Riding the merger wave: Uncertainty, reduced monitoring, and bad acquisitions

Journal of Financial Economics 107 (2013) 69–88

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Riding the merger wave: Uncertainty, reduced monitoring, and bad acquisitions$ Ran Duchin a,n, Breno Schmidt b a b

Foster School of Business, University of Washington, Seattle, WA 98195, USA Goizueta Business School, Emory University, USA

a r t i c l e i n f o

abstract

Article history: Received 16 December 2010 Received in revised form 21 November 2011 Accepted 21 December 2011 Available online 15 August 2012

We show that acquisitions initiated during periods of high merger activity (‘‘merger waves’’) are accompanied by poorer quality of analysts’ forecasts, greater uncertainty, and weaker CEO turnover-performance sensitivity. These conditions imply reduced monitoring and lower penalties for initiating inefficient mergers. Therefore, merger waves may foster agency-driven behavior, which, along with managerial herding, could lead to worse mergers. Consistent with this hypothesis, we find that the average longterm performance of acquisitions initiated during merger waves is significantly worse. We also find that corporate governance of in-wave acquirers is weaker, suggesting that agency problems may be present in merger wave acquisitions. & 2012 Elsevier B.V. All rights reserved.

JEL classification: G34 G14 L22 Keywords: Mergers and acquisitions Governance Merger waves Turnover Uncertainty

1. Introduction The observation that mergers tend to cluster by time and industry may be one of the most consistent empirical regularities found in the merger literature.1 Industry

$ We gratefully acknowledge the helpful comments from an anonymous referee, Kenneth Ahern, Harry DeAngelo, Clifton Green, John Matsusaka, Micah Officer, Oguzhan Ozbas, Mark Rachwalski, Uday Rajan, Soojin Yim, Dexin Zhao. This research was conducted when Ran Duchin was at the Ross School of Business at the University of Michigan. An earlier version of this paper was circulated under the title ‘‘Riding the Merger Wave.’’ n Corresponding author. Tel.: þ 1 206 543 4377; fax: þ 1 206 543 7472. E-mail address: [email protected] (R. Duchin). 1 See, e.g., Mitchell and Mulherin (1996), Mulherin and Boone (2000), Andrade, Mitchell, and Stafford (2001), and Harford (2005).

0304-405X/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jfineco.2012.07.003

merger waves can, in fact, be of impressive magnitudes. Focusing on acquisitions of at least $100 million, Harford (2005) identifies 35 waves from 1981 to 2000, with an average of 34 mergers per wave. Various theories have been put forth to explain this pervasive pattern. Mitchell and Mulherin (1996) suggest that waves are driven by industry shocks that trigger restructuring and consolidation of industries. Shleifer and Vishny (2003) and RhodesKropf and Viswanathan (2004) argue that waves are instead triggered by stock market overvaluation. In contrast to prior work, which focuses on understanding what drives merger waves, this paper investigates their consequences for managerial incentives and firm value. In particular, using a large sample of 9,854 mergers from 1980 to 2009, we study the properties and implications of the information and monitoring environment that surrounds merger waves. We start by analyzing the accuracy of analysts’ forecasts and uncertainty during merger waves. We consider

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two competing hypotheses. Our first hypothesis is that the quality of analysis of any individual merger may decrease with the number of mergers. This is because of limited resources available to both analysts and investors. Indeed, prior research (e.g., Clement, 1999; Clement and Tse, 2005) finds that the accuracy of analysts’ forecasts declines with the number of companies and industries analysts follow. The alternative hypothesis is that prior and contemporary deals provide valuable information, which may in turn increase the accuracy of analysts’ forecasts and reduce uncertainty. In addition, the value of analysis may increase during merger waves, which may also improve the quality of analysts’ forecasts. We therefore study how different measures of uncertainty and quality of analysis vary with merger activity. Using the dispersion and error in analysts’ forecasts, the implied volatility of options, and Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-based estimates of volatility as proxies, our findings suggest that merger waves are characterized by greater uncertainty and poorer quality of analysis. The magnitudes are substantial: for example, the implied volatility is approximately 4.4 percentage points higher during merger waves, whereas the normalized dispersion in analysts’ forecasts is about 20.9% higher. These results hold when we measure uncertainty both at the industry and at the firm level. Next, we investigate whether managers are more likely to be favorably evaluated ex post if their ex ante behavior was similar to that of other managers. This idea is formalized in theoretical works such as Scharfstein and Stein (1990), in which managers are evaluated not only on the success of their decisions but also on how they compare with their peers.2 If decisions have systematic, unpredictable components, some ‘‘good’’ managers could be unlucky. This implies that merger waves offer the possibility of ‘‘sharing the blame’’ of unsuccessful mergers with other managers. To test this hypothesis, we study how likely managers are to be removed from their jobs following bad merger outcomes. Prior research by Lehn and Zhao (2006) finds a positive relation between poor merger performance and managerial turnover. Our tests are more nuanced. We investigate whether the positive relation between bad mergers and turnover is weaker when the mergers are initiated inside waves. We find that the turnover of managers is less sensitive to bad post-merger performance when the merger is initiated during merger waves. The magnitudes are economically significant. For example, outside waves, a decrease of one standard deviation in post-merger returns corresponds to an increase of 35% in the predicted probability of turnover. In contrast, during waves, the corresponding increase in the predicted probability of turnover is only 11%, a decrease of 68%. Lower in-wave turnover-performance sensitivity is 2 Holmstrom (1982) identifies various agency-related distortions that remain despite the positive role of career concerns. Other examples of such distortions include Zwiebel (1995) and Avery and Chevalier (1999).

