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CEO tenure and mergers and acquisitions
Finance Research Letters xxx (xxxx) xxx–xxx
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CEO tenure and mergers and acquisitions Bing Zhoua, Shantanu Duttab, , Pengcheng Zhuc ⁎
a
School of Accounting, Chongqing Technology and Business University, Research Center for Economy of Upper Reaches of the Yangtse River, Chongqing Technology and Business University, 19 XueFu Ave, Nan'an District, Chongqing,400067, China b Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa K1N6N5 Ontario, Canada c School of Business, University of San Diego, 5998 Alcala Park, San Diego 92110-2492 California, United States
ARTICLE INFO
ABSTRACT
Keywords: CEO tenure Mergers and acquisitions Performance Event study
In this study, we examine the relationship between CEO tenure and corporate mergers and acquisitions (M&A) performance. Using a large sample of 16,516 M&As in the United States between 1999 and 2015, we find that long-tenured CEOs tend to create more shareholder value than short-tenured CEOs in M&A deals. We also find that long-tenured CEOs are more likely to acquire private target firms and to make acquisitions in the same industries and the domestic market. Finally, we find that long-tenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions.
JEL classification: G34
1. Introduction Does CEO tenure matter in mergers and acquisitions (M&As) decisions? M&As are important corporate events and CEOs play a critical role in the M&A decision making process, including target assessment, deal negotiation and completion, and integration of the newly acquired target. Extant literature shows that several CEO characteristics (e.g., CEO overconfidence, power, education, and age) affect her decision making process (Malmendier and Tate, 2008; Bebchuk et al., 2011). We add to this literature by demonstrating that CEO tenure can also influence M&A decisions significantly. Earlier studies have examined the effect of CEO tenure on a number of firm related and strategic issues, such as, “firm performance (Miller, 1991), firm value (Brookman & Thistle, 2009), strategic change (Zhang & Rajagopalan, 2010), commitment to the status quo (Musteen, Barker & Baeten, 2006), innovation (Wu, Levitas, & Priem, 2005), and risk taking (Simsek, 2007)” (Boling et al. 2016, p. 893). However, there exists no systematic study that would explore the relation between CEO tenure and M&A decisions. We fill this gap by focusing on a number of related research questions as follows: (i) Do long-tenured CEOs make better M&A deals? (ii) What are the channels (e.g., deal characteristics) that impact the performance of M&A deals undertaken by longtenured CEOs? (iii) Are long-tenured CEOs compensated for undertaking value-creating M&A deals? According to the dominant view presented in the literature, long-tenured CEOs are likely to be entrenched and have a myopic view of the business environment. This may lead to poor M&A decisions and destroy shareholder value (Westphal and Zajac, 1995; Shleifer and Vishny, 1989; Hermalin and Weisbach, 1998; Zajac and Westphal, 1996; Kroll et al., 1997; Walters et al., 2007; Audia et al., 2000; Hambrick and Fukutomi, 1991; Kroll et al., 2000; Finkelstein and Hambrick, 1996). On the other hand, a number of other studies argue that long-tenured CEOs may take better strategic decisions due to their familiarity with business risk and their firm/ industry specific business experience (Hambrick and Fukutomi, 1991; Carpenter, Pollock, and Leary, 2003; Sitkin and Pablo, 1992). Given the competing viewpoints in the literature, it is not clear ex ante whether long-tenured CEOs would make better M&A
⁎
Corresponding author. E-mail addresses: [email protected] (B. Zhou), [email protected] (S. Dutta), [email protected] (P. Zhu).
https://doi.org/10.1016/j.frl.2019.08.025 Received 18 February 2019; Received in revised form 27 August 2019; Accepted 27 August 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Bing Zhou, Shantanu Dutta and Pengcheng Zhu, Finance Research Letters, https://doi.org/10.1016/j.frl.2019.08.025
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decisions. We examine this issue empirically with a sample of 16,516 M&A deals over the 1999–2015 period. Our results show that long-tenured CEOs make better M&A deals that have higher abnormal returns around the announcements. We also find that longtenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions. Our study differs from the limited literature on the relation between CEO tenure and M&A decisions in the following ways. First, in comparison to other studies (e.g., Walters et al., 2007) ,1 we use a comprehensive M&A dataset, which enhances the generality of our results. Second, we examine the channels that impact M&A deal performance and related personal benefits accrued to longtenured CEOs, which provides a holistic perspective on the relation between CEO tenure and M&A decisions. 2. Data and sample Our initial sample consists of all S&P 1500 firms included in the ExecuComp database between 1999 and 2015. For each of these firms we collect M&A data and relevant variable information (such as payment type, related/unrelated acquisition tag, transaction value, announcement date, domestic vs. cross-border deals, and target industry information) from the SDC database. The M&A transaction value must have been at least $1 million USD and the acquisition status classified as ‘complete’ to be included in the analysis. We obtain CEO tenure, age and compensation data from ExecuComp database and use CRSP, Compustat and BoardEx to gather the financial and governance characteristics. The initial M&A sample contains 16,516 deal observations. Due to missing stock returns data or the deletion of nonpublicly listed acquiring firms, the sample size decreased to 7823 deal observations with short-term stock performance measure. For the firm-level analysis, our panel dataset includes 8148 firm-year observations in which a firm made at least one acquisition in a year and 21,478 firm-year observations including firms that did not have any acquisitions in a year. Appendix A presents a brief description of all the relevant variables used in the paper. 3. Measures and methods We employ a standard event study method by estimating a market model in the (−240, −41) window before the announcement dates and use cumulative abnormal returns, CAR (−2, +2) as the main independent variable, to capture the stock market reaction effect of the M&A announcements. The key independent variable is CEO tenure. In the spirit of Ali and Zhang (2015), we adopted an indicator variable to contrast long-tenured CEOs with short-tenured CEOs. We create a dummy variable, “CEO tenure dummy”, which equals 1 if a firm's CEO tenure in a given year is greater than or equal to the sample median value of CEO tenure in that year and 0 if it is less than the sample median value. We have taken this approach based on the view that a CEO's learning aptitude, experience, and risk-taking behavior do not change linearly over time. Because of career concerns, monitoring, environmental familiarity and relative power with board members, a CEO's behavior varies distinctly between early and later years (Fama, 1980; Hermalin and Weisbach, 1998; Holmstrom, 1982,1999; Milbourn, 2003; Oyer, 2008; Ali and Zhang, 2015). Accordingly, we divide our CEO sample into the following two groups: the early career group (short-tenured CEOs) and the experientially mature group (long-tenured CEOs). We also include a set of control variables in our regression models. Table 1 shows the descriptive statistics of the main variables used in the paper. Panel A presents the summary of the main firm characteristics. Panel B presents M&A deal characteristics. Panel C contains statistics on CEO compensation and Panel D presents statistics on the panel data sample. Our deal-level analysis is based on pooled-sample multiple linear regressions. In the firm-level analysis, we use the random-effect panel data regression method to examine our research questions.2 4. Empirical results 4.1. Impact of CEO tenure on M&A performance First, we examine the direct relationship between CEO tenure and stock performance around the M&A announcement dates. We regress CAR (−2, +2) on the indicator variable of CEO tenure. Model 1 results (Table 2) show that the regression coefficient for the CEO tenure dummy is positive and significant (p value <0.01). This result supports the view that long-tenured CEOs tend to make better M&A deals and that stock markets react positively to the deal announcements. On average, the acquisitions made by longtenured CEOs generate 0.36% higher CARs in the five days window around M&A announcements than those made by short-tenured CEOs. Given that the average CAR value is 0.8%, this difference accounts for almost 45% (i.e., 0.36%/0.8% = 45%) of the value creation in an average deal, which is economically significant for shareholders. In Model 2 of Table 2, we include more deal-level control variables and find that the regression coefficient for the CEO tenure dummy is still positive and significant (p value <0.05).
1 Walter et al. (2007) use only 100 randomly selected M&A deals in their study and do not investigate the channels that are likely to affect M&A performance undertaken by long-tenured CEOs. 2 Since our main variable of interest, the CEO tenure dummy, is a dichotomous variable (in the spirit of Ali and Zhang, 2015) that does not vary significantly over firm-year, we have adopted random effect panel data regression in this study.
2
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Table 1 Descriptive statistics. Panel A. Firm characteristics (event-based sample) Number
Mean
Std. dev.
Min
Max
CEO tenure (in years) CEO tenure dummy CEO age (in years) CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability
8.523 0.503 55.620 0.394 8.449 3.338 0.179 9.509 0.763 0.133 0.100 0.504
6.961 0.500 6.838 0.125 1.751 3.462 0.157 2.691 0.164 0.283 0.073 0.734
1.000 0.000 40.000 0.047 1.754 (9.818) 0.000 3.000 0.000 (0.539) (1.055) 0.048
35.000 1.000 71.000 0.763 12.189 31.944 0.845 32.000 1.000 4.914 0.326 8.807
16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516
Panel B. M&A characteristics (event-based sample) Number
Mean
Std. dev.
Min
Max
Acq. CAR (−2 to +2) Target relative size HighTech dummy Private target dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy
0.008 0.100 0.495 0.561 0.126 0.335 0.734 0.062
0.054 0.215 0.500 0.496 0.332 0.472 0.442 0.241
(0.158) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.226 2.573 1.000 1.000 1.000 1.000 1.000 1.000
7823 8109 16,516 16,516 16,516 16,516 16,516 5742
Panel C. CEO compensation (event-based sample) – all values are in thousand USD Number Mean
Std. dev.
Min
Max
Full sample CEO total compensation Pre-post CEO total pay difference CEO equity-based compensation Pre-post equity-based CEO pay difference CEO salary and bonus Pre-post CEO salary difference
10,120.000 7372.739 8873.996 7294.380 2052.217 1584.708
355.600 (30,161.080) 0.000 (30,559.720) 207.692 (7725.423)
64,005.280 35,523.120 58,456.100 38,085.170 13,078.100 6163.930
16,516 15,762 16,516 15,762 16,516 15,762
Panel D. Panel data sample (only acquiring firms sample)
Yearly total acquisitions Ratio of yearly positive CAR acquisitions Ratio of yearly positive CAR acquisitions (with zero replacement)* Number of yearly positive CAR acquisitions (with zero replacement)* Number of yearly positive CAR acquisitions Yearly average Acq. CAR (−2 to +2) Yearly Average Acq. CAR (−2 to +2) (with zero replacement)*
8288.056 626.756 6327.329 676.130 1848.437 (32.285) Number
Mean
Std. dev.
