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Abstract
The rate of failure in quantitative finance is high, particularly in financial machine learning applications. The few managers who succeed amass a large amount of assets and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that the author explains in this article. In the author's experience, 10 critical mistakes underlie those failures.
TOPIC: Big data/machine learning
- © 2018 Pageant Media Ltd
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- Article
- Abstract
- PITFALL #1: THE SISYPHUS PARADIGM
- SOLUTION #1: THE META-STRATEGY PARADIGM
- PITFALL #2: RESEARCH THROUGH BACKTESTING
- SOLUTION #2: FEATURE IMPORTANCE ANALYSIS
- PITFALL #3: CHRONOLOGICAL SAMPLING
- SOLUTION #3: THE VOLUME CLOCK
- PITFALL #4: INTEGER DIFFERENTIATION
- SOLUTION #4: FRACTIONAL DIFFERENTIATION
- PITFALL #5: FIXED-TIME HORIZON LABELING
- SOLUTION #5: THE TRIPLE-BARRIER METHOD
- PITFALL #6: LEARNING SIDE AND SIZE SIMULTANEOUSLY
- SOLUTION #6: META-LABELING
- PITFALL #7: WEIGHTING OF NON-INDEPENDENT IDENTICALLY DISTRIBUTED SAMPLES
- SOLUTION #7: UNIQUENESS WEIGHTING AND SEQUENTIAL BOOTSTRAPPING
- PITFALL #8: CROSS-VALIDATION LEAKAGE
- SOLUTION #8: PURGING AND EMBARGOING
- PITFALL #9: WF (OR HISTORICAL) BACKTESTING
- SOLUTION #9: COMBINATORIAL PURGED CROSS-VALIDATION
- PITFALL #10: BACKTEST OVERFITTING
- SOLUTION #10: THE DEFLATED SHARPE RATIO
- CONCLUSIONS
- ACKNOWLEDGMENT
- REFERENCES
- Info & Metrics
- PDF (Subscribers Only)
- PDF (Subscribers Only)
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