consistent with more favorable evaluation of managers whose actions conform to those of their peers. The above findings suggest that the information environment and managerial incentives during merger waves may lead to an increase in initiating bad mergers. Specifically, the poorer quality of analysts’ forecasts and elevated levels of uncertainty during merger waves may reduce external monitoring. This in turn can increase the volume of agency-driven acquisitions because managers might be able to ‘‘get away with it’’ in a way analogous to that studied in the crime literature. There, criminals are less likely to be caught during periods of high crime rates because of limited enforcement resources (e.g., Sah, 1991; Freeman, Grogger, and Sonstelie, 1996; Huang, Laing, and Wang, 2004).3 In addition, managers are likely concerned about the long-term impact of unsuccessful acquisitions on their reputations and careers (e.g., Fama, 1980; Lazear and Rosen, 1981).4 However, merger waves could mitigate these concerns by reducing the penalties for making bad acquisitions as acquirers ‘‘share the blame’’ of unsuccessful mergers with other managers. Indeed, prior empirical work finds that career concerns motivate individuals to take similar actions (e.g., Chevalier and Ellison, 1999). The above arguments imply that merger waves may lead to inefficient mergers. To test these implications, we compare the performance of in-wave mergers and outwave mergers. Controlling for firm characteristics, we find that in-wave acquirers have annualized buy-and-hold abnormal returns that are, on average, 4.65–6.25 percentage points lower than other acquirers.5 A similar pattern emerges when we compare operating performance: the 2year post-merger change in Return On Assets (ROA) of inwave acquirers is 0.75–2.14 percentage points lower than that of out-wave acquirers. Our results are robust to different measures of long-term performance such as buy-and-hold returns, calendar time portfolio returns, and various measures of operating performance. They also persist after controlling for stock market overvaluation, differences in the method of payment, acquirers’ size, and the ownership status of the target company. In contrast, we do not find significant differences in merger announcement returns between in-wave and out-wave mergers.

3 There are, however, important distinctions between the crime literature and agency-driven mergers. First, in the crime literature, criminals’ actions and the scale of enforcement are rationally optimal. In contrast, in a rational expectations equilibrium, shareholders will anticipate managerial empire building during waves. Second, in the crime literature, crime waves lead to even more crime through a feedback effect owing to limited enforcement resources. In contrast, we argue that during merger waves, managers are able to hide agencydriven acquisitions by ‘‘pooling’’ with value-maximizing acquisitions owing to the lower quality of analysis. 4 For empirical evidence that poor job performance leads to poor labor market outcomes for managers, see, e.g., Weisbach (1988), Kaplan and Reishus (1990), Gibbons and Murphy (1992), and Gompers and Lerner (1999). 5 Bouwman, Fuller, and Nain (2009) find a similar pattern when they examine mergers initiated during periods of high stock-market valuation.

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To further investigate the agency channel, we also study the governance of acquirers during merger waves. We argue that if managers are indeed intentionally initiating bad mergers during waves, we should observe poorer governance for in-wave acquirers relative to out-wave acquirers. Specifically, we use various measures of governance (e.g., board characteristics, CEO incentives, shareholder monitoring) to estimate the relation between the quality of governance and the timing of the merger. Our findings suggest that the governance of in-wave acquirers is weaker than the governance of out-wave acquirers. Although waves might lead to more agency-driven acquisitions, they could also be associated with managerial herding. In this case, value-maximizing managers rely on the information content of their predecessors. This explanation is modeled in Persons and Warther (1997), for example, who assume that the only way managers can learn about quality is from the experience of early movers. Their model thus predicts that managers will continue to follow their predecessors until their experience is poor enough, which implies that late movers will perform poorly. Finally, we consider the possibility that our results are driven by inherent differences between in- and out-wave mergers. Specifically, in-wave acquirers might be forced to merge in response to technological or regulatory shocks. These ‘‘mergers of necessity’’ are expected to yield lower returns because they are not done by choice. They are also expected to generate a lower CEO turnoverperformance sensitivity because investors realize that the CEO did not have a choice. To investigate this alternative hypothesis, we reestimate our tests, focusing on merger waves in the industry of the target firm. That is, we focus on mergers in which the target’s industry undergoes a merger wave but the acquirer’s industry does not. We further exclude mergers in which the acquirer and target industries are vertically linked via the supply chain. The idea is that ‘‘target waves’’ are not initiated by shocks in the acquirer’s industry (or industries) and therefore are unlikely to be driven by mergers of necessity. Our hypothesis is that these acquisitions are still accompanied by elevated levels of uncertainty and worse analysts’ forecasts due to the limited monitoring capacity in the target industry. Therefore, they may still foster agency-driven acquisitions. Our findings are consistent with this hypothesis, suggesting that mergers of necessity alone cannot explain our results. Our paper adds to a large body of research on mergers. Some researchers suggest that mergers are valuemaximizing (e.g., Matsusaka, 2001; Jovanovic and Braguinsky, 2004; Maksimovic and Phillips, 2001; Maksimovic, Phillips, and Prabhala, 2011; Maksimovic, Phillips, and Yang, forthcoming), while others suggest they are inefficient, potentially driven by agency conflicts (e.g., Baumol, 1959; Jensen, 1986, 1993; Stulz, 1990). We suggest that the latter are more likely to take place during merger waves. There is also a vast literature on merger waves. The economic theory does not necessarily predict negative value implications. For example, if merger waves are driven by industry shocks that trigger restructuring and consolidation of industries (e.g., Mitchell and Mulherin, 1996; Jovanovic and Braguinsky, 2004), they

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may create value by facilitating efficient asset reallocation. If they are driven by stock market overvaluation (e.g., Shleifer and Vishny, 2003; Rhodes-Kropf and Viswanathan, 2004), they may benefit the acquirer’s shareholders. In contrast, our results imply that in-wave mergers perform substantially worse than out-wave mergers. The results in this paper are also related to the growing literature on inattention in finance (e.g., Barber and Odean, 2008; DellaVigna and Pollet, 2009; Hirshleifer and Teoh, 2003). In particular, we argue that agency-driven managers may get away with predictably bad acquisitions due to constrained information processing of analysts and investors during merger waves. Our findings that announcement returns of in-wave acquisitions are not lower than those of out-wave acquisitions are consistent with temporary mispricing and, more broadly, with the real impact of investor inattention on asset prices. To our knowledge, this paper is among the first to highlight the role of investor inattention on real corporate decisions. The paper proceeds as follows. Section 2 describes the data. Section 3 investigates the uncertainty and quality of analysis during merger waves. Section 4 studies the performance-turnover sensitivity of in-wave mergers. Section 5 considers the long-term performance and corporate governance inside and outside merger waves. Section 6 studies target waves. Section 7 concludes. 2. Data sources and sample construction We start with all merger bids reported by the Securities Data Company (SDC) from 1980 to 2009 and impose the following data requirements: (i) the acquirer is a nonutility, publicly traded company with common stock data available on the Center for Research in Security Prices (CRSP) tapes at the time of the announcement; (ii) the acquirer gained control over the target company (i.e., it had a minority stake of less than 50% before the deal and a majority stake of 51% or more after it); (iii) the deal value, as reported by the SDC, was at least $10 million, and at least 5% of the value of the acquirer at the time of the announcement; (iv) the deal was completed; and (v) the acquirer had data available from Compustat for the fiscal year preceding the merger. Our final sample consists of 9,854 acquisitions of both public and private target companies, of which 1,677 occurred in 1980–1989, 4,869 occurred in 1990–1999, and 3,308 occurred in 2000–2009. Our empirical investigation relies on the comparison between mergers initiated inside and outside intense merger periods. We use three measures of merger intensity. First, we follow Harford (2005) and identify a potential industry merger wave as the 24-month period of highest merger concentration in each decade. Industry classification is based on the Fama and French (1997) 48 industries. Of these potential waves, we define as merger waves those in which the number of mergers is higher than the 95th percentile of a simulated uniform distribution of all the mergers that took place in that industry over the decade (see Harford, 2005, for details). Like Harford, we allow each industry to have only one merger

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wave per decade (thus, an industry cannot have more than three waves in our sample). We define the three ‘‘decades’’ as the periods 1980–1989, 1990–1999, and 2000–2009. Moreover, we consider only waves consisting of at least ten mergers. By employing the above procedure, we calculate 77 merger waves during our sample period. There are 38 industries with merger waves in at least one decade, 28 industries with waves in two decades or more, and 11 industries with waves in all three decades. The average (median) number of mergers per wave is 50 (31). The largest wave in our sample occurred in the banking industry between October 1996 and September 1998 and included 292 mergers. The second-largest wave took place in the computer software industry between October 1997 and September 1999.6 These numbers suggest that merger waves are ubiquitous, of significant magnitudes, and well spread across time and industries. This allows us to test our hypotheses without restricting attention to specific industries or time periods. As discussed above, our hypotheses predict that the costs of monitoring increase after the initiation of a wave, when many acquisitions have already taken place. We therefore construct two measures that record the number of mergers (% N deals) and the total value of mergers (% Deal value) in each industry over the past 12 months, the midpoint of the 24-month merger waves described above. Since these variables are industry-specific, we normalize them by the total number and total value of mergers in the industry-decade, respectively. Thus, these are continuous measures intended to capture the relative intensity of the merger activity over the past year. They do not classify periods as in-wave or outwave. Instead, they identify mergers that were initiated following periods of intensified merger activity, including the peak and later parts of merger waves. As shown in Table 1, both variables exhibit large variation. While mergers in the 25th percentile are initiated following 10% of the mergers in the decade (4% of total deal value) over the previous 12 months, mergers in the 75th percentile follow 17% of such mergers (19% of total deal value). We next describe the construction of the long-term performance measures used throughout this paper. Ideally, we would like to compare the post-merger performance of in-wave acquirers against that of otherwise identical out-wave acquirers. While we recognize that this is extremely difficult to do, our empirical approach is to compare the benchmark-adjusted performance of inwave and out-wave acquirers. We focus on two benchmark portfolios. The first is a weighted average of two industry portfolios: the acquirer industry portfolio and the target industry portfolio. Each

6 Harford (2005) identifies waves in the banking and software industry during similar periods. He attributes these waves to changes in deregulation and information technology. In general, our merger waves differ from Harford’s in three ways. First, we consider one more decade (2000–2009). Second, we include acquisitions of private as well as public firms. Third, Harford considers mergers with deal value of at least $100 million, whereas we consider acquisitions of $10 million or more and with a relative deal value of at least 5%.

