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<p>News is reaching investors at an unprecedented rate since pop-up notifications and news recommendations are so common on websites and applications. For investors, these news sources have established themselves as an essential resource for stock market information. To study the impact of news on stock prediction and further enhance its predictive ability, this study innovatively merges the capacity of attention mechanisms to focus crucial information with the ability of temporal convolutional network (TCN) to discern temporal patterns, proposing a new algorithm named “Attention-TCN”. The results indicate that the Attention-TCN model has the smallest prediction error compared to well-known stock prediction algorithms such as Long Short-Term Memory and Gated Recurrent Units. The study demonstrates that incorporating a news perspective and utilizing a TCN model combined with an attention mechanism can hold the promise of helping investors to make more informed decisions and achieving higher returns.</p>

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Enhancing Stock Prediction ability through News Perspective and Deep Learning with attention mechanisms

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Mei Yang,
  • Fanjie Fu,
  • Du Ni,
  • Zhi Xiao

摘要

News is reaching investors at an unprecedented rate since pop-up notifications and news recommendations are so common on websites and applications. For investors, these news sources have established themselves as an essential resource for stock market information. To study the impact of news on stock prediction and further enhance its predictive ability, this study innovatively merges the capacity of attention mechanisms to focus crucial information with the ability of temporal convolutional network (TCN) to discern temporal patterns, proposing a new algorithm named “Attention-TCN”. The results indicate that the Attention-TCN model has the smallest prediction error compared to well-known stock prediction algorithms such as Long Short-Term Memory and Gated Recurrent Units. The study demonstrates that incorporating a news perspective and utilizing a TCN model combined with an attention mechanism can hold the promise of helping investors to make more informed decisions and achieving higher returns.