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Measuring the Relevance of Factors on Cross-Sectional Returns with Decision Trees

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Listed:
  • Paul Felix Reiter

Abstract

This study is concerned with new ways to identify and analyse the factors on cross-sectional returns in financial markets with respect to their time-variability. Therefore, classification and regression trees and conventional regression models are applied. This study uses data on the S&P 500 from 1999 to 2019. Empirical findings show high time variability of factors on cross-sectional returns. The high level of time-variability is not dependent on the applied model. It is also shown that CARTs and conventional regression models have low power when it comes to identifying the factors on cross-sectional returns or predicting the returns themself.

Suggested Citation

  • Paul Felix Reiter, 2023. "Measuring the Relevance of Factors on Cross-Sectional Returns with Decision Trees," Applied Economics and Finance, Redfame publishing, vol. 10(4), pages 14-25, November.
    • Handle: RePEc:rfa:aefjnl:v:10:y:2023:i:4:p:14-25
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    References listed on IDEAS

    as
    1. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    2. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    3. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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