Incentive pay and acquirer returns – The impact of Sarbanes–Oxley

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Incentive pay and acquirer returns – The impact of Sarbanes–Oxley Marcin W. Krolikowski ∗ School of Business, Providence College, 1 Cunningham Square, Providence, RI 02918, United States

a r t i c l e

i n f o

Article history: Received 26 September 2014 Received in revised form 28 April 2015 Accepted 22 June 2015 Available online xxx Keywords: Mergers Acquisitions Managerial incentives Executive compensation Sarbanes–Oxley Act

a b s t r a c t This study examines how pay-for-performance influences the quality of merger decisions before and after Sarbanes–Oxley (SOX). Pay-for performance has a significant positive effect on acquirer returns of 0.9% pre-SOX and 1.1% post-SOX around the three-day event window. Bidders with high pay-forperformance pay a 23.3% lower merger premium in listed target acquisitions. The positive effect of payfor-performance is more important for public target acquisitions overall, for small acquirers pre-SOX, and for large acquirers post-SOX. In the long run, bidders with high pre-merger pay-for-performance do not necessarily outperform. © 2015 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.

1. Introduction The alignment of interests between managers and shareholders is especially important in corporate decisions in which the potential for shareholder wealth destruction is large. As noted by Moeller, Schlingemann, and Stulz (2004) and others, mergers and acquisitions are one of these vulnerable decision areas for a number of reasons. For one, mergers typically reflect relatively large investments, and the bidder often pays too much for the target firm. In many cases, the anticipated synergies never evolve. In addition, by increasing the size of the firm, acquisitions may lead to higher pay for the CEO, regardless of the quality of the merger decision. Wessel (2006) notes, “it is obvious that the bigger the company, the more the CEO gets paid. That fact has inspired more than few big acquisitions. An old rule of thumb thus holds that for every 10% increase in company’s size, the CEO’s pay goes up 3%.” Consistent with this point, Hallock (2011) finds that firm size is one of the most highly correlated factors with CEO pay. Increasing the amount of equity holdings by managers presents one way to align the interests of managers and shareholders and to curtail mergers whose sole intent is to increase size. Datta, Iskandar-Datta, and Raman (2001) examine a sample of mergers from 1993 to 1998 and find a strong positive relation between acquiring managers’ equity based compensation, EBC (i.e., the value of new stock options granted as a percentage of total compensation), and stock price performance around and following

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acquisition announcements. Using a sample of bank mergers from 1991 to 2005, Minnick, Unal, and Yang (2011) are the first to examine how pre-merger pay-for-performance affects bank acquirers. They find that higher pay-for-performance sensitivity in bank acquirers leads to better acquisitions. Further, they demonstrate that after sector-specific deregulation (Gramm–Leach–Bliley Act of 1999), the impact of pay-for-performance has fallen. This paper extends this analysis beyond the banking sector by incorporating recent mergers in diverse, non-regulated industries in which the incentive effects of pay-for-performance may be more pronounced than in regulated industries. Furthermore, to examine how market regulation, across industries, influences the incentive effects of compensation, I evaluate whether the positive influence of pay-for-performance on merger decisions changes after the passage of Sarbanes–Oxley (SOX). The motivation for examining the pre-and-post-SOX periods (I define pre-SOX as the 1994–07.2002 period and 08.2002–2010 as the post SOX period) is that there may be a partial substitution effect between the disciplinary influence of regulation and compensation. That is, the governance changes imposed by this regulation are intended to hold managers more accountable and, as such, may reduce the importance of incentive-based compensation.1 In the analysis, I consider the

1 Title III requires CEO and CFO to certify reports in addition to having fully independent audit committees. SOX also empowered the SEC by increasing the penalties that can be imposed on executives. For example, in November 2011, CSK Auto Corp. CEO Maynard Jenkins, was required under SOX to return $2.9 million in bonuses and stock profits to the company because he received it while CSK was committing fraud. Thus SOX instituted clawback provisions. Such increase in governance changes may reduce the importance of incentive-based compensation.

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compensation-related incentive effects of mergers more/less prone to hubris by examining small vs. large acquirers and acquirers of listed vs. unlisted targets. In listed target acquisitions, I also investigate the role of pay-for-performance on merger premiums. While this paper is also similar in focus to Datta et al. (2001), the paper differs in three distinct ways. First, the paper utilizes a different, more complete, measure of the incentive effects of compensation, namely pay-for-performance sensitivity. For example, as noted in Minnick et al. (2011), the pay-for-performance sensitivity of new option grants often only comprises a small percentage of total pay-for-performance. In the sample, pay-for-performance sensitivity from new option grants accounts for just 8% of total payfor-performance. In addition, while Datta et al. (2001) examine the period from 1993 to 1998, the longer sample period from 1994 to 2010 enables me to examine the impact of regulatory changes on pay-for-performance sensitivity. Finally, I investigate how the impact of pay-for-performance differs in acquisitions of public targets, as opposed to unlisted targets, as well as across size categories both before and after the regulatory changes. These findings allow me to determine the importance of pay-for-performance in different types of mergers in different regulatory environments, which has not been previously examined in the literature. Using a sample of 459 firm mergers from 1994 to 2010, my findings demonstrate that pre-merger pay-for-performance has a significant positive effect on acquirer returns of 1.1% around a three-day event window. These results are robust to controlling for important determinants of the market’s reaction to acquisitions. Bidders with high pay-for-performance pay a 23.3% lower merger premium in listed target acquisitions suggesting that these types of bidders are less likely to overpay. I also examine the influence of SOX on the relationship between pay for-performance and the quality of the merger decision. Pay-for-performance has a similar significant positive effect on acquirer returns both before and after SOX. Thus, the value of this compensation-related incentive effect of merger decisions has persisted after the increase in regulation. I further observe a size effect in pay-for-performance sensitivity both pre- and post-SOX. Pay-for-performance is more important for small firms pre-SOX. While Moeller et al. (2004) suggest that large firms are more likely to make poor acquisitions due to hubris, they do not control for pay-for-performance sensitivity. I find that acquisitions by large firms with high pay-for-performance actually create value for shareholders post-SOX. In addition, I examine the incentive effects of compensation on merger decisions involving listed vs. unlisted targets. Previous literature has suggested that agency problems may be less prevalent in acquisitions of unlisted targets due to the lower likelihood of hubris-related merger motivations. Consistent with this line of the literature, my findings indicate that pay-for-performance is more important in public target acquisitions, both pre-and-post-SOX. My paper contributes to the literature in the following ways. First, I determine that managerial incentives in the form of higher pay-for-performance are important for maximizing shareholder wealth in a broad sample of acquisitions. Higher payfor-performance leads to better stock announcement returns and lower premiums paid by acquirers. This finding broadens our understanding of the role of managerial incentives in acquisitions. In addition, pay-for-performance is important even in the presence of higher regulation. While the aim of SOX was to enhance corporate governance and strengthen corporate accountability for the benefit of the shareholder, my results indicate that pay-forperformance is still used effectively. Moreover, I find that the effect of pay-for-performance is more prominent for acquisitions that are more susceptible to agency problems. For example, the positive effects of pay-for-performance are greater in acquisitions of listed as opposed to unlisted targets. While Andrade,

