative drawdown/profit for the hedge.

5. M%en the signal ends (when the condition is FALSE again), the 145 contract short sale is covered. However, this will not be known until the end of that week when the closing figures are inputted into the model. Therefore, when looking at the history of signals, the P&L calculations start with a one week lag and continue for an extra week. The final column shows the portfolio value on a hedged basis.

Table 5 gives the details of all the signals: each obsenation with start and end date, the number of weeks that the signal is in effect, the start and end values for the S&P 500. the percentage change in the S&P .500, and finally the total effect of hedging the S&P 500 portfolio with futures contracts.

Performance There have been 9 obsewations. each of which has lasted am-

where from 3 Iseeks to 11 weeks. The average period lasted about i weeks. Six out of nine signals were profitable, or 67 pet. The biggest gain from hedging occurred during the 1987 crash (26.13 pet), while the largest drawdown occurred in 1990, totaling 5.75 pet. The average gain from hedging is 4.57 percentage points. The difference between the change in the portfolio value and the return of the hedge can be attributed to transaction costs and the rounding up or down of the number of contracts that need to be sold short.

Table 5

9 Observations

#l begin end

#2 begin end

#3 begin end

#4 begin end

#5 begin end

#6 beoin end

%7 begin end

#8 begin end

#9 begin end

Summary of Observations Chgin

Date #Weeks S&P500 S&P500

4113184 156.27 6115184 9 151.36 -3.14% 4110187 322.36 615187 8 292.06 -9.40% 914187 322.36

10130187 8 238.14 -26.13% 3/16/90 338.30 5/25/90 10 357.74 5.75% 8/24/90 317.10

10/19/90 8 303.83 -4.18% 4124192 410.17 5/15/92 3 414.89 1.15%

516194 451.39

Pet P/L of Hedge

3.05%

9.29%

26.43%

-5.75%

4.13%

-1 .l 6%

7122194 11 453.28 0.42% -0.45% 1017194 455.46 1125194 7 452.65 -0.62% 0.59% 6128196 668.18 7119196 3 634.91 -4.98% 4.96%

Average ex-1987

7.4 -4.57% 4.57% 7.4 -1.88% 1.83%

14 MTA JOURSAL * \Yinter - Spring 1998

Since the beginning of this study in 1983, the average annual total return of a buy-and-hold portfolio has been 15.90 pet, while the average annual return of an actively hedged portfolio using this timiig indicator has been 19.64 pet. Hence, the average yearly excess return is 3.74 percentage points.

Chart8

Chart 8 shows the cumulative total return of our hypothetical portfolio on an actively hedged basis. The dotted line shows the total return on a buy-and-hold basis.

Analysis X few issues arise when we look at the results.

The biggest problem is immediately apparent when looking at the bottom two rows of the table as well as the chart with the cumulative total returns: while it is desirable that our indicator captured the 1987 stock market crash, the problem is that it accounts for a big part of the overall profitability. Including the crash, the average excess return is 4.57 pet, but excluding the crash, the excess return is only 1.83 pet. However, from the standpoint of eliminating market risk from a portfolio from time to time, this is still an acceptable performance (because all we’re giving up is upside performance, as opposed to being outright short).

The study only includes the bull market of the 1980s and 9Os, and therefore we are unsure whether it will stand the test of bull and bear markets. The problem is that we want a fonvard- looking indicator and are limited bp the availability of earnings estimates, leaving only 1982 and after. However, we can go back farther in time to assess the validitv of the unorthodox discounting method using actual earnings’instead of expected

Chart9

earnings. If the correlation of the indicator stands up over a longer period of time (through the same regression analysis as before), we can at least ascertain that the fundamental idea behind E/I is valid. Chart 9 shows E/I using expected earnings after 1982 and actual earnings before 1982. The regression study (not shown) reveals an R’ of 92 pet, which is still pretty good for a period of 35 years. As a result, we can maintain confidence that the idea behind the E/I indicator is valid.

Chart 5 on page 12 shows that while the signal captured some corrections perfectly (namely the 198i crash and the sell-off in Julv ‘96), it has been on the late side in other instances, such as 1990 and 1994. It appears that the sharp price corrections are handled well br our indicator, while the more triangle shaped time-based corrections are handled with less suc-

cess. Bp the time the signals occur in the latter type corrections, it seems a better time to buy than to sell. The problem is that if the three studies used for this exercise are made more sensitive (by giving an earlier signal), the sharp advances prior to the 1987 crash and July ‘96 sell-off are hedged out, leaving profits on the table. The latter point is a valid one, and the performance table does shows a rather large maximum drawdown of 3.75 pet in 1990. One way to improve the effectiveness of the signal is to trv different indicators and to run them through a computer spreadsheet (Microsoft Excel was used for this study).

The indicator has not only been adept in signaling risk periods, but was very effective in signaling market bottoms as well. The chart shows that while E/I peaks well in advance of the S&P 500 index, it bottoms at the same time as the index. In other words, it is a kading indicator at tops and a roincidmt indicator at bottoms.

Relating to the previous point, we see that E/I was also valuable as a confirming indicator of a rising stock market. Know- ing that E/I tended to peak well in advance of the stock market, we can assume that as long as E/I is in its uptrend (making new highs, uptrend lines intact, etc.), it is safe to be ag- gressively invested in stocks (note the 1993 period). In other words, the indicator worked both ways. M’hen both stocks and E/I are rising and making new highs, stay invested. \1’hen the bearish divergence first occurs, it is OK to remain invested, but caution is warranted. ;Is the divergence gets progressirel!- worse and the retracement of E/Is preceding advance deep- ens, it is time to get ready to hedge. M%en the timing model kicks in, or when other technical studies indicate risk, the S&P is sold, at which point the end of the signal can begin to be anticipated (in terms of the percentage retracement and the time of the correction). Finally the signal ends, and the S&P is bought back.

Conclusion M’e know that the correlation of E/I is 95 pet, meaning that 95

pet of the action in the stock market has been explained b’ E/I. That is pretty valuable. Therefore, if we compliment the timing indicator with traditional technical analysis (on both E/I as well as the stock market itself), perhaps we can increase its value in identi@ng risky periods in the stock market, as well as periods during which a fullpinvested portfolio stance should be adopted. Complementing our indicator with traditional measures such as the Advance-Decline Line, Low~~‘s Buying Power, Cash Flows into mutual funds and other sentiment measures should nicely round its effectiveness.

MTA JOCRUL 0 Itinter - Spring 1998

One example of performing additional ad hoc analysis on E/ I is shown in Chart 10. There have been four major corrections in E/I (excluding the 1996 decline), each of which retraced between 46 pet and 5’2 pet of the preceding advance. The average correction is 48 pet. This falls right in the middle of the traditional Fibonacci retracement objectives of 38.2 pet, 50.0 pet, and 61.8 pet. Note also that out of those four corrections, three were doubles (two sell signals). Hence, there is a repeated pattern evident in the behavior of E/I, and that can be very valuable. For instance, we can deduce that the July ‘96 correction v-as perhaps only the first of two correction phases, and that the stock market will remain at risk until E/I corrects by the 48 pet average. It will be up to the technical strategist to combine his or her skills with the knowledge that E/I is a valid leading indicator of stock market peaks. Anything from trendline analysis to time cycles may be of value here.

The indicator developed in this paper, E/I, can be of use when investing in the stock market in several ways. For portfolio managers and position traders alike, utilizing a fundamentally oriented indicator with a 95 pet correlation to stock prices is useful, either through the use of a timing indicator as done in this paper, or merely as an indicator of risk in combination with other indicators. The indicator works both as a risk indicator and as a full! invested indicator. M%ile this paper has focused on portfolio managers who can use E/I to hedge during high risk periods, obviously it can be equally valuable to position traders who can use stop-reverse strategies for either a long-neutral strategy or a long-short strategy. 1C’Tjften in dugwt 1996.

Chart10

Update - One Year later Updating the model from the report’s initial publication in

August 1996, we find that no further sell signals hare been issued since the June/July 1996 signal that captured that sell-off so well. 1l?lile the indicator E/I did not issue a sell signal going into the April 1997 correction, a more subtle warning was evident in that a bearish divergence occurred at the final high leading into the correction. This reinforces my point that the value of E/I as an indicator is not limited to a buy/sell algorithm, but rather that traditional technical analysis can be performed just as would be appropriate for an advance-decline line for example. Since the correction in April ‘97 was so short lived, in retrospect the failure of E/I to issue a sell signal probably worked out for the better. Since the April 14th low, the indicator has been making new highs,

lereby confirming the bullish price action. ;1~grrsl 18, 1 YYi.

Chart11

<I, ml P*,*llri~l*flil ",P s&Pm

References Paper written by Paul hlontgommery for 1996 MT;\ seminar where he was the featured speaker

John Murphy, Technical Analysis of the Futures Markets

CQG, Inc.

M’elles M’ilder, New Concepts in Technical Trading- Systems

Biography Jurrien Timmer, CMT, was born and raised in Aruba, and

came to the U.S. to attend Babson College in 1981. He graduated with a B.S. in finance 8: investments and went to work for ABS/AMRO Bank, where he worked for 10 years and ended up running a fixed income trading 8: sales desk. It was there he developed an interest in technical analpis, and started to write a weekly report for institutional clients. His interests changed from trading/sales to research. Currentl! he is a senior technical analyst at Fidelity Investments, and advises the investment professionals on developments in the financial markets (stocks and bonds, as well as sectors and industry groups). He applies quantitative methods to back up his technical ideas, since he believes drawing trend lines is not enough to add an? value to the investment process. He writes several in-house reports and holds monthly technical reviews for the investment professionals.

16 MTA JOLRNAL * \\‘inter - Spring 1998

Sector Rotation in an Environment of Rising Interest Rates

Gatis N. Roze

Introduction In their respective works, John Murphy and Martin Pring dis-

cuss to varying degrees the linkage of (a) the business cycle, (b) intermarket relationships, (c) sector rotation, and (d) stock price movement.

They point out that over time the linkages and, to a certain degree the sequences of relationships within the linkages, have recurred over many decades. It is specifically these sequences among industry sectors that interest me most and are the subject of this article.

This article is a condensation of a broader paper written for the ChlT certificate program. As such, the key points, observations and conclusions have been included, but some elaboration and detail have been deleted.

My observation has been that many large mutual funds toda! make decisions based to a great degree on sector rotation. Un- derstanding how sector rotation has occurred in the past after an interest rate trend change may give insight into how sectors might sequence again during future interest rate changes, hence pro- ducing some potential trading opportunities.

Objective The objective of this research was to focus explicitly on only

one portion of the Market Life Cycle: specifically that period of rising interest rates in 1994 which began with the Federal Reserve raising rates on Friday February 4, 1994. For the purpose of this paper I chose to use the second week of February, because, tech- nically speaking, the charts showed that to be the trend change period (see chart below). Prior to that time, interest rates were in a multi-vear downtrend.

By focusing explicitly on one significant window of time and on one common set of industry groups-the ninety-four S & P Industy Sectors, (weekly data provided bp Dial Data/NYC), one can focus on how each sector reacted to this “rate shock” and

arrange these sectors according to their change in trend at or about the time of this shift in interest rates.

I used the weekly S 8c P Industry Sectors because no other bench mark is more widely followed than the S & P 500 and all ninety-four S & P sectors are created using the S & P 300 companies. The market value of these companies represents ‘iO$% of all LT. S. equities traded. Each sector is a market-value-weighted index (shares outstanding times stock price).

Purpose The objective then is to look for significant trend changes in

each sector at about the same time as the interest rate “Pivot Date” change (which I haye identified as a technical trend change having occurred in the second week of Februan; 1994) and to then cluster the various sector groups as either (a) Leading, (b) Coin- cident, (c) Lagging, or (d) Independent.

A major benefit of clustering these sectors into groups is that. while it’s highly unlikely the exact sequential sector rotation will ever recur, focusing on these clusters of Leading or Coincident sectors that have historicallv moved together allow the technician to look for confirmations among these groups.

Chart Analysis In the original paper I produced at least one graph of all sec-

tor charts showing w price action for a period of approxi- mately thirw months. The charts are produced on a hlacintosh using Trendsetter’s technical analysis software with data provided via Dial Data in New York. Each chart shows:

weekly data

three simple moving averages: (3 week/l0 week/29 week averages)

Directional Movement Index: Both the positive and negative nine period DMI is plotted beneath each graph.

----------

Ad&stntde Mortgage Rate Cdifmia hbkipl Bonds

7.0

65

60

55

50

4.5

4.0

2ND WEEK 35

3.0

CCD 4nnr d 2.5 4 rcu. IYY+

FMAMJJASONDJFMAMJJ

1992 1993 1994

INTEREST RATES AND YIELDS

Prime Rate

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

MTA JOURNAL e \\‘inter - Spring 1998 17

18

The objective here was to show on one chart both the first and fourth quarters of 1994, as interest rate trends reversed in each of these quarters. Although the focus of this study was to concen- trate on the interest rate change in early February 1994, it was helpful to see how a sector reacted or didn’t react in the fourth quarter of 1994, when interest rates changed in the opposite direction. These results are. however, not reported upon here as they are beyond the scope of this paper. In this article, for illus- trative purposes, 1 present two representative sectors, Automo- biles and Building Materials to illustrate the methodology.

Using the S & P Automobile Sector data as an example, I noted that prices peaked in late January \2‘e have a head and shoulders pattern with the right shoulder peaking in late February. confirmed also by the crossover of the 5 week simple moving average and 10 week simple moving average. The intermediate term trendline is broken soon after as well. By late March we receive confirmation of a definite trend change as the 10 week moving average has rolled over as well, and the Directional Nol-ement Index clearly shows a crossover of the nine period positive and negative trend indicators, with the negative DMl now above the positive DMI, signaling a shift to a negative trend. These signals all come together showing the S S: P Auto Sector as entering a negative trend the fourth week in Larch, and hence being classified in the Lagging Secton

AUTOMOBILES (112ooO) Line Weekly O&%,93 lo 02/14tW UNDEFINED

12 MonIh High 246.22 12 k&h Low l&.47 SC& 1 Inch = 21.56 zoom LllyBl4 2QbarMAl lObarMk25barMA39barDMI

,T-Podr( W-LI UlL‘Ic oc: mekc.at.

I’ve included a second typical example - that being the S 8: P Building Materials Sector. From the chart, we can see that this sector peaked in mid-JanuaT and then broke a well-established intermediate trendline in mid-February The price broke steep) and quickly below both the 5 and IO-week moving averages in mid-Februaq and the negative DMI trend indicator soon thereafter crossed above the positive DhII, indicating a negative trend.

BLDG MATERIALS (1150001 Urn Weaklv 081lBJB3 lo 02/14/E15 UNDEFINED

12 Mmlh High 331.77 12 Mmlh LOW 253.98 Bde 1 Inch I 27.55 Zoom LawI 4

2ebarMA110bww25barWQbarDMI

I

All of this occurred in Februan; hence, this sector was classified in the coincident group.

In some sectors such as Cosmetics, this methodology helped validate the theorv that this sector did not react significantly to interest rate changes either in early 1994 or in late 1993. and was thus unaffected and rated “independent” for the purpose of this stud\-.

11; most sectors, the rising of interest rates in early 1994 correlated with falling price trends. Conrersel!; price trends in these same sectors tended to reverse downward and then show positive movement later in the !-ear with falling interest rates. This was true in the majority of charts. The sectors which reacted positively to an interest rate hike are listed as “positively correlated‘ on the charts. The majority reacted negatively and are therefore listed as “negativel!- correlated.”

All charts were marked with a “P,” indicating the most recent peak in price. If this occurred prior to the second week of Febru- an; 1994, it was marked with a “-P.” I f it occurred afterwards, it was marked with a “tP” followed by the approximate number of weeks that the peak preceded or followed the interest rate trend change of Februar); 1994.

The purpose of the peak analysis is simply to differentiate those sectors that changed trend the same week and to provide a better indication as to the obviousness of many trend changes. A cli- matic peak followed by a significant trend change and price shift is more significant than a subtle peak and subtle trend change. I’ve used this information in some cases, but I have not attempted to quantie this for all sectors.

All charts were also marked with a vertical line showing the

second week in February 199-l, n-hen the interest rate trend change occurred.

The results do, however, point out that some sectors are more

interest-rate-sensitive and volatile and that interest rate trend changes do have a more dramatic effect on certain sectors. \\‘ith these sectors, it would seem wise to tighten one’s grip on the trigger during environments Ichere potential interest rate changes may occur. These sectors may present some unique trading opportunities on both the long as well as the short side.

Certain sectors such as Savings &: Loans and Home Building which historically have been more sensitive to interest rate hikes, may provide a ti-ading opportunity in that thev can be shorted with a higher probability of going down, less likelihood of going up, and can be confirmed by similar moves amongst sister sectors such as Banks and Sewspapers, which ha\-e also historicallr- show themselves to be amongst the coincident classification. i‘he op portunity therefore exists for higher probability trades with the use of sector analysis.

Trend Changes \Yith the benefit of hindsight analysis, it is relatively easy to

identify trend changes. I chose not to employ a purely mechanical approach or a “perfect hindsight” approach, but used ml- own discretion as I do in my real-world trading. For the most part, I relied on these four inputs: a) trendlines,

b) five period (week) simple moving average,

c) ten period (week) simple mo\ing average, and

d) crossover of the tD1 and the -DI components of the Direc- tional hlol-ement Index. (See Exhibit E)

Using these four inputs, I inserted a vertical trend change marker line at a point where one could have likely noted the change at the time it was happening, based upon the data to date instead of some idealistic point that could have provided an earlier signal, but may not have been as apparent without the help of future data. Each of those lines is marked with the month and week of the trend change. For example, if the trend changed during the second week in March, it would read “M2” or “hlarch 2.” It is important to note that the numeral designates the week, not the date, since this are m data.

The purpose of this stud:- was not to focus on indicators. Nev- ertheless, I would like to add that, with respect to trendline breaks, mv analysis is relatively standard analrsis. I considered long term trendlines where they existed, but I focused more on infw~&‘nt~ term trendlines under the key assumption that no sector could anticipate interest rate changes more than six months out.

\2’ith the moving averages, I looked at both five and ten period crossovers, as well as change in slope for each moving average line. M?th respect to DRII, I focused on the crossover of the nine period tDM1 and -DI lines versus the ADX value.

In some cases, no significant trend change was perceived just prior to or after the February period. I listed these particular sectors as “Not Applicable” and labeled them “Independent.”

As discussed earlier, the accompanying charts (see charts on page 18) show the typical analysis performed on each and even sector. It is obviously not practical to present all the charts; hence, showing the Building Materials Sector and the Automobile Sec- tor illustrate the methodology applied to all sector graphs. The complete results are presented in the following tables.

Sector Rotation Results

A. leading Sectors - Negative Correlation Leadiie Sectors Trend Chance Week

Telecommm~ications (Low D.) Sept. / 4 Insurance Property C&ual{ Insurance Index Composite Insurance hlulti Line Financial Life Insurance Personal Loam DoIvJones Utilities Telephone Publishing Retail Gen. hldse. Chains Retail Stores Composite Photography/Imaging Retail S & P Broadcast Media Foods Containers: Metal & Glass Oil Domestic Integrated Retail Special5 Beverages: Soft Drinks Health Care Diversified Health Care Miscellaneous Housewares

Sept. / 4

Oct. / 2

Oct. / 2 Oct. / 3 Oct. / 3 Oct. / 4 Oct. / -I

Nov. / 1 Dec. / 1 Dec. / 2 Dec. / 2 Dec. / 3 Dec. / 3 Dec. / 4 Dec. / 1

g:: ; 1 1

:,,,: dll ! 2 l

i":: dI1 ; : 2 4 -l

Positive Correlation Trend Chance Week Oct. / 2 Oct. / 3

leading Sectors Leading- Sectors Chemical Composite Chemicals Miscellaneous Insurance Brokers Beverages: Alcoholic

B. Coincident Sectors - Negative Correlation Coincident Sectors

Gold Mining Health Care Drugs Health Care Composite Rlanufactured Housing Pharmaceutical Composite Pollution Control Save S: Loan Hold. Cos. Building Materials Comp. Homebuilding hlachine Tools Oil Exploration & Production Oil M’ell Equip. k SKS. Dow Jones Transport Transportation Misc. Energy Composite Medical Products & Supplies hloney Center Banks Newspapers Oil Composite Office Equipment Railroads Specialized Services Tobacco Cigarette Mfrs. Transportation Misc.

Trend Chanee Week

Feb. Feb. : 1 1 Feb. Feb. : 1 1 Feb. Feb. : 1 1 Feb. Feb.

: 2 1

Feb. / 2 Feb. Feb. ; 2 2 Feb. Feb. r 2 2 Feb. / 2 Feb. Feb. : 3 3 Feb. Feb. / 4 4 Feb. Feb. ; 3 4 Feb. Feb. : 3 4 Feb. / 4 Feb. / 4

MTA JOURN,iL l \\‘inter - Spring 1998 19

C. lagging Sectors - Negative Correlation Lazing Sectors Trend Chance Week Oil International March / Specialty Printing March / Consumer Goods March / Containers Paper March / Distributors (Consumer Prod.) March / Household Products March / Paper & Forest Products March / Aerospace/Defense March / Auto Parts After Mkt. March / Chemicals Specialty March / Conglomerates March / Computer Software & Services March / Electrical Equipment March / Hotel-Motel March / Manufacturing: Divec/Industq March / Aluminum March / Automobiles March / Heay Duty Trucks & Parts March / Capital Goods March / Communication-Equipment/Mfg March / Computer Systems March / Electronics Inst. March / Entertainment March / Entertainment & Leisure Comp. March / Hardware & Tools March / High Tech Composite March / Leisure Time March / Metals Misc. March / Restaurants March / Truckers March / Computer Systems Excl. IBM April / Electronic Defense April / Electronics Semicon. April / Hospital Management April / Machinery Diversified April / Engineering 82 Construction May / Retail (Specialty Apparel) May /

D. Independent Sectors Cosmetics Oil & Gas Drilling Shoes Steel Textiles: Apparel Mfrs. Toys

1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 2 2 1 1

Summarized Results The specific sector rotation results are organized into the four

clusters previously discussed: a. Leading Sectors (January 1994 or earlier),

b. Coincident Sectors (Februay 1994),

c. Lagging Sectors (!vIarch 1994 or later), and

d. Independent sectors. (No correlation)

Within each category, the results were organized in order from the earliest turning sector to the latest sector. Once again, it should be noted that these are weekly data.

1.

2.

3.

4.

5.

6.

Conclusions and Interpretations Since I realized just how many inaccurate preconceived no- tions I held about certain sectors (Utilities, Financials, Gold, and the like), I found it enlightening to sequence all the sectors. Some surprised me, such as Healthcare and Telecommu- nications which were leading sectors with a negative correlation, but I couldn’t argue with the charts and so was pleased to see them organized by clusters and sequenced by sector.

One overwhelming conclusion was to show the importance of confirmations.

A. One must look at price action confirmation among “sector sisters”, i.e. Healthcare Drugs and Healthcare Diversified.

B. Another level of confirmation is among “sequence sisters,” i.e. the Financial Sector historicalh turns before the Entertain- ment Sector. I f not, any price mo\‘e could be deemed suspect.

