<s> Wiley Trading
Me eo Umer ech eCE RR Kee incorporating establishedstatistical practices into the analysis and interpretation of technicalanalysis and other market theories. I can say without qualification thatthis book shouldbe in every serious market practitioner's collection.”
—vVictor Niederhoffer, Chief Speculator, Manchester Partnersauthor of The Education of a Speculator and Practical Speculation
HSN aaeANALYSIS
Applying the Scientific Methodand Statistical Inference toTrading Signals
 
 
 Praise for EVIDENCE-BASED TECHNICAL ANALYSIS
“In clear language, Aronson demonstrates the theoretical flaws in interpretative techni-cal analysis methodologies, the flawed premises and conclusions of the Efficient MarketHypothesis, and the appropriate techniques for developing and testing technical analysismethods that do have validity. Readers will learn a lot from this book.”
—Jack Schwager, author of Market Wizards and the Schwager on Futures book series
“Aronson’s explanation of data mining is a must-read for every analyst, and his overalldiscussion of statistical inference is critical to success. The book is filled with common-sense examples and provides a testing and validation process that saves time, frustra-tion, and money.”
—Perry Kaufman, author of New Trading Systems and Methods, Fourth Edition
“This book debunks many of the myths of technical analysis. One should read this bookbefore buying a technical system. The book is a good reference to the literature on thesubject with extensive footnotes and bibliography.”
—Sandor Straus, Managing Member, Merfin, LLC
“You may not agree with everything David Aronson says in this controversial, but com-pelling new study. Still, every trader who wants to invest technical analysis with thedignity of a great science should read this discerning account.”
—Nelson Freeburg, Editor, Formula Research
“There is an emerging trend in the technical analysis community toward a new, morerigorous type of analysis that utilizes the scientific process and depends on statisticaltesting to gauge the quality of one’s results. Just as quantitative analysts have longused the tools of technical analysis, market technicians are taking up the quant's tools.Evidence-Based Technical Analysis is a welcome milestone on a road that leads towarda better, more dependable type of analysis.”
—John Bollinger, CFA, CMT, BollingerBands.com
“There are illusions of the mind that are every bit as real as optical illusions. Aronson’scriticisms of popular forms of technical analysis are right on target.”
—Fred Gehm, author of Quantitative Trading and Money Management
ISBN O0-470-00874-1Peeerreees 59500
  
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sis has suffered because it is a “discipline”
practiced without discipline. In order fortechnical analysis to deliver useful knowledgethat can be applied to trading, it must evolveinto a rigorous observational science.
A s an approach to research, technical analy-
Over the past two decades, numerous articlesin respected academic journals have approachedtechnical analysis in a scientifically rigorousand intellectually honest manner, and now,Evidence-Based Technical Analysis looks tocontinue down this path. Organized into twoparts, this valuable resource first establishes themethodological, philosophical, and _ statisticalfoundations of evidenced-based technicalanalysis (EBTA), and then demonstrates thisapproach—by using twenty-five years of historicaldata to test 6,400 binary buy/sell rules on theS&P 500.
Evidence-Based Technical Analysis examineshow you can apply the scientific method, andrecently developed statistical tests, to determine the true effectiveness of technical tradingsignals. Throughout these pages, expert DavidAronson details this new type of technical anal-ysis that—unlike traditional technical analysis—is restricted to objective rules, whose historicalprofitability can be quantified and scrutinized.
Filled with in-depth insights and practical advice,Evidence-Based Technical Analysis providesyou with comprehensive coverage of this newmethodology, which is specifically designed forevaluating the performance of rules/signals thatare discovered by data mining. Experimentalresults presented in the book will showyou that data mining—a process in whichmany rules are back-tested and the bestperforming rules are selected—is an effective
( continued on back flap )( continued from front flap )
procedure for discovering useful rules/signals.However, since the historical performanceof the rules/signals discovered by data min-ing are upwardly biased, new statistical testsare required to make reasonable inferencesabout future profitability. Two such tests, oneof which has never been discussed anywhereheretofore, are described and illustrated.
If you want to use technical analysis to navigatetoday’s markets, you must first abandon thesubjective, interpretive methods traditionallyassociated with this discipline, and embracean approach that is scientifically and statisti-cally valid. Grounded in objective observationand statistical inference, EBTA is the approachto technical analysis you need to succeed inyour trading endeavors.
DAVID ARONSON is an adjunct professor atBaruch College, where he teaches a graduate-level course in technical analysis. He is alsoa Chartered Market Technician and has pub-lished articles on technical analysis. Previously,Aronson was a proprietary trader and technicalanalyst for Spear Leeds & Kellogg. He foundedRaden Research Group, a firm that was an earlyadopter of data mining within financial markets.Prior to that, Aronson founded AdvoCom, a firmthat specialized in the evaluation of commoditymoney managers and hedge funds, theirperformance, and trading methods. For freeaccess to the algorithm for testing data minedrules, go to www.evidencebasedta.com.
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Evidence-BasedTechnicalAnalysisApplying the Scientific
Statistical Inferenceto Trading Signals
DAVID R. ARONSON
John Wiley & Sons, Inc.
 