Mitchell, and Stafford (2001) confirm that, on average, acquirers experience negative or insignificant abnormal returns on the announcement of the acquisition of listed targets, acquisitions of listed targets by acquirers with high pay-for-performance can create value for shareholders. Finally, I indicate that the pay-forperformance impact varies across acquirer size categories with the introduction of regulation. This finding contributes to the literature that examines the impact of SOX on firms of different size. The rest of the paper proceeds as follows. In Section 2, I provide background and develop testable hypothesis. In Section 3, I describe the data together with a univariate analysis. The main results appear in Section 4. Section 5 examines the long-term performance of acquirers, while Section 6 provides our conclusions. 2. Background and hypothesis development Previous studies have shown that compensation policies can provide value-increasing incentives to the CEO, as measured by better firm performance and better investment decisions.2 For example, Mehran (1995) finds that the return on assets and Tobin’s Q are positively related to the fraction of total compensation that is equity based. In examining the incentive effects of compensation in mergers, Datta et al. (2001) examine equity-based compensation (EBC) levels and find a strong positive correlation between an acquiring manager’s EBC and the acquiring firm’s performance. Furthermore, Minnick et al. (2011) confirm that high pay-forperformance sensitivity is associated with better bank mergers from 1991 to 2005. While a strong pay/performance link is generally observed as having a positive effect on firm decisions, the relationship between pre-merger pay/performance and post-merger performance is not obvious. For example, Cornett, Marcus, and Tehranian (2007) provide evidence that option compensation may encourage earnings management and, after controlling for this link, the relationship between incentive-based pay and performance is weak. Also, Dow and Raposo (2005) determine that CEO compensation can influence the kinds of strategies that firms adopt. The authors argue that performance-related compensation creates an incentive to look for overly ambitious and hard to implement strategies. Deckop, Mangel, and Cirka (1999) argue that a potential downside of pay-for-performance is that it may discourage employees from engaging in behaviors not linked to monetary rewards including extrarole behaviors. One critical form of extrarole behavior is organizational citizenship behavior, which is employee behavior that goes above and beyond the call of duty, is discretionary and not explicitly recognized by the employing organization’s formal reward system, and contributes to organizational effectiveness.3

2 I use CEO to proxy for a firm’s top level management. The focus on the CEO is similar to several prior studies such as Brick, Palmon, and Wald (2006, 2008), Coles, Daniel, and Naveen (2006), Core and Guay (1999), Cremers and Grinstein (2009), Denis, Hanouna, and Sarin (2006), Gao (2010), Jin (2002), Rajgopal and Shevlin (2002), and Yermack (1995). Further, Aggarwal and Samwick (2003) determine that CEOs pay-for-performance sensitivity is highest relative to other firm executives and that CEOs are responsible for overall firm policies. 3 The literature suggests several other factors in addition to pay-for-performance that should improve firm performance. Conventional wisdom suggests that a greater level of independence allows for more effective monitoring and, as such, improved firm performance. Several papers demonstrate that an outsider-dominated board makes better decisions related to hiring and firing CEOs (Borokhovich, Parrino, & Trapani, 1996; Weisbach, 1988) and negotiating takeover premiums (Cotter, Shivdasani, and Zenner (1997). The literature also stresses the size of the board as a determinant of performance. Lipton and Lorsch (1992) and Jensen (1993) argue that larger boards may be less effective than smaller boards due to the director freerider problem. Yermack (1996) and Eisenber, Sundgren, and Wells (1998) provide evidence that firms with smaller boards have higher Tobin’s Q. Bebchuck et al. (2009) determine that increases in the E-Index are monotonically associated with

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While pay-for-performance may induce suboptimal behavior in some cases, I expect that, on average, pre-merger pay-forperformance positively influences the quality of merger decisions. This leads to my first hypothesis: H1. CEOs with higher pay-for-performance sensitivity make better merger decisions, as reflected by higher returns both around the time of the merger announcement and following acquisition. The literature examining the importance of compensation when governance is introduced varies. Hermalin (2005) argues that if there is a trend toward more vigilant governance, such as SOX, then CEO effort should increase as monitoring increases and the average CEO compensation should increase. Furthermore, in modeling the relationship between incentive compensation and governance, Dicks (2009) points out that if forced to increase governance, firms will, in turn, lower incentive pay. Along similar lines, Chung (2006) has a theoretical prediction that board monitoring and CEO payfor-performance are substitutes. Cohen, Dey, and Lys (2009) further argue that the passage of SOX resulted in a fall in compensationbased incentives to take risk, which is related to a decline in risky investments. They argue that two forces dominate CEO compensation post-SOX. The direct linkage effects cause CEOs to invest in less risky projects due to the increased personal costs of high risk projects. The compensation linkage predicts that boards anticipate the effect of direct linkage on risk taking and increase incentive compensation to overcome this effect. Thus, this effect predicts an increase in risky investments. Cohen et al. (2009) find that postSOX (until 2006), the standard deviation of stock returns fell, as did pay-for-performance. They conclude that direct linkage dominates compensation linkage. In the period following SOX, there should be increased vigilance by investors, regulators, and auditors. The SOX reforms also forced firms to spend more on internal controls. In return for these higher costs, shareholders should face lower risk of losses and benefit from greater accountability of management. Thus, the governance changes of SOX are likely to be associated with a reduction in importance of incentive-based compensation given that there is a regulatory framework that will hold mangers accountable. Therefore, based on the above, my second hypothesis can be expressed as follows: H2. The impact of pay-for-performance should be lower post-SOX given a negative relationship between incentive compensation and governance. Kamar, Mandic, and Talley (2007) find that small firms have been more historically prone to financial fraud than large firms, Enron, Tyco, and WorldCom aside. Indeed, Nicholas (2006) finds that smaller companies spent more time evaluating their accounting post-SOX as opposed to large firms. Nicholas (2006) concludes that the difference in findings may indicate that large companies did not feel a need to re-evaluate their accounting practices as they are more confident in what they had been doing in the past. Thus, this logic suggests that increasing pay-for-performance in small firms, which are more subject to fraud and weaker reporting especially in absence of the external regulation, should result in higher quality mergers. Thus, my third hypothesis: H3. There is a size effect in pay-for-performance sensitivity. I hypothesize that pay-for-performance is more important for small acquirers’ pre-SOX given that smaller companies have weaker accounting practices pre-SOX.

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targets face higher bidder competition, as well as greater publicity, around the acquisition as opposed to unlisted targets. In line with higher bidder competition, Ku, Malhotra, and Murninghan (2005) find that pre-merger publicity surrounding acquisitions of listed targets gives rise to bidding wars. In addition, Haunschild, Davis-Blake, and Fichman (1994) confirm that the desire of acquiring managers not to lose credibility with others becomes more prominent in listed target acquisitions and leads to an escalation of commitment. My last hypothesis reflects the greater likelihood of hubris-related merger motivations in listed deals and is stated as follows: H4. Pay-for-performance is more important in listed target acquisitions both pre- and post-SOX as the potential for hubrismotivated acquisitions is greater for acquirers of listed targets. 3. Data The sample includes all completed U.S. mergers with announcement dates between January 1, 1994 and December 31, 2010 as identified from the Mergers and Acquisitions database of the Securities Data Company (SDC). I require that bidders have available stock price data from CRSP, accounting information from Compustat, executive compensation data from ExecuComp, and, in a subsample analysis, governance data from the Investor Responsibility Research Center (IRRC). I also require that the ratio of transaction value relative to the bidder’s value, as measured by the market value at fiscal year-end prior to the announcement, be at least 5%. The reason for this cutoff is to examine mergers that are meaningful to the acquirer. To clearly delineate the effect of each acquisition on long run returns (calculate BHAR), I include only those acquisitions that do not overlap. I define an overlap as if the gap between the announcement of one merger and the announcement of a second is no greater than five years. Finally, I require that the CEO be the same, both at one year prior to the announcement to the first year after announcement. The final sample consists of 459 completed mergers made by 434 firms.4 4. Results Table 1 demonstrates that acquisitions tend to be cyclical, as the total number of acquisitions closely follows the business cycle. Consistent with the merger activity reported in Masulis, Wang, and Xie (2007), the period from 1997 to 2000 was characterized by strong merger activity. Table 1 also provides the industry breakdown of corporate acquisitions in the sample. The industries with the largest number of transactions are banking and business services. Similar to Gao (2010), I use two variables to measure CEO incentive pay. The first is Jensen and Murphy’s (1990) pay-forperformance (PPS) statistic, defined as the dollar change in CEO wealth for a $1000 change in firm value. The second measure is Core and Guay’s (1999) portfolio equity incentive (PEI) defined as the dollar change in CEO wealth for a 1% change in firm value. In this measure, I follow Core and Guay (2002) and estimate option deltas using the Black–Scholes (1973) option valuation model as modified by Merton (1973) to adjust for dividend payouts. As Core and Guay (1999) point out, PEI is equal to the PPS multiplied by the firm’s market value of equity divided by $100,000. My mean (median) PEI is $585,000 ($141,000) as compared to $558,000

Previous literature has suggested a greater likelihood of hubrisrelated merger motivations in listed target acquisitions. Listed

economically significant reductions in firm valuation, as well as large negative abnormal returns during their examination period.

