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Selecting Directors Using Machine Learning

Author

Listed:
  • Erel, Isil

    (Ohio State University)

  • Stern, Lea Henny

    (University of Washington)

  • Tan, Chenhao

    (University of Colorado)

  • Weisbach, Michael S.

    (Ohio State University)

Abstract

Can an algorithm assist firms in their hiring decisions of corporate directors? This paper proposes a method of selecting boards of directors that relies on machine learning. We develop algorithms with the goal of selecting directors that would be preferred by the shareholders of a particular firm. Using shareholder support for individual directors in subsequent elections and firm profitability as performance measures, we construct algorithms to make out-of-sample predictions of these measures of director performance. We then run tests of the quality of these predictions and show that, when compared with a realistic pool of potential candidates, directors predicted to do poorly by our algorithms indeed rank much lower in performance than directors who were predicted to do well. Deviations from the benchmark provided by the algorithms suggest that firm-selected directors are more likely to be male, have previously held more directorships, have fewer qualifications and larger networks. Machine learning holds promise for understanding the process by which existing governance structures are chosen, and has potential to help real world firms improve their governance.

Suggested Citation

  • Erel, Isil & Stern, Lea Henny & Tan, Chenhao & Weisbach, Michael S., 2018. "Selecting Directors Using Machine Learning," Working Paper Series 2018-05, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  • Handle: RePEc:ecl:ohidic:2018-05
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    References listed on IDEAS

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    Cited by:

    1. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers and Journal Articles 19-309, W.E. Upjohn Institute for Employment Research.
    2. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2020. "Why are some Chinese firms failing in the US capital markets? A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    3. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    4. Giannetti, Mariassunta & Wang, Tracy Yue, 2020. "Public Attention to Gender Equality and the Demand for Female Directors," CEPR Discussion Papers 14503, C.E.P.R. Discussion Papers.
    5. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.

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    More about this item

    JEL classification:

    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions

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