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Beyond Sentiment in Stock Price Prediction: Integrating News Sentiment and Investor Attention with Temporal Fusion Transformer

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Artificial Intelligence Applications and Innovations (AIAI 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 713))

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Abstract

News sentiment is attracting considerable interest in stock market prediction, given its crucial role in shaping stock prices. Previous research has mainly focused on improving prediction accuracy by exploiting news sentiment, without adequately considering the different levels of attention that individual news articles receive. Furthermore, despite the advanced predictive capabilities of deep learning models, there has been a lack of focus on the interpretability of these models, leading to predictions that are not transparent. This study presents an innovative prediction model that integrates a FinBERT-based analysis of news sentiment and investor attention metrics with an attention-based Temporal Fusion Transformer framework. This approach not only enables highly effective forecasting, but also provides insights into the temporal dynamics that influence the stock market. The effectiveness of the model is demonstrated by analyzing stock price data for 41 of the largest market capitalization companies over the period 2010 to 2021. The results confirm the superiority of the proposed model over existing deep learning approaches, and the attention analysis underscores the critical role of synthesizing news sentiment and attention metrics in predicting stock prices.

Supported by the scientific research project of the Czech Science Foundation Grant No: 22-22586S.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Administration, Science and Research Centre, University of Pardubice, Studentska 84, Pardubice, Czech Republic

    Petr Hajek

  2. Faculty of Economics and Administration, Institute of Business Economics and Management, University of Pardubice, Studentska 84, Pardubice, Czech Republic

    Josef Novotny

Authors
  1. Petr Hajek
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Correspondence to Petr Hajek .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Abertay, Dundee, UK

    John Macintyre

  4. Ionian University, Corfu, Greece

    Markos Avlonitis

  5. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

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Hajek, P., Novotny, J. (2024). Beyond Sentiment in Stock Price Prediction: Integrating News Sentiment and Investor Attention with Temporal Fusion Transformer. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-63219-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-63219-8_3

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