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

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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 the book, expert David Aronson 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.
- Sales Rank: #731273 in Books
- Published on: 2006-11-03
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.80" w x 6.50" l, 1.91 pounds
- Binding: Hardcover
- 544 pages
Review
"…his book is well written and contains a great deal of information that is of value…." (The Technical Analyst, May/June 2007)
From the Inside Flap
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.
From the Back Cover
Praise for EVIDENCE-BASED TECHNICAL ANALYSIS
"In clear language, Aronson demonstrates the theoretical flaws in interpretative technical analysis methodologies, the flawed premises and conclusions of the Efficient Market Hypothesis, and the appropriate techniques for developing and testing technical analysis methods 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 overall discussion of statistical inference is critical to success. The book is filled with commonsense examples and provides a testing and validation process that saves time, frustration, 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 book before buying a technical system. The book is a good reference to the literature on the subject with extensive footnotes and bibliography."
—Sandor Straus, Managing Member, Merfin, LLC
"You may not agree with everything David Aronson says in this controversial, but compelling new study. Still, every trader who wants to invest technical analysis with the dignity of a great science should read this discerning account."
—Nelson Freeburg, Editor, Formula Research
"There are illusions of the mind that are every bit as real as optical illusions. Aronson's criticisms of popular forms of technical analysis are right on target."
—Fred Gehm, author of Quantitative Trading and Money Management
Most helpful customer reviews
185 of 197 people found the following review helpful.
Great book
By Mike Carr, CMT
In this thought-provoking work, David Aronson tests more than 6,400 technical analysis rules and finds that none of them offer statistically significant returns when applied to trading the S&P 500. This result, presented at the end of his work, is not disappointing to dedicated students of technical analysis who draw from the book not a new trading technique but instead take away a new, and more effective, approach to system development and trading. Those seeking the single best indicator or day trading pattern will be disappointed after reading Evidence-Based Technical Analysis, just as they will be disappointed in their trading until they advance beyond seeking the Holy Grail of Trading.
Most books and articles about technical analysis focus on applying a specific technique in pursuit of success in the markets. This one is different in that it outlines an entirely new process of thinking, and through the application of this new thought process, success can be attained. Part I of Evidence-Based Technical Analysis is called, "Methodological, Psychological, Philosophical, and Statistical Foundations" and Aronson uses this title as an outline to define the processes which should underlie system development.
The scientific method changed the world, and made the advances of modern society possible. Until now, technical analysis has been considered more of an art than a science to many practitioners and escaped the scrutiny of the scientific method. With recent advances in computing power and analysis software, it is now possible for virtually anyone to search through years of data and identify seemingly profitable trading rules. Aronson presents the scientific method, combined with the philosophy of science as explained by Karl Popper, as an antidote to this very real danger.
Well designed experiments in any scientific inquiry are based upon a verifiable hypothesis grounded in detailed observations. Popper contributed the concept of falsification to this framework, which readily lends itself to mechanical trading system design. As Aronson writes, "Popper's central contention was that a scientific inquiry was unable to prove a hypothesis to be true. Rather, science was limited to identifying which hypotheses were false."
In technical analysis, we can never prove that if the NYSE Advance-Decline Line reaches a new high, the Dow Jones Industrial Average will always be higher thirty days later. But, we can test this hypothesis to see if it is not true. This simple example illustrates the beginning of Aronson's scientific approach to the markets.
Many of the dangers of data mining and curve fitting are grounded in psychology, and Aronson thoroughly explains many of the common problems that can contribute to inaccurate observations. Carefully studying his sections on logic and psychology should lead to better market observations, which should lead to profitable systems.
The chapters on statistical analysis are worth more than the price of the book in itself. Aronson presents a clear primer on statistics, and leaves the reader with all they need to understand how to design a statistically valid experiment. In what may very well be a publishing first, he presents clear, detailed and understandable descriptions of bootstrap and Monte Carlo randomization methods.