The importance of confirmations is that they increase the likelihood or probability of a good trade. The more confirmations by sister sectors which have historically moved together, the higher the probability of the sector in question being a true trending move.

Aside from individual sector rotation, I found simply monitoring the percent of sectors rolling over was a good indication of general market conditions. It was obvious from this study that there existed a significant number of trend changes in the first quarter of 1994. This would have therefore alerted one to the need to change one’s overall market strategy

Trend analyis works better on sectors versus individual stocks within the intermediate time frame because sectors produce fewer whipsaws and seem to have better follow-through once momentum is established. My observation was that the) simply trended better than individual stocks.

I haven’t talked much about DMI, but this study cominced me of the benefit of DMI as a trend indicator. I found the crossovers of the t DI and -DI more powerful than absolute ADX values and slope when combined with trendlines and moling averages.

One of the prime limitations of the study was the assumption that interest rate changes alone drive indust? sectors’ price trends. This is indeed simplistic and unrealistic.

On the other hand, many analysts are inclined to tiy to explain various sectors’ behavior using fundamental analysis and economic reasoning. I believe it is important to focus on letting the charts “tell their stories.” One could easily get bogged down trying to hypothesize whp one sector leads another or doesn’t lead a third. The conclusion, therefore, simply reinforces my focus on the charts and not the economics. This is both a strength as well as a limitation, some could claim.

As a side note, if we were to insert the S & P 500 as a sector, it would be classified in the Coincident category. The Shearson Bond Index, on the other hand, would be in the Leading category.

8. The benefit of this research is that it provides a hypothetical road map with which to compare future interest rate changes and sector rotation. In an environment of increasing rates, the present rotation would obviouslp be more relevant. In an environment of decreasing rates, however, it’s likely that the majority of sector groupings would remain relatively similar and would move together, but not necessarily maintain indi-

20 MTA JOLXML * Itinter - Spring 1998

vidual sector sequences. In other words, Pharmaceuticals, which have been historically part of the coincident group, ma! join the leading group, but it is unlikely that its other hventy- three sister sectors in the coincident group would all suddenly join the leading group. By looking at the behavior of the majority of sectors, we can better set the stage for higher prob abilitv trades.

References q Murphy John J., Technical Analvsis of the Futures Markets,

New York Institute of Finance, Prentice-Hall.

n Pring, Martin J., The All Season Investor, John Wiley and Sons, Inc.

W Pring, Martin J., Technical Analysis Explained, McGraw-Hill.

n Stovall, Sam, Sector Investing, McGraw-Hill.

n Data Source: Dial Data, New York City New York.

n Charts: Personal Hotline by Trendsetter Software created on a Macintosh computer.

Biography Gatis N. Roze is a full-time trader and past president of

the Technical Securities Analysts Association of San Francisco. After graduation from Stanford in 1981 with an MBA, he co- founded a NXSDhQtraded high-tech company For the past eleven years, he has traded his own account with the assis- tance of a small staff. His primary focus is small-cap stocks and international mutual funds traded for an intermediate- term trend. Gatis now works in Seattle.

MTA JOURNAL l \tinter - Spring 1998

22 MTA J0URN.X l \\‘inter - Spring 1908

The Risk/Reward/Probability Diffusion Index: Increasing the probability of successful trading by using psychology, support and

resistance expectations, momentum, wave structure, and reversal patterns.

Larry M. Berman, CMT

Introduction In order to use technical analysis successfully one must fully un-

derstand the market one is analyzing and trad’ing. Fe\< will argue the fact that most technical indicators do not work well across all markets, with the exception of support and resistance expectations. Because of the human interaction with trading, support and resistance rules do tend to work across all markets. In my research and trading, I had developed a need for a comprehensive \$‘a\- of analpz- ing fixed-income markets which was consistent across ‘all market cyles. Some indicators worked well in bull markets, others worked well in bear markets, but I had not seen an indicator that was consistent across all market cycles. This was the starting point for my theory.

I have developed a methodology of trading and analyzing fixed- income markets that I believe takes advantage of most of the strengths of technical analysis and risk control. The theoF sets a trading bias bv assigning a buy or sell weighting to several key technical facets of’the market. Th’e results of these weightings summed together creates a diffusion index which allows the analyst or trader to have a strong technical bias. Long positions are favoured when the index shows a strong bullish bias, and short positions are favoured when the index shows a bearish bias.

I began by learning the inner mechanics of indicators while testing many systems and observing thousands of price patterns. I came up with one indicator that was a blend of hMCD and RSl using Bollinger bands as volatility triggers. It worked and it was very profitable trading S&P options. It took advantage of extreme shifts in momentum. In ten years of back testing, it worked well in most market conditions. I did some optimizing and paper-traded it for a while with good results. Under actual trading conditions, I realized the psychology of the market had changed. PII! model only captured a very small part of price action; it was missing too much. I clearlv needed a method of trading that would tell me when the trading bias had shifted so that I could avoid making these mistakes again.

U’hat I discovered was that the short-term trading bias (usually from 55 day) can shift from day to day. I also learned that there are leading and lagging indicators and that most technical studies that are used today react to price movements (lagging). t\%en used in isolation, they can be a financial and emotional roller-coaster. MXat I needed was to combine the power of support and resistance analvsis, wave structure analysis, trend analysis, and candlestick reversal patterns which I’ll show are leading indicators, and to use lagging indicators for confirmation only

Therefore, I hare developed a methodolog: of analyzing and trading based on support and resistance expectations and probabili- ties. I will introduce the Risk/Reward/Probability Diffusion Index, the basis for its development, the methodolog of its construction, and then provide real-time test results. I will summarize my find- ings, point out some weaknesses, and will indicate areas for additional research. I believe this approach to trading will attempt to quantib and quali6 the trading bias, and will provide analysts and traders with an edge.

Method

The Psychology of Support and Resistance Expectations

First we need to define what support and resistance levels are and what they mean psychologically. hccording to John Murphy in Technical ;Inalysis of the Futures Markets, “Support is a level or area on a chart below the market where buying interest is sufficienth strong enough to overcome selling pressure.” It is actually much more than that. The market constantly looks for places to do business. \Ve understand that as the market approaches an area where support is expected, hying pressure is expected again. Because sellers are in control of the trading process as these levels are ap- proached, the selling pressure will continue until buying interest is strong enough to take control. \\71en sellers become cominced the market can’t more lower, these sellers mav become buyers, or will just refrain from selling until the market reaches a level where thel believe it is overpriced. Mllen a shift in the psycholog of the market occurs, traders gain confidence in the position and will then attempt to support the current trend. Comersel!; resistance is a level or area on the chart above the market where selling interest is sufficiently strong to overcome buying pressure. Support and resistance levels are therefore leading indicators because we know where they are before the market gets there and we can develop expectations for them.

Determining Areas of High and Low Risk

The following section summarizes various types of support and resistance levels. Because it is our core belief that the maximum risk vs. reward equation exists at support and resistance levels, what they are and how they are determined are very important to analyzing our trading model. Market psychology is most prevalent at major support and resistance levels.

Let’s start with the basic premise that we want to buy expected support levels and sell expected resistance levels. b\‘e will assume the highest probability of success at these points, not necessarih 100% because one just can’t be that certain. M’e w-ill call it maximum probability or highest trading bias. The middle of a support and resistance zone will be called the minimum probability or no trading bias, because the risk of trading toward either end of the range is independently equal from a riskvs. reward perspective. The first step in the theory is determining which support and resistance level will be more significant. Each of the following are examined with this framework in mind.

There are Many Different Kinds of Support and Resistance levels

Horizontal Levels Mhen a shift in the trading process occurs, it creates an ex-

treme point or price consolidation on a chart. I f we project a horizontal line from the point or consolidation area into the future, we have a good idea where we can expect support or resis-

MTA JOURNAL l I\‘inter - Spring 1998 23

tance again. The market has an internal memory which is built from expectations.

The following example illustrates the process: Investor A wants to own the stock of company Qb\T at 15 dollars. Every time the market gets to this price, buy orders are placed. At some point there will be no more sellers at that price and the stock should begin to trade higher. After moving higher for several weeks, investor A decides to distribute the stock at 20 dollars. Some time passes and we’ll assume price begins to approach buying levels again at 15 dollars. Investor A decides to buy again until there are no further sellers, and the process continues. But the next time the market gets to this level, investor A decides not to buy this time. Something has changed. MThether it was a fundamental report or an asset shift into another stock, the buyer that sup-

ported this level has decided not to buy again. Support has now vanished and the market could move lower if no other buvers are found. Horizontal support/resistance levels are associaied with previous financial commitments. F171en the market reverses trend, buyers who bought a support level may try to break e\-en if the market gets back there after it breaks, in turn, creating resistance. This market rule is known as, “Support, once broken, becomes resistance.” The horizontal support or resistance level is the most significant because of its association with a previous financial com- mitment, and therefore carries a higher probability of providing support or resistance in the future.

Moving Average Levels Mo\lng averages, of which there are many variations, in their

most basic form track the current trend of the market. The value of the mol-ing average can represent a support or resistance level when enough people follow it. For example, bond market players often follow the lOO- and 200-day simple moving averages for long-term trends, and markets tend to pause at these levels. How- ever, there is no consistent level that provides a high enough probability to warrant a strong trading bias. kloring averages are best used for determining trend direction and speed, and not for trade execution. There are many different time cycles that work best in specific markets, but there are few that consistently work well, all the time. Moving averages, therefore, should not be relied on as much as other types of support and resistance levels, but work well as a complement to trend-trading and dip-buying strategies. The important issue, again, is the expectation of what is going to happen as the market approaches a key technical support or resistance level.

Trendlines A trendline is a line drawn connecting a minimum of two ex-

treme trading levels. The more points that support a trendline, the more reliable it becomes. These extreme levels represent significant supply or demand points where market forces have altered the auctioning process. The slope that this line creates is expected to act as support or resistance as the trend continues. 1e expect support to hold in an up trend and resistance in a downtrend. N’ben one breaks, the more significant the break, the greater the shift in psychology and indication that the primary trend of the market may be changing. \Ve expect a supportive trendline to act as resistance once it is penetrated and for a resistance trendline to act as support once penetrated. If this doesn’t occur, then the initial penetration was false and the trend usualh resumes. N%en a trendline holds, it adds confidence and increases the probability of the trend continuing. M’hen a trendline breaks, a shift in bias has occurred and the probability of a new trend

developing increases. It is enough to give a trading bias, but not

a strong one.

Market Profile

The market has three distinct memory banks: The first and most widely-followed is price. Price is the most obvious because it is the level at which market participants exchange risk. The second is time, which is also widely-used in determining value and places to exchange risk. The third, and perhaps the most important, is volume. Market Profile, developed bT Peter Steidlmaver, allows its followers to combine these three pieces of history and to project future support and resistance levels based on price and volume distributions. Narket Profile allows us to find price levels on a chart where high or low volume has occurred as well as what the market considers value areas (where 70% of volume has been done). U’e know that prices that are at high volume levels indicate that market participants have strong emotional and financial commitments made at these levels. \Ve therefore expect them to act as support and resistance levels in the future. The basic tenet of the psychology of support and resistance levels is that what was once support can become resistance and vice I-ersa. Low volume levels also represent support and resistance in that the market has not traded there much or at all, and investors will err on the side of caution and show a fear of the unknown. Steidlmayer’s research has shown that these high and low volume areas as well as value areas tend to offer strong support and resistance levels in the future. These support and resistance levels are also very good leading indicators as strong expectations can be based on them. They therefore carry a strong trading bias.

Fibonacci Retracements Leonardo Fibonacci was a 12th century mathematician who is

best known for developing a mathematical numbering sequence that seems to dominate the world in which we live. From plan- etary cycles to outbreaks of measles in America, the ratios and extensions of his number sequence seem to have a dominating influence on human behavior. The sequence: 1 .1,2,3,5,&l 3,21, 34,55,89,144... where the next number in the sequence is calculated by adding the previous two numbers: i.e. ltl=2, 1+2=3, and so on. One important property of the sequence is that when each number is divided by the subsequent number it approaches 61.8% as you move further down the sequence: i.e. 3/5=60%, 5/8=62.5%, and g/13=61.5% etc. \271en each number is divided bp the second subsequent number it approaches 38.2% as you move further down the sequence: i.e. 3/8=37.5%, 5/13=38.5%. and 8/ 21=3&l% etc. $\“nen trending markets begin to consolidate, we often look between the 38.2%’ and 61.8% retracement levels for expected support and resistance levels. \Ve are not entirely sure why this happens, but this retracement zone often acts as a potential reversal area. Fibonacci ratios of ,382 and.618 are also closeh associated with Elliott pl’ave Theory, which has its place in trading strategies as well. ,Yt-thur Merrill’s research has shown that no one retracement level is dominant, )-et traders and analysts still expect markets to pause at these levels. Therefore, less reliance should be placed on any one specific level. I f a market does reverse in this zone, it does provide a slight edge, and is therefore useful in determining a trading bias.

Event-Driven Price Gaps and Reversal Points M’hen key economic data is released or a key official say some-

thing that lit11 affect public policy, the market moves very quickh to a new trading range. F\‘hen a kev event drives the market, it creates a low volume area or price sap which is very important

MTA JOURNAL l \Vinter-Spring 1998

when analyzing support and resistance expectations. For example: \2hen Mr. Greenspan delivered his Humphrey-Hawkins testimon! to congress in February 1996, he indicated that the recent easing cycle was implemented as insurance and that a prolonged easing cycle should not be expected. Belds immediately rose 15-40 basis points along the yield curve, which created a low-volume gap between 6.25% and 6.30% in the US 30-Year bond. This major event provided resistance for the balance of 1996 and continues to do so. Another example would be when the market reverses trend after a key Non-farm Pay-011 report. The price points that are created from these important market moving events become much more important support and resistance levels in the future.

Technical Structure The following section deals with technical structure. Pure price

movement tells us so much about what the market is doing. It has strong leading indications and adds a strong trading bias.

Il”nen a market is trading well and trending, the most obvious characteristic is that it is consistently making higher/lower highs and higher/lower lows. This is a very strong leading indicator and helps to set a strong trading bias. \\1en today’s session has a higher high, higher low, and higher close than the yesterdays session, the trend should be expected to continue. If, however, the market made a higher high, a lower low, and a lower close, this could be a good indication that the bullish trend could be coming to an end or consolidating - a rejection of higher levels. The wave structure as it relates to range extensions sets a leading trading bias.

It has been well-documented that the market prices have a primary or impulsive wave and a secondar) or consolidative wave. b\Then a shorter period time frame wave (hourly) is in the same direction as the longer-period time frame ware, (daily) the trading bias is usually very strong. 1Ve therefore assign a stronger weighting than if the two time frames were conflicting. This generates a leading trading bias.

Another important part of technical structure is the breaking of support and resistance levels. M’hen the market is in a primal? bullish trend, we expect support levels to hold and resistance levels to break. If in fact we see the opposite happening, it gives us a good leading indication that the prima? trend may be changing. From a psychological perspective, minor support level breaks are not as important as major breaks. \\71en the trading bias is strong, the market has good technical structure and a higher probabilib of continuing.

Reversal or Continuation Days LVhen a key support or resistance level is reached, it is impera-

tive that we utilize a time or price filter before determining if psychology has shifted or not. Our experience has show that when levels are penetrated and rejected quickly (several minutes), the impending move in the opposite dire&on is usually ven strong. This type of rejection is usually called a key reversal da)- after the fact, and is typical of a market probing for new business. X key reversal is usually preceded by an established trend, a rapid approach of the support or resistance level, and is often related to a much-anticipated news event.

Consider the bond market in front of a key payroll report. LVe know the market is always looking for support and resistance levels to probe for new business. After a key report is delivered and a level is broken, if there is no follow-through trading, it has a very high probability of reversing trend. Since market partici-

pants collecti\-ely realize that the market will not continue in that direction, a shift in bias occurs and traders will push the market in the opposite direction. Traders who went with the break get squeezed and those who fought it add more, because it works for them, causing additional momentum to build and a key reversal day to occur. Days like these offer the maximum probability that a shift in trading bias has occurred and are leading indicators.

Continuation days usuall!- accelerate the reversal pattern of the previous session. L\Xen this confirmation occurs, it adds a strong trading bias and is often a leading indicator that the trend will continue. Mrhen we don’t see follow-through after a reversal pattern, it is a strong negative, and removes or reverses the positive bias that the initial reversal created.

These price patterns are captured very nicely in candlestick charts. There are many types of reversal and continuation patterns that develop. By interpreting these patterns we can add to our probability of making a successful trade.

Momentum Studies and Oscillators: lagging or Coincident Indicators

The moving average, convergence, divergence (RMCD) indicator is an excellent tool for measuring price momentum. i\‘hen this study is converging, momentum is slowing. 1lihen the market reaches a key technical support or resistance level, we should expect it to hold. At least the probability of it holding is strong if momentum has slowed. A similar indicator that measures momentum is rate of chance (ROC), which gives us an idea of the speed of the market. M’hen ROC is strong, the probability is higher that a support or resistance level breaks. Using momentum tools this way removes the pure reliance we had on momentum in our original model many years ago.

Mien a market becomes overbought or oversold b!- oscillator measurements (RSI and Stochastics), it indicates extreme strength or weakness. In the short-term (3-5 days), these internal measurements support the prevailing trend. 1z‘e can expect a period of profit taking when the market reaches a key support or resistance level, but similar to momentum studies,‘oscillators can indicate how strong or weak the market is and if the probability of the trend continuing is high or low.

However, because these studies are derived from prices themselves, they tend to be coincident or lagging indicators. \\7ien studies are derived from price, they become excellent tools for comparing price movements with momentum or internal measurements. This is known as divergence analysis and it represents a strong form of anticipating a shift in trend. In general, the! need to be compared to price action to add value.

An Explanation of The Risk/Reward/Probability Diffusion Index

The index is constructed in a manner similar to hlartin Zweig’s Diffusion Index for Equities, Alan Shaw’s S&P 500 Group Rank- ing System, and Phil Roth’s Stock Market Barometer Index. The index sets a bias whether we should be long or short by examin- ing several key technical aspects of the market.

Vsing support and resistance expectations, technical structure, reversal and continuation patterns, as well as momentum and oscillator analysis, we’re constructed 7 leading inputs and 3 coincident inputs to comprise 10 important psychological and technical aspects of trading and risk/reward/probability analysis. The total index score is made up from a total of each input’s weight. Mien the daily values of the index are joined, an oscillator is

MTA JOURNAL l lvinter - Spring I998

created that fluctuates between t/- 20. Each question is assigned a weighting of (sell) t2,tl.O,-l,-2 (buy) and the total score is the sum of the answers to all 10 questions. The minus sign on bullish signal was chosen so that buy signals are given when the oscillator is low and sell signals are given when the oscillator is high, corresponding to price movement. The buying bias zone is any index value lover than or equal to -5; the selling bias zone is any index value at greater than or equal to +5. Once a bias is set, it is maintained until an opposite bias is generated, even if the index v&e motes into neutral readings. These levels were initially chosen b,- the observation that a significant bias would be set if more than 50% of the net inputs showed a bias. The model is not purel! quantified, and there is some subjectivity involved in valuing each technical input. For the purpose of historical testing, all trades were executed at the close. All questions are independent, and the total of the answers to all the questions gives us the R/R/P index score. The position is held until the opposite signal is given. Only one contract is traded and there is no pyramiding. Index values are determined as follows:

The Definitions of How to Ask and Score the Questions are:

Question 1 What is the proximity to a major support/resistance level?

t2: N?thin 8 ticks or 2 basis points of resistance. Price observd- tions of historical charts indicate that this is the level where bias shifts are strongest.

tl: Between 9 and 16 ticks or 2 to 4 basis points of resistance. Not as strong a bias and slightly higher risk, but still close enough to have a bias.

=0: Greater than 16 ticks or 4 basis points is far enough away that the risk of executing a trade is too high.

-1: Between 9 and 16 ticks or 2 to 4 basis points of support. Sot as strong a bias and slightlv higher risk. but still close enough to have a bias.

-2: M’ithin 8 ticks or 2 basis points of support. Price obserw tions of historical charts indicate that this is the level where

bias shifts are strongest.

Question 2 What is the proximity to a minor support/resistance level?

t2: \Vithin 4 ticks or 1 basis point of resistance. Price observations of historical charts indicate that this is the level where bias shifts are strongest.

tl: Between 5 and 8 ticks or 1 to 2 basis points of resistance. Not as strong a bias and slightly higher risk, but still close enough to have a bias.

=0: Greater than 8 ticks or 2 basis points is far enough away that the risk of executing a trade is too high.

-1: Between 5 and 8 ticks or 1 to 2 basis points of support. Sot as strong of a bias and slightly higher risk, but still close enough to have a bias.

-2: M’ithin 4 ticks or 1 basis point of support. Price observations of historical charts indicate that this is the level where bias shifts are strongest.

Question 3 What is the direction of the primary/major trend?

t2: The market has been in an established downtrend and con-

tinues to break support levels. Monthly and/or weekly charts continue to make lower lows. Generallv a move more than 50 basis points.

tl: .I period of consolidation once a strong downtrend has been established. It could be indicating a shift in bias is developing, but has not occurred. X counter-trend break of a -1 cow solidation phase: the beginning stages of a new primal? trend. but more work is needed.

=0: h long-term narrow range has developed. Greater than 6 months and less than 50 basis points.

-1: .I period of consolidation once a strong up trend has been established. It could be indicating a shift in bias is developing, but has not occurred. X counter-trend break of a t 1 consolidation phase; the beginning stages of a new primary trend. but more work is needed.

-2: The market has been in an established up trend and continues to break resistance levels. Monthly and/or weekly charts continue to make higher highs. Generally a move more than 50 basis points.

Question 4 What is the direction of the secondary/minor trend?

t2: The hourlv trend is in the same direction as the primary trend and continues to break support levels. Dail!- charts continue to make lower lows. Generally a move more than 15 basis points or two full points on the contract has been established.

tl: Generally a range-bound market with well-defined support and resistance levels on an hourly chart. Less than 15 basis points or two points on the contract. Can be counter to primary trend and therefore offsetting.

=0: A short-term narrow range has developed. Less than a few days and 10 basis points. Characteristic of inside day.

-1: Generally a range-bound market with \vell-defined support and resistance levels on an hourly chart. Less than 15 basis points or two points on the contract. Can be counter to primary trend and therefore offsetting.

-2: The hourlv trend is in the same direction as the primary trend and continues to break support levels. Daily charts continue to make lower lows. GeneraIll- a move more than 13 basis points or two full points on the contract has been established.

Question 5 What is the wave structure? Higher/lower highs/lows/close?

~2: X lower high, low and close are indications of a strong wave I structure and continuing trend. X higher high, lower Lowe and a close below the previous day’s low are key: reversal indications that a new counter-trend wave structure is beginning.

tl: X lower close with higher high and lower low A lower close, lower high, but not lower low. Both maintain trend direction, but are not as strong as t2

=0: X short-term narrow range has developed. Less than a few day and 10 basis points. Characteristic of inside days.

