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Characteristic-Sorted Portfolios: Estimation and Inference

Author

Listed:
  • Matias D. Cattaneo

    (Princeton University)

  • Richard K. Crump

    (Federal Reserve Bank of New York)

  • Max H. Farrell

    (University of Chicago)

  • Ernst Schaumburg

    (AQR Capital Management)

Abstract

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.

Suggested Citation

  • Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Ernst Schaumburg, 2020. "Characteristic-Sorted Portfolios: Estimation and Inference," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 531-551, July.
    • Handle: RePEc:tpr:restat:v:102:y:2020:i:3:p:531-551
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    Cited by:

    1. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2024. "On Binscatter," American Economic Review, American Economic Association, vol. 114(5), pages 1488-1514, May.
    2. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2022. "Beta-Sorted Portfolios," Papers 2208.10974, arXiv.org, revised Nov 2024.
    3. Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020. "Dissecting Characteristics Nonparametrically," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    4. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    5. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    6. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.
    7. Christophe J. GODLEWSKI & Katarzyna BYRKA-KITA & Renata GOLA & Jacek CYPRYJANSKI, 2022. "Silence is not golden anymore? Social media activity and stock market valuation in Europe," Working Papers of LaRGE Research Center 2022-04, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    8. Jianyuan Zhong & Zhijian Xu & Saizhuo Wang & Xiangyu Wen & Jian Guo & Qiang Xu, 2024. "DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction," Papers 2405.15833, arXiv.org.
    9. Yu, Xiufan & Yao, Jiawei & Xue, Lingzhou, 2024. "Power enhancement for testing multi-factor asset pricing models via Fisher’s method," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Kim, Junyong, 2024. "Zoom in on momentum," International Review of Financial Analysis, Elsevier, vol. 94(C).
    11. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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