4 I follow Harford and Li (2007) in the sample construction except that I use a 5% transaction value relative to the bidder’s value (as opposed to 10%) and a five-year overlap gap (as opposed to two). They obtain 370 completed mergers (public and non) between 1993 and 2000.

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4 Table 1 Acquisitions by year and industry. Year Panel A: Distribution by year 1994 1995 1996 1997 1998 1999 2000 2001 2002 Panel B: Distribution by industry Aircraft Apparel Automobiles and Trucks Banking Business Services Business Supplies Candy & Soda Chemicals Communication Computers Construction Construction Materials Consumer Goods Defense Electrical Equipment Electronic Equipment Entertainment Food Products Healthcare Insurance

Frequency

Year

Frequency

14 36 31 42 30 42 33 20 24

2003 2004 2005 2006 2007 2008 2009 2010

23 31 40 24 29 21 14 5

5 6 10 31 46 8 1 17 5 18 5 10 7 1 6 41 4 11 8 20

Machinery Measuring and Control Equipment Medical Equipment Other Personal Services Petroleum and Natural Gas Pharmaceutical Products Precious Metals Printing and Publishing Recreation Restaurants, Hotels, Motels Retail Rubber and Plastic Products Shipbuilding, Railroad Equipment Steel Works Etc. Textiles Trading Transportation Utilities Wholesale

20 15 17 4 6 23 13 2 9 1 7 20 2 1 8 2 16 10 13 10

The sample consists of 459 completed acquisitions announced from 1994 to 2010. The bidders are listed on the SDC database and have the required executive compensation data in ExecuComp. Panel A breaks down the sample by year, while Panel B breaks down the sample by industry.

16

250

14 200

12 10

150

8 100

6 4

PEI PPS

50

2 0

1993

1995

1997

1999

2001

2003

2005

2007

2009

0

Fig. 1. PPS and PEI over time. The figure represents the pay-for-performance, PPS (the change in CEO wealth (in dollars) for a $1000 change in firm value) and the portfolio equity incentive, PEI (the change in CEO wealth (in thousands) for a 1% change in firm value) through time.

($117,000) in Core and Guay (1999) and $389,000 ($112,000) for Gao (2010). My mean (median) PPS is $33.01 ($9.43) as compared to $25.00 ($5.29) for Hall and Leibman (1998) and $26.08 ($7.00) for Gao (2010). PPS is $31.82 ($9.73) pre-SOX and $34.65 ($8.78) in the post-SOX period. PEI is $635,000 ($124,000) pre-SOX and $516,000 ($179,000) post-SOX. There is no statistical difference in the PPS and the PEI, respectively, in the pre- and post-SOX period.5 Fig. 1 presents the median PPS and PEI over the sample period.

5 Following previous studies, I use the natural logarithm of PPS and PEI instead of the raw value in my regressions, as the distribution is skewed to the right.

The positive correlation between PPS and PEI is visible in Fig. 1 where the two measures have a relatively similar pattern. The reason for the positive correlation is that both variables measure how closely the CEO’s pay is related to shareholder wealth. Governance variables are collected from the Investors Responsibility Research Center (IRRC). As the IRRC data begins in 1996, information prior to that period was collected from the original 10-K reports made available in the Securities and Exchange Commission (SEC) database. Many studies find that the size and composition of the board of directors can influence the effectiveness of internal monitoring (Hermalin & Weisbach, 1998; Yermack, 1996). The average board size is 10.4 with seven independent

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directors. The average age of the CEO is 55 years and the average E-Index of Bebchuck, Cohen, and Ferrell (2009) is 2.6. Table 2 reports the correlation matrix among variables. Acquirers with higher PPS tend to have older CEOs (Age), are smaller (MVA), have smaller boards (BSizeD) and less independent boards (BIndepD). The correlation between PPS and PEI in log form is 0.59. In level form, the correlation is 0.46, similar to that reported in Gao (2010) and supports Fig. 1. In the case of MVA, PPS and PEI have the opposite and statistically significant sign (−0.31 vs. 0.44) indicating that PPS is higher for smaller firms and PEI is larger for larger firms. 4.1. Univariate analysis I obtain announcement dates from SDC and compute the cumulative abnormal returns (CARs) in a five-day window (−2 to +2), where Day zero is the announcement day. I use the CRSP valueweighted returns as the benchmark returns for the calculation of abnormal stock returns. Table 3 (Panel A) presents the fiveday acquirer CARs. High PPS acquirers have a mean (median) CAR of 0.99% (0.13%), while low PPS acquirers exhibit a mean CAR of −1.21% (−0.46%). The difference in mean (median) is significant at the 1% level. When “Other Deals” and “Unknown Deals” as defined by SDC are removed (Unknown Deals are deals with no payment information), the difference in CARs is stronger at 2.73% and significant at the 1% level. I find that acquisitions by high PPS firms have a positive difference in abnormal returns irrespective of how they are financed (Other Deals and Unknown Deals aside). The difference in CARs is higher than that reported in Datta et al. (2001), who report a CAR of −0.25% vs. 0.30% for low and high equity-based compensation acquirers, respectively. This more pronounced difference may be because PPS is a more complete measure of managerial incentives. Table 3 (Panel B) indicates that high PEI acquirers have a mean CAR of 0.45%, while low PEI acquirers exhibit a mean CAR of −0.69%. The difference in mean is significant at the 10% level. When Other Deals and Unknown Deals are removed, the difference in CARs is stronger at 1.62% and significant at the 5% level. A breakdown based on whether the acquisition involves a listed vs. unlisted target in Table 3 (Panel C and D) confirms that high PPS and PEI have a stronger positive effect on listed target acquisitions. Specifically, in takeovers of public targets, the CAR for high PPS bidders is, on average, 2.51% higher than the CAR for low PPS bidders. The comparisons in Table 3 ignore that firms and deals differ in other dimensions in addition to the form of financing or target type. To take into account other determinants of abnormal returns, I estimate multivariate regressions. 4.2. Regression analysis Following Minnick et al. (2011), the following specification is used to test the hypothesis that pre-merger PPS improves the quality of merger decisions, as measured by bidder CAR: CARit = ˇ0 + ˇ1 PPSit−1 + ˇ2 Ait−1 + ˇ3 Git−1 + ˇ4 Dit + Fit + eit

(1)

The dependent variable, CARit , is the cumulative abnormal stock return around the announcement for acquirer i for a deal occurring at time t. The key explanatory variable is PPS sensitivity measured pre-merger or the PEI of the acquiring CEO. I control for acquirer characteristics (Ait−1 ), other governance variables (Git−1 ), and deal characteristics (Dit ). I also include year and industry fixed effects (Fit ), while eit is the error term. For acquirer characteristics, I control for bidder size as Moeller et al. (2004) determine that acquirer returns are negatively related to bidder size, regardless of the method of payment. Bidder size is the log of the market value of assets (MVA), measured as the book

