CEO power, M&A decisions, and market reactions

J. of Multi. Fin. Manag. 21 (2011) 257– 278

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CEO power, M&A decisions, and market reactions

Shantanu Duttaa,∗, Kenneth MacAulayb,1, Samir Saadic,2

a University of Ontario Institute of Technology, Faculty of Business and Information Technology, 2000 Simcoe Street North,Oshawa, ON, Canada L1H 7K4b St. Francis Xavier University, Gerald Schwartz School of Business and Information Systems, 3090 Martha Drive,Antigonish, NS, Canada B2G 2W5c Queen’s University, Queen’s School of Business, 143 Union Street, Kingston, ON, Canada K7L 3N6

a r t i c l e i n f o

Article history:Received 5 January 2010Accepted 18 July 2011Available online 4 August 2011

JEL classification:G14G34

Keywords:Mergers and acquisitionsCEO powerEvent studyCorporate governanceLong-term performance

a b s t r a c t

In this study we examine the relationship between CEO power,corresponding acquisition activities and market reactions to merg-ers and acquisitions (M&A) announcements with a Canadian M&Adataset (1997–2005). We use CEO excess pay as a proxy for CEOpower. Our empirical results show that the market reactions toM&A announcements are not related to CEO power. It impliesthat powerful CEOs do not necessarily make value destroyingacquisitions. Our results further show that CEO power levels aresignificantly higher for acquiring firms compared to the CEOs ofnon-acquiring firms. In other words, CEOs with more relative powermake more acquisitions. Such acquisitions will increase the size ofthe firm and will allow CEOs to demand a higher compensation levelfor managing larger asset pools and to derive higher performanceincentives that are also generally tied to firm size.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Mergers and acquisitions (M&A) activities are risky in nature and demand more time and effortfrom CEOs. However, as it appears from the ever increasing level of M&A activities, CEOs are not

∗ Corresponding author. Tel.: +1 905 721 8668.E-mail addresses: [email protected], [email protected] (S. Dutta), [email protected] (K. MacAulay),

[email protected] (S. Saadi).1 Tel.: +1 902 867 5548.2 Tel.: +1 613 767 6476.

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258 S. Dutta et al. / J. of Multi. Fin. Manag. 21 (2011) 257– 278

apprehensive about such challenging activities. Such an observation leads to a number of relevantquestions: What motivates a CEO to make an acquisition? Does a CEO obtain significant personal benefitsthrough an acquisition? How does he/she receive any personal benefits? In this study, we focus on thesequestions by examining the relationship between CEO power, corresponding acquisition activities andmarket reactions to M&A announcements.

Extant literature shows that almost all gains from merger activities accrue to target shareholdersand zero or negative gains to acquirer shareholders. This leads to a natural question: why do firmsmake acquisitions? Roll (1986) attributed such actions to CEO “hubris”. He argued that CEOs with morehubris make acquisitions and overbid for targets. In the process they transfer most of the benefits froman acquisition to the target shareholders. Hietala et al. (2003) have also related acquisition decisionsto CEO hubris. In a recent paper, Malmendier and Tate (2008) explore the relationship between CEOoverconfidence, acquisition decisions and corresponding market reactions. They consider CEOs’ optionexercise behavior to distinguish between “overconfident” and “rational” CEOs. Overconfident CEOshold company stock options until the last year before exercise, whereas rational CEOs exercise theirstock options well before expiration. Consistent with earlier studies, they find that overconfidentCEOs are likely to undertake more acquisition activities and conduct diversifying acquisitions. Further,Malmendier and Tate (2008) report that outside investors react more negatively to the announcementof a bid by an “overconfident CEO” than by a “rational CEO”.

Although the CEO hubris or overconfidence hypothesis attempts to explain the acquisition decisionby CEOs, it does not explore or explain the economic benefits that accrue to CEOs through an acquisitionevent. Few studies have investigated the level of CEO compensation and bonuses after mergers. Blissand Rosen (2001) find that CEO compensation increases after bank mergers even if the market reactsnegatively to such deals. Hartzel et al. (2004), alternatively, examined the compensation of the CEOsof the target firms. Grinstein and Hribar (2004) examine the incentive compensation related to M&Adeals. They find that “M&A bonuses are positively related to managerial power: managers who havemore board power are likely to get substantially higher bonuses, to engage in larger deals, and to havesubstantially smaller announcement returns” (p. 121).

In summary, earlier studies have exposed the irrational behavior (hubris or overconfidence hypoth-esis) or the opportunistic behavior (deriving higher transaction benefits such as greater M&A bonuses)of CEOs. However, such views of CEO behavior with respect to M&A decisions look puzzling for thefollowing reasons: First, in an effective and competitive labor market, CEOs are likely to be concernedabout their reputation. Therefore, they are not likely to make an acquisition which might hurt theirreputation in the future without rational analysis. Second, most of the CEOs have long-term perfor-mance incentives (such as stock options and profit sharing schemes) in a firm. Therefore, they mightnot be interested just in short-term M&A bonuses. Rather, they might be interested in acquiring a goodtarget, which will give long-term benefits to them (in terms of a total CEO compensation package).

Further, we argue that a CEO’s M&A decisions should be viewed in the context of her/his totalpay-off structure (total compensation package) and relative board power. Only a powerful CEO canimpose her/his decision on a firm that might be used to maximize her/his own benefits. However, thedecision of a powerful CEO to acquire a target is dependent on the alternative opportunities availableto her/him. A CEO can maximize her/his benefit in two ways: (i) by increasing the size of the firm(for example by acquisitions) thus demanding higher compensation for managing larger asset pools(Core et al., 1999),3 or (ii) by stealing or expropriation (La Porta et al., 2002). We argue that if thecost of stealing is quite high, powerful CEOs will be inclined to make acquisitions to increase thesize of the firm to maximize total personal benefits. Extant literature has shown that the level ofCEO compensation has a significant positive relationship with firm size (Core et al., 1999; Duffhuesand Kabir, 2008). Therefore, CEOs might to resort to M&A activities as a means to increase their owncompensation level.

In this study, we investigate the role of CEO power in the M&A decision making process and thecorresponding market reactions in two steps: (i) by developing a theoretical model, and (ii) by testing

3 CEOs benefits from stock options are also indirectly related to firm size. Conventionally, CEOs managing larger investmentsare likely to get more stock options. In the Canadian context, Chourou et al. (2008) have shown a significant positive relationshipbetween the stock option mix and the firm size.

S. Dutta et al. / J. of Multi. Fin. Manag. 21 (2011) 257– 278 259

the theoretical arguments with empirical investigations. In the theoretical model, we identify a CEO’stotal benefit as a function of her/his relative board power. We show that as CEOs become more pow-erful, the incentive for stealing or expropriation decreases in countries with better legal systems andprotection. Therefore, more powerful CEOs are likely to engage into more M&A activities to increasethe size of a firm in a country with a stronger legal system. Subsequently, we carry out detailed empir-ical investigations with an out-of-sample dataset that involves all completed Canadian M&A dealsfrom 1997 to 2005. We use CEO excess pay as a proxy for CEO power. Our empirical results show thatthe market reactions to M&A announcements are not related to CEO power. It implies that powerfulCEOs do not necessarily make value destroying acquisitions. Our results further show that CEO powerlevels are significantly higher for acquiring firms compared to the CEOs of non-acquiring firms. Inother words, CEOs with more relative power make more acquisitions. Such acquisitions will increasethe size of the firm and will allow CEOs to demand a higher compensation level for managing largerasset pools. Our results are robust to the inclusion of different proxies of CEO power. In the spirit ofCore et al. (1999), we use CEO excess pay as a proxy for CEO power and as a robustness check, we useCEO pay slice (Bebchuk et al., 2009, 2011) as a proxy for CEO power.4

Our study contributes to the literature in several ways. First, in this paper, we examine Canadianacquiring firms and thus present out-of-sample evidence with a different developed country capitalmarket. We take the view that differences in the size of the economy and in the capital market andregulatory environment may lead to different results. Most of the prior studies focus on USA and UKacquisition markets, where most of the M&A deals take place. However, the Canadian M&A market isalso large and vibrant. Crosbie & Co., a Toronto-based merchant bank, report that the total transactionvalue of the 1941 announced deals during 2007 was $370 billion. This was a record in CanadianM&A history, with 60 transactions in excess of $1 billion. By examining Canadian acquiring firms, wepresent out-of-sample evidence for a different developed country capital market. Dutta and Jog (2009)identify many important differences between the Canadian and the US M&A markets5 and show thatthe market reacts differently to the M&A announcements in each of the respective countries.

