Data and variables

Our data set is constructed from several well-established databases. We collect data on the board of directors from the ISS database, which begins in 1996 and, therefore, sets the lower bound of our sample. In the base case, our sample period ranges from 1996 to 2015.¹⁵ We obtain daily stock returns, used to calculate our VOV measures, from CRSP, and financial and accounting data from Compustat. Additionally, we collect information on the firms’ CEOs from ExecuComp. In line with the literature investigating aspects of board structure and stock market-based measures, we exclude financial firms (SIC Codes 6000-6999) and utilities (SIC Codes 4900-4999) from the data set.

Because of our empirical setting, the firms included in our analyses must meet three additional criteria. First, we only include firms for which we have data on board independence for fiscal year 2001, in which we assign firms to the treated or control group based on whether they comply with the new listing rules or not. Second, we require that firms have at least one observation with full data available after 2001. That is, those firms for which necessary data are only available in the pre-listing rules period are excluded. Third, we require the firms’ stock to be listed on the NYSE or NASDAQ from at least 2001 to 2005. Because the final compliance deadlines were in 2004 or 2005, only firms that were still listed at the end of that period were affected. Together, these requirements ensure that we only consider firms to which the new rules actually applied and that our results are not driven by firms that are in the sample only in the pre or post period.

Our final matched sample consists of 954 firms, 245 of which are classified as treated. This corresponds to 14,025 firm-year observations in the models containing all control variables.

Our dependent variable of interest is the VOV of stock returns, which we calculate in two different ways. The first approach begins by computing the standard deviation of daily stock returns for each month of the firm’s fiscal year. We then calculate the standard deviation of the monthly standard deviations, to obtain the annualV OV. We only include months with at least 15 return observations and years with a minimum of six months of return data available on CRSP. This version represents a “raw” VOV measure that picks up the fluctuation of a firm’s stock return volatility, independent of its level of volatility. In absolute terms, however, changes in volatility tend to be larger for stocks with high volatility (Baltussen et al., 2018). To account

15We also provide results for reduced sample periods in Section 4.4.3.

for this effect, we calculate a second version, theScaled VOV, similar to that defined in Baltussen et al. (2018),¹⁶ by simply dividing the raw VOV by the mean of monthly volatilities of a firm’s fiscal year. Scaled VOV thus measures the fluctuation of volatility in relation to the level of volatility. For robustness, we also construct idiosyncratic versions of the two VOV measures.

Their definitions and the respective results are presented in Section 4.4.8.

In addition to the two main explanatory variablesP ostandT reated, our models include an array of additional control variables that have been shown to be associated with stock return volatility and firms’ risk-related policy choices and, thus, may explain time-varying differences in VOV between treated and control firms. Among them are CEO variables, including a CEO-chairperson indicator, CEO tenure, and CEO ownership¹⁷, to control for the substantial influence of CEOs on firm policies and outcomes, as well as proxies for several firm characteristics. We control for firm size, leverage, operating performance, cash holdings, R&D expenditures¹⁸, and growth opportunities. We also check the robustness of our results by including additional controls. The results are discussed in the robustness section. All variables and their definitions can be found in Table C.1 in Appendix C.

4.3.4 Descriptive statistics

Table 4.1 presents the full matched sample summary statistics for both the VOV measures and the control variables used throughout the empirical analyses. Variables obtained from Compustat are winsorized at the 1% and 99% level.

To test if the treated firms still differ systematically from the control firms after matching, we also compare the two groups in the year in which we assign the firms to either group. In Table 4.2, we check for differences in means of important characteristics of the treated and control firms in 2001.

First of all, we can see that the two groups show no statistical differences in the dependent variable, neither in the raw VOV nor in the scaled VOV, which provides initial support for our choice of matching variables. The differences in board independence are, of course, expected.

16The main difference between our approach and that of Baltussen et al. (2018) is that they use option-implied volatilities. We, instead, use historical volatilities to calculate the VOV, as using implied values would diminish our sample by approximately 50%.

17This variable replacesInside and Linked Vote from the PSM. The latter is not available for the whole sample period and would thus reduce the sample. Our results are robust to using either of the two,Inside and Linked VoteorCEO Ownership, as the control variable throughout the analyses.

18In line with the literature, we set missing values for R&D expenditures to zero (e.g., Coles et al., 2006; Bernile et al., 2018).

Table 4.1: Summary statistics

Obs. Mean SD 25th Pct. Median 75th Pct.

