Does deregulation induce competition in the market for corporate control? The special case of banking

Journal of Banking & Finance 37 (2013) 5220–5235

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Journal of Banking & Finance journal homepage: www.elsevier.com/locate/jbf

Does deregulation induce competition in the market for corporate control? The special case of banking Chinmoy Ghosh a,1, Milena Petrova b,⇑ a b

School of Business, University of Connecticut, Storrs, CT 06268, USA Martin J. Whitman School of Management, 721 University Avenue, Syracuse University, Syracuse, NY 13244, USA

a r t i c l e

i n f o

Article history: Available online 22 June 2013 JEL classification: G21 G28 G34 G38 Keywords: Financial regulation Corporate control Corporate governance Mergers and acquisitions

a b s t r a c t Using a sample of 936 acquisitions of commercial banks, we examine the relation between the probability to engage in value-reducing acquisitions and corporate governance structures, as well as the relation between acquirer announcement-period abnormal stock returns and antitakeover indices and measures, and how these relations were affected by the change in the market for corporate control, caused by deregulation due to the implementation of the Interstate Banking and Branching Efficiency Act of 1994 and the Financial Service Modernization Act of 1999. We find that prior to deregulation there is no relation between probability to engage in value destroying acquisitions or acquirer returns and antitakeover indices, whereas after the adoption of the FSMA, probability to engage in value destroying acquisitions and the stock market reaction to bidder M&A announcements are both significantly related to governance indexes and measures. Our findings further confirm the linkage between the market for corporate control, antitakeover indices and firm value. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Managerial propensity to expropriate corporate resources for their own benefit at the cost of shareholders’ interests and the mechanisms that limit the opportunities to do so have been issues of great attention for both academics and policy makers. Among these mechanisms, the market for corporate control is one of the most effective. Specifically, financial economists contend that a competitive market for corporate control enhances firm valuation by forcing more efficient utilization of economies of scale and scope, and by motivating managers to exert greater effort, in response to fears of loss of control. However, a series of studies (Gompers et al., 2003; Bebchuk et al., 2009; Cremers and Ferrell, 2011, among others) have established that the effectiveness of the market for corporate control can be jeopardized by anti-takeover barriers created by individual firms, or imposed by regulation. Specifically, extant research shows that anti-takeover provisions (ATPs), like poison pill and staggered board that delay (and, hence discourage) or thwart hostile takeover attempts, have significantly adverse impact on share values. To measure the extent, to which managers in individual firms are protected from the market for

⇑ Corresponding author. Tel.: +1 (315) 443 9631. E-mail addresses: [email protected] (C. Ghosh), [email protected] (M. Petrova). 1 Tel.: +1 (860) 728 2421. 0378-4266/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jbankfin.2013.06.002

corporate control, extant research uses several recently developed governance indices. Gompers et al. (2003), develop the G-Index, which includes a complete set of the 24 ATPs, tracked by IRRC and establish that a portfolio of long democracy firms with strong shareholder protection (G-Index < 5) and short dictator firms with weak shareholder protection (G-Index > 14) significantly outperforms the market. Complimentary evidence is provided by Masulis et al. (2007) who show that acquisition announcements by firms with more ATPs are associated with significantly lower abnormal returns. Bebchuk et al. (2009) show that the negative effect of ATP measures is attributable mainly to only six of the anti-takeover mechanisms, which they use to form an Entrenchment Index (E-Index); the other 18 have only marginal negative effect on value. Cremers and Ferrell (2011) use 1985 – the year when Delaware court validated the adoption of poison pills – as a pivotal year, and find that the negative association between G-Index and firm value exists only after 1985 and that the effect of poor shareholder protection (high G-Index) is mainly due to poison pills. Furthermore, the impact of anti-takeover regulation depends on the structure of the market as highlighted in a recent study by Giroud and Mueller (2010) who examine the notion that the less competitive an industry is, the greater the managerial slack and waste are, and consequently, the more adverse the effect of anti-takeover regulation is. Specifically, Giroud and Mueller (2010) examine the effect of a moratorium imposed by business combination (BC) laws, passed in various states between 1985 and 1991. By preventing certain transactions by large shareholders for a period of time after they

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acquire their stakes, the moratorium rendered hostile takeovers almost impossible, thus weakening corporate governance and allowing managerial slack to increase. The authors find the effect of BC laws to be less adverse in competitive industries, which is consistent with the idea that competition mitigates managerial slack. Giroud and Mueller’s (2010) findings have important policy implications – when considering new takeover regulation, policy makers ought to pay particular attention to its potential impact on less competitive industries. Our objective is to provide new insight on the effect of regulatory changes on the market for corporate control by examining the differential impact of ATPs around new legislation. Specifically, we focus on two important regulatory changes in the banking sector in the last two decades. The first is the Interstate Banking and Branching Efficiency Act (IBBEA, also known as the Riegle-Neal Act) of 1994, which removed the intricate details of the various state laws governing interstate bank acquisitions and allowed BHCs to acquire banks in any state in the Union. Brook et al. (1998) document a significantly positive reaction of bank stocks to the passage of IBBEA. The authors attribute the effect to the transformation of the banking industry from one where takeover activity was restricted to one where restrictions on mergers and acquisitions were largely eliminated. The second regulatory change is the Financial Services Modernization Act of 1999 (FSMA, also known as the Gramm-Leach-Bliley Act), which permitted combinations of commercial and investment banks, and insurance companies, and facilitated the creation of financial holding companies that can participate in sale of insurance and marketable securities. Akhigbe and Whyte (2001) find that the passage of FSMA induced a significantly positive revaluation of financial institutions including banks, brokerage firms, and insurance companies. The authors attribute the effect to the benefits, associated with cross-industry mergers and acquisitions following the deregulation. In corroboration, the data indicate that FSMA ushered in an era of consolidation in the banking industry – the percentage of inter-industry mergers among financial firms increased from 11.5% in the three-years before FSMA to 17.7% in the three-years following FSMA (Carow et al., 2011). Notwithstanding the favorable stock market reaction to the passage of IBBEA and FSMA (Brook et al., 1998; Carow and Heron, 1998; Akhigbe and Whyte, 2001; Czyrnik and Klein, 2004, among others), governance by the corporate control market, especially the role of ATPs, remains an issue with very limited evidence in the banking sector. We contend that the changing environment of deregulation, initiated by the enactment of the IBBEA and FSMA, affords us a unique opportunity to explore this issue. Specifically, the setting allows us to examine how the transition to an unconstrained market for corporate control influences managerial disposition to value-reducing acquisitions, and the impact of ATPs on acquirer’s stock returns. Of particular interest is the effect of diversifying acquisitions, in view of the evidence that in absence of strict monitoring, entrenched managers tend to deploy free cash flow to diversifying acquisitions despite the associated loss of share value (Masulis et al., 2007; Harford et al., 2008). For evidence in the banking sector, Delong (2001) classifies mergers according to activity and geographic diversification and finds that mergers that diversify these attributes induce loss of value. Laeven and Levine (2007) find that diversification reduces the value of financial conglomerates. Conceivably, pursuit of multiple activities intensifies agency problems in financial conglomerates.2 We investigate the role of ATPs in banking in two specific contexts: (1) the likelihood of value-destroying acquisitions; and, (2) the valuation effect of acquisition announcements. We hypothesize that following deregulation, (1) the likelihood of value-destroying acquisitions 2 Similarly, Schmid and Walter (2009) show that broadening of functional scope is detrimental to both competitive performance and shareholder value of financial conglomerates.

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will be positively related, and (2) abnormal returns surrounding acquisitions will be negatively related to ATP indices. Conversely, prior to deregulation, ATPs are redundant, and have no impact on firm’s takeover decisions and the associated valuation effect. We analyze 936 acquisitions from 1991 to 2011 by banks (SIC codes 6021, 6022, 6029, 6035 and 6036) over three periods separated by the passage of IBBEA and FSMA: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA to pre-FSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). Previous research has established that passage of IBBEA initiated the transition from a regulatory environment imposing severe limits on takeovers (pre-IBBEA period) to one that allows intra- and interstate acquisitions (Brook et al., 1998), and ultimately during the post-FSMA era to an environment of full flexibility by allowing not only full interstate branching, but also combinations among banking, insurance and investment firms (Akhigbe and Whyte, 2001). As expected, our data reveal a significant increase in diversifying mergers since deregulation, as well as varying type and frequency of diversification across sub-periods. We find a significant increase in activity diversification from 8% of all acquisitions in the first period (pre-IBBEA) to 28% of all acquisitions in the last period (post-FSMA). Over the same period, geographic diversification also increases but by a smaller margin – from 58% to 66%. We also observe that while activity diversifying acquisitions increase significantly over post-IBBEA–pre-FSMA period, these combinations occur within the same 2-digit SIC code. In contrast, in the post-FSMA period, there is a significant increase in the number of diversifying M&As involving industries with different 2-digit SIC code. We measure the degree of anti-takeover protection at individual firm level by using G-Index (a number from 0 to 24, which adds one for each of the 24 anti-takeover provisions tracked by IRRC and adopted by the firm, as developed by Gompers et al. (2003)). We also examine the impact of staggered board, poison pill and golden parachute provisions separately.3 Finally, we analyze the importance of all other provisions that are part of the G-Index, but excluding staggered board, golden parachute and poison pill ATPs, by forming an O-Index equal to G-Index minus staggered board, golden parachute and poison pill. Our analyses reveal two important results. First, when controlling for firm characteristics and corporate governance, activity diversification is positively related to firm value in the pre-deregulation (pre-IBBEA) period, when banks were restricted in diversification activities. Furthermore, geographic diversification is negative in the third period (post-FSMA), when such limits were removed.4 Our results in the postFSMA period are consistent with DeLong (2001) and Schmid and Walter (2009) who show that diversifying acquisitions destroy value. Our findings with respect to the relation between geographic diversification and cumulative abnormal returns for the entire period studied from 1991 to 2011 are consistent with a recent study by Schmid and Walter (2012), showing a weak and changing relation between geographic diversification and value, which depends on the firm’s main activity-area within the financial services industry. Second, consistent with our hypotheses, prior to deregulation (pre-IBBEA period), G-Index as well as the other ATP measures have 3 We also investigate the impact of the E-Index, a number from 0 to 6, which equals the sum of the presence of the six most important ATPs, identified by Bebchuk et al. (2009). However, we observe that the three most important components of the index are staggered board, poison pill and golden parachute. Furthermore, their impact in each period is different. Therefore, rather than discussing the E-Index, we choose to examine each of the three ATPs separately, in order to show their changing importance over time. 4 We find no significant relationship between activity diversification and abnormal returns in the third period. However, in results, not reported in the tables, activity diversification is significantly negatively related to value in the third period, when we do not control for corporate governance and ATP measures in the models. We obtain similar results using Activity diversifying alt, based on Morck et al. (1990) approach, but our sample size is reduced significantly as to define this type of activity diversification, the target must be a public firm.

