Deep reinforcement learning in stock portfolios

Abstract

This paper investigates stock portfolios by application of Deep Reinforcement Learning (DRL) Models to achieve an optimal tactical asset allocation. The research problem is described as an optimization scenario that seeks to maximize the portfolio risk adjusted returns for a given portfolio asset allocation. The problem is set up with an initial capital investment which is invested in a set of assets. The initial strategic allocation is determined, which in our case is the equal weight allocation, and all of the capital is invested in the set of assets. At each point in time, the assets are reallocated according to the allocation which will increase the portfolio value. Two DRL models are implemented. The performance of the DRL models is compared with the uniform weights portfolio. The results show that, generally, two DRL models have higher cumulative returns.