VOV Measures

VOV (%) 14122 13.592 9.052 7.516 11.249 16.906

Scaled VOV (%) 14122 33.764 11.527 25.826 31.956 39.520

Governance and CEO

Independence (%) 14122 71.242 16.878 60.000 75.000 85.714

CEO Duality 14122 0.596 0.491 0.000 1.000 1.000

CEO Tenure 14025 7.783 7.256 3.000 6.000 10.000

CEO Ownership (%) 14025 1.776 5.190 0.000 0.120 0.891

Inside and Linked Vote (%) 13598 6.683 14.692 0.000 1.500 5.840

E Index 14122 2.865 1.457 2.000 3.000 4.000

Firm Characteristics

Total Assets 14122 8189.431 18487.913 792.925 2027.160 6445.000 Book Leverage (%) 14122 21.812 16.135 8.566 21.117 32.189

ROA (%) 14122 4.938 8.652 2.360 5.651 9.263

Cash/Assets (%) 14122 13.943 15.258 2.715 8.075 20.041

R&D/Assets (%) 14122 3.160 5.011 0.000 0.537 4.276

MTB (%) 14122 326.287 352.529 157.114 239.670 379.610

Firm Age 14122 29.613 20.490 14.000 25.000 39.000

Our sample consists of all firm-years with relevant data from the ISS, CRSP, Compustat, and ExecuComp databases during the period between 1996 and 2015. Financial and utility firms are excluded as well as firms without board independence data in 2001 and firms that only appear in the pre listing rules period or that had not been listed on the NYSE or NASDAQ during the full period between 2001 and 2005. Data forInside and Linked Vote, which we use for the propensity score estimation, are not available in 1996, which explains the lower number of observations.

The variables constructed from Compustat data are winsorized at the one and 99% level. All variable definitions can be found in Table C.1 in Appendix C.

Table 4.2: Treated vs. control firms in 2001 Obs.

Treated

Obs.

Control

Mean Treated

Mean

Control Difference p-Value

VOV (%) 245 709 18.922 18.369 0.553 0.4372

Scaled VOV (%) 245 709 35.241 34.759 0.482 0.5400

Independence (%) 245 709 40.145 72.061 -31.915 0.0000

CEO Duality 245 709 0.567 0.694 -0.127 0.0003

CEO Tenure 245 709 8.800 6.845 1.955 0.0003

Inside and Linked Vote (%) 245 709 16.756 5.778 10.978 0.0000

E Index 245 709 1.784 2.271 -0.487 0.0000

Total Assets 245 709 4286.678 6187.411 -1900.733 0.0823

Book Leverage (%) 245 709 21.777 23.195 -1.418 0.2632

ROA (%) 245 709 3.423 1.690 1.734 0.0224

Cash/Assets (%) 245 709 13.900 14.502 -0.602 0.6364

R&D/Assets (%) 245 709 2.409 3.835 -1.426 0.0003

MTB (%) 245 709 314.247 324.179 -9.932 0.6841

Our sample includes all non-financial, non-utility firms for which ISS board data are available for 2001 and that meet the additional sample criteria outlined in Section 4.3.3. The treated firms are those that did not have a majority of independent directors on the board in fiscal year 2001, while control firms are the matched firms that met the independence requirement. All remaining variable definitions can be found in Table C.1 in Appendix C.

Thep-values are based on t-test of differences in means.

With respect to the other matching variables, Shipman et al. (2017) point out that it is unrealistic to expect all differences to be insignificant in large data sets, especially in continuous variables.

Rather, the quality of the matching approach should be evaluated on the economic magnitude of the differences. Moreover, in our setting, it is not uncommon to have differences between the treatment and the control group (Guo and Masulis, 2015; Humphery-Jenner et al., 2019). Table 4.2 shows some remaining differences. Compliant firms tend to be smaller, yet only at the 10%

level, and given that we only consider S&P 1500 firms, the economic effect should be negligible (Banerjee et al., 2015). The two groups also differ according to the two CEO variables. However, the two measures point in opposite directions. CEO duality is more likely in compliant firms, while CEO tenure is more common in noncompliant firms. Moreover, the difference in mean tenure is only two years, which is not likely to make a substantial difference in the power of the CEO. Similarly, the difference in the E index is only half a point, which does not appear to be an economically meaningful difference. The percentage of inside and linked votes is much greater in the treatment group than in the control group. According to Boone et al. (2007), this suggests that insiders have more influence and can avoid control through more independent directors.

Conversely, it is also plausible that a greater ownership stake more strongly aligns the directors’

interests with those of the shareholders, making control through independent directors less of a necessity. Lastly, we also see statistical differences in performance and R&D spending, yet, again, the economic magnitude appears to be small. It is thus not immediately clear if these differences will unduly influence our estimations, especially given that they are similar to what prior studies report (e.g., Guo and Masulis, 2015).

Nevertheless, we take several steps to mitigate concerns that this might violate the assumptions of the DID and affect our results. First, we not only include all of these variables as controls in either the main analyses or the robustness checks, but also run all models with firm fixed effects that account for any unobserved or omitted time-invariant firm characteristics. Second, we present a battery of robustness checks in Section 4.4.8, comprising alternative PSM specifications (e.g., in which we directly match on the remaining differences), a different matching approach (i.e., a coarsened exact matching (CEM)), as well as tests for the parallel trends assumption. The results indicate that there are no discernible deviations in pre-treatment trends. Finally, even if there still remained any omitted effects that we cannot include in our econometric model, market participants would include them in pre-event market prices if markets are efficient. That is, these pre-treatment differences are unlikely to drive post-treatment changes in our market-based measure of the VOV (Duchin et al., 2010). In summary, we remain confident that the data are

suited for a DID approach.

4.4 Results