Deep Reinforcement Learning and Sustainable Dynamic Portfolio Management

Abstract

Deep Reinforcement Learning is actively used in modern financial literature. However, there is a major gap regarding the inclusion of investor preferences for sustainable and green investments. This paper applies a DRL framework that brings those preferences into dynamic portfolio management with sustainability constraints. We employ the Soft Actor-Critic (SAC) and Proximal Policy Optimization algorithm to design a continuous-time investment model that allocates wealth between fossil-based, green and risk-free assets. The model aims to optimize a utility function derived from both consumption and green innovation, and our agents are trained to navigate stochastic return dynamics, transition costs, and the behavioral effects of investor short-termism. When benchmarked against Nonlinear Model Predictive Control (NMPC) investor, the DRL agent achieves both comparable long-run wealth and greener portfolio allocations under the realistic uncertainty. The paper incorporates the regime-switching process, which affects the investor's diversification strategy. The paper shows how the Deep Reinforcement Learning agents can successfully learn the strategy of diversification during the turbulent financial regimes.

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DOI

10.2139/ssrn.5463316

Publisher page

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5463316

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