Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance optimization is used with the aim of maximizing returns while minimizing risk, however, more recently, deep reinforcement learning formulations have been explored. Increasingly, investors have demonstrated an interest in incorporating ESG objectives when making investment decisions, and modifications to the classical mean-variance optimization framework have been developed. In this work, we study the use of deep reinforcement learning for responsible portfolio optimization, by incorporating ESG states and objectives, and provide comparisons against modified mean-variance approaches. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation across additive and multiplicative utility functions of financial and ESG responsibility objectives.

翻译：投资组合优化旨在通过确定投资组合资产的最优配置，以实现特定的投资目标。传统上，通常采用某种形式的均值-方差优化方法，以在最大化收益的同时最小化风险；然而，近年来深度强化学习框架也逐渐被探索。投资者在做出投资决策时，对纳入ESG目标的兴趣日益增长，相应的经典均值-方差优化框架的改进版本也已开发出来。本文通过引入ESG状态和目标，研究了深度强化学习在负责任投资组合优化中的应用，并将其与改进后的均值-方差方法进行了比较。我们的结果表明，在财务与ESG责任目标的加性及乘性效用函数下，深度强化学习策略在负责任投资组合配置方面可提供与均值-方差方法相竞争的性能。