Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals

528 Pages·2011·2.058 MB·English

by  Aronson, David

Evidence-Based
Technical
Analysis
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Evidence-Based
Technical
Analysis
Applying the Scientific
Method and
Statistical Inference
to 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.
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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 2007
332.63'2042—dc22
2006014664
Printed in the United States of America.
10 9 8 7 6 5 4 3 2 1
To Jack and Belma
Contents
Acknowledgments ix
About the Author xi
Introduction 1
PART I Methodological, Psychological,
Philosophical, and Statistical
Foundations
CHAPTER 1 Objective Rules and Their Evaluation 15
CHAPTER 2 The Illusory Validity of Subjective
Technical Analysis 33
CHAPTER 3 The Scientific Method and Technical
Analysis 103
CHAPTER 4 Statistical Analysis 165
CHAPTER 5 Hypothesis Tests and Confidence Intervals 217
CHAPTER 6 Data-Mining Bias: The Fool’s Gold
of Objective TA 255
CHAPTER 7 Theories of Nonrandom Price Motion 331
PART II Case Study: Signal Rules
for the S&P 500 Index
CHAPTER 8 Case Study of Rule Data Mining
for the S&P 500 389
CHAPTER 9 Case Study Results and the Future of TA 441
vii
viii CONTENTS
APPENDIX Proof That Detrending Is Equivalent to
Benchmarking Based on Position Bias 475
Notes 477
Index 517
Acknowledgments
T
hough a book is attributed to its author(s), it truly reflects the efforts
of many more people. I wish to acknowledge those individuals
without whom this book would have been impossible or a much
lesser work.
I am most indebted to Dr. Timothy Masters, whom I have had the plea-
sure of knowing for over 10 years. His patient and intelligent guidance
kept me on a solid statistical footing. Tim not only gave me important feed-
back on technical issues but was responsible for coding and running the
ATR rule experiments and the statistical routines used to test the over
6,400 rules examined. Tim also innovated the Monte Carlo permutation
method as an alterative to the patented method of White, called Reality-
Check, for testing the statistical significance of rules discovered by data
mining. Tim has graciously decided to put the method in the public domain
and has allowed it to be published for the first time here.
Also crucial were the programming talents of Stuart Okorofsky and
the database creation by Dr. John Wolberg. I am indebted Dr. Halbert
White, inventor of Reality-Check and for the help of Professor David
Jensen, director of the Knowledge Discovery Lab at the University of
Massachusetts–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, and
Kenny 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, and
Emilie Herman, for her steady hand in editing the book. Thanks as well to
Michael Lisk and Laura Walsh.
ix
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-level
course in technical analysis to MBA and financial-engineering students,
and vice-president of Hood River Research Inc., a firm that develops signal
filters and predictive models. He was formerly a proprietary trader and
technical 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 a
BA in philosophy from Lafayette College in 1967 and served in the Peace
Corps in El Salvador.
xi
Evidence-Based Technical Analysis: Applying the Scientific
Method and Statistical Inference to Trading Signals
by David R. Aronson
Copyright © 2007 David R. Aronson
Introduction
T
echnical analysis (TA) is the study of recurring patterns in financial
market data with the intent of forecasting future price movements.1
It is comprised of numerous analysis methods, patterns, signals, indi-
cators, and trading strategies, each with its own cheerleaders claiming
that their approach works.
Much of popular or traditional TA stands where medicine stood before
it evolved from a faith-based folk art into a practice based on science. Its
claims are supported by colorful narratives and carefully chosen (cherry
picked) anecdotes rather than objective statistical evidence.
This book’s central contention is that TA must evolve into a rigorous
observational science if it is to deliver on its claims and remain relevant.
The scientific method is the only rational way to extract useful knowledge
from market data and the only rational approach for determining which
TA methods have predictive power. I call this evidence-based technical
analysis (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 a
random walker.
Approaching TA, or any discipline for that matter, in a scientific man-
ner is not easy. Scientific conclusions frequently conflict with what seems
intuitively obvious. To early humans it seemed obvious that the sun cir-
cled the earth. It took science to demonstrate that this intuition was
wrong. An informal, intuitive approach to knowledge acquisition is espe-
cially likely to result in erroneous beliefs when phenomena are complex
or highly random, two prominent features of financial market behavior.
Although the scientific method is not guaranteed to extract gold from the
mountains of market data, an unscientific approach is almost certain to
produce fool’s gold.
This book’s second contention is that much of the wisdom comprising
the popular version of TA does not qualify as legitimate knowledge.
1