Min
Max
8148 5272 8148 8148 5272 5272 8148
2.041 0.558 0.361 0.537 0.830 0.009 0.006
2.013 0.458 0.455 0.796 0.859 0.053 0.038
1.000 0.000 0.000 0.000 0.000 (0.156) (0.093)
36.000 1.000 1.000 15.000 15.000 0.203 0.127
If a firm did not make any acquisition in a year, the values are replaced with zero. Table 1 presents the descriptive statistics of relevant variables used in the study. Panel A includes variable information on firm characteristics used in the event based sample. Panel B includes information on M&A performance related variables (event-based sample), Panel C includes information on CEO compensation (event based sample) and Panel D presents information on M&A variables in a panel data set-up (i.e., based on panel data firm-year information). All variables are described in Appendix A. ⁎
4.2. Effect of CEO tenure on M&A deal characteristics Next, we study the differences between long-tenured CEOs and short-tenured CEOs in striking various mergers and acquisition deals, including acquiring private vs. public target firms, making acquisitions in related industries vs. unrelated industries, and making domestic vs. cross-border acquisitions. We use logistic regression to test the impact of CEO tenure on choices regarding these acquisition decisions. Model 1, 2 and 3 use private target dummy, related acquisition dummy and domestic target dummy as dependent variables, respectively. We find a positive and significant coefficient of the CEO tenure dummy in these three models, suggesting that long-tenured CEOs are more likely to acquire private targets, related targets and make domestic acquisitions than cross-border acquisitions. Based on prior literature (e.g., Dutta et al., 2013; Fuller et al., 2002) and our findings in Table 2, these deal characteristics are all associated with better M&A performance (i.e., CAR (−2, +2)). Therefore, we believe that long-tenured CEOs are able to systematically strike better deals in terms of these features that can create more value for shareholders. 3
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Table 2 CEO tenure and acquisition performance. Variables CEO tenure dummy
(1) Model
(2) Model
0.0036⁎⁎⁎ (0.001) −0.0001 (0.000) −0.0020 (0.005) −0.0029⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0048 (0.004) −0.0001 (0.000) −0.0006 (0.004) −0.0000 (0.000) −0.0055 (0.013) 0.0000 (0.000)
Constant
0.0558⁎⁎⁎ (0.010)
0.0042⁎⁎ (0.002) −0.0001 (0.000) −0.0080 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0054 (0.005) 0.0003 (0.000) −0.0030 (0.005) 0.0000 (0.000) 0.0091 (0.014) 0.0000 (0.000) 0.0071 (0.006) 0.0028 (0.003) −0.0082⁎⁎⁎ (0.002) 0.0078⁎⁎⁎ (0.002) 0.0044⁎⁎ (0.002) −0.0138⁎⁎⁎ (0.004) 0.0445⁎⁎⁎ (0.013)
Year & industry fixed effects
Yes
Yes
Observations R-square
7823 0.021
5357 0.035
CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy
Table 2 presents OLS regression results examining the association between CEO tenure and M&A short-term performance. The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (−2, +2) period around M&A announcement. In Model 1, we control for a set of firm-specific characteristics and in Model 2, we add other M&A deal related control variables. The main independent variable, ‘CEO Tenure Dummy’ is a dummy variable. It is equal to 1 if CEO tenure in a given year is equal to or greater than median CEO tenure of sample firms in that year. ‘CEO Age’ denotes the CEO age in years. ‘CEO Pay Slice’ is the fraction of CEO payout of the total compensation to the group of minimum top-two and maximum top-five executives, including CEO (Bebchuk et al., 2011). ‘Log (Firm Size)’ denotes acquiring firm's total market value (log transformed) in the fiscal year end before the acquisition. ‘Market to Book Value’ denotes market value of assets over book value of assets in the fiscal year end before the acquisition (Masulis et al., 2007). ‘Leverage’ denotes acquiring firm's long term debt to assets ratio in the fiscal year end before the acquisition. ‘Board Size’ denotes the total number of board members. ‘Independent Board Ratio’ is calculated as number of unrelated board members divided by total number of board members. ‘Sales Growth’ denotes the percentage change in sales over two consecutive fiscal years. ‘ROA’ denotes EBIT (Earnings before interest and tax) divided by total assets. ‘Stock Return Variability’ represents the standard deviation of yearly stock returns over last five years. ‘Target Relative Size’ is defined as M&A transaction value divided by the acquiring firm's market cap. ‘HighTech Dummy’ is a dummy variable that equals 1 if the acquiring firm and the target firm are both in the high-tech industry (see Appendix A for details). ‘Public Target Dummy’ is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. ‘Related Acquisition’ is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. ‘Domestic Target Dummy’ is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0. ‘Pure Stock Dummy’ is a dummy variable that equals 1 if the payment is made by 100% stock, otherwise it equals 0. In both models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed. 4
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Table 3 CEO tenure and characteristics of M&A deals. (1) Private target
(2) Related acquisition
(3) Domestic target
0.0729⁎⁎ (0.035) −0.0042* (0.003) −0.2508* (0.134) −0.0577⁎⁎⁎ (0.012) −0.0006 (0.001) −0.6758⁎⁎⁎ (0.107) −0.0415⁎⁎⁎ (0.008) −0.3408⁎⁎⁎ (0.111) 0.0028 (0.002) 1.6345⁎⁎⁎ (0.239) −0.0030 (0.002) 0.9948⁎⁎⁎ (0.241)
0.1051⁎⁎⁎ (0.036) −0.0057⁎⁎ (0.003) −0.1528 (0.139) −0.0912⁎⁎⁎ (0.013) 0.0019* (0.001) −0.4874⁎⁎⁎ (0.112) −0.0336⁎⁎⁎ (0.008) 0.1605 (0.117) −0.0028 (0.002) 0.7428⁎⁎⁎ (0.237) −0.0005 (0.001) 0.4006 (0.261)
0.1684⁎⁎⁎ (0.039) −0.0007 (0.003) 0.1134 (0.149) −0.1669⁎⁎⁎ (0.013) −0.0001 (0.001) 0.4354⁎⁎⁎ (0.125) −0.0023 (0.008) −0.7991⁎⁎⁎ (0.131) 0.0093⁎⁎⁎ (0.002) 0.8512⁎⁎⁎ (0.260) 0.0000 (0.001) 2.3842⁎⁎⁎ (0.258)
Year & industry fixed effects
Yes
Yes
Yes
Observations Pseudo R-square Chi-square
16,516 0.0384 868.941⁎⁎⁎
16,516 0.0326 688.470⁎⁎⁎
16,516 0.0527 1008.48⁎⁎⁎
VARIABLES CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant
Table 3 presents Logistic Regression results examining the association between CEO tenure and various M&A deal characteristics, namely, Private Target Dummy (Model 1), Related Acquisition (Model 2), and Domestic Target Dummy (Model 3). Accordingly we use these variables as dependent variables in Model 1, 2, and 3 respectively. ‘Public Target Dummy’ is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. ‘Related Acquisition’ is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. ‘Domestic Target Dummy’ is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0. In all three models, we regress on the ‘CEO Tenure Dummy’ (defined in Table 2 or Appendix A) and a set of control variables similar to the ones used in Table 2 (detailed definitions of all variables can be found in Appendix A). In all three models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
4.3. CEO tenure, compensation and M&A performance Based on prior results, we may question what drives long-tenured CEOs to make better acquisitions for shareholders. In order to explore this aspect, for each acquisition deal, we gather a CEO's compensation in the year before and after the deal and find the difference between the two compensation values. We then regress the difference of the CEO compensation on M&A performance (i.e., CAR (−2, +2)), the CEO tenure dummy, and most importantly, the multiplicative interaction term between the two variables. Model 1 in Table 4 shows the main effect model. Neither M&A performance nor the CEO tenure dummy is significant in explaining CEO compensation change in a deal. However, we find a positive and significant interaction term between M&A performance and the CEO tenure dummy. The coefficient is not only statistically significant (p value <0.05) but also economically significant. Based on Model 1, one standard deviation change in acquirer CAR (standard deviation is 0.054) leads to 0.490 million USD (= 0.054*−12.342 + 0.054*21.406) change in total compensation for the long-tenured CEOs.3 To understand the compensation contract design better, we further split CEO total compensation change into the following two parts: incentive pay change (Models 3 and 4) versus fixed pay change (Models 5 and 6). We find that the results are mainly driven by the incentive pay change (Model 4). 4.4. CEO tenure and acquisition propensity and quality Next, using a firm-level panel data setup, we examine the relation between CEO tenure and M&A quality (Table 5). Models 1 and 2 use ‘Ratio of Yearly Positive CAR Acquisitions’ as the dependent variable, whereas, Models 3 and 4 use ‘Number of Yearly Positive CAR Acquisitions’ as the dependent variable. Further, Models 1 and 3 use only acquiring firm sample, whereas Models 2 and 4 use full 3
In model 4, pre-post compensation difference values are presented in million USD. 5
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Table 4 Do long-tenured CEOs benefit from good acquisitions? VARIABLES
Acq. CAR (−2 to +2) CEO Tenure Dummy Acq. CAR (−2 to +2) × CEO tenure dummy CEO age Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant Year & industry fixed effects Observations R-square
(1) Pre-post CEO total pay difference
(2) Pre-post CEO total pay difference
(3) Pre-post equitybased CEO pay difference
(4) Pre-post equitybased CEO pay difference
(5) Pre-post CEO salary difference
(6) Pre-post CEO salary difference
−0.8012 (4.611) −0.1464 (0.473)
−12.3424 (9.187) −0.3163 (0.445) 21.4063⁎⁎
−1.6700 (4.582) −0.1354 (0.465)
−13.3031 (9.186) −0.3066 (0.436) 21.5767⁎⁎
0.8689 (0.742) −0.0111 (0.083)
0.9607 (1.088) −0.0097 (0.083) −0.1704
0.0038 (0.026) −0.2549 (0.177) 0.0148* (0.009) 2.9104⁎⁎ (1.188) 0.0141 (0.145) −1.8790 (1.205) 0.0033⁎⁎⁎ (0.001) 16.8066⁎⁎⁎ (5.024) −0.0532 (0.051) 24.2042⁎⁎⁎ (3.704) Yes
(9.636) 0.0046 (0.026) −0.2600 (0.178) 0.0146* (0.009) 2.9610⁎⁎ (1.196) 0.0151 (0.145) −1.8590 (1.199) 0.0030⁎⁎⁎ (0.001) 16.9184⁎⁎⁎ (5.017) −0.0492 (0.052) 24.1978⁎⁎⁎ (3.698) Yes