“. . .For example, one of the most important differences in these two countries is the form ofM&A antitrust regulation. Antitrust regulation is stricter, more developed, and less favorable toacquiring firms in the U.S. than in Canada. While in the U.S., courts may proceed against acquisi-tions on market concentration grounds alone, section 92(2) of the Competition Act (of Canada)expressly prohibits a finding of merger harm – i.e., a substantial lessening of competition – solelyon the basis of concentration or market share (Green, 1993, p. 193). Furthermore, compared toCanadian firms, U.S. firms more frequently adopt anti-takeover strategies such as shareholder’srights plan, poison pills, and shark repellent. In Canada, anti-takeover plans are typically ren-dered ineffective by the securities commission(s) at the request of the bidder (Brealey et al.,2006). Also, cash payments represent the majority of Canadian acquisitions, whereas stock pay-ments represent the majority of U.S. deals, (Eckbo and Thorburn, 2000; Loughran and Vijh, 1997)and thus have implications on the choice of the target. Overall, such differences between the U.S.and Canadian markets could affect the target selection process, the propensity of M&A activities,the price paid, and, more importantly, post-acquisition performance (Dutta and Jog, 2009; pp.1400–1401).”

In addition to the above characteristics, Institutional Shareholder Services (ISS) gives Canada thehighest corporate governance rating (Doidge et al., 2007). A different corporate governance environ-ment and a high governance rating make Canada a suitable case for our study. In particular, a Canadiansample is deemed to be a good fit because, as part of our study, we examine the impact of strong legaland governance environment on CEO power and related activities.

Second, we focus on CEO power and its impact on market reactions to M&A announcements. Most ofthe earlier studies focus on CEO overconfidence and show that the market generally reacts negatively

4 We thank an anonymous referee for proposing this proxy.5 In addition, unlike their U.S. counterparts, Canadian firms tend to have a more concentrated ownership structure and family

control (e.g., Morck et al., 2000; Bozec, 2007).


to the M&A announcement by firms managed by an overconfident CEO (Malmendier and Tate, 2008;Doukas and Petmezas, 2007). Our perspective on CEO power is different from that of CEO hubris oroverconfidence. We view CEO power as the level of control of a CEO over the board. A firm with weakercorporate governance will have a more powerful CEO. We argue that any appropriate proxy for CEOpower should have a relationship with the CEO’s compensation package. Accordingly, we use CEOexcess pay as a proxy for CEO power. Third, we check whether the market reactions to M&A dealannouncements are influenced by the power of a firm’s CEO (i.e. CEO excess pay). We find that themarket reactions to M&A announcements are not related to CEO power. In other words, the marketparticipants do not view the acquisitions by high-power CEOs negatively and do not anticipate anynegative effect on long-term operating performance of the acquiring firms. In order to validate thisview, we examine the long-term operating performance of acquisitions with respect to different levelsof CEO power and find that acquisitions made by high-power CEOs do not result in any significantdeterioration in the acquirer’s long-term operating performance.

The rest of this paper proceeds as follows. Section 2 presents the theoretical model and relevanthypotheses. Section 3 presents our sample and is followed by the discussion on CEO power in Section4. Section 5 presents the empirical results on hypotheses testing and Section 6 presents the robustnesstests. Section 7 concludes the paper.

2. The model6

In this section, we present a model for the total benefits (U) accrued to a CEO of a firm. In general, aCEO has two components in the compensation package: (i) a cash salary component which is stronglyrelated to the firm size (or total investment size) (Core et al., 1999; Grinstein and Hribar, 2004), and(ii) a performance based incentive component. A risk-averse CEO would like to convince the board ofthe need for a weaker incentive based compensation contract and a higher base salary (Bebchuk andFried, 2005). Beside base compensation and incentive based compensation, a CEO can derive personalbenefits through expropriation (such as direct theft, transfer pricing, non-arms-length dealings withother firms, exorbitant perks). However, such expropriation will come at a cost (La Porta et al., 2002).In a country with a strong legal and governance environment, such a cost could be quite high whichin turn may prohibit extensive ‘expropriation’ or ‘stealing’ by CEOs.

A number of earlier studies have extensively examined the determinants of CEO compensation (forexample, see Core et al., 1999; Grinstein & Hribar, 2004). Yet, it is not clear how a powerful CEO wouldmanage and manipulate a firm’s investments in order to increase his/her own benefits. In line withBebchuk and Fried (2005), we conjecture that managerial power would impact the CEO pay packageand other benefits. Our key argument is that firm size (or the investment size) is dependent on CEOpower.

Below, we present a total benefit function for a CEO in Eq. (1) that includes CEO power and itsdynamics with firm investment. The total CEO’s benefit function (U) is:

CEO’s benefit (U) = benefits attributed to managing the firm’s assets + benefits attributed to firmperformance + benefits attributed to stealing or expropriation − cost of expropriation.

Alternatively,

U = ˛I + (1 − P)(1 − s)RI + sRI − c(k, s)RI + sPI − c(k, s)PI (1)

U = ˛I + RI − PRI + sPRI − c(k, s)RI + sPI − c(k, s)PI (1a)

We assume that the firm has a total investment of ‘I’ (i.e. the size of the firm) and this investmentlevel is dependent on the CEO’s relative power (‘P’) on the board. A weak board will give more powerto a CEO. ‘R’ is the firm’s return on investment. ‘s’ represents the expropriation of wealth by the CEO.c(k, s) is the share of profits s/he loses when ‘s’ is expropriated. Both c(k, s) and ‘s’ are expressed in

6 This model is inspired by La Porta et al. (2002, pp. 1149–1153). In their model, they considered “a firm fully controlled bya shareholder, called the entrepreneur” (p. 1149). We have expanded the model by considering the overall benefits of a CEO,who is not necessarily a controlling shareholder. Further, a CEO’s payoff function is different as they may receive both fixedcash benefits and performance based incentives.


fractions. Similar to the La Porta et al. (2002) model, we call c(k, s) the cost-of-theft function, where ‘k’is the quality of legal system and shareholder protection. As ‘k’ increases (i.e. if the law is very strongin a country), c(k, s) also increases.

Other key assumptions7 of the model are presented below:

(a) The firm’s investment size (‘I’) is a function of CEO relative power (‘P’). ‘P’ has a value of 0–1.The higher the value of ‘P’, the greater the power of the CEO over the board. ‘R’ is the return oninvestment; for a profitable firm, the value of ‘R’ is between 0 and 1. The value of ‘s’ is between 0and 1 as it is expressed as a percentage of return.

(b) ck > 0; expropriation is costlier in a country with stronger legal system.(c) cs > 0; the marginal cost of expropriation is positive.(d) css > 0; the marginal cost of expropriation rises as more is stolen.(e) cks > 0; the marginal cost of expropriation is higher when investors are better protected or the

country’s legal system is stronger.(f) (s − c(k, s)) > = 0. Cost of stealing has to be less than stealing itself. A continuing CEO is unlikely to

indulge in expropriation or stealing that will have a net negative personal wealth effect.

The various components of the CEO’s benefit function (U) as they appear in Eq. (1) are as follows.The first component captures the CEO cash salary ̨ × I, where ̨ is a fixed coefficient with a value

range 0–1. This component captures the size effect on the CEO’s cash compensation (Core et al., 1999;Grinstein & Hribar, 2004). ‘I’ (i.e. the size of the firm) is a function of ‘P’ (CEO relative power).

The second component (1 − P)(1 − s)RI captures the CEO’s performance based incentive pay. Weargue that the CEO’s incentive pay will depend on her/his relative power (‘P’) over the board. The valueof ‘P’ is between 0 and 1. The higher the value of ‘P’, the more powerful is the CEO. As a risk-averseCEO becomes more powerful, s/he would like to convince the board to give her/him a contract thatis less sensitive to performance. As Bebchuk and Fried (2005) posit, “The managerial power approachpredicts that executives who have more power should receive higher pay – or pay that is less sensitiveto performance – than their less powerful counterparts” (p. 15). In order to capture this effect, we haveincluded the ‘(1 − P)’ term in the second component of the equation. Further, as a benefit of managingthe firm, the CEO might divert a portion of profit ‘s’ from the firm to her/himself. ‘s’ represents theexpropriation of wealth by the CEO. The value of ‘s’ is between 0 and 1 as it is expressed as a percentageof return.8

The third and fourth components capture the expropriation of profit resulting from a firm’s oper-ation and relevant costs (La Porta et al., 2002). sRI is the amount of expropriation by a CEO out of totalprofit (RI). However, such expropriation will come at a cost and the fourth component c(k, s)RI capturesthe cost of expropriation. c(k, s) is the share of profits s/he loses when ‘s’ is expropriated.

The fifth and sixth components capture the ‘camouflage and stealth compensation’ and related costsas outlined by Bebchuk and Fried (2005). The ‘camouflage and stealth compensation’ are not necessar-ily related to the firm’s profitability and are devised to minimize outsiders’ outrage over excessive CEOcompensation. Bebchuk and Fried explain why a firm might opt for such ‘camouflage and stealth com-pensation’; “The critical role of outsiders’ perception of executives’ compensation and the significanceof outrage costs explain the importance of yet another component of the managerial power approach“camouflage.” The desire to minimize outrage gives designers of compensation arrangements a strongincentive to try to legitimize, justify, or obscure – or, more generally, to camouflage – the amount andperformance-insensitivity of executive compensation (p. 16).” Subsequently Bebchuk and Fried listthe arrangements “that disguise or downplay the amount and performance–insensitivity of compen-sation (p. 16)”. The list includes executive pension plans, deferred compensation arrangements, andpost-retirement perks. None of these items are related to firm performance. We take a view that such

7 Assumptions (b)–(e) are similar to the assumptions of La Porta et al.’s (2002) model.8 In our model we do not include a CEO’s excess pay in ‘s’ as long as such compensation is approved by the board. The aspect

of CEO excess pay is already incorporated in the first two components of our model, as these two components are dependenton the CEO’s relative power level.


