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Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management

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  • Gang Hu
  • Ming Gu

Abstract

Investment portfolios, central to finance, balance potential returns and risks. This paper introduces a hybrid approach combining Markowitz's portfolio theory with reinforcement learning, utilizing knowledge distillation for training agents. In particular, our proposed method, called KDD (Knowledge Distillation DDPG), consist of two training stages: supervised and reinforcement learning stages. The trained agents optimize portfolio assembly. A comparative analysis against standard financial models and AI frameworks, using metrics like returns, the Sharpe ratio, and nine evaluation indices, reveals our model's superiority. It notably achieves the highest yield and Sharpe ratio of 2.03, ensuring top profitability with the lowest risk in comparable return scenarios.

Suggested Citation

  • Gang Hu & Ming Gu, 2024. "Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management," Papers 2405.05449, arXiv.org.
    • Handle: RePEc:arx:papers:2405.05449
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    References listed on IDEAS

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    5. Peter A. Griffin, 1984. "Different Measures of Win Rate for Optimal Proportional Betting," Management Science, INFORMS, vol. 30(12), pages 1540-1547, December.
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