-1: X higher close with higher high and lower low. X higher close, higher low, but not higher high. Both maintain trend direction, but are not as strong as -2

-2: X higher high, low and close are indications of a strong wave structure and continuing trend. .A lower low, higher high, and a close above the previous day’s high are kev reversal indications that a new counter-trend wave structure is beginning.

26 MTA JOURN,U. l \\‘inter - Spring 1998

Question 6 Did the market break/hold a major or minor support/

resistance level? t2: A test and rejection of a major resistance level. X break and

close below a major support level.

tl: A test and rejection of a minor resistance level. h break and close below a minor support level.

=0: Generally in the middle of a range with no test of support or resistance.

-1: X test and rejection of a minor support level. X break and close above a minor resistance level.

-2: X test and rejection of a major support level. A break and close above a major resistance level.

Question 7 Are there any reversal or continuation patterns?

t2: Days with long selling tails (long upper shadow candlestick). Bearish engulfing patterns or outside down day (strongest when preceded by a strong up trend). X bearish break of a triangle or pennant.

tl: A continuation or confirmation of a bearish reversal pattern.

=0: Inside days or multi-day consolidations that have not formed a triangle or pennant.

-1: A continuation or confirmation of a bullish reversal pattern.

-2: Days with long buying tails (long lower shadow candlestick). Bullish engulfing patterns or outside up days (strongest when preceded by a strong downtrend). X bullish break of a triangle or pennant.

Question 8 What is the short-term (hourly, daily) oscillator (RSI,

Stochastics) alignment? t2: Both hourly and daily studies are aligned or accelerating bear-

ishly The &as is stronger if it’s in the same direction as the primary trend, not as strong if it’s a counter-trend move.

tl: X counter-trend move with favorable alignment, but not as strong as t2, or indicating signs of acceleration. A reversal from overbought levels, but without acceleration.

=0: Hourly and daily studies are mixed or at neutral levels. So obvious signs of strength or weakness in the studies, typicall) in tight range markets.

-1:

-2:

t2:

tl:

A counter-trend move with favorable alignment, but not as strong as -2, or indicating signs of acceleration. h reversal from oversold levels, but without acceleration.

Both hourly and daily studies are aligned or accelerating bullishly. The bias is stronger if it’s in the same direction as the primary trend. Not as strong if it’s a counter-trend move.

Question 9 Is short-term momentum (MAUI, ROC) con-

firming trend (primary/secondary)? Ivlomentum is bearish in hourly and daily studies and confirms the primary and secondary trends. Strong bearish divergence to price showing acceleration.

Momentum is bearish in hourly or daily studies and is aligned with the secondary trend. These

% .E 3

5

116

counter-trend moves can lead to stronger moves, but until the? do, the!; are not as strong as the primary trend bias. An alignment with the major trend, but without acceleration.

=0: X market that lacks momentum or has very mixed indications.

-1: Momentum is bearish in hour& or daily studies and is aligned with the secondary trend. These counter-trend moves can lead to stronger moves, but until they do, the! are not as strong as the primary trend bias. ;\n alignment with the major trend, but without acceleration.

-2: Momentum is bullish in hourly and daily studies and confirms the primary and secondary trends. Strong bullish divergence to price showing acceleration.

Question 10 What is the retracement to the previous primary trend?

t2: A market that has held or rejected the 100% retracement of the primary trend, This development also suggests a major double top formation, and the probability is higher that a market will reverse from this point.

tl: The probability is higher that a market will reverse between the 38.2% level and the 61.8% level. Though no specific level has been proven to consistently reverse markets, a higher percentage reverse inside this zone than not. It has enough merit to carry a slight bias.

=0: X market that is anywhere else than within the 38.2%-61.8% band or at 100%.

-1: The probability is higher that a market will reverse between the 38.2% level and the 61.8%’ level. Though no specific level has been proven to consistently reverse markets, a higher percentage reverse inside this zone than not. It has enough merit to carry a slight bias.

-2: A market that has held or rejected the 100% retracement of the primary trend. This development also suggests a major double bottom formation, and the probability is higher that a market will reverse from this point.

The R/R/P model for front-month bonds from July 8, 1996 to February 7, 1997 with equity curve is shown below:

Chart 1: R/R/P Model

1”‘

100

07iOU96 0&‘27;96 lo/l&‘96 13lli96 OYW9

10.00 g N

f 5.00 m

-15.00

MTA JOURML * Itinter - Spring 1998 27

719196 7/10/96 7111196 7/12/96 7/15/96 7116/96 7117196

%Z 7122196 7123196 7124196 7/25196 7126196

:;i;;ii 7131196

g/20/96

%Zi 9125196 9126196

EE 10/l/96 10/2/96 lo/3196 1014196 1017196

1 Ei lo/lo/96 10111196 lo/15196 10116196 10/17/96 10/18/96

Table 1: Data for R/R/P Model Price

105.94 106.44 106.94 107.22 107.81 107.28 107.97 107.84 109.19 108.53 108.03 108.56 107.66 107.91 108.03 107.28 107.91 108.59 110.16 111.00 111.22 111.06 110.94 110.78 111.56 111.38 110.41 110.50 110.19 110.78 110.41 110.25 110.16 110.06 109.03 108.13 108.44 108.28 107.63 106.78 107.34 106.97 106.41 106.75 107.22 106.66 106.78 107.25 108.72 108.81 108.13 108.06 107.66 107.75 108.09 108.63 109.20 109.81 109.47 109.19 109.91 110.31

10.31 11.69 11.16 Il.09 10.56 10.03 10.59 10.50 10.31 11.13 ii.28

Score -;! -10

1;

-i

1;

: -a

-2 -6

-1: -10 -16 -6 4

7

-:

E -2

-1: 4

:

i 14 -3

11 -5

1: -a

-13

-l -9 -3

1;

-7

-i

-;; 6

-! -9 2

i 13

; -10

2

-A 5

Date 718196 719196

7110196

:;1% 7115196 7116196 7117196 7118196

:;:% 7123196 7124196 7125196

Ei 7/30/96

"83/%

;%i 816196

%ii 8/9/96

a/12/96 a/13/96 8114196 a/15/96 a/16/96

Eii 8121196

LEi 8126196

KZ~

%E~ 913196

%ii 916196 919196

g/10/96 9111196

Z%

~~1% 9/18196

%% 9123196 9124196 9125196

99~;;;~~ g/30/96 10/l/96

1% 1014196 1017196 1018196 10/g/96

lo/lo/96 10111196 10115196 10/16/96 lo/17196 10/18196

Position long long long long long short long long long short short long short short long long long long long long long short short short long short short short short long long long short short short short short short short short long short short long long long long long long long short short short short short long long long short short long long long long short short short short long long long long short

p&L 0.00 0.50 1.00 1.28 1.88 1.34 0.66 0.53 1.88 1.22 1.72 1.19 0.28 0.03

/i:i:\ 'y;'

2.03 2.88 3.09 2.94 3.06 3.22 2.44 2.25 3.22 3.13 3.44 2.84 2.47 2.31 2.22 2.31 3.34 4.25 3.94 4.09 4.75 5.59 5.03 4.66 5.22 4.88 5.34 4.78 4.91 5.38 6.84 6.94 6.25 6.31 6.72 6.63 6.28 5.75 6.33 6.94 6.59 6.88 6.16 6.56 6.56 7.94 7.41 7.47 a.00 a.53 7.97 7.88 7.69 8.50 8.66

10121196 10122196 10123196 10124196 lo/25196 10/28/96 10/29/96 10/30/96 10131196 11/l/96 1114196 1115196 11/6/96 1117196 1118196 11112196 11113196 11/14/96 11/15/96 11/18196 11/19/96 11120196 11121196 11122196 11125196 11/26/96 11127196 11/29/96 1212196 1213196 1214196 1215196 1216196 1219196 12110/96 12111/96 12112196 12113/96 12116196 12117196 12/18/96 12119196 12/20/96 12123196 12124196 12126196 12127196 12/30/96 12131/96 112197

1;;;;: 117197

1;Ki:

1;; % 1114197 l/15/97 l/16/97

1;::;i: l/22/97

Ei: 1127197 1128197

Ez:

:;%'

;;;;z: 216197 217197

111.06

i% 110.59 111.03 110.91 112.59 112.59 113.00

11% 113.78 113.63 114.28

1% 114.72

X 114.84

E::; 115.53 115.31

11% 115.31 115.91 115.88 116.00 115.34

11E

1% 112.91 112.50 113.25 112.75 112.53 112.06 113.16 113.31 113.44 113.41

1% 113.88 112.63 w.38 111.56 111.25 110.88 110.72 111.41 110.34 110.41 111.41 111.28 110.72

FE 111.03 110.84 110.25

1 %i 110.25

FE

11%; 112.03 111.91 112.47

11

-1:

-;;:

-iFi

:;

i -3

-1; -11 -16 -10 -12 -1

-1: -7

1: 7

-z -17 -13

i

-! -14 -1

-i -12 -2

-;

1; -1

1; -9 -7

1:

; 10

-; 13

-Y

7 3

1:

: 13

;

1; -12 -12

2 1

-9

10/21/96 10122196 10/23/96 10124196 10/25/96 10/28/96 10129196 10/30/96 10131196

1%;

11 ;;;ii 1117196 1118196

11112196 11/13/96 11/14196 11/15/96 11118196 11/19/96 11/20/96 11121196 11122196 11125196 11126196 11127196 11129196

1212196 1213196 1214196 1215196 1216196 1219196

12110/96 12/11/96 12112196 12113196 12116196 12/17196 12118/96 12119196 12/20/96 12123196 12124196 12126196 12127196 12130196 12131196

112197

%z: 117197 l/a/97 l/9/97

l/10/97

1;134;;: l/15/97

K:

1;;:;z: 1123197 1124197 1127197

E: 1130197

'K: 214197 215197 216197 217197

short short long long long long long long long short short short short long long long long long long long long long short short short short long long long short short shot-l short long long short long long long long long long long long long long long long short short short short short short short short short short short short short short short short short short short short short long long long long long long

a.88 9.25

98:;; 9.29 9.16

lo.85 10.85 11.26 11.01 10.76 9.98

1!2i 9.26

10.10 9.91

1 ;:;'i 10.04 10.41 10.92 10.73 10.95 10.79 10.91 10.95 11.54 11.51 11.63 12.29

1 El

1% 11.66

1%

11E 11.63

1Ei 13.01 12.98 12.91 13.51 13.45 12.20

'1%

1% 14.10 13.41 14.48 14.41 13.41 13.54 14.10

1 i:Z! 13.79 13.98 14.57 15.04 14.73 14.57 14.26 13.38 14.04

E 13:e 14.41

28 MTA JOURNAL l \Cinter - Spring 19%

Conclusion In the period tested, the net results before commissions were

very impressive. ;Z buy-and-hold strategy would have shown a profit of 6.53 dollars per hundred while the model netted 14.41 dollars per hundred. There was a total of 33 trades (switches from long to short or vice-versa); 17 long, and 16 short. The average holding period for a long trade was 4.58 day lvith a mode of 3, and the average period for a short trade was 4.375 days with a mode of 4, both showing very consistent results. The shortest holding period was 1 day for both long and short positions (these were attributed to false breakouts). The longest holding period for a long position was 12 day and 21 days for a short position. These were both observed during consolidation phases. These results confirmed our belief that the duration of tradable trends lasts on average between 3-5 days. Other tests we conducted have shown similar or better results in other time frames as well as with pyramiding strategies, but for the sake of simplicity we will analyze these results.

The results indicate that the model captured most of the tradable trends in the market bp judging the technical bias. \Ve can apply the model to weekly and monthly charts with similar results; however, drawdowns and risk grow substantiall!; when such long time frames are used with leveraged futures trading. Clearl! the best time frame to trade is focused between 3 and 5 day, for tradable trends in fixed income markets rarely last longer without giving back a substantial part of the trend.

The model did well at avoiding dramatic whipsaws, as evidenced by the relatively low drawdowns from peak to trough on the eq- LILLY curve. The standard deviation of the day-to-day changes in equity was .5X7 or 20/32nds. The worst period was from July 12, 1996 to July 29,1996 when the returns dropped from a pro& of l-28/32 to a loss of 27/32 on July 29, 1996, and after that did not have a greater drawdown period. There were, however, 10 trades (30%) that only lasted for 1 or 2 day and that is an area for improvement. Me attribute most of these signals to false range breaks or periods when major support or resistance levels are broken b) only one or two days. This is definitely an area for improvement.

Future Research The theory is valid and showed a dramatic advantage in trad-

ing and analyzing the market from a risk/reward/probability perspective. Though not perfect, as we have shown, it does require some additional work and refinement in the time filter for intraday execution to avoid signals that last 1-2 days. 1 feel that this area could be developed further bp historically quantieing the magnitude of the valid range break on intra-day levels, but this will be the topic of a follow-up research paper. !~ly research into the psycholog?: shift-s at support and resistance levels is new and exciting. I believe it has major implications for further development and study. Though little comprehensive research has been written on the entire subject of trade execution and maximizing probability I intend to devote my career to further developing this body of research.

I have followed our initial research into support and resistance expectations by adding several fundamental expectations questions, and while performance w-as increased modestly it has not been significant enough to date to alter the initial model.

My model is ideally suited for programming into a neuro-net (a logical computer algorithm) because of its quantifiable inputs. While there is a small degree of subjecti\-@, rules can still be

formed to account for some of the “gray” areas of my: analysis. I have also tested a similar model for spot CAD/USD and for Cana- dian cash bonds with similar or better real-time results. The method has proven to be vet profitable in real-time execution as well as historical testing.

Summary 111~ theor? is a methodology that I have developed into a new

trading tool. I’m trying to emphasize the techniques that a good technician/trader should employ when following markets in an effort to reduce risk and maximize the value that the technical approach brings to the markets.

Building support and resistance expectations are an essential part of trading psychology Understanding the market one is trading and knowing when the probability of success is at maximum levels are important parts being successful and managing risk. In order to maximize return one has to control risk by only trading when one has highest probability of being correct along with the smallest risk, and that occurs at support and resistance levels.

Bibliography II Dalton, James F., Jones, Eric T., and Dalton, Robert B., w

o\‘er Markets: Power Tradinc M’ith Market Generated lnfor- mation, Chicago: Probus Publishing Company, 1993

I Edwards, Robert D. and Magee, John, Technical Analvsis of StockTrends. 6th edition. NewYork Institute of Finance, 1992.

q Elder, Dr. Alex, Trading- for a Living: Psvcholoa: Trading Tac- tics. Money Management. New York: John Wiler &: Sons, Inc., 1993.

I Elliott, Ralph N., (edited by Robert Prechter) The Sfaior FVorks of R. N. Elliott. Chappaqua, hy New Classics Library, 1980.

t# Mamis, Justin, The Sature of Risk: Stock Market Sunival and The Sleanitw of Life. Addison-W’esley Publishing Company. 1993.

Murphy, John J., Technical Analysis of the Futures Markets. SewYork Institute of Finance, X Prentice-Hall Company, 1986.

Sison, Steve, JaDanese Candlestick Charting Technioues: A Contemporary Guide to the Ancient Investment Technique of the Far East. NewYork Institute of Finance, Simon 8: Shuster, A1 Paramount Communications Company 1991.

Pring, Martin, Technical ,Analysis Esnlained. 2nd edition. New York: McGraw-Hill, 1985.

!# Merrill, Arthur. Research articles from Market Technicians Assoication Library, Sew York, NY

Sperandeo, \‘ictor, (with T Sullivan Brown) Trader \Tc - Meth- ods of a pi-all Street Master. Sew York: John \VileT &: Sons, Inc., 1993.

Zweig, Martin E., 12’inning. on i2’all Street. New York, 1Varner Books, Inc., 1990

MTA JOURNAL l \Vinter - Spring 1998 29

Biography Larry M. Berman, CMT, CT,\, is senior technical analyst

and executive director in the financial markets research group of CIBC LVood Gundy. He provides Canadian and US fixed- income and foreign exchange technical analysis for CIBCW’G’s clients, sales, and trading groups. He is also part of CIBCM’G’s proprietary trading group.

Prior to joining CIBCWG in March 1997, Larry was a senior technical analvst for Technical Data in Boston, where he concentrated on the US fixed-income markets specializ- ing in the Eurodollar market. Before joining Technical Data, he was senior technical analyst for Marleau, Lemire Futures in Toronto, where he traded commodity and financial futures for the firm’s clients.

Larry was graduated from York University in Toronto in 1989 with a BA in Economics. He has completed the Cana- dian securities course, Canadian options course, Canadian futures course, investment funds course, CPH course, and holds the US series 32 and series 3 exams.

Larry is a registered Commodity Trading Advisor (CTA), and Chartered Market Technician (CMT) , He has also passed level one of the Chartered Financial Analysts (CFA) exam. He completed a technical internship with the Market Tech- nicians Association in New York in 1994. Larry began his career as a investment advisor in 1989.

MTA JOURML l 1finter Spring 1998

The Bridge-CRB Bond Ratio An Alternate View

Vincent Jay, CMT

Overview The interplay between commodities, interest rates and infla-

tion, as well as some of the well-known fundamental economic indicators of inflation are concepts that the reader is assumed to be familiar with, or can study in Intermarket Technical Analysis by John J. Murphy, or a basic college level economics textbook. Instead, the Bridge-CRB Index will be briefly discussed before shifting focus to some of the well-known technical trading tools employed by both noyice and veteran investors in determining whether to direct funds into or out of interest-rate-sensitive investments. h less-frequently used indicator, the MACD-Momen- turn oscillator (MACD-M) ‘, which is a variation of Gerald Appel’s MACD oscillator, is examined and used to operate on price data of the Bridge-CRB Index and U.S. Treasun Bond Futures from September 1977 to October 1996. The uliimate endeavor is to uncover a more timely, and it is hoped, a more profitable application of this indicator as part of an overall disciplined regimen of money management of U.S. Treasury Bond investments. The computerized technical analysis software used to generate charts, modi indicators, test trading systems and to generate trading system P/L is SuperCharts \‘ersion 2.0 for M’indows 3.1.

Introduction Let’s take a closer look at the Bridge-CRB (Commodity Re-

search Bureau) Index - formerly the KR-CRB Index - and how it is used as a leading indicator of inflation. The Bridge-CRB Index is composed of six categories of commodities including meats, metals, grains and industrials. These categories in turn consist of individual commodities such as gold, silver, sugar, soybean, cot- ton and crude oil, to name a few. The Bridge-CRB’lndex was revised in December 1995 to better reflect commodity costs in- curred by goods producers. The Index is derived by computing an arithmetic, then geometric average of the component prices, which is then adjusted for a base-year value for the Index and adjusted for changes in the number of components comprising the Index. As one can surmise, a rise (fall) in the Bridge-CRB Index is generally inflationary (deflationary) for the econom) which tends to be bearish (bullish) for Treasury Bonds. Another commonly used indicator of inflation is the Bridge-CRB/Bond Ratio. Correspondingl!; a rising (falling) Bridge-CRB/Bond Ra- tio is bearish (bullish) for Treasury Bonds. This can be seen in Figure 1.

IGUREl

CRm; : 125

Source: Bridge Profit Center

Indicator Construction

MACD Oscillator

The MACD (hIo\ing Average Convergence-Divergence oscil- rtor, developed by Gerald Appel to signal changes and direction If trends)‘, rests on the general concept of a moving average which 5 a smoothing process. It is simply an average of ‘n’ data points Iver a period of ‘n’ days, weeks or any other time frame under xamination. 1Vith the passage of each unit of time, the data rom the first period is dropped while the data from the ‘ntl’ beriod is added and the next ‘n’ period average is recomputed - lence the name ‘moving ayerage.’ h si$&moving average (SSW) ieighs each data point equally for the entire ‘n’ periods, whereas .n exponenti@J weighted moving average (EMA) weighs recent data nore heavily. By virtue of their computation method, moving werages tend to lag movement of the underlying market. Be- ‘ause the EMA also includes past price action, and hence is more ,omprehensive in subsequent recomputations, it will be used in he balance of this article when reference is made to moving aver- Iges, unless otherwise stated.

The MACD oscillator is useful in non-trending markets by alert- ng the technician to short-term extremes such as overbought and wersold conditions. The MACD oscillator consists of two lines, he MACD line which is the difference between the longer 2& jeriod moving average and the shorter 1Speriod moving aver- tge, and the signal (trigger) line which is an exponentiall! ,moothed MACD line. Sometimes, an accompanying histogram Aot is also used. This is the difference between the RWCD line md the exponentially smoothed signal line, and is shown in the ‘arm of vertical bars above and below the zero line. The 13- and %-period averages mentioned above are most commonly used ‘or the stock and commodity markets, while other markets ma! dictate different parameters. \l%enever the MACD and signal lines u-e below (above) the zero line and the hZ4CD line crosses above

MTA JOURNAL l \Vinter - Spring I998 31

(below) the smoother signal line, one is alerted to a possible buy (sell) signal. Another characteristic to look for is a bullish (bearish) divergence - when the underlying market makes new lows (highs) while the MACD oscillator makes higher (lower) lows (highs). In Figure 2, note that the X4U made a lower low in late 1992 whereas the MACD oscillator made a higher low during the same period-a bullish divergence. In early 1993, both the MACD and signal line climbed above the zero link while the MACD line crossed above the signal line to further confirm the rally in the XAU Index. During the 199394 period, note the bearish direr- gence as the XX continued rallying above 140 while the MACD oscillator made lower highs in early in 1994 and even lower highs in late in 1994 before crossing back below the zero line as the MACD line pierced below the signal line.

FIGURE2

PHLX scaLn.aWVBR INDW ---xtw

Earn

Source: Bridge Pro@ Cent0

Momentum Oscillator Whereas the MACD oscillator tends to Lug the underlying mar-

ket, the Momentum oscillator tends to lpnd the underlying market. Momentum measures the rate of change of the market as opposed to the actual levels of the market itself. A g-period Mo- mentum oscillator is the difference between the current market level and the market level nine periods earlier. In Figure 3, I illustrate the use of the Momentum oscillator as applied to the XAU Index. Early in 1993, the Momentum line was rising and was above the zero level - positive momentum - which means that the uptrend was accelerating. During 1995, the XX! failed to rally above the 130 level and began leveling off. Simultaneously, the Momentum line started declining late in 1995 and eventuall? dipped below the zero line - negative momentum - indicating that the downtrend was accelerating.

FIGURE3

Source: Bridge Profit Center

Summarizing, I briefly described the Bridge-G% Index and the Bridge-CRB/Bond Ratio with regard to inflation, as well as the uses and interpretations of the M4CD and Momentum oscillators. Now I embark on a project that extends the Bridge-CRB/ Bond Ratio concept explained in John J. Murphy’s text on Intermarket Technical Analysis. This can be accomplished by introducing the M4CD Momentum (MACD-M) oscillator which is then used to ‘filter’ the Bridge-CRB Bond Ratio to see if a more consistent signal can be unveiled that alerts us to earlier turning points in the Treasury Bond market. In addition, I explore whether the indicator can stay with the intermediate- and long- term market trend.

