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