The 10 Reasons Most Machine Learning Funds Fail

ArXiv ID: ssrn-3104816 “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, Predictive Analytics, Trading Strategy, Quantitative Finance / Equities

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 1.5/10
• Quadrant: Philosophers
• Why: The paper focuses on high-level methodological pitfalls and organizational paradigms in financial machine learning, with minimal advanced mathematical formalism. It lacks empirical backtests, statistical code, or implementation-heavy data analysis, making it more of a conceptual framework than a backtest-ready study.

  flowchart TD
    Q["Research Question:<br>Why do ML funds fail?"] --> D["Data: Financial ML<br>papers & strategies"]
    D --> M["Methodology: Cross-sectional<br>analysis of failures"]
    M --> C["Computational Process:<br>Identify recurring pitfalls"]
    C --> F["Findings: 10 systemic reasons<br>e.g., overfitting, data snooping"]
    F --> O["Outcome: Risk management<br>framework for ML funds"]