PolyModel for Hedge Funds’ Portfolio Construction Using Machine Learning

ArXiv ID: 2412.11019 “View on arXiv”

Authors: Unknown

Abstract

The domain of hedge fund investments is undergoing significant transformation, influenced by the rapid expansion of data availability and the advancement of analytical technologies. This study explores the enhancement of hedge fund investment performance through the integration of machine learning techniques, the application of PolyModel feature selection, and the analysis of fund size. We address three critical questions: (1) the effect of machine learning on trading performance, (2) the role of PolyModel feature selection in fund selection and performance, and (3) the comparative reliability of larger versus smaller funds. Our findings offer compelling insights. We observe that while machine learning techniques enhance cumulative returns, they also increase annual volatility, indicating variability in performance. PolyModel feature selection proves to be a robust strategy, with approaches that utilize a comprehensive set of features for fund selection outperforming more selective methodologies. Notably, Long-Term Stability (LTS) effectively manages portfolio volatility while delivering favorable returns. Contrary to popular belief, our results suggest that larger funds do not consistently yield better investment outcomes, challenging the assumption of their inherent reliability. This research highlights the transformative impact of data-driven approaches in the hedge fund investment arena and provides valuable implications for investors and asset managers. By leveraging machine learning and PolyModel feature selection, investors can enhance portfolio optimization and reassess the dependability of larger funds, leading to more informed investment strategies.

Keywords: hedge funds, feature selection, portfolio optimization, machine learning, fund selection, Hedge Funds

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper introduces a novel theoretical framework (PolyModel) and integrates advanced machine learning, indicating moderate-to-high mathematical complexity; however, the empirical section relies on aggregated summary statistics (cumulative returns, volatility) without detailed backtest mechanics, implementation specifics, or data processing pipelines, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal: Enhance Hedge Fund<br>Portfolio Construction with ML"] --> B{"Methodology: PolyModel ML Framework"}
    B --> C["Input Data: Historical Hedge Fund<br>Performance & Features"]
    C --> D["Computational Process:<br>ML Models with Feature Selection"]
    D --> E{"Findings/Outcomes"}
    
    E --> F1["ML boosts returns<br>but increases volatility"]
    E --> F2["PolyModel Feature Selection<br>is robust for fund selection"]
    E --> F3["LTS manages volatility<br>effectively"]
    E --> F4["Larger funds not consistently<br>more reliable than smaller ones"]