AI RESEARCH
A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
arXiv CS.LG
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ArXi:2503.09655v2 Announce Type: replace-cross 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