MACD-Momentum Oscillator (MACD-M) The MACD-M is a composite indicator combining the Moving

Average, MACD and Momentum oscillator concepts just discussed. Specifically the MACD-M indicator is calculated (Appendix A, Formulas) as a Simple Moving Average of the Momentum of the MACD histogram. Construction of this indicator involves three steps: 1) computing the MACD histogram -the difference between the

MACD line and the exponentially smoothed signal line,

2) computing a lo-period Momentum of this MACD histogram, and finally

3) smoothing the lo-period Momentum of the MXD histogram with a three-period Simple Moving Average.

Use and interpretation of the MACD-M is similar to that of the traditional MACD oscillator, but with a modification to the parameters as presented by Thomas &pray in the September and August 1991 issues of Technical Analysis of Stocks &- Commodi- &. Instead of the traditional 13- and 26-period simple moving averages, Aspray proposed using lo- and 20-period simple moving averages but retaining use of a lo-period exponential moving average to smooth the YL4CD line. Figure 4 below shows the MACD-M of the weekly continuation chart of Treasury Bond futures. The plot consists of the MACD-M oscillator line superim- posed onto the MACD histogram. During the June-August 1980 period, the MACD-M declined, indicating a potential downside reversal, but the histogram was still positive. Early in July the MACD-M crossed below the zero line, but the actual sell signal occurred late in July when the histogram also crossed below the zero line.

32 MTA JOURNAL l Winter - Spring 1998

FIGURE4

In brief, as Barbara Starr, Ph.D.” notes, the most important consideration is the direction of the MACD-M indicator, followed br zero line crossovers by the MACD-M and the MACD histogram. Figure 4 also indicates another useful characteristic of the MACD- M indicator - it is a leading indicator. Note that the May 1980 peak of the MACD-M preceded the peak of T-Bond futures bv 4-5 weeks. Meanwhile, the *MACD histogram failed to make new highs and began weakening during the June-August period before crossing below the zero line late in July. This not only confirmed the downtrend as indicated by the declining MACD-M, but also triggered a sell signal. Again, it is cautioned that though this consti- tutes a sell signal, one should consult other technical indicators before following through and initiating a short position. The same discipline also applies to long positions.

Modified MACD-Momentum Oscillator (MACD-(M)) Recall that moving averages, whether simple or exponential,

tend to lag the underlying market. Taking a closer look at the method of calculation of the MACD histogram - it is the difference behveen the MACD line and the exponentially-smoothed signal line. Retaining the exponential average, compute the momentum of the exponentially-smoothed signal line, thereby par- tially offsetting the lagging characteristic, before subtracting it from the MACD line to compute the MACD histogram. Next, calculate the momentum of the MACD histogram as was done in computing the MACD-M oscillator, but with the major difference that the simple moving average is not computed as the last step. Succinctly stated, the difference behveen the previously discussed MACD-M oscillator (Simple MO&g Average of the Momentum of the M&ZD histogram) and the modified MACD-M oscillator, hereafter referred to as the MACD-(M) oscillator (Momentum of the MACD histogram - see Appendix A, Formulas), is the computation of the MACD histogram and momentum of the MACD histogram.

Figure 5 illustrates a comparison of the use and interpretation of both the WED-M and MACD-( M) oscillators for weekly T-Bond futures. In 1990, T-Bond futures retraced the 1989 rally before bottoming in September 1990. The MACD-M troughed in early September signaling the potential rebound in T-Bonds while the MACD histogram began to increase shortly thereafter. The buy signal was triggered in late October when both the MACD-M and MACD histogram crossed above the zero line. The MACD-(M) oscillator, however, was more astute in picking up the September 1990 bottom. The LMACD-(M) and its MACD histogram bottomed in late August 1990 and simultaneously reversed directions to cross- confirm each other with regard to market direction and issued a buy signal despite both being below the zero line. The important

point to note is that the MACD-(M) oscillator not only led the underlying market, but it also led the MACD-M oscillator as well - precisely the improvement that is sought.

FIGURE5

Source: Super-Charts

A more obvious example of this lead/lag relationship in Fig- ure 5 is the peak late in December 1989 which coincided with the peak in the MACD-M while the MACD histogram issued a sell signal by crossing below the zero line. The KKD-(M) peaked in late November, warning of the upcoming top and subsequent reversal while its MACD histogram promptly began displaying weakening market momentum and confirmed the T-Bond decline. Again, the MACD-(M) led the market top as well as the MACD-M.

Aside from this leading/lagging characteristic of the MACD- (M) oscillator, another characteristic of added value is its tendenc! to stay with the longer-term trend. In Figure 5, during the June- December 1991 period, though the &ED-(M) broke below the zero line twice, its histogram remained above the zero line, thereb! maintaining a long position for the T-Bond rally during this period. Contrast this to the MACD-M whose histogram was below the zero line from June till August 1991.

Interpretation of Results

Chart Presentations

Recapping the description of the MACD-(M) oscillator: the MACD-(M) line alerts us of a potential trend change while its MACD histogram indicates the momentum of the current and potential longer-term trend. From this, let me summarize the rules of interpretation for the short- and long-term. For the short- term: 1) A potential buy (sell) signal occurs when both the MACD-(M)

and the MACD histogram are rising (falling).

2) Should divergences occur between the MACD-(M) and MACD histogram, trade in the direction of the MACD histogram while waiting for further confirmation when both the MACD-(M) and MACD histogram trend in the same direction, then trade in this direction.

3) Trendline analysis is applicable to the MACD-(M) line as well as the MACD histogram.

For the longer-term: 1) A potential buy (sell) signal occurs when the MACD histogram

crosses above (below) the zero line indicating bullish (bearish) trend.

2) Again, trendline analysis map be applied.

Note that for a long-term trader, the buy/sell criteria are sim-

MTA JOURNAL l LVinter - Spring 1998 33

plified: is the hMCD histogram, hence the trend of the momentum, positive or negative? Note that the MACD-(MM) indicator allows one the option of trading short- or long-term by the mere expedient of taking an alternate I-iew of the same indicator.

Track Record: MACD-(M) Oscillator

To begin, I apply the long-term buy/sell criteria to the MACD-(!vl) in filtering the Bridge- CRB/Bond Ratio below (see Appendix A, For- mulas for criteria) in Figure 6(a) --weekly charts for comparison, Figure 6(b) - performance summary (see Appendix B, Sotes for explanation of performance statistics) and Figure 6(c) - trade-by-trade with P/L.

Figure 6(a) shows the weekly plots of the Bridge-CRB/Bond ratio (top chart) and T-Bond futures (bottom chart) with their respective MACD-(M) indicators. Sote the up and down arrows on the Bridge-(X%/Bond ratio chart; they indicate buy (long entry) and sell (short entry) signals generated by SuperCharts as per the specified long-term buy/sell criteria. Since a rising (falling) Bridge-CRB/Bond ratio is inflationary (deflationary), this is bearish (bullish) for T-Bond futures. Let us see how useful the MACD-(MM) indicator is in ‘filtering’ an inflation indicator for use in trading T-Bond futures. M’hen a buy (sell) is indicated in the Bridge-CRB/Bond ratio, a sell (buy) signal for T- Bond futures should logically follow, given their intermarket relationship.

FIGURE 6(b) Bridge-CRB Bond Ratio

8/26/77-l O/l 8/96

Performance Summary -All Trades

Total Net Profit $4,820.00 Open Position P/L Gross Profit $5,320.00 Gross Loss

Total # of trades 14 Percent Profitable Number winning trades 10 Number losing trades

Largest winning trade $1,790.00 Largest losing trade Average winning trade $ 532.00 Average losing trade Ratio avg winlavg loss 4.26 Avg trade (win & loss)

Max consec. winners 4 Max consec. losers Avg # bars in winners 77 Avg # bars in losers

Max intraday drawdown -$340.00 Profit factor 10.46 Max # contracts held Account size required $340.00 Return on account

-$I 60.00 -$500.00

71% 4

-$180.00 -$125.00 $344.29

1 36

1 1,418%

Source: Suj$erCharts

Perlormance Summary - Long Trades



Largest winning trade 31,790.OO Largest losing trade Average winning trade $ 516.00 Average losing trade Ratio avg win/avg loss 4.30 Avg trade (win & loss)


Max intraday drawdown -$290.00 Profit factor 10.75 Max # contracts held Account size required $290.00 Return on account

Performance Summary - Short Trades



Largest winning trade $1,170.00 Largest losing trade Average winning trade $ 548.00 Average losing trade Ratio avg winlavg loss 4.22 Avg trade (win & loss)


Max intraday drawdown -$SSO.OO Profit factor 10.54 Max # contracts held Account size required $360.00 Return on account

Source: SuperCharts

-$160.00 -$240.00

71% 2

-$180.00 -$120.00 $334.29

43

1 807%

-

s 0.00 -$260.00

71% 2

-5180.00 -$130.00 $354.29

30

1 689%

----

MTA JOURN.% 9 \Vinter - Spring 19%

FIGURE 6(c)

Date

12/01/78 06/05/81 06105181 07/l 7181 07/l 7181 09/l 8181 09/18/81 01101182

01/01/82 02/l 9182 02/l 9182 09123183

09/23/83 10/26/84

1 O/26/84 06/l 9187 06/l 9187 01113189 01 I1 3189 04/06/90 04106190 1 O/l 9190

1 O/l 9/90 02111 I94 02/l 1 I94 05/05/95 05/05/95 05/l 7196 05117196

Bridge-MB Bond Ratio 8126177-l O/l 8196

mQl&

Buy 1 LExit 1 Sell 1 SExit 1

Buy 1 LExit 1

Sell SExit

BUY LExit Sell SExit

BUY LExit

Sell SExit


BUY LExit

Sell SExit


BUY

1 1 1 1

1 1 1 1 1 1 1 1

1 1

1 1 1 1 1 1

1 1 1

p&e!

2.47 4.26 4.26 4.34 4.34 4.52

4.52 4.12 4.12 4.29 4.29 3.83 3.83 3.65

3.65 2.48 2.48 2.74 2.74 2.59 2.59 2.53

2.53 1.97 1.97 2.15 2.15 2.33 2.33

Entrv P/L Cumulative

$1,790.00 s1,790.00

-$ 80.00 $1,710.00

$ 180.00 $1,890.00

$ 400.00 $2,290.00

$ 170.00 $2,460.00

$ 460.00 $2,920.00

-$ 180.00 $2.740.00

$1,170.00 $3,910.00

$ 260.00 $4,170.00

$ 150.00 $4,320.00

-$ 60.00 $4,260.00

$ 560.00 $4,820.00

$ 180.00 $5,000.00

-$ 180.00 $4,820.00

Source: Supdharts

Therefore, from Figure 6(c), I take note of the dates on which i/q and srll signals were selected by the trading system for the Bridge-CRB/Bond ratio. and then execute corresponding selland bq transactions for T-Bond futures at their corresponding prices on those dates. Note that in contrast to the 1-3 week lead time that the Bridge-CRB/Bond ratio has historically had over T-Bond futures, I now assume time coincident buy/sell transactions. Fig- ure 5 tabulates the T-Bond futures results including the profit and loss.

FIGURE 7

Bridge-CRB Date Bond Ratio

12/01/78 Buy 6105181 Long Exit

6/05/81 Sell 7117181 Short Exit

7117181 Buy 9118181 Long Exit

9/l 8181 Sell 1101182 Short Exit

1101182 Buy 2/l 9/82 Long Exit

2/l 9182 Sell g/23/83 Short Exit

g/23/83 Buy 1 O/26/84 Long Exit

1 O/26/84 Sell 6/l 9/87 Short Exit

6/l 9187 Buy l/l 3/89 Long Exit

l/l 3189 Sell 4/06/90 Short Exit

4106190 Buy 10119190 Long Exit

1 O/l 9/90 Sell 2/l 1194 Short Exit

2111194 Buy 5/05/95 Long Exit

5/05/95 Sell 5/l 7/96 Short Exit

5117196 Buy

Source: SuperCharts

Bond

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell Short Exit

BUY Long Exit

Sell

Bond Entry p&e KL

93.00 65.59 $27,410.00

65.59 63.78 -S 1 ,810.OO

63.78 60.19 S 3,590.OO

60.19 61.91 S 1,720.OO

61.91 60.31 S 1,600.OO

60.31 72.59 $29,000.00

72.59 69.81 $ 2.780.00 l

69.81 92.47 $22,660.00

92.47 90.41 $ 2,060.OO

90.41 93.09 $ 2,680.OO

93.09 91.31 $ 1,780.OO

91.31

Cumulative

$ 27,410.OO

$ 25,600.OO

$ 29,190.oo

$ 30,910.00

$ 32,510.OO

$ 61,510.OO

$ 64,290.OO

$ 86.950.00

$ 89,010.OO

$ 91.690.00

$ 93.470.00

15.09 $23,780.00 $117.250.00

15.09 09.00 $ 6,090.OO $123.340.00

09.00 09.59 $ 590.00 $123.930.00

09.59

The resulting total net profit is $123,930.00. Now, I apply the MACD-(hl) indicator to T-Bond futures directly. and compare the total net profit results. See Figure S(a) and Figure 8(b) for the performance summa? and trade-by-trade with P/L results.

FIGURE B(a) T-Bond Futures

812617 7 - 1 O/l 8196

Performance Summary-All Trades

Total Net Profit $44,510.00 Open Position P/L -S 4,280.OO Gross Profit $65,530.00 Gross Loss -$21,020.00

Total % of trades 16 Percent Profitable 56% Number winning trades 9 Number losing trades 7

Largest winning trade $21,470.00 Largest losing trade -s9,000.00 Average winning trade $ 7,281 .ll Average losing trade -S3,002.86 Ratio avg win/avg loss 2.42 Avg trade (win & loss) $2,781.88

Max consec. winners 4 Max consec. losers 2 Avg # bars in winners 84 Avg # bars in losers 22

Max intraday drawdown -$18,190.00 Profit factor 3.12 Max # contracts held 1 Account size required $18.190.00 Return on account 245%

Source: SuperCharts

MTA JOCRSAL * \tinter - Spring 1998 35

FIGURE 8(b)

Date

12/08178 09/12/80

09/12/80 10/31/80

10/31/80 03/05/82

03105182 04/30/82

04/30/82 0706182

07116182 10/14/83

10/14/83 11/16/84

11/16/84 07/10/87

07/10/87 05/06/88

05/06/88 07129188

07/29/88 01/06/89

01/06/89 04/20/90

04/20/90 02/08/91

02/08/91 05/13/94

05/13/94 04/28/95

02/08/91 06/07/96

06/07/96

T-Bond Futures

08126177 - 10/18/96 IyJ&Cnts

Sell 1 SExit 1

Buy 1 LExit 1

Sell 1 SExit 1

BUY 1 LExit 1

Sell 1 SExit 1

BUY 1 LExit 1

Sell 1 SExit 1

Buy 1 LExit 1

Sell 1 SExit 1

Buy 1 LExit 1

Sell 1 SExit 1

Buy 1 LExit 1

Sell 1 SExit 1

BUY 1 LExit 1

Sell SExit :

Buy 1 LExit 1

Buy 1

pr&

92.19 73.31

73.31 67.81

67.81 63.63

63.63 63.19

63.19 63.28

63.28 71.59

71.59 70.81

70.81 92.28

92.28 87.47

87.47 86.19

86.19 88.69

88.69 89.53

89.53 98.53

98.53 103.13

103.13 105.34

105.34 107.00

107.00

P/L Entrv

$la,aoo.oo

-$ 5,500.oo

$ 4,iao.oo

-$ 440.00

-$ 90.00

$ 8,310.oo

$ 780.00

$21,470.00

$ 4,aio.oo

4 i,280.00

-$ 2,500.OO

$ 840.00

-$ 9,ooo.oo

$ 4,600.OO

-$2,210.00

$1,660.00

$la,aoo.oo

$13,380.00

$17,560.00

$17,120.00

$17,030.00

$24,340.00

$26,120.00

$47,590.00

$52,400.00

$51,120.00

$48,620.00

$49,460.00

$40,460.00

$45,060.00

$42,850.00

$4.4510.00

Source: Sup-Charts

The resulting total net profit is only $44,510.00, which is a little more than one third (35.9% to be more precise) of the $123,930 obtained by applying the -MACD-(M) to the Bridge-CRB/Bond Ratio directly and executing the complimentary transactions on T-Bond futures - that is, when MACD-(M) generates a buy (sell) for Bridge-CRB/Bond ratio, sell (by?) T-Bond futures. Though the performance summary cites additional statistics, just comparison of the total net profit reveals the improved profitabiliy of trading T-Bond futures by using buy/sell signals generated by Xl- tering’ the Bridge-CRB/Bond ratio through the MACD-(M) indicator. Further, in our earlier discussion on interpreting the results, I mentioned one set of criteria for short-term interpretation and another set of criteria for longer-term trading. The longer-term trade view results in fewer trades, which has the advantage of lower commissions and less concern with slippage costs while keeping the trader “friendly with the trend.” X more ag- gressive trader has the option of utilizing the longer-term as a “baseline” and, if he or she so chooses, to overlay short-term buy/ sell criteria on top of the baseline trading ystem to buy dips and sell rallies in attempting to squeeze additional profits over and above those derived from the baseline long-term trading system.

Mp research has shown that though the MACD-(M) indicator is applicable to daily weekly and monthly data, the most profitable signals are generated by weekly data.

Track Record - Other Technical Indicators Having gone through the lengthy discussion of the MACD-RI

indicator, introduced by Thomas Aspray as a variation of Gerald Appel’s !vLKD oscillator, as well as the improved version which I call MACD-( M) , let us now briefI>: compare the MACD-( hl) oscillator (Figure 9 (a) ) to the stochastic indicator (Figure 9 (b) ) , parabolic SAR(Figure 9(c)), and the MACD oscillator (Figure 9(d)) as applied to the weekly Bridge-CRB/Bond Ratio together with a summar!; table of respective statistics and relative profitability of each indicator (Figure 10). The buT/sell criteria for each of these indicators is detailed in Appendix A-Formulas, together with their respective input parameters, and assigned numerical values in brackets. These numerical values are commonly used values - that is, they have not been optimized using SuperCharts. Opti- mizing involves selecting a range of values for each input parameter, and then testing all possible combinations of input parameter values till one is found that maximizes profitability.

MTA JOURML l Itinter - Spring 1998

FIGURE 9(a) FIGURE 9(a)

1MBRG C~2.17 O-221 L-2.,7 I I

” ; I 07 m 89 90 9l 92 93

I I

Source: &@-Charts Source: &@-Charts

FIGURE 9(b) FIGURE 9(b)

-3m

-2.m

-280

-2.40

-220

Source: SufwrCharts

FIGURE 9(c)

Source: SuperCharts

MTA JOURML * \Vinter - Spring 1998

FIGURE 9(d) appear to be undesirable as it results in late participation dur-

i t

280

the MACD histogram component of the W&D-(M) indicator, it more than com- pensates by making every buy and sell es- ecution more reliable.


FIGURE10 SIOW Parabolic

Stochastic MACD-(M) SAR MACD

Winning Trades 10 80 29 22 Losing Trades 4 111 44 46 Total Trades 14 191 73 68 Largest Winning Trade($) 1790 1040 1240 870 Largest Losing Trade($) 180 380 390 450 Maximum Drawdown 340 1160 1760 1300 Return on Account 1418% 515% 302% 138%

Source: SuperCharts

As I show shortly even without optimization, the MACD-(M) oscillator leads the rest of the pack in terms of profitabilic because it enters positions in the direction of the major trend on the basis of trend sustainability as indicated by whether the ;LLL\CD histogram is above or below the zero line. Analysis of the above charts and P/L confirms that compared with the other technical indicators, the MACD-(M) oscillator is quite profitable. On a weekly basis, it tallied a 1,418% return on account for the August 26, 1977 to October 18, 1996 period. The total number of trades spans this period whereas the closest runner-up was the slow stochastic which managed a respectable 515% return. In addition, the MACD-(M) also leads in the categoy of percent profitability (winning trades/total trades) with 71%, well ahead of the 42% profitability of the slow stochastic system and far outperforming the MACD-M which had a 32% return. Also of importance is that the MACD-(M) registered the highest profitability with the minimum number of total trades, the lowest $ drawdown, the highest $ winning trade and the lowest $ losing trade.

Limitations - False Signals Several observations can be made bp inspection of the price

charts: 1) Because the MACD-(M) oscillator closely tracks the longer-term

trend, the few buy and sell signals that it generates are less subject to whipsaws, and hence are more reliable.

2) This de facto buy-and-hold strategy minimizes commission and slippage costs associated with trying to time the markets by trading every buy or sell signal that is triggered.

3) The MACD-(M) oscillator generates delayed buy and sell signals compared to other indicators. On the surface, this ma)

ing rallies and overexposure during de- clines. However, because the buy and sell signals track momentum of the trend via

4) The RIACD-(hl) oscillator works best during steadily trending markets and is less effective in narrow-range, non-trending markets where it tends to generate less reliable buy and sell signals.

In Figure 11, note the volatile 1981 period where the MACD-(M) oscillator’s MACD histogram was losing momentum early in May before crossing below the

zero line and issuing a sell signal (see the first down arrow). This resulted in a loss as the buy signal that followed was at a higher closing price while a short-term uptrend followed shortl!- thereafter! As one can see, as good as the RWCD-(M) oscillator may be, it is not immune to false signals.

38 MTA JOURNAL * \\‘inter - Spring 1998

FIGURE11

Source: SuperCharts

The false signal just mentioned might have been avoided b! noting a caveat that all practitioners of technical analvsis should heed - treat any signal as a potential buv or sell signal till one gets confirmation from other technical indicators. In Figure 12, the MACD-(M) oscillator line began leveling off and declined during the February to blarch 1990 period - before the buy signal. The Momentum oscillator also weakened during this period and failed to make new highs thereby indicating weakness. Additional confirmation was given by the slow stochastic which issued a crossover sell signal in overbought territory when the Bridge-CRB/ Bond ratio first peaked. Finally, the directional indicator began narrowing just prior to the first price peak. Trend analysis would also have revealed underlying weakness. In addition, the trendline connecting the April and May 1990 oscillator line peaks shows a bearish divergence - the Bridge-CRB/Bond Ratio made higher highs but the MACD-(M) oscillator line failed to confirm this. Another alternative is to ‘optimize’ the MACD-(M) oscillator in- pLt parameters by letting SuperCharts automate the process. The results including trade and profitability statistics are shown in Fig- ure 13.

FIGURE12

III


MTA JOURNAL l \tinter - Spring I998 39

FIGURE13 Sext, in a comparison of performance, I found that the hIACD- (RI) outperformed other common technical indicators and that the parameter values proposed by Starr’ were optimal. It was also discovered that the RMCD-(RI) oscillator is most reliable when using weekly data and taking the longer-term perspective which serves as the basis for

conservative money management. Finally, despite its shortcomings, the

NACD-(>I) oscillator has been found to add value bv: 1) minimizing the number of buv and sell signals, thereby cutting commission costs, untimely trades resulting in drawdowns, and

2) the ability to track and stay with the

Winning Trades 15 Largest Winning Trade Losing Trades 12 Largest Losing Trade Total Trades 27 Maximum Drawdown

Return on Account 593%

$1630 $380 $900

Source: SuperCharts

The ‘optimized’ values for the input parameters FastMA, SlowhN, SlacdMA and Length are 4, 12, 12 and 10 respectively In comparing these result to those shown in Figure 9(a) and Fig- ure 10, I find several interesting anomalies: 1) the optimized MACD-(M) is more responsive, but in contrast

to the unoptimized MACD-(>I), 60th the oscillator line and histogram component of the indicator now track the short- term trend,

2) the optimized MACD-(XI) oscillator zcnd~r~e~forms the unoptimized results in Figure 9(a) and Figure 101

It appears that the original numerical I-alues for the hWCD- (M) input parameters mar already be the optimal values.

