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Hilton Atlanta, Grand Ballroom B
American Finance Association
Monitoring by Shareholders and Directors
Saturday, Jan. 5, 2019 2:30 PM - 4:30 PM
- Chair: Nadya Malenko, Boston College
Does Board Size Matter?
AbstractThis paper uses minimum board size requirements to assess whether large boards reduce firm performance. Since 1976, the legally required minimum size of German supervisory boards increases from 12 to 16 directors as firms pass 10,000 domestic employees. Board sizes increase sharply at this threshold, indicating that the mandate is binding for many firms. Using a regression discontinuity design around the threshold and a difference-in-differences analysis around the law’s introduction, we find robust evidence that forcing firms to have large boards lowers performance and value. At the threshold, operating return on assets drops by 2-3 percentage points and Tobin’s Q by 0.20-0.25, with similar declines for treated firms after the law’s introduction. Firms just above the threshold also generate lower acquisition announcement returns than firms just below, suggesting that large boards undertake worse acquisitions.
AbstractThis paper estimates a spatial model of proxy voting, the W-NOMINATE method for scaling legislatures, and maps institutional investors onto a left-right dimension based on their votes for fiscal year 2012. The far-left are socially responsible and the far-right are “money-conscious” investors. Significant ideological differences reflect an absence of shareholder unanimity. The proxy adviser ISS, similar to a political leader makes voting recommendations that place it center-left; to the left of most mutual funds. Public pension funds and other investors on the left support a more social and environment-friendly orientation of the firm and fewer executive compensation proposals.
Selecting Directors Using Machine Learning
AbstractCan 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.
University of Texas-Austin
University of California-San Diego
University of Colorado
- G3 - Corporate Finance and Governance