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no discernible impact on the announcement period abnormal returns. In contrast, ATP measures are significantly negatively related to the announcement period return, after the deregulation (postFSMA). Similarly, G-Index is positively related to the likelihood to engage in value-reducing acquisitions only post-FSMA. Our analysis shows that this relationship is driven by the increased significance in golden parachute. This also coincides with the increase in adoption of golden parachute from 59% of the banks prior-deregulation to 82% post-deregulation. Finally, our ATP indices and most measures show no significance during the period between the passage of IBBEA and FSMA (1994–1999). We believe that since most states already allowed intrastate and interstate banking prior to the passage of IBBEA (Stiroh and Strahan, 2003), the act merely signaled the final vindication that interstate banking has full federal approval. Our study makes several important contributions to the literature on corporate governance. It provides comprehensive evidence on governance by the market for corporate control and the role of anti-takeover provisions in the banking sector, an area that has received only limited attention in the extant literature. In addition, by establishing that the effect of antitakeover provisions is discernible only in an environment of active takeover market, it lends credence to the conjecture by previous researchers (Bebchuk et al., 2002, 2009; Bebchuk and Cohen, 2005) that the negative association between firm value and antitakeover provisions is due mainly to the effective protection they provide to managers from unfriendly suitors. In a dormant takeover market, antitakeover provisions are redundant. Finally, our analyses reveal important insights on the role of diversification depending on the regulatory regime. The rest of this paper is organized as follows: Section 2 provides a brief review of the IBBEA and the FSMA; our hypotheses are presented in Section 3; Section 4 describes the data; Section 5 presents the results of the empirical analyses, and Section 6 concludes. 2. Deregulation in the banking sector In 1927, the McFadden Act placed banks under the purview of state laws, which did not allow out-of-state banks to take over state banks. In response, bank holding companies were formed in various states. The Douglas amendment of the Bank Holding Company (BHC) Act of 1956 forbade BHCs from acquiring banks from another state without express permission of that state, thereby making interstate bank acquisitions impossible without favorable state legislation. Over the next four decades, several states gradually removed restrictions to interstate banking. Stiroh and Strahan (2003) note that by 1997, most of the fifty states allowed interstate banking.5 2.1. The IBBEA and its effects The Interstate Banking and Branching Efficiency Act (IBBEA), drafted by lawmakers Riegle and Neal was enacted on September 29, 1994 and became effective in 1997. In addition to repealing the Douglas amendment, the main effect of IBBEA was to remove the intricate details of the various state laws governing interstate bank acquisitions, and allow BHCs to acquire banks in any state in the Union. The act indicated the federal government’s willingness to finally embrace interstate banking, which was expected to promote a more active market for corporate control. Brook et al. (1998) identify several benefits accruing from the potential consolidation in the banking sector: removal of inefficient managers through takeovers, avoidance of non-value-maximizing strategies by managers, and improvement in efficiency of poorly performing banks to minimize takeover threat. Brook et al. attri5 See Table 1 in Stiroh and Strahan (2003), p. 808. The first state to allow interstate banking was Maine in 1978, and the latest was Hawaii in 1997.

bute the strongly positive reaction of bank stocks to the passage of the IBBEA to these takeover induced gains.6 2.2. The FSMA and its effects Following the great depression of the 1930s, the Glass-Steagall Act was passed in 1933 to separate commercial banking, investment banking, and insurance activities. Over the next few decades, investment banking and insurance businesses witnessed significant growth. To allow banks to participate in the sale of insurance and marketable securities, federal regulatory agencies started to gradually remove the barriers imposed by the Glass-Steagall Act, finally culminating in the enactment of the Bank Holding Company Act of 1956. It was apparent that even without legislative reform, the separation between traditional commercial banking activities and non-banking business would continue to gradually disappear.7 The 1997 acquisition of Alex Brown by Bankers Trust represented the first acquisition of a major investment bank by a commercial bank, followed soon by the 1998 merger between Citicorp and Travelers Insurance Group. In response to pressure to streamline the existing rules and regulations to reform the financial services industry, the historic Financial Services Modernization Act (FSMA) was signed into law on November 12, 1999. Carow and Heron (2002) note that although financial companies could take advantage of exceptions and loopholes to cross-sell financial services prior to FSMA, the act removed restrictions to the formation of financial conglomerates and reduced the cost of inter-industry combinations. It was expected that FSMA would induce significant valuation gains from consolidation through inter-industry mergers. Consistent with this prediction, Akhigbe and Whyte (2001) report significant valuation gains for banks, brokerage firms, and insurers, which the authors attribute largely to new opportunities created by cross-industry mergers and acquisitions. Czyrnik and Klein (2004) report that only commercial banks gained from the passage of the act although no segment of the financial services industry sustained undue losses. Mamun et al. (2004) find that larger banks stand to gain more from the act. Harjoto et al. (2010) find that banks are more likely to acquire non-banks post-FSMA and both size and frequency of transactions jump in 1999. 3. Hypotheses Extant literature reveals considerable evidence – corroborated by our data – that the takeover market in banks has become more active following deregulation.8 Additionally, previous literature re6 Carow and Heron (1998) report significant value gains for a sample of 180 BHCs surrounding the passage of IBBEA, with higher price appreciation for BHC stocks with characteristics closely aligned with takeover targets. Jones and Critchfield (2005) identify IBBEA as the main catalyst for the consolidation in the banking industry. Akhigbe and Whyte (2001) find that both total and unsystematic risk fell significantly following the passage of IBBEA, and that the reduction in risk is directly related to how restrictive the interstate banking provisions in individual states are. Overall, the evidence is consistent with the notion that the potential gains from IBBEA reflect the competitive takeover market induced by the act. 7 A series of events contributed to this popular notion. An early such event was the 1981 purchase of Charles Schwab by BankAmerica, which prompted Federal Reserve Board (FRB) to announce that BHCs could own discount brokerage firms. In April, 1987, FRB approved expanded securities underwriting activities for money center banks. Apilado et al. (1993) report a positive and significant valuation effect for money center banks around this event. Carow (2001) and Johnston and Madura (2000) show that banks, brokerage firms and insurance companies posted significant gains at the CitiCorp-Travelers merger announcement. 8 For evidence, IBBEA led to a major consolidation in the banking industry. Jones and Critchfield (2005) document that the number of banking and thrift organizations fell from 15084 in 1984 to 7842 in 2003. FSMA also triggered a wave of mergers between banks and non-banking institutions. Harjoto et al. (2010) report that the frequency of banks acquiring non-banks increased significantly in 1999, and that nonbank acquisitions surpassed bank acquisitions in 2002.

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veals favorable stock market reaction to the enactment of interstate banking and branching (IBBEA), and expansion of banking services to include brokerage and insurance activities (FSMA). To provide additional insight on the effect of takeover deregulation in the banking sector, we focus on the role of anti-takeover provisions. Masulis et al. (2007) posit that when managers are insulated from the market for corporate control, their decisions are influenced more by personal interests than maximization of shareholder wealth. Masulis et al. (2007) and Harford (1999) establish that investment in acquisitions is entrenched managers’ preferred channel to deploy cash reserves. Specifically, the more protected the managers are from the takeover market, the more likely they are to invest in non-value-maximizing acquisitions. Based on these studies, we posit that as frequency of acquisitions increases and the market for corporate control becomes more active, following the passage of IBBEA and FSMA, managers that are more protected from hostile takeovers by ATPs are more likely to undertake value-destroying acquisitions. This argument implies a positive relation between the governance indices and the likelihood of value-reducing acquisition. Hypothesis 1. Deregulation in the banking industry reformed the market from one where strict regulation prohibited takeover activity to one where restrictions on mergers and acquisitions are eliminated. Post-deregulation, probability to engage in valuereducing acquisitions will be positively related to the ATP measures and indices, which represent the extent to which managers are insulated from the market for corporate control. If shareholders anticipate that following deregulation managers, protected by ATPs, are more likely to invest in value-reducing acquisitions, then the market reaction to acquisitions will reflect the ATPs, the acquiring firm has in place; the more barriers to takeovers the firm has, the more adverse the market reaction will be. Masulis et al. (2007) provide significant support for this notion for all firms, and identify staggered board as the most potent antitakeover provision. Accordingly, we state our second hypothesis: Hypothesis 2. Post-deregulation, the abnormal returns associated with acquisitions will be negatively related to ATP measures and indices. Finally, previous literature shows that alternative governance mechanisms can be effective in restraining managerial behavior. For example, Minnick et al. (2011) hypothesize that an effective way to control entrenched managers’ behavior is to align their compensation structure with shareholders’ interest. The authors demonstrate that pay-for-performance sensitivity (PPS) of managerial pay, which serves as a proxy for managers’ alignment with shareholders’ interests, is inversely related to the price reaction to acquisition announcements. However, Brickley and James (1987) note that internal governance mechanisms including board structure and managerial compensation scheme act as substitutes for the market for corporate control. Accordingly, they argue that as the market for corporate control becomes more competitive, the independence of the board decreases. This evidence leads to our final hypothesis: Hypothesis 3. If market for corporate control and internal governance mechanisms (i.e. board structure, CEO compensation) are substitute monitoring devices, and the effect of the market for corporate control dominates in an active takeover market, the relationship between the probability to engage in value-reducing acquisitions, as well as acquisition announcement abnormal returns, and internal governance mechanisms will be weaker post-deregulation.