‘camouflage and stealth compensation’ would depend on CEO power and firm (or investment) size.The sixth component captures the associated costs, which is termed as ‘outrage costs’ by Bebchuk andFried (2005). As the authors explain, directors and executives bear economic costs (such as heightenedrisk of takeover) and social costs (such as embarrassment) when a board approves a compensationpackage that is too favorable to managers.

2.1. Relationship between cost of expropriation and rate of stealing

Differentiating Eq. (1a) with respect to ‘s’:

dU

ds= PRI − cs(k, s)RI + PI − cs(k, s)PI = 0

cs(k, s)I(R + P) = PI(1 + R)

cs(k, s) = P(1 + R)(R + P)

(2)

Eq. (2) shows that marginal cost of stealing (expropriation) is positive, as values of ‘P’ and ‘R’ arepositive as per the assumptions.

Differentiating Eq. (2) with respect to ‘k’:

cks(k, s) + cs(k, s)ds

× ds

dk= 0

ds

dk= − cks(k, s)

css(k, s)< 0 (3)

Result 1a: In a country with better legal protection (i.e. Cks > 0), there is less expropriation by theCEO.

2.2. Relationship between CEO relative power and rate of stealing

Differentiating Eq. (2) with respect to ‘P’:

cs(k, s)dP

= cs(k, s)ds

× ds

dP= d

dP

[P

R + P+ PR

R + P

]

= (R + P) − P

(R + P)2+ (R + P)d(PR)/dP − PRd(R + P)/dP

(R + P)2

= (R)

(R + P)2+ (R + P)R − PR

(R + P)2

= (R)

(R + P)2+ (R + P)R − PR

(R + P)2

= (R + R2)

(R + P)2= R(1 + R)

(R + P)2

ds

dP= 1

css(k, s)R(1 + R)

(R + P)2> 0 (4)

Result 1b: Under the condition when css > 0, the rate of change of ‘s’ with respect to ‘P’ is positive.However, from the equation (4), we can see that the value of ds/dP will decrease as CEO power ‘P’ andthe change in the marginal cost of expropriation “css(k,s)” increase. Although, high relative power ‘P’gives a CEO more opportunity to ‘steal’ or ‘expropriate’, as css(k, s) goes up in a business environment,ds/dP will be significantly lower. In other words, ‘stealing’ or ‘expropriation’ will be more constrainedfor a powerful CEO in a country with strong legal and governance practices.


2.3. Relationship between CEO relative power and size of investment

Differentiating Eq. (1a) with respect to ‘P’:

dU

dP= ˛

dI

dP+ R

dI

dP− R

[P

dI

dP+ I

]+ R

[P

d(sI)dP

+ sI]

− c(k, s)RdI

dP+

[P

d(SI)dP

+ sI]

− c(k, s)[

PdI

dP+ I

]= 0

dU

dP= ˛

dI

dP+ R

dI

dP− R

[P

dI

dP+ I

]+ R

[P(

Ids

dP+ s

dI

dP

)+ sI

]

− c(k, s)RdI

dP+

[P(

Ids

dP+ s

dI

dP

)+ sI

]− c(k, s)

[P

dI

dP+ I

]= 0

dI

dP[ ̨ + R − RP + RPs − c(k, s)R + Ps − c(k, s)P] − I

[R − RP

ds

dP− Rs − P

ds

dP− s + c(k, s)

]= 0

dI

dP=

I[R(1 − s) − P(1 + R) ds

dP − (s − c(k, s))]

[ ̨ + R(1 − P + Ps − c(k, s)) + P(s − c(k, s))]

By replacing the value of ds/dP=(1/css(k,s))(R(1+R)/(R + P)2) (based on Eq. (4)), we obtain:

dI

dP=

I[R(1 − s) − P(1 + R)1/css(k, s)R(1 + R)/(R + P)2 − (s − c(k, s))

][ ̨ + R(1 − P + Ps − c(k, s)) + P(s − c(k, s))]

= I [X]Y

(5)

In Eq. (5), the value of the rate of change dI/dP will be positive if I[X] (numerator) and Y (denom-inator) are both positive (or are both negative). Appendix A presents the simulated values for thenumerator ‘X’.9 In the context of operationalizing Eq. (5), we assume that the value of ‘s’ and ‘(s − c(k,s))’will be relatively small. It is expected that in a country with a better legal and governance environ-ment, the values of ‘s’ and ‘c(k,s))’ will be lower and ‘c(k,s))’ would increase with ‘s’. From Table A1(Appendix A) we find that the sign of ‘X’ would vary with respect to the value of ‘css(k,s)’ and CEOpower ‘P’. As ‘css(k,s)’ and ‘P’ increase, the values of ‘X’ tend to be positive. In a country with a stronglegal and governance environment, the value of ‘css(k,s)’ is expected to be higher.

As shown in Table A2 (Appendix A), for a given set of values for ˛, R, P, s, c(k, s) the value ofthe denominator ‘Y’ will be always positive. As per the assumptions presented earlier, the range ofparameters ˛, R, P, s, c(k, s) is 0–1. Therefore, in light of equation (5), we may conjecture that the rateof change dI/dP will be positive (i.e. investment ‘I’ would increase as ‘P’ increases), if the change in themarginal cost of stealing ‘css(k,s)’ is higher and the CEO has higher power. In other words, we arguethat in a country with a higher value for a change in the marginal cost of stealing, powerful CEOswould be more interested in expanding the firm size (‘I’). Also, CEOs with high power can implementthe expansion plan more effectively. As CEO compensation is directed related to firm size (Core et al.,1999), a CEO would benefit in the process as firm size increases. Alternatively, if the change in themarginal cost of stealing ‘css(k,s)’ is lower, the value of the rate of change dI/dP will be negative. Insuch an environment, CEOs will be more interested in direct ‘expropriation’ or ‘stealing’ rather thanexpanding the size of investment.

Result 2: In a country where the marginal cost of stealing rises significantly as more is stolen (i.e.‘css(k,s)’ is higher), a powerful CEO would be more inclined to increase the size of the investment (orfirm size).

The size of the firm can be increased in a number of ways, such as through internal growth oracquisitions of other firms or assets. In this study, we focus on CEO power and its impact on M&A

9 We assume that firm size ‘I’ is positive.


activities. Based on the above results, we propose the following testable hypotheses in the context ofM&A.

Results 1a and 1b imply that in a country with better legal protection, CEOs are unlikely to expro-priate shareholder wealth in a manner that will be easily visible to outsiders. M&A activities are closelywatched by the market participants. Thus, it is unlikely that CEOs will deliberately make bad acqui-sitions that will harm the CEOs’ reputations. As a result, market participants are unlikely to have anegative view with respect to the acquisition activities pursued by powerful CEOs.

Hypothesis 1. CEO power does not have any systematic relation with market reactions to M&Aannouncements.

As we have argued earlier, CEOs are unlikely to commit outright theft in the event of an acqui-sition. In a country with better legal protection, a CEO’s role in M&A activities will be subject tostricter scrutiny. Therefore, CEOs are likely to use M&A activities indirectly to increase their compen-sation level. Through M&A activities, firm size increases. Extant literature shows that firm size directlyinfluences CEO compensation levels. Result 2 in the above model shows that in a strong legal and gov-ernance environment, as the relative power of the CEO increases, s/he will have more incentives toincrease the size of the investment (or firm size) to increase her/his total benefits. Therefore, powerfulCEOs (relative to board) are likely to undertake more M&A activities in order to maximize personalbenefits.

Hypothesis 2. The CEO’s relative power to the board is likely to be higher in acquiring firms comparedto non-acquiring firms.

3. Data and sample

This study considered all Canadian M&A deals that occurred from 1997 to 2005 and involved aTSX-listed bidding company. The nine year period (1997–2005) is important as it captures a periodof corporate governance reform in Canada. First, the reform of corporate governance in Canada beganwith the recommendations of the Dey Report in 1994 which was followed by the Toronto StockExchange governance guidelines in 1995. These guidelines were designed to work in the environ-ment of the Canadian capital markets. The Canadian market has proportionately more small publiccompanies than capital markets in countries like the US and the costs of mandatory compliance witha set of governance rules could be prohibitive for many small firms. As a result Canada adopted aprinciples-based approach to governance where compliance with governance principles was volun-tary but disclosure of company compliance was required. Second, the 1997–2005 period also coversthe introduction of SOX in the US in 2002. The requirements of SOX impacted a number of Cana-dian companies because of their cross listing in the US. Finally, in June 2005, after several years ofconsultation, the Canadian Securities Administrators (CSA) issued National Policy 58-201 (Corpo-rate Governance Guidelines) and National Instrument 58-101 (Disclosure of Corporate GovernancePractices). The CSA principles were generally consistent with the previous guidelines but disclosurepractices were improved.

