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

DESCRIPTION

Evidence-Based Technical Analysis is a technical guide that digs further into scientific concepts and methodologies and the relativity of these research-based variables to technical trading signals. This book poses questions on how to utilize scientifically proven methods and tests in determining the effectiveness of indicators and signals commonly used in technical trading. 

As a discipline considered dependent on research, technical analysis has magnified inadequate results mainly from malpractice. Traders utilize science just as a supplementary tool, but what they fail to understand is both disciplines are a continuum. This means that in order for technical analysis to bring out laudable and substantial results that can be employed in trading, the process must become a meticulous and thorough observational science. 

From cover to cover, professional trader David Aronson addressed the problem by presenting a comprehensive introduction and intensive lectures of a newfound methodology bound in both science and technical analysis. Such was mainly designed and wired for performance evaluation of indicators and signals that data mining brought out. 

ABOUT THE AUTHOR

David Aronson is an esteemed figure in the field of technical analysis and data mining. He began his teaching career back in 2002 as he stayed to work as a professor of finance at the Zicklin School of Business. His fields of specialization involved development researches. Data mining application and enhancement of traditional computerized trading strategies. 

TABLE OF CONTENTS 

Acknowledgments

About the Author

Introduction

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

  1. Objective Rules and Their Evaluation
  2. The Illusory Validity of Subjective Technical Analysis
  3. The Scientific Method and Technical Analysis
  4. Statistical Analysis
  5. Hypothesis Tests and Confidence Intervals
  6. Data-Mining Bias: The Fool’s Gold of Objective TA
  7. Theories of Nonrandom Price Motion

PART II– Case Study: Signal Rules for the S&P Index

  1. Case Study of Rule Data Mining for the S&P 500
  2. 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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