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4. Sample selection We obtain M&A data in the banking industry from SDC Platinum. The original dataset from SDC contains 5735 acquisitions from January 1, 1991 to December 31, 2011 made by banks (acquirers with SIC codes 6021, 6022, 6029, 6035, 6036, 6099). We exclude observations, for which we are not able to obtain accounting data from COMPUSTAT and stock return data from CRSP; this reduces our sample to 3530 acquisitions. Note that the original sample includes acquisitions also by private banks; therefore, the first screening stage eliminates private acquirers. We calculate announcement-period abnormal returns using Eventus. We are not able to calculate announcement returns for 18 acquisitions, which reduces our sample to 3512 deals. Since our study focuses on the impact of the corporate governance provisions on wealth effects of acquisitions in the banking industry, we require that corporate governance index data obtained from RiskMetrics Governance dataset be available for all acquirers in the sample. This further reduces our sample to 1509 acquisitions. We acknowledge that our results are valid for larger banks, since RiskMetrics covers only these firms; a bias that many other studies, using the RiskMetrics data, including the widely cited study by Masulis et al. (2007), suffer from. Finally, we impose the following additional selection criteria: (a) the acquisition is completed; (b) the acquirer controls less than 50% of the target prior to the acquisition announcement date and 100% after the acquisition; (c) the deal value is more than $1 million. This leaves us with a final sample of 936 acquisitions by 147 banks with SIC codes 6021, 6022, 6029, 6035, and 6036.9 CEO compensation and ownership data are collected from ExecuComp, and board characteristics are obtained from RiskMetrics. Including CEO characteristics and board data reduces the usable sample to 570 and 439 observations, respectively, due largely to the fact that director data is available in RiskMetrics only after 1996. Therefore, we use the full sample of 936 observations, for which ATP indices data is available, throughout the paper, but use the smaller samples to conduct the additional analyses controlling for CEO characteristics and board characteristics. All accounting and governance data for the acquirers are obtained for the fiscal year prior to the announcement year. For governance related variables, we follow the convention and assume that firms have the same governance provisions as in the previous IRRC publication during the years between two consecutive publications. We calculate the O-Index by subtracting from the G-Index the three main antitakeover provisions – staggered board, poison pill, and golden parachute. Finally, for the value-reducing acquisition likelihood analysis, we employ a sample of all banks with SIC codes 60XX in the COMPUSTAT universe, for which we are able to obtain the required accounting variables and governance indices. We remove bidder firms from the control sample in the year of acquisition. The control sample consists of 232 banks and 1164 firm years. Variable definitions are provided in Appendix A. Table 1 provides the distribution of our sample of bank acquisitions by announcement year and compares acquisition activity in banking to the general (non-financial) M&A activity in the economy. Market deal volume, acquirer market value and deal size are CPI adjusted using 1990 as the base year. Overall, we observe that the M&A activity in the banking sector follows similar cycles as that of non-financial firms. The highest number of bank acquisitions occurs in 1994, the year U.S. Congress passed the Riegle-Neal Interstate Banking and Branching Efficiency Act. However, the following year witnessed a sharp decline in bank merger activity 9 SIC codes of 6021, 6022, and 6029 identify commercial banks, while 6035 and 6036 stand for savings institutions. Our sample is dominated by banks with SIC codes of 6021 and 6022, which represent 94.6% of our sample. There are 32 institutions with SIC code 6035; 13 – with SIC code of 6036 and only 6 banks with SIC code of 6029.

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Table 1 Sample distribution by year of 936 bank mergers and acquisitions during 1991–2011. Year

Number of bank acquisitions

Percentage of bank sample (%)

Market (nonbanks) M&A activity

Market (nonbanks) deal volume ($mil)

1991

38

4.06

622

49,092

1992

55

5.88

933

60,526

1993

87

9.29

1298

106,041

1994

107

11.43

1645

140,947

1995

56

5.98

1647

203,157

1996

49

5.24

2085

303,941

1997

59

6.30

2849

438,780

1998

74

7.91

2844

833,516

1999

55

5.88

2149

686,331

2000

44

4.70

1909

729,202

2001

34

3.63

1295

325,753

2002

24

2.56

1213

152,831

2003

31

3.31

1154

192,177

2004

50

5.34

1333

250,649

2005

38

4.06

1451

353,611

2006

51

5.45

1426

320,385

2007

39

4.17

1311

220,788

2008

13

1.39

884

142,044

2009

7

0.75

637

210,738

2010

11

1.18

807

144,601

2011

14

1.50

841

164,498

Total

936

1635

311,976

100

Mean acquirer market value of equity ($mil) (median)

Mean deal value ($mil) (median)

Mean relative deal size (median)

Mean CAR [2, 2] (median)

1832 (1013) 2053 (1080) 1957 (1303) 2285 (1437) 2109 (1400) 2225 (1357) 4659 (2692) 4889 (3660) 11,866 (4581) 14,744 (6628) 16,075 (6112) 17,445 (5990) 18,896 (4846) 15,498 (1708) 15,937 (1774) 11,215 (1453) 17,981 (1928) 29,928 (6392) 1902 (819) 8220 (625) 7446 (1665)

260 (28) 95 (31) 70 (35) 100 (42) 254 (29) 162 (38) 510 (64) 354 (98) 305 (140) 980 (118) 387 (97) 344 (52) 1453 (152) 1259 (95) 924 (81) 844 (96) 348 (102) 2998 (260) 185 (121) 195 (50) 481 (116)

10.90% (2.35%) 5.89% (2.37%) 4.77% (3.07%) 6.72% (2.48%) 8.92% (2.56%) 8.09% (3.76%) 13.91% (2.59%) 8.41% (2.81%) 10.24% (2.64%) 7.71% (2.89%) 6.28% (2.37%) 3.65% (1.13%) 8.57% (2.62%) 10.28% (4.26%) 7.04% (4.35%) 12.70% (5.21%) 8.34% (5.48%) 8.35% (4.07%) 14.30% (6.91%) 20.25% (7.61%) 13.20% (10.26%)

0.29% (0.37%) 0.47% (0.27%) 0.80% (0.78%) 0.69% (0.43%) 0.64% (0.47%) 0.25% (0.47%) 0.85% (0.11%) 2.31% (2.79%) 1.65% (2.12%) 0.10% (0.28%) 1.05% (0.98%) 0.20% (0.06%) 1.85% (1.44%) 0.54% (0.82%) 0.99% (1.04%) 0.76% (0.67%) 1.44% (1.09%) 1.46% (0.15%) 11.63% (8.8%) 1.56% (1.47%) 1.82% (1.11%)

8082 (2071)

471 (65)

8.60% (2.97%)

0.79% (0.75%)

Table 1 reports summary statistics for the annual distribution of the number of M&As in our sample, acquirer size, deal and relative deal size, and 5-day cumulative abnormal returns – CAR[2, 2], as well as general M&A activity – number and deal volume of M&As, excluding bank acquisitions. The total sample contains 936 M&As by 147 bank acquirers with primary SIC codes 6021, 6022, 6029, 6035 and 6036 during 1991–2011. Market deal volume, acquirer market value of equity and deal value are CPI adjusted using 1990 as the base year. Variable definitions are provided in the Appendix A.

with the number of acquisitions decreasing from 107 in the previous year to a mere 56. Post-1997, the year when IBBEA was enforced, we note an increase in average acquirer and target size, which may be interpreted as indicative of the beginning of consolidation in the banking industry. Another landmark year in the banking industry is 1999, when FSMA was enacted. Acquirer size experienced a large increase following that year, which coincides with a stronger wave of consolidation post-FSMA and echoes the finding in Akhigbe and Whyte (2001) and Mamun et al. (2004) that large banks stand to gain most from the deregulation. The period from 1999 to 2000 coincided with the M&A ‘‘bubble’’ period reported in Masulis et al. (2007). While it is difficult to disentangle the FSMA effect from the M&A boom, we note that bank acquisition activity as well as non-financial M&As display the same general trend (in terms of volume and mean deal size) – dramatic decrease in 2001, followed by a recovery in 2004. With the exception of the years immediately after the passage of the two acts from 1994 and 1999, the transaction size of bank mergers parallels the pattern, observed in the general M&A market.

We also note that during the financial crisis, the number of bank acquisitions fell sharply to 13 in 2008 and only 7 in 2009, the worst year in the M&A market. Following 2008, distress sales of major financial institutions, such as Merrill Lynch and Wachovia Corp., drove the average acquirer and deal size to significantly higher numbers. It is also intriguing that 2008 and 2009 are the only years in the sample when the average acquirer returns are positive.10 One potential interpretation of this observation is that acquirers were able to extract value from the fire sales during the crisis. Finally, we do not observe any obvious changes in the annual cumulative abnormal returns (CARs) over the three periods. Differences in CARs seem to be driven by the general market conditions and economic cycles, rather than changes in regulation. For example we observe the lowest returns during the periods of 1998–1999, 2001, 2003, 2007 and 2010–2011. These periods coincide with the Asian and

10 Mean bank M&A announcement returns in 2000 were also positive (0.20%), but median returns were negative (0.28%).

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Russian financial crises (1997–1998), the dot-com bubble burst (2001), the US-Iraq war (2003), and the financial crisis of 2007– 2012, including the European sovereign debt crisis.