We obtained our M&A dataset from the SDC Thomson Financial Database. Our data met the follow-ing criteria: (i) the deals were completed, (ii) the acquiring firm was not from the financial industry,(iii) acquiring firms with multiple acquisitions during 1997–2005 period were considered, (iv) deals ofall sizes were considered (i.e., there were no minimum or maximum transaction values). Governancevariables were hand collected from company filings on the “System for Electronic Document Analysisand Retrieval (SEDAR)” or were downloaded (with financial variables) from the StockGuide database.Additional data was hand gathered from SEDAR for the 2003–2005 period to calculate CEO pay slice(CPS) similar to Bebchuk et al. (2009). A robustness test was performed using CEO pay slice (CPS) inplace of CEO excess pay.

Table 1 presents several important observations: (i) in line with overall Canadian M&A activity, wesee an increase in M&A deals between 1997 and 2000 and a decline in the post 2000 period. The dealsizes also peaked around 2000 – in an era of the tech bubble; (ii) most of the acquirers (673 out of848 acquiring firms) are single acquirers (that is, completed only one deal in a calendar year) – the


Table 1Description of acquisitions by Canadian companies listed on Toronto Stock Exchange The sample size is 1109 acquisition eventsover 1997–2005 period by Canadian acquirers listed on the TSX. The sample includes multiple acquirers. ‘Multiple acquirers’refers to the acquiring firms that acquire more than one target in a calendar year. ‘Single acquirers’ acquire only one target inany calendar year.

# of completeddeals with financialand accountinginformation

# of acquirer # of singleacquirer

# of multipleacquirer

Total transactionvalue (in $ mil. CDN)

Avg. transactionvalue (in $ mil. CDN)

1997 159 127 101 26 11,294 71.01998 160 109 81 28 40,007 250.01999 135 105 84 21 30,468 225.72000 150 107 85 22 54,740 364.92001 134 100 75 25 18,440 137.62002 118 90 71 19 18,923 160.42003 89 73 60 13 21,916 246.32004 83 73 63 10 20,511 247.12005 81 64 50 11 5545 68.5

Total 1109 848 673 175 221843.0 200.0

The average transaction value for 2005 initially seems low given that the value of announced deals for 2005 was up 48% over2004 (Financial Post Crosbie). It is important to realize that our study focuses on completed deals and not announced deals.The deferral from announcement to completion would be significant since the high value deals were back-end loaded into thesecond half of 2005. Further, given that our focus is on Canadian acquirers listed on the TSX, we do not consider any of thelarge acquisitions of Canadian companies by foreigners. Finally we note that the decrease in the average transaction value isconsistent with the data reported by Crosbie for 2005. For example, the average transaction value of US companies acquiredby Canadian companies fell from $244.4 million in 2004 to $80.4 million in 2005. Further the average transaction value of allforeign companies acquired by Canadian companies fell from $179.4 million in 2004 to $84.3 million in 2005.

rest of the firms made more than one acquisition in a given year. Table 2 summarizes various firm andgovernance specific characteristics of acquiring and non-acquiring firms.

4. Determinants of CEO power

CEO excess pay, which is determined by the governance characteristics of a firm,10 is used as aproxy for CEO power. As Core et al. (1999) have argued, CEO excess pay is the manifestation of afirm’s weak governance. A number of other studies have posited that a firm with a weak governancesystem gives more power to the CEO, who in turn, can influence the other directors with respect tothe compensation decision (Bebchuk and Fried, 2003; Grinstein and Hribar, 2004). These studies haveshown that firms with less effective governance tend to pay greater CEO compensation. In other words,a weaker governance system in a firm gives more power to CEOs to extract more personal benefits.Consistent with this view, we use CEO excess pay as a proxy for CEO power.

In order to determine CEO excess pay, we have followed the methodology proposed by Core et al.(1999). First, the predicted value of CEO pay has been determined with the help of the followingcross-sectional multiple regression using 1997–2005 data:

CEO Pay = Fn (Economic determinants) + Fn (governance variables) = Fn (Revenue, avg. price to bookvalue, return on asset, 1-year stock return, standard deviation of return on asset, standard deviationof stock return) + Fn (Chairman is related, percentage of inside directors, board size, CEO ownership,directors’ ownership, blockholder ownership).

The regression equation includes two variations of CEO pay: (i) CEO cash pay; and (ii) CEO totalpay. “CEO cash pay” is the sum of salary and annual cash bonus. “CEO total pay” is the sum of salary,cash bonus, and realized stock option pay in that year. The compensation data were obtained frommanagement information circulars. Subsequently, two versions of CEO excess pay (one for cash payand one for total pay) are determined using the following equation:

10 Hayward and Hambrick (1997) state, “there is no reliable instrument to measure CEO hubris directly” (p. 107). As an indirectmeasure, they recommend to use past firm performance and board characteristics along with media praise for the CEO.


Table 2Descriptive statistics of CEO compensation and its determinants.

(in $ CDN) Cash pay Total pay

Panel A. CEO payMean $ 638,219 $ 1,221,712Median $ 385,655 $ 456,171Std. dev. $ 906,087 $ 5,323,721“CEO cash pay” is the sum of salary and annual cash bonus. “CEO total pay” is the sum of salary, cash bonus, andrealized stock option pay in that year. The compensation data were obtained from management information circulars.

Revenue(in’000$)

Avg. price tobook value(ratio)

Return onasset (%)

1-year stockreturn(decimal)

Std. Dev. ofROA (%)

Std. Dev. ofstock return(decimal)

Panel B. Economic determinantsMean $ 1,197,380 2.3 −4.3 0.4 12.8 0.7Median $ 127,485 1.8 3.1 0.1 5.2 0.4Std. dev. $ 3,617,351 2.2 29.9 1.5 21.5 0.9“Revenue” is for the year prior to the year in which compensation is awarded. “Average price to book value” is overfive years prior to the year in which CEO compensation was awarded. “Return on asset” is the percentage return ontotal assets for the prior year. “1-year Stock Return” is calculated based on year-end stock prices of the prior year. “Std.dev. of return on asset” is the standard deviation of the firm’s return on assets over five prior years. “Std. dev. of stockreturn” is the standard deviation of the 1-year stock return over four years prior to the year in which compensationwas awarded.

Chair is related % of insidedirectors

Board size CEO ownership % Blockholderownership %

Panel C. Governance variablesMean 0.6 31.8 8.2 9.7 11.6Median 1.0 28.6 8.0 1.4 0.0Std Dev 0.5 17.2 2.8 30.3 20.8“Chair is related” is a dummy variable equal to “1” if the chair is related to management or the board, “0” otherwise.For example, if the CEO is also the chair of the board, the dummy variable is equal to “1”. “Percentage of insidedirectors” is the ratio of related directors to total board size. “Board size” is the number of directors on the board. “CEOownership” is the percentage of outstanding shares owned by CEO. “Blockholder ownership” is the percentage ofoutstanding shares owned by individuals or institutions other than the directors. If the blockholder ownership is lessthan 10%, the input value for this variable is “0”.

The sample consists of total 1309 annual observations for acquiring (1109) and non-acquiring (200) firms from 1997 to 2005.For acquiring firms, only one event is considered in case of multiple acquisitions by the firm in any year.

CEO Excess Pay = Coefficient × Governance variables = Coefficient × Board variables +Coefficient × Ownership variables

Variable descriptions and descriptive statistics for each variable are presented in Table 2 and theresults of OLS multiple regressions are presented in Table 3.

4.1. Conjectures and proxies for CEO power components

Prior theories and empirical evidence suggest that larger firms with higher growth potentialsand better performances will pay higher compensation (Carpenter and Sanders, 2002; Cordeiro andVeliyath, 2003; Core et al., 1999; Smith and Watts, 1992). We use revenue as a proxy for firm size,five-year average price to book value as a proxy for growth and investment potential, and return onasset and stock return as proxy for past performance. We also control for firm risk (standard deviationof ROA and standard deviation of stock return) as suggested by a number of prior research studies(Core et al., 1999; Smith and Watts, 1992).

Earlier studies have argued that the presence of a substantial blockholder would negatively impactCEO pay (Dong and Ozkan, 2008; Ozkan, 2007; Cordeiro and Veliyath, 2003; Werner and Tosi, 1995).Core et al. (1999) and Conyon and Peck (1998) have shown that firms with a related board chairman andmore inside directors tend to pay more to a CEO. We do not see any comprehensive economic theoryrelating the size of a corporate board with performance (Wu, 2000). However, Jensen (1993), Liptonand Lorsch (1992) and Ozkan (2007) have suggested that large boards can be less effective than small


Table 3CEO compensation and its economica and governanceb determinants.

