Deep Reinforcement Learning: Extending Traditional Financial Portfolio Methods

Abstract

Portfolio allocation, a key part of investment management, aims to balance risk and return. Traditional methodologies, rooted in modern portfolio theory, have been widely used for this purpose. Recently, deep reinforcement learning (DRL) has emerged as a powerful
tool to tackle these complex problems, allowing finding new solutions through a trial-and-error process. The central idea of this paper is to demonstrate that traditional portfolio allocation
strategies can be reframed in the DRL framework. It shows that a short-sighted agent, driven by immediate rewards and only considering the first two moments, converges to the Markowitz portfolio. By supplying this agent with more information, such as contextual data and additional future rewards, the DRL model can outperform traditional methods, though this comes with added complexity. Experiments confirm the usefulness of contextual data and show that DRL can improve traditional financial methods.