Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons

TL;DR

This paper enhances portfolio optimization by applying G-Learning and GIRL algorithms within a reinforcement learning framework to maximize portfolio value by a target date, achieving improved risk-adjusted returns in volatile markets.

Contribution

It introduces a combined G-Learning and GIRL approach for dynamic portfolio optimization, demonstrating the effectiveness of reinforcement learning in financial decision-making.

Findings

Sharpe Ratio improved from 0.42 to 0.483

GIRL's reward parameter tuning has marginal impact on performance

Reinforcement learning methods enable robust portfolio optimization

Abstract

This research proposes an enhancement to the innovative portfolio optimization approach using the G-Learning algorithm, combined with parametric optimization via the GIRL algorithm (G-learning approach to the setting of Inverse Reinforcement Learning) as presented by. The goal is to maximize portfolio value by a target date while minimizing the investor's periodic contributions. Our model operates in a highly volatile market with a well-diversified portfolio, ensuring a low-risk level for the investor, and leverages reinforcement learning to dynamically adjust portfolio positions over time. Results show that we improved the Sharpe Ratio from 0.42, as suggested by recent studies using the same approach, to a value of 0.483 a notable achievement in highly volatile markets with diversified portfolios. The comparison between G-Learning and GIRL reveals that while GIRL optimizes the reward function parameters (e.g., lambda = 0.0012 compared to 0.002), its impact on portfolio performance remains marginal. This suggests that reinforcement learning methods, like G-Learning, already enable robust optimization. This research contributes to the growing development of reinforcement learning applications in financial decision-making, demonstrating that probabilistic learning algorithms can effectively align portfolio management strategies with investor needs.