CEO compensation variable

Cash pay Total pay

1. Economic determinantsConstant Beta 9.758*** 10.147***

t-stat 50.60 38.37Sig. 0.000 0.000

Revenue (LN REV) Beta 0.211*** 0.229***

t-stat 15.02 11.96Sig. 0.000 0.000

Avg. price to book value (LN AVGPRBV) Beta 0.130** 0.302***

t-stat 2.50 4.16Sig. 0.013 0.000

Return on Asset (RASST1) Beta 0.002 0.002t-stat 1.00 1.00Sig. 0.317 0.320

1-year Stock Return (LN RET1) Beta 0.070** 0.184***

t-stat 1.97 3.71Sig. 0.050 0.000

Std. dev. of return on asset (LN STDROA) Beta 0.031 −0.031t-stat 1.07 −0.78Sig. 0.29 0.434

Std. dev. of stock return (LN STDRET) Beta −0.128 0.012t-stat −1.64 0.12Sig. 0.102 0.908

2. Governance variablesChair is related (CHRREL) Beta −.034 −0.016

t-stat −0.73 −0.25Sig. 0.469 0.802

Percentage of inside directors (PINDIR) Beta .267** 0.009t-stat 1.96 0.05Sig. 0.050 0.96

Board size (BDSIZE) Beta 0.065*** 0.050***

t-stat 6.76 3.80Sig. 0.000 0.000

CEO ownership (PCEOOWN) Beta −0.060 −0.223t-stat −0.42 −1.12Sig. 0.677 0.261

Blockholder ownership (BLKOWN) Beta −0.004*** −0.005***

t-stat −3.35 −2.91Sig. 0.001 0.004

N 669 668

Adj. R square 0.58 0.46F-Stat 41.20 25.69Sig. 0.000 0.000

The sample consists of 1309 observations for acquiring (1109) and non-acquiring (200) firms from 1997 to 2005. For acquiring firms, only oneevent is considered in case of multiple acquisitions by the firm in any year. In the regression model, the number of cases is less than 1309 dueto list-wise deletion of the cases with missing data. “CEO cash pay” is the sum of salary and annual cash bonus. “CEO total pay” is the sum ofsalary, cash bonus, and realized stock option pay in that year. The compensation data were obtained from management information circulars.As do Core et al. (1999), all independent variables are organized in two main categories: “Economic Determinants” and “Governance Variables”.

a Economic determinants: Revenue is for the year prior to the year in which compensation is awarded. “Average price to book value” is overfive years prior to the year in which CEO compensation was awarded. “Return on asset” is the percentage return on total asset for the prior year.“1-year Stock Return” is calculated based on year-end stock prices of the prior year. “Std. dev. of return on asset” is the standard deviation ofthe firm’s return on asset over five prior years. “Std. dev. of stock return” is the standard deviation of 1-year stock return over four years priorto the year in which compensation was awarded. All economic determinants except “return on asset” were transformed using the natural logof the variable.

b Governance variables: “Chair is related” is a dummy variable equal to “1” if the chair is related to management or the board, “0” otherwise.For example, if the CEO is also chair of the board, the dummy variable is equal to “1”. “Percentage of inside directors” is the ratio of relateddirectors to total board size. “Board size” is the number of directors on the board. “CEO ownership” is the percentage of outstanding sharesowned by CEO. “Blockholder ownership” is the percentage of outstanding shares owned by individuals or institutions other than the directors.If the blockholder ownership is less than 10%, the input value for this variable is “0”. Beta coefficient value, t-statistics and significance levelobtained through OLS regression are reported. Eight dummy variables are included to control for the “year” effect and four dummy variablesare included to control for the “industry” effect (in line with Dutta and Jog, 2009). Results for these dummy variables are not reported in thetable. In order to avoid the extreme value (outlier) problem, the top 1% and bottom 1% of data for each variable in the regression model aredeleted.* Statistical significance at 10% level.

** Statistical significance at 5% level.*** Statistical significance at 1% level.


boards. When boards become too big, agency problems (such as director free-riding) increase and theboard becomes more symbolic and less a part of the management process (Hermalin and Weisbach,2003; Jensen, 1993). Consequently, it can be argued that firms with larger board sizes would pay moreto a CEO.

The relationship between CEO ownership and compensation is not obvious. Higher levels of CEOownership will give the CEO the power and leverage to extract more cash salary and bonus. However,CEOs holding a large fraction of their firms’ equity are less likely to demand further stock-basedcompensation, for at least for two reasons: (i) the interests of CEOs and shareholders are alreadyrelatively aligned (Jensen and Meckling, 1976); and (ii) since CEOs have already invested their humancapital in the firm they would try to diversify risk by not taking a very high ownership stake in thesame firm (Smith and Watts, 1992; Yermack, 1995). Accordingly, it has been argued that when CEOshold a large fraction of their firm’s equity, their boards of directors decrease the intensity of incentivesprovided by stock-based awards and shift the mix of CEO pay toward cash compensation (Beaty andZajac, 1994; Bryan et al., 2000; Yermack, 1995).

Results of the regression models are presented in Table 3. Consistent with prior evidence and pre-vious arguments, we find that CEO pay is an increasing function with revenue, average price to bookvalue, and past stock return. That is, larger firms with better growth/investment opportunities andbetter past performance tend to pay more to a CEO. With respect to governance variables, we find thatfirms with larger board sizes and more inside directors tend to pay higher CEO compensations. Boardswith more members and proportionately higher inside directors are considered to be less efficient.Such boards are like to favor higher CEO compensation. Further, consistent with the conjectures pre-sented earlier, we find that blockholder ownership has a significant negative relationship with CEOcompensation. In both regression models, dummy variables are included (not reported) to controlfor yearly variations and industry effects. Overall, the results show that a less effective (or weaker)governance system in a firm will lead to excess CEO pay. Therefore, CEO excess pay appears to be areasonable proxy for CEO power.

5. Hypotheses testing

5.1. CEO power and market reactions to M&A deal announcements

We perform a standard event study (market model) to calculate cumulative abnormal returns(CAR). Subsequently we regress CAR on the CEO power variable.

CAR = Fn (CEO power) + Fn (control variables)Table 4 presents the CARs around the announcement dates. We used four different CARs depend-

ing on the window around the announcement dates. All CAR values show significant results. Thisimplies that market participants react positively to the M&A announcements in Canada. These resultscorroborate with the results of earlier Canadian studies.

Table 5 presents the results of various regression models examining the impact of CEO power onmarket reactions to M&A announcements. CAR1 (1, +1) and CAR2 (−2, +2) values are used as dependentvariables. We use CEO excess pay, which is the portion of CEO pay attributed to the firm’s governancevariables, as a proxy for CEO power. Two different forms of CEO excess pay were used in the regression:(i) CEO cash excess pay, and (ii) CEO total excess pay. Further, we have controlled for commonly usedvariables in the M&A literature in all models. The natural log of total assets is used as a proxy for firmsize. We created three dummy variables based on the medium of acquisition (cash, stock, or mixed).“Cash dummy” is a dummy variable. Its value is one if the medium of acquisition is cash and zerootherwise. “Stock dummy” is a dummy variable. Its value is one if the medium of acquisition is stockand zero otherwise. “Public target dummy” value is one if the target firm is a public company andzero otherwise. “Tender dummy” value is one if the acquisition is done through a tender offer, andzero otherwise. “Cross border acquisition dummy” value is one, if the target is Canadian, and zerootherwise. “Related acquisition dummy” value is one, if the target is from the related industry, andzero otherwise. We matched 4-digit SIC code to determine the relatedness of a target firm.

We find that the coefficient of the CEO power variable is not significant in any model. This impliesthat CEOs with more power are not systematically engaged in bad acquisitions, especially in a country


Table 4Short-run cumulative abnormal return: market model.

Period All cases Excluding multiple acquirers

Avg. CAR t-stat Avg. CAR t-stat

CAR1 (−1, +1) 0.014*** 6.27 0.015*** 4.36CAR2 (−2, +2) 0.016*** 5.47 0.017*** 3.87CAR3 (0, +2) 0.013*** 5.96 0.013*** 3.98CAR4 (−5, +5) 0.013*** 3.04 0.015** 2.28

Day All cases Excluding multiple acquirers

AR t-stat AR t-stat

−5 0.0014 1.11 0.0035 1.79−4 −0.0000 −0.02 −0.0010 −0.50−3 0.0001 0.09 0.0005 0.27−2 0.0016 1.29 0.0036 1.87−1 0.0008 0.61 −0.0002 −0.11

0 0.0081*** 6.40 0.0105*** 5.45+1 0.0049*** 3.85 0.0043** 2.21+2 0.0000 0.07 −0.0015 −0.76+3 −0.0016 −1.24 −0.0019 −0.96+4 −0.0009 −0.74 −0.0012 −.062+5 −0.0017 −1.34 −0.0021 −1.07

The sample size is 1184 for all acquiring events (this number is greater than the 1109 transactions in Tables 1 and 2 becausemarket data was available for some firms for which financial and governance data was not available) and 666 for all acquiringevents excluding multiple acquisitions by a firm in the same calendar year. We use the market model ARjt = Rjt − (˛j + ˇjRmt )

to determine the abnormal return and adjusted abnormal return. We compute the security-specific parameters ˛j and ˇj overthe estimation period t−31 to t−120 trading days. Some studies use a longer estimation window (e.g., t−41 to t−240 days). As theestimation window increases, the chance of encountering other external events during this estimation period also increases.Since many acquirers make multiple acquisitions, we chose to use a shorter estimation window in our analysis. We exclude the30-day time interval t−30 to t−1 days to avoid including information about the event that may affect security returns. “CAR” isthe average of the summation of the abnormal returns for each stock for a specific period. Four different versions of CAR wereused in the analysis. The statistical significance of the abnormal return was examined by t-statistics which provides a resultsimilar to the Z-statistics introduced by Linn and McConnell (1983). CAR results are reported in decimals (not in percentage).* Statistical significance at 10% level.

