Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management
On the map: Knowledge distillation → On-policy and self-distillation for LLMs
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in On-policy and self-distillation for LLMs, one of 12 directions in Knowledge distillation. Builds on 4 of its 38 references that are in the map. First posted on arXiv 8 May 2024.
Builds on
4 of 38 references.
References · 4
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 154
- 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36
- 2023Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Managementcited by 1
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