Table 1 Summary statistics. This table reports the summary statistics for the variables employed in this study. In Panel A, we report the mean, standard deviation, and the 25th, 50th, and 75th percentiles for each variable. In each case, we compute summary statistics for the entire sample (All) and two subsets, corresponding to merger wave and non-wave acquisitions. Accounting measures are calculated for the fiscal year end prior to the announcement of the acquisition. Sample characteristics and a detailed description of each variable are included in Table 9. In Panel B, we present the proportion of deals in which (i) the target industry is different than the acquirer’s (Diversifying), (ii) the target is a public company (Public tgt), and (iii) cash was the method of payment (Cash only). For each case, we show the proportion of deals for the entire sample, non-wave acquisitions, and wave acquisitions, respectively. The last column of Panel B (Out–In) represents the difference between non-wave and wave numbers. Panel A: Summary statistics Mean Deal value ($ million) All 337.39 In wave 394.28 Out wave 311.21 Relative size (%) All 37.92 In wave 38.40 Out wave 37.70 Tobin’s Q All 1.81 In wave 1.91 Out wave 1.76 Price run-up (%) All 12.96 In wave 13.74 Out wave 12.60 Acq size ($ billion) All 2.66 In wave 2.73 Out wave 2.62 % N deals All 13.77 In wave 17.58 Out wave 12.02 % Deal value All 13.47 In wave 18.80 Out wave 11.02

Std. dev.

25th

Median

75th

892.60 976.63 849.96

30.85 33.50 30.00

78.41 88.50 74.47

235.00 281.79 220.00

50.47 50.03 50.68

9.92 10.12 9.84

19.36 20.10 19.03

42.97 44.73 42.10

1.22 1.34 1.15

1.08 1.11 1.06

1.39 1.43 1.37

2.02 2.15 1.96

50.46 51.24 50.10

 17.28  16.13  17.73

4.54 5.89 4.09

31.46 31.77 31.31

6.80 7.23 6.60

0.16 0.16 0.16

0.54 0.53 0.54

1.87 1.82 1.88

6.01 5.34 5.46

10.00 14.12 8.70

13.48 17.30 11.95

17.34 20.90 14.84

12.95 13.48 11.92

4.01 8.75 3.05

9.45 14.97 7.02

18.63 26.01 14.31

Panel B: Proportions and number of deals All % Diversifying % Public tgt % Cash only No. of deals

36.53 32.68 30.46 9,854

Out wave 36.23 30.36 32.15 6,749

In wave 37.20 37.71 26.80 3,105

Out–In  0.97  7.35 5.36 3,644

of the two portfolios is measured as the value-weighted portfolio of all same-industry firms not involved in acquisitions. (Industry is defined based on the Fama and French 48 industries classification.) The weight given to each of these two portfolios is determined by the weight of the acquiring and target firms, respectively, in the combined firm. To isolate the direct effects of the merger on the returns, we include in the industry portfolio only firms that did not participate in any merger activity for the 3 years surrounding the merger date. We also require

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that these firms have at least 2 years of returns after the merger. We use the combined industry portfolio as a benchmark because there is ample evidence suggesting that merger waves are strongly clustered by industry (e.g., Mitchell and Mulherin, 1996; Mulherin and Boone, 2000; Andrade, Mitchell, and Stafford, 2001; Harford, 2005). We include both the acquirer’s and the target’s industry to account for the potential change in the industry composition of the combined firm’s output. All our results hold if the industry portfolio comprises only firms from the acquirer’s industry. The second benchmark portfolio comprises propensity score-matched nonacquirers. Each acquirer is matched to three nonacquiring firms based on a probit model estimating the propensity to make an acquisition in the year preceding the announcement of the acquisition. We follow the model in Harford (1999), which predicts bidders using average abnormal return, sales growth, noncash working capital, leverage, market-to-book, price-to-earnings, size, and cash deviation, augmented with industry dummies. We estimate the model separately for each year during our sample period and match each acquirer to the three firms in the acquirer’s industry with the closest propensity scores. Our procedure is restricted to firms that (i) did not participate in any merger activity for the 3 years surrounding the merger date and (ii) have at least 2 years of returns after the merger. The goal of this benchmark portfolio is to address selection, that is, the choice of nonacquiring firms not to merge. All our results are robust to an alternative definition of this benchmark portfolio based on same-industry comparable firms of similar size and market-to-book. We employ two commonly used measures of longterm abnormal performance: Buy and Hold Abnormal Returns (BHAR)7 and the intercept of a factor model applied to a calendar time portfolio. We define individual firms’ BHAR as BHARi,t ¼

