Portfolio management using online reinforcement learning with adaptive exploration and Multi-task self-supervised representation

Abstract

Reinforcement learning (RL) has been widely used to make continuous trading decisions in portfolio management. However, traditional quantitative trading methods often generalize poorly under certain market conditions, whereas the output of prediction-based approaches cannot be easily translated into actionable insights for trading. Market volatility, noisy signals, and unrealistic simulation environments also exacerbate these challenges. To address the aforementioned limitations, we developed a novel framework that combines Multi-task self-supervised learning (MTSSL) and adaptive exploration (AdapExp) modules. The MTSSL module leverages auxiliary tasks to learn meaningful financial market representations from alternative data, whereas the AdapExp module enhances RL training efficiency by improving the fidelity of the simulation environment. Experimental results obtained in backtesting conducted in real financial markets indicate that the proposed framework achieved approximately 13% higher returns relative to state-of-the-art models. Furthermore, this framework can be used with various RL methods to considerably improve their performance.