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A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection

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  • Damian Kisiel
  • Denise Gorse

Abstract

This work proposes a novel portfolio management technique, the Meta Portfolio Method (MPM), inspired by the successes of meta approaches in the field of bioinformatics and elsewhere. The MPM uses XGBoost to learn how to switch between two risk-based portfolio allocation strategies, the Hierarchical Risk Parity (HRP) and more classical Na\"ive Risk Parity (NRP). It is demonstrated that the MPM is able to successfully take advantage of the best characteristics of each strategy (the NRP's fast growth during market uptrends, and the HRP's protection against drawdowns during market turmoil). As a result, the MPM is shown to possess an excellent out-of-sample risk-reward profile, as measured by the Sharpe ratio, and in addition offers a high degree of interpretability of its asset allocation decisions.

Suggested Citation

  • Damian Kisiel & Denise Gorse, 2021. "A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection," Papers 2111.05935, arXiv.org.
    • Handle: RePEc:arx:papers:2111.05935
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    References listed on IDEAS

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    Cited by:

    1. Damian Kisiel & Denise Gorse, 2022. "Portfolio Transformer for Attention-Based Asset Allocation," Papers 2206.03246, arXiv.org.
    2. Damian Kisiel & Denise Gorse, 2022. "Axial-LOB: High-Frequency Trading with Axial Attention," Papers 2212.01807, arXiv.org.

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