Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE
This addresses the challenge of optimal asset allocation in volatile financial markets for investors, though it appears incremental as it combines existing RL methods with a time-series encoder.
This study tackled the optimal asset allocation problem between risky and risk-free assets by formulating it as a sequential decision-making task within a Markov Decision Process and integrating the Time-series Dense Encoder (TiDE) into the Deep Deterministic Policy Gradient (DDPG) reinforcement learning framework. The results showed that DDPG-TiDE outperformed Q-learning and generated higher risk-adjusted returns than a passive buy-and-hold strategy.
The optimal asset allocation between risky and risk-free assets is a persistent challenge due to the inherent volatility in financial markets. Conventional methods rely on strict distributional assumptions or non-additive reward ratios, which limit their robustness and applicability to investment goals. To overcome these constraints, this study formulates the optimal two-asset allocation problem as a sequential decision-making task within a Markov Decision Process (MDP). This framework enables the application of reinforcement learning (RL) mechanisms to develop dynamic policies based on simulated financial scenarios, regardless of prerequisites. We use the Kelly criterion to balance immediate reward signals against long-term investment objectives, and we take the novel step of integrating the Time-series Dense Encoder (TiDE) into the Deep Deterministic Policy Gradient (DDPG) RL framework for continuous decision-making. We compare DDPG-TiDE with a simple discrete-action Q-learning RL framework and a passive buy-and-hold investment strategy. Empirical results show that DDPG-TiDE outperforms Q-learning and generates higher risk adjusted returns than buy-and-hold. These findings suggest that tackling the optimal asset allocation problem by integrating TiDE within a DDPG reinforcement learning framework is a fruitful avenue for further exploration.