# A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks

arXiv: https://arxiv.org/abs/2503.09655 · HTML: https://mlsanity.com/paper/2503.09655/

## Key facts

- Cited by 1 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 4 of its 9 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## 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, 4 of 4)

- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks](https://mlsanity.com/paper/2410.22391/) (2024)
- [A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks](https://mlsanity.com/paper/2212.02721/) (2022)
- [Deep Reinforcement Learning Approach for Trading Automation in The Stock Market](https://mlsanity.com/paper/2208.07165/) (2022)

## Built on by (most cited first, 1 of 1)

- [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025)

## Abstract (arXiv)

Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems.