Reinforcement learning for bond portfolio management: an actor-critic approach

Abstract

Portfolio management poses unique challenges for traditional forecasting methods due to its complex, sequential decision-making process. This study leverages reinforcement learning (RL) to address these challenges, focusing on fixed income portfolio management. We develop a novel autonomous RL system using a custom environment for bond exchange-traded fund (ETF) dynamics and the Deep Deterministic Policy Gradient (DDPG) algorithm. Unlike prior studies that merely report algorithmic instability, our work systematically addresses this issue by introducing a robust agent selection process during training. To illustrate the practical benefits, we construct a simple equally weighted ensemble of selected agents that outperforms the static buy-and-hold benchmark by 4.3% and achieves a total return comparable to the portfolio's best-performing asset, while exhibiting superior risk characteristics during periods of market stress. Our methodology also incorporates methodological innovations, including a scaled reward structure to improve learning in bond markets. While instability is observed in the DDPG algorithm, our results demonstrate that this challenge can be systematically mitigated through robust agent selection and ensemble methods. These findings establish RL as a powerful tool for financial strategies where direct forecasting is complex and uncertain, offering a practical framework for implementation in fixed income markets.