Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management
This work addresses portfolio optimization for finance, offering a hybrid method that improves returns and risk management, though it appears incremental by integrating existing techniques.
The paper tackled portfolio management by combining Markowitz's theory with reinforcement learning using knowledge distillation, achieving a Sharpe ratio of 2.03 and the highest yield in comparative evaluations.
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.