Summary and Conclusions The aim of constructing a technical indicator that improves

upon Thomas Aspray‘s JMCD Momentum Oscillator appears to have been achieved. Background material was introduced briefl! to describe commonlv-used inflation indicators. Fundamental concepts of the WKD and Momentum oscillators were reviewed before building up to the topic of this research paper. I found that by modifying the construction of the histogram component of the MACD Momentum oscillator, also called MACD-SI, and phasing out elements of the indicator that causes it to lag the underlying market, I arrived at the KKD-(RI) oscillator. Upon comparison with the MACD-!vI, the MACD-(RI) issued short-term buy/sell signals that led the market as well as its predecessor - the MACD-M - in addition to closely tracking the longer-term market trend. The YvIACD-(11) was then used to filter the Bridge- CRB/Bond Ratio to see if I could uncover more profitable by and sell signals in trading L’S T-Bond futures.

AM!; research found that it was more profitable to chronologi- call!; time T-Bond futures trades to Bridge-CRB/Bond Ratio trades as dictated by the %&D-(M) oscillator, as opposed to trading T- Bond futures based upon buy/sell signals issued by the W&D- (M) oscillator applied to T-Bond futures data directly. Like an!

technical indicator, false signals are as inevitable as ;he need to use multiple indicators for confirmation before executing trades.

longer-term ‘trend. As indicated in the article, this Tyas accomplished by changing the way SLICD is computed in the technical analysis software literature.

I added momentum to the computation of the hI.KD histogram which in effect speeded ~1 the occurrence of the short-term trigger. The reward was a more profitable longer-term trend- tracking indicator.

Footnotes

(see also Appendix C - Bibliography)

1. Thomas .ispraJ, Par-t I and Put II 2. Thom Hurtle, John Ehlers

3. Barbara Stan; Ph.D.

MACD-(M)

Appendix A - Formulas

Indicators

Oscillator = Momentum (KICD (Close, FasthI& SlowAZ~) - Mo- mentum (XXverage (MXCD (Close, FastMA. Slow>L4), MacdKl), Length), Length)

Histogram = SIXCD (Close, FastMA, Slow>l.~) Momentum (XAverage (hl;\CD (Close, FastNX, SlowM~~) , MacdMA) Length)

Input Parameters (Time period refers to daily, weekly and monthly)

Close - Closing Price

Fasth,IA- Time period of shorter-term simple moiing average; [lo]

SlowNA - Time period of longer-term simple moving average; [ 201

MacdKZ - Time period of exponential moving average: [lo]

Length - Time period of momentum oscillator; [lo]

MACD-M

Oscillator = AAverage(Momentum(MhCD(Close, FasthI& SLowMA) - XXverage( hIACD (Close, FastMA, SlowMA), MacdhIA), Lengthl), LengthP)

Histogram = RIACD (Close, FastMA, SlowSlA) XAverage (RIACD (Close, FastMA, SlowMA), hIacdM4)

Input Parameters


FastK4 - Time period of shorter-term simple moving average; [lo]

MTA JOURNAL l I\‘inter - Spring 1998

SlowbIA - Time period of longer-term simple maying average; [ 201

MacdhL1- Time period of exponential moving average; [lo]

Length1 - Time period of momentum oscillator; [lo]

Length2 - Time period of simple moving average; [3]

Tradine System Buv(,on close)/Sell(on close) Criteria

MACD-(M)

Buy if: (hL%CD(Close, FastMA, SlowM.\) - Momentum (XAverage( MXCD (Close, FastMX, SlowhlA) , MacdMA), Length) > MACD(Close, FastMA, SlowhIA) - Momentum(XA1verage(MACD(Close, FastMX, SlowhIA), MacdMA) , Length) [ l] ) and (MACD (Close, FastMA, SlowMA) - Momentum(XAverage(MACD(Close, FastMA, SlowMA), MacdMA), Length) > 0)

Sell if: (MACD (Close, FastMA, SlowMA) - Momentum (XAverage( MACD (Close, FastMA, SlowMA), MacdM.1) , Length) < MACD (Close, FastMA, SlowMA) - Mo- men turn (XAverage (MACD (Close, FasthlX, SlowMA), MacdMA) , Length) [ 1 ] ) and (MACD (Close, FastMA, S~ONMA) - Momentum(XAverage(hlXCD(Close, FastMA, SlowMA), MacdMA), Length) < 0)

Input Parameters (Time period refers to daily, weekly and monthly)


FastMA- Time period of shorter-term simple moving ayerage; [lo]

SlowhfA- Time period of longer-term simple moving average; [20]

MacdWA - Time period of exponential moving average; [lo]

Length - Time period of momentum oscillator; [lo]

MACD-M

BUY iti Average (Momentum (MACD (Close, FastMA, SlowMA) - XAverage (MACD (Close, FastMA, SlowMA), MacdMA) , Lengthl), LengthP) > 0 and MACD(Close, FastMA, SLowMA) - XAyerage (MACD (Close, FastMA, SlowMA), MacdK1) > 0

Sell if: Average(Momentum(MACD(Close, FasthI& SlowMA) - XAverage (MXCD (Close, FastMA, SlowMA), MacdMA) , Lengthl), LengthP) < 0 and MACD(Close, FasthLA, SLowhZA) - average (MACD (Close, FastRZA, SlowMA), MacdMA) < 0

Input Parameters


FastbM-Time period of shorter-term simple moving average; [lo]

SlowMA- Time period of longer-term simple moving average; [ 201

hlacdhl.4 - Time period of exponential mo\-ing average; [lo]

Length1 - Time period of momentum oscillator; [lo]

Length2 - Time period of simple mating average; [ 31

Stochastic

Buy if: CurrentBar > 1 and SlowK(Length) crosses above SlowD(length)

Sell if: CurrentBar > 1 and SlowK(Length) crosses below SlowD (Length)

Input Parameters (Time period refers to daily weekly and monthly)

Length - Time period over which price range of interest is taken; [lo]

Parabolic

Buy if: CurrentBar = 1 or MarketPosition <> 1

Sell if: CurrentBar > 1 or 1farketPosition = 1

MACD

Buy if: CurrentBar > 1 and KKD(Close, FasthlA, SlowMA) [ 11 < XAverage (MACD (Close, FastMA, SLowMA), MacdMA) [l] and MACD(Close, FastMA, SlowMA\) > X-Average (MACD (Close, FastMA, Slow~M), MacdhL1)

Sell if: CurrentBar > 1 and MACD(Close, FastMA, SlowhM) [l] > XAverage (MACD (Close, FasthL1, SLowhU) , MacdMA) [l] and MACD(Close, FastMA, SlowhU) < XAverage (hL%D (Close, FastMA, Slow1L\), MacdMA)

Input Parameters (Time period refers to daily, weekly and month]!)


FastlLZ- Time period of shorter-term simple moving average: [ 121

S~ONNA - Time period of longer-term simple moving ayerage; [ 261

SlacdK! - Time period of exponential moving average; [9]

MTA JOURNAL l \Vinter - Spring 1998

Appendix B - Notes

Performance Statistics Glossary

Total Set Profit

Open Position P/L

Gross Profit

Gross Loss

Total # of Trades

Percent Profitable

Number of Il’inning Trades

Number of Losing Trades

Largest 12’inning Trade

Largest Losing Trade

Ayerage Winning Trade

Average Losing Trade

Ratio of Average Win/Loss

Average Trade (M’in 8: Loss)

Maximum Consecutive \Vinners

Maximum Consecutive Losers

Average # of Bars in 1Vinners

Average # of Bars in Losers

Maximum Intraday Drawdown

Profit Factor

Maximum # of Contracts Held

Account Size Required

Gross profit minus gross loss

Profit/Loss of current open trade

Cumulative total of all winning trades

Cumulative total of all losing trades

Number of winning trades plus number of losing trades

Number of It-inning trades divided b! total trades

Total count of winning trades

Total count of losing trades

Largest closed out winning trade

Largest closed out losing trade

Gross profits divided by number of winning trades

Gross profits divided by number of losing trades

Average winning trade divided b\ average losing trade

Set profits divided by total trades

Longest string of consecutive winning trades

Longest string of consecutive losing trades

Cumulative total bars in winning trades divided by number of winning trades

Cumulative total bars in losing trades divided by number of losing trades

The largest equity dip based on closed out trades plus open position profit/loss on an intra-day basis. Maximum Intra-Day Drawdown is the true number of dollars needed to sustain the largest equit\ dip during the life of the test period.

Gross profits divided by gross loss. Thus number represents how man!- dollars the system made for every dollar it lost

Slaximum number of contractS held at any one time during the life of the test period

Maximum Intra-Day Drawdown plus margin

Appendix C

Bibliography

Aspra:; Thomas, “W&D momentum Part I.” Technical .Anall-- sis of Stocks 8: Commodities. A1ugust 1988: p. 33-36.

Aspray, Thomas, “MACD momentum Part II,” Technical Anal\: sis of Stocks & Commodities. September 1988: p. 650.

Murphy, John J., Intermarket Technical Analvsis: Trading Strat- eg-ies for the Global Stock, Bond Commoditv and Currency Markets. New York: John M’iley 8; Sons, Inc., 1991: p. 19.

q Hartle, Thorn, “Moving Average Convergence/Divergence (MACD) .” Technical Analysis of Stocks & Commodities. March 1991: p. 35.

q Ehlers, John F.,“The IvIACD Indicator Revisited,” Technical Analwis of Stocks 8c Commodities. October 1991: p. 18-23.

q Starr, Barbara, Ph.D., “The hMCD Momentum Oscillator,” Technical Analysis of Stocks & Commodities. Februay 1994: 73-58.

SuperCharts \‘ersion 2.0 for M?ndows 3.1. Omega Research

Incorporated, Miami, Florida.

Biography \‘incent Jay, CMT, is a Technical Analyst at McCarthy,

Crisanti & Maffei, Inc. H e is responsible for analyzing US

Treasuries futures and cash, Eurobonds and commodities. He holds a Capital Markets Certificate from Sew York Uni-

versi? School of Continuing Education. In addition, he also holds a B.Sc. degree in Engineering.

\‘incent received his CMT designation in Februaq 1998, and is currently pursuing the CFA Charter. His professional associations include Membership in the Market Technicians

Association, New York Society of Security Analysts and the Society of Quantitative Analysts.

MTA JOURNAL l \\‘inter - Spring I998

Fibonacci Ratios & Time Cycles Observation and Applications

Song Hua Huang

Introduction Time cyle studies hale been one of the most important areas

in technical analvsis. So far, most of the cvcle studies are for svmmetrical time cwles. However, cycle movements are not always symmetrical. They often appear longer in one ware and shorter in the other. Under this situation, svmmetrical time cycle stud\ does not work well. N%at can a tech&cian do with the’ non-y&- metrical cycles? Fibonacci numbers can provide powerful solu- tions. Some technicians have used 0.618 and 1.618 to measure time cycles, and that has pro\-ed to be very effective in forecasting time cycle turning points. This research has explored further into other important Fibonacci ratios and observed their effects on time cycles. Observations have been done on 0.236( 1.236), 0.382(1.382), 0.5(1.5), 0.809(1.809).

11~ research has found that these Fibonacci ratios are also ve? effeciive in predicting cycle turns. N’ith the help of these ratios, technicians should become more capable in dealing with different cycle lengths, and be able to predict or forecast the markets more effectively. The outline of this research paper is as follows:

Brief Review of Fibonacci Numbers and Ratios

Basic Tenets

Xpplication & Chart Illustrations

Conclusion

Brief Review of Fibonacci Numbers & Golden Ratios 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144 . . . . . . These summation series or Fibonacci numbers were discov-

ered b!- Leonardo Fibonacci, a medieval Italian mathematician. reputedly after he visited the Egyptian pyramids. The summation series have several important properties:

1) The sum of any hvo adjacent numbers equals the next higher number.

2) Any given number (3 or above) approximates 0.618 of the number following it.

3) Any given number (5 or above) approximates 1.618 times the number preceding it.

4) Between alternate numbers, the second higher number ap proximates 2.618 times the first number.

5) Between alternate numbers, the second number down has the ratio of 0.382.

M’e can see 0.618 and 1.618 ratios play a key role in the summation series. The ratios calculated from the summation series are also called Fibonacci ratios.

The magic numbers 0.618 and 1.618 have been known to human beings since B.C. The ancient Greek mathematician Euclid (330? - ?255 B.C.) discovered the ratios 0.618 and 1.618 in the course of his study of geomety Euclid studied the proportions of hvo sections in a straight line. In order to ha\-e L:a = a:b, Euclid found the requirement was met when point C = 0.618 (propor-

tion of L). From the following graph we can see: \\71en C = 0.618 then L:a = a:b = 1.618. a:L = b:a = 0.618. This ratio is also called the golden ratio. The names golden ratio and Fibonacci ratio are often used interchangeably.

A

4 a

C b B

L w

The mathematical formula for 0.618 is.

x= -1tt;s 2

r2n important property for S is 1 - X = X’ Other important Fibonacci ratios include: 1.618 x 0.5 = 0.809 0.618 x 0.618 = 0.382 0.618 x 0.382 = 0.236 ;\nd notice that all the ratios above are 0.618 or 1.618 related.

Remember 0.5 and 1 are also important Fibonacci ratios. The golden ratio is a natural phenomenon. Researchers have

found that the golden ratio exists everywhere in our universe. For example, the structures of the spiral of galax?, shell of snails, sunflowers, human body proportions and human DNA twist in- terval ratios are all integrated with the golden ratios.

\‘hat about human products? They too, are found to be integrated with golden ratios. The Great Pyramid of Gizeh is one of the most outstanding examples of golden ratio application. Golden ratios are also found integrated with the size of houses. windows and books. People subconsciouslv find objects lvith the golden ratio integration stable, firm and aksthetically pleasing.

In this century, R.S. Elliott fomided a school of stock market analysis that reflects the order of Fibonacci numbers. Thus,Fibonacci numbers and ratios have become a powerful tool for technical analysis ever since.

Basic Tenets [Ve encounter two basic questions when we study Fibonacci

ratios and time cycles: 121~ can Fibonacci ratios be applied to human activities?

\l’hy do Fibonacci ratios have an effect on time cycles?

To answer these two questions, some basic tenets are offered:

Fibonacci ratios are natural phenomena. They exist every where, from the spirals of galaxies to our human DNA structure.

Nature has a tendency to exist and change in an optimal wa): Golden ratios or Fibonacci ratios are optimal ratios. Golden ratios are nature’s phenomena for structure and change.

Human beings are a product of nature. Human beings also have a tendencl- to act in an optimal way. Much of human behavior (quantitatively and in terms of time) is unconsciously integrated with Fibonacci ratios.

Stock and commodity price charts record and present human behavior in the markets. Fibonacci ratios can be applied to


observe human behavior by measuring the time as well as the price levels (Fibonacci retracements, for example) on the charts.

Application & Chart Illustration A great observation from R.N. Elliott about the importance of

time in market movements came from his statement: “All human activities have three distinctive features, pattern, time and ratio, all of which observe the Fibonacci summation series.”

In our time cycle study, we replace Elliott’s 5\vave up and 3- wave down pattern with conventional technical patterns (e.g. trend lines, gaps, double bottoms and tops, support and resistance, etc.), and we make those replacements the Fibonacci summation series with Fibonacci ratios, in order to make our cvcle studies more practical. So the three basic features for time’cyzle observation now become technical patterns, time, and Fibonacci ratios. The basic steps in our Fibonacci time cycle approach are:

To watch the technical chart pattern development in the current time cycle.

To project the Fibonacci ratio set 0.236(1.236), 0.382(1.382),0.5(1.5),0.618(1.618)...onthefuturedates based on the previous cycle lengtb

To look for the likely cycle turning point when the price moves to a high or low level and meets a projected Fi- bonacci ratio point.

To act accordingly (initiate or liquidate a position), if the chart pattern and Fibonacci ratio factors line up and the price reversal is taking place.

If the price reversal doesn’t happen, watch the next Fi- bonacci subset ratio date for the probable reversal and so on.

Certain chart reading experience and estimation are needed to apply this approach. Cp to the current research level, we don’t know for sure beforehand if the cycle jvill turn at 1.236, 1.382 or other ratio point until the projected date arrives or until afterwards. One of the things a technician can do, when the price arrives to the projected Fibonacci date but the chart pattern is not ready to reverse, is to exclude the current date as a likely turning point and look to the next Fibonacci ratio date for the likely reversal.

dne clear advantage in this approach is that the Fibonacci ratio points are the high-probability turning points in the time cvcle movements. This knowledge can be exploited for our appiica- tion.

The following charts illustrate how Fibonacci ratios work on time cvcles in the real world situations.

Ma& of us are already familiar with the symmetrical time cycles.

Bank America

&America is an example. It showed 104day synmetrical cycles )rn early 1993 to earlv 1993. Remember 1 is also a Fibonacci .io, so we can regard symmetrical cycle as Fibonacci ratio 1 x : length of the previous cyle. Let’s look at the following non-symmetrical cycle charts.

Goodyear Tore 01131197 /

Goodyear Tire showed 123-day cycle (or 2 small cycles com-

ned) from late 1994 to early 199.5. The length of the second cle was 123 x 1.236 = 152 days. The third cycle was 1.52 x 1.236 188 days. TJVO consecutive increases by 1.236 times.

Ratio 1.236 showed on General Dynamics time cycle move- .ents in 19941995 period.

MTA JOURNAL l \Vinter - Spring 1998

h very short term 1.382 ratio at work on Nextel time cycle in March 1995.

Bally Gaming 01130196

13.0

After the 52-day cycle in mid Jwe 1995, Sally Gaming price shot up and started a new cycle. Five day later, the price retraced along a straight trend line. Since it went up and retraced sharply, it would be likely to form a shorter time cycle than the previous 52-day cvcle. Projecting ratios 0.382 and 0.5. 0.382 would land on July (2 and 0.5 would land on July 21. On July 12, the price retraced to g-5/8, no support appeared in this level, the price continued to edge down. So 0.382 should be excluded as a turning point. On the 0.5 ratio day (July 21), the price reached 9-l/8 level and filled the price gap, which was a technical support. Both the time and pattern factors lined up and indicated a probable reversal at this point. In fact, price bounced up in the following days.

Because a technician could not be 100%’ sure in the case above, price would reverse on the 0.5 ratio da): He ma!- wait until the price reverses to initiate a long position or liquidate a short position on the 0.5 ratio dav.

The chart also sho& ratio 1.382 pointed to the cycle top in September 1995.

AT&T 09/30/g

Ratios 0.618 and 0.5 in effect on AT&T price cycles in 1996-

L

Naxos Res 12123196

1

,,jl I:, j ‘95 w$! !w DE;

Naxos Resources (traded on the Alberta Stock Exchange before June 1996) showed a fascinating Fibonacci phenomenon on its price turning points during JanuaryMay 1996.

The character in the Naxos chart was that prices moved up

and down very sharplv and frequentlv. Traders could start to project the Fidonacci ratios to forecast ;he next topping day right after the current top, or wait until the price started to move up

sharply again in the next cycle. The latter method can narrow down the Fibonacci ratio subset choices, and therefore can save some steps for forecasting.

The Naxos chart shows another great example of the way that human activities are unconsciously integrated with Fibonacci ratios in terms of time.

Ratio 0.809 at work on Repap Enterprises weekly chart in 1993 to 1994 period. (stock traded on the Toronto Stock Exchange).

MTA JOURKIL l \Cinter Spring 1998 45

Quarterdeck Quarterdeck

5.0 5.0 t.5 t.5 4.0 4.0 3.5 3.5 3.0 3.0 2.5 2.5

'45 Rb '45 Rb EC EC CCT CCT Mu Mu nc nc '97 '97

Ratio 1.809 at work on Quarterdeck cycle tops. Ratio 1.809 at work on Quarterdeck cycle tops.

There are a few rules to follow when we apply Fibonacci ratios There are a few rules to follow when we apply Fibonacci ratios on time cycles:

The Fibonacci time cycle approach should be combined with chart pattern analysis. Remember that the three basic features in our approach are pattern, time and ratios.

This approach should be applied to the charts with clear cycle patterns only; otherwise results may be incorrect or unsatis- factory.

Fibonacci ratios on time cycles can be applied on daily weekly monthlv or intraday charts.

For daily charts, count the trading days only, non-trading days should not be included in the cycle length measure.

Allow for a unit (one day for daily chart, one week for weekly chart, etc.) deviation from the predicted Fibonacci ratio turning point.

Fibonacci ratio points are high-probability turning points in the time cycle movements. Still, not all cycle turning points can be measured by symmetrical or Fibonacci ratios.

Conclusion Fibonacci ratios are a natural phenomena; they exist everywhere from the spiral of a galaxy to our human DM structure.

Human beings have a tendency to behave in an optimal way and Fibonacci ratios are optimal ratios. Much human behavior, including stock market behavior, is unconsciously integrated with Fibonacci ratios.

In addition to the ratios 0.618 and 1.618, ratios 0.236, 0.382, 0.5, and 0.809 are also important Fibonacci ratios. They all play active roles in human activities. Therefore, they are important supplements to symmetrical and the 0.618 (1.618) ratios for time cycle study.

The application of Fibonacci ratios to time cycles is in a sense an optimum seeking method. M’ith this method, traders may find the most likely cycle turning points much more effectively.

The Fibonacci approach has shown the ability to forecast the time cycle turning dates with impressive accuracy while the traditional technical approaches are mainly trend-following approaches.

The Fibonacci cycle approach does not require advanced math knowledge to learn it or to apply it; it can be a powerful tool for the average trader.

Like any other approach. Fibonacci and symmetrical time cycle analysis also have their limitations. The main ones are that they are not able to forecast all the cl-cle turning points, and ceriain chart reading experience and estimation are still needed to apply this approach properlv.

The preliminary results of this exploratory research show that Fibonacci ratio approach can be a promising way for observing the time cycle behavior and forecasting the time cycle turning points. ‘Il’ith common efforts in research and discoveries from other students in the field of technical analyis, this approach should become more accurate and effecti\-e in the future.

Sources of Reference Zhong, Ling Song and Tao, Chong En, Short-Term Trading LVeapons. Hong Kong

Frost, A.J. and Prechter, Robert R., Elliott \\‘ave Principle. New York, hY (1990)

Murphy John J., Technical Xnalvsis of the Futures Markets. New York, NY, (1986)

Fischer, Robert, Fibonacci ADglication and Strategies for Trad- m. Sew York (1993)

Hua, Luo Geng, X Plain Talk On Omimum Seeking- Methods. M’u Han, China (1971)

Biography Song Hua Huang is an active pri\-ate trader. He was born

and raised in China. He came to Sorth America in 1984 and currently lives in Toronto, Canada. For the last 11 years, Mr. Huang has concentrated his major efforts on the study of technical analysis. The main areas of his studies include trends, chart patterns, volume, analogy. time cycles, Fibonacci numbers and momentum.

