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Selective news selection model for explainable stock prediction via cross-attention integration

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  • Jang, Junkyu

Abstract

This study proposes a News Selection Model (NSM) that adaptively identifies market-relevant news from daily information streams for stock price prediction. Traditional approaches either aggregate all available news or rely on predefined filters, both suffering from information overload or missing critical market-moving news. Our framework employs a differentiable Top-K selection mechanism to identify the most informative headlines, which are then integrated with historical price data through cross-attention mechanisms. Experimental results on four major global indices (S&P 500, NASDAQ, CSI-300, KOSPI 200) demonstrate that NSM achieves an average of 16.5% performance improvement in MAE compared to state-of-the-art models while processing only five selected news items daily. The framework provides explainable predictions by transparently presenting which specific news headlines influenced the model’s decisions, addressing the black-box problem in financial AI. This explainability feature enables financial practitioners to understand and trust AI-driven predictions, making the approach practically valuable for investment decision-making.

Suggested Citation

  • Jang, Junkyu, 2025. "Selective news selection model for explainable stock prediction via cross-attention integration," Finance Research Letters, Elsevier, vol. 85(PD).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pd:s1544612325014011
    DOI: 10.1016/j.frl.2025.108146
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    1. Xue, Xiaorui & Li, Shaofang & Wang, Xiaonan & Ren, Tingting, 2026. "Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).

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