This book is well-researched and presents actionable ideas to advance the study of technical analysis. Although none of the rules Aronson tested proved to be statistically significant, he helpfully devotes a section to explaining the limitations of his test results. Armed with this information, and the knowledge provided in the rest of the book, the thoughtful analyst can develop better insights into the market and perform better backtests to identify profitable strategies.
36 of 38 people found the following review helpful.
Required reading for professional investors
By Vampa
David Aronson's Evidence Based Technical Analysis ("EBTA") is a fantastic book, and one which our industry has sorely needed. It is a "How to Do Research" book that details the scientific method with regard to the markets. Everyone in the field should both read the book and practice what it preaches. But that won't happen, which is both bad news and good news. The bad news is that the vast majority of market traders who do not practice what the book preaches will lose money. The good news is that those who do will most certainly prosper. As the numbers of the former outnumber those of the latter, the few will earn a lot from the many.
The long (over 100 pages) psychology "preface" is extremely important to Aronson's body of work. I found it hugely interesting, but fear that others may not, or worse. In fact, the psychology preface itself indicates that this work will be reviled (my words) by the multitude. People do not like their sacred cows criticized.
The problem is that most market practitioners use methods with little or no scientific basis. Even if shown evidence of faulty logic, people continue to believe its validity. This is also true in the medical profession as Aronson illustrated and which scared the daylights out of me.
For anything to be scientifically testable, it must be possible to prove it wrong. However, many of the technical analysis disciplines cannot be defined. Thus they cannot be disproved. Consequently they have no scientific validity. They may have some anecdotal importance, but true science is lacking.
Let us say that one of the market gurus espouses that when the chart of XYZ resembles "Pattern A", the stock is destined to rally. To test that we have to define Pattern A and we have to define "rally", and we should provide some time parameters in which to work or fail. The trouble is that the guru cannot define any of that. But the guru still believes in his work, and all of the investors who pay monthly fees for his expertise believe it also. Anyone who criticizes the guru or the validity of Pattern A is looking to get flamed.
EBTA preaches that technical analysis research should be conducted like quantitative analysis research. Those who treat TA as a casual discipline will get casual results. The book is not an easy read, but it is an easier and much more interesting read than the "bible" of the CFA community, Quantitative Methods for Investment Analysis (DeFusco, et al.). I own both books and certainly consider EBTA more valuable than the CFA manual, worshipped by thousands. Don't expect to download all of Aronson's knowledge the first time - read it again. I did and learned more the second time through.
Aronson is meticulous and provides "service after the sale". I had recently traded emails with him about an article he had cited. He was prompt to respond and discus the implications of our expanded research. I have the feeling that he is like this with everyone.
In conclusion I have to say, that if you cannot do what EBTA preaches, at least get yourself a money manager who does.
Bill Rafter
President
Mathematical Investment Decisions, Inc.
65 of 73 people found the following review helpful.
Why not to include in the book the other, successful studies? Why only the negative?
By Paulo C. Rios Jr.
The author mentions: "[while] the set of rules I tested did not give significant results many footnotes point to studies that do" and "I certainly don't claim that there are no TA rules that work. In fact I cite references to numerous peer reviewed studies that discuss TA rules that have proven out in rigorous testing. However, for the rule set that I specified, which I felt at the outset would contain some good rules, none had sufficient performance to reject the null hypothesis".
In other words, the chosen rule set did not contain some good rules as the author expected. The question is then:
why not to include in the book (= to submit to test and select those that in fact worked) some of these "numerous peer reviewed studies that have proven out in rigorous testing"? Any fear that getting them published would make them no longer useful? Well, but they have already been published as cited in the book.
The author does mention that "the purpose of the book was to present a method of testing rules". But instead of just using this method to disprove every single study presented in the book, it would make a lot more sense to use the very same method to confirm at least some successful studies too. This would make the book a lot more useful, well-balanced and positive.
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