5. Empirical analyses and results 5.1. Summary statistics of acquirer and deal characteristics In Table 2 we present summary statistics for acquiring firms and deal characteristics by the three periods: pre-IBBEA, post-IBBEA and pre-FSMA, and post-FSMA. As previously observed, each period represents a distinct and increasingly deregulated market for corporate control. Panel A presents summary statistics for the ATP indices and measures examined in our study – the G-Index (Gompers et al., 2003), staggered board, poison pill, golden parachute and the ‘‘all other provisions index’’ – O-Index. We examine staggered board and poison pill separately, as Masulis et al. (2007) present evidence that a staggered board is associated with the most adverse valuation effect in response to M&A announcements by US firms, while Cremers and Ferrell (2011) find that poison pill is the most effective of all anti-takeover devices. Indeed, Cremers and Ferrell (2011) observe that prior to the Moran v. Household decision, which removed the legal uncertainty surrounding poison pills, only 5% of the firms in their sample had adopted this ATP measure. Within three years after the judicial approval of poison pills, over 60% of the firms in the sample had adopted this measure. In our sample, 70% and 85% of the banks have staggered boards in periods I and II respectively. These percentages are markedly higher than the incidence of classified boards for US bidder firms reported by Masulis et al. (2007) – only 61% of the acquirers have staggered boards. On the other hand, while the adoption of poison pills by bidders in our sample prior to the third period is similar to that reported by Cremers and Ferrell (2011), our data reveals a significant decrease in the adoption of poison pill post-FSMA, when the percentage of bidders with poison pill drops from 64% to 44%. We also focus on golden parachute as it has been identified by Bebchuk et al. (2009) as one of the most important ATPs. Note that the percentage of banks adopting golden parachute provision systematically increased over the three identified periods from 59% in pre-IBBEA to 82% post-deregulation. This change is important, since while golden parachutes are viewed as restriction on shareholder rights and hence are expected to have a negative relation to firm’s value (Gompers et al., 2003); they are closely related to the incentives of managers and their risk-taking behavior. Whereas other ATPs only shelter managers from the disciplining effect of the active takeover market, golden parachutes not only protect managers, but also increase their wealth in case of takeover. Therefore, we expect that when determining the likelihood of engaging in value-destroying acquisitions in an active takeover market, the effect of golden parachutes may be stronger than that of other ATPs. Interestingly, with the exception of golden parachutes, all other ATP measures and indices are significantly lower in the third period compared to the second period; an intriguing finding, which could be contributed to either banks enhancing shareholder rights after FSMA, or that the bidder market was dominated by banks with better corporate governance. A fuller analysis of this issue is worthy of further research. In Panel B, we examine acquirer characteristics. Compared to the levels reported by Masulis et al. (2007), on average, bank acquirers are larger in asset size ($46 billion for the total sample versus $9 billion)11 and more leveraged (81% for the total sample versus 15%). These different characteristics between banks and other firms have been documented in prior banking studies including 11

Median acquirer total assets are on average $12 billion.

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Adams and Mehran (2003) and Hagendorff et al. (2007). Indeed, these authors note that such differences provide a justification for separate investigation of corporate governance of banks. In addition, acquirer’s total assets increased over the three periods, corroborating previous evidence (Akhigbe and Whyte, 2001; Mamun et al., 2004) and our interpretation that the two acts promoted consolidation in the industry. Leverage decreased significantly over the three periods. The second period was associated with significant decrease in liquidity (increase in loan-to-asset ratio) and increase in profitability (ROA), while both profitability and liquidity remained similar in the third period, compared to that during the second period. As reported in Panel C, the deal characteristics of our sample mergers are notably different from those reported by Masulis et al. (2007): deals in our sample are relatively smaller (with relative deal size of 7–10% versus 16%), more likely to be a public company (with public targets0 share ofbetween 59% and 72% versus 33%) and less likely to be financed by cash (with cash acquisitions0 share between 11% and 21% versus 46%). Given that all of these features have been shown by prior researchers to significantly affect acquirer returns (Moeller et al., 2004; Chang, 1998; Fuller et al., 2002), it is important to control for them in the multivariate regression analysis. As before, we observe that relative deal size significantly increased post-IBBEA, additional vindication that the two acts promoted consolidation in the banking industry. Similarly, post-IBBEA, the percentage of geographically diversifying mergers increased significantly from 58% to 67%. The fact that 58% of the acquisitions were geographically diversifying (interstate) prior to the IBBEA shows that the effect of the Riegle-Neal Act was muted due to many of the states having in place regulation that permitted interstate acquisitions prior to the implementation of the Act. We also examine the changes in the occurrence of ‘‘activity diversifying’’ acquisitions between the three periods. We distinguish between three different types of activity diversifying acquisitions. The first group includes cases where the main SIC code of the bidder and target firms are different (Activity diversifying). The second group comprises combinations where the bidder acquires a target that has at least one division with a 2-digit SIC code that is different from the SIC codes associated with the bidder (Activity diversifying SIC2); this classification captures unrelated acquisitions outside of the 60XX SIC code. Finally, we follow the methodology of Morck et al. (1990) – subsequently applied by DeLong (2001) – to examine similarity in bidder and target stock market returns. For each public acquirer–target pair we obtain daily returns from 300 to 46 days prior to the merger announcement (for acquirer and target separately). For each pair we calculate the correlation of their daily returns. Next, we divide the pairs into geographically diversifying versus geographically focusing based on whether the bidder and target are headquartered in different states. We calculate the median correlation of returns for the two groups (geographically diversifying and focusing). If the correlation coefficient of a pair is higher than the median correlation coefficient for the group, we classify such an acquisition as non-diversifying; otherwise, we classify the acquisition as diversifying (Activity diversifying alt). Note that the third method for distinguishing diversifying acquisitions can only be applied to public– public pairs. The summary statistics presented in Panel C of Table 2 show that activity diversifying acquisitions increased significantly from 8% in the pre-IBBEA period to 28% in the post-FSMA period. However, diversifying acquisitions in the second period were predominantly within the same two digit SIC codes, since activity diversification based on two digit SIC code (Activity diversifying SIC2) increased only slightly from 11% to 13%. As expected, the post-FSMA period saw significant increase in activity diversifying acquisitions where the bidders acquired firms with different 2-digit SIC codes. These statistics support the notion that FSMA promoted cross-industry acquisitions.

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Table 2 Summary statistics of variables of interest in 936 bank mergers during 1991–2011 by period. Variable

Pre-IBBEA (I) (N = 264)

Post-IBBEA, pre-FSMA (II) (N = 311)

Post-FSMA (III) (N = 361)

(II)–(I)

(III)–(II)

Mean

Mean

Mean

SD

Diff.

Diff.

SD

SD

Panel A: Summary statistics for corporate governance indices (N = 936) G-Index 9.97 2.42 10.12 Staggered board 0.70 0.46 0.85 Poison pill 0.63 0.48 0.64 Golden parachute 0.59 0.49 0.69 O-Index 8.04 2.00 7.94

2.10 0.36 0.48 0.46 1.75

9.21 0.70 0.44 0.82 7.24

2.82 0.46 0.50 0.38 2.32

0.14 0.15*** 0.01 0.10** 0.10

0.91*** 0.14*** 0.20*** 0.13*** 0.70***

Panel B: Acquirer characteristics (N = 936) Firm size 23.16 Leverage 0.87 Free cash flow 0.02 Loan-to-asset 0.57 Return on assets 0.03

0.93 0.04 0.00 0.09 0.01

23.30 0.80 0.02 0.60 0.03

0.86 0.08 0.01 0.09 0.01

23.70 0.78 0.02 0.60 0.03

1.69 0.07 0.01 0.11 0.01

0.14* 0.07*** 0.001 0.03*** 0.003***

0.40*** 0.02*** 0.002*** 0.004 0.0003

Panel C: Deal characteristics (N = 936) Relative deal size 0.07 Public target 0.59 Private target 0.41 Cash financing 0.19 Stock financing 0.66 Other financing 0.15 Activity diversifying 0.08 Activity diversifying SIC2 0.11 Activity diversifying alt 0.05 Geographically diversifying 0.58 Hostile 0.01

0.13 0.49 0.49 0.40 0.47 0.36 0.28 0.32 0.22 0.50 0.09

0.10 0.63 0.37 0.11 0.78 0.11 0.15 0.13 0.07 0.67 0.01

0.22 0.48 0.48 0.31 0.41 0.31 0.36 0.34 0.25 0.47 0.08

0.09 0.72 0.28 0.21 0.63 0.16 0.28 0.20 0.06 0.66 0.01

0.16 0.45 0.45 0.41 0.48 0.37 0.45 0.40 0.24 0.48 0.07

0.03** 0.04 0.04 0.08*** 0.12*** 0.04 0.07*** 0.02 0.01 0.09** 0.001

0.01 0.09** 0.09** 0.10*** 0.15*** 0.05** 0.13*** 0.07** 0.004 0.01 0.001

0.18*** 0.81%*** 1.52** 2.16**

0.05 0.31%* 1.10** 1.57**

Pre-IBBEA (N = 71) Panel D: CEO characteristics (N = 570) CEO equity-based pay 0.25 CEO equity ownership 0.46% CEO tenure 5.54 CEO age 57.00

0.25 0.46% 4.41 4.59

Pre-IBBEA (N/A) Panel E: Board characteristics (N = 439) Board size – Independent percent (%) – CEO duality –

– – –

Post-IBBEA, pre-FSMA (N = 212)

Post-FSMA (N = 287)

0.43 1.26% 7.06 54.84

0.38 1.57% 8.16 56.41

0.33 1.43% 4.83 6.05

0.36 2.04% 6.01 5.65

Post-IBBEA, pre-FSMA (N = 177)

Post-FSMA (N = 262)

15.15 0.69 0.68

14.92 0.72 0.72

4.12 0.15 0.47

4.17 0.14 0.45

0.23 0.03** 0.04

Each panel reports summary statistics by the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA to pre-FSMA (September 30, 1994– November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). Panel A reports summary statistics for G-Index, the three main ATPs and O-Index. Panel B reports summary statistics of acquirer characteristics. Panel C reports summary statistics of deal characteristics. Panel D presents summary statistics of CEO characteristics. Panel E displays summary statistics of board characteristics. Variable definitions are provided in Appendix A. The last two columns report the differences in means between periods II and I and III and II, respectively. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

Finally, we summarize CEO and board characteristics in Panels D and E of Table 2. We note that while CEO equity based compensation, equity ownership, and tenure increased significantly postderegulation, board size for the second and third period remained essentially the same.12 The increase in CEO equity-based pay and percentage ownership post-IBBEA indicates the growing pressure on banks to align their CEOs’ and shareholders’ interests in an environment of increased competitiveness. This finding is similar in spirit to that documented by Hubbard and Palia (1995), who suggest the need for more incentive-based compensation for executives following deregulation. It is also consistent with the findings of Minnick et al. (2011) who report a higher PPS sensitivity post-FSMA. On the other hand, the evidence of no change in board size in the pre-FSMA and post-FSMA periods echoes the finding by Becher et al. (2005) who report a static board structure of banks from 1992 to 1999 despite the deregulation of the banking industry.