 Copyright © 2007 by David R. Aronson. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, ortransmitted in any form or by any means, electronic, mechanical, photocopying,recording, scanning, or otherwise, except as permitted under Section 107 or 108of the 1976 United States Copyright Act, without either the prior writtenpermission of the Publisher, or authorization through payment of the appropriateper-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive,Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web atwww.copyright.com. Requests to the Publisher for permission should beaddressed to the Permissions Department, John Wiley & Sons, Inc., 111 RiverStreet, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online athttp://www. wiley.conV/go/permissions.
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Library of Congress Cataloging-in-Publication Data:
Aronson, David R., 1945-
Evidence-based technical analysis : applying the scientific method and
statistical inference to trading signals / David R. Aronson.p. cm.—(Wiley trading series)
Includes bibliographical references and index.
ISBN-13: 978-0-470-00874-4 (cloth)
ISBN-10: 0-470-00874-1 (cloth)
1. Investment analysis. I. Title. II. Series.HG4529.A77 2007332.63'2042—dc22
2006014664
Printed in the United States of America.1098765 4 3 2To Jack and Belma 
 
 
Contents
 
Acknowledgments ixAbout the Author xiIntroduction 1PART I Methodological, Psychological,
Philosophical, and Statistical
FoundationsCHAPTER 1 = Objective Rules and Their Evaluation 15CHAPTER 2 The Illusory Validity of Subjective
Technical Analysis 33CHAPTER 3 ‘The Scientific Method and Technical
Analysis 103CHAPTER 4 ss Statistical Analysis 165
CHAPTER 5 Hypothesis Tests and Confidence Intervals 217CHAPTER 6 = Data-Mining Bias: The Fool’s Gold
of Objective TA 255CHAPTER 7 ‘Theories of Nonrandom Price Motion 331PART II Case Study: Signal Rules
for the S&P 500 Index
 
CHAPTER 8 Case Study of Rule Data Miningfor the S&P 500 389
CHAPTER 9 Case Study Results and the Future of TA 441
viivill CONTENTS
APPENDIX Proof That Detrending Is Equivalent toBenchmarking Based on Position Bias 475
Notes 477
Index 517 
Acknowledgments
 
 
 
of many more people. I wish to acknowledge those individualswithout whom this book would have been impossible or a muchlesser work.
Iam most indebted to Dr. Timothy Masters, whom I have had the plea-sure of knowing for over 10 years. His patient and intelligent guidancekept me on a solid statistical footing. Tim not only gave me important feed-back on technical issues but was responsible for coding and running theATR rule experiments and the statistical routines used to test the over6,400 rules examined. Tim also innovated the Monte Carlo permutationmethod as an alterative to the patented method of White, called Reality-Check, for testing the statistical significance of rules discovered by datamining. Tim has graciously decided to put the method in the public domainand has allowed it to be published for the first time here.
Also crucial were the programming talents of Stuart Okorofsky andthe database creation by Dr. John Wolberg. I am indebted Dr. HalbertWhite, inventor of Reality-Check and for the help of Professor DavidJensen, director of the Knowledge Discovery Lab at the University ofMassachusetts—Amherst.
I also wish to express my appreciation to the following people for re-viewing and commenting on various chapters. Their feedback was essen-tial: Charles Neumann, Lance Rembar, Dr. Samuel Aronson, Dennis Katz,Hayes Martin, George Butler, Dr. John Wolberg, Jay Bono, Dr. Andre Shle-fier, Dr. John Nofsinger, Doyle Delaney, Ken Byerly, James Kunstler, andKenny Rome.
Special thanks to the helpful folks at John Wiley & Sons: Kevin Com-mins, for seeing the value of a critical appraisal of technical analysis, andEmilie Herman, for her steady hand in editing the book. Thanks as well toMichael Lisk and Laura Walsh.
T hough a book is attributed to its author(s), it truly reflects the efforts 
About the Author
 
