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PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning

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  • Siqiao Zhao
  • Dan Wang
  • Raphael Douady

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.

Suggested Citation

  • Siqiao Zhao & Dan Wang & Raphael Douady, 2024. "PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning," Papers 2412.11019, arXiv.org.
    • Handle: RePEc:arx:papers:2412.11019
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    References listed on IDEAS

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    1. Siqiao Zhao & Zhikang Dong & Zeyu Cao & Raphael Douady, 2024. "Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer," Papers 2408.03320, arXiv.org, revised Aug 2024.
    2. Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
    3. Andrew W. Lo & Mila Getmansky & Peter A. Lee, 2015. "Hedge Funds: A Dynamic Industry in Transition," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 483-577, December.
    4. Alexander Cherny & Raphael Douady & Stanislav Molchanov, 2010. "On measuring nonlinear risk with scarce observations," Finance and Stochastics, Springer, vol. 14(3), pages 375-395, September.
    5. William Fung & David A. Hsieh, 2004. "Hedge Fund Benchmarks: A Risk-Based Approach," Financial Analysts Journal, Taylor & Francis Journals, vol. 60(5), pages 65-80, September.
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