0.0078 (0.025) −0.2420 (0.175) 0.0131* (0.008) 3.7872⁎⁎⁎ (0.912) 0.0472 (0.144) −1.1751 (1.177) 0.0035⁎⁎⁎ (0.001) 16.0449⁎⁎⁎ (4.884) −0.0453 (0.043) 22.0245⁎⁎⁎ (3.654) Yes
(9.580) 0.0086 (0.026) −0.2471 (0.176) 0.0129* (0.008) 3.8382⁎⁎⁎ (0.921) 0.0481 (0.144) −1.1549 (1.171) 0.0032⁎⁎⁎ (0.001) 16.1576⁎⁎⁎ (4.878) −0.0413 (0.045) 22.0180⁎⁎⁎ (3.648) Yes
−0.0040 (0.005) −0.0129 (0.032) 0.0017 (0.001) −0.8768 (0.685) −0.0330* (0.018) −0.7039⁎⁎⁎ (0.244) −0.0002 (0.000) 0.7617 (1.044) −0.0079 (0.008) 2.1797⁎⁎⁎ (0.494) Yes
(1.506) −0.0041 (0.005) −0.0129 (0.032) 0.0017 (0.001) −0.8772 (0.686) −0.0330* (0.018) −0.7041⁎⁎⁎ (0.244) −0.0002 (0.000) 0.7608 (1.047) −0.0079 (0.008) 2.1797⁎⁎⁎ (0.495) Yes
6042 0.068
6042 0.069
6042 0.066
6042 0.067
6042 0.034
6042 0.034
Table 4 presents OLS Regression results examining the moderating effect of acquirer M&A deal performance on the relation between CEO tenure and M&A pre-post CEO pay difference. We use three different dependent variables: Pre-Post CEO Total Pay Difference (in Model 1 and 2), Pre-Post Equity-based CEO Pay Difference (in Model 3 and 4), and Pre-Post CEO Salary Difference (in Model 5 and 6). Specifically, ‘Pre-Post CEO Total Pay Difference’ denotes the difference in CEO total compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. ‘Pre-Post Equity-based CEO Pay Difference’ denotes the difference in equity-based CEO compensation between ‘t + 1′ and ‘t − 1' fiscal years, relative to the acquisition event year. ‘Pre-Post CEO Salary Difference’ denotes the difference in CEO salary and bonus between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. All three dependent variables are expressed in million USD in the regression models. In order to examine the moderating effect of acquirer M&A deal performance, we introduce an interaction term (‘Acq. CAR (−2 to +2) × CEO Tenure Dummy’) in Model 2, 4 and 6. In all models, we control for a set of firm-specific characteristics similar to the ones in Tables 2 and 3 (defined in Appendix A). In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
sample. Results in these models show that long-tenured CEOs are likely to make more positive CAR deals than short-tenured CEOs. 4.5. Moderating effects of firm and governance characteristics Next, we test if our results are contingent on any firm characteristics or corporate governance features. Specifically, we test the moderating effect of CEO power (i.e., CEO pay slice), firm size, firm valuations (i.e., market-to-book value), financial risk taking (i.e., leverage), and governance (i.e., independent board ratio). However, we do not find any significant moderating effects in any model in Table 6. This suggests that our main results regarding CEO tenure and M&A performance are not contingent on these various conditions. 4.6. Robustness tests First, we address the concern of reverse causality that may introduce endogeneity bias into our empirical results. There is a possibility that CEO tenure is affected by M&A performance. Lehn and Zhao (2006) show that M&A performance can play an important role in CEO turnover decisions, which may induce reverse causality bias. If short-tenured CEOs lose their jobs due to poor M&A performance, we will not have an opportunity to examine their M&A performance in later years. To address this issue, we create 6
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Table 5 CEO tenure and (i) Probability of M&A deals, (ii) ratio of positive CAR deals, (iii) number of positive CAR deals. Variables
CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant Year & industry fixed effects Observations Number of unique firms R-square overall Chi-square
(1) Ratio of yearly positive CAR acquisitions (acquiring firm sample)
(2) Ratio of yearly positive CAR acquisitions (full sample)
(3) Number of yearly positive CAR acquisitions (acquiring firm sample)
(4) Number of yearly positive CAR acquisitions (full sample)
0.0278⁎⁎ (0.013) −0.0004 (0.001) −0.0385 (0.054) −0.0185⁎⁎⁎ (0.005) 0.0003⁎⁎⁎ (0.000) 0.0253 (0.040) −0.0016 (0.003) 0.0092
0.0092* (0.005) −0.0013⁎⁎⁎ (0.000) 0.0400* (0.021) 0.0186⁎⁎⁎ (0.002) 0.0000 (0.000) −0.0127 (0.013) −0.0005 (0.001) −0.0042
0.0912⁎⁎ (0.047) −0.0044* (0.003) 0.1116 (0.136) 0.0422* (0.026) 0000 (0.000) 0.1484 (0.111) −0.0133* (0.007) −0.1697
0.0919⁎⁎ (0.046) −0.0111⁎⁎⁎ (0.004) 0.3904⁎⁎ −0.176 0.1897⁎⁎⁎ (0.019) 0.0002 (0.000) −0.1619 (0.219) −0.0170 (0.011) −0.1548
(0.046) −0.0005⁎⁎⁎ (0.000) 0.1521* (0.082) 0.0004⁎⁎ (0.000) 0.8939⁎⁎⁎ (0.107) Yes
(0.018) −0.0000⁎⁎⁎ (0.000) 0.0958⁎⁎⁎ (0.022) −0.0000 (0.000) 0.1376⁎⁎ (0.061) Yes
(0.106) 0.0035⁎⁎⁎ (0.001) 0.1514 (0.166) 0.0006 (0.000) 0.7306* (0.423) Yes
(0.144) 0.0003 (0.000) 1.1565⁎⁎⁎ (0.331) −0.0002 (0.001) −1.6795⁎⁎⁎ (0.351) Yes
5272 1612 0.018
21,478 2322 0.024
5272 1612
21,478 2322
2820.12⁎⁎⁎
4106.06⁎⁎
Table 5 presents Panel Data Regression results examining the relation between CEO tenure and (ii) ratio of positive CAR deals, and (ii) number of positive CAR deals. Accordingly, we use two different dependent variables: Ratio of Yearly Positive CAR Acquisitions (in Model 1 and 2), and Number of Yearly Positive CAR Acquisitions (in Model 3 and 4). Model 1 and 3 use only acquiring firm sample, whereas, Model 2 and 4 use full sample. Further, given the nature of dependent variables, Model 2 and 3 use panel data regression, and Model 4 and 5 use panel data Poisson regression. All these models use random-effect regression. Specifically, ‘Ratio of Yearly Positive CAR Acquisitions’ denotes the ratio of number of positive CAR M&A deals to total M&A deals in a fiscal year. ‘Number of Yearly Positive CAR Acquisitions’ denotes the number of positive CAR M&A deals in a fiscal year. We regress these variables on the main independent variable, ‘CEO Tenure Dummy’, and control for a similar set of firmspecific characteristics used in previous tables (defined in Appendix A). In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
different subsamples by retaining observations that show which particular CEO has retained her position in a firm for at least (i) three years (to account for early turnover) and (ii) six years (to account for intermediate CEO tenure) .4 This subsample creation alleviates the concern of reverse causality, as we only include cases where CEOs have avoided the possibility of losing their job in the early years of tenure. For these subsamples, we again examine the association between CEO tenure and M&A short-term performance (similarly to Table 2). The results are presented in Table 7 and show that long-tenured CEOs still tend to make better M&A decisions. Second, we use alternative measures of M&A performance based on CARs in various event windows, such as CAR (−1, +1) and CAR (0, +1). Our results are not sensitive to M&A performance measures. Third, we conduct our analysis on various subsamples, such as excluding financial and utilities firms, excluding the financial crisis years (2000–2001 and 2008–2009), and excluding infrequent acquirers. Our results remain qualitatively similar. For the sake of brevity, these results are not reported in the paper. 5. Discussion and conclusion Recent debates in media and among researchers demonstrate a strong interest in the question as to whether CEO tenure matters and how CEO tenure affects a firm's strategy and shareholder value (e.g., Lublin, 2010, Wall Street Journal; and Stoll, 2018, Wall Street Journal). It is reported that CEO tenure is becoming shorter in major corporations (Laughlin, 2018, New York Times). According to a recent Equilar study, the median tenure for S&P 500 CEOs was approximately five years in 2017, and many of these 4
Median CEO tenure in our sample is six years. 7
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Table 6 Moderating effects of various firm-specific and governance characteristics on M&A performance. Variables CEO tenure dummy CEO tenure dummy × CEO pay slice CEO tenure dummy × Log (firm size)
(1) Model
(2) Model
(3) Model
(4) Model
(5) Model
0.0021 (0.006) 0.0050 (0.013)
0.0027 (0.008)
0.0045⁎⁎ (0.002)
0.0055⁎⁎ (0.002)
0.0028 (0.007)
0.0002 (0.001)
CEO tenure dummy × Market to book value CEO tenure dummy × Leverage
−0.0001 (0.000)
CEO tenure dummy × Independent board ratio CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy Constant Year & industry fixed effects Observations R-square
−0.0001 (0.000) −0.0099 (0.010) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0030 (0.005) 0.0000 (0.000) 0.0088 (0.013) 0.0000 (0.000) 0.0069 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0467⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0001 (0.000) −0.0074 (0.007) −0.0028⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0029 (0.005) 0.0000 (0.000) 0.0087 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0465⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0001 (0.000) −0.0076 (0.007) −0.0028⁎⁎⁎ (0.001) 0.0001 (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0029 (0.005) 0.0000 (0.000) 0.0082 (0.013) 0.0000 (0.000) 0.0069 (0.006) 0.0025 (0.002) −0.0075⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0136⁎⁎⁎ (0.004) 0.0451⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0081 (0.009) −0.0001 (0.000) −0.0072 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0086 (0.007) 0.0002 (0.000) −0.0031 (0.005) 0.0000 (0.000) 0.0079 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0024 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0453⁎⁎⁎ (0.013) Yes 5357 0.035
0.0017 (0.009) −0.0001 (0.000) −0.0074 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0047 (0.005) 0.0002 (0.000) −0.0038 (0.007) 0.0000 (0.000) 0.0086 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0461⁎⁎⁎ (0.013) Yes 5357 0.035