 
 
David Aronson is an adjunct professor of finance at Baruch College's Zick-lin School of Business in New York, where he teaches a graduate-levelcourse in technical analysis to MBA and financial-engineering students,and vice-president of Hood River Research Inc., a firm that develops signalfilters and predictive models. He was formerly a proprietary trader andtechnical analyst at Spear, Leeds and Kellogg and president of Raden Re-search Group Inc., a consulting firm that developed the data-mining soft-ware PRISM and filters and systems for various trading firms. Prior to that,he founded AdvoCom Corporation, which managed client funds in portfo-lios of futures trading advisors using portfolio optimization. He received aBA in philosophy from Lafayette College in 1967 and served in the PeaceCorps in El Salvador. 
 
Introduction |
 
market data with the intent of forecasting future price movements.!
It is comprised of numerous analysis methods, patterns, signals, indi-cators, and trading strategies, each with its own cheerleaders claimingthat their approach works.
Much of popular or traditional TA stands where medicine stood beforeit evolved from a faith-based folk art into a practice based on science. Itsclaims are supported by colorful narratives and carefully chosen (cherrypicked) anecdotes rather than objective statistical evidence.
This book’s central contention is that TA must evolve into a rigorousobservational science if it is to deliver on its claims and remain relevant.The scientific method is the only rational way to extract useful knowledgefrom market data and the only rational approach for determining whichTA methods have predictive power. I call this evidence-based technicalanalysis (EBTA). Grounded in objective observation and statistical infer-ence (i.e., the scientific method), EBTA charts a course between the magi-cal thinking and gullibility of a true believer and the relentless doubt of arandom walker.
Approaching TA, or any discipline for that matter, in a scientific man-ner is not easy. Scientific conclusions frequently conflict with what seemsintuitively obvious. To early humans it seemed obvious that the sun cir-cled the earth. It took science to demonstrate that this intuition waswrong. An informal, intuitive approach to knowledge acquisition is espe-cially likely to result in erroneous beliefs when phenomena are complexor highly random, two prominent features of financial market behavior.Although the scientific method is not guaranteed to extract gold from themountains of market data, an unscientific approach is almost certain toproduce fool's gold.
This book’s second contention is that much of the wisdom comprisingthe popular version of TA does not qualify as legitimate knowledge.
[nant analysis (TA) is the study of recurring patterns in financial2 INTRODUCTION
KEY DEFINITIONS: PROPOSITIONS AND CLAIMS,BELIEF AND KNOWLEDGE
 