H Y j¼1

ð1 þ r i,t þ j Þ

H Y

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sample are initiated during waves, whereas the other 68% are initiated outside waves. The table shows that the average deal value is larger during waves. Relative size (i.e., the ratio of deal value to acquirer market value) and size of the acquiring firm are all only marginally larger during merger waves. Given previous findings that size is negatively correlated with the acquirer’s announcement returns (e.g., Moeller, Schlingemann, and Stulz, 2004; Ahern, 2010), this emphasizes the importance of controlling for deal and acquirer size. Table 1 also indicates that the industry-level Q is higher during merger waves. This result is consistent with the results in Harford (2005), who finds a positive relation between the lagged market-to-book ratio and the occurrence of a merger wave. Price run-up is also higher during waves. To the extent that stock market valuation and past performance are correlated with subsequent long-term performance (e.g., Rau and Vermaelen, 1998), it is important that we control for these factors in our tests. Panel B indicates that there is a higher fraction of public targets during waves and a lower fraction of cash transactions. Prior literature documents that acquisitions of private targets and cash acquisitions are associated with higher acquirer returns (e.g., Fuller, Netter, and Stegemoller, 2002). Therefore, our tests control for the method of payment and for the ownership status of the target firm. Summary statistics for the two continuous measures of merger intensity are also presented in Table 1. Not surprisingly, both measures are about 1.5–1.7 times bigger during Harford’s (2005) merger waves. These measures, however, are not restricted to merger waves. They are intended to consider later parts of waves and, more generally, acquisitions initiated following periods of intense merger activity. 3. The quality of analysis and uncertainty 3.1. The quality of analysis

ð1 þ r benchmark,t þ j Þ

ð1Þ

j¼1

in which H is the holding period (typically 24–36 months). We also compare the long-term performance of in-wave and out-wave acquirers using a calendar time portfolio. This portfolio buys stocks of out-wave acquirers and sells stocks of in-wave acquirers. The portfolio is rebalanced monthly. For robustness, we consider two specifications, in which the stocks are held for a period of either 2 or 3 years, respectively. Table 1 gives summary statistics for our sample and compares between firm and deal characteristics inside and outside waves. All variable definitions are given in Table 9. Using Harford’s method of wave identification, we find that approximately 32% of the mergers in our 7 Viswanathan and Wei (2008) show that if the past performance predicts future events and the event process is well behaved (in particular, stationary), BHAR measures are negatively biased. Because we concentrate on differences in performance inside and outside waves, our results should not be affected by this bias as long as such bias is not greater in one case versus the other.

First, we consider links between the volume of merger activity and the quality of analysis of investors and analysts. We consider two competing hypotheses. Our first hypothesis is that merger waves increase the workload of analysts and the quantity of information that analysts and investors need to monitor. Analysts’ workload increases because it requires specialized labor, which is in fixed supply in the short run. Therefore, it is possible that the quality of analysis of any individual merger decreases with overall merger activity. A similar mechanism is modeled in the work of Khanna, Noe, and Sonti (2009), which finds that banks reduce IPO screening in hot markets because it requires specialized labor, which is in fixed supply. The alternative hypothesis is that prior mergers provide useful information, which is likely to improve the quality of analysis. Further, to the extent that outside investors rationally predict the impact of merger waves on incentives and quality, the marginal benefit of analysts’ forecasts may be higher. This hypothesis therefore predicts higher quality of analysis during merger waves.

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Table 2 The quality of analysis during merger waves. This table compares different measures of uncertainty and announcement returns inside and outside waves. The variables of interest are the dispersion and error in analysts’ forecasts (Forecast std and Forecast error, respectively) and two measures of announcement returns. CAR[  1,1] represents cumulative abnormal returns during the one-day window surrounding the announcement date. CAR[  3,3] uses a 3-day window. Sample characteristics and a detailed description of each variable are included in Table 9. The first column of Panel A (All) presents simple averages of each measure, whereas columns Out wave and In wave contain averages for mergers outside and inside waves, respectively. The difference between these two columns is presented in column Out–In. For the first three columns, simple t-tests are run for each average, where the null is an average of zero. For the last column, a mean difference test is performed, where the null is no difference between the averages outside and inside the wave. In Panel B, we regress each variable of interest on industry fixed effects and our proxies for merger waves. Standard errors are clustered at the industry level. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Panel A: Forecasts and announcement returns (averages)

Forecast std Forecast error CAR[  1,1] CAR[  3,3]

All

Out wave

In wave

Out–In

0.0046nnn 0.0165nnn 0.0090nnn 0.0109nnn

0.0043nnn 0.0150nnn 0.0088nnn 0.0105nnn

0.0052nnn 0.0194nnn 0.0097nnn 0.0124nnn

 0.0009nn  0.0044nnn  0.0008  0.0019

CAR[  1,1]

CAR[  3,3]

 0.0015  0.0208  0.0153n

0.0012  0.0083  0.0171

Panel B: Forecasts and announcement returns (regressions) Forecast std Wave % N deals % Deal value

n

0.1140 0.6890nn 0.1310

Forecast error nn

0.1850 1.6090nnn 0.4790nn

To study the quality of analysis, we compare analysts’ forecasts and announcement returns inside and outside merger waves. In particular, we study potential differences in the dispersion and error in analysts’ forecasts about the earnings of acquiring firms. If forecasts are more dispersed and less accurate during merger waves, it indicates that analysts are less able to make accurate forecasts during such periods.8 It could be that investors fully account for the differences in the quality of acquisitions during merger waves. If this change in quality is priced, it should be reflected in announcement returns. Therefore, in addition to analyst forecasts, we also compare announcement returns inside and outside waves. Panel A of Table 2 reports differences in means between in-wave and out-wave mergers. The definition of merger waves in this panel is based on Harford (2005). Our results are compelling and suggest that the dispersion and error in analysts’ forecasts are greater during merger waves. The magnitudes of these differences are substantial: during waves, the (normalized) standard deviation of analysts’ forecasts is 21% higher, and the error in analysts’ forecasts is 29% higher. The differences are also significant at the 5% level or better.