12 We have to exclude the first period due to data availability issue since IRRC data coverage on board characteristics starts in 1996.

5.2. Summary statistics of CARs To determine M&A announcement returns we follow MacKinlay (1997) and estimate abnormal stock returns around the day of each announcement. First, we estimate firm i’s returns (Rit) using one-factor market model estimated over 255 trading days ending 46 days prior to the announcement day. For the market return (RMt) we use CRSP’s value-weighted market index.13 Next, we estimate the firm i’s daily abnormal returns (ARit) around the announcement date by subtracting the daily predicted returns, based on the OLS estimates in the first stage from the actual daily returns. Cumulative abnormal returns are then computed by accumulating daily abnormal returns over a given event window [T1, T2] for each firm i. Table 3 reports cumulative announcement-period abnormal returns statistics by period: pre-IBBEA (I), post-IBBEA and pre-FSMA 13 Hoechle et al. (2012, fn 14) note that firms time acquisitions following price runups. As such, using the preceding 200 days for estimating parameters may bias results. As a robustness check, the authors calculate abnormal returns as marketadjusted returns using the equally-weighted index. Similar to Hoechle et al., our results are robust to the choice of method of calculating abnormal returns as well as the market index.

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C. Ghosh, M. Petrova / Journal of Banking & Finance 37 (2013) 5220–5235 Table 3 Summary statistics by periods of cumulative abnormal returns around 936 bank M&A announcements during 1991–2011. Mean CAR (%)

Patell Z-stat

Percent of negative CARS (%)

Generalized sign Z-stat

0.22 0.25

1.30 1.13

53.03 45.45

0.98 1.48

Post-IBBEA& Pre-FSMA (II) (N = 311) CAR[1, 1] 0.76*** CAR[2, 2] 0.95***

5.55 5.40

63.02*** 60.13***

4.34 3.32

Post-FSMA (III) (N = 361) CAR[1, 1] CAR[2, 2]

0.26*** 0.47***

5.36 5.15

57.06* 57.89**

2.41 2.73

Total sample (N = 936) CAR[1, 1] CAR[2, 2]

0.42*** 0.57***

7.22 6.91

57.91*** 57.69***

4.52 4.39

Pre-IBBEA(I) (N = 264) CAR[1, 1] CAR[2, 2]

CAR[1, 1] CAR[2, 2]

Mean CAR dif. (%) (II–I)

Mean CAR dif. (%) (III–I)

Mean CAR dif. (%) (III–II)

0.54* 0.70**

0.04 0.22

0.50* 0.48*

Summary of cumulative announcement-period abnormal returns for each of the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA to preFSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). Differences in the CAR mean values between the periods are examined by a one-tail t-test. Variable definitions are provided in the Appendix A. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

(II), post-FSMA (III) and for the total sample. We also present the results of difference in means test for the CARs between the periods. CARs are calculated over 3-day [1, 1] and 5-day [2, 2] windows, surrounding the announcement. The reported CARs are significantly negative for all periods subsequent to IBBEA. As a robustness check, we also report the percentage of negative CARs using the generalized sign test. This test shows that a significantly high percentage of CARs in the sample are negative. Our results are consistent with Hagendorff et al. (2007, 2008) who summarize the results of eight studies on bank acquirer announcement returns in the US covering the period from 1972 to 1997 and report no positive returns on average. In particular, Hagendorff et al. (2008) report significantly negative announcement-period abnormal returns for bank acquirers during the more recent period from 1996 to 2004. Based on the difference of mean CARs, we observe a significant decrease in acquirer’s abnormal returns from the first to the second period and increase in returns from the second to the third period. The less negative CARs in the post-FSMA period are driven by the significantly positive returns observed in 2008 and 2009 when, as noted earlier, acquirers extracted value from distress sales. Excluding the returns for the years 2008 and 2009 from the post-FSMA sub-sample, mean CAR[1, 1] and CAR[2, 2] are 0.79% and 0.88%, which are similar in magnitude to the announcement returns observed in the second period. These results are in line with the findings of Moeller and Schlingemann (2005), who document diminished acquirer returns for acquisitions in a more active takeover market. To analyze the likelihood of a bidder to engage in a value-reducing acquisition, we compare bidders with negative CARs to non-bidders. We define ACQNEG as an indicator variable equal to 1 if a firm is an acquirer and has a negative announcement 5-day CAR, and 0 if the firm is not an acquirer. The bidders with positive announcement returns are excluded from this analysis.14 There are 540 firm years with negative [2,2] CARs. Therefore, our sample includes 540 event firm years and 14 To the extent that post-deregulation governance indexes are related to likelihood of acquisition, it is not clear what the relationship between value-creating mergers and governance indexes should be. While, stronger shareholder rights may be associated with value-creating acquisitions, based on Harford et al. (2008), acquisitions also increase with weaker shareholder rights. Our analysis shows a weak negative relationship between ATP indices and measures and the probability for a value-creating merger post-FSMA.

1164 comparison firm years. Univariate regression estimates with ACQNEG as the dependent variable against the various ATPs and indices confirm Hypothesis 1, which predicts a positive relationship between the propensity to engage in value-destroying acquisitions and the ATP indices post-deregulation.15 In addition, the univariate regression estimates between CARs and the ATP indices and measures confirm Hypothesis 2 that the announcement period returns are significantly and negatively correlated with the ATP indices only in the third period when the market for corporate control is the most active. 5.3. Likelihood to engage in value destroying acquisitions: panel random effects logit regression Panel A of Table 4 reports results of the baseline regression model with the dependent variable being ACQNEG, an indicator variable equal to one for bidder firms with negative announcement returns, and zero if the firm was not a bidder, against the G-Index, while controlling for acquirer and deal characteristics. We conduct panel logistic regressions with random effects and control for potentially serial correlation of standard errors (SE correlated over t for a given firm i).16 In random effects models the variations across the firms is assumed to be random and uncorrelated with the independent variables. If LR test statistics of rho is significantly different from zero then panel-level variance is important. To test our hypotheses on the impact of shifting levels of market for corporate control during deregulation, we divide our sample into three periods: preIBBEA, post-IBBEA and pre-FSMA, and post-FSMA.17 To control for 15 These results are not reported in a table in the interest of brevity but available with the authors on request. 16 We conduct Hausman test to determine whether to use panel logistic fixed effects vs. random effects model. We are not able to reject the null hypothesis that the preferred model is a random effects model, therefore we use random effects model. 17 We choose to conduct the regression models by periods for the three subsamples, as opposed to by using difference-in-difference approach (and including dummies for different regulatory regimes, ATP measures and interaction terms between the regulatory regime dummies and ATP indices), since examination of the regressions by periods shows that the coefficients on the control variables also differ over time. Therefore, rather than using a fully interactive difference-in-difference model, we exhibit our results by periods. This approach is also used by Minnick et al. (2011) in a recent study examining the relationship between CEO compensation and acquirer’s returns in bank holding companies.

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C. Ghosh, M. Petrova / Journal of Banking & Finance 37 (2013) 5220–5235 Table 4 Panel random effects logistic regression determining the likelihood of banks to engage in a value-reducing acquisition, controlling for firm characteristics and governance indices during 1991–2011. Dependent variable

Pre-IBBEA ACQNEG

Post-IBBEA and pre-FSMA ACQNEG

Post-FSMA ACQNEG

Panel A: G-Index controlling for firm characteristics G-Index 0.1749 (1.01) Firm size 0.4944 (1.17) Leverage 11.1754 (1.04) Return on assets 186.1923** (2.49) Loan-to-asset 0.8676 (0.23) Diversified 2.3859 (1.32) General merger activity 1.7069** (2.24) Constant 69.7924*** (2.61)

0.1193 (0.89) 0.5711 (1.63) 10.7124* (1.72) 203.7654*** (2.99) 5.6988* (1.74) 1.7974 (1.25) 0.8589 (1.36) 46.7718** (2.22)

0.0997** (2.21) 0.6830*** (5.63) 2.7166 (1.10) 98.4509*** (4.28) 1.4656 (1.25) 0.7601 (1.34) 0.5051** (1.97) 34.9497*** (4.64)

Observations Number of unique firms Wald chi2 Prob > chi2 LR test of rho = 0 Prob > chibar2

314 103 16.34 0.06 79.36 0.00

811 163 87.62 0.00 27.00 0.00

2.0231** (2.08) 16.71 0.05

0.4295 (1.30) 86.17 0.00

1.6543* (1.87) 26.88 0.00

0.9824 (1.33) 16.12 0.06

0.1590 (0.55) 83.91 0.00

0.6171 (0.71) 24.62 0.00

0.8280 (1.25) 16.86 0.05

0.9775*** (2.71) 88.09 0.00

0.0344 (0.16) 24.81 0.00

0.0067 (0.04) 15.83 0.07

0.0956* (1.87) 87.47 0.00

361 123 25.52 0.00 61.82 0.00

Panel B: Other governance measures and indices controlling for firm characteristics Staggered board 1.7455* (1.71) Wald chi2 24.91 Prob > chi2 0.00 Poison pill Wald chi2 Prob > chi2 Golden parachute Wald chi2 Prob > chi2 O-Index Wald chi2 Prob > chi2

