Risk Budgeting Portfolio Optimization with Deep Reinforcement Learning

Abstract

Risk budgeting (RB) portfolio optimization is one of the popular methods in asset allocation. The key benefit of this method is to control the risk contribution of each asset individually and reduce the unnecessary fluctuation in the allocation by not relying on the expected return of assets. The RB portfolio optimization requires one important parameter, a risk budget vector, and the portfolio performance is strongly influenced by the delicate choice of the values in this vector. Moreover, if the risk strategy allows deviation from a predefined risk budget, then it introduces the problem of finding the optimal time-dependent risk budget deviations. In this article, the author presents a reinforcement learning framework that can select this critical parameter optimally by learning how to control time-dynamic risk budgets in an automated and efficient manner. The experiment result shows that our agent can improve the target performance metric with statistical significance in the different asset universes, indicating that our agent can pick close to optimal risk budget deviations based on the learned policy.