AFA2019会议论文（40）：Monitoring by Shareholders and Directors

AFA2019会议论文（40）：Monitoring by Shareholders and Directors

金融经济学 昨天

1、Investors' Attention to Corporate Governance

Peter Iliev, Pennsylvania State University

Jonathan Kalodimos, Oregon State University

Michelle Lowry, Drexel University

Abstract

We use unique data to assess the extent of governance-related research on EDGAR conducted by 96 mutual fund families in 3,425 companies, over a five-year period. Our governance measure is based on the number of times each investor accesses each firm’s proxy and proxy-related SEC filings, over the months leading up to the firm’s annual meeting. We find that both investors’ and ISS’s governance research is significantly concentrated within larger firms, firms with poorer recent performance, and firms with more contentious items up for vote. Investors are strategic in where they concentrate their governance efforts: they focus on firms where their holdings justify the costs of research, and they conduct significantly less governance research when other shareholders in the firm are more active monitors along this dimension. Finally, we find that investors’ governance research is related to both their investment policies and to companies’ compensation policies.

原文链接：

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=AFA2019&paper_id=1429

2、Does Board Size Matter?

Dirk Jenter, London School of Economics

Thomas Schmid, University of Hong Kong

Daniel Urban,Technische Universität München

Abstract

This paper uses minimum board size requirements in Germany to assess whether large boards reduce firm performance. Since 1976, the legally required minimum size of the supervisory board increases from 12 to 16 directors as German firms pass 10,000 domestic employees. There is a sharp increase in board size at this threshold, indicating that the mandate is binding for many firms. Using a regression discontinuity design around the threshold and a differencein-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. Consistent with the main result, we also observe that firms above the threshold are more likely to engage in value-destroying acquisitions.

原文链接：

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=AFA2019&paper_id=488

3、Investor Ideology

Patrick Bolton, Columbia University

Tao Li, University of Florida

Enrichetta Ravina, Northwestern University

Howard Rosenthal, New York University

Abstract

This paper analyzes the voting patterns of institutional investors from their proxy voting records. It estimates a spatial model of voting, using the W-NOMINATE scaling for voting in legislatures. We find that institutional investors’ ideology (or ideal points) can be mapped onto a left-right dimension, just as legislators’ ideologies can be represented along a left-right spectrum. The far-left investors are socially responsible investors and the far-right investors are “greedy” investors, those opposed to proposals that could financially cost shareholders. There are significant ideological differences across institutional investors and there is no shareholder unanimity. The proxy adviser Institutional Shareholder Services (ISS) plays a role similar to a political party. A second adviser, Glass Lewis, has fewer followers. We find that the ideology of ISS is centerleft, to the left of most institutional investors and Glass Lewis. Furthermore, Vanguard and Blackrock are center-right, and the ideology reflected in management proposals and voting recommendations is far to the right. Investors on the left support a more social orientation of the firm on environmental and other issues. They also support fewer executive compensation proposals.

原文链接：

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=AFA2019&paper_id=932

4、Selecting Directors Using Machine Learning 

Lea Stern, University of Washington

Isil Erel, Ohio State University

Chenhao Tan, University of Colorado

Michael Weisbach, 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.

原文链接：

https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=AFA2019&paper_id=1178

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