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Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals / Edition 1

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Overview

As an approach to research, technical analysis has suffered because it is a "discipline" practiced without discipline. In order for technical analysis to deliver useful knowledge that can be applied to trading, it must evolve into a rigorous observational science.

Over the past two decades, numerous articles in respected academic journals have approached technical analysis in a scientifically rigorous and intellectually honest manner, and now, Evidence-Based Technical Analysis looks to continue down this path. Organized into two parts, this valuable resource first establishes the methodological, philosophical, and statistical foundations of evidenced-based technical analysis (EBTA), and then demonstrates this approach—by using twenty-five years of historical data to test 6,400 binary buy/sell rules on the S&P 500.

Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout these pages, expert David Aronson details this new type of technical analysis that—unlike traditional technical analysis—is restricted to objective rules, whose historical profitability can be quantified and scrutinized.

Filled with in-depth insights and practical advice, Evidence-Based Technical Analysis provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining. Experimental results presented in the book will show you that data mining—a process in which many rules are back-tested and the best performing rules are selected—is an effective procedure for discovering useful rules/signals. However, since the historical performance of the rules/signals discovered by data mining are upwardly biased, new statistical tests are required to make reasonable inferences about future profitability. Two such tests, one of which has never been discussed anywhere heretofore, are described and illustrated.

If you want to use technical analysis to navigate today's markets, you must first abandon the subjective, interpretive methods traditionally associated with this discipline, and embrace an approach that is scientifically and statistically valid. Grounded in objective observation and statistical inference, EBTA is the approach to technical analysis you need to succeed in your trading endeavors.

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Editorial Reviews

From the Publisher
"…his book is well written and contains a great deal of information that is of value…." (The Technical Analyst, May/June 2007)
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Product Details

  • ISBN-13: 9780470008744
  • Publisher: Wiley
  • Publication date: 11/3/2006
  • Series: Wiley Trading Series , #274
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 544
  • Sales rank: 436,082
  • Product dimensions: 6.46 (w) x 9.09 (h) x 1.66 (d)

Meet the Author

DAVID ARONSON is an adjunct professor at Baruch College, where he teaches a graduate- level course in technical analysis. He is also a Chartered Market Technician and has published articles on technical analysis. Previously, Aronson was a proprietary trader and technical analyst for Spear Leeds & Kellogg. He founded Raden Research Group, a firm that was an early adopter of data mining within financial markets. Prior to that, Aronson founded AdvoCom, a firm that specialized in the evaluation of commodity money managers and hedge funds, their performance, and trading methods. For free access to the algorithm for testing data mined rules, go to www.evidencebasedta.com.
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Table of Contents

Acknowledgments.

About the Author.

Introduction.

PART I Methodological, Psychological, Philosophical, and Statistical Foundations.

CHAPTER 1 Objective Rules and Their Evaluation.

CHAPTER 2 The Illusory Validity of Subjective Technical Analysis.

CHAPTER 3 The Scientific Method and Technical Analysis.

CHAPTER 4 Statistical Analysis.

CHAPTER 5 Hypothesis Tests and Confidence Intervals.

CHAPTER 6 Data-Mining Bias: The Fool’s Gold of Objective TA.

CHAPTER 7 Theories of Nonrandom Price Motion.

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.

CHAPTER 9 Case Study Results and the Future of TA.

APPENDIX Proof That Detrending Is Equivalent to Benchmarking Based on Position Bias.

Notes.

Index.

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  • Anonymous

    Posted August 15, 2013

    Great book but why do i have to always pay more for eBooks on No

    Great book but why do i have to always pay more for eBooks on Nook than I do on Kindle? I think is time to trash the nook as kindle is $2.49 cheaper

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  • Anonymous

    Posted November 20, 2006

    Excellent!

    David Aronson persuasively presents his case - the scientific method and proper statistical evaluations must be applied to technical analysis in order to assure its relevance. Most technical indicators and concepts are subjective and untrustworthy as they lack predictive power. Objective technical methods are better because they can be tested to see if they contain the legitimate knowledge and predictive power we seek. But evidence-based technical analysis (EBTA) goes further. Reports of profitable back tested results are not enough because testing which has not taken into account data mining biases or that shades statistical methods can produce deceptive or mediocre results. The value of this book will be reinforced by the future research of supporters of EBTA. Critics of EBTA will declare that technical analysis is more art than science. Aronson exposes the weakness of that claim by showing how statistical methods can be applied to commonly used technical indicators, taking into account the data-mining biases he so well describes, to seek trading signals with statistically significant returns. He shares his results in Part II of the book. This book is well-written and organized. Aronson¿s lifetime pursuit of intellectual honesty is evident, and his Wall Street experience is solid. Numerous footnotes stimulate further study.

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