Machine Learning for Stock Selection

ArXiv ID: ssrn-3330946 “View on arXiv”

Authors: Unknown

Abstract

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning technique

Keywords: Machine learning, Quantitative finance, Predictive accuracy, Quantitative Strategies

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper provides a conceptual overview of machine learning techniques in finance with minimal advanced mathematical derivations, focusing more on the debate and methodology rather than deep theoretical proofs. Empirical rigor is limited as it discusses general challenges like overfitting and proposes forecast combinations without presenting detailed backtest results, code, or specific implementation datasets.

  flowchart TD
    A["Research Goal: Evaluate ML for Stock Selection"] --> B["Data: Historical Prices, Fundamentals, Sentiment"]
    B --> C["Methodology: Train ML Models e.g., Gradient Boosting, Neural Networks"]
    C --> D{"Computational Process: Backtest on Out-of-Sample Data"}
    D --> E["Key Finding: ML Models Achieve High Predictive Accuracy"]
    D --> F["Key Finding: Significant Risk of Overfitting"]
    E & F --> G["Outcome: Mixed Results; Strategy Viability Depends on Rigorous Validation"]
    style A fill:#e1f5fe
    style G fill:#fff3e0