Table 6 presents OLS Regression results examining the moderating effect of firm-specific and governance characteristics on the relation between CEO tenure and M&A short-term performance. The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (−2, +2) period around M&A announcement. We examine the moderating effects of following variables: CEO Pay Slice (in Model 1), Log (Firm Size) (in Model 2), Market to Book Value (in Model 3), Leverage (in Model 4), and Independent Board Ratio (in Model 5). Accordingly, we introduce interaction effect with ‘CEO Tenure Dummy’ variable in each model. We control for a similar set of firm and deal characteristics used in previous tables. All variables are defined in Appendix A. In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
leaders did not even survive two years (Sonnenfeld, 2018). However, if CEOs can withstand the early storms in their career, they become more prudent and might make better investment decisions. We provide a robust result showing that long-tenured CEOs tend to make better M&As and create more shareholder value than short-tenured CEOs. In order to obtain a holistic perspective on the relation between CEO tenure and M&A decisions, we also examine a few channels that could potentially explain our results. We find that long-tenured CEOs show more prudence in M&A decisions – they are more likely to acquire private target firms and make acquisitions in the same industries and the domestic market. Generally, these 8
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Table 7 Robustness test: addressing endogeneity bias. VARIABLES CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size
(1) CEO min. tenure >3 years
(2) CEO min. tenure >3 years
(3) CEO min. tenure >6 years
(4) CEO min. tenure >6 years
0.0036⁎⁎⁎ (0.001) −0.0000 (0.000) −0.0035 (0.006) −0.0030⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0069* (0.004) 0.0001 (0.000) 0.0004 (0.004) −0.0000 (0.000) −0.0017 (0.014) 0.0003⁎⁎⁎ (0.000)
0.0042⁎⁎ (0.002) −0.0000 (0.000) −0.0107 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0094* (0.005) 0.0005 (0.000) −0.0009 (0.006) 0.0000 (0.000) 0.0184 (0.014) 0.0004⁎⁎⁎ (0.000) 0.0084 (0.006) 0.0036 (0.003) −0.0079⁎⁎⁎ (0.002) 0.0079⁎⁎⁎ (0.002) 0.0066⁎⁎⁎ (0.002) −0.0138⁎⁎⁎ (0.004) 0.0362⁎⁎ (0.014) Yes 4845 0.040
0.0029* (0.002) −0.0001 (0.000) 0.0030 (0.006) −0.0030⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0002 (0.005) 0.0001 (0.000) −0.0011 (0.004) 0.0061** (0.003) −0.0169 (0.018) 0.0011⁎⁎ (0.001)
0.0044⁎⁎ (0.002) −0.0002 (0.000) −0.0021 (0.008) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) −0.0007 (0.006) 0.0005 (0.000) −0.0031 (0.006) 0.0072* (0.004) 0.0024 (0.015) 0.0009* (0.001) 0.0120 (0.007) 0.0035 (0.003) −0.0080⁎⁎⁎ (0.002) 0.0078⁎⁎⁎ (0.002) 0.0053⁎⁎ (0.002) −0.0136⁎⁎⁎ (0.005) 0.0410⁎⁎⁎ (0.015) Yes 3860 0.043
High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy Constant Year & industry fixed effects Observations R-square
0.0519⁎⁎⁎ (0.010) Yes 7117 0.022
0.0552⁎⁎⁎ (0.012) Yes 5692 0.026
Table 7 presents OLS regression results examining the association between CEO tenure and M&A short-term performance using different subsamples to address reverse causality bias. Models 1 and 2 use a sample in which CEO minimum tenure in a firm is more than 3 years, and Model 3 and 4 use a sample in which CEO minimum tenure in a firm is more than 6 years. In order to obtain CEO minimum tenure in firm, first we determine a CEO's overall tenure in a firm-CEO setup. Subsequently, we create different sub-samples by retaining observations for which a particular CEO has retained her position in a firm for at least (i) three years (to account for early turnover), and (ii) six years (to account for median CEO tenure). The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (-2, +2) period around M&A announcement. In Model 1 and 3, we control for a set of firm-specific characteristics and in Model 2 and 4, we add other M&A deal related control variables. ‘CEO Tenure Dummy’ is the main independent variable. We also control for a similar set of control variables used in previous Table 2 (see Appendix A for detailed definitions). In all models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
attributes are more favored by market participants. Furthermore, we show that there is an incentive channel for long-tenured CEOs to undertake high-quality M&A deals; long-tenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions. Overall, our findings suggest that boards should be careful in enforcing premature CEO turnovers. A resilient board should be able to preserve long-term goals under disturbing outside pressures. Future studies may focus on other channels (e.g. CEO overconfidence, conservatism, risk taking), that would enhance our understanding of the relation between CEO characteristics and M&A performance.
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Funding None. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.08.025. Appendix A List of variables and measurement Deal characteristics and other M&A related variables Acquisition dummy Acquirer CAR (M&A announcement return) Ratio of Yearly Positive CAR Acquisitions Number of Yearly Positive CAR Acquisitions Public target dummy Related acquisition Target relative size Pure stock payment High tech acquisitions
Domestic target dummy
It is a dummy variable that equals 1 if the firm made any acquisitions in the year, otherwise it equals zero. It is measured by the acquiring firm's cumulative abnormal return (CAR) over a 5-day (−2, +2) period around M&A announcements. It denotes the ratio of number of positive CAR M&A deals to total M&A deals in a fiscal year. It denotes the number of positive CAR M&A deals in a fiscal year. It is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. It is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. M&A transaction value divided by the acquiring firm's market cap. It is a dummy variable that equals 1 if the payment is made by 100% stock, otherwise it equals 0. It is a dummy variable that equals 1 if the acquiring firm and the target firm are both in the high-tech industry (SIC codes: 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3671, 3672, 3674, 3675, 3677, 3678, 3679 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 3841, 3845 (medical instruments), 4812, 4813 (telephone equipment), 4899 (communications services), and 7371, 7372, 7373, 7374, 7375, 7378, and 7379 (software). (Loughran and Ritter, 2004, page.35) It is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0.
Firm characteristics (acquiring firm) CEO tenure dummy CEO age Log (firm size) Market to book value Leverage Stock return variability Sales growth ROA Board size Independent board ratio CEO pay slice CEO total compensation CEO equity-based compensation CEO salary and bonus Pre-post total CEO Pay difference Pre-post equity-based CEO pay difference Pre-post CEO salary difference
It is a dummy variable. It is equal to 1 if CEO tenure in a given year is equal to or greater than median CEO tenure of sample firms in that year. It denotes the CEO age in years. The acquiring firm's total market value (log transformed) in the fiscal year end before the acquisition. It denotes market value of assets over book value of assets in the fiscal year end before the acquisition (Masulis et al., 2007). The acquiring firm's long term debt to assets ratio in the fiscal year end before the acquisition. It represents the standard deviation of yearly stock returns over last five years. Yearly stock return is calculated based on twelve monthly buy-and-hold returns in a fiscal year. Subsequently standard deviation is calculation based on last five yearly stock return values. Percentage change in sales over two consecutive fiscal years. It denotes EBIT (Earnings before interest and tax) divided by total assets. It denotes the total number of board members. It is calculated as number of unrelated board members divided by total number of board members. It is the fraction of CEO payout of the total compensation to the group of minimum top-two and maximum top-five executives, including CEO (Bebchuk et al., 2011). From an agency theory perspective, a higher CEO pay slice may indicate a higher level of agency problem in a firm. It considers both fixed and variable salary paid to a CEO. It includes cash salary, bonuses, long-term incentive plans and any stock grant and stock option values. CEO Equity-based compensation variable includes the total value of stock grants and stock options awarded to a CEO. It considers fixed salary paid to a CEO. It includes cash salary and bonuses. It denotes the difference in CEO total compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. It denotes the difference in equity-based CEO compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. It denotes the difference in CEO salary and bonus between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year.
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Finance Research Letters journal homepage: www.elsevier.com/locate/frl
CEO tenure and mergers and acquisitions Bing Zhoua, Shantanu Duttab, , Pengcheng Zhuc ⁎
a
School of Accounting, Chongqing Technology and Business University, Research Center for Economy of Upper Reaches of the Yangtse River, Chongqing Technology and Business University, 19 XueFu Ave, Nan'an District, Chongqing,400067, China b Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa K1N6N5 Ontario, Canada c School of Business, University of San Diego, 5998 Alcala Park, San Diego 92110-2492 California, United States
ARTICLE INFO
ABSTRACT
Keywords: CEO tenure Mergers and acquisitions Performance Event study
In this study, we examine the relationship between CEO tenure and corporate mergers and acquisitions (M&A) performance. Using a large sample of 16,516 M&As in the United States between 1999 and 2015, we find that long-tenured CEOs tend to create more shareholder value than short-tenured CEOs in M&A deals. We also find that long-tenured CEOs are more likely to acquire private target firms and to make acquisitions in the same industries and the domestic market. Finally, we find that long-tenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions.
