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

ArXiv ID: 2503.09655 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: xLSTM (Extended LSTM), Deep Reinforcement Learning (DRL), Proximal Policy Optimization (PPO), Automated Stock Trading, Time Series Analysis, Equities

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced concepts like PPO and xLSTM architectures with mathematical formulations, warranting a high math score. It includes backtesting on real financial data with specific metrics (Sharpe ratio, etc.), establishing a moderate empirical rigor level.
  flowchart TD
    A["Research Goal:<br/>Enhance DRL-based Stock Trading<br/>by using xLSTM Networks"] --> B["Methodology: xLSTM + Deep RL<br/>(Proximal Policy Optimization)"]
    
    B --> C["Data Input:<br/>Financial Data of Major Tech Companies"]
    
    C --> D["Computational Process:<br/>xLSTM networks serve as<br/>Actor and Critic components<br/>for time-series analysis"]
    
    D --> E["Key Findings:<br/>xLSTM outperforms LSTM<br/>in Cumulative Return,<br/>Sharpe Ratio, and other metrics"]