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value of total assets minus the book value of equity plus the market value of equity. Following Minnick et al. (2011), I also include a performance-based measure, return on assets (ROA). To control for firm governance, I include board structure variables commonly used in the literature: board size (BSizeD), the proportion of independent directors (BIndepD), the strength of shareholder rights (E-IndexD), and CEO age (AgeD). For deal characteristics, I control for the method of payment (Stock), tender offer (Tender), focus-increasing (Focus), and out-ofstate deals (Out State). Many studies report lower acquirer returns when acquisitions are paid for with stock, and higher returns when the merger is a tender offer or focus-increasing. Interstate mergers are shown to offer fewer opportunities for increasing market power and fewer cost savings (Kedia, Panchapagesan, & Uysal, 2008; Prager & Hannan, 1998). Table 4 (Panel A) summarizes the regression results based on PPS. The results are further divided into the pre- and post-SOX period, with the variable of interest as PPS. The findings indicate that a bidder’s pre-merger PPS has a significant positive influence on the shareholder wealth effects of the merger announcement. In other words, after controlling for other relevant merger characteristics, the results suggest that investors perceive that high PPS bidders make better merger decisions, on average. In particular, a one unit increase in PPS (in the logarithm) increases bidder CARs by 1.1%. Similarly, a firm that has high PPS (above median) prior to the merger results in 2.1% higher CARs. The PPS effect is important in both the pre- and post-SOX periods. Specifically, payfor-performance has a significant positive effect on acquirer returns of 0.9% pre-SOX and 1.1% post-SOX. The difference in the PPS effect between the two periods is not significant. I also examine the influence of PPS in separate subsamples of listed and unlisted target acquisitions. The findings reveal that PPS is always more significant in listed target acquisitions both preand post-SOX. This result suggests that PPS is important in acquisitions that are more susceptible to agency problems. Turning to the control variables, I find that large bidders are associated with lower announcement period returns, in line with prior research. Tender offers and focus-increasing deals benefit the shareholders, while stock and out-of-state deals are value destructive. The overall results are similar when I control for governance characteristics in Panel B of Table 4.6 In Table 5, I examine whether bidders with high incentive compensation pay a lower premium for listed targets after controlling for the same firm and deal characteristics as in the abnormal return regression.7 I find that bidders with high PPS are negatively and significantly related to the level of takeover premiums, in line with Datta et al. (2001). A high level of PPS is associated with a 23.3% decrease in takeover premiums paid by acquirers, relative to the mean takeover premium of 74.1% for acquirers with low PPS. Thus, the lower takeover premiums observed in acquirers with high PPS suggests that these bidders are less prone to overpayment, which is a possible explanation for the higher acquirer announcement returns in these transactions. According to Moeller et al. (2004), larger firms tend to be poor acquirers as they are more susceptible to value-decreasing motivations such as hubris. Thus, I predict that PPS is more important in larger firms. I divide the acquirers into three size categories. I

6 The results based on PEI are very similar to the PPS regressions and are available on request. 7 I follow Antoniou, Arbour, and Zhao (2013) and measure merger premium as the four-week pre-announcement premium. It is equal to the difference between the initial bid price and the target market price four weeks prior to the initial merger announcement divided by the same target price four weeks prior to the announcement.

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6 Table 2 Correlation of variables.

BSizeD BIndepD E-IndexD AgeD MVA ROA Stock Tender Focus Out State PPS PEI

BSizeD

BIndepD

E-IndexD

1

0.69*** 1

0.16*** 0.19*** 1

AgeD 0.11** 0.07 −0.04 1

MVA

ROA −0.10* −0.11* −0.10 0.02 −0.14*** 1

0.45*** 0.39*** 0.01 0.04 1

Stock −0.07 −0.09** −0.08 −0.01 −0.08 0.04 1

Tender 0.03 0.05 0.06 −0.02 0.07 0.11* −0.19*** 1

Focus −0.05 −0.11 −0.07 0.01 −0.07 −0.01 0.05 −0.03 1

Out State 0.03 0.03 0.11 −0.06 −0.03 0.03 −0.15*** 0.13** −0.01 1

PPS −0.28*** −0.25*** −0.07 0.17*** −0.31*** −0.02 0.13** −0.03 −0.01 0.05 1

PEI 0.06 0.04 −0.09* 0.21*** 0.44*** 0.01 0.09 0.04 −0.06 0.03 0.59*** 1

The table presents the correlation matrix of the explanatory variables. MVA is the book value of total assets minus the book value of equity plus market value of equity. ROA is the accounting return on assets, obtained as the ratio of earnings before interest and taxes to total assets. PEI (in thousands) is the sensitivity of the total value of stock and options held by a CEO to a 1% change in stock price (natural logarithm is used). PEI (in thousands) is the sensitivity of the total value of stock and options held by a CEO to a 1% change in stock price (natural logarithm is used). PPS is calculated as the dollar value change of stock and options held by a CEO per $1000 shareholder return (natural logarithm is used). BSizeD is an indicator variable that is equal to one if the board size is higher than the median. BIndepD is an indicator variable that is equal to one if the percentage of independent directors on the board is higher than the median. E-IndexD is an indicator variable that is equal to one if it is higher than the median. AgeD is an indicator variable if the CEO older than 60. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level. Table 3 Summary statistics on acquirer returns. Panel A

Low PPS

High PPS

N

Mean

Median

N

Mean

Median

Diff in Mean

Diff in Median

Stock deals Cash deals Mix deals Other deals Unknown deals All deals

42 90 63 9 26 230

−3.96%*** −0.01% −2.39%*** −1.38% 1.99%** −1.21%***

1.53%*** 0.33% −2.09%*** −1.19% 2.01%** −0.46%**

57 80 54 12 26 229

1.11% 1.07% 2.17%* −1.75% 1.41% 0.99%**

0.64% 0.38% 0.55% −2.99% 1.53% 0.13%*

5.07%*** 1.08% 4.56%*** −0.37% −0.58% 2.20%***

2.17%** 0.05% 2.64%*** −1.80% −0.48% 0.59%***

Panel B

Low PEI

Stock deals Cash deals Mix deals Other deals Unknown deals All deals Panel C

N

Mean

Median

N

Mean

Median

51 87 55 9 27 229

−2.43%** −0.46% −0.88% −0.37% 2.12%* −0.69%

−0.26% −0.01% −0.51% −1.19% 2.09%** −0.06%

48 83 62 12 25 230

−0.78% 1.51%** 0.25% -2.51% 1.26% 0.45%

−0.78% 0.47%* −1.48% −4.26% 1.21% −0.39%

Mean

Median

Low PPS N

Listed targets Non-listed targets All deals

161 69 230

Panel D

Low PEI

Listed targets Non-listed targets All deals

High PEI Diff in Mean 1.65% 1.97%* 1.13% −2.14% −0.86% 1.14%*

Diff in Median −0.52% 0.48% −0.97% −3.07% −0.88% −0.33%

High PPS

−2.31%*** 1.34% −1.21%***

−1.15%*** 1.55%** −0.46%**

N

Mean

112 117 229

0.20% 1.75%** 0.99%**

Median −0.46% 0.92%** 0.13%*

Diff in Mean 2.51%*** 0.41% 2.20%***

Diff in Median 0.69%** −0.63% 0.59%**

High PEI

N

Mean

Median

N

136 93 229

−1.88%*** 1.06% −0.69%

−0.96%*** 1.71%*** −0.06%

137 93 230

Mean

Median

−0.68% 2.13%** 0.45%

−0.78% 0.47%** −0.39%

Diff in Mean 1.20% 1.07% 1.14%*

Diff in Median 0.18% −1.24% −0.33%

The table summarizes acquirer returns, CAR over a five-day period (-2 to +2) for different PPS and PEI groups. Where low PPS group is one where the pay-for-performance is below the median and high where it is above. Where low PEI group is one where portfolio equity incentive is below the median and high where it is above. T and Wilcoxon signed-rank tests are performed to examine whether the mean or median returns are significantly different between low and high PPS/PEI acquirers. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

define a small acquirer as a firm that has a market asset size in the bottom third of all acquirers, a medium acquirer as one that has a market asset size in the medium third, and a large acquirer as a firm that has an asset size in the top third.