46 MTA JOURh?L l \Cinter - Spring 1998

OEX Trading System

David Kirkman

Introduction Probabb the most widely-traded option is the Standard 3 Poori 100

(OEX) in&x opfion. B~WZIUP options NIP big/$ IPrwmgf$ f/w amount of money that can be made i.s tremendous. ‘That is one reason that players are l&ed to the option markets. HourPun; a lot of monq can be 10s; as ruell, so vlonn management is WI-\ crucial in /wesewing longevity in the option markets.

It zuas these characteristics of the option markets that Ld to mu focus on hozcr to trade potentially profitable moves in the OEX, while creating a .rimfile, Jet objective system, using popular technical methods and indiccr- tors. This sptem hasstrict entq rules, and also strict stop loss/exit rules.

This paper explains rn? system, broken douw into the following compo- nmts:

1. Quantijication of each indicator used in the system.

2. Options basics.

3. Criteria for thP system.

4. Entry and exit rules.

5. A running equity table from Januar;v 1987 to September 1996, using the entq signals and monq management rules.

6. dp@-ation of this MU OEX ystem in toda)‘s portfolio mix, and comparisons.

7. Glossa,? and explanation of technical terms and OEX options pricing theory

8. Appendix and references.

Description of Indicators and Usage

Stochastics Oscillator I’ve always been vey comfortable using stochastics and/or the

relative strength index (RSI) to trade stocks and index movemena. I use both for divergence analysis, and for also checking overbought!oversold readings for the non-trending markets that I trade. But as I got more seasoned trading OEX options and momentum reversals, I realized that I didn’t need to use both to get my OEX signals. The formula for both oscillators is in the appendix section of this paper.

I realized that for this ystem, I was more successful with the stochastics than with both indicators or the RSI standing alone. I didn’t need the RSI oscillator, which gave me a ratio of the arer- age movement on up/down days for my n-periods because if there were volatile market conditions, and there was a large point gain (sa? 15 points), and the 13-point gain was actually right in the middle of the day’s trading range, then the RSI would calculate this strength by placing 15 into part of the total summation for the up day for the n-period. This could significantly increase the true RSI when in fact, it could be just a one day anomaly Stochastics, on the other hand, would see this event as a wide trading range, and while the closing price was still up, it was in the middle of the trading range, giving it a one-period weighting of 3: a less than stellar event for stochastics, and one that would not skew the oscillator one way or the other in such a dramatic fashion.

I needed to use the stochastic oscillator because I needed to

know where in the trading range the index was closing. I’ve sub- scribed to my own theory that if the momentmli is going to change to the upside, then the momentum has to be toward the upper end of the trading range, regardless of the intraday trading range action.

Seeing where the close was in relation to its high or low gave me a better indication where the momentum was hrdding. I believe that if the index goes from 600 high, 590 low, and the close at 599, with the previous day’s close at 600.10. this shows that while there were forces at work during the da!, that there was more accumulation toward the close when day traders were un- winding their intraday positions at the close. M%ile such a da! would possibly raise the stochastics value, it would add a -1 .lO to the average of down closes for the n-period in the RSI.

The lo-Period Stochastic

When I first started using stochastics to trade swings in the OEX, I used the standard 5-period SK, with a J-period smoothing value for SD. It didn’t take long to realize that I was trading too often and unsuccessfully Losing money can do that to a trader. I really liked the stochastics indicator and I thought that I might be able to get a more reliable reading by extending the %k value. I first tried the Wperiod average because of the usefulness of the 20-day moving average that I use regularly in my work.

By visual inspection, it was very obvious that the ‘LO-period stochastics ws clearly too long. Signals and crossovers were be- coming apparent too late for trading, and in some instances. the bulk of the upmove was almost over by the time the appropriate crossover was met.

I decided to shorten the stochastics value from ‘LO to 10 because 10 was a half cycle of the 20-period and 10 was also a dou- bling of the j-period \-alue. Itjust happened to coincide that half cycles and double-length cycles are very important in cycle theory that I was studying at that time. .A length of 10 periods. as I found out later; is used by many analysts today.

I noticed a remarkable visual improvement as the computer signals were better suited for the crossovers than the TO-period value. I also noticed that the lo-period value didn’t compromise the amomit of signals to a great degree versus the .?-period value. I didn’t want a ystem that gave one signal per year, but I did want the signals to be accurate.

In my technical arsenal, I have become very reliant upon the lo-period value for my stochastics as well as for other indicators (moving averages, momentum, RSI).

Arms Index At first, this tool wasn’t even a consideration for this system. I

first heard of the Arms Index when I watched CNBC’s John Murphy talk about the index and the rise and fall of stock indi- ces. I learned further of its capabilities by reading more about it when I was studying John Murphy’s book Technical Analysis of the Futures Markets.

After reading Murphy’s book about the Arms Index, I started to pa! attention to the lo-da)- Arms Index reading on CNBC’s morning market call and I would watch the relationship of mar-

ket action to the Arms Index readings. I found I-alidity in what John Murphy was saying about Arms Index readings and market

MTA JOURNAL l I\‘inter - Spring 1998 47

action relationships, but wasn’t knowledgeable enough to include this indicator in my system.

However, as I was ‘experimenting with this reading in my sys- tern, I did find that if the lo-day Arms Index was under 1.0, m) OEX signals to the long side bkcame more profitable with the stochastics crossing over to the upside within the parameters.

I then purchased Richard Arms’ book, The Arms Index, to get inside the mind of the man who invented this tool. I’m the type of person who can’t use an indicator without knowing as much as possible about its creation and the mathematics behind it. Two key phrases in his Arms’ book that got my attention as to the ero- lution of this measurement were on page 3.

“The numbers reflect the internal structure of the market and can help to reveal that structure without its being obscured bp other external factors. The index is showing us where the buying and selling pressures really are, regardless of the message being imparted by the popular averages.”

After learning about how the Arms Index evolved, I turned my attention to Arms’ work on the lo-period moving average of the Arms Index because my stochastic period was 10, and the lo- period Arms Index seemed popular among technicians. Arms describes the 10 day Arms Index as:

“In using arithmetic averages, the most common use has been the IO-day moving average this is a natural outgrowth of our decimal system, and it was adopted to suit the convenience of the analysts rather than the rhythm of the markets. It speaks well for the underlying validity of the index that it was so successful while having the constraints of convenience imposed upon it.”

Arms also states that the lo-day moving average is “very widel! followed, and numerous advisors key much of their forecasting to this computation.”

Knowing the efficacy of the Arms Index, and how it was inad- vertently helpful at first, I looked further into applying lo-day Arms Index readings into my system.

Altering the 1.0 Level I originally starting using the Arms Index by following its gen-

eral definition. If it’s below 1.0, then volume is going into advancing stocks, and if it’s above 1.0, the volume is going into declining stocks. While trying to put the best and most profitable system together with the tools I was using, I started to closely ex- amine the results of the alert signals by adjusting the 10 day average numerical requirement to try and find the more reliable signals (signals that got you in before the move started). After visu- ally inspecting the results, by changing the 10 day value requirements between .90 to 1.10 in increments of .05, I found that the best results in this system were obtained with the value requirement at 1.05.

I finally decided to stick with 1.05 because if the lo-day average of the Arms Index was between .90 and 1.00, the accumulation had already been underway and part of the move was missed. With the lo-peiiod average above 1.05, distribution was still stronger than accumulation. But, interestingly, 1.05 worked well. My explanation for this is that before the uptrend starts, the average of the Arms Index gets close to 1.0 (even accumulation/distribution) but it need not be 1.0 or less.

Exponential Moving Average More Recent

I used the exponential moving average for the Arms Index (Arms uses simple moving averages for his calculations) for one reason only. Since the equity markets react to current information, and the exponential moving average places more relevance on more recent data, we can assume that it is more relevant than

:he simple moving average, which gives all the days in the n-period weighting. Because these index options that we are trading are for shorter-term trading opportunities, we can assume that the more recent day’s events influence the markets more than the already-absorbed market sentiment reaction from several days past.

Importantly;, I found that by switching to the exponential mov ing average, that there were a handful of signals that were caught by the exponential moving average, and not by the simple moving average. I also noticed that the exponential mo\-ing average also confirmed the simple moving average alerts.

Higher Low An uptrend is often defined as having higher lows and higher

highs. The whole purpose of timing the OEX trade is to try and find the closest bottoming point (with improving internal strength) and entering to the long side.

M%ile there are different “set-up” patterns in technical anal!-- sis, I found that this system’s signals were a lot more reliable if the alert day had a higher low than the previous day’s low. \2%ile it was not necessary to have a higher low on the alert day, I found that ensuing rallies had a far greater occurrence rate with a higher low than if there were a lower low.

I implemented the following rule: The current da: must make a higher low than the previous

period. This is an important piece of evidence that the previous day may have hit some measure of support or that the market is short-term orersold at that point and a rally may be starting. To see evidence of a possible reversal is key, because the signals are

48 MTA JOLRSAL l \Vinter - Spring 1998

more reliable with signs of improVing strength in the index. Here is an example of both types of signals:

The Trendline Interestingly enough, one of technical analysis’ basic compo-

nents is what differentiated this system as being good or great. At first, I was using moving average crossovers coupled lz-ith the Arms Index average and the stochastics crossover. For the crossover, I used a j-10-20 crossover. It was a variation of the more infamous 4-9-18 crossover method used in futures. I used the parameters that I did because of the efficacy that I found by using the 10 periods, and that 5 periods was a half cycle of 10, and 20 was double the lo-day period. Using different combinations for entry with the crossovers, the signals still lagged, but the best results at that time were obtained by using the 5-10 crossover.

My OEX chart was filled with trendlines ranging from short- term to longer-term trends. Abandoning the moving average crossover methodology for the same reason that I abandoned the 20-period stochastic reading, I started experimenting with the use of trendlines in this system when I saw two consecutive signals that were initiated with a penetration of an already existing trendline.

My curiositv was very much awakened and I went back as far as my data permitted, and I started drawing short-term trendlines. I noticed a significant number of signals grouped very close to a break in the short-term downtrend line. Some signals were on the exact day that the downtrend line was broken, but the number of signals that fit this ideal scenario were a lot fewer than desired.

I went back over the data again repeated17 and noticed that ven significant moves in the OEX happened within a few days of the’signal. I had to narrow down a time frame that caught most of the major moves and that could be implemented into a trading system. After analyzing the data again, I decided that if 10 periods [stochastics and Arms Index EM41 were indicative for the OEX signal, then the trendline should break within that half cycle of the 10 periods. If the price of the OEX couldn’t break within the j-period half cycle, the prior forces that were giving a signal were at that point invalid for short-term trading purposes. The thought was that if the market momentum couldn’t at least change short-term after 5 periods, than other factors (i.e. economic news) might have a more dominant influence over the market prices going forward.

OEX (courtesy Chicago Board Options Exchange)

OEX is the symbol for options on the S&P 100 Index. This is a capitalization-weighted index, covering a broad-range of indus- tries. Each index option contract represents 100 times the current value of the index. For example, when the index is 500, the dollar value of the index will equal $50,000. 100 (the multiplier) times 500.

The OEX was created by the Chicago Board Options Exchange and originally represented the top 100 stocks that traded on the CBOE in 1983. However, Standard & Poor’s now manages the S&P 100 and is responsible for the addition and deletion of securities.

Options Basics An option gives the holder the right, but not the obligation, to

buy or sell a security at a set price until a specific future date.

There are two ?;pes of options: calls and puts. X call option gives the owner the nght to buy the underlving security at a specified price, the put option gives the owner the right to sell the underlying security at a specified price. The “specified” price is also referred to as the strike price.

Other references to options include “in,” “at” and “out of the money. ” “In the money” options are call options that have lower strike prices than the current prices, and put options with strike prices above current price. “At the money” options are call op tions that have the same strike price as the current market price, and put prices the same as the current market price. “Out of the money” options are call options with strike prices higher than the current market price, and put options that have lower strike prices than current market prices.

For this paper, we are only interested in call options that are at least $5 in the money for the next expiration period. I trade these because they seem to appreciate at an acceptable rate for the necessan price objective, yet are less expensive than calls that are “deeper” in the money

,Vote: Options pricing and price movement theory in relation to the un-

der@ng index arc addressed in the glossq oftems.

Criteria For an alert signal, the following criteria must be met:

1. The lo-day EMA of the Arms Index must be below 1.05.

2. The FastD on the stochastics value must cross above the 30 line from below the 30 line. %K is often volatile, and we are using the smoothing factor of %D for a more reliable signal.

3. The current low must be higher than the previous bar’s low.

An alert signal becomes an entry (buy) signal when:

4. The short-term (minor) downtrend must have been broken prior to signal, or do so within 5 trading days after the alert signal date, on at least an intraday basis.

Then buy market on close.

MTA JOL’RNAL l \Cinter - Spring 1998 49

Example Charts Example chart 1 shows a clear bu! signal, while chart 2 shows

an alert signal without the buy criteria being met.

I

=-------~--._-- --’ -.__.- -_------. ---- Iax

I- lsssco YSLal

Exit Once you get the confirmation from !-our broker that you are

in the OEX call position, immediately put in an order to sell 4 points above your purchase price.

Why 4 points? This requirement needed very little thought because I wanted

a system, whose signals were profitable, and one which pro\-ided a predetermined exit point.

The four points and out derives from the fact that the minimum gain that was achier-ed on a consistent basis was 4-l/1 points. Accounting for the difference between the bid and the ask prices, I rounded the number dew to 4 points. I also didn’t want to theoretically lose more than I could gain, so the maximum loss I allowed myself would be 4 points. This kept the risk/reward ratio at 1:l. 1V<ile more could have been made on certain trades, the main idea was to be able to stay in the game long enough to play another round. Because greed is a nemesis to a trader, I lranted to eliminate the emotionality of getting out of the trade.

Money Management Cntil your 4 points are achieved, also make sure that ~-our bro-

ker knows to put a stop loss in 30% below what you paid for the option. Ifpou bought an option at $10, then your objective would be $14, stop loss of $7.

The 30% or maximum 4 point stop, whichever is LESS, was devised after looking at the daily option prices. If the option went down 30%. which on some trades was only 2-3 points, the option ended going dolvn a lot more. some even expiring worth- less. The maximum loss of 4 points keeps the risk/reward at 1 :l.

The table on the next page represents the simulated results beginning from January 2, 198i and ending September 30. 1996.

Results

The running equitv table shows that one can realize impressive gains consistently’if one follow the trading rules. But most importantly it shows ho\\- well technical analysis methods can be combined to form powerful and vev profitable trading systems that can compliment portfolio strategies.

-Just hov significant are the results? One of the most widel:-accepted methodologies of investing

is the buy-and-hold method. Bw-and-hold is based on the premise that market timing is not possible, and an investor would be better off buying quality stocks and holding them through good times and bad. Let’s compare this theory against my trading system.

I don’t believe that an!-one can dispute that the Dow Jones Industrial Average contains some of the highest-quality most- popular stocks available for investing purposes.

Also represented are the S&P 500, the NASDXQ Composite, Russell 2000, and two ver!- popular mutual funds: \Bnguard In- dex 500 Trust (tracks the performance of the S&P 500), and Fi- delity Rlagellan.

Below is a table with the annualized returns for each index from January 2, 1987 through September 30, 1996. All index benchmarks, and all mutual fund returns, assume the reinvest- ment of dividends. hlutual fund returns also include the rein- vestment of any capital

I f ains distributions. omparison Table

(112187 through g/30/96)

Net Annualized Difference from Strategy Return Return OEX system

OEX System 1004.28% 27.92 N/A Fidelity Magellan 315.97% 15.32% -12.60% S&P 500 282.07% 14.73% -13.19% Vanguard Index 500 293.44% 14.68% -13.24% DJIA 257.58% 13.84% -14.08% Nasdaq Comp 251.78% 13.77% -14.15% Russell 2000 156.62% 10.14% -17.78% ,\70fP: .111 stt-otPgiP.5 or? Dli~-nnrl-hold with lhr twPplion of t/w om S~rtrtn.

rlll di~~idmds, ~f~ll/llicnld~, 01-f winmId; m utucll /II r1d,5 also hmv ccrl,ilnl pin.5 minrmtrd. Sourm: Bloonrbq Finnnrinl, Strrl~dntd ti Poor’s. Fidrlr!) .4~t

LIImlclgPvwn/, I hgrtnrff .4mt Jfnnng-r!nPnt

;\ccording to The Motley Fool’s Investment Guide, “the most damning statistic in the world of finance: over 75% of all mutual funds underperform the market’s average return each year.” It goes on to say that, “Only T80 mutual funds have a lo-year track record. Of those, 87.3% lost to the S&P 500’s annual return.”

Not only did this svstem beat the S&P 500’s annualized return ahnost two-fold, but one could conclude that this system beat over 87% of professionally managed mutual funds. This is quite a statement for an options trading sytem using basic technical analysis techniques.

50 MTA JOCRML l \Vinter Spring 1998

Results Table Running equity table from January 2, 1987 through September 30, 1996

Irade

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

Entrv Date

l/2/87 4116187 5126187 2110188

416188 4125188 5116188 5124188 7/l 4188 7129188 9129188

11 I22188 1 I5189

2/l 6189 313189

3/31189 5112189 8124189 9121 I89

12126189 2/l 190

2126190 5/2190

10118190 1116191 3126191

512191 6/14/91

711/91 8121191

10111/91 416192

6/29192 7128192 1 /I 3193 5/l 8193 6111193

115194 2/l 6194 416194

4121194 5/l 6194

715194 9/l 4194

10/l 0194 11/15194 8/l 4195 8125195

12126195 l/l 9196 4/l 2196 6121196

919196

Contract

230~ 275~ 280~ 235~ 245~ 240~ 240~ 235~ 250~ 255~ 250~ 245~ 260~ 275~ 270 270 285 315 315 315 300 300 310 285 290 350 355 355 350 360 350 375 380 380 385 400 410 425 430 405 405 405 405 430 420 425 525 520 580 575 610 640 625

Strike1 Month

2187 5187 7187 3188 5188 6188 6188 7188 8188 9188

11188 l/89 2189 3189 4189 5189 6189

1 O/89 11189 2/90 3/90 4190 6190

11190 2191 5191 6/91 7191 8191

10/91 11191 5192 8192 9192 2193 6193 7193 2194 3194 5194 6194 6194 8194

10/94 11194 12194 9195

1 O/95 2196 3196 5196 8196

10196

Entry stop p&e [in points)

8 314 2 518 11 314 3 518 13 118 4 16 4 12114 3 114 15112 4 11 114 3 318 12 314 3 718 11 112 3 112 11 114 3 318 12112 3 314 13 518 4 9 718 3 9 318 2 718 11 518 3 112 11 l/4 3 318 12114 3 314 19 4 14 518 4 16 718 4 15 II4 4 15 318 4 13 II4 4 12 318 3 314 15 114 4 13 718 4 12 114 3 314 13114 4 14 718 4 16112 4 12 314 3 718 10 718 3 318 11 118 3 318 13 318 4 11 112 3 112 11 3 318 11 112 3 112 10 518 3 l/4 13 4 15 4 14 318 4 11 114 3 318 12 112 3 314 9 718 3 11 3 318 12 314 3 718 12 114 3 314 17 314 4 16 4 17 318 4 16 718 4 19 4 23 314 4

Contract Sell Date:

l/5/87 4121 I87 611 O/87 2122188 4112188

515188 5118188

611 I88 7122188

819188 10120188 11/30/88 l/19/89 2122189 3116189 4118189 5119189

g/6189 lo14189 1 I2190 218190

2128190 5/8/90

10119/90 l/17/91 419191

5110/91 6119191 7119191

919191 10115/91

417192 718192

7130192 1115193 5119193 6121 I93 l/l O/94 2124194 4113194

519194 5117194 7114194 9120194

10/11/94 11121194

8121195 915195 1 I3196

1 I24196 4/l 6196

715196 9112196

Sell p&e

12 314 15 314 17118 20 16114 11 112 7 318 20 718 8 7 314 16112 18114 14 118 6 112 16114 19 17112 15 21 114 21 112 19114 19 318 17 114 16 318 23 314 9 718 8 l/2 8 112 19 12 112 17 314 7 l/2 7 314 17 314 15 112 15 8 14 518 8 318 11 10 318 15 114 17 114 13 718 15 a 718 8 l/2 22 314 20 l/2 23 118 20 718 12 l/2 31

4 points Achieved7 -

Y Y Y Y Y N N- Y* N N- Y Y' Y' N Y* Y* Y* N Y* Y* Y Y Y Y Y* N N N- Y* N Y* N N Y* Y Y N Y N- N N Y Y* Y Y N N Y* Y* Y* Y N- Y*

Running Eauitv

S12.750 S16.750 S20.750 S24.750 S28.750 S24.750 S20.875 s29,ooo $25.500 $22.000 S26.000 $30.625 $34,875 532.000 S36.625 S44.375 $49,625 $45.625 $52,250 $56,875 $60,875 $64,875 $68,875 $72,875 $81,375 $77,375 $73,625 $68,875 $73,000 $69,000 $74.000 $70,625 $67,250 $71,625 $75,625 $79,625 $76,125 $80,125 $75,500 $71,500 $67,500 $71,500 $76,250 $80,250 $84,250 $80,375 $76,625 $81,625 $86,125 $91,875 $95,875 $89,375 $96,625

+ Starting equity is $8730; 10 contracts at 8-j/4 entq price on l/2/87 * Option opened higher than 4 point object& Preoious day’s open, high, low, and close were below objectizje including assmed bid/ask spread.

Option opened lozuer than maximum stop/loss. Previous day ‘F open, high, low, and close zjere above stop objectizle including a.rsumed bid/ask spread. Assumptions: 10 contracts purrhased and lo-up rule in pffpct; l/4 point bidark spad; commissions not included due to diffrrPnt prices rhqed for commissions at different brokerage firms; orders executed at stop prices; entry orders execute on closing/nice. Sote: h complete set of graphs representing all the entry signals from the results table is located in the Appendix.

MTA JOURNAL l Itinter - Spring 1998

Backtesting This study goes almost 10 years, which 1 believe is sufficient

enough to draw the conclusions that 1 have regarding this study. F$%ile 1 would like to have back-tested this system further, 1 was handicapped by the following facts:

1. The OEX was invented by the CBOE in 1983; therefore, m\ analysis couldn’t have gone back any farther than that.

2. Options on the OEX only started trading on March 11, 1983.

3. U’hile the S&P 500 has been maintained bv Standard & Poor’s since 1957, options for the S&P 500 weren’t traded until Jul) 1, 1983, later than the OEX.

