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

arXiv: https://arxiv.org/abs/2407.09557 · HTML: https://mlsanity.com/paper/2407.09557/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance](https://mlsanity.com/paper/2111.05188/) (2021)
- [CNN-DRL with Shuffled Features in Finance](https://mlsanity.com/paper/2402.03338/) (2024)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning Pair Trading: A Dynamic Scaling approach](https://mlsanity.com/paper/2407.16103/) (2024)
- [Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions](https://mlsanity.com/paper/2507.19639/) (2025)

## Abstract (arXiv)

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.