Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity

TL;DR

This paper investigates the trading behaviors and asset holding tendencies of various deep reinforcement learning algorithms in finance, revealing distinct strategies and performance patterns among them.

Contribution

It provides the first comprehensive analysis of DRL algorithms' trading behaviors and decision-making tendencies in financial applications.

Findings

A2C outperforms others in cumulative rewards

PPO and SAC trade frequently with fewer stocks

DGP, A2C, and TD3 hold positions longer

Abstract

Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance applications. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards. While PPO and SAC engage in significant trades with a limited number of stocks, DDPG and TD3 adopt a more balanced approach. Furthermore, SAC and PPO tend to hold positions for shorter durations, whereas DDPG, A2C, and TD3 display a propensity to remain stationary for extended periods.