JEL classification: G34
1. Introduction Does CEO tenure matter in mergers and acquisitions (M&As) decisions? M&As are important corporate events and CEOs play a critical role in the M&A decision making process, including target assessment, deal negotiation and completion, and integration of the newly acquired target. Extant literature shows that several CEO characteristics (e.g., CEO overconfidence, power, education, and age) affect her decision making process (Malmendier and Tate, 2008; Bebchuk et al., 2011). We add to this literature by demonstrating that CEO tenure can also influence M&A decisions significantly. Earlier studies have examined the effect of CEO tenure on a number of firm related and strategic issues, such as, “firm performance (Miller, 1991), firm value (Brookman & Thistle, 2009), strategic change (Zhang & Rajagopalan, 2010), commitment to the status quo (Musteen, Barker & Baeten, 2006), innovation (Wu, Levitas, & Priem, 2005), and risk taking (Simsek, 2007)” (Boling et al. 2016, p. 893). However, there exists no systematic study that would explore the relation between CEO tenure and M&A decisions. We fill this gap by focusing on a number of related research questions as follows: (i) Do long-tenured CEOs make better M&A deals? (ii) What are the channels (e.g., deal characteristics) that impact the performance of M&A deals undertaken by longtenured CEOs? (iii) Are long-tenured CEOs compensated for undertaking value-creating M&A deals? According to the dominant view presented in the literature, long-tenured CEOs are likely to be entrenched and have a myopic view of the business environment. This may lead to poor M&A decisions and destroy shareholder value (Westphal and Zajac, 1995; Shleifer and Vishny, 1989; Hermalin and Weisbach, 1998; Zajac and Westphal, 1996; Kroll et al., 1997; Walters et al., 2007; Audia et al., 2000; Hambrick and Fukutomi, 1991; Kroll et al., 2000; Finkelstein and Hambrick, 1996). On the other hand, a number of other studies argue that long-tenured CEOs may take better strategic decisions due to their familiarity with business risk and their firm/ industry specific business experience (Hambrick and Fukutomi, 1991; Carpenter, Pollock, and Leary, 2003; Sitkin and Pablo, 1992). Given the competing viewpoints in the literature, it is not clear ex ante whether long-tenured CEOs would make better M&A
⁎
Corresponding author. E-mail addresses: [email protected] (B. Zhou), [email protected] (S. Dutta), [email protected] (P. Zhu).
https://doi.org/10.1016/j.frl.2019.08.025 Received 18 February 2019; Received in revised form 27 August 2019; Accepted 27 August 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Bing Zhou, Shantanu Dutta and Pengcheng Zhu, Finance Research Letters, https://doi.org/10.1016/j.frl.2019.08.025
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decisions. We examine this issue empirically with a sample of 16,516 M&A deals over the 1999–2015 period. Our results show that long-tenured CEOs make better M&A deals that have higher abnormal returns around the announcements. We also find that longtenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions. Our study differs from the limited literature on the relation between CEO tenure and M&A decisions in the following ways. First, in comparison to other studies (e.g., Walters et al., 2007) ,1 we use a comprehensive M&A dataset, which enhances the generality of our results. Second, we examine the channels that impact M&A deal performance and related personal benefits accrued to longtenured CEOs, which provides a holistic perspective on the relation between CEO tenure and M&A decisions. 2. Data and sample Our initial sample consists of all S&P 1500 firms included in the ExecuComp database between 1999 and 2015. For each of these firms we collect M&A data and relevant variable information (such as payment type, related/unrelated acquisition tag, transaction value, announcement date, domestic vs. cross-border deals, and target industry information) from the SDC database. The M&A transaction value must have been at least $1 million USD and the acquisition status classified as ‘complete’ to be included in the analysis. We obtain CEO tenure, age and compensation data from ExecuComp database and use CRSP, Compustat and BoardEx to gather the financial and governance characteristics. The initial M&A sample contains 16,516 deal observations. Due to missing stock returns data or the deletion of nonpublicly listed acquiring firms, the sample size decreased to 7823 deal observations with short-term stock performance measure. For the firm-level analysis, our panel dataset includes 8148 firm-year observations in which a firm made at least one acquisition in a year and 21,478 firm-year observations including firms that did not have any acquisitions in a year. Appendix A presents a brief description of all the relevant variables used in the paper. 3. Measures and methods We employ a standard event study method by estimating a market model in the (−240, −41) window before the announcement dates and use cumulative abnormal returns, CAR (−2, +2) as the main independent variable, to capture the stock market reaction effect of the M&A announcements. The key independent variable is CEO tenure. In the spirit of Ali and Zhang (2015), we adopted an indicator variable to contrast long-tenured CEOs with short-tenured CEOs. We create a dummy variable, “CEO tenure dummy”, which equals 1 if a firm's CEO tenure in a given year is greater than or equal to the sample median value of CEO tenure in that year and 0 if it is less than the sample median value. We have taken this approach based on the view that a CEO's learning aptitude, experience, and risk-taking behavior do not change linearly over time. Because of career concerns, monitoring, environmental familiarity and relative power with board members, a CEO's behavior varies distinctly between early and later years (Fama, 1980; Hermalin and Weisbach, 1998; Holmstrom, 1982,1999; Milbourn, 2003; Oyer, 2008; Ali and Zhang, 2015). Accordingly, we divide our CEO sample into the following two groups: the early career group (short-tenured CEOs) and the experientially mature group (long-tenured CEOs). We also include a set of control variables in our regression models. Table 1 shows the descriptive statistics of the main variables used in the paper. Panel A presents the summary of the main firm characteristics. Panel B presents M&A deal characteristics. Panel C contains statistics on CEO compensation and Panel D presents statistics on the panel data sample. Our deal-level analysis is based on pooled-sample multiple linear regressions. In the firm-level analysis, we use the random-effect panel data regression method to examine our research questions.2 4. Empirical results 4.1. Impact of CEO tenure on M&A performance First, we examine the direct relationship between CEO tenure and stock performance around the M&A announcement dates. We regress CAR (−2, +2) on the indicator variable of CEO tenure. Model 1 results (Table 2) show that the regression coefficient for the CEO tenure dummy is positive and significant (p value <0.01). This result supports the view that long-tenured CEOs tend to make better M&A deals and that stock markets react positively to the deal announcements. On average, the acquisitions made by longtenured CEOs generate 0.36% higher CARs in the five days window around M&A announcements than those made by short-tenured CEOs. Given that the average CAR value is 0.8%, this difference accounts for almost 45% (i.e., 0.36%/0.8% = 45%) of the value creation in an average deal, which is economically significant for shareholders. In Model 2 of Table 2, we include more deal-level control variables and find that the regression coefficient for the CEO tenure dummy is still positive and significant (p value <0.05).
1 Walter et al. (2007) use only 100 randomly selected M&A deals in their study and do not investigate the channels that are likely to affect M&A performance undertaken by long-tenured CEOs. 2 Since our main variable of interest, the CEO tenure dummy, is a dichotomous variable (in the spirit of Ali and Zhang, 2015) that does not vary significantly over firm-year, we have adopted random effect panel data regression in this study.
2
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Table 1 Descriptive statistics. Panel A. Firm characteristics (event-based sample) Number
Mean
Std. dev.
Min
Max
CEO tenure (in years) CEO tenure dummy CEO age (in years) CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability
8.523 0.503 55.620 0.394 8.449 3.338 0.179 9.509 0.763 0.133 0.100 0.504
6.961 0.500 6.838 0.125 1.751 3.462 0.157 2.691 0.164 0.283 0.073 0.734
1.000 0.000 40.000 0.047 1.754 (9.818) 0.000 3.000 0.000 (0.539) (1.055) 0.048
35.000 1.000 71.000 0.763 12.189 31.944 0.845 32.000 1.000 4.914 0.326 8.807
16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516 16,516
Panel B. M&A characteristics (event-based sample) Number
Mean
Std. dev.
Min
Max
Acq. CAR (−2 to +2) Target relative size HighTech dummy Private target dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy
0.008 0.100 0.495 0.561 0.126 0.335 0.734 0.062
0.054 0.215 0.500 0.496 0.332 0.472 0.442 0.241
(0.158) 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.226 2.573 1.000 1.000 1.000 1.000 1.000 1.000
7823 8109 16,516 16,516 16,516 16,516 16,516 5742
Panel C. CEO compensation (event-based sample) – all values are in thousand USD Number Mean
Std. dev.
Min
Max
Full sample CEO total compensation Pre-post CEO total pay difference CEO equity-based compensation Pre-post equity-based CEO pay difference CEO salary and bonus Pre-post CEO salary difference
10,120.000 7372.739 8873.996 7294.380 2052.217 1584.708
355.600 (30,161.080) 0.000 (30,559.720) 207.692 (7725.423)
64,005.280 35,523.120 58,456.100 38,085.170 13,078.100 6163.930
16,516 15,762 16,516 15,762 16,516 15,762
Panel D. Panel data sample (only acquiring firms sample)
Yearly total acquisitions Ratio of yearly positive CAR acquisitions Ratio of yearly positive CAR acquisitions (with zero replacement)* Number of yearly positive CAR acquisitions (with zero replacement)* Number of yearly positive CAR acquisitions Yearly average Acq. CAR (−2 to +2) Yearly Average Acq. CAR (−2 to +2) (with zero replacement)*
8288.056 626.756 6327.329 676.130 1848.437 (32.285) Number
Mean
Std. dev.