I have already used the terms knowledge and belief but have not rigor-ously defined them. These and several other key terms will be used re-peatedly in this book, so some formal definitions are needed.
The fundamental building block of knowledge is a declarative state-ment, also known as a claim or a proposition. A declarative statement isone of four types of utterances that also include exclamations, questions,and commands. Declarative statements are distinguished from the othersin that they have truth value. That is to say, they can be characterized aseither true or false or probably true or probably false.
The statement “Oranges are on sale at the supermarket for five cents adozen” is declarative. It makes a claim about a state of affairs existing atthe local market. It may be true or false. In contrast, the exclamatorystatement “Holy cow, what a deal,” the command “Go buy me a dozen,” orthe question “What is an orange?” cannot be called true or false.
Our inquiry into TA will be concerned with declarative statements,such as, “Rule X has predictive power.” Our goal is to determine which ofthese declarative statements warrant our belief.
What does it mean to say, “I believe X.”? “With regard to states of af-fairs in general (i.e., ‘matters of fact’ or ‘what will happen’) believing Xamounts to expecting to experience X if and when we are in a position todo so.”” Therefore, if I believe the claim that oranges are on sale for fivecents a dozen, it means that I expect to be able to buy oranges for fivecents a dozen if I go to the store. However, the command to buy some or-anges or the exclamation that I am happy about the opportunity, set up nosuch expectation.
What does all this all means for us? For any statement to even be con-sidered as a candidate for belief, it must “assert some state of affairs thatcan be expected.’ Such statements are said to have cognitive content—they convey something that can be known. “If the statement containsnothing to know then there is nothing there to be believe.”!
Although all declarative statements presumably have cognitive con-tent, not all actually do. This is not a problem if the lack of cognitivecontent is obvious, for example, the declaration “The square root ofTuesday is a prime number.” This utterance is, on its face, nonsense.There are other declarative statements, however, whose lack of cogni-tive content is not so obvious. This can be a problem, because suchstatements can fool us into thinking that a claim has been made thatsets up an expectation, when, in fact, no claim has really been put for-ward. These pseudo-declarative-statements are essentially meaninglessclaims or empty propositions.Introduction 3
Although meaningless claims are not valid candidates for belief, thisdoes not stop many people from believing in them. The vague predictionsmade in the daily astrology column or the nebulous promises made bypromoters of bogus health cures are examples of meaningless claims.Those who believe these empty propositions simply do not realize thatwhat they have been told has no cognitive content.
A way to tell if a statement has cognitive content and is, thus, a validcandidate for belief is the discernible-difference test® described by Hall.“Utterances with cognitive content make claims that are either true orfalse; and whether they are true or false makes a difference that can bediscerned. That is why these utterances offer something to believe andwhy there is no point in trying to believe an utterance that makes no suchoffer”’ In other words, a proposition that passes the discernible-differencetest sets up an expectation such that the state of affairs, if the statementwere true, is recognizably different from the state of affairs, if the state-ment were false.
The discernible-difference criterion can be applied to statements pur-porting to be predictions. A prediction is a claim to know something aboutthe future. If a prediction has cognitive content, it will be clearly dis-cernible in the outcome if the prediction was accurate or not. Many, if notmost, of the forecasts issued by practitioners of popular TA are devoid ofcognitive content on these grounds. In other words, the predictions aretypically too vague to ever determine if they were wrong.
The truth or falsity of the claim oranges are on sale for five cents adozen will make a discernible difference when I get to the market. It isthis discernible difference that allows the claim to be tested. As will be de-scribed in Chapter 3, testing a claim on the basis of a discernible differ-ence is central to the scientific method.
Hall, in his book Practically Profound, explains why he findsFreudian psychoanalysis to be meaningless when examined in light of thediscernible-difference test.
“Certain Freudian claims about human sexual development are com-patible with all possible states of affairs. There is no way to confirm or dis-confirm either ‘penis envy’ or ‘castration complex’ because there is nodistinguishable difference between evidence affirming and evidence deny-ing these interpretations of behavior. Exactly opposite behaviors areequally predictable, depending on whether the alleged psychosexualstress is overt or repressed.” The requirement of “cognitive content rulesout all utterances that are so loose, poorly formed or obsessively held(e.g., conspiracy theories) that there is no recognizable difference be-tween what would be the case if they were so, and what would be the caseif they were not.” In a like vein, the Intelligent Design Theory carries nocognitive freight in the sense that no matter what life form is observed it is4 INTRODUCTION
consistent with the notion that it manifests an underlying form specifiedby some intelligent designer.®
What then is knowledge? Knowledge can be defined as justified truebelief. Hence, in order for a declarative statement to qualify as knowl-edge, not only must it be a candidate for belief, because it has cognitivecontent, but it must meet two other conditions as well. First, it must betrue (or probably true). Second, the statement must be believed with jus-tification. A belief is justified when it is based on sound inferences fromsolid evidence.
Prehistoric humans held the false belief that the sun moved acrossthe sky because the sun orbited the earth. Clearly they were not in pos-session of knowledge, but suppose that there was a prehistoric personwho believed correctly that the sun moved across the sky because ofthe earth’s rotation. Although this belief was true, this individual couldnot be described as possessing knowledge. Even though they believedwhat astronomers ultimately proved to be true, there was no evidenceyet to justify that belief. Without justification, a true belief doesnot attain the status of knowledge. These concepts are illustrated inFigure I.1.
    