4. OEX historical option contract data is very hard to obtain and very expensive once a viable source has been obtained.

Because this system’s inherent success relies on the results of profitable option trades, and options were not trading on either index before 1983, there were no surrogate benchmarks to back- test the data against that had corresponding option data.

sLlmmary

\2llile there is still investor belief that the markets can’t be “timed” and that technical analysis doesn’t work, this paper has attempted to alleviate some of those claims.

The system contained in these pages uses well-known and widely accepted technical indicators, that when used in combination produced results that were approximatelv 2-times better than some traditional buy-and-hold strategies.

Considering the fact that 87% of professional money managers haven’t beaten the stock market average returns in lo-year performance, 1 feel that developing a mechanical trading system, that for the past 10 years has at least doubled the market’s annualized returns by usmg traditional technical analysis methods, is very significant.

Glossary of Technical Terms

Moving averages

There are several types of moving averages. The most basic is the simple moving ayerage. A simple moving average gives equal weight to all the days under consideration (e.g. If you hare a 10 day simple moving average, simply add all 10 figures up and di- vide by 10). Other types of moving averages include exponential, triangular, weighted, and more. For this stud!; 1 used an exponential mo\ing average.

An exponential moving average gives more weight to the most recent prices (YesterdaT’s price gets more weight than four days ago, etc.). 1 believe that the exponential moving average works very well with this stud!; and that the more recent day’s action contains more sentiment than was evident several day past.

There are several benefits of using moving averages. A great benefit of moving averages is that they smooth out volatile price fluctuations, eliminating price “noise.” Moving averages are also used by traders for support and resistance levels, stop loss imple- mentaiion, and identification of trend direction.

The moving average does have its limitations. Moving average crossover systems are prone to getting whipsawed in non-trending markets. Moving ayerages often lag behind price action when the markets speed up. They don’t provide overbought and oversold information, nor do moving averages identify the strength of the trend.

The exponential moving average is very hard to calculate b! hand, but it is included in most charting software packages. The

Formula for calculating the exponential moling average is in the appendix of this paper.

Stochastics

Stochastics have been popularized by George Lane. It is ex- pressed in terms of %I; and SD. %K stochastic values represent the relationship between today’s close and the high/low range over the last n-trading days. %D is a 3-day moving average of ‘%K.

As more data are added, the %K and ‘%D will be displayed as data lines that range from 0 to 100, making an oscillator. The formula for stochastics is included in the appendix.

Traditional interpretation states that values of less than 20 indicate an oversold condition, while values over 80 indicate an overbought condition (some traders prefer to use 25/75, or 30/ 70 levels as well). 1 used 30 for oversold and 70 as overbought. This study was only interested in the %#D value of 30 for oversold.

The basic stochastic signal is generated by the crossing of the ‘j%K and %D lines. Also taken into account are the levels of %I( and %D in respect to being oversold or overbought. Example: if %K crosses ‘%D and both values were pre\-iously under 20, one may enter a long position.

Stochastics are most effective in markets that are non-trending or have only a slight upward or downward bias. The\- are also put to use in stochastic crossover systems. Two benefits of stochastics are that this indicator identifies bottoms and tops reli- ably in non-trending markets, and it indicates overbought or oversold levels in price ranges. One of the most important uses of stochastics is in divergence analysis.

Inherent problems with stochastics, however, include whipsaws and lower degrees of reliability in trending markets. Stochastics may also get you in or out of a trade too soon because of inaccurate oversold or overbought levels in trending markets.

Trendlines

.A trendline is a line that connects significant points within the prevailing trend. It must have at least two points. A rising trendline is created when it is connecting two or more troughs (lows). h declining trendline is created bp connecting two or more peaks (tops). This study only concerned itself with declining trendlines. General technical analysis theory states that a trend remains intact until it is broken. Please refer to the “criteria” section and example charts for trendline break criteria rules for this system.

ARMS Index

The Arms Index was named after its creator, Richard Arms. It is also known as TRIS (short-term trading index). The Arms Index is a short-term trading tool that is designed to indicate if volume is going into advancing or declining securities. The formula for calculating the Arms Index is in the appendix.

In general, readings below 1.0 indicate that more volume is going into advancing issues and is considered bullish, while readings above 1 .O indicate that more volume is going into declining issues and is considered bearish. Because the Arms Index is verv volatile with extreme readings at times, it is best to smooth it wi& a moving average.

Some benefits of the Arms Index are these: It indicates where volume is going (up or down issues), indicates overbought or oversold le\-els, and can be used to enter on the long or short sides of the market when its reading gets to extreme levels.

OEX options pricing theory (courtesy Chicago Board Options Exchange): The discussion in this section is an excerpt (Chapter

9 pp 297-307) from the book ODtions: Essential Concepts and Trading Stratepies, written and edited b? the Educational Divi-

MTA JOURNAL l \tinter - Spring 1998

sion of the Chicago Board Options Exchange and published b! Irwin Professional Publishing, 1995 (Chicago, London, Singapore).

Option Premium (CBOE) The amount by which an option price exceeds its intrinsic value

is called the premium. An option’s intrinsic value is the difference between the current value of the stock and the strike price of the option. Premiums are the “product” of the options market. All of the fundamental, psychological, and mathematical factors in the market are incorporated in their value. Monitoring the level of premiums and their relationship is one of the sim- plest - and probably one of the most important - ways option market information can be used to make stock decisions. If the strike price of the option is at or above the price of the stock (in the case of a call), or at or below the price of the stock (in the case of a put), the entire price of the option is premium. The level of premiums is an important consideration for option traders - and for stock traders. Traders in general want to be sellers when premiums are high, buyers when they are low. The problem is that what is relatively high or low is known for certain only after the fact. In other words, by looking at a chart of historical option premiums, the viewer can say what would have been the best approach for a trader to have taken. For example, if premiums had been declining, the trader should have been a seller, since the level obviously had been too high in the past.

Implied Volatility (CBOE) An option pricing model requires the following input variables:

stock price, exercise price, time, interest rate, stock dividend, and expected volatility. Of these, volatility is the only unknown variable, yet it is the most important. Volatility is defined for pricing model purposes as the standard deviation of the daily percentage price change for the stock, annualized. To estimate the volatilit! of a stock, the stock price changes for a recent time period can be analvzed. The volatilitv for the stock determined in this manner is called historical volatilitv.

Calculating the value ok an option using historic price volatility often results in a theoretical price that is different from that in the actual market. This means either that the option is out of line with its value, or that the volatility being used by others in the market varies from the historic. Because the option market has become relatively efficient since its birth in 1973, it is more often the latter reason - that expected volatility is different from historical volatility To determine what this assumed volatility factor is, the pricing model can be employed. If the price of the option is used as an input, and the model is solved for the volatility assumption, the resulting factor is called implied volatility.

Implied volatility can help an investor analyze the market in

several ways. A chart of implied volatility provides the same perspective as the chart of option premiums discussed in the previous section. If a stock’s implied volatility has been declining, it can be assumed that option writers have been earning a higher rate than expected. If implied volatility is in an uptrend, option buyers hare probably been net winners. A sudden change in the trend of implied volatilitv might indicate a substantial fundamental event in the cornpa@ or the market.

CBOE Market Volatility Index (CBOE) In 1993, the Chicago Board Options Exchange introduced the

CBOE Market Volatility Index. The CBOE Market Yolatility In- dex, known by its ticker synbol L’IX, measures the volatility of the U.S. equity market. It provides investors with up-to-the-minute

market estimates of expected volatility by using real-time OEX index option bid/ask quotes.

The 17X is calculated by averaging S&P 100 Stock Index at- the-money put and call implied volatilities. The availability of the index enables investors to make more informed investment decisions.

Appendix Arms Index:

(ad\-ancing issues/declining issues)

(advancing volume/declining volume)

Exponential moving average formula:

EM, = E>L\,., t (SF * (P;EMA.,))

where

ESL-\, = present E1\M value

ELLA,-, = prior E&L1 value

SF = smoothing factor (most common smoothing factor is SF=2/‘ntl,

where II is the number of periods in the calculation)

Relative Strength Index: RSI= 100 - 100

1tRS

where RS = A1verage of wday’s up closes

Average of n-day’s down closes

Stochastics formula: %K=

(Today’s close - Lowest Low in %K periods)

*100

(Highest high in ‘%K periods - Lowest Low in %K periods)

SuperCharts Formula:

This formula will identib an alert signal. The technician must then

use the short-term trendline analysis to get the buy signal.

Make a new “ShowMe” study and enter the follo\ving formula:

the XAverage (close,lO) of datal<=l.Oj and the FastD( 10) of data2

is>30 and the FastD( 10) [I] of data2 is<=30 and low of data2>=low[ l]

where datal= Arms Index

where data2= OEX data

Graphs of trades:

Key to graphs:

A dot on the low of the bar, with the symbol ‘a’ indicates an alert day.

X thick bar from high to low indicates an options expiration period.

MTA JOURML 9 \l‘inter - Spring 1998 53

305fol ,,,,l’l,~l~/~“~““‘~ g5am ‘1 I, ti 1Il't r mxm .17

mlw

MTA JOLXhNL l \Cinter - Spring 1998

ma&

I=-- -w/--i,,m- -.---Y----c- - ..__ ___ lrn ,

m m

MTA JOURNAL l U-inter - Spring 1998 55

References 1. Achelis, Steven B. (1993), Technical Analysis From A to Z.

Chicago, IL

2. Arms, Richard M’. (1989), The Arms Index. Homewood, IL

3. Chicago Board Options Exchange, Chicago:

http://ww~v.cboe.con~/indes/oex/oexfaq.html

http://ww.cboe.com,/index/oex/xadrance.html.

4. Gardner, David and Gardner, Tom, (1996). The Xlotlev Fool Investment Guide. New York. 17’

3. Le Beau. Charles & Lucas. Da\id\V., (1992)) Commuter Anal\.- sis of the Futures Market. Burr Ridge, IL

6. XlchZillan, Lawrence G., (1993), ODtions As X Strategic In- vestment, 3rd Edition. New York, NY

7. Murphy John J., (1986), Technical ,%nalvsis of the Futures New ‘;ork, r\il’ Markets.

Data Providers OEX and Arms Index data provided by Pinnacle Data Corpora-

tion: Mebster, Q’

OEX option data provided by CSI: Boca Raton, FL

Biography David Kirkman is a Microsoft Certified Systems Engineer,

and currently works as an I/T Specialist with IBM Global

Services’ Consulting for !vIicrosoft Technologies division. He has been a personal trader and trading systems developer for the last eight years. He has also worked as a technical

analyst for two Chairman’s Council members at a major brokerage firm. He has been stud+g technical analysis for the

last eight years and provides technical analysis consultations on co-managed personal accom7ts.

56 MTA JOURNAL l \Vinter - Spring 1998

A Comparative Study of a Technical Neural Network vs. a Fundamental Neural Network in the Forecasting of

the long Bond Rate

Robert J. Van Eyden, Ph.D.

Introduction Economirs is often reJ~rrpd to as thp dismal sripnce, owing to the disbar-

if! bptween fheovy and realit), and is ronsistent!y speking new modeling

techniques to bridge the diffPrentia1. JIodels based upon eronovnir theoy,

work 711~11 on the dv-awing boards and allou~ one to stud) the interactions of

aslerts of an eronomj. Houwuet; their prpdirtiue and pxplanator~ ,bowev

tend to be weak ruhpn tested on a real ulorld test-bed.

0~7~ of the foundations of econometric models/vnodpling arose from ~77-

g?neering whereb? aspprts of control theor) wre applied.

thought to be a control sytevn.

The erononl! was

Thpse sytevns t$iral(y involve a largp and

rompbx series of equations that nped to be solupd to obtain a solution. Onr

of the first areas of sripnre to be utilized ulithin ~ronovnirs was statistical

modeling. The most popular modpling terhniques to be utilized werp sto-

&astir proresses (e.g. queuing theo1:)) and tivne series forecasting (e.g. ;iR,

d1-l. AR\L-I. ,4RCH, and GAIRCH). Houw~pv; the main problevn with

these models is that thq required the estimation of parameters, a lengthy

and diSficult task. ‘ind thq arp overtly linpar: they modpl s!stpms via a

collection of lines in space and as a rtwl( uev> strong-M not linear changes

in a rovnp1e.x systevn (such as an ~~0~10777~) were often missed.

Ozing to the (ynamisvn of econovnips, eronovnir modelers and ivmestiga-

tors turned to nonlinear models with the aim of using non-linear+) to bet-

ter explain the rhanges in an pronovnl. Some examples includp uwplets,

Fourier Series and Taylor Series. ;inother field that arosp frovvl ph!sirs, in

partirular dwavnirs, ulas thp rhaos theory: the search for order under@ng

a .seemivzgl~ chaotic or random sytevn. Others turned to the branch of rovn-

@tev- srie&e rallfd artifirial intelligenre, with the aim of building “intelli-

gent” systems, whirh could be applied for vnaking economic predirtion and

pxplanation. From this latter area, nwal netulorks, genetic algorithms.

Juzzl logic, rough sets and IiJbrids of thpsp terhnologi~s haue arisen. Of thpse terhnologies, neural networks havp bprome the most ulidely used, and

it is on this vnethod that this paper concentrates. HOU~O; it vn.ust be noted

that the use of non-linear systpvns in desrribing the behavior of ecovvomir

variables revnains rontroveriial.

The @rpose of this paper is covnpare t7oo npural network vnpthodologies

With parh othev: The neural networks distinguish thpmselues in terms ofthp

inputs used to forecast theSwth Af v-u-an long bond rate. The one netroork Jorused 077 Jundamental inputs, zuhilst the other utilized purely tpchniral

analysis inputs.

Bqorp r&wing thp rpsearch methodolog it is important to notp one OJ

the problems in romparing alternativp modpls is that therp is not a single

tprhniral vnodpl that technicians would subsrvibe to, just as there is not one

fUndamenta1 model.

tal” neural networks.

The same ran be said of “technical” and “fundame,2-

This seemingly trillial point vneans that the funda-

mental model that the neural netro&k generates need not wflert rereiued

marroeronomic theory Similarly, terhnirians might rontest the value of the

terhniral neural network model that is d&fled.

The Fundamental Neural Network Design

Selection of Inputs and Outputs The selection of inputs was achieved bp employing the follow

ing approaches:

Emnirical Inductive AiDDrOaCh - This approach lends itself to the identification of attributes regarding early warning behav ior such as time-lagged structure of the factors. and the calculation of correlations between time-lagged input time series and their corresponding target values (Van Eyden, 1995).

Lozical Deductive kmroach - This approach supports expert queries concerning the following:

* -tie there any technical indicators that have proved their significance in the field of technical analvsis?

a \\“nich alternating effects of the variables can be described in a mathematical form? and

* Does a common theory exist for the specific forecast task?

The model built up for the fundamental bond rate prediction encompasses three of the four major investment sectors, viz. equities, bonds and cash. Inter and intra sector relationships have been defined as well as foreign and other extra-sectoral factors. h total of 23 (twenty-three) inputs were used by the individual models. These 23 inputs are summarized in Table 1.

Table 1 Inputs Used in the Construction of the Neural Network Model(s) input name Abbreviation Calculation

Date Date

Bond risk premium -BRP Bond yield - cash yield

Trend in BRP Tbrp - BRp‘“rren: - ~Rpprevlous

Inflation INF

Real Long bond return -Rbond BY - inflation

ALSI EN ALSley -

Basis point movement BoNDmov BL~: - Bypm”, - Stock Return AlSIre AL%rrent - Aqmus Dividend yield (ALSI) - ALSldy

Stock/bond premium Sbprem Stock return-1 BY

Trend SBP Tsbprem SBP CUrlent - sBppre”lous _ Equity valuation ALSlpe --ALSI (P/E)

Equity risk premium ERP .~

Stock return - CY

Trend in ERP - Terp ERpc”rren~ - ERpore”lo”s Cash Yield Cash

keal cash yield &ash CY - Inflation

M3 (Monthly) Mmsupply .~~ M4”rie”t - M3pre”lo”s M3 (Annual) Amsupply %M3 (Annual)

US Inflation Usinf

us30 Uslong

USTB Ustb

US$ - Exchange rate Forex

Offshore differential Diff FUSB - R150

MTA JOLWUL l \\‘inter Spring 1998

The variables used in the model need explanation. The stock/ bond premium is simpl? the expected return differential between stocks and bonds. It is supposed to illustrate the relative attractiveness of stocks vs. bonds. The bond and equity risk premiums are also expected return differentials, but with respect to the no- risk. The cash yield indicates the relative attractiveness of bonds and equities vs. no-risk cash. In addition to these relative measures, absolute measures of each of the three sectors is utilized; viz. the Johannesburg All Share Index (AL%), All Bond Yield (RljO) and the 3-Month Banker Acceptance Rate (B&i). Allied to the bond sector is the bond excess return (second differential). This figure illustrates how the actual bond differentials compare to the average differentials. The idea behind this differential is to illustrate spurious movements in the bond rates, whilst the equity sectors are represented by dividend yields and P/E ratios.

Relative and trend measurements of the equity risk premium are also calculated (i.e. bond risk premium and stock/bond premium); these relative measures represent the actual rate (price) and the average rate (price) differentials. Furthermore, ther illustrate how the market has moved relative to the market ovkr a previous period. Trend measures are equivalent to momentum, simpl: illustrating the strength of a more in rates or prices.

Furthermore, both stock and bond movement are utilized to illustrate the rate of movements in prices or rates, as is South Africa (SA’s) annual Money Supply (M3) growth.

A basket of United States of America (US) market indicators, including the US Treasury Bill Rate, Dow Jones Industrial Aver- age, US Inflation, US 30-year Long Bond Rate and the US$/Fi- nancial Rand (Foreign exchange rate) were also included. These variables represent the major aspects of the CS economy and were deemed to have an influence on the SA economy as SX often takes its cue from the US markets. The initial output chosen for this study was to predict the actual yield one month in the future. The results indicated that the neural networks (individual) were generally good at predicting the direction correctly, but magnitude forecasting was extremelr poor. It was, therefore, decided that the output variable was the long bond yield (known as the RljO) one month ahead, stated as a percentage relative to the current month’s closing yield.

The model is depicted graphically in Figure 1 (Intra-sector relationships are represented by dotted boxes).

The Model Development A total of 15 neural network models were developed. hddi-

tionally, a benchmark was created to which the neural networks could be compared. The benchmark is referred to as the simple model.’ The neural network models distinguished themselves in terms of learning rules (Adaptive gradient & Kalman filters) and variable selection (Neural network or multiple regression). A genetic algorithm was employed for the variable selection and this was another reason as to why forecast values had to be combined. Genetic algorithms by iheir nature will yield different models using the same data. It is therefore recommended that the output values should be combined so that the reflected value is the combined wisdom of each of these experts (Neuralworks Predict, 1995).

- LOCi r

Figure 1 - The Fundamental Bond Rate Prediction

II Variables S’cck to Bond t’rem~um

Real . ..d(ield 1 Real i”“:” Yield

\ Real Cash Yield

I i

Foreign Variables

Dow Jones Indusmal

I 4

US Long Bond Rate US 6 : Financial Rand

In choosing indicators, the idea was to keep the number chosen and the number of variations thereof to a minimum. Certain indicators are inapplicable for use in the South African bond market, such as the bull/bear ratio, the puts-calls ratio and the breadth thrust, as the data required only exists for equities or is

Rationale for the Development of a Technical Neural Network

The author’s specific research to date has been the development of models based upon fundamental indicators used to for Forecast the local South African long bond rate. However, in addition to knowing the long-term movements of a financial ele- ment, the shorter-term movements also need to be considered to aid in the timing of purchases. Furthermore, it was determined with the one-month-ahead long bond forecasters, the accuracies of the longer-term models were better; this could be owing to the omission of technical indicators in the input set (Baise, 1996).

There exists a plethora of technical indicators, with each trader choosing a small set of favorites. The question arises: “\Vhich indicators or set of indicators yields a very good indication as to the short-term movements in the price/rate of a particular financial instrument?” Furthermore, the times used for each of the technical indicators are also quintessential. To exhaustively determine the optimal indicator sets as well as their respective periods would require an inordinately large amount of time. For this and other reasons, a genetic-algonthm-based discriminator is used to determine these optimal sets.

Indicators Chosen

58 MTA JOURNAL l \tinter - Spring 1998

unavailable. Trend lines are also inapplicable to a numerical model; they are only useful for analyzing a graph. Basing periods are very similar to the volatility calculations, and as volatility will be included, they will be ignored. Concerning hIAs, the variable MA yielded abysmal results and so was ignored. The remaining fi\-e types, were all similar, so the three best - the weighted (S- week), exponential (8-week) and pyramidal (S-week) (1’an E!-den, 1991) hIAs were chosen.

For the SIACDs, the variable M-based one is too volatile to be useful. Al the others are acceptable and, owing to their great potential, were added to the data set. Stochastics illustrate great I-alue as well. As indicated above, using the turning points appears to be very advantageous. In addition, comparing the stochastic to its pvramidal M-2 yielded decent results. So a fast 21. week stochastic will be used for the turning point method and a (21,i) fast stochastic with the difference between the stochastic and its MA being input. ;Uso, a slow (16,7) stochastic will be added: the difference between the stochastic and its i-pyramidal MA.”

Bollinger bands are also deemed useful, although they present more and better information in graphical form. As it is deemed one of the most useful indicators, it should be used in conjunc- tion with the other indicators; viz. hZ-\CD and stochastics.

Concerning the RSI, using a 9. 16 and 21 week RSI and using the turning points therein appears to be the most prudent choice. Using either of the levels-based approaches is inapplicable for short-term trading.

The OBV and Accumulation/ Distribution will also be included per se. For the volatility measures, one of each of the standard, Chaikin’s and $\‘ilder’s ;.olatility measures were included. The standard and \\‘ilder’s volatility measure are vel? erratic, and some erratic aspect is needed for volatilitv. The 19 inputs are summarized in Table 2.

Table 2 - Inputs Utilized in the Technical Neural Network

Technical indicator Transformation

Moving Averages Weighted (8 weeks)

Exponential (8 weeks) ~

Pyramidal (8 weeks)

MAC0 Weighted (l&26 weeks)

Exponential (9.21 weeks)

Exponential (8,21 weeks)

Simple (8,26)

Pyramidal (8,16)

On Balance Volume

Accumulation/Distribution

Volatility Measures Standard

Chaikin’s

Wilder’s

Relative Strength Index 9 weeks

16 weeks

21 weeks

Stochastics Fast (21 weeks)

Fast (21,7)

Slow (16,7)

Building the Technical Neural Network Model Four sets” of models were built: prediction models without

:he Rl30 rate as an input, prediction models with the R130 rate 1s an input, classification models without the Rl.50 rate as an in- 3ut, and classification models with the Rl30 rate as an input. For the classification models, the R150 rate differences were normal- lzed to between the maximum and minimum rate movements 3aer four weeks. This as opposed to classifying the rates themselves, which is deemed to have little value. The results of each of the four model sets are as below. Four sets of technical models hvere constructed: a four-week-ahead forecaster that uses the current value of the long bond, a four-week-ahead forecaster that does not use the current value of the long bond: a four-week- ahead classifier that uses the current value of the long bond, and a four-week-ahead classifier that uses the current value of the long bond. This is depicted in figure 2.