Min
Max
8148 5272 8148 8148 5272 5272 8148
2.041 0.558 0.361 0.537 0.830 0.009 0.006
2.013 0.458 0.455 0.796 0.859 0.053 0.038
1.000 0.000 0.000 0.000 0.000 (0.156) (0.093)
36.000 1.000 1.000 15.000 15.000 0.203 0.127
If a firm did not make any acquisition in a year, the values are replaced with zero. Table 1 presents the descriptive statistics of relevant variables used in the study. Panel A includes variable information on firm characteristics used in the event based sample. Panel B includes information on M&A performance related variables (event-based sample), Panel C includes information on CEO compensation (event based sample) and Panel D presents information on M&A variables in a panel data set-up (i.e., based on panel data firm-year information). All variables are described in Appendix A. ⁎
4.2. Effect of CEO tenure on M&A deal characteristics Next, we study the differences between long-tenured CEOs and short-tenured CEOs in striking various mergers and acquisition deals, including acquiring private vs. public target firms, making acquisitions in related industries vs. unrelated industries, and making domestic vs. cross-border acquisitions. We use logistic regression to test the impact of CEO tenure on choices regarding these acquisition decisions. Model 1, 2 and 3 use private target dummy, related acquisition dummy and domestic target dummy as dependent variables, respectively. We find a positive and significant coefficient of the CEO tenure dummy in these three models, suggesting that long-tenured CEOs are more likely to acquire private targets, related targets and make domestic acquisitions than cross-border acquisitions. Based on prior literature (e.g., Dutta et al., 2013; Fuller et al., 2002) and our findings in Table 2, these deal characteristics are all associated with better M&A performance (i.e., CAR (−2, +2)). Therefore, we believe that long-tenured CEOs are able to systematically strike better deals in terms of these features that can create more value for shareholders. 3
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Table 2 CEO tenure and acquisition performance. Variables CEO tenure dummy
(1) Model
(2) Model
0.0036⁎⁎⁎ (0.001) −0.0001 (0.000) −0.0020 (0.005) −0.0029⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0048 (0.004) −0.0001 (0.000) −0.0006 (0.004) −0.0000 (0.000) −0.0055 (0.013) 0.0000 (0.000)
Constant
0.0558⁎⁎⁎ (0.010)
0.0042⁎⁎ (0.002) −0.0001 (0.000) −0.0080 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0054 (0.005) 0.0003 (0.000) −0.0030 (0.005) 0.0000 (0.000) 0.0091 (0.014) 0.0000 (0.000) 0.0071 (0.006) 0.0028 (0.003) −0.0082⁎⁎⁎ (0.002) 0.0078⁎⁎⁎ (0.002) 0.0044⁎⁎ (0.002) −0.0138⁎⁎⁎ (0.004) 0.0445⁎⁎⁎ (0.013)
Year & industry fixed effects
Yes
Yes
Observations R-square
7823 0.021
5357 0.035
CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy
Table 2 presents OLS regression results examining the association between CEO tenure and M&A short-term performance. The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (−2, +2) period around M&A announcement. In Model 1, we control for a set of firm-specific characteristics and in Model 2, we add other M&A deal related control variables. The main independent variable, ‘CEO Tenure Dummy’ is a dummy variable. It is equal to 1 if CEO tenure in a given year is equal to or greater than median CEO tenure of sample firms in that year. ‘CEO Age’ denotes the CEO age in years. ‘CEO Pay Slice’ is the fraction of CEO payout of the total compensation to the group of minimum top-two and maximum top-five executives, including CEO (Bebchuk et al., 2011). ‘Log (Firm Size)’ denotes acquiring firm's total market value (log transformed) in the fiscal year end before the acquisition. ‘Market to Book Value’ denotes market value of assets over book value of assets in the fiscal year end before the acquisition (Masulis et al., 2007). ‘Leverage’ denotes acquiring firm's long term debt to assets ratio in the fiscal year end before the acquisition. ‘Board Size’ denotes the total number of board members. ‘Independent Board Ratio’ is calculated as number of unrelated board members divided by total number of board members. ‘Sales Growth’ denotes the percentage change in sales over two consecutive fiscal years. ‘ROA’ denotes EBIT (Earnings before interest and tax) divided by total assets. ‘Stock Return Variability’ represents the standard deviation of yearly stock returns over last five years. ‘Target Relative Size’ is defined as M&A transaction value divided by the acquiring firm's market cap. ‘HighTech Dummy’ is a dummy variable that equals 1 if the acquiring firm and the target firm are both in the high-tech industry (see Appendix A for details). ‘Public Target Dummy’ is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. ‘Related Acquisition’ is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. ‘Domestic Target Dummy’ is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0. ‘Pure Stock Dummy’ is a dummy variable that equals 1 if the payment is made by 100% stock, otherwise it equals 0. In both models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed. 4
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Table 3 CEO tenure and characteristics of M&A deals. (1) Private target
(2) Related acquisition
(3) Domestic target
0.0729⁎⁎ (0.035) −0.0042* (0.003) −0.2508* (0.134) −0.0577⁎⁎⁎ (0.012) −0.0006 (0.001) −0.6758⁎⁎⁎ (0.107) −0.0415⁎⁎⁎ (0.008) −0.3408⁎⁎⁎ (0.111) 0.0028 (0.002) 1.6345⁎⁎⁎ (0.239) −0.0030 (0.002) 0.9948⁎⁎⁎ (0.241)
0.1051⁎⁎⁎ (0.036) −0.0057⁎⁎ (0.003) −0.1528 (0.139) −0.0912⁎⁎⁎ (0.013) 0.0019* (0.001) −0.4874⁎⁎⁎ (0.112) −0.0336⁎⁎⁎ (0.008) 0.1605 (0.117) −0.0028 (0.002) 0.7428⁎⁎⁎ (0.237) −0.0005 (0.001) 0.4006 (0.261)
0.1684⁎⁎⁎ (0.039) −0.0007 (0.003) 0.1134 (0.149) −0.1669⁎⁎⁎ (0.013) −0.0001 (0.001) 0.4354⁎⁎⁎ (0.125) −0.0023 (0.008) −0.7991⁎⁎⁎ (0.131) 0.0093⁎⁎⁎ (0.002) 0.8512⁎⁎⁎ (0.260) 0.0000 (0.001) 2.3842⁎⁎⁎ (0.258)
Year & industry fixed effects
Yes
Yes
Yes
Observations Pseudo R-square Chi-square
16,516 0.0384 868.941⁎⁎⁎
16,516 0.0326 688.470⁎⁎⁎
16,516 0.0527 1008.48⁎⁎⁎
VARIABLES CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant
Table 3 presents Logistic Regression results examining the association between CEO tenure and various M&A deal characteristics, namely, Private Target Dummy (Model 1), Related Acquisition (Model 2), and Domestic Target Dummy (Model 3). Accordingly we use these variables as dependent variables in Model 1, 2, and 3 respectively. ‘Public Target Dummy’ is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. ‘Related Acquisition’ is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. ‘Domestic Target Dummy’ is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0. In all three models, we regress on the ‘CEO Tenure Dummy’ (defined in Table 2 or Appendix A) and a set of control variables similar to the ones used in Table 2 (detailed definitions of all variables can be found in Appendix A). In all three models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
4.3. CEO tenure, compensation and M&A performance Based on prior results, we may question what drives long-tenured CEOs to make better acquisitions for shareholders. In order to explore this aspect, for each acquisition deal, we gather a CEO's compensation in the year before and after the deal and find the difference between the two compensation values. We then regress the difference of the CEO compensation on M&A performance (i.e., CAR (−2, +2)), the CEO tenure dummy, and most importantly, the multiplicative interaction term between the two variables. Model 1 in Table 4 shows the main effect model. Neither M&A performance nor the CEO tenure dummy is significant in explaining CEO compensation change in a deal. However, we find a positive and significant interaction term between M&A performance and the CEO tenure dummy. The coefficient is not only statistically significant (p value <0.05) but also economically significant. Based on Model 1, one standard deviation change in acquirer CAR (standard deviation is 0.054) leads to 0.490 million USD (= 0.054*−12.342 + 0.054*21.406) change in total compensation for the long-tenured CEOs.3 To understand the compensation contract design better, we further split CEO total compensation change into the following two parts: incentive pay change (Models 3 and 4) versus fixed pay change (Models 5 and 6). We find that the results are mainly driven by the incentive pay change (Model 4). 4.4. CEO tenure and acquisition propensity and quality Next, using a firm-level panel data setup, we examine the relation between CEO tenure and M&A quality (Table 5). Models 1 and 2 use ‘Ratio of Yearly Positive CAR Acquisitions’ as the dependent variable, whereas, Models 3 and 4 use ‘Number of Yearly Positive CAR Acquisitions’ as the dependent variable. Further, Models 1 and 3 use only acquiring firm sample, whereas Models 2 and 4 use full 3
In model 4, pre-post compensation difference values are presented in million USD. 5
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Table 4 Do long-tenured CEOs benefit from good acquisitions? VARIABLES
Acq. CAR (−2 to +2) CEO Tenure Dummy Acq. CAR (−2 to +2) × CEO tenure dummy CEO age Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant Year & industry fixed effects Observations R-square
(1) Pre-post CEO total pay difference
(2) Pre-post CEO total pay difference
(3) Pre-post equitybased CEO pay difference
(4) Pre-post equitybased CEO pay difference
(5) Pre-post CEO salary difference
(6) Pre-post CEO salary difference
−0.8012 (4.611) −0.1464 (0.473)
−12.3424 (9.187) −0.3163 (0.445) 21.4063⁎⁎
−1.6700 (4.582) −0.1354 (0.465)
−13.3031 (9.186) −0.3066 (0.436) 21.5767⁎⁎
0.8689 (0.742) −0.0111 (0.083)
0.9607 (1.088) −0.0097 (0.083) −0.1704
0.0038 (0.026) −0.2549 (0.177) 0.0148* (0.009) 2.9104⁎⁎ (1.188) 0.0141 (0.145) −1.8790 (1.205) 0.0033⁎⁎⁎ (0.001) 16.8066⁎⁎⁎ (5.024) −0.0532 (0.051) 24.2042⁎⁎⁎ (3.704) Yes