8 Data on analysts’ forecasts are taken from the Institutional Brokers Estimate System (I/B/E/S). For each merger announcement date, we consider all forecasts made about the next quarterly earnings of the acquiring firm in the month before the merger. The dispersion in analyst forecasts is defined as the standard deviation of earnings forecasts across analysts in the month surrounding a merger announcement, normalized by the firm’s total book assets. Analyst forecast error is defined as the absolute difference between the mean analyst earnings forecast in the month surrounding a merger announcement and the actual earnings, normalized by the firm’s total book assets.

In contrast, we do not find significant differences between announcement returns inside and outside merger waves. In fact, though the differences are not statistically significant, we find higher announcement returns inside merger waves. One explanation for these findings is that investors fail to recognize the systematic deterioration in the quality of acquisitions during waves. This explanation is supported by the evidence in Section 5. In Panel B we estimate regressions explaining the dispersion and error in analysts’ forecasts and merger announcement returns. In addition to the merger wave indicator (Wave), we consider mergers that happen later in a wave or after other mergers (% N deals, % Deal value). To control for interindustry differences and intraindustry correlations, all regressions include industry fixed effects and standard errors clustered at the industry level. Consistent with the results in Panel A, we find a positive relation between the intensity of merger activity and the dispersion/error in analysts’ forecasts. This relation is statistically significant at the 5% level or better in four of the six regressions. The regression results also indicate that there is no clear relation between announcement returns inside and outside waves. We find a positive difference in one of the six cases and a negative difference in the remaining five cases. These differences are statistically insignificant (except for one case in which the difference is significant at the 10% level). 3.2. Uncertainty In this section, we show that merger waves are associated with greater levels of uncertainty, both about the companies involved and about the industry in which the merger wave takes place. One possible reason for the results we document in this section is that waves are

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initially triggered by technological or regulatory shocks (e.g., Mitchell and Mulherin, 1996; Harford, 2005), which are likely accompanied by elevated levels of uncertainty about the prospects of the shock. To test whether merger waves are indeed accompanied by elevated levels of uncertainty, we use a number of stock return volatility measures. Specifically, we consider the acquiring firm’s stock return implied volatility obtained from options prices, as well as GARCH-based estimates of industry volatility. These analyses are presented in Table 3. Implied volatility is a forward-looking measure of the market’s expectations about the future distribution of stock returns. Therefore, it can be used to estimate the market’s level of uncertainty about the firm value. We test

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the hypothesis that acquirers’ implied volatilities are higher during merger waves, which in turn suggests that there is greater uncertainty about the value of the acquisition during merger waves. Our empirical investigation follows Bargeron, Lehn, Moeller, and Schlingemann (2009). We collect data on the acquiring firms’ implied volatility from the estimated volatility surface in the Optionmetrics database for 91day at-the-money (ATM) options. This database contains interpolated values of implied volatility on a daily basis for all firms in the sample. We compute two measures of implied volatility. The first measure (Implied vol(t)) is the average implied volatility of the ATM call and the ATM put at the time of the acquisition announcement. The

Table 3 Uncertainty during merger waves. This table compares different measures of volatility inside and outside waves. The variables of interest are the two measures of implied volatility and the GARCH estimates. Implied vol (t) is measured at the time of the announcement. Implied vol (t 30, t) is the median implied volatility over the 22 trading days preceding the announcement date. Sample characteristics and a detailed description of each variable are included in Table 9. The first column of Panel A (All) presents simple averages of each measure, whereas columns Out wave and In wave contain averages for mergers outside and inside waves, respectively. The difference between these two columns is presented in column Out In. For the first three columns, simple t-tests are run for each average, where the null is an average of zero. For the last column, a mean difference test is performed, where the null is no difference between the averages outside and inside the wave. In Panel B, we regress each variable of interest on industry fixed effects and our proxies for merger waves. Standard errors are clustered at the industry level. Panel C presents the mean, standard deviation, and the 25th, 50th, and 75th percentiles across estimates from GARCH models similar to those in French, Schwert, and Stambaugh (1987), except that we allow for the variance to depend on our proxies for merger waves. The baseline model is (

r i r f

¼ a þ bðr m rf Þþ et þ yet1 ,

s2t

¼ expðg 1 þ g 2 WÞþ bs2t1 þ c1 e2t1 þ c2 e2t2 ,

where W represents one of our proxies for merger waves, and ri, rm, and rf correspond to returns on the industry portfolio, the CRSP value-weighted index, and the risk-free asset. We estimate the baseline model for each one of the 48 Fama and French industries separately, using daily returns from 1980 to 2009. Estimates for a, b, c1, c2, b, and y come from the baseline model using the wave dummy Wave. The baseline model is reestimated for the two other proxies for merger waves and the coefficients g1 and g2 are presented for each case. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Panel A: Implied volatility (averages)

