Reexamining staggered boards and shareholder value

Abstract

Cohen and Wang (2013) (CW2013) provide evidence consistent with market participants perceiving staggered boards to be value reducing. Amihud and Stoyanov (2016) (AS2016) contests these findings, reporting some specifications under which the results are not statistically significant. We show that the results retain their significance under a wide array of robustness tests that address the concerns expressed by AS2016. Our empirical findings reinforce the conclusions of CW2013.

Introduction

In a paper published in the Journal of Financial Economics in 2013, “How do staggered boards affect shareholder value? Evidence from a natural experiment”, Cohen and Wang (2013; CW2013), we provide evidence that market participants perceive staggered boards to be, on average, value-reducing.¹ Amihud and Stoyanov (2016; AS2016) contests our findings, arguing that excluding some observations or amending some specifications renders our results not statistically significant (though they largely retain their sign). In this paper, we carry out empirical tests that address the concerns of AS2016, and we show that the evidence is overall consistent with the conclusions of CW2013.

CW2013 reports that the two rulings by the Delaware courts in Air Products & Chemicals Inc. v. Airgas, Inc. are accompanied by abnormal stock returns that are statistically significant and consistent with the view that staggered boards are value-decreasing. With the exception of its replication of the CW2013 specifications, AS2016 does not present results that are statistically significant, and the results based on our sample largely have a sign consistent with the conclusions of CW2013. Thus, these results are not by themselves inconsistent with the view that staggered boards are value-decreasing.

In any event, our comprehensive analysis of the stock returns accompanying the Airgas case indicates that the evidence is overall consistent with the view that staggered boards are value-decreasing. When an event study is not based on a large number of observations, the statistical significance of its results is often sensitive to the removal of a small number of observations. However, in the case of our study, our results retain their significance under a wide range of tests conducted to address the concerns raised by AS2016.

The remainder of this paper is organized as follows. Section 2 describes the results of CW2013 and the analysis of AS2016. Given the concerns regarding robustness raised by AS2016, Section 2 also discusses two alternative definitions of treated companies to improve robustness by expanding the sets of treated firms to include firms that are affected less strongly by the rulings. We show that these specifications yield conclusions that are consistent with CW2013.

Section 3 focuses on the central issue raised by AS2016, that is, the results of CW2013 become statistically insignificant when excluding a handful of very small companies and, thus, cannot inform the assessment of how staggered boards affect value in normal-size firms. We first show that, when imposing the same sample filters recommended by AS2016, the results are statistically significant using the two alternative definitions of treated firms. We then demonstrate that, using all three treatment definitions, the results of CW2013 are robust to excluding all companies with market capitalization below $500 million or $1 billion instead of excluding the handful of small firms suggested by AS2016. These findings are inconsistent with the claim that the CW2013 results are driven by small companies and that they do not hold when such firms are excluded.

Turning to examine the source of the nonsignificance results presented in AS2016, we show that they are not due to a differential size effect. Instead, these results are due to the happenstance that some of the firms excluded by AS2016 have large returns that go in one direction; that is, the sample restrictions of AS2016 happen to remove extreme observations asymmetrically, from one side of the return distribution. After excluding large returns symmetrically from both sides of the distribution, we obtain an array of results (across various alternative specifications and samples excluding small firms) that are consistent with the results and conclusions of CW2013.

Section 4 examines the AS2016 claim that the results of CW2013 are unduly driven by a few particular observations with extreme returns. We first show that, when excluding the observations suggested by AS2016, our results still retain their significance using the two alternative definitions of treatment firms. Furthermore, when excluding extreme returns in a symmetric fashion, we obtain results that are statistically significant and consistent with the conclusions of CW2013 under each of the three alternative definitions of treated firms.

Finally, Section 5 considers the sensitivity of the CW2013 results to our using industry fixed effects based on six-digit Global Industry Classicification Standard (GICS-6). AS2016 suggests using four-digit GICS (GICS-4), as opposed to GICS-6, and argues that doing so renders our results not statistically significant. We show that the results retain their significance even when using GICS-4 fixed effects under either of the two alternative definitions of treated firms. Furthermore, under each of the alternative treatment definitions, the results retain their significance when no industry fixed effects are used, as is common in event studies (e.g., Larcker et al., 2011, Cunat et al., 2012, Becker et al., 2013, Cohn et al., 2016).²

Finally, we conclude in Section 6. Overall, the wide array of results from our reexamination of the data are consistent with and reinforce the conclusions of CW2013.