  
Well FormedDeclarative Statements(True or False)
       
Py
   
JustifiedLoa wale (teRca
Knowledge
    
FIGURE Lf) Knowledge: justified true belief.Introduction 5
From this it follows that erroneous beliefs or false knowledge fail tomeet one or more of the necessary conditions of knowledge. Thus, an er-roneous belief can arise either because it concerns a meaningless claim orbecause it concerns a claim that, though meaningful, is not justified byvalid inferences from solid evidence.
Still, even when we have done everything right, by drawing the bestpossible inference from sound evidence, we can still wind up adoptingerroneous beliefs. In other words, we can be justified in believing afalsehood, and honestly claim to know something, if it appears to betrue according to logically sound inferences from the preponderance ofavailable evidence. “We are entitled to say ‘I know’ when the targetof that claim is supported beyond reasonable doubt in the networkof well-tested evidence. But that is not enough to guarantee that wedo know.”!”
Falsehoods are an unavoidable fact of life when we attempt to knowthings about the world based on observed evidence. Thus, knowledgebased on the scientific method is inherently uncertain, and provisional,though less uncertain than knowledge acquired by less formal methods.However, over time, scientific knowledge improves, as it comes to de-scribe reality in a progressively more accurate manner. It is a continualwork in progress. The goal of EBTA is a body of knowledge about marketbehavior that is as good as can be had, given the limits of evidence gather-ing and the powers of inference.
ERRONEOUS TA KNOWLEDGE: THE COSTOF UNDISCIPLINED ANALYSIS
 
To understand why the knowledge produced by the popular version of TAis untrustworthy, we must consider two distinct forms of TA: subjectiveand objective. Both approaches can lead to erroneous beliefs, but they doso in distinct ways.
Objective TA methods are well defined repeatable procedures that is-sue unambiguous signals. This allows them to be implemented as comput-erized algorithms and back-tested on historical data. Results produced bya back test can be evaluated in a rigorous quantitative manner.
Subjective TA methods are not well-defined analysis procedures. Be-cause of their vagueness, an analyst’s private interpretations are required.This thwarts computerization, back testing, and objective performanceevaluation. In other words, it is impossible to either confirm or deny asubjective method's efficacy. For this reason they are insulated from evi-dentiary challenge.6 INTRODUCTION
From the standpoint of EBTA, subjective methods are the most prob-lematic. They are essentially meaningless claims that give the illusion ofconveying cognitive content. Because the methods do not specify howthey are to be applied, different analysts applying it to the same set ofmarket data can reach different conclusions. This makes it impossible todetermine if the method provides useful predictions. Classical chart pat-tern analysis,'! hand-drawn trend lines, Elliott Wave Principle,'* Gann pat-terns, Magic T’s and numerous other subjective methods fall into thiscategory.'® Subjective TA is religion—it is based on faith. No amount ofcherry-picked examples showing where the method succeeded can curethis deficiency.
Despite their lack of cognitive content and the impossibility of everbeing supported by sound evidence, there is no shortage of fervent believ-ers in various subjective methods. Chapter 2 explains how flaws in humanthinking can produce strong beliefs in the absence of evidence or even inthe face of contradictory evidence.
Objective TA can also spawn erroneous beliefs but they come aboutdifferently. They are traceable to faulty inferences from objective evi-dence. The mere fact that an objective method has been profitable in aback test is not sufficient grounds for concluding that it has merit. Pastperformance can fool us. Historical success is a necessary but not a suffi-cient condition for concluding that a method has predictive power and,therefore, is likely to be profitable in the future. Favorable past perfor-mance can occur by luck or because of an upward bias produced by oneform of back testing called data mining. Determining when back-testprofits are attributable to a good method rather than good luck is a ques-tion that can only be answered by rigorous statistical inference. This isdiscussed in Chapters 4 and 5. Chapter 6 considers the problem of data-mining bias. Although I will assert that data mining, when done correctly,is the modern technician's best method for knowledge discovery, special-ized statistical tests must be applied to the results obtained with datamining.
HOW EBTA IS DIFFERENT
 