Implied vol (t) Implied vol (t  30, t)

All

Out wave

In wave

Out–In

0.5340nnn 0.5300nnn

0.5130nnn 0.5110nnn

0.5570nnn 0.5510nnn

 0.0441nnn  0.0399nnn

Implied vol (t)

Implied vol (t  30, t)

Panel B: Implied volatility (regressions)

0.1150n 1.0910nnn 0.2570nnn

Wave % N deals % Deal value

0.1100n 1.0270nnn 0.2470nnn

Panel C: GARCH estimates Mean

a b c1 c2

b y g1(W ¼Wave) g2(W ¼Wave) g1(W ¼% N deals) g2(W ¼% N deals) g1(W ¼% Deal value) g2(W ¼% Deal value)

0.0004nnn 0.9187nnn 0.2218nnn  0.1333nnn 0.6562nnn 0.0443nnn  14.3189nnn 0.1822nn  14.4099nnn 0.9429nnn  14.4309nnn 0.5775nnn

Std. dev. 0.0003 0.0583 0.4091 0.2460 0.1319 0.077 0.9274 0.5052 1.1083 1.2606 1.1179 0.9259

25th

Median

75th

0.0003 0.9096 0.1383  0.1168 0.5627  0.0079  14.9864  0.1812  15.0689  0.0484  15.0897  0.1676

0.0004 0.9285 0.1553  0.0933 0.6689 0.0445  14.5292 0.1721  14.4706 0.7268  14.5091 0.2906

0.0005 0.9459 0.1754  0.071 0.7434 0.0915  13.8788 0.4836  14.0630 1.8595  13.9779 1.3311

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second measure (Implied vol(t  30,t)) is the average implied volatility from one month before the announcement to the day of the announcement. Both Panels A and B of Table 3 indicate that implied volatility is higher during periods of intensified merger activity. Panel A presents differences-in-means estimates. Implied volatility is 4.4 percentage points higher during merger waves, and these differences are statistically significant at the 1% level. Panel B reports the results from three regressions for each of the two measures of implied volatility. Each regression corresponds to a different measure of merger intensity (Wave, % N deals, and % Deal value). All regressions include industry fixed effects and standard errors clustered by industry. The results in Panel B suggest that all three measures of merger intensity are positively and significantly related to implied volatility. These effects are statistically significant at the 1% level in four of the six cases and at the 10% level in the remaining two cases. Panel C of Table 3 considers an alternative measure of expected stock return volatility. Specifically, we augment the GARCH model of French, Schwert, and Stambaugh (1987) with our measures of merger intensity to estimate the effect of merger waves on return volatility. We estimate the following model:   ( r i r f ¼ a þ b r m r f þ et þ yet1 ,   ð2Þ s2t ¼ exp g 1 þ g 2 W þ bs2t1 þ c1 e2t1 þ c2 e2t2 , in which W represents one of the three different measures of merger intensity, r i is the return on the industry portfolio, r m is the return on the CRSP value-weighted index, and r f is the return on the risk-free asset. We consider the industry portfolio to capture the industrylevel uncertainty. The above model is estimated for each of the 48 Fama and French industries separately using daily returns from 1980 to 2009. We then use the cross-section of estimates to draw our inferences. For our purposes, the coefficient of interest is g 2 , which captures the impact of the intensity of merger activity on return volatility. Panel C reports estimates of g 2 for all three measures of merger intensity. In all three cases, the average effect is positive and statistically significant at the 5% level or better. These results suggest that the expected volatility of industry returns is higher during merger waves. Overall, the results suggest that there is greater uncertainty about the value of in-wave acquisitions. Uncertainty may be yet another mechanism through which merger waves reduce the quality of monitoring. This is because it increases the costs of obtaining more informative signals, which are required by monitors to better infer the quality of the merger. 4. CEO turnover Next, we test whether the CEO is less likely to be dismissed following bad mergers taking place during periods of high merger activity. To this end, we study the CEO turnover–performance sensitivity as a function of the intensity of the merger activity. We collect available data on CEO turnover from ExecuComp and BoardEx, resulting in 2,831 observations. Our specification is similar to the

