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

arXiv:2405.05449v1 Announce Type: cross
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 …

abstract agents arxiv balance cs.lg ddpg distillation finance hybrid hybrid approach investment kdd knowledge management paper portfolio q-fin.cp reinforcement reinforcement learning returns risks theory training type