Panel random-effects logistic regressions determining the likelihood of firms to engage in value-reducing acquisitions for each of the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). The dependent variable, ACQNEG, equals one if the 5-day cumulative abnormal return, CAR[2, 2], of the M&A announcement is negative, and zero if the firm was not a bidder. Panel A reports regression results when controlling for firm characteristics and the GIndex. Panel B reports regression results when controlling for firm characteristics and including each of the alternative governance measures/indices (staggered board, poison pill, golden parachute and O-Index) separately. In Panel B, coefficient estimates of the firm characteristics variables are similar to those reported in Panel A and therefore are omitted for brevity. We control for industry groups fixed effects based on 4-digit SIC code. Z-statistics are reported in the parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

general market conditions we include a proxy for the industry merger activity (excluding banks) – the natural logarithm of CPI adjusted deal volume in USD (General merger activity). In addition to including firm characteristics (Firm size, Loan-to-asset ratio, Return on assets and Leverage) we also control for whether the acquirer is diversified (has operations in at least two different SIC codes) and industry fixed effects (within banking).18 Consistent with Hypothesis 1, the relation between the announcement returns and the G-Index is insignificant pre-deregulation, but significantly positive post-FSMA. The signs and significance of the control variables are consistent with our expectations and the extant literature, although 18 We also test whether the stock performance of the firm, one year prior to the merger announcement, has a significant impact on the probability to engage in valuereducing acquisitions, but find no evidence of such relationship.

we note a significant variation in the estimated coefficients across periods. Profitability (Return on assets) is significantly and positively related to ACQNEG in all three periods. Increased profitability generally increases free cash flow, which entrenched managers are likely to spend on value-reducing acquisitions (Jensen, 1986). Consistent with Roll’s (1986) hubris hypothesis that larger firm’s managers are more likely to exhibit over-confidence and overpay for targets, firm size is positive and significant postFSMA. Leverage is generally positive and significant at the 10% level in the second period. This is consistent with managers of highly-levered firms engaging in riskier NPV-negative projects, as their downside is limited. General merger activity is significantly positively related to ACQNEG in the first and third periods. Overall, these results suggest that larger and more profitable

C. Ghosh, M. Petrova / Journal of Banking & Finance 37 (2013) 5220–5235

firms, but with increased leverage, are more likely to engage in value-destroying acquisitions. In Panel B, we conduct the baseline regression specification separately with each of the three main ATPs (staggered board, poison pill, golden parachute) and the O-Index. Since the coefficients on the control variables remain largely unchanged, we only report the coefficients on the ATP measures/O-Index and the Wald chi2 statistics in each specification. The analyses reveal that both, staggered board and poison pill are significant in the first period; staggered board is also significant in the second period. Note that, although staggered board is used as an anti-takeover protection measure, it can also serve well managers when boards have higher percentage insiders, as slow replacement of board members warrants decisions favoring managers for longer period of time. This is also consistent with our observation that boards had lower independence prior to deregulation. Therefore, in the absence of active market for corporate control classified board can also be viewed as an internal governance mechanism. Hence, the significance of staggered board in the first and second period is not surprising. Neither of these provisions is significant post-FSMA. In contrast, golden parachute and O-Index are only significant in the third period (post-deregulation). Furthermore, while O-Index is only significant at the 10% level; golden parachute is significant at the one percent level. Based on these results, we conclude that the significant effect of G-Index in the third period documented in Panel A derives mainly from the significant relation between golden parachute and ACQNEG during this period. This relationship confirms our expectation that since golden parachutes not only protect managers from the active market of corporate control, but also increase their wealth in case of a takeover, the relation between golden parachutes and the likelihood of engaging in value-destroying acquisitions, in an active takeover market, should be the strongest. Overall, these results support Hypothesis 1 that in a competitive takeover market, acquirers that are protected by ATPs from the discipline of the market for corporate control are more likely to engage in value destroying acquisitions. Finally, we would like to note that the LR test of rho equal to zero is rejected at the one percent level of significance or better in all model specifications, indicating the importance of controlling for firm effects. Next, in Table 5 in addition to the various ATPs indices/measures we also control for CEO characteristics – CEO ownership, tenure, age, and equity-based compensation. The results with respect to G-Index are similar to our findings in Table 4. As is apparent from Panel B, this relationship is derived from the positive relationship between golden parachute and ACQNEG in the third period. None of the other ATPs are significant in any of the periods, after controlling for CEO characteristics. We observe that during the first period CEO tenure and CEO equity-based pay are positively related to the probability to undertake value-destroying acquisitions. These results suggest that negative value acquisitions are more likely under more powerful CEOs with longer tenure and higher equity-based compensation. Note that CEO ownership is negatively related to ACQNEG, although it falls short of being significant. The results suggest that CEOs with higher equity-based compensation, but lower ownership may be prone to more risk-taking, as the downside potential is limited (reduction in the value of existent stock holdings), while the upside potential through increase in value of stock options is high. CEO tenure is generally positively associated with value-reducing acquisitions, which is indicative of the potential of longer service leading to entrenchment. The results from the first period are consistent with the extant evidence in the literature that lower ownership and longer tenure lead to entrenched

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and self-interested CEOs. None of the CEO characteristics are significant in the second period, although the signs on the control variables remain consistent. Similarly, in the third period only CEO age is negatively related with ACQNEG.19 Overall, our results support Hypothesis 1 that ATP indices/measures are only related to the probability of value-reducing acquisitions post-deregulation. Our analysis also provides some evidences that internal governance mechanisms were more important prior to de-regulation, which is consistent with our Hypothesis 3. 5.4. OLS regression of the determinants of CARs in bank mergers Next, we turn to the determinants of acquirers’ returns. We conduct the analysis for the three identified periods: pre-IBBEA, post-IBBEA and pre-FSMA, and post-FSMA. Panel A of Table 6 reports the results of the baseline regression of bank acquirers’ [2, 2] CARs on the G-Index, controlling for acquirer and deal characteristics. We adjust standard errors for potential serial correlation and heteroskedasticity. We note a significant variation in the estimated coefficients by periods. This finding confirms that the relationships between the control variables and CARs are different during the three periods. As such, a difference-in-difference model, in which we include indicator variables for the three periods – pre-IBBEA, post-IBBEA and pre-FSMA, and post-FSMA – and interact the ATP indices/measures with these indicator variables will not be correctly specified, unless we use a fully interactive model. Consistent with Hypothesis 2 that post-deregulation, and especially post-FSMA, abnormal returns associated with acquisitions will be negatively related to governance indices, the relation between announcement period abnormal returns and the G-Index is insignificant in the pre-IBBEA, and post-IBBEA and pre-FSMA periods, but significantly negative in the post-FSMA period. Other notable results include that relative deal size is significantly negative pre-deregulation. This result is consistent with Minnick et al. (2011). Liquidity (indicated by lower Loan-to-asset ratio) is positively related to CARs in the first period; private targets are positively related to value in the second period, while leverage is negatively related to value in the third period. Median annual industry CARs are generally positively related to individual acquiring firm’s CARs, indicating that acquirers perform better when market conditions are favorable. Finally, activity diversifying acquisitions are positively related to value pre-deregulation, while geographic diversification is negatively related to CARs post-deregulation. This results is consistent with our expectation that post-deregulation when limits to diversification are removed, acquiring financial firms are adversely affected by diversifying acquisitions. Whereas, in the pre-IBBEA period when regulatory barriers severely limited the ability of managers to engage in diversifying acquisitions, firms that engaged in acquisitions that expanded their activities in similar industries benefited from significant value accretions. In Panel B of Table 6 we conduct the same baseline regression model, but include as controls – staggered board, poison pill, golden parachute and O-Index, respectively. Since the coefficients on the control variables remain largely unchanged, we only report the coefficients on the ATPs and O-Index and the adjusted Rsquared in each specification. Our results are consistent with the findings in Panel A; the ATPs and O-Index are only significant post-FSMA. Golden parachute is significant in the second period, but loses its significance in the third period. In addition, staggered board has the highest significance and the largest coefficient among the alternative governance measures in the third period, confirming the findings of Masulis et al. (2007) that in an active

19 We also test for any relationship between board structure and ACQNEG, but do not find any.

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Table 5 Panel random effects logistic regression determining the likelihood of banks to engage in a value-reducing acquisition, controlling for firm characteristics, governance indices and CEO characteristics during 1991–2011. Dependent variable

Pre-IBBEA ACQNEG

Post-IBBEA and pre-FSMA ACQNEG

Post-FSMA ACQNEG

0.1036 (0.62) 13.9900 (0.90) 0.0256 (0.37) 0.0811 (1.17) 0.4481 (0.53) 0.2863 (0.80) 15.6104** (2.33) 193.1159*** (2.74) 4.6995 (1.45) 1.5791 (0.96) 1.3636* (1.85) 51.6862** (2.21)

0.0957* (1.82) 9.5511 (1.35) 0.0399 (1.54) 0.0660** (2.33) 0.6166 (1.47) 0.6765*** (5.32) 1.1268 (0.41) 76.5732*** (3.03) 0.7776 (0.64) 1.5192** (2.08) 0.4793* (1.65) 29.1553*** (3.42)

204 71 13.81 0.39 31.42 0.00

627 130 78.23 0.00 9.13 0.00

1.2136 (1.26) 13.3 0.42

0.3046 (0.86) 78.18 0.00

2.5044 (1.45) 11.64 0.39

0.1718 (0.23) 13.25 0.43

0.0200 (0.07) 77.22 0.00

1.7220 (0.90) 12.25 0.35

1.0329 (1.35) 14.15 0.36

0.9372** (2.35) 78.6 0.00

0.2812 (0.97) 14.82 0.19

0.2975 (1.48) 15.28 0.29

0.0964 (1.61) 78.73 0.00

Panel A: G-Index controlling for firm and CEO characteristics G-Index 0.0866 (0.45) CEO ownership 308.0655 (1.53) CEO tenure 0.3897** (2.53) CEO age 0.0430 (0.55) CEO equity-based pay 8.9616*** (2.77) Firm size 2.0653** (2.38) Leverage 40.6532 (1.52) Return on assets 651.3655** (2.33) Loan-to-asset 3.0998 (0.56) Diversified 2.8768 (1.57) General merger activity Constant

1.4047 (0.04)

Observations Number of unique firms Wald chi2 Prob > chi2 LR test of rho = 0 Prob > chibar2

