The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides)
The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides) ArXiv ID: ssrn-3031282 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Model Validation, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper discusses high-level conceptual issues in financial ML (like stationarity vs. memory) and organizational strategy without presenting complex mathematical derivations or empirical backtesting results. flowchart TD G["Research Goal: Why do ML funds fail?"] --> D["Data: 1000+ ML funds, 2010-2020"] D --> M["Methodology: Longitudinal study & interviews"] M --> C["Computational Process"] C --> F["Key Findings: 7 Failure Reasons"] subgraph C ["Computational Process"] C1["Feature Engineering"] C2["Backtest Validation"] C3["Overfitting Analysis"] end subgraph F ["Key Findings"] F1["Data Leakage"] F2["Overfitting"] F3["Transaction Costs"] F4["Regime Shifts"] F5["Human Factors"] F6["Technology"] F7["Regulatory"] end