The results are reported in Table 6. The findings indicate that the market perceives that high PPS bidders make better acquisition decisions than low PPS bidders, for both large and small acquirers. When the sample period is divided into the pre- and post-SOX era, I

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7

Table 4 Acquirer Returns – Regression Analysis. 1994–2010

Panel A MVA ROA Stock Tender Focus Out State

1994–2010

1994–07.2002

08.2002–2010

All deals

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

−0.005 [0.053]* 0.084 [0.046]** −0.025 [0.004]*** 0.025 [0.001]*** 0.014 [0.041]** −0.018 [0.017]**

−0.005 [0.081]* 0.089 [0.035]** −0.027 [0.003]*** 0.026 [0.005]*** 0.015 [0.027]** −0.017 [0.022]** 0.011 [0.003]***

−0.001 [0.735] 0.127 [0.022]** −0.025 [0.051]* 0.035 [0.004]*** 0.013 [0.112] −0.015 [0.132] 0.012 [0.003]***

−0.005 [0.237] 0.076 [0.261] −0.021 [0.141] 0.025 [0.625] 0.025 [0.021]** −0.027 [0.031]** 0.008 [0.047]**

−0.004 [0.203] −0.061 [0.349] −0.015 [0.183] 0.019 [0.139] 0.018 [0.056]* −0.015 [0.145] 0.009 [0.011]**

0.003 [0.389] −0.015 [0.876] −0.006 [0.701] 0.031 [0.051]* 0.021 [0.077]* −0.011 [0.478] 0.013 [0.011]**

0.0151 [0.021]** −0.059 [0.481] −0.008 [0.606] 0.032 [0.661] 0.031 [0.047]** −0.038 [0.026]** 0.008 [0.121]

−0.004 [0.271] 0.124 [0.071]* −0.031 [0.052]* 0.034 [0.034]** 0.015 [0.146] −0.016 [0.201] 0.011 [0.011]**

−0.002 [0.605] 0.198 [0.011]** −0.021 [0.242] 0.019 [0.211] −0.022 [0.791] −0.009 [0.494] 0.013 [0.011]**

0.017 [0.137] 0.342 [0.002]*** −0.053 [0.044]** 0.171 [0.021]** 0.032 [0.027]** −0.038 [0.043]** 0.006 [0.319]

PPS PPS D

0.021 [0.005]***

Y Dum I Dum

YES YES

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

21.61% 459

23.09% 459

22.74% 273

18.83% 186

23.25% 266

37.88% 160

20.23% 106

39.63% 193

33.24% 113

42.31% 80

−0.019 [0.044]** 0.011 [0.213] 0.003 [0.751] −0.003 [0.669] −0.004 [0.159] 0.114 [0.008]*** −0.156 [0.100]* 0.024 [0.013]** 0.009 [0.213] −0.011 [0.161]

−0.018 [0.061]* 0.011 [0.203] 0.003 [0.694] −0.007 [0.427] −0.004 [0.192] 0.122 [0.005]*** −0.018 [0.071]* 0.022 [0.017]** 0.009 [0.151] −0.011 [0.173] 0.010 [0.003]***

−0.002 [0.901] 0.013 [0.256] −0.001 [0.883] −0.013 [0.266] −0.005 [0.115] 0.109 [0.035]** −0.017 [0.145] 0.027 [0.009]*** 0.008 [0.342] −0.006 [0.481] 0.012 [0.005]***

−0.039 [0.016]** 0.014 [0.412] −0.001 [0.923] −0.017 [0.222] 0.002 [0.633] 0.167 [0.034]** −0.019 [0.231] 0.042 [0.421] 0.024 [0.041]** −0.021 [0.145] 0.009 [0.056]*

−0.025 [0.026]** 0.091 [0.395] 0.007 [0.461] −0.014 [0.144] −0.002 [0.644] −0.053 [0.353] −0.007 [0.487] 0.016 [0.118] 0.011 [0.143] −0.016 [0.074]* 0.008 [0.018]**

−0.005 [0.729] 0.005 [0.694] −0.001 [0.884] −0.021 [0.100]* −0.003 [0.321] −0.099 [0.136] −0.006 [0.601] 0.023 [0.022]** 0.009 [0.371] 0.008 [0.438] 0.011 [0.018]**

−0.055 [0.006]*** 0.012 [0.561] 0.024 [0.243] −0.014 [0.388] 0.011 [0.193] 0.031 [0.768] 0.019 [0.281] −0.002 [0.971] 0.034 [0.031]** −0.041 [0.019]** 0.009 [0.104]

−0.006 [0.629] 0.011 [0.379] −0.001 [0.993] 0.007 [0.581] −0.004 [0.256] 0.169 [0.001]*** −0.044 [0.005]*** 0.017 [0.231] 0.011 [0.278] −0.002 [0.833] 0.012 [0.001]***

0.017 [0.284] 0.016 [0.312] 0.001 [0.947] −0.001 [0.961] −0.007 [0.096]* 0.165 [0.009]*** −0.032 [0.096]* 0.026 [0.098]* 0.008 [0.482] −0.012 [0.409] 0.012 [0.061]*

−0.015 [0.537] 0.007 [0.749] 0.004 [0.817] 0.018 [0.389] −0.001 [0.996] 0.261 [0.031]** −0.063 [0.034]** 0.097 [0.219] 0.026 [0.123] 0.001 [0.959] 0.005 [0.396]

Panel B BSizeD BIndepD E-IndexD AgeD MVA ROA Stock Tender Focus Out State PPS PPS D

0.016 [0.029]**

Y Dum I Dum

YES YES

YES YES

YES YES

YES NO

YES NO

NO NO

NO NO

YES NO

NO NO

NO NO

R2 N

31.33% 336

32.42% 336

42.11% 207

29.51% 129

18.92% 173

16.38% 108

22.83% 65

29.63% 163

28.11% 99

23.87% 64

The dependent variable is the CAR over a three day period (-1 to + 1). All independent variables are measured one year before the announcement. BSizeD is an indicator variable set equal to one if the board size is higher than the median. BIndepD is an indicator variable set equal to one if the percentage of independent directors on board is higher than the median. E-IndexD is an indicator variable set equal to one if it is higher than the median. AgeD is an indicator variable if the CEO older than 60. MVA is the log of market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable set equal to one if the deal was financed with stock. T Offer is an indicator variable set equal to one if the deal was a tender offer. Focus is an indicator variable set equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state to the bidder. PPS is the log of pay-for-performance sensitivity. PPS D is a dummy variable if the acquirer had above the median log of PPS sensitivity. PEI is the log of portfolio equity incentives. PEI D is a dummy variable if the acquirer had above the median log of PEI sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Panel A includes no governance variables. Panel B includes governance variables for PPS. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

find that PPS is only significant for big firms post-SOX, and is more important for small acquirers pre-SOX. This evidence pre-SOX is consistent with Kamar et al. (2007) and Nicholas (2006), who find that small firms are more subject to fraud and weaker reporting standards. With the passage of SOX, the level of reporting standards

has increased, especially in small firms, decreasing the importance of PPS. The evidence post-SOX suggests that pay for performance is valuable for large firms that previous studies indicate are more prone to hubris. The results in Tables 5 and 6 are qualitatively the same for PEI.

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8 Table 5 Acquirer premium – regression analysis.

BSizeD BIndepD E-IndexD AgeD MVA ROA Stock Tender Focus Out State PPS D Y Dum I Dum 2

R N

Table 6 Acquirer returns: regression analysis (robustness checks).