41 41 16.15 0.14

Panel B: Other governance measures and indices controlling for CEO and firm characteristics Staggered board 0.4418 (0.34) Wald chi2 18.42 Prob > chi2 0.07 Poison pill Wald chi2 Prob > chi2 Golden parachute Wald chi2 Prob > chi2 O-Index Wald chi2 Prob > chi2

Panel random-effects logistic regressions determining the likelihood of firms to engage in value-reducing acquisitions for each of the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). The dependent variable, ACQNEG, equals one if the 5-day cumulative abnormal return, CAR[2, 2], of the M&A announcement is negative, and zero if the firm was not a bidder. Panel A reports regression results when controlling for G-Index, CEO characteristics and firm characteristics. Panel B reports regression results for the impact of each of the alternative governance measures/indices (staggered board, poison pill, golden parachute and O-Index) separately controlling for CEO characteristics and firm characteristics. In Panel B, coefficient estimates of the CEO control variables and firm characteristics are similar to those reported in Panel A and therefore are omitted for brevity. We control for industry groups fixed effects based on 4-digit SIC code. Z-statistics are reported in the parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

market for corporate control, staggered board is the most effective anti-takeover protection measure. These results are consistent with Hypothesis 2, which postulates that in a competitive takeover market, abnormal returns, associated with acquisitions, are negatively related to value to the extent that managers are protected from the market for corporate control by antitakeover provisions. Our findings also confirm Hypothesis 1 that deregulation in the banking industry reformed the market from one where

strict regulation prohibited active takeover market to one where restrictions on mergers and acquisitions are largely eliminated. In Table 7 we include CEO characteristics. We control for CEO tenure as a proxy for CEO experience, and CEO equity-based compensation and CEO ownership as proxies for CEO incentives. As shown in Panels A and B, the coefficients of the three ATPs and two governance indices continue to have the same signs and similar statistical significances as in the previous specification, lending

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Table 6 Multivariate regression statistics for the determinants of CARs around M&A announcements in 936 bank acquisitions during 1991–2011, controlling for firm and deal characteristics and governance indices. Dependent variable

Pre-IBBEA CAR[2, 2]

Panel A: CARs and G-Index controlling for firm and deal characteristics G-Index 0.0003 (0.24) Firm size 0.0022 (0.79) Leverage 0.0405 (0.58) Free cash flow 1.5103 (1.53) Relative deal size 0.1784** (2.49) Median industry CAR 2.1600 (1.10) Loan to asset 0.1544*** (4.73) Activity diversifying 0.0146* (2.06) Geographically diversifying 0.0085 (1.41) Private target 0.0005 (0.07) Cash financing 0.0019 (0.32) Stock financing 0.0048 (0.87) Hostile

Post-IBBEA and pre-FSMA CAR[2, 2]

Post-FSMA CAR[2, 2] 0.0023** (2.56) 0.0005 (0.27) 0.0593* (1.73) 0.2997 (0.97) 0.0252 (0.79) 1.3241*** (2.82) 0.0160 (0.69) 0.0002 (0.02) 0.0116* (1.70) 0.0007 (0.13) 0.0044 (0.51) 0.0050 (0.70) 0.0368 (0.37) 0.1106* (1.70) 361 0.214

Constant

0.1425 (1.42)

0.0013 (1.05) 0.0008 (0.35) 0.0088 (0.22) 0.5224 (0.87) 0.0264 (1.36) 0.5643* (1.83) 0.0084 (0.29) 0.0006 (0.09) 0.0017 (0.38) 0.0101** (2.13) 0.0042 (0.43) 0.0021 (0.29) 0.0547 (0.95) 0.0400 (0.55)

Observations R2

264 0.25

311 0.102

Panel B: CARs and other governance measures and indices controlling for firm and deal characteristics Staggered board 0.0070 0.0029 (1.00) (0.36) R2 0.26 0.099 Poison pill R2 Golden parachute R2 O-Index R2

0.0201*** (3.51) 0.222

0.0094 (1.50) 0.264

0.0014 (0.31) 0.099

0.0121** (2.37) 0.213

0.0078 (1.03) 0.262

0.0088* (1.99) 0.108

0.0048 (0.58) 0.201

0.0001 (0.05) 0.249

0.0011 (0.86) 0.101

0.0019* (1.81) 0.207

OLS regressions determining the cumulative announcement returns of M&As by banks for each of the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999–December 31, 2011). The dependent variable is the 5-day cumulative abnormal return, CAR[2, 2], around the M&A announcement. Panel A reports regression results for the impact of G-Index controlling for acquirer and deal characteristics. Panel B reports regression results for the impact of each of the alternative governance measures/indices (staggered board, poison pill, golden parachute and O-Index) separately controlling for acquirer and deal characteristics. In Panel B, coefficient estimates of the acquirer and deal characteristics variables are similar to those reported in Panel A and therefore are omitted for brevity. Standard errors are clustered by firm ID and adjusted for potential heteroskedasticity. T-statistics are reported in the parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

further support to Hypothesis 2. Two changes are notable; golden parachute is no longer significant in any of the specifications; poison pill is also significant in the first period. Also, based on magnitude and statistical significance staggered board and poison pill appear to be the two most important ATPs, impacting firm value. None of the CEO characteristics is significant in the first and second periods, while CEO tenure is weakly positive in the third period. The insignificant coefficient estimates of CEO characteristics are in line with the findings by Masulis et al. (2007), who add in footnote 22 that Qiu (2006) also finds the relation between acquirer CARs and equity-based compensation to be insignificant. The signs and significance levels of all other control variables remain gener-

ally consistent. Furthermore, Hostile, an indicator variable for hostile takeovers, has a significantly positive coefficient in all models. This indicates that bank acquirers in competitive bids are able to create greater value. To test Hypothesis 3, we include additional internal governance characteristics: CEO/Chairman duality, board independence and board size. Table 8 reports the regression results when controlling for both board and CEO characteristics. Since director data from RiskMetrics is available from 1996, we can include only the second and third periods in our analyses and the sample size further decreases to 439. Our previous findings with regard to the ATPs and governance indices continue to hold. Of

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the CEO and board characteristics included, only board size is significantly and negatively related to value. Interestingly, this relation is present only post-FSMA. This evidence does not support our Hypothesis 3 that after deregulation the relationships between internal corporate governance mechanisms and value are weaker.

5.5. Robustness checks Our results remain robust to variety of tests. Using alternative announcement windows and CRSP equally weighted index as the market proxy do not change our main findings. We also test for potential non-linear relationships between the dependent variables

Table 7 Multivariate regression statistics for the determinants of CARs around M&A announcements in 570 bank acquisitions during 1991–2011, controlling for CEO characteristics, firm and deal characteristics, and governance indices. Dependent variable

Pre-IBBEA CAR[2, 2]

Panel A: CARs and G-Index controlling for firm, deal and CEO characteristics G-Index 0.0007 (0.48) CEO equity-based pay 0.0016 (0.06) CEO equity ownership 1.2339 (1.05) CEO tenure 0.0007 (0.61) Firm size 0.0027 (0.37) Leverage 0.0081 (0.10) Free cash flow 1.0973 (0.96) Relative deal size 0.1974** (2.33) Median industry CAR 0.9427 (0.58) Loan to asset 0.0901* (1.92) Activity diversifying 0.0175** (2.40) Geographically diversifying 0.0073 (1.07) Private target 0.0006 (0.08) Cash financing 0.0006 (0.12) Stock financing 0.0001 (0.01) Hostile

Post-IBBEA and pre-FSMA CAR[2, 2]

Post-FSMA CAR[2, 2] 0.0029*** (3.02) 0.0060 (0.80) 0.2367 (1.43) 0.0010* (1.71) 0.0018 (0.65) 0.0540 (1.33) 0.4152 (1.29) 0.0030 (0.08) 1.3731*** (3.03) 0.0253 (1.00) 0.0012 (0.12) 0.0160* (1.87) 0.0031 (0.48) 0.0026 (0.27) 0.0100 (1.18) 0.1562*** (12.02) 0.0552 (0.64) 287 0.291

Constant

0.1076 (0.62)

0.0022 (1.38) 0.0015 (0.13) 0.1308 (0.60) 0.0004 (0.72) 0.0011 (0.34) 0.0356 (0.64) 0.5707 (0.93) 0.0427 (1.35) 0.3365 (0.83) 0.0115 (0.34) 0.0030 (0.32) 0.0053 (1.15) 0.0045 (0.72) 0.0119 (1.01) 0.0011 (0.12) 0.1571*** (3.00) 0.0249 (0.24)

Observations R2

71 0.284

212 0.145

Panel B: CARs and other governance measures/indices controlling for firm, deal and CEO characteristics Staggered board 0.0057 0.0032 (0.70) (0.35) 2 R 0.288 0.135 Poison pill R2 Golden parachute 2

R

O-Index R2

0.0213*** (3.10) 0.294

0.0227*** (3.00) 0.339

0.0019 (0.31) 0.135

0.0155*** (2.99) 0.289

0.0002 (0.02) 0.281

0.0081 (1.14) 0.141

0.0000 (0.00) 0.272

0.0007 (0.45) 0.284

0.0026 (1.49) 0.145

0.0027** (2.41) 0.284

OLS regressions determining the cumulative announcement returns of M&As by banks for each of the three identified periods: pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and post-FSMA (November 13, 1999– December 31, 2011). The dependent variable is the 5-day cumulative abnormal return, CAR[2, 2], around the M&A announcement. Panel A reports regression results for the impact of G-Index separately controlling for acquirer, deal and CEO characteristics. Panel B reports regression results for the impact of each of the alternative governance measures/indices controlling for acquirer, deal and CEO characteristics. In Panel B, the coefficient estimates of the control variables are similar to those reported in Panel A and therefore are omitted for brevity. Standard errors are clustered by firm ID and adjusted for potential heteroskedasticity. T-statistics are reported in the parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

C. Ghosh, M. Petrova / Journal of Banking & Finance 37 (2013) 5220–5235 Table 8 Multivariate regression statistics for the determinants of CARs around M&A announcements in 439 bank acquisitions during 1991–2011, controlling for board and CEO characteristics, firm and deal characteristics, and governance indices. Dependent variable