** Statistical significance at 5% level.*** Statistical significance at 1% level.

characterized by good governance practice. The results support Hypothesis 1. In a country with stronglegal and governance environment, CEOs are likely to be more concerned about their reputation andhence would be more careful with their decisions. The Canadian corporate governance environmentis rated as one of the best in the world (Doidge et al., 2007). Hence, it is likely that in Canada powerfulCEOs would not deliberately make bad acquisitions.

5.2. CEO power and acquisition activity

The results for the impact of CEO power on the acquisition decision are presented in Table 6.Univariate analysis and logistic regression analysis generally confirm that CEO power levels are sig-nificantly higher for acquiring firms compared to the CEO power levels for non-acquiring firms. In otherwords, CEOs in acquiring firms enjoyed more power in making acquisition decisions. These findingsare consistent with the viewpoints of Roll (1986), Berkovitch and Narayanan (1993), and Haywardand Hambrick (1997). However, unlike other studies, we used a direct and integrated approach ininvestigating the impact of CEO power on a firm’s acquisition decision.11 This supports ‘Hypothesis 2’and Result 2 of our model. As the legal environment is quite strong in Canada, CEOs are less likely to

11 Earlier studies have either tested the impact of CEO hubris on takeover premium (Roll, 1986), or used a number of separatevariables to proxy for CEO hubris without considering the confounding effect (Hayward & Hambrick, 1997).


Table 5CEO power and market reactions to M&A deal announcements.

Dependent variable Model 1 Model 2 Model 3 Model 4CAR1 (−1,+1) CAR1 (−1,+1) CAR2 (−2,+2) CAR2 (−2,+2)

Constant Beta 0.086*** 0.084*** 0.099*** 0.094***

t-stat 4.95 4.74 4.24 3.94Sig. 0.000 0.000 0.000 0.000

CEO cash excess pay Beta 0.016 −0.002(CEXPAY1T) t-stat 1.05 −0.10

Sig. 0.295 0.921CEO total excess pay Beta 0.006 −0.020(TEXPAY1T) t-stat 0.32 −0.85

Sig. 0.746 0.394Natural log of total asset Beta −.006*** −.005*** −0.005** −0.005**

(LNTAST1F) t-stat −3.47 −3.12 −2.53 −2.20Sig. 0.001 0.002 0.012 0.028

Cash dummy Beta −0.002 −0.002 0.001 0.001(CASHD) t-stat −0.39 −0.41 0.14 0.11

Sig. 0.696 0.681 0.887 0.909Stock dummy Beta −0.006 −0.006 −0.018* −0.018*

(STOCKD) t-stat −0.73 −0.74 −1.71 −1.70Sig. 0.469 0.462 0.088 0.090

Public target dummy Beta −0.007 −0.007 −0.012 −0.012(PUBD) t-stat −1.12 −1.09 −1.44 −1.43

Sig. 0.263 0.275 0.151 0.154Tender dummy Beta 0.002 0.002 0.015 0.015(TENMER) t-stat 0.21 0.17 1.21 1.23

Sig. 0.832 0.866 0.227 0.221Cross border acquisition dummy Beta −0.004 −0.004 −0.004 −0.004(CROSSBD1) t-stat −0.79 −0.80 −0.57 −0.60

Sig. 0.431 0.423 0.568 0.552Related acquisition dummy Beta −0.005 −0.005 −0.008 −0.008(REL4DIG) t-stat −1.04 −1.13 −1.33 −1.36

Sig. 0.298 0.260 0.184 0.176N 675 675 690 690Adj. R2 0.019 0.018 0.019 0.020F-stat 2.66*** 2.53*** 2.65*** 2.74***

Sig. 0.007 0.010 0.007 0.006

For acquiring firms (1997–2005), all acquisition events are considered in case of multiple acquisitions by the firms in any year.CAR1 (−1, +1) and CAR2 (−2, +2) values are used as dependent variables. “CEO Power” is the portion of CEO pay attributed tothe firm’s governance variables. Two different forms of CEO excess pay were used in the regression: (i) CEO cash excess pay,and (ii) CEO total excess pay. The natural log of total asset is used as a proxy for firm size. We created three dummy variablesbased on the medium of acquisition (cash, stock, or mixed). “Cash dummy” is a dummy variable. Its value is one, if the mediumof acquisition is cash and zero otherwise. “Stock dummy” is a dummy variable. Its value is one, if the medium of acquisition isstock and zero otherwise. “Public target dummy” value is one, if the target firm is a public company and zero otherwise. “Tenderdummy” value is one, if the acquisition is done through a tender offer, and zero otherwise. “Cross border acquisition dummy”value is one, if the target is Canadian, and zero otherwise. “Related acquisition dummy” value is one, if the target is from therelated industry, and zero otherwise. We matched 4-digit SIC code to determine the relatedness of a target firm.

* Statistical significance at 10% level.** Statistical significance at 5% level.

*** Statistical significance at 1% level.

engage in stealing or expropriation as their power increases. Rather they will acquire other targets tomaximize their benefit function.

6. Robustness checks

6.1. Long-term operating performance of acquiring firms

Results presented in Table 5 indicate that market participants do not react negatively to the M&Aannouncements by the acquiring firms with a higher level of CEO power. Such an observation leads to


Table 6Analysis of the impact of CEO power on M&A decision.

Acquiring firmN = 710

Non acquiring firmN = 143

Mean difference t-stat Sig.

Panel A. Univariate analysisCEO cash excess pay (CEXPAY1T) 0.5536 0.5314 0.0222 1.35 0.178CEO total excess pay (TEXPAY1T) 0.3377 0.3002 0.0375*** 2.58 0.010

“CEO Power” is the CEO excess pay, which is the portion of CEO pay attributed to the firm’s governance variables. Twodifferent forms of CEO excess pay were used in the regression: (i) CEO cash excess pay, and (ii) CEO total excess pay.Means of CEO cash excess pay and CEO total excess pay were calculated and compared for the two groups of acquiringfirms and non-acquiring firms over 1997–2005 period.

Model 1 Model 2

Panel B. Logit regression on CEO powerCEO cash excess pay (CEXPAY1T) Beta 1.08**

Wald 4.62Sig. 0.032

CEO total excess pay (TEXPAY1T) Beta 1.61***

Wald 7.56Sig. 0.006

Constant Beta 1.28*** 1.37***

Wald 20.37 46.89Sig. 0.000 0.000

N 1078 1081

−2 Log likelihood 839.02 836.80Cox & Snell R square 0.004 0.007Nagelkerke R square 0.008 0.013

Hosmer and Lemeshow testChi-square 10.05 13.71df 8 8Sig. 0.26 0.09

Logit regression of “acquire” dichotomous variable on CEO power is presented. If a firm made an acquisition in a yearfrom 1997 to 2005, “acquire” = 1, otherwise “acquire” = 0. “CEO power” is the CEO excess pay, which is the portion ofCEO pay attributed to the firm’s governance variables. Two different forms of CEO excess pay are used in theregression: (i) CEO cash excess pay, and (ii) CEO total excess pay. We did not include any other firm-specific orgovernance variables in the model as those variables were used to obtain the estimate of CEO excess pay. However, asa robustness check we ran a number of other models including proxies for firm size, growth prospect, and governancevariables. Our main results do not change. We find that the effect of CEO excess pay remains significant in all othermodels.



a few related questions. Do market participants believe that CEO power will not have any significantimpact in the long-term performance of the acquiring firms? Do powerful CEOs act in a similar way asthe not so powerful CEOs in selecting a target? To gain a deeper insight into these issues, we examinethe long-term operating performance of acquisitions with respect to different levels of CEO power.Based on the level of CEO power (i.e. CEO excess pay), we have divided all acquiring firms into threegroups. The firms with the higher (lower) level of CEO excess pay are termed as the ‘high (low)-powerCEO’ group.

