Balancing Profit, Risk, and Sustainability for Portfolio Management

TL;DR

This paper introduces a reinforcement learning-based portfolio optimization method that balances profit, risk, and sustainability by using a novel utility function and genetic algorithm optimization, outperforming existing approaches.

Contribution

It presents a new utility function combining Sharpe ratio and ESG scores and replaces gradient descent with a genetic algorithm for better optimization.

Findings

Outperforms MADDPG in portfolio optimization tasks

Improves on deep Q-learning by enabling continuous actions

Effectively incorporates risk and sustainability into the optimization process

Abstract

Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as daily rewards are compounding and objective functions may include more than just profit, e.g., risk and sustainability. We developed a novel utility function with the Sharpe ratio representing risk and the environmental, social, and governance score (ESG) representing sustainability. We show that a state-of-the-art policy gradient method - multi-agent deep deterministic policy gradients (MADDPG) - fails to find the optimum policy due to flat policy gradients and we therefore replaced gradient descent with a genetic algorithm for parameter optimization. We show that our system outperforms MADDPG while improving on deep Q-learning approaches by allowing for continuous action spaces. Crucially, by incorporating risk and sustainability criteria in the utility function, we improve on the state-of-the-art in reinforcement learning for portfolio optimization; risk and sustainability are essential in any modern trading strategy and we propose a system that does not merely report these metrics, but that actively optimizes the portfolio to improve on them.