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xLSTM networks enhance deep reinforcement learning for automated stock trading

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new automated stock trading system utilizing Extended Long Short-Term Memory (xLSTM) networks combined with deep reinforcement learning (DRL). This approach aims to overcome the limitations of traditional LSTMs in handling long-term dependencies and dynamic market conditions. Experiments showed that the xLSTM-based DRL model outperformed standard LSTM models across several key trading metrics, including cumulative return and Sharpe ratio. AI

IMPACT Introduces a novel architecture for DRL-based financial trading, potentially improving algorithmic strategy performance.

RANK_REASON Academic paper detailing a new approach to automated stock trading using xLSTM networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

xLSTM networks enhance deep reinforcement learning for automated stock trading

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Academic paper detailing a new approach to automated stock trading using xLSTM networks.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Faezeh Sarlakifar, Mohammadreza Mohammadzadeh Asl, Sajjad Rezvani Khaledi, Armin Salimi-Badr ·

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

    arXiv:2503.09655v2 Announce Type: replace-cross 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 perfo…

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