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Experimental Results Presented In New Book Show That Data Mining Is an Effective Approach for Discovering Useful Rules - 'Evidence-Based Technical Analysis.


DUBLIN, Ireland -- Research and Markets (http://www.researchandmarkets.com/reports/c47135) has announced the addition of Evidence-Based Technical Analysis
Technical Analysis
A method of evaluating securities by analyzing statistics generated by market activity, such as past prices and volume. Technical analysts
Technical analysts
Also called chartists or technicians, analysts who use mechanical rules to detect changes in the supply of and demand for a stock, and to capitalize on the expected change.
 do not attempt to measure a security's intrinsic value, but instead use charts to identify patterns that can suggest future activity.

Notes:
Technical analysts believe that the historical performance of stocks and markets are indications of future performance.
: Applying the Scientific Method and Statistical Inference to Trading Signals to their offering.

Evidence-Based Technical Analysis is a breakthrough book in that it rigorously applies the scientific method sci·en·tif·ic method (sn-tf and recently developed statistical tests to determine the true effectiveness of trading strategies, rules or systems discovered by data mining.

Traditional technical analysis - as currently practiced - is more like a faith-based folk art than a science, the author asserts. These subjective interpretive methods cannot be back-tested or evaluated, yet many believe that they are effective. The author explains that because of various cognitive biases and illusions, such as hindsight bias, illusory correlations, etc., people often adopt beliefs that are unsupported by evidence or even contradicted by evidence. For example, the famous head and shoulders pattern
Head and Shoulders Pattern
A technical analysis term used to describe a chart formation in which a stock's price:

1. Rises to a peak and subsequently declines.
2. Then, the price rises above the former peak and again declines.
3. And finally, rises again, but not to the second peak, and declines once more.

The first and third peaks are shoulders, and the second peak forms the head.
 - a cornerstone of traditional TA when tested objectively - has been shown to have no predictive power. Yet many TA texts and most TA experts believe in the pattern's efficacy. To move technical analysis forward, the author proposes a new type of technical analysis, which he calls: evidence-based technical analysis or EBTA EBTA - Electronic Bonding Trouble Administration
EBTA - Emissions Banking and Trading of Allowances
EBTA - Enhanced Business Travel Account
EBTA - European Bobath Tutors Association
EBTA - European Brief Therapy Association
EBTA - Evaluasi Belajar Tahap Akhir
. Unlike traditional technical analysis, EBTA is restricted to objective methods whose historical profitability can be quantified and then rigorously scrutinized. The author provides a new statistical methodology specifically designed for evaluating the performance of rules that are discovered by data mining, a process in which many rules are back-tested and the best performing rule(s) is selected.

Experimental results presented in the book show that data mining is an effective approach for discovering useful rules. However, the historical performance of the best rule (s) is upwardly biased - a combined effect of randomness and data mining. Thus new statistical tests are needed to make reasonable inferences about the future profitability of rules discovered by data mining. Most importantly, in a data mining case study the author evaluates more than 6,400 signalling rules applied to the S&P500 Index using these new tests. For technical analysts and traders, the book is a wake-up call to abandon subjective, interpretive methods and embrace an approach that is scientifically and statistically valid. For other traders, the rigorous testing of trading signals/rules may make their data mining efforts more productive and stimulate the development of new systems, signalling rules.

Author information

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.

Content Outline:

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
Fool's Gold
Also known as Iron Pyrite, fool's gold is a gold colored mineral that is often mistaken for real gold.

Notes:
Unlike the real stuff, fool's gold is relatively worthless.
See also: Bre-X Minerals Ltd., Bullion, Precious Metal
 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

For more information visit http://www.researchandmarkets.com/reports/c47135
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No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2006, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

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Date:Dec 15, 2006
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