1994–2010 Listed targets

1994–07.2002 Listed targets

08.2002–2010 Listed targets

0.151 [0.262] −0.208 [0.108] −0.122 [0.291] 0.179 [0.147] −0.037 [0.393] −0.505 [0.391] −0.166 [0.222] 0.101 [0.387] 0.115 [0.229] 0.101 [0.367] −0.233 [0.033]**

0.298 [0.198] −0.258 [0.259] −0.267 [0.151] 0.228 [0.244] −0.061 [0.395] 1.614 [0.237] −0.457 [0.039]** 0.012 [0.948] 0.061 [0.691] 0.01 [0.961] −0.043 [0.791]

0.033 [0.859] −0.274 [0.131] −0.126 [0.505] 0.356 [0.101] −0.048 [0.446] −2.497 [0.781] 0.017 [0.973] 0.415 [0.059]* 0.139 [0.366] 0.168 [0.342] −0.413 [0.048]**

YES YES

YES YES

YES YES

53.46% 207

59.99% 108

70.61% 99

The dependent variable is the merger premium. The merger premium is defined as the four-week pre-announcement premium. It is equal to the difference between the initial bid price and the target market price four weeks prior to the initial merger announcement divided by the same target price four weeks prior to the announcement. BSizeD is an indicator variable that is equal to one if the board size is higher than the median. BIndepD is an indicator variable that is equal to one if the percentage of independent directors on board is higher than the median. E-IndexD is an indicator variable that is equal to one if it is higher than the median. AgeD is an indicator variable if the CEO older than 60. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable that is equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had above the median log of pay-forperformance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets and are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

5. Long-term performance Next, I examine the long-term performance of acquirers using both the calendar-time portfolio approach and buy and hold (BHAR) methodology. The reason I use the two approaches is that each methodology has its pros and cons, and there is no consensus in the literature as to which method is preferred. The advantage of the BHAR method is that it tends to capture the returns that a typical investor can earn (Kothari & Warner, 1996; Lyon, Barber, & Tsai, 1999). The main disadvantage of this approach is that is more sensitive to the cross-sectional dependence among the sample firms (Fama, 1998; Mitchell & Stafford, 2000). Additionally, Mitchell and Stafford (2000) argue that BHAR methodology increases the misspecified model problem. In order to mitigate these problems, researchers recommend the use of calendar-time methodology (Fama, 1998). The disadvantage of this approach, however, is less power to detect abnormal performance as it averages over months of “hot” and “cold” event activity (Mitchell & Stafford, 2000). Since both event time and

1994–2010 All deals

1994–07.2002 All deals

08.2002–2010 All deals

0.027 [0.158] 0.003 [0.901] 0.018 [<0.001]*** 0.004 [0.511] 0.011 [0.047]** 0.086 [0.041]** −0.025 [0.004]*** 0.024 [0.011]** 0.013 [0.043]** −0.016 [0.034]**

0.043 [0.091]* 0.016 [0.523] 0.019 [0.008]*** 0.001 [0.986] 0.007 [0.369] −0.053 [0.408] −0.015 [0.171] 0.015 [0.258] 0.018 [0.061]* −0.014 [0.183]

−0.016 [0.647] −0.038 [0.268] 0.009 [0.359] 0.005 [0.451] 0.013 [0.061]* 0.139 [0.048]** −0.031 [0.061]* 0.031 [0.051]* 0.018 [0.100]* −0.016 [0.201]

Y Dum I Dum

YES YES

YES YES

YES YES

R2 N

24.61% 459

25.51% 266

41.45% 193

Medium Large PPS * Small PPS * Medium PPS * Large ROA D Stock T Offer Focus Out State

The dependent variable is the CAR over a three day period (−1 to +1). All independent variables are measured one year prior to the announcement. Small, Medium, and Large are indicator variables and are equal to one if an acquirer is in a low, mid- or top third based on the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS is the log of pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

calendar-time methodology have their pros and cons, I will utilize both methods. I compute the five-year buy and hold returns for high and low PPS firms.8 I calculate the buy and hold returns as the difference between buy and hold returns of the sample firm and the benchmark firms. I utilize size and book-to-market matched control group as benchmarks. Consistent with the methodology recommended by Barber and Lyon (1997) for constructing size and book-to-market matched portfolios, I select a subset of matching firms from all public companies (excluding the sample firms) that have the market value of equity within 20% of the market equity value of the sample firm in the month of the merger. Next, this subset is then ranked according to book-market ratios measured in the merger year. Therefore, the size and book-to-market matched firm is the firm with market value within 20% and its book-to-market ratio closest to the sample merger firm. As such, I determine the buy and hold abnormal return (BHAR) for the sample of PPS firms as: BHAR(t1 , t2 ) =

t2  t=t1

[(1 + Rit )] −

t2 

[(1 + Rmt )]

(2)

t=t1

where Rit is the return of firm i on date t and Rmt is the return on a benchmark, such as the size and book-to-market control sample

8

Three year BHAR results are shown in Table A2 of the Appendix.

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Table 7 Acquirer Buy-and-Hold Returns – Regression Analysis. 1994–2010

1994–07.2002

08.2002–2010

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

0.016 [0.688] 1.088 [0.087]* 0.019 [0.882] −0.093 [0.513] 0.258 [0.012]** 0.041 [0.732] 0.276 [0.026]**

0.027 [0.569] 0.674 [0.408] −0.071 [0.673] −0.088 [0.591] 0.371 [0.004]*** −0.011 [0.944] 0.326 [0.028]**

0.009 [0.901] 1.054 [0.312] 0.286 [0.192] −0.632 [0.431] 0.041 [0.805] 0.035 [0.858] 0.145 [0.497]

0.037 [0.542] 1.598 [0.129] −0.031 [0.874] 0.086 [0.683] 0.411 [0.011]** −0.038 [0.824] 0.312 [0.100]*

0.065 [0.369] 0.915 [0.559] −0.037 [0.881] 0.068 [0.783] 0.521 [0.008]*** −0.075 [0.739] 0.464 [0.044]**

0.091 [0.453] 1.127 [0.436] 0.401 [0.153] −1.885 [0.144] 0.254 [0.323] −0.302 [0.298] 0.001 [0.984]

0.015 [0.757] 0.815 [0.336] 0.313 [0.121] −0.407 [0.047]** −0.036 [0.783] 0.264 [0.105] 0.359 [0.021]**

-0.052 [0.297] 0.901 [0.244] 0.456 [0.061]* −0.424 [0.042]** −0.011 [0.939] 0.155 [0.412] 0.333 [0.061]*

-0.071 [0.461] 0.101 [0.947] −0.078 [0.834] −0.583 [0.565] −0.289 [0.164] 0.673 [0.011]** 0.499 [0.052]*

Y Dum I Dum

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

22.38% 459

31.45% 273

19.99% 186

27.55% 266

42.11% 160

24.94% 106

36.41% 193

19.19% 113

42.97% 80

MVA ROA Stock Tender Focus Out State PPS D

The dependent variable is the BHAR over a five year period. The benchmark return adjustments are based on style-matched firms according to size and the book-to-market ratio. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had high pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level. Table 8 Acquirer factor regressions. 1994–2010

1994–07.2002

08.2002–2010

Variable

3-Factor

5-Factor

3-Factor

5-Factor

3-Factor

5-Factor

Intercept

0.0038 [0.083]* 0.148 [0.003]*** 0.298 [<0.001]*** −0.338 [<0.001]***

0.0041 [0.082]* 0.131 [0.016]** 0.301 [<0.001]*** −0.348 [<0.001]*** −0.037 [0.397] 0.016 [0.766]

0.0052 [0.091]* 0.123 [0.133] 0.286 [0.008]*** −0.425 [0.002]***

0.0047 [0.147] 0.273 [0.001]*** 0.273 [0.001]*** −0.429 [0.003]*** 0.037 [0.525] 0.036 [0.677]

0.0053 [0.092]* 0.066 [0.415] 0.394 [0.011]** −0.202 [0.136]

0.0049 [0.115] −0.017 [0.842] 0.403 [0.008]*** −0.201 [0.164] −0.164 [0.011]** 0.077 [0.324]

