Deep Reinforcement Learning for Asset Allocation: Reward Clipping
This is an incremental improvement for asset allocation in finance.
The paper tackles portfolio optimization in finance by comparing reinforcement learning algorithms and introducing a Reward Clipping model, which outperforms existing models and traditional strategies in both bull and bear markets.
Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the advanced algorithm, so called Reward clipping model. It seems that the Reward Clipping model is better than other existing models in finance domain, especially portfolio optimization - it has strength both in bull and bear markets. Finally, we compare the performance for these models with traditional investment strategies during decreasing and increasing markets.