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

Book Description

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.

Table of Contents

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Acknowledgments
  7. About the Author
  8. Introduction
  9. Part I: Methodological, Psychological, Philosophical, and Statistical Foundations
    1. Chapter 1: Objective Rules and Their Evaluation
      1. The Great Divide: Objective Versus Subjective Technical Analysis
      2. TA Rules
      3. Traditional Rules and Inverse Rules
      4. The Use of Benchmarks in Rule Evaluation
      5. Other Details: The Look-Ahead Bias and Trading Costs
    2. Chapter 2: The Illusory Validity of Subjective Technical Analysis
      1. Subjective TA is Not Legitimate Knowledge
      2. A Personal Anecdote: First A True TA Believer, Then A Skeptic
      3. The Mind: A Natural Pattern Finder
      4. The Epidemic of Weird Beliefs
      5. Cognitive Psychology: Heuristics, Biases, and Illusions
      6. Human Information Processing Limitations
      7. Too Dang Certain: The Overconfidence BIAS
      8. Second-Hand Information BIAS: The Power of A Good Story
      9. Confirmation BIAS: How Existing Beliefs Filter Experience and Survive Contradicting Evidence
      10. Illusory Correlations
      11. Misplaced Faith in Chart Analysis
      12. The Intuitive Judgment and The Role of Heuristics
      13. The Representativeness Heuristic and The Illusion Trends and Patterns in Charts: Real and Fake
      14. The Antidote To Illusory Knowledge: The Scientific Method
    3. Chapter 3: The Scientific Method and Technical Analysis
      1. The Most Important Knowledge of All: A Method to Get More
      2. The Legacy of Greek Science: A Mixed Blessing
      3. The Birth of The Scientific Revolution
      4. Faith in Objective Reality and Objective Observations
      5. The Nature of Scientific Knowledge
      6. The Role of Logic In Science
      7. The Philosophy of Science
      8. The End Result: The Hypothetico-Deductive Method
      9. Rigorous and Critical Analysis of Observed Results
      10. Summary of Key Aspects of The Scientific Method
      11. If TA Were to Adopt The Scientific Method
      12. Objectification of Subjective TA: An Example
      13. Subsets of TA
    4. Chapter 4: Statistical Analysis
      1. A Preview of Statistical Reasoning
      2. The Need for Rigorous Statistical Analysis
      3. An Example of Sampling and Statistical Inference
      4. Probability Experiments and Random Variables
      5. Statistical Theory
      6. Descriptive Statistics
      7. Probability
      8. Probability Distributions of Random Variables
      9. Relationship Between Probability and Fractional Area of The Probability Distribution
      10. The Sampling Distribution: The Most Important Concept in Statistical Inference
      11. Deriving The Sampling Distribution: The Classical Approach
      12. Deriving The Sampling Distribution With The Computer-Intensive Approach
      13. Preview of Next Chapter
    5. Chapter 5: Hypothesis Tests and Confidence Intervals
      1. Two Types of Statistical Inference
      2. Hypothesis Tests Versus Informal Inference
      3. Rationale of The Hypothesis Test
      4. Hypothesis Testing: The Mechanics
      5. Computer-Intensive Methods for Generating The Sampling Distribution
      6. Estimation
    6. Chapter 6: Data-Mining Bias: The Fool’s Gold of Objective TA
      1. Falling into The PIT: Tales of The Data-Mining BIAS
      2. The Problem of Erroneous Knowledge in Objective Technical Analysis
      3. Data Mining
      4. Objective TA Research
      5. Data Mining and Statistical Inference
      6. Data-Mining BIAS: An Effect With Two Causes
      7. Experimental Investigation of The Data-Mining BIAS
      8. Solutions: Dealing With The Data-Mining BIAS
    7. Chapter 7: Theories of Nonrandom Price Motion
      1. The Importance of Theory
      2. Scientific Theories
      3. What is Wrong With Popular TA Theory?
      4. The Enemy’s Position: Efficient Markets and Random Walks
      5. Challenging EMH
      6. Behavioral Finance: A Theory of Nonrandom Price Motion
      7. Nonra NDOM Price Motion in The Context of Efficient Markets
      8. Conclusion
  10. Part II: Case Study: Signal Rules for the S&P 500 Index
    1. Chapter 8: Case Study of Rule Data Mining for the S&P 500
      1. Data Mining BIAS and Rule Evaluation
      2. Avoidance of Data Snooping BIAS
      3. Analyzed Data Series
      4. Technical Analysis Themes
      5. Performance Statistic: Average Return
      6. No Complex Rules Were Evaluated
      7. The Case Study Defined in Statistical Terms
      8. Rules: Transforming Data Series Into Market Positions
      9. Time-Series Operators
      10. Input Series To Rules: Raw Time Series and Indicators
      11. Table of 40 Input Series Used in Case Study
      12. The Rules
    2. Chapter 9: Case Study Results and the Future of TA
      1. Presentation of Results
      2. Critique of Case Study
      3. Possible Case Study Extensions
      4. The Future of Technical Analysis
  11. Appendix: Proof That Detrending Is Equivalent to Benchmarking Based on Position Bias
  12. Index