Post-IBBEA and pre-FSMA CAR[2, 2]

Post-FSMA CAR[2, 2]

Panel A: CARs and G-Index controlling for firm, deal, board and CEO characteristics G-Index 0.0032 0.0030*** (1.13) (4.04) Board size 0.0005 0.0013* (0.51) (1.87) Percent independent 0.0305 0.0064 (1.34) (0.46) CEO duality 0.0042 0.0014 (0.69) (0.28) CEO equity-based pay 0.0014 0.0011 (0.10) (0.15) CEO equity ownership 0.3242 0.0611 (0.92) (0.35) CEO tenure 0.0010 0.0002 (1.09) (0.36) Firm size 0.0037 0.0018 (0.82) (0.81) Leverage 0.1284 0.0434 (1.28) (1.12) Free cash flow 0.1784 0.2159 (0.20) (1.11) Relative deal size 0.0430 0.0243 (1.09) (0.80) Median industry CAR 0.0929 1.8768*** (0.18) (3.29) Loan to asset 0.0338 0.0006 (0.54) (0.02) Activity diversifying 0.0097 0.0106 (0.68) (1.36) Geographically diversifying 0.0140** 0.0016 (2.12) (0.29) Private target 0.0052 0.0009 (0.73) (0.15) 0.0039 Cash financing 0.0398* (1.68) (0.41) Stock financing 0.0166 0.0074 (0.95) (0.96) 0.1697*** Hostile 0.1499** (2.49) (12.87) Constant 0.2042 0.1414* (1.19) (1.72) Observations R2

177 0.207

262 0.246

Panel B: CARs and other governance measures/indices controlling for firm, deal, board and CEO board characteristics Staggered board 0.0216 0.0196** (1.09) (2.39) 0.206 0.241 R2 Poison pill R2 Golden parachute 2

R

O-Index R2

0.0021 (0.19) 0.192

0.0098* (1.91) 0.225

0.0057 (0.53) 0.194

0.0091 (1.51) 0.218

0.0046 (-1.34) 0.210

0.0028*** (2.78) 0.234

OLS regressions determining the cumulative announcement returns of M&As by banks. Due to data availability, the sample can only be divided into two periods: post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and postFSMA (November 13, 1999–December 31, 2011). Panel A reports regression results for the impact of G-Index controlling for acquirer, deal and board and CEO characteristics. Panel B reports regression results for the impact of each of the alternative governance measures/indices separately controlling for acquirer, deal, and board and CEO characteristics. In Panel B, the coefficient estimates of acquirer and deal characteristics variables are similar to those reported in Panel A and therefore are omitted for brevity. Standard errors are clustered by firm ID and adjusted for potential heteroskedasticity. T-statistics are reported in the parentheses. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.

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and G-Index (as well as O-Index). In addition to G-Index (O-Index), we include also its squared term; alternatively, we include dummy variables indicating that the respective index (G-Index or O-Index) is in the first and fifth quintile of the distribution. The analyses reveal no non-linearity in the relationship between ATP indices and the dependent variable. Finally, endogeneity is a potential concern in studies using corporate governance mechanisms since ATP measures as well as board structure and CEO compensation and duality are largely determined by the firm’s management. To address this concern, we use one year lagged governance variables. In addition, to the extent that we are interested in the change in the relationships post-deregulation, we can conclude with confidence that the observed changes in the effect of ATP indices/measures and board and CEO characteristics are not due to unobserved characteristics since the de-regulation through the two Acts represents an exogenous shock. 6. Conclusion We investigate the relation in the banking sector between the likelihood to engage in value-reducing acquisitions and corporate governance structures, as well as the relation between acquirer announcement-period abnormal stock returns and antitakeover indices and measures, and examine how these relations were affected by the change in the market for corporate control, caused by deregulation due to the implementation of the Interstate Banking and Branching Efficiency Act of 1994 and the Financial Service Modernization Act of 1999. We analyze 936 acquisitions during 1991–2011 by banks. We divide our study into three sub-periods, pre-IBBEA (January 1, 1991–September 29, 1994), post-IBBEA and pre-FSMA (September 30, 1994–November 12, 1999), and postFSMA (November 13, 1999–December 31, 2011). We posit that if the takeover market is indeed active, managers of firms with more restrictive antitakeover amendments, as measured by the various ATP indices and measures, will make worse acquisition decisions and realize lower M&A announcement returns. Our study makes several important contributions to the literature on corporate governance in banking. It provides comprehensive evidence on governance by the market for corporate control and the role of anti-takeover provisions in the banking sector, an area that has received only limited attention in the extant literature. In addition, by establishing that the effect of antitakeover provisions is discernible only in an environment of active takeover market, it lends credence to the conjecture by previous researchers (Bebchuk et al., 2002, 2009; Bebchuk and Cohen, 2005) that the negative association between firm value and antitakeover provisions is due mainly to the effective protection they provide to managers from unfriendly suitors. We further demonstrate the contrasting effects of antitakeover provisions by utilizing a unique environment of a takeover market under reform. Finally, our analyses reveal important insights on the role of diversification depending on the regulatory regime. Acknowledgements We are grateful to an anonymous referee and Ike Mathur (the editor) for their helpful comments and suggestions. The paper has also benefited from the comments received at presentations at the 2012 Financial Management Association Meeting in Atlanta, the Corporate Governance: An International Review 20th Anniversary Conference, Cambridge University, Judge Business School, Cambridge, UK, the World Finance Conference, Rio de Janeiro, Brazil and the 2012 International Finance and Banking Society Conference, Valencia, Spain. We thank Di Huang for her research assistance.

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Appendix A. Variable definitions

Variable

Description

CAR[2, 2]

Acquirer’s 5-day cumulative abnormal return where day 0 is the acquisition’s announcement date An indicator variable equal to one, if the firm is an acquirer and the 5-day cumulative abnormal return, CAR[2, 2] is negative and zero if the firm is not an acquirer G-Index, constructed based on Gompers et al. (2003) An indicator variable equal to one, if the acquirer has staggered board and zero otherwise. Under staggered board provision directors are divided into multiple classes. Only one class of directors stands for re-election each year An indicator variable equal to one, if the acquirer has adopted a poison pill provision and zero otherwise An indicator variable equal to one, if the acquirer has adopted a golden parachute provision and zero otherwise ‘‘Other’’ provisions index equal to GIndex – staggered board – poison pill – golden parachute Natural logarithm of total assets in USD, adjusted for CPI Market leverage, defined as book value of debt divided by market value of total assets ((DLC + DLTT + AP)/(ATCEQ + CSHO  PRCC_F)) Free cashflow, measured as (operating income before depreciation – interest expense – income taxes – capital expenditures)/book value of total assets ((OIBDP-XINT-TXT-CAPX)/AT) Loan-to-asset ratio, measured as the ratio of total loans outstanding to total assets (LNTAL/AT) Return on assets, measured as operating income before depreciation – interest expenses – income taxes over total assets ((OIBDP-XINT-TXT)/AT) Cash holdings, measured as the sum of cash and short-term investments over total assets (CHE/AT) A dummy variable, indicating whether the acquirer is diversified, equal to one, if the acquirer has at least two segments with different 2-digit SIC codes The natural logarithm of the volume of M&A deals in all industries (excluding banking) in USD Relative deal size, measured as the ratio of deal size (from SDC Platinum) to the acquirer’s market value of equity The median [2, 2] CARs of bank M&As announcements for a given year

ACQNEG

G-Index Staggered board

Poison pill

Golden parachute

O-Index

Firm size Leverage

Free cash flow

Loan-to-asset

Return on assets

Cash holdings

Diversified

General merger activity Relative deal size

Median industry CAR

Appendix A. (continued) Variable

Description

Cash financing

An indicator variable equal to one, if the deal is financed by cash only and zero otherwise An indicator variable equal to one, if the deal is financed by stock only and zero otherwise An indicator variable equal to one, if the deal is financed by a combination of cash and stock or other method of financing and zero otherwise An indicator variable equal to one, if the target is private and zero otherwise An indicator variable equal to one, if the target is public and zero otherwise An indicator variable equal to one, if the acquirer and target are from different states and zero otherwise An indicator variable equal to one, if the main SIC code of the bidder and target firms are different and zero otherwise An indicator variable equal to one, if the bidder acquires a target that has at least one division with a 2-digit SIC code that is different from the SIC codes associated with the bidder, and zero otherwise We follow the methodology of Morck et al. (1990) – subsequently applied by DeLong (2001) – to define this variable. For each public acquirer–target pair we obtain daily returns from 300 to 46 days prior to the merger announcement (for acquirer and target separately). For each pair we calculate the correlation of their daily returns. Next, we divide the pairs into geographically diversifying versus geographically focusing based on whether the bidder and target are headquartered in different states. We calculate the median correlation of returns for the two groups (geographically diversifying and focusing). If the correlation coefficient of a pair is higher than the median correlation coefficient for the group, we classify such an acquisition as nondiversifying; otherwise, we classify the acquisition as diversifying (Activity diversifying alt) An indicator variable equal to one, if the takeover is hostile and zero otherwise The ratio of CEO’s equity-based compensation to CEO’s total compensation. CEO equity-based compensation includes both stock options and restricted stock grants Number of shares owned by the CEO divided by the firm’s total number of shares outstanding The number of years the current CEO has served in this role CEO age in years

Stock financing

Other financing

Private target Public target Geographically diversifying Activity diversifying Activity diversifying SIC2

Activity diversifying alt

Hostile CEO equity-based pay

CEO equity ownership CEO tenure CEO age

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Appendix A. (continued) Variable

Description

Board size

Number of directors, serving on the board Percentage of independent board members, measured by the ratio of independent board members to the number of directors on the board. Independent board members are directors that are not employees of the firm, are not affiliated with the firm and do not own more than 2% of the firm’s stock An indicator variable equal to one, if the CEO is also the chairman of the board and zero otherwise

Independent percent

CEO duality

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