Click to login and read the full article.
Don’t have access? Click here to request a demo
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
EMEA: +44 0207 139 1600
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
Don’t have access? Click here to request a demo
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
UK: 0207 139 1600
Log in using your username and password
Jump to section
- 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
Similar Articles
Cited By...
- The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning-Time Series Approach
- Machine Learning Algorithms to Classify Future Returns Using Structured and Unstructured Data
- Carry Strategies and the US Dollar Risk of US and Global Bonds
- Selecting Computational Models for Asset Management: Financial Econometrics versus Machine Learning--Is There a Conflict?
- A Network and Machine Learning Approach to Factor, Asset, and Blended Allocation
- Interactions and Interconnectedness Shape Financial Market Research
- Machine Learning in Asset Management--Part 1: Portfolio Construction--Trading Strategies
- AI and Machine Learning in Real Estate Investment
- A Backtesting Protocol in the Era of Machine Learning