We present the results for pre- and post-acquisition operating performance using cash flow tototal assets as a proxy for operating performance that is somewhat standard in this literature (Healyet al., 1992). To ensure that the results do not depend on the methodological choices we use matchingfirm adjusted (Ghosh, 2001) cash flow to total assets in the pre- and post-event period.12 The reasonfor using this methodology (as opposed to industry adjusted cash flows) is two fold: First, Ghoshargues that larger firms generally make acquisitions within an industry segment and they are likely

12 Matching firm benchmarks are selected in the spirit of Barber and Lyon’s (1997) arguments.


Table 7Operating performance (cash flow to total assets) for pre- and post-merger period.

Year relative to M&A Matching firm adjusted cash flow to total asset(acquiring firm − matching firm)

Mean t-stat Sig.

Panel A. Operating performance of acquiring firms with a high-power CEO3 0.022* 1.779 0.0772 0.036*** 2.830 0.0051 0.027** 2.363 0.019−1 0.033*** 3.441 0.001−2 0.019** 2.212 0.028−3 0.020* 1.851 0.066

Post average: mean of years 3, 2, and 1 0.028*** 2.725 0.007Pre average: mean of years −3, −2, and −1 0.024*** 3.004 0.003(Post–pre) difference 0.004 0.578 0.564

Year relative to M&A Matching firm adjusted cash flow to total asset(acquiring firm − matching firm)

Mean t-stat Sig.

Panel B. Operating performance of acquiring firms with a low-power CEO3 0.037 1.189 0.2372 0.074*** 2.693 0.0081 0.095*** 3.906 0.000−1 0.057*** 2.805 0.006−2 0.044* 1.832 0.069−3 0.083*** 3.432 0.001

Post average: mean of years 3, 2, and 1 0.073*** 2.949 0.004Pre average: mean of years −3, −2, and −1 0.058*** 2.942 0.004(Post–pre) difference 0.015 0.804 0.346

“Matching firm adjusted cash flow to total asset” is the average difference in the operating performance (cash flow to totalasset) between the acquiring firm and matching firm for a given year relative to the acquisition year. The “Individual matchingfirm” was selected based on the nearest propensity score with respect to firm size and price to book value. “Matching adjustedpost average cash flow to total asset” is the average of “matching firm adjusted cash flow to total asset” for post acquisitionperiod (year +1, +2 and +3). “Matching adjusted pre average cash flow to total asset” is the average of “Matching firm adjustedcash flow to total asset” for the pre-acquisition period (year −1, −2 and −3). “Matching firm adjusted post and pre difference”is the average of the difference between “Matching adjusted post average cash flow to total asset” and “Matching adjusted preaverage cash flow to total asset”. t-statistics and significance level are reported for each mean difference. In the case of multipleacquisitions by a firm in any year, only one event was considered in the analysis. All operating performance variables areexpressed in decimals. Mean differences in operating performance are expressed in decimals (not in percentage). High-powerCEOs have the higher CEO excess pay and low-power CEOs have the lower CEO excess pay.



to be more profitable compared to the industry average benchmark just because of the size effect(Fama and French, 1995). Second, acquiring firms generally make acquisitions following a periodof above industry average performance. Therefore, industry mean adjusted operating performanceresults might be biased. In order to select a matching firm, we follow a two-stage procedure. First, weidentify all the TSX firms that have not made any acquisitions in the period of 1997–2005. Second, weperform an OLS regression considering all acquiring firms and matching firms. We regress the firms’return on equity on firm size and market-to-book value variables and select matching firms based onthe nearest propensity score obtained by using the coefficients of firm size and price-to-book valuefactors.

Table 7 presents the univariate results for matching firm adjusted operating performance. Panel Apresents the results for the ‘high-power CEO’ group. We do not see any significant difference in pre- andpost-acquisition performance once we consider matching firm adjusted operating performance (mean


Table 8Comparison to Bebchuk’s CPS measure.

Statistics Our sample Bebchuk et al. (2009)

Cash CPS Total CPS Non-Equity-CPS Total CPS

Mean 0.3375 0.3365 0.351 0.357Std. dev. 0.155 0.184 0.100 0.114Minimum 0 0 0 0Maximum 1 1 1 1Observations 248 248 8659 8659

For Bebchuk et al. (2009), “Total CPS” is based on total compensation (ExecuComp item TDC1) and “Non-Equity-CPS” is the“Total CPS” less “Equity CPS”. In a similar spirit, our calculation of “Cash CPS” is based on salary and bonus. “Total CPS” is basedon the Cash CPS plus the value of options exercised during the year.

difference is 0.4% per year and the significance level is 0.564). That is, acquisitions made by high-powerCEOs do not result in any significant deterioration in the acquirer’s long-term operating performance.These results are consistent with the short-term stock performance presented in Table 5 where wefound that CEO power does not impact the market reaction (i.e. CAR) significantly. In other words,high-power CEOs are not likely to make a bad acquisition deliberately that would be detrimental totheir reputation. Panel B presents the results for the ‘low-power CEO’ group. Similar to the ‘high-powerCEO’ group, this group also does not show any significant changes in operating performance in thelong-run.

6.2. CEO pay slice as an alternative measure of CEO power13

Recently, Bebchuk et al. (2009, 2011)14 have proposed using CEO pay slice (CPS) as a measure of CEOpower. CPS is the amount of total compensation received by the CEO relative to the total compensationreceived by the top five executives. We have collected relevant compensation data for the CEO andother executives to develop a measure of CPS in the spirit of Bebchuk et al.’s study. As a robustnesstest, we repeated our analyses from Tables 5 and 6 using CPS instead of CEO excess pay for the period2003–2005. The comparison of our measure of CPS to Bebchuk et al.’s measure can be found in Table 8.The results of the robustness tests can be found in Table 9 .

In comparing the two measures, it is important to keep in mind that Bebchuk’s data was gatheredfor the 1993–2004 period whereas ours was gathered for the 2003–2005 period. For Bebchuk et al.(2009) “Total CPS” is based on total compensation (ExecuComp item TDC1) and “Non-Equity-CPS” isthe “Total CPS” less “Equity CPS”. In a similar spirit, our calculation of “Cash CPS” is based on salaryand bonus. “Total CPS” is based on the Cash CPS plus the value of options exercised during the year.As we can see from Table 8, the average CPS is qualitatively similar to Bebchuk et al. (2009) althoughthere appears to be significantly more variation in our scores.

The use of CPS instead of CEO excess pay in Panel A of Table 9 does not change our conclusions.None of the models or coefficients were significant which is consistent with our earlier conclusionthat CEOs with more power are not systematically engaged in bad acquisitions, at least as indicated bymarket reactions. The results of Panel B are in the direction predicted but not significant. This suggeststhat CEO power as measured by CPS may not be significantly differently between acquirers and non-acquirers. This is not surprising as CPS does not measure the same facet of CEO power that CEO excesspay does. CPS measures the CEO’s power relative to other executives. Although this is an importantrelationship, it is likely not the key relationship in determining whether an acquisition will occur.15

13 We thank a referee for proposing CEO pay slice as an alternative measure.14 We referred to both versions as Bebchuk et al. (2011) focuses on CEO pay slice (CPS) based on total compensation.15 CPS determines the relative CEO power compared to other executives but may not be a good measure of management’s

relationship to the board. For example, it may not be able to determine if the management team is getting overpaid due to poorgovernance. If everyone on the team is overpaid then CPS may not work well at identifying key governance issues. CEO excesspay solves this issue by examining CEO pay relative to other firms. This provides a measure of the relationship of the CEO to theboard and this measure is likely more relevant to examining acquisition decisions.


Table 9Robustness test: CEO pay slice.

Dependent variable Model 1 Model 2 Model 3 Model 4CAR1 (−1, +1) CAR1 (−1, +1) CAR2 (−2, +2) CAR2 (−2, +2)

Panel A. CEO power and market reactions to M&A deal announcementsConstant Beta 0.038 0.039 0.027 0.030

t-stat 1.16 1.21 0.68 0.76Sig. 0.248 0.227 0.495 0.447

CEO cash pay slice Beta 0.007 0.013(CASHSLICE1) t-stat 0.23 0.343

Sig. 0.822 0.731CEO total pay slice Beta −0.001 −0.002(TOTSLICE1) t-stat −0.05 −0.05

Sig. 0.963 0.958Natural log of total asset Beta −0.003 −0.003 −0.002 −0.002(LNTAST1F) t-stat −1.16 −1.14 −0.64 −0.60

Sig. 0.248 0.258 0.525 0.551Cash dummy Beta 0.009 0.009 0.004 0.004(CASHD) t-stat 0.80 0.79 0.31 0.30

Sig. 0.423 0.430 0.756 0.767Stock dummy Beta 0.008 0.008 −0.008 −0.008(STOCKD) t-stat 0.55 0.56 −0.45 −0.43

Sig. 0.586 0.577 0.656 0.670Public target dummy Beta −0.019* −0.019* −0.014 −0.014(PUBD) t-stat −1.68 −1.67 −1.04 −1.03

Sig. 0.096 0.098 0.299 0.306Tender dummy Beta 0.014 0.013 0.014 0.014(TENMER) t-stat 0.58 0.57 0.51 0.50

