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AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics

Author

Listed:
  • Jiajun Gu
  • Zichen Yang
  • Xintong Lin
  • Sixun Chen
  • YuTing Lu

Abstract

This project investigates the interplay of technical, market, and statistical factors in predicting stock market performance, with a primary focus on S&P 500 companies. Utilizing a comprehensive dataset spanning multiple years, the analysis constructs advanced financial metrics, such as momentum indicators, volatility measures, and liquidity adjustments. The machine learning framework is employed to identify patterns, relationships, and predictive capabilities of these factors. The integration of traditional financial analytics with machine learning enables enhanced predictive accuracy, offering valuable insights into market behavior and guiding investment strategies. This research highlights the potential of combining domain-specific financial expertise with modern computational tools to address complex market dynamics.

Suggested Citation

  • Jiajun Gu & Zichen Yang & Xintong Lin & Sixun Chen & YuTing Lu, 2024. "AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics," Papers 2412.12438, arXiv.org.
    • Handle: RePEc:arx:papers:2412.12438
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    References listed on IDEAS

    as
    1. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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