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Selecting Directors Using Machine Learning

Author

Listed:
  • Isil Erel
  • Léa H. Stern
  • Chenhao Tan
  • Michael S. Weisbach

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

  • Isil Erel & Léa H. Stern & Chenhao Tan & Michael S. Weisbach, 2018. "Selecting Directors Using Machine Learning," NBER Working Papers 24435, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24435
    Note: CF LE LS
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    References listed on IDEAS

    as
    1. Mitchell Hoffman & Lisa B. Kahn & Danielle Li, 2015. "Discretion in Hiring," NBER Working Papers 21709, National Bureau of Economic Research, Inc.
    2. David F. Larcker & Allan L. McCall & Gaizka Ormazabal, 2015. "Outsourcing Shareholder Voting to Proxy Advisory Firms," Journal of Law and Economics, University of Chicago Press, vol. 58(1), pages 173-204.
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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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