Deep Reinforcement Learning for Asset Allocation: Reward Clipping

TL;DR

This paper compares various reinforcement learning algorithms for asset allocation, introduces a reward clipping model, and demonstrates its superior performance in both bullish and bearish market conditions.

Contribution

It introduces a reward clipping reinforcement learning model tailored for finance, showing improved stability and performance over existing methods in portfolio optimization.

Findings

Reward clipping model outperforms other RL models in finance.

The model maintains stability in both bull and bear markets.

Compared to traditional strategies, RL models show competitive results.

Abstract

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.