Balancing Profit, Risk, and Sustainability for Portfolio Management
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Date
2022Metadata
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Maree, C. & Omlin, C. W. P. (2022). Balancing Profit, Risk, and Sustainability for Portfolio Management. IEEE Symposium on Computational Intelligence for Financial Engineering and Economics, 2022, 1-8. IEEE. https://doi.org/10.1109/CIFEr52523.2022.9776048Abstract
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.