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Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal

Author

Listed:
  • Vidhi Agrawal
  • Eesha Khalid
  • Tianyu Tan
  • Doris Xu

Abstract

This study applies machine learning to predict S&P 500 membership changes: key events that profoundly impact investor behavior and market dynamics. Quarterly data from WRDS datasets (2013 onwards) was used, incorporating features such as industry classification, financial data, market data, and corporate governance indicators. Using a Random Forest model, we achieved a test F1 score of 0.85, outperforming logistic regression and SVC models. This research not only showcases the power of machine learning for financial forecasting but also emphasizes model transparency through SHAP analysis and feature engineering. The model's real world applicability is demonstrated with predicted changes for Q3 2023, such as the addition of Uber (UBER) and the removal of SolarEdge Technologies (SEDG). By incorporating these predictions into a trading strategy i.e. buying stocks announced for addition and shorting those marked for removal, we anticipate capturing alpha and enhancing investment decision making, offering valuable insights into index dynamics

Suggested Citation

  • Vidhi Agrawal & Eesha Khalid & Tianyu Tan & Doris Xu, 2024. "Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal," Papers 2412.12539, arXiv.org.
    • Handle: RePEc:arx:papers:2412.12539
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    File URL: http://arxiv.org/pdf/2412.12539
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    References listed on IDEAS

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    1. Gregory W. Martin & Wayne B. Thomas & Matthew M. Wieland, 2016. "S&P 500 Membership and Managers’ Supply of Conservative Financial Reports," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 43(5-6), pages 543-571, May.
    2. Kumar, Rajnish & Lawrence, Edward R. & Prakash, Arun & Rodríguez, Iván M., 2023. "Additions to and deletions from the S&P 500 index: A resolution to the asymmetric price response puzzle," Journal of Banking & Finance, Elsevier, vol. 154(C).
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