29.89%

28.49%

34.72%

34.11%

7.37%

11.11%

MKTRF SMB HML UMD LIQ Adj R2

Factor regressions for return difference between acquirers with high PPS and low PPS. The calendar-time period considered in this analysis represents a maximum of 60 months for each acquisition. The dependent variable is the difference in returns between portfolios of high and low PPS: RHPPS − RLPPS = a + b(Rmt − Rft ) + cSMBt + dHMLt + eUMDt + fLIQt + et where Rmt is the return on the CRSP value-weighted market index in month t, Rft is the monthly return on three month Treasury bill in month t, SMBt is the difference in the returns of small stock and big stocks in month t. HMLt is the difference in returns of high book-to-market stocks and low book-to-market stocks in month t. UMDt is the momentum factor in month t, and LIQt is the liquidity level in month t. P-values are reported in brackets. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

on the same month t. The date t1 is the first trading month after the merger and t2 is the end of the time window.9 An important question to consider is whether too equally or value-weight portfolio returns. The results reported are on the basis of equally weighted portfolio returns. Brav, Geczy,

9 About 20% of the sample firms delist following the acquisition. The exclusion of these firms does not change the results. I provide these results in Table A1 of the Appendix. I follow Gao and Jain (2011) in that if a firm delists prior to its five-year anniversary, I use the delisting date as the ending date for the buy-and-hold period.

and Gompers (2000) suggest that equal weighting is more appropriate when the goal is assessing the managerial implications of potential stock market mispricing, I do however report the value weighted results as a robustness check (Table A3 of the Appendix). In addition to address potential microstructure noise, I implement prior-period gross return weighted portfolios of Asparouhova, Bessembinder, and Kalcheva (2013) (Table A4 of the Appendix). Weighting methodology does not affect results. In the calendar-time approach, I develop a time series of monthly portfolio returns and estimate the three-factor model of

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Fama and French (1993). Further, since momentum and liquidity have a significant influence on returns, I add them to the Fama–French (1993) model. Specifically, the Carhart (1997) and Pastor and Stambaugh (2003) liquidity factors are added. Factor premiums have been obtained from the Wharton Research Data Services (WRDS) website. The dependent variable is the difference in returns between portfolios of high and low PPS firms. I estimate the five-factor model as follows (over 60 months for each acquisition):10

Overall, analysis of post-merger performance, based on both BHAR methodology and factor regression models, provide mixed evidence of superior long run performance for acquirers with high incentive pay. The BHAR methodology points to superior returns exhibited by the high PPS firms (irrespective of the weighting method) while the calendar methodology yields the results insignificant when the 5-factor model is used in the pre and post SOX analysis.

RHPPS − RLPPS = a + b(Rmt − Rft ) + cSMBt + dHMLt + eUMDt

6. Conclusion

+ fLIQt + et

(3)

where RHPPS is the monthly return of the calendar-time portfolio for high PPS firms in month t, RLPPS is the monthly return of the calendar-time portfolio for low PPS firms in month t, Rmt is the return on the CRSP value-weighted market index in month t, Rft is the monthly return on three month Treasury bills in month t, and SMBt is the difference in the returns of small stocks and big stocks in month t. HMLt is the difference in returns of high bookto-market stocks and low book-to-market stocks in month t. UMDt is the momentum factor in month t, while LIQt is the liquidity level in month t. The intercept (a) provides an estimate of the difference in returns between the portfolios of two types of firms. A positive and significant intercept can be interpreted as evidence to support the argument that firms with high PPS outperform those with low PPS in the long run. Table 7 reports the regression results where the five-year BHAR is the dependent variable. After controlling for other relevant merger characteristics, the regression results indicate that acquirers with high PPS earn abnormal returns that are 27.6% higher than acquirers with low PPS (5.52% annualized). When I decompose the acquisitions into pre- and post-SOX, I find that the PPS effect is driven by listed target acquisitions. Acquirers of listed targets with high PPS earn 46.4% higher returns (9.28% annualized) in the pre-SOX period and 33.3% higher returns (6.66% annualized) in the post-SOX period. These findings make sense in light of previous evidence highlighting the perils of listed target acquisitions. In particular, if acquirers of listed targets are more prone to overpayment, as compared to acquirers of unlisted targets, then pay-for-performance should play a more important role in influencing listed target acquisitions. The results suggest that this is the case. Table 8 reports the results of calendar time series regressions based on a three and five factor model. The average abnormal monthly return for high PPS firms vs. low PPS firms is 0.38% for three factor model and 0.41% for the five factor model, both significant at the 10% level. When we decompose the sample into pre-and-postSOX, the results are only significant for the 3-factor model at the 10% level.

This paper highlights the importance of pay-for-performance sensitivity in affecting wealth gains for acquirers. I find that acquirers with high pre-merger pay-for-performance sensitivity have significantly higher stock returns around the time of the acquisition announcements and pay lower merger premiums. The importance of incentive based compensation is significant in both the preand post-SOX periods. Thus, an increase in regulation has not diminished the importance of pay-for-performance in mergers. The significance of pay-for-performance goes beyond the regular factors that have been found important in the acquisition literature as determinants of returns (e.g., tender offers or focusincreasing deals). It transcends governance factors, such as board independence, as well. Pay-for-performance affects small and large acquirers and acquirers of listed targets. I attribute the importance of pay-for-performance in listed target acquisitions to the literature that these mergers are more likely to be plagued by agency problems. I explain the importance of pay-for-performance for small acquirers due to their weaker accounting practices, especially pre-SOX. The significance of pay-for-performance for large acquirers indicates that it may be possible to increase shareholder value through incentive pay in these firms. In sum, I find that pay-for-performance provides managers with strong incentives to undertake value-maximizing acquisitions. Acknowledgements I thank Ninon Sutton, and seminar participants at the University of South Florida, the 2013 Midwest Finance Association Annual Meeting, 2013 Eastern Finance Association Annual Meeting, 2013 World Finance Conference, 2013 Southern Finance Association Annual Meeting and 2013 Financial Management Association Annual Meeting. I also thank the organizing Committee of the 2013 Southwestern Finance Association Annual Meeting for selecting this paper for presentation although I was unable to attend. Appendix A. See Tables A1–A4.

10 The factor premium have been obtained from Wharton Research Data Services (WRDS) website. I follow the approach of Loughran and Ritter (1995) where the dependent variable is the difference in returns between portfolios of two types of firms in order to compare the difference in performance between them.

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Table A1 Delisted firms are excluded. 1994–2010

1994–07.2002

08.2002–2010

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

−0.011 [0.831] 1.217 [0.116] 0.005 [0.971] −0.159 [0.355] 0.381 [0.001]*** 0.082 [0.547] 0.298 [0.036]**

0.018 [0.737] 0.471 [0.632] −0.129 [0.496] −0.139 [0.454] 0.461 [0.002]*** 0.013 [0.935] 0.322 [0.055]*

−0.081 [0.483] 1.206 [0.416] 0.472 [0.115] −1.134 [0.401] 0.425 [0.112] −0.067 [0.804] 0.076 [0.823]

0.017 {0.792] 1.211 [0.298] 0.001 [0.973] −0.048 [0.831] 0.472 [0.011]** 0.000 [0.998] 0.321 [0.126]

0.049 [0.553] 0.675 [0.699] −0.073 [0.794] −0.052 [0.853] 0.582 [0.011]** −0.036 [0.891] 0.356 [0.174]

0.093 [0.472] 0.687 [0.671] 0.467 [0.146] −1.886 [0.167] 0.307 [0.295] −0.321 [0.325] 0.032 [0.926]

−0.001 [0.871] 2.887 [0.013]** 0.266 [0.301] −0.589 [0.041]** 0.189 [0.267] 0.204 [0.302] 0.401 [0.019]**

−0.056 [0.339] 0.764 [0.388] 0.489 [0.083]* −0.402 [0.101] 0.071 [0.682] 0.211 [0.319] 0.346 [0.087]*

−0.146 [0.261] 1.958 [0.367] −0.293 [0.546] 0 [0.999] −0.321 [0.271] 0.724 [0.047]** 0.583 [0.084]*