(9.636) 0.0046 (0.026) −0.2600 (0.178) 0.0146* (0.009) 2.9610⁎⁎ (1.196) 0.0151 (0.145) −1.8590 (1.199) 0.0030⁎⁎⁎ (0.001) 16.9184⁎⁎⁎ (5.017) −0.0492 (0.052) 24.1978⁎⁎⁎ (3.698) Yes
0.0078 (0.025) −0.2420 (0.175) 0.0131* (0.008) 3.7872⁎⁎⁎ (0.912) 0.0472 (0.144) −1.1751 (1.177) 0.0035⁎⁎⁎ (0.001) 16.0449⁎⁎⁎ (4.884) −0.0453 (0.043) 22.0245⁎⁎⁎ (3.654) Yes
(9.580) 0.0086 (0.026) −0.2471 (0.176) 0.0129* (0.008) 3.8382⁎⁎⁎ (0.921) 0.0481 (0.144) −1.1549 (1.171) 0.0032⁎⁎⁎ (0.001) 16.1576⁎⁎⁎ (4.878) −0.0413 (0.045) 22.0180⁎⁎⁎ (3.648) Yes
−0.0040 (0.005) −0.0129 (0.032) 0.0017 (0.001) −0.8768 (0.685) −0.0330* (0.018) −0.7039⁎⁎⁎ (0.244) −0.0002 (0.000) 0.7617 (1.044) −0.0079 (0.008) 2.1797⁎⁎⁎ (0.494) Yes
(1.506) −0.0041 (0.005) −0.0129 (0.032) 0.0017 (0.001) −0.8772 (0.686) −0.0330* (0.018) −0.7041⁎⁎⁎ (0.244) −0.0002 (0.000) 0.7608 (1.047) −0.0079 (0.008) 2.1797⁎⁎⁎ (0.495) Yes
6042 0.068
6042 0.069
6042 0.066
6042 0.067
6042 0.034
6042 0.034
Table 4 presents OLS Regression results examining the moderating effect of acquirer M&A deal performance on the relation between CEO tenure and M&A pre-post CEO pay difference. We use three different dependent variables: Pre-Post CEO Total Pay Difference (in Model 1 and 2), Pre-Post Equity-based CEO Pay Difference (in Model 3 and 4), and Pre-Post CEO Salary Difference (in Model 5 and 6). Specifically, ‘Pre-Post CEO Total Pay Difference’ denotes the difference in CEO total compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. ‘Pre-Post Equity-based CEO Pay Difference’ denotes the difference in equity-based CEO compensation between ‘t + 1′ and ‘t − 1' fiscal years, relative to the acquisition event year. ‘Pre-Post CEO Salary Difference’ denotes the difference in CEO salary and bonus between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. All three dependent variables are expressed in million USD in the regression models. In order to examine the moderating effect of acquirer M&A deal performance, we introduce an interaction term (‘Acq. CAR (−2 to +2) × CEO Tenure Dummy’) in Model 2, 4 and 6. In all models, we control for a set of firm-specific characteristics similar to the ones in Tables 2 and 3 (defined in Appendix A). In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
sample. Results in these models show that long-tenured CEOs are likely to make more positive CAR deals than short-tenured CEOs. 4.5. Moderating effects of firm and governance characteristics Next, we test if our results are contingent on any firm characteristics or corporate governance features. Specifically, we test the moderating effect of CEO power (i.e., CEO pay slice), firm size, firm valuations (i.e., market-to-book value), financial risk taking (i.e., leverage), and governance (i.e., independent board ratio). However, we do not find any significant moderating effects in any model in Table 6. This suggests that our main results regarding CEO tenure and M&A performance are not contingent on these various conditions. 4.6. Robustness tests First, we address the concern of reverse causality that may introduce endogeneity bias into our empirical results. There is a possibility that CEO tenure is affected by M&A performance. Lehn and Zhao (2006) show that M&A performance can play an important role in CEO turnover decisions, which may induce reverse causality bias. If short-tenured CEOs lose their jobs due to poor M&A performance, we will not have an opportunity to examine their M&A performance in later years. To address this issue, we create 6
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Table 5 CEO tenure and (i) Probability of M&A deals, (ii) ratio of positive CAR deals, (iii) number of positive CAR deals. Variables
CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Constant Year & industry fixed effects Observations Number of unique firms R-square overall Chi-square
(1) Ratio of yearly positive CAR acquisitions (acquiring firm sample)
(2) Ratio of yearly positive CAR acquisitions (full sample)
(3) Number of yearly positive CAR acquisitions (acquiring firm sample)
(4) Number of yearly positive CAR acquisitions (full sample)
0.0278⁎⁎ (0.013) −0.0004 (0.001) −0.0385 (0.054) −0.0185⁎⁎⁎ (0.005) 0.0003⁎⁎⁎ (0.000) 0.0253 (0.040) −0.0016 (0.003) 0.0092
0.0092* (0.005) −0.0013⁎⁎⁎ (0.000) 0.0400* (0.021) 0.0186⁎⁎⁎ (0.002) 0.0000 (0.000) −0.0127 (0.013) −0.0005 (0.001) −0.0042
0.0912⁎⁎ (0.047) −0.0044* (0.003) 0.1116 (0.136) 0.0422* (0.026) 0000 (0.000) 0.1484 (0.111) −0.0133* (0.007) −0.1697
0.0919⁎⁎ (0.046) −0.0111⁎⁎⁎ (0.004) 0.3904⁎⁎ −0.176 0.1897⁎⁎⁎ (0.019) 0.0002 (0.000) −0.1619 (0.219) −0.0170 (0.011) −0.1548
(0.046) −0.0005⁎⁎⁎ (0.000) 0.1521* (0.082) 0.0004⁎⁎ (0.000) 0.8939⁎⁎⁎ (0.107) Yes
(0.018) −0.0000⁎⁎⁎ (0.000) 0.0958⁎⁎⁎ (0.022) −0.0000 (0.000) 0.1376⁎⁎ (0.061) Yes
(0.106) 0.0035⁎⁎⁎ (0.001) 0.1514 (0.166) 0.0006 (0.000) 0.7306* (0.423) Yes
(0.144) 0.0003 (0.000) 1.1565⁎⁎⁎ (0.331) −0.0002 (0.001) −1.6795⁎⁎⁎ (0.351) Yes
5272 1612 0.018
21,478 2322 0.024
5272 1612
21,478 2322
2820.12⁎⁎⁎
4106.06⁎⁎
Table 5 presents Panel Data Regression results examining the relation between CEO tenure and (ii) ratio of positive CAR deals, and (ii) number of positive CAR deals. Accordingly, we use two different dependent variables: Ratio of Yearly Positive CAR Acquisitions (in Model 1 and 2), and Number of Yearly Positive CAR Acquisitions (in Model 3 and 4). Model 1 and 3 use only acquiring firm sample, whereas, Model 2 and 4 use full sample. Further, given the nature of dependent variables, Model 2 and 3 use panel data regression, and Model 4 and 5 use panel data Poisson regression. All these models use random-effect regression. Specifically, ‘Ratio of Yearly Positive CAR Acquisitions’ denotes the ratio of number of positive CAR M&A deals to total M&A deals in a fiscal year. ‘Number of Yearly Positive CAR Acquisitions’ denotes the number of positive CAR M&A deals in a fiscal year. We regress these variables on the main independent variable, ‘CEO Tenure Dummy’, and control for a similar set of firmspecific characteristics used in previous tables (defined in Appendix A). In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
different subsamples by retaining observations that show which particular CEO has retained her position in a firm for at least (i) three years (to account for early turnover) and (ii) six years (to account for intermediate CEO tenure) .4 This subsample creation alleviates the concern of reverse causality, as we only include cases where CEOs have avoided the possibility of losing their job in the early years of tenure. For these subsamples, we again examine the association between CEO tenure and M&A short-term performance (similarly to Table 2). The results are presented in Table 7 and show that long-tenured CEOs still tend to make better M&A decisions. Second, we use alternative measures of M&A performance based on CARs in various event windows, such as CAR (−1, +1) and CAR (0, +1). Our results are not sensitive to M&A performance measures. Third, we conduct our analysis on various subsamples, such as excluding financial and utilities firms, excluding the financial crisis years (2000–2001 and 2008–2009), and excluding infrequent acquirers. Our results remain qualitatively similar. For the sake of brevity, these results are not reported in the paper. 5. Discussion and conclusion Recent debates in media and among researchers demonstrate a strong interest in the question as to whether CEO tenure matters and how CEO tenure affects a firm's strategy and shareholder value (e.g., Lublin, 2010, Wall Street Journal; and Stoll, 2018, Wall Street Journal). It is reported that CEO tenure is becoming shorter in major corporations (Laughlin, 2018, New York Times). According to a recent Equilar study, the median tenure for S&P 500 CEOs was approximately five years in 2017, and many of these 4
Median CEO tenure in our sample is six years. 7
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Table 6 Moderating effects of various firm-specific and governance characteristics on M&A performance. Variables CEO tenure dummy CEO tenure dummy × CEO pay slice CEO tenure dummy × Log (firm size)
(1) Model
(2) Model
(3) Model
(4) Model
(5) Model
0.0021 (0.006) 0.0050 (0.013)
0.0027 (0.008)
0.0045⁎⁎ (0.002)
0.0055⁎⁎ (0.002)
0.0028 (0.007)
0.0002 (0.001)
CEO tenure dummy × Market to book value CEO tenure dummy × Leverage
−0.0001 (0.000)
CEO tenure dummy × Independent board ratio CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy Constant Year & industry fixed effects Observations R-square
−0.0001 (0.000) −0.0099 (0.010) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0030 (0.005) 0.0000 (0.000) 0.0088 (0.013) 0.0000 (0.000) 0.0069 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0467⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0001 (0.000) −0.0074 (0.007) −0.0028⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0029 (0.005) 0.0000 (0.000) 0.0087 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0465⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0001 (0.000) −0.0076 (0.007) −0.0028⁎⁎⁎ (0.001) 0.0001 (0.000) 0.0046 (0.005) 0.0002 (0.000) −0.0029 (0.005) 0.0000 (0.000) 0.0082 (0.013) 0.0000 (0.000) 0.0069 (0.006) 0.0025 (0.002) −0.0075⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0136⁎⁎⁎ (0.004) 0.0451⁎⁎⁎ (0.013) Yes 5357 0.035
−0.0081 (0.009) −0.0001 (0.000) −0.0072 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0086 (0.007) 0.0002 (0.000) −0.0031 (0.005) 0.0000 (0.000) 0.0079 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0024 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0453⁎⁎⁎ (0.013) Yes 5357 0.035