Sig. 0.564 0.568 0.613 0.619Cross border acquisition dummy Beta 0.015* 0.015* 0.012 0.012(CROSSBD1) t-stat 1.66 1.66 1.15 1.14

Sig. 0.099 0.100 0.254 0.255Related acquisition dummy Beta 0.005 0.005 0.004 0.004(REL4DIG) t-stat 0.60 0.63 0.37 0.41

Sig. 0.547 0.531 0.709 0.684

N 154 154 154 154

Adj. R2 0.000 −0.001 −0.029 −0.030F-stat 0.996 0.989 0.460 0.445Sig. 0.442 0.447 0.882 0.892

The sample consists of 154 M&A announcements from 2003 to 2005. For acquiring firms, all acquisition events are consideredin case of multiple acquisitions by the firms in any year. CAR1 (−1, +1) and CAR2 (−2, +2) values are used as dependentvariables. “CEO Power” is the CEO pay slice, which is the CEO’s pay as a percentage of the pay of the top five executives. Thegreater the pay slice, the greater the CEO’s power, all else equal. Two different forms of CEO pay slice were used in theregression: (i) CEO cash pay slice, and (ii) CEO total pay slice. Natural log of total asset is used as a proxy for firm size. Wecreated three dummy variables based on the medium of acquisition (cash, stock, or mixed). “Cash dummy” is a dummyvariable. Its value is one, if the medium of acquisition is cash and zero otherwise. “Stock dummy” is a dummy variable. Itsvalue is one, if the medium of acquisition is stock and zero otherwise. “Public target dummy” value is one, if the target firm isa public company and zero otherwise. “Tender dummy” value is one, if the acquisition is done through a tender offer, and zerootherwise. “Cross border acquisition dummy” value is one, if the target is Canadian, and zero otherwise. “Related acquisitiondummy” value is one, if the target is from the related industry, and zero otherwise. We matched 4-digit SIC code to determinethe relatedness of a target firm.

Acquiring firm N = 161 Non acquiring firm N = 87 Mean difference t-stat Sig.

Panel B. Analysis of the impact of CEO power on M&A decisionCEO cash pay slice (C SLICE1) 0.3436 0.3261 0.0175 0.85 0.396CEO total pay slice (T SLICE1) 0.3444 0.3219 0.0225 0.92 0.360

Part (i). Univariate analysis “CEO Power” is the CEO pay slice, which is the CEO’s pay as a percentage of the pay of the top fiveexecutives. Two different forms of CEO pay slice were used in the regression: (i) CEO cash pay slice, and (ii) CEO total pay slice.Means of CEO cash pay slice and CEO total pay slice were calculated and compared for the two groups of acquiring firms andnon-acquiring firms over 2003–2005 period


Table 9 (Continued )

Model 1 Model 2

Initial model without governance variablesCEO cash pay slice (C SLICE1) Beta 0.747

Wald 0.722Sig. 0.395

CEO total pay slice (T SLICE1) Beta 0.683Wald 0.842Sig. 0.359

Constant Beta 0.365 0.388Wald 1.298 1.932Sig. 0.255 0.164

N 248 248



Part (ii). Logit regression on CEO Power Logit regression of “acquire” dichotomous variable on CEO power is presented. If a firmmade an acquisition in a year between 2003 and 2005, “acquire” = 1, otherwise “acquire” = 0. “CEO power” is the CEO pay slice,which is the CEO’s pay as a percentage of the pay of the top five executives. Two different forms of CEO pay slice are used inthe regression: (i) CEO cash pay slice, and (ii) CEO total pay slice

Model 1 Model 2

Full model with governance variablesConstant Beta 0.942 0.923

Wald 1.452 1.515Sig. 0.228 0.218

CEO cash pay slice (C SLICE1) Beta 0.144Wald 0.020Sig. 0.888

CEO total pay slice (T SLICE1) Beta 0.188Wald 0.050Sig. 0.823

Chair is related (CHRREL) Beta 0.752** 0.751**

Wald 5.295 5.292Sig. 0.021 0.021

Percent of inside directors (PINDIR) Beta −2.091** −2.087**

Wald 5.485 5.506Sig. 0.019 0.019

Board size (BDSIZE) Beta −0.026 −0.026Wald 0.219 0.208Sig. 0.640 0.648

CEO ownership (PCEOOWN) Beta 0.253 0.242Wald 0.048 0.044Sig. 0.827 0.834

Blockholder ownership (BLKOWN) Beta 0.013* 0.013*

Wald 3.318 3.355Sig. 0.069 0.067

N 232 232






CEO excess pay measures the CEO’s power relative to the board of directors. Hence this measure maybe more suitable in the context of our study, where we examine the relationship between CEO powerand the propensity for acquisitions.

7. Summary and conclusions

In this study we examine the relationship between CEO power, corresponding acquisition activ-ities and market reactions to M&A announcements with an out-of-sample dataset that involves allcompleted Canadian M&A deals from 1997 to 2005. We use CEO excess pay as a proxy for CEO power.We carry out the analyses in two steps: (i) by developing a theoretical model, (ii) by testing theoreticalarguments with empirical investigations. In the theoretical model, we identify a CEO’s total benefit as afunction of her/his relative board power. We show that as CEOs become more powerful, the incentivesof stealing or expropriation decrease in a country with better legal systems and protection. Therefore,more powerful CEOs are likely to engage in more M&A activities to increase the size of a firm in acountry with a stronger legal system.

Our empirical results show that the market reactions to M&A announcements are not related toCEO power. It implies that powerful CEOs do not necessarily make value destroying acquisitions. Inother words, they do not use acquisitions as a means of wealth expropriation. In order to substantiatethis finding, we examine the long-term operating performance of acquisitions with respect to differentlevels of CEO power. We find that acquisitions made by high-power CEOs do not show any signifi-cant deterioration in the acquirer’s long-term operating performance. Our results further show thatCEO power levels are significantly higher for acquiring firms compared to the CEOs of non-acquiringfirms. In other words, CEOs with more relative power make more acquisitions. Such acquisitions willincrease the size of the firm. Extant literature shows that CEO cash and option based compensationsare generally tied to firm size. The results presented in this study show support to the view that CEOswith more relative power and operating in a country with strong legal environment do not necessarilymake bad acquisitions; however, such acquisitions increase firm size. As firm size is tied to CEO pay,eventually powerful CEOs benefit from M&A activities.

Appendix A.

Table A1Simulated values for′X ′ :

[R(1 − s) − P(1 + R) 1

css(k,s)R(1+R)

(R+P)2 − (s − c(k, s))]

.

Parameters Columns1 2 3 4 5 6 7 8 9

css(k,s) 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00R 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25P 0.30 0.30 0.30 0.50 0.50 0.50 0.70 0.70 0.70s 0.03 0.05 0.10 0.03 0.05 0.10 0.03 0.05 0.10c 0.02 0.03 0.08 0.02 0.03 0.08 0.02 0.03 0.08X 0.0388 0.0238 0.0113 0.0589 0.0439 0.0314 0.0810 0.0660 0.0535

css(k,s) 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50R 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25P 0.30 0.30 0.30 0.50 0.50 0.50 0.70 0.70 0.70s 0.03 0.05 0.10 0.03 0.05 0.10 0.03 0.05 0.10c 0.02 0.03 0.08 0.02 0.03 0.08 0.02 0.03 0.08X −0.0258 −0.0408 −0.0533 0.0010 −0.0140 −0.0265 0.0305 0.0155 0.0030

css(k,s) 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70R 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25P 0.30 0.30 0.30 0.50 0.50 0.50 0.70 0.70 0.70s 0.03 0.05 0.10 0.03 0.05 0.10 0.03 0.05 0.10c 0.02 0.03 0.08 0.02 0.03 0.08 0.02 0.03 0.08X −0.3209 −0.3359 −0.3484 −0.2635 −0.2785 −0.2910 −0.2003 −0.2153 −0.2278


Table A2Simulated values for ’ Y ’ : [ ̨ + R(1 − P + Ps − c(k, s)) + P(s − c(k, s))].

Parameters Columns

1 2 3 4 5 6 7 8 9

˛ 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01R 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25P 0.30 0.30 0.30 0.50 0.50 0.50 0.70 0.70 0.70s 0.03 0.05 0.1 0.03 0.05 0.1 0.03 0.05 0.1c 0.02 0.03 0.08 0.02 0.03 0.08 0.02 0.03 0.08Y 0.1853 0.1873 0.1785 0.1388 0.1438 0.1375 0.0923 0.1003 0.0965

˛ 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01R 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15P 0.30 0.30 0.30 0.50 0.50 0.50 0.70 0.70 0.70s 0.03 0.05 0.1 0.03 0.05 0.1 0.03 0.05 0.1c 0.02 0.03 0.08 0.02 0.03 0.08 0.02 0.03 0.08Y 0.1164 0.1188 0.1135 0.0893 0.0943 0.0905 0.0622 0.0698 0.0675

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CEO power, M&A decisions, and market reactions

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