Figure 2: Taxonomy of the Technical Models Built

I Technical Models

I

Set 1: Prediction Models Excluding the R150 Rate The first model type to be built was the prediction model with

the current Rl.iO rate excluded. The results are contained in appendix h and were encouraging. ’ The correlations were ver\ high as are the directional accuracy and the profits generated. Relative to that of the one-month-ahead fundamental models, the correlations are equivalent, the actual to forecast accuracv is also equivalent, the forecast-to-forecast accuracy is slightly’ worse, whereas the average profits generated are definitely higher. As profit is the ultimate aim, the results favor the technical models over the fundamental models.

Appendix B contains a comparison between the technical classification models built and the fundamental one-month-ahead models. Two aspects are apparent: the directional accurac? is much higher and the average profits (profit per trade) are much higher. For the former aspect, the direction is from one rate range to another rate range as opposed from one rate to another rate. This allows for greater leeway and appears to explain the much higher directional accuracy. For the latter aspect, no explanation may be readily determined, yet the technicals generate more profit in the short term than do the fundamental models.

Variables Chosen Concerning the forecasting models built, the orders of the

input sets were small, four or five out of a possible total of 19 inputs. Of these, the following four variables consistently appear in both sets’ inputs:”

Exponential (Half-Life-Based) hJACD (g-Week - 21-FVeek)

RSI (Pl-M’eek)

RSI (S-M’eek)


MTA JOLXML * \Vinter - Spring 1998

In addition, the On Balance Volume indicator made a notable, albeit lesser, appearance. The differences between the two sets are minimal: the Exponential Moving Average (8 weeks) appears in almost all of the models in set 1, whereas it appears in no models of set 2. Instead the current value of the Rl50 rate appears in half the models in set 2. It appears that including the current value of the Rl50 in the input set is significant for forecasting purposes when the inputs are all technical. For the classification models, the orders of the input sets were larger than that of the forecasting models, being around 10 of a possible total of 19 inputs.” Of these, the following seven variables consistently appear in both sets’ inputs:

Weighted MA (8-LVeek)

Weighted MACD (16-LVeek - 2&M’eek)

Simple MACD (8-1Veek - 26-M’eek)

Exponential (Half-Life-Based) SMCD (9-\Veek - 21-M’eek)

RSI (Pl-Week)

RSI (9-LVeek)


MThen the current R150 classification was excluded, the following variables are utilized in addition to the above seven:

Exponential MA (8-M’eek)

Exponential MACD (9.N’eek - 21-FVeek)

On Balance l’olume

Chaikin’s Volatility (IO-week Pyramidal MA)

These four variables did not feature prominently when the current classification of the Rl50 was included; rather the new addition featured prominentlv when it was included. FVhether the R150 was excluded or not,’ the following four variables made a consistently prominent feature in nearly all of the 40 models

Exponential (Half-Life-Based) RWCD (9Week - Pl-M’eek)


M71en the current R150 rate or classification was added, it featured prominentlv as well. Consider, for a while, these variables. What do they illustrate? Obviously the RSIs, accumulation/distribution and MACDs feature prominently. Exponential-based MAs and MACDs are significant, as are the weighted variants, albeit to a lesser degree. It was evident from a graphical inspec- tions that the indicators (RSIs, Accumulations/Distributions and MACDs) all yield remarkable indications as to the future and current movement of the long bond, the MACDs being particularl! good for medium-term forecasting. So the commonality of these variables in the input sets of all the models built appears coher- ent. That the current rate/classification of the Rl50 featured prominently when added is significant in that it indicates the reliance of the future rate on the current rate/classification.

So, when taken together, RSIs, (exponential) MACDs and Ac- cumulation/Distribution yield excellent indications as to where long bond rates are headed in the very short term (4 weeks). Determining which variables the models are most sensitive to and the degree of that sensitivity is of great significance. The following section outlines an approach to the sensitivity analysis of these technical models.

Sensitivity Analysis Owing to the complexity of nonlinear models, sensitivity anal?-

sis of them is currently an unsolved problem; rather no method exists for the determination of sensitivities to inputs.’ One other method is to ident@ those inputs that come up most often in a set of models and to perturb them in unison in certain directions by a said amount, say lo’%, then compare the variation in the forecasts generated by each model to the original forecasts. In this instance, the following variable sets would be pertinent:

Set 1: Forecasting Models; No Current R150 Rate Exponential (Half-Life-Based) MACD (S-M’eek - Pl-FVeek)

RSI (21.LVeek)

RSI (9-LVeek)


Set 2: Forecasting Models; Current R150 Rate Added

Exponential (Half-Life-Based) MACD (9-N’eek - 21.\\‘eek)

RSI (21-ireek)

RSI (9-LVeek)

Accumulation/Distribution Current Rl50 Rate

Set 3: Forecasting Models; No Current R150 Classification

\Veighted hIA (8-N’eek)

1Veighted MACD (ltY\Veek - 26-FVeek)

Simple MACD (8-\Veek - 26-FVeek)

Exponential (Half-Life-Based) MACD (9-\Veek - Pl-\Veek)

RSI (21-FVeek)

RSI (9.1Veek)


Chaikin’s \‘olatility (lo-week Pyramidal M\)

Set 4: Forecasting Models; Current R150 Rate Classifi- cation Added

TVeighted h’Z-\ (8-ireek)

\Veighted RMCD (16-M’eek - 2&M’eek)

Simple hMCD (8-\Veek - 26-\\leek)

Exponential (Half-Life-Based) M.KD (9-M’eek - 21-\\‘eek)

RSI (21-\Veek)

RSI (9-LVeek)


Current Rl50 Rate Change Classification

The problem with these technical models is that a 10% move in a technical indicator appears to have little meaning, as some indicators are only relevant above certain levels (oscillators) and others may have nonlinear effects in opposite directions (e.g. Volatilitv indicators). Others have obvious effects as they encode the prediction indirectly (e.g. accumulation/distributio& on balance volume and current R150 rate) so an increase in them would almost always imply an increase in the forecast. For these reasons, no real utility could be obtained from sensitivitv analysis on technical indicators and so none was performed. 14 sensitivit! analysis on the major inputs to a fundamental model would be informative, and significant.

MTA JOURNAL l Winter - Spring 1998

Conclusions Interestingly no noticeable increase in accuracy or profits was

obsened when the current rate of the R150 was used to forecast the next rate compared to when it was omitted. Also, since moves over four weeks are often less compared to longer terms, the classification model would be more appropriate for eight, twelve or sixteen weeks ahead forecasts.

The variables that came up consistently Pl-week RSI, the 9- week RSI, the (9,21) week MACD and the accumulation/distribution curves as well as the current Rl50 rate/classification when it was added, indicate the significance of oscillators, namely RSIs over stochastics, the difference behveen two moving averages that is better than mo\ing averages themselves, and the volume graded by a rate change for accumulation distribution (i.e. a momentum indicator). This is significant as it results in a (small) suite of indicators that give strong indications as to where the long bonds are headed.

An interesting obsemation is that if a buy or sell is performed when a move from one classification to another is forecast, rather than a significant move is forecast, the chance of making a profit is far greater, about 85% versus around 68%!” So normalizing actual movements and then forecasting them provides more utility for short-term forecasting than forecasting rates.

Appendices h and B contain the results of a “simple” forecaster, one in which the prelious more in the rates is used as the next move. In this case, the entries in a table must be compared with each other, i.e. intra-table comparisons. For the rate forecasting models (appendix A), the results favor the “simple” forecaster especially in terms of profits generated. The other measures, being average absolute deviations, correlations and root mean square errors (RMSE), are very similar to that of the neural models. How- ever the directional accuracy of the “simple” forecaster tended to be better. For the classification models, the simple forecaster performed worse when class moves were considered as a basis of accuracy yet better when the correct class was forecast. This latter case’is obvious as it is more incremental in nature and so tends to follow the actual rates closer, overall. The RMSE and absolute deviations were equivalent in accuracy, however the correlations of the simple forecaster were extremely low.

However, the simple forecaster misses all the turning points and as turning points are of cardinal importance, it has little value from an investment (as opposed to trading) point of view. A measure of turning point accuracy would be extremely useful and is to be investigated.

Appendices A and B also contain the equivalent statistics for the monthly fundamental models. Interestingly comparing the simple forecaster to the fundamental models in the same table, the accuracy and average profits generated tended to be lower, in contrast to that of the technical models. Nevertheless, the correlations and RMSEs are as in the case of the technical models. Mhat could account for this is the resolution of the time series to the forecast period,” i.e. one to one for the monthlr forecasters and four to one for the weekly forecasters. It is possible that the larger the ratio the better the simple forecaster performs, or is there a point (ratio) where the accuracy utility starts to decline? Further investigation is warranted.

To conclude, compared to the fundamental models, the technical models were, overall, more profitable, although the directional accuracv and correlations may be considered to be similar.“’ However, the RMSEs of the fundamental models were con- siderably lower, by a factor of three to fire! In the case of techni-

cal models, the simple forecaster returned superior results when the forecasting of rates was considered, yet worse when classifica- tions, rather class moves, were considered. For the fundamental \rersus the technical models, the simple forecasters returned better results in the case of the technical models. Thus the technical classification models appear superior for one-month/four-week- ahead forecasts and are thus chosen as the models to be used.

Considerations for Further Work As is apparent from the aforementioned technical indicators,

the MA forms the basis of almost all of them. Using digital filters, such as Fourier Spectra and Kalman filters, instead of MAs would largely preclude the lag effect of MS, which is their major dowm- fall, and hence lead to more accurate (better timing) indicators.

Another interesting stud? would be the comparison of using differences of MS (such as m the MACD) to using digital filters. Both largely eliminate the aforementioned lag effect. Of cardinal importance is the development of a measure of the accurac) of a model with respect to the major turning points of the long bond rates. Although the classification-based technical models were chosen as the models for four-week-ahead forecasts, investi- gations of how they perform over twelve weeks (three months) could be interesting.

Finally, checking the accuracy of the models versus the simple forecasters for longer period forecasts or resolutions, say three or six months, is to be investigated, i.e. to investigate the effects of using larger ratios on the various accuracy measures.

Appendix A

One-Month-Ahead Fundamental Models vs. the Four-Week-Ahead Technical Forecaster Models

Tables comparing the results of the one-month-ahead fundamental models and the four-week-ahead technical forecasting models. Note that the alTerage profits quoted are according to the weighted trading policy and are the number of percentage points gained per transaction, on average. Also, the results are obtained from the models generated from an input set excluding the current R150 rate.

Table 1: Correlations (Technical Model)

Model 1989/11 To 1996/01 1990/N To 1996/01 1995/01 To 1996/N

Simple 0.95 0.95 0.95

1 0.95 0.96 0.97

2 0.96 0.96 0.96

3 0.97 0.97 0.97

4 0.97 0.96 0.97

5 0.96 0.96 0.96

6 0.95 0.95 0.96

7 0.95 0.95 0.95

8 0.97 0.97 0.96

9 0.95 0.95 0.96

10 0.94 0.95 0.97

MTA JOCRXU * \Vinter - Spring 1998 61

Table 2: Correlations (Fundamental Model)

Model 1980/01 To 1995/10 1990/01 To 1995/10 1994/06 Onwards

Simple 0.96 0.94 0.89

1 0.97 0.97 0.98

2 0.96 0.96 0.98

3 0.96 0.96 0.98

4 0.94 0.96 0.96

5 0.86 0.92 0.96

6 0.88 0.95 0.95

7 0.97 0.96 0.98

8 0.97 0.96 0.98-

9 0.95 0.94 0.96

10 0.91 0.94 0.94- -

Table 3: Directional Accuracy-Actual to Forecast (Technical Model)

Model 1989/11 To 1996/01 1990/01 To 1996/01 1995/01 To 1996/01

Simple 0.82 0.82 0.82

1 0.69 0.68 0.71

2 0.65 0.65 0.78

3 0.77 0.77 0.85

4 0.72 0.73 0.78

5 0.69 0.67 0.69

- 6 0.69 0.66 0.76

7 0.70 0.67 0.71

- 8 0.71 0.69 0.76

- 9 0.66 0.65 0.76

10 0.65 0.63 0.73

Table 4: Directional Accuracy-Actual to Forecast (Fundamental Model)


Simple 0.62 0.70 0.74

1 0.75 0.78 0.85

2 0.71 0.76 0.77

3 0.70 0.78 0.85

4 0.70 0.69 0.62

5 0.61 0.68 0.69

6 0.61 0.63 0.46

7 0.68 0.75 0.85

8 0.73 0.76 0.77

9 0.61 0.68 0.77

-~ 10 0.63 0.67 0.62

Table 5: Directional Accuracy - Forecast to Forecast (Technical Model)

Model 1989/11 To 1996/01 1990/01 To 1996/01 1995/01 To 1996/01

Simple 0.73 0.75 0.75

1 0.67 0.68 0.62

2 0.66 0.66 0.71

3 0.71 0.74 0.84

4 0.68 0.65 0.75

5 0.65 0.64 0.69

6 0.70 0.70 0.73

7 0.67 0.67 0.73

8 0.69 0.69 0.71

9 0.64 0.64 0.64

10 0.67 0.70 0.67-

Table 6: Directional Accuracy - Forecast to Forecast (Fundamental Model)


Simple 0.59 0.62 0.74

1 0.69 0.76 0.77-

2 0.67 0.69 0.77

3 0.67 0.75 0.85-

4 0.67 0.72 0.77

5 0.61 0.67 0.85

6 0.64 0.74 0.77

7 0.65 0.74 0.85

8 0.67 0.72 0.85

9 0.65 0.74 0.77

10 0.60 0.69 0.69

Table 7: Average Profits -Actual to Forecast (Technical Model)

Model 1989/11 To 1996/01 1990/01 To 1996/01 1995/01 To 1996/01

Simple 3.25 4.00 2.66

1 0.81 1.13 0.72

2 0.72 0.95 0.73

3 1.09 1.48 0.97

4 1.06 1.53 0.96

5 0.70 0.92 0.73

6 0.88 1.17 0.69

7 0.79 0.98 0.62

8 0.83 1.08 0.76

9 0.70 0.92 0.77

10 0.71 1.03 0.89

MTA JOURML l \Vinter - Spring 1998

Table 8: Average Profits -Actual to Forecast (Fundamental Model)

Model 1980/01 To 1995/l 0 199OAl To 1995/10 1994/06 Onwards

Simple 0.93 0.94 0.97

1 0.81 0.90 0.88

2 1.02- 1.07 0.89-

3 0.79 1.02 1.03

4 0.69 0.66 0.76

5 0.84 0.90 0.90

6 0.49 0.30 0.56

7 1.11 1.09 1.05

8 1.12 1.08 0.92

9 0.81 0.93 0.72

10 0.82 0.92 0.78

Table 9: Average Profits - Forecast to Forecast (Technical Model)


Simple 3.13 3.84 2.52

- 1 0.80 1.06 0.73

2 0.69 0.91 ~.0.68

3 1.07 1.52 1.03

4 0.89 1.24 -- 0.90

- 5 0.61 0.83 0.76

6 0.90 1.20 0.64

7 0.74 0.89 0.60

-~ 8 0.84 1.09 0.73

9- 0.68 0.86 0.74

10 0.76 1107 0.82

Table 10: Average Profits - Forecast to Forecast (Fundamental Model)


Simple 0.91 0.71 0.76

1 1.05 0.92 0.87

2 1.04. 0.90 0.93

3 0.93 0.92 0.89

4 0.98 c8.89 0.97

5 0.98 0.86 0.94

6 1.00 0.91 0.90

7 1.01 0.96 1.06

8 1.06 0.90 0.97

9 1.04 0.91 0.93

10 0.90 0.93 0.99

Model

Simple

1

i

3

4

5

6

7

8

9

10 -

Table 11: Root Mean Square Error (Fundamental Model)

1980/01 To 1995/10 1990/01 To 1995/10 1994/06 Onwards

0.18 0.20 0.17

0.09 0.10 0.09

0.09 0.10 0.11

0.09 0.10 0.09

0.08 0.10 0.09

0.11 0.12 0.12

0.08 0.09 0.08

0.11 0.12 0.11

0.09 0.09- 0.09

0.10 0.11 0.10

- - 0.12 0.13 -0.11

Model

Simple

1

2

3

4

5

6

7

8

9

10

Table 12: Root Mean Square Error (Technical Model)

1980/01 To 1995/10 1990/01 To 1995/10 1994/06 Onwards

0.46 0.50 0.42

-~-~ 0.41 0.41 0.39

0.37 0.37 0.32

0.40 0.40 0.63

0.37 0.37 0.36

6.41 0.41 0.39

- 0.42 0.42 0.36

0.44 0.44 0.36

0% 0.36 0.32

0.43 0.43 0.40

0.44 0.44 0.32

Appendix B

One-Month-Ahead Fundamental Models vs. the Four-Week-Ahead Technical Classification

Models The following tables compare the results of the one-month-

ahead fundamental models and the four-week-ahead technical classification models. Note that the average profits quoted are according to the weighted trading police and are the number of percentage points gained per transact&n, on average. Also, the results are obtained from the models generated from an input set including the current R150 rate.

MTA JOURNAL * \\‘inter - Spling 1998 63

Table 1: Correlations (Technical Model)

Model 1989/l 1 To 1996/01 1990/01 To 1996/01 1995/01 To 1996/01

Simple 0.95 0.95 0.95

1 0.75 0.75 0.59

2 0.09 0.09 0.50

3 0.17 0.16 0.53

4 0.09 0.12 0.55

5 0.00 0.04 0.75

- 6 0.11 0.10 0.54

7 0.05 -0.01 0.09

8 0.08 0.08 0.60

9 0.03 0.03 0.72

10 0.11 0.20 0.50

Table 2: Correlations (Fundamental Model)

Model 1980/01 To 1995/10 199ODl To 1995/10 1994/06 Onwards

Simple 0.96 0.94 0.89

1 0.97 0.97 0.98

2 0.96 0.96 0.98

3 0.96 0.96 0.98

4 0.94 0.96 0.96

5 0.86 0.92 0.96

6 0.88 0.95 0.95

7 0.97 0.96 0.98

8 0.97 0.96 0.98

9 0.95 0.94 0.96

10 0.91 0.94 0.94

Table 3: Directional Accuracy -Actual to Forecast (Technical Model)

Model 1989/U To 1996/01 1990/01 To 1996/01 1995jUl To 1996/01

Simple 0.62 0.70 0.74

1 0.86 0.83 0.80

2 0.89 0.90 0.82

3 0.89 0.90 0.88

4 0.88 0.85 0.86

5 0.81 0.82 0.76

6 0.91 0.90 0.90

7 0.84 0.80 0.80

8 0.88 0.87 0.80

9 0.86 0.84 0.84

10 0.89 0.86 0.82

Table 4: Directional Accuracy-Actual to Forecast (Fundamental Model)


Simple 0.62 0.70 0.74

1 0.75 0.78 0.85

2 0.71 0.76 0.77

3 0.70 0.78 0.85

4 0.70 0.69 0.62

5 0.61 0.68 0.69

6 0.61 0.63 0.46

7 0.68 0.75 0.85

8 0.73 0.76 0.77

9 0.61 0.68 0.77

10 0.63 0.67 0.62

Table 5: Average Profits -Actual to Forecast Class Moves (Technical Model)

Model 1989/11 To 1996/01 1990/01 To 1996/01 1995/01 To 1996/01

Simple 0.58 0.69 0.48

1 2.01 2.30 1.86

2 2.93 3.32 2.11

3 2.52 2.27 2.53

4 2.73 2.87 3.05

5 1.68 1.38 1.38

6 2.68 2.93 2.11

7 1.53 1.39 1.33

8 3.14 3.85 2.74

9 1.28 0.93 0.54

10 1.09 0.94 0.22

Table 6: Average Profits - Actual to Forecast (Fundamental Model)


Simple 0.93 0.94 0.97

1 1.07 1.03 0.63

2 1.02 1.09 0.61

3 1.12 1.15 0.59

4 1.06 0.97 0.50

5 0.72 0.82 0.51

6 0.77 0.89 -0.01

7 1.10 1.08 0.63

a 1.12 1.10 0.62

9 1.01 1.04 0.53

10 0.82 0.91 0.47

MTA JOURNAL * \Vinter - Spring 1998

1.

2.

3.

4.

5.

6.

7.

8.

9.

Footnotes

Simple model = Prpuiousn~, - (Prpoious,,., - Previous wz ) converted into

a percentage rate change.

Recall that there were no significant diff erences between stochastirs when the periods were altered.

Each of order 10.

Kate that the periods used are slightly dqferent owing to the nature of the time scales used. Also, the average weighted profits (per transaction profits) were quoted instead of the total profits generated so as to 1argelJ eliminate the time jzviod diffPrences.

20 Inptts for the second set.

20 In@ts for the second set.

I f the internals of the neural network model are accessible, then b! checking the weights on the inputs to neurons and by computing de- vivatiues for those neuron inputs an indication of the influences of the inputs may be gauged. C!nfortunateb, the software used in this analpis does not support these methods.

This is evident by comparing the appropriate graphs in Appendices A and B.

Let r be the resolution of the time sevl’es (e.g. weeks, months, etc); the time period used Then the ratio is defined as f/rwheref is the nuvnbev of time periods (resolution) that theforecast is done oueI; e.g. a 4 week ahead forecast done using weekly data yields a ratio of 4.

10. Excluding the classification models.

References and Further Readings Baise, P., Forecasting the Tohannesbure Stock Exchange Finan- cial and Industrial Earnincs Yield, Sandton, South Africa: Un- published internal report - Rand Merchant Bank Asset Man- agement, 1996.

Neuralware, Neuralworks Predict “Comnlete Solution for Seu- ral Data Modeling,” Neuralware, Inc, Pittsburgh, PA., 1995.

\‘an Evden. R.J., The Evaluation of Certain Technical Analysis Tech&ues to Determine the Future Market Price of Shares on the Tohannesbuw Stock Exchange, Pretoria, South Africa: Unpublished M. Corn thesis - Cnirersity of Pretoria, 1991.

1’an Eyden, R.J., The hnnlication of Neural Networks in the Forecasting of Share Prices, Finance & Technology Publish- ing, Haymarket, \‘A., 1995.

Software: Neurogenetic Optimizer Version 2.5. Biocomp sys- terns, Inc. Hyperlink http://wwv.biocomps~stems.com

Biography Robert J. \‘an Eyden, Ph.D, is currently employed as an

quantitative analyst by Rand Merchant Bank Asset Manage- ment - South Africa. He specializes in the development of quantitative and art&al intelligence forecasting models, incorporating technical, fundamental, and inter-market analysis factors. Furthemore, he develops and manages muti- factor equity models. He can be reached at phone at t27-1 l- 2821521, Fax: t27-11-2821500 or E-mail: Rveyden@ RMBAM.co.za

Acknowledgements The author thanks Paul Bais who conducted the primary re-

search. Furthermore, both anonymous referees of the Market Technicians Association Journal are thanked for their construc- tive comments.


Notes Notes

66 MTA JOWUL * IVinter - Spring 1998

journal of technical analysis (jota). issue 49 (1998, winter)

Economy & Finance

cmt kirkpatrick

cmt dory

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