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Clustering-based sector investing

Author

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  • Bagnara, Matteo
  • Goodarzi, Milad

Abstract

Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.

Suggested Citation

  • Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:397
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    References listed on IDEAS

    as
    1. Kahle, Kathleen M. & Walkling, Ralph A., 1996. "The Impact of Industry Classifications on Financial Research," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(3), pages 309-335, September.
    2. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
    3. Clarke, Richard N, 1989. "SICs as Delineators of Economic Markets," The Journal of Business, University of Chicago Press, vol. 62(1), pages 17-31, January.
    4. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    5. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    6. Hameed, Allaudeen & Mian, G. Mujtaba, 2015. "Industries and Stock Return Reversals," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 50(1-2), pages 89-117, April.
    7. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    8. Tomohiro Ando & Jushan Bai, 2017. "Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1182-1198, July.
    9. Hoberg, Gerard & Phillips, Gordon M., 2018. "Text-Based Industry Momentum," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(6), pages 2355-2388, December.
    10. Jayanthi Krishnan & Eric Press, 2003. "The North American Industry Classification System and Its Implications for Accounting Research," Contemporary Accounting Research, John Wiley & Sons, vol. 20(4), pages 685-717, December.
    11. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    12. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    13. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    14. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    15. Hrazdil, Karel & Trottier, Kim & Zhang, Ray, 2013. "A comparison of industry classification schemes: A large sample study," Economics Letters, Elsevier, vol. 118(1), pages 77-80.
    16. Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
    17. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    18. Fama, Eugene F. & French, Kenneth R., 1997. "Industry costs of equity," Journal of Financial Economics, Elsevier, vol. 43(2), pages 153-193, February.
    19. Belén Villalonga, 2004. "Does Diversification Cause the "Diversification Discount"?," Financial Management, Financial Management Association, vol. 33(2), Summer.
    20. Jame, Russell & Tong, Qing, 2014. "Industry-based style investing," Journal of Financial Markets, Elsevier, vol. 19(C), pages 110-130.
    21. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    22. Sanjeev Bhojraj & Charles M. C. Lee & Derek K. Oler, 2003. "What's My Line? A Comparison of Industry Classification Schemes for Capital Market Research," Journal of Accounting Research, Wiley Blackwell, vol. 41(5), pages 745-774, December.
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    More about this item

    Keywords

    Empirical Asset Pricing; Risk Premium; Machine Learning; Industry Classification; Clustering;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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