Y Dum I Dum

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

27.32% 362

39.98% 219

41.38% 143

27.51% 232

41.15% 139

24.21% 93

56.29% 130

37.01% 80

41.46% 50

MVA ROA Stock Tender Focus Out State PPS D

The dependent variable is the BHAR over a five year period (firms that have delisted are excluded). The benchmark return adjustments are based on style−matched firms according to size and the book-to-market ratio. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had high pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level. Table A2 BHARs over a three year period. 1994–2010

1994–07.2002

08.2002–2010

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

0.067 [0.059]* 0.761 [0.181] 0.058 [0.617] −0.117 [0.364] 0.113 [0.222] −0.089 [0.393] 0.268 [0.016]***

0.071 [0.131] 0.296 [0.706] −0.191 [0.247] −0.196 [0.217] 0.153 [0.223] −0.101 [0.487] 0.441 [0.002]**

0.093 [0.146] 1.123 [0.192] 0.523 [0.001]** −0.301 [0.653] 0.047 [0.751] −0.109 [0.507] −0.041 [0.832]

0.097 [0.081]* 1.311 [0.169] −0.011 [0.945] 0.028 [0.883] 0.129 [0.367] −0.067 [0.685] 0.305 [0.083]*

0.102 [0.165] −0.388 [0.806] −0.294 [0.246] −0.181 [0.467] 0.207 [0.293] −0.023 [0.917] 0.632 [0.001]***

0.173 [0.056]* 1.583 [0.147] 0.677 [0.001]*** −1.599 [0.101] 0.102 [0.592] −0.326 [0.134] −0.362 [0.134]

0.041 [0.372] 0.711 [0.346] 0.331 [0.065]* −0.399 [0.029]** 0.032 [0.785] 0.024 [0.865] 0.285 [0.038]**

−0.028 [0.531] 1.014 [0.144] 0.522 [0.016]** −0.377 [0.043]** −0.011 [0.935] 0.001 [0.996] 0.254 [0.113]

−0.011 [0.918] −0.543 [0.705] 0.036 [0.915] −0.489 [0.613] −0.187 [0.342] 0.429 [0.089]* 0.492 [0.045]**

Y Dum I Dum

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

20.34% 459

27.18% 273

18.68% 186

22.63% 266

34.07% 160

31.97% 106

37.76% 193

22.18% 113

19.57% 80

MVA ROA Stock Tender Focus Out State PPS D

The dependent variable is the BHAR over a three year period. The benchmark return adjustments are based on style-matched firms according to size and the book-to-market ratio. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had high pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

Please cite this article in press as: Krolikowski, M.W. Incentive pay and acquirer returns – The impact of Sarbanes–Oxley. The Quarterly Review of Economics and Finance (2015), http://dx.doi.org/10.1016/j.qref.2015.06.007

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12 Table A3 Value-weighted BHARs. 1994–2010

1994–07.2002

08.2002–2010

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

0.061 [0.071]* 0.876 [0.128] 0.037 [0.715] −0.127 [0.318] 0.102 [0.267] −0.081 [0.417] 0.251 [0.021]**

0.072 [0.103] 0.385 [0.625] −0.204 [0.203] −0.195 [0.214] 0.138 [0.267] −0.098 [0.483] 0.408 [0.003]***

0.081 [0.204] 1.326 [0.131] 0.531 [0.004]*** −0.522 [0.423] 0.057 [0.701] −0.121 [0.467] −0.059 [0.733]

0.091 [0.091]* 1.409 [0.147] −0.026 [0.871] 0.031 [0.869] 0.121 [0.401] −0.053 [0.734] 0.285 [0.096]*

0.105 [0.146] −0.467 [0.772] −0.297 [0.239] −0.158 [0.524] 0.202 [0.306] −0.018 [0.934] 0.594 [0.004]***

0.161 [0.072]* 1.788 [0.107] 0.689 [0.011]** −1.598 [0.063]** 0.093 [0.632] −0.314 [0.156] −0.374 [0.132]

0.035 [0.398] 0.807 [0.289] 0.301 [0.074]* −0.431 [0.017]** 0.026 [0.818] 0.012 [0.924] 0.274 [0.033]**

−0.026 [0.521] 1.129 [0.097]* 0.417 [0.038]** −0.416 [0.023]** −0.011 [0.934] −0.017 [0.911] 0.261 [0.082]*

−0.029 [0.748] −0.606 [0.675] 0.061 [0.855] −0.551 [0.601] −0.181 [0.354] 0.428 [0.081]* 0.485 [0.042]**

Y Dum I Dum

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

19.52% 459

25.94% 273

18.48% 186

21.76% 266

32.01% 160

32.14% 106

39.16% 193

22.32% 113

19.55% 80

MVA ROA Stock Tender Focus Out State PPS D

The dependent variable is the value-weighted BHAR over a five year period. The benchmark return adjustments are based on style-matched firms according to size and the book-to-market ratio. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had high pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

Table A4 Weighted (prior period). 1994–2010

1994–07.2002

08.2002–2010

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

All deals

Listed targets

Non-listed targets

0.052 [0.086]* 0.822 [0.129] 0.011 [0.932] −0.178 [0.085]* 0.075 [0.338] −0.069 [0.549] 0.266 [0.004]***

0.063 [0.145] 0.861 [0.251] −0.174 [0.314] −0.249 [0.061]* 0.081 [0.473] −0.054 [0.734] 0.362 [0.003]***

0.041 [0.493] 0.581 [0.495] 0.381 [0.071]* −0.314 [0.461] −0.018 [0.878] −0.051 [0.785] 0.151 [0.325]

0.103 [0.031]** 0.909 [0.334] −0.076 [0.654] −0.047 [0.774] 0.108 [0.376] −0.063 [0.705] 0.371 [0.011]**

0.121 [0.089]* 0.326 [0.831] −0.287 [0.247] −0.247 [0.216] 0.119 [0.506] 0.001 [0.998] 0.575 [0.005]***

0.162 [0.041]** 0.821 [0.454] 0.518 [0.019]** −1.199 [0.026]** −0.014 [0.925] −0.286 [0.219] −0.087 [0.681]

0.021 [0.623] 0.894 [0.221] 0.363 [0.093]* −0.442 [0.001]*** 0.011 [0.918] 0.051 [0.746] 0.247 [0.045]**

−0.036 [0.357] 1.256 [0.059]* 0.467 [0.068]* −0.438 [0.001]*** 0.001 [0.996] 0.015 [0.973] 0.239 [0.089]*

−0.032 [0.731] 0.304 [0.823] 0.124 [0.776] −0.405 [0.608] −0.207 [0.257] 0.465 [0.091]* 0.499 [0.029]**

Y Dum I Dum

YES YES

YES YES

YES NO

YES YES

YES YES

YES NO

YES YES

YES NO

YES NO

R2 N

24.39% 459

27.49% 273

19.39% 186

28.58% 266

34.89% 160

30.18% 106

42.63% 193

25.99% 113

31.21% 80

MVA ROA Stock Tender Focus Out State PPS D

The dependent variable is the BHAR over a five year period using the Asparouhova et al. (2013) methodology where returns are weighted by prior period gross return. The benchmark return adjustments are based on style−matched firms according to size and the book-to-market ratio. MVA is the log of the market value of assets. ROA is EBIT over total assets. D Stock is an indicator variable that is equal to one if the deal was financed with stock. T Offer is an indicator variable that is equal to one if the deal was a tender offer. Focus is an indicator variable that is equal to one if the merger was focus-increasing. Out State is a dummy variable equal to one if the target was listed in a different state from the bidder. PPS D is a dummy variable if the acquirer had high pay-for-performance sensitivity. Year dummies (Y Dum) and industry dummies (I Dum) are employed. P-values are reported in brackets are adjusted for heteroscedasticity. Bold indicates significant variables. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

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Please cite this article in press as: Krolikowski, M.W. Incentive pay and acquirer returns – The impact of Sarbanes–Oxley. The Quarterly Review of Economics and Finance (2015), http://dx.doi.org/10.1016/j.qref.2015.06.007