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Dynamic and context-dependent stock price prediction using attention modules and news sentiment

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  • Nicole Königstein

    (Impactvise AG
    quantmate)

Abstract

The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $$\alpha _{t}$$ α t -RIM (recurrent independent mechanism). This architecture makes use of key–value attention to integrate top-down and bottom-up information in a context-dependent and dynamic way. To model the data in such a dynamic manner, the $$\alpha _{t}$$ α t -RIM utilizes an exponentially smoothed recurrent neural network, which can model non-stationary times series data, combined with a modular and independent recurrent structure. We apply our approach to the closing prices of three selected stocks of the S &P 500 universe as well as their news sentiment score. The results suggest that the $$\alpha _{t}$$ α t -RIM is capable of reflecting the causal structure between stock prices and news sentiment, as well as the seasonality and trends. Consequently, this modeling approach markedly improves the generalization performance, that is, the prediction of unseen data, and outperforms state-of-the-art networks, such as long–short-term memory models.

Suggested Citation

  • Nicole Königstein, 2023. "Dynamic and context-dependent stock price prediction using attention modules and news sentiment," Digital Finance, Springer, vol. 5(3), pages 449-481, December.
  • Handle: RePEc:spr:digfin:v:5:y:2023:i:3:d:10.1007_s42521-023-00089-7
    DOI: 10.1007/s42521-023-00089-7
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    References listed on IDEAS

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    1. Sangyeon Kim & Myungjoo Kang, 2019. "Financial series prediction using Attention LSTM," Papers 1902.10877, arXiv.org.
    2. Mohsen Pourahmadi, 2016. "Time Series Modelling with Unobserved Components , by Matteo M. Pelagatti . Published by CRC Press , 2015 , pages: 257 . ISBN-13: 978-1-4822-2500-6," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 575-576, July.
    3. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    4. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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