What sets EBTA apart from the popular form of TA? First, it is restrictedto meaningful claims—objective methods that can be tested on historicaldata. Second, it utilizes advanced forms of statistical inference to deter-mine if a profitable back test is indicative of an effective method. Thus,Introduction 7
the prime focus of EBTA is determining which objective methods are wor-thy of actual use.
EBTA rejects all forms of subjective TA. Subjective TA is not evenwrong. It is worse than wrong. Statements that can be qualified as wrong(untrue) at least convey cognitive content that can be tested. The proposi-tions of subjective TA offer no such thing. Though, at first blush, theyseem to convey knowledge, when they are examined critically, it becomesclear they are empty claims.
Promoters of New Age health cures excel at empty claims. They tellyou that wearing their magic copper bracelet will make you will feel bet-ter and put more bounce in your step. They suggest your golf game willimprove and maybe even your love life. However, the claim’s lack of speci-ficity makes it impossible to nail down exactly what is being promised orhow it can be tested. Such claims can never be confirmed or contradictedwith objective evidence. On these same grounds, it can be said that thepropositions of subjective TA are empty and thus insulated from empiricalchallenge. They must be taken on faith.
In contrast, a meaningful claim is testable because it makes measur-able promises. It states specifically how much your golf game will im-prove or how bouncy your steps will be. This specificity opens the claimto being contradicted with empirical evidence.
From the perspective of EBTA, proponents of subjective methods arefaced with a choice: They can reformulate the method to be objective, asone practitioner of the Elliott Wave Principle has done," thus exposing itto empirical refutation, or they must admit the method must be acceptedon faith. Perhaps Gann lines actually provide useful information. In theirpresent form, we are denied this knowledge.
With respect to objective TA, EBTA does not take profitable backtests at face value. Instead, they are subjected to rigorous statisticalevaluation to determine if profits were due to luck or biased research.As will be pointed out in Chapter 6, in many instances, profitable backtests may be a data miner fool’s gold. This may explain why many objec-tive TA methods that perform well in a back testing perform worse whenapplied to new data. Evidence-based technical analysis uses computer-intensive statistical methods that minimize problems stemming from thedata-mining bias.
The evolution of TA to EBTA also has ethical implications. It is theethical and legal responsibility of all analysts, whatever form of analysisthey practice, to make recommendations that have a reasonable basisand not to make unwarranted claims.'* The only reasonable basis for as-serting an analysis method has value is objective evidence. Subjective8 INTRODUCTION
TA methods cannot meet this standard. Objective TA, conducted in accor-dance with the standards of EBTA can.
EBTA RESULTS FROM ACADEMIA
 
Evidence-based technical analysis is not a new idea. Over the past twodecades, numerous articles in respected academic journals!® have ap-proached TA in the rigorous manner advocated by this book.!” The evi-dence is not uniform. Some studies show TA does not work, but someshow that it does. Because each study is confined to a particular aspect ofTA and a specific body of data, it is possible for studies to reach differentconclusions. This is often the case in science.
The following are a few of the findings from academic TA. It showsthat, when approached in a rigorous and intellectually honest manner, TAis a worthwhile area of study.
e Expert chartists are unable to distinguish actual price charts of stocksfrom charts produced by a random process. '*
¢ There is empirical evidence of trends in commodities!’ and foreign ex-change markets that can be exploited with the simple objective trendindicators. In addition, the profits earned by trend-following specula-tors may be justified by economic theory” because their activitiesprovide commercial hedgers with a valuable economic service, thetransference of price risk from hedger to speculator.
e Simple technical rules used individually and in combinations canyield statistically and economically significant profits when applied tostock market averages composed of relatively young companies (Rus-sell 2000 and NASDAQ Composite).”!
© Neural networks have been able to combine buy/sell signals of simplemoving-average rules into nonlinear models that displayed good pre-dictive performance on the Dow Jones Average over the period 1897to 1988.”
¢ Trends in industry groups and sectors persist long enough after detec-tion by simple momentum indicators to earn excess returns.”
¢ Stocks that have displayed prior relative strength and relative weak-ness continue to display above-average and below-average perfor-mance over horizons of 3 to 12 months.”
¢ United States stocks, selling near their 52-week highs, outperformother stocks. An indicator defined as the differential between astock’s current price and its 52