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From Votes to Volatility Predicting the Stock Market on Election Day

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  • Igor L. R. Azevedo
  • Toyotaro Suzumura

Abstract

Stock market forecasting has been a topic of extensive research, aiming to provide investors with optimal stock recommendations for higher returns. In recent years, this field has gained even more attention due to the widespread adoption of deep learning models. While these models have achieved impressive accuracy in predicting stock behavior, tailoring them to specific scenarios has become increasingly important. Election Day represents one such critical scenario, characterized by intensified market volatility, as the winning candidate's policies significantly impact various economic sectors and companies. To address this challenge, we propose the Election Day Stock Market Forecasting (EDSMF) Model. Our approach leverages the contextual capabilities of large language models alongside specialized agents designed to analyze the political and economic consequences of elections. By building on a state-of-the-art architecture, we demonstrate that EDSMF improves the predictive performance of the S&P 500 during this uniquely volatile day.

Suggested Citation

  • Igor L. R. Azevedo & Toyotaro Suzumura, 2024. "From Votes to Volatility Predicting the Stock Market on Election Day," Papers 2412.11192, arXiv.org.
    • Handle: RePEc:arx:papers:2412.11192
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    References listed on IDEAS

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    1. Thanh Trung Huynh & Minh Hieu Nguyen & Thanh Tam Nguyen & Phi Le Nguyen & Matthias Weidlich & Quoc Viet Hung Nguyen & Karl Aberer, 2022. "Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction," Papers 2211.07400, arXiv.org, revised Nov 2022.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. André F. Perold, 2004. "The Capital Asset Pricing Model," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 3-24, Summer.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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