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Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals Hardcover – November 3, 2006


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Product Details

  • Hardcover: 544 pages
  • Publisher: Wiley; 1 edition (November 3, 2006)
  • Language: English
  • ISBN-10: 0470008741
  • ISBN-13: 978-0470008744
  • Product Dimensions: 9.1 x 6.3 x 1.6 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (66 customer reviews)
  • Amazon Best Sellers Rank: #265,897 in Books (See Top 100 in Books)

Editorial Reviews

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.


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Customer Reviews

I found this book well written, informative and entertaining.
EE
EBTA is a great book for any technical analysis (TA) based trading system developer.
Braden Craig
This part of the book has some very good ideas, as well as some unfortunate errors.
Rob Ryley

Most Helpful Customer Reviews

170 of 181 people found the following review helpful By Mike Carr, CMT on November 12, 2006
Format: Hardcover
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.
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29 of 30 people found the following review helpful By Vampa on March 7, 2007
Format: Hardcover Verified Purchase
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.
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51 of 57 people found the following review helpful By Sam Levine, CFA on November 15, 2006
Format: Hardcover
Professor Aronson's book is a fascinating read for anyone frustrated with the current state of technical research and a must-read for those new to the field. I believe the Market Technicians Association should include it in its Chartered Market Technician curriculum.

After a few years of studying and using technical analysis, I was left with the distinct feeling that there was an elephant in the room: most of the methods used by market technicians haven't been rigorously examined for risk-adjusted performance. Elaborate and often contradictory theories and strategies have been presented by saying "my personal experience has been..." or something similar. Eventually, TA began to seem more a religious choice rather than a science of observing and predicting the markets (let alone successful investing).

Aronson's book follows a structure that is designed to break through generations of instruction from pontificating gurus. He discusses the reason TA's rules are suspect, provides a brief history of empiricism ("the scientific method") and then delves into descriptive and inductive statistics to move the field forward. Those readers fortunate enough to have an undergraduate background in philosophy and statistics will find the reading somewhat basic but the application of these fields to a critical appraisal of TA refreshing. Finally, he applies his rigorous testing to a large set of TA rules.

Key takeaway: The way to develop and test strategies going forward.
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53 of 60 people found the following review helpful By Paulo C. Rios Jr. on October 14, 2007
Format: Hardcover Verified Purchase
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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