0.0017 (0.009) −0.0001 (0.000) −0.0074 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0047 (0.005) 0.0002 (0.000) −0.0038 (0.007) 0.0000 (0.000) 0.0086 (0.013) 0.0000 (0.000) 0.0068 (0.006) 0.0025 (0.002) −0.0076⁎⁎⁎ (0.002) 0.0074⁎⁎⁎ (0.002) 0.0040⁎⁎ (0.002) −0.0135⁎⁎⁎ (0.004) 0.0461⁎⁎⁎ (0.013) Yes 5357 0.035
Table 6 presents OLS Regression results examining the moderating effect of firm-specific and governance characteristics on the relation between CEO tenure and M&A short-term performance. The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (−2, +2) period around M&A announcement. We examine the moderating effects of following variables: CEO Pay Slice (in Model 1), Log (Firm Size) (in Model 2), Market to Book Value (in Model 3), Leverage (in Model 4), and Independent Board Ratio (in Model 5). Accordingly, we introduce interaction effect with ‘CEO Tenure Dummy’ variable in each model. We control for a similar set of firm and deal characteristics used in previous tables. All variables are defined in Appendix A. In all the models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
leaders did not even survive two years (Sonnenfeld, 2018). However, if CEOs can withstand the early storms in their career, they become more prudent and might make better investment decisions. We provide a robust result showing that long-tenured CEOs tend to make better M&As and create more shareholder value than short-tenured CEOs. In order to obtain a holistic perspective on the relation between CEO tenure and M&A decisions, we also examine a few channels that could potentially explain our results. We find that long-tenured CEOs show more prudence in M&A decisions – they are more likely to acquire private target firms and make acquisitions in the same industries and the domestic market. Generally, these 8
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Table 7 Robustness test: addressing endogeneity bias. VARIABLES CEO tenure dummy CEO age CEO pay slice Log (firm size) Market to book value Leverage Board size Independent board ratio Sales growth ROA Stock return variability Target relative size
(1) CEO min. tenure >3 years
(2) CEO min. tenure >3 years
(3) CEO min. tenure >6 years
(4) CEO min. tenure >6 years
0.0036⁎⁎⁎ (0.001) −0.0000 (0.000) −0.0035 (0.006) −0.0030⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0069* (0.004) 0.0001 (0.000) 0.0004 (0.004) −0.0000 (0.000) −0.0017 (0.014) 0.0003⁎⁎⁎ (0.000)
0.0042⁎⁎ (0.002) −0.0000 (0.000) −0.0107 (0.007) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) 0.0094* (0.005) 0.0005 (0.000) −0.0009 (0.006) 0.0000 (0.000) 0.0184 (0.014) 0.0004⁎⁎⁎ (0.000) 0.0084 (0.006) 0.0036 (0.003) −0.0079⁎⁎⁎ (0.002) 0.0079⁎⁎⁎ (0.002) 0.0066⁎⁎⁎ (0.002) −0.0138⁎⁎⁎ (0.004) 0.0362⁎⁎ (0.014) Yes 4845 0.040
0.0029* (0.002) −0.0001 (0.000) 0.0030 (0.006) −0.0030⁎⁎⁎ (0.000) 0.0000⁎⁎ (0.000) 0.0002 (0.005) 0.0001 (0.000) −0.0011 (0.004) 0.0061** (0.003) −0.0169 (0.018) 0.0011⁎⁎ (0.001)
0.0044⁎⁎ (0.002) −0.0002 (0.000) −0.0021 (0.008) −0.0027⁎⁎⁎ (0.001) 0.0000⁎⁎⁎ (0.000) −0.0007 (0.006) 0.0005 (0.000) −0.0031 (0.006) 0.0072* (0.004) 0.0024 (0.015) 0.0009* (0.001) 0.0120 (0.007) 0.0035 (0.003) −0.0080⁎⁎⁎ (0.002) 0.0078⁎⁎⁎ (0.002) 0.0053⁎⁎ (0.002) −0.0136⁎⁎⁎ (0.005) 0.0410⁎⁎⁎ (0.015) Yes 3860 0.043
High tech dummy Public target dummy Related acquisition Domestic target dummy Pure stock dummy Constant Year & industry fixed effects Observations R-square
0.0519⁎⁎⁎ (0.010) Yes 7117 0.022
0.0552⁎⁎⁎ (0.012) Yes 5692 0.026
Table 7 presents OLS regression results examining the association between CEO tenure and M&A short-term performance using different subsamples to address reverse causality bias. Models 1 and 2 use a sample in which CEO minimum tenure in a firm is more than 3 years, and Model 3 and 4 use a sample in which CEO minimum tenure in a firm is more than 6 years. In order to obtain CEO minimum tenure in firm, first we determine a CEO's overall tenure in a firm-CEO setup. Subsequently, we create different sub-samples by retaining observations for which a particular CEO has retained her position in a firm for at least (i) three years (to account for early turnover), and (ii) six years (to account for median CEO tenure). The dependent variable is M&A short-term performance - Acquirer CAR (−2 to +2), which denotes acquirer's cumulative abnormal return over a 5-day (-2, +2) period around M&A announcement. In Model 1 and 3, we control for a set of firm-specific characteristics and in Model 2 and 4, we add other M&A deal related control variables. ‘CEO Tenure Dummy’ is the main independent variable. We also control for a similar set of control variables used in previous Table 2 (see Appendix A for detailed definitions). In all models, we control for year and industry fixed-effects. Robust standard errors are reported in parentheses. ⁎⁎⁎ denotes 1% significance, ⁎⁎ denotes 5% significance, and * denotes 10% significance, all two-tailed.
attributes are more favored by market participants. Furthermore, we show that there is an incentive channel for long-tenured CEOs to undertake high-quality M&A deals; long-tenured CEOs receive higher compensation compared to the pre-acquisition period if they make better acquisitions. Overall, our findings suggest that boards should be careful in enforcing premature CEO turnovers. A resilient board should be able to preserve long-term goals under disturbing outside pressures. Future studies may focus on other channels (e.g. CEO overconfidence, conservatism, risk taking), that would enhance our understanding of the relation between CEO characteristics and M&A performance.
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Funding None. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.frl.2019.08.025. Appendix A List of variables and measurement Deal characteristics and other M&A related variables Acquisition dummy Acquirer CAR (M&A announcement return) Ratio of Yearly Positive CAR Acquisitions Number of Yearly Positive CAR Acquisitions Public target dummy Related acquisition Target relative size Pure stock payment High tech acquisitions
Domestic target dummy
It is a dummy variable that equals 1 if the firm made any acquisitions in the year, otherwise it equals zero. It is measured by the acquiring firm's cumulative abnormal return (CAR) over a 5-day (−2, +2) period around M&A announcements. It denotes the ratio of number of positive CAR M&A deals to total M&A deals in a fiscal year. It denotes the number of positive CAR M&A deals in a fiscal year. It is a dummy variable that equals 1 if the target firm is public, otherwise it equals 0. It is a dummy variable that equals 1 if it is a related acquisition (based on 4 sic code of the acquiring and target industry), otherwise it equals 0. M&A transaction value divided by the acquiring firm's market cap. It is a dummy variable that equals 1 if the payment is made by 100% stock, otherwise it equals 0. It is a dummy variable that equals 1 if the acquiring firm and the target firm are both in the high-tech industry (SIC codes: 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3671, 3672, 3674, 3675, 3677, 3678, 3679 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 3841, 3845 (medical instruments), 4812, 4813 (telephone equipment), 4899 (communications services), and 7371, 7372, 7373, 7374, 7375, 7378, and 7379 (software). (Loughran and Ritter, 2004, page.35) It is a dummy variable that equals 1 if the target firm is from USA, otherwise it equals 0.
Firm characteristics (acquiring firm) CEO tenure dummy CEO age Log (firm size) Market to book value Leverage Stock return variability Sales growth ROA Board size Independent board ratio CEO pay slice CEO total compensation CEO equity-based compensation CEO salary and bonus Pre-post total CEO Pay difference Pre-post equity-based CEO pay difference Pre-post CEO salary difference
It is a dummy variable. It is equal to 1 if CEO tenure in a given year is equal to or greater than median CEO tenure of sample firms in that year. It denotes the CEO age in years. The acquiring firm's total market value (log transformed) in the fiscal year end before the acquisition. It denotes market value of assets over book value of assets in the fiscal year end before the acquisition (Masulis et al., 2007). The acquiring firm's long term debt to assets ratio in the fiscal year end before the acquisition. It represents the standard deviation of yearly stock returns over last five years. Yearly stock return is calculated based on twelve monthly buy-and-hold returns in a fiscal year. Subsequently standard deviation is calculation based on last five yearly stock return values. Percentage change in sales over two consecutive fiscal years. It denotes EBIT (Earnings before interest and tax) divided by total assets. It denotes the total number of board members. It is calculated as number of unrelated board members divided by total number of board members. It is the fraction of CEO payout of the total compensation to the group of minimum top-two and maximum top-five executives, including CEO (Bebchuk et al., 2011). From an agency theory perspective, a higher CEO pay slice may indicate a higher level of agency problem in a firm. It considers both fixed and variable salary paid to a CEO. It includes cash salary, bonuses, long-term incentive plans and any stock grant and stock option values. CEO Equity-based compensation variable includes the total value of stock grants and stock options awarded to a CEO. It considers fixed salary paid to a CEO. It includes cash salary and bonuses. It denotes the difference in CEO total compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. It denotes the difference in equity-based CEO compensation between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year. It denotes the difference in CEO salary and bonus between ‘t + 1′ and ‘t − 1′ fiscal years, relative to the acquisition event year.
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