one in Lehn and Zhao (2006), who study the relation between merger performance and CEO turnover. We augment their specification with our three measures of merger intensity. Specifically, we follow their paper and compute buy-and-hold abnormal returns during the three years before the acquisition (Pre-merger BHAR) and during the 3 years following the acquisition (Post-merger BHAR). For the cases in which turnover occurs within this 3-year period, Post-merger BHAR represents the buy-and-hold returns up to the year the CEO is replaced. We handle overlapping deal windows in the CEO turnover analysis by considering only the largest acquisition within that period. For consistency, we use the industry portfolio to adjust the raw returns. Table 4 reports results of cross-sectional probit regressions in which the dependent variable is an indicator that takes the value of one if the CEO left the firm and zero otherwise. The independent variable of interest is the interaction term between each of our three merger wave variables and the post-merger BHAR measure. Similar to Lehn and Zhao (2006), we include the relative deal size, method of payment, and CEO age as control variables. Since CEO age is poorly populated, we set missing observations to zero and include an indicator variable (Missing CEO age) equal to one if CEO age is missing and zero otherwise. We further augment their controls with the method of payment and the ownership status of the target, since our sample includes both public and private deals. In columns 1–3 we consider the full sample. However, the impact of bad acquisitions on turnover is likely a function of the importance of the acquisition for the firm. In that case, we would expect the results to strengthen once we focus on those largest, most important deals. Thus, in columns 4–9 we study the turnover-to-performance sensitivity in subsamples that include large acquisitions. Columns 4–6 include only the largest acquisition initiated by each firm. Columns 7–9 include deals whose relative deal value is greater than the sample median. Consistent with Lehn and Zhao (2006), we find that the direct effect of post-merger BHAR on CEO turnover is negative (that is, poorer performance is associated with a greater likelihood of CEO turnover). However, the interaction between post-merger performance and the merger intensity measures is positive. This suggests that during periods of intensified merger activity, the sensitivity of CEO turnover to post-merger performance is weaker, everything else being equal. The interaction term is statistically significant in six of the nine regressions (at the 1% level in five of the six cases). Consistent with our hypothesis, we obtain stronger results in columns 4–9. These results imply that the effect of merger waves on turnover–performance sensitivity is especially pronounced in larger acquisitions. These findings suggest that initiating mergers in periods of intense merger activity might mitigate long-term career concerns because managers may be evaluated more favorably ex post if their ex ante behavior was similar to that of their peers.9 Underlying this line of

9 Fama (1980) and Lazear and Rosen (1981) were the first to note that career concerns may curb agency-driven managerial behavior that hurts shareholders.

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Table 4 CEO turnover and merger waves. This table presents estimates from probit regressions explaining CEO turnover using pre- and post-merger performance similar to those in Lehn and Zhao (2006). We only include mergers announced on or after 1990 due to data limitations. The variables of interest are the interactions between postmerger performance and our proxies for merger waves. We collect CEO turnover data from ExecuComp and BoardEx. Following Lehn and Zhao (2006), we compute buy-and-hold abnormal returns during the 3 years before the acquisition (Pre-Merger BHAR) and during the 3 years following the acquisition (Post-merger BHAR). Matched firms are used to adjust raw returns. We deal with overlapping deal windows by considering only the largest acquisition within that period. Sample characteristics and a detailed description of each variable are included in Table 9. In columns 1–3, all acquisitions are included. In columns 4–6, only the largest acquisition (in terms of relative value) is included for each company. In columns 7–9, we limit the sample to acquisitions with relative deal value above the overall median. Robust standard errors (clustered at the industry level) are reported in parentheses. All coefficients represent marginal effects. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Full sample (1) Post-merger BHAR  Wave

(2)

(3)

0.020n (0.011)

Acq size Public tgt Cash only CEO age (  100) Missing CEO age

R-squared Observations

(8)

0.330nnn (0.086) 0.046 (0.030) 0.012 (0.018)

0.069 (0.124)

 0.029 (0.006) 0.111 (0.251)  0.022nn (0.011) 0.007n (0.004)  0.001 (0.011) 0.043nnn (0.015) 0.025 (0.033) 0.062nn (0.029)

 0.058 (0.011) 0.107 (0.249)  0.024nn (0.011) 0.007nn (0.004)  0.001 (0.011) 0.043nnn (0.015) 0.020 (0.033) 0.059nn (0.029)

0.032 (0.032)  0.024nnn (0.008) 0.103 (0.253)  0.023nn (0.011) 0.007n (0.004)  0.001 (0.011) 0.043nnn (0.015) 0.025 (0.033) 0.062nn (0.029)

0.024 2,859

0.028 2,859

0.023 2,859

reasoning is the assumption that the labor market penalizes poor managerial performance (see, e.g., Weisbach, 1988; Kaplan and Reishus, 1990; Gibbons and Murphy, 1992; Gompers and Lerner, 1999). Our findings are consistent with the theoretical works of Scharfstein and Stein (1990) and others, which suggest that when managers are evaluated relative to their peers, they tend to be evaluated more favorably if their actions were similar to those of other managers.10

5. Implications for performance and corporate governance The above findings suggest that the information and incentive environment during merger waves may lead to 10 For evidence in support of relative peer evaluation of management, see, e.g., Gibbons and Murphy (1990), and Morck, Shleifer, and Vishny (1990).

(9)

0.029nnn (0.008)

0.007 (0.022)

nnn

(7)

0.097nn (0.038)

0.228nnn (0.087)

nnn

Largest relative deal values

0.405nnn (0.121)

0.016 (0.013)

% Deal value

Relative deal size

(6)

0.015 (0.022)

% N deals

Pre-Merger BHAR (  100)

(5)

0.033nnn (0.010)

Post-merger BHAR  % Deal value

Post-merger BHAR

(4)

0.256nnn (0.081)

Post-merger BHAR  % N deals

Wave

Firm’s largest acquisition

nnn

 0.036 (0.009) 0.036 (0.268)  0.040nnn (0.015) 0.004 (0.005) 0.005 (0.018) 0.043n (0.024)  0.040 (0.046) 0.039 (0.038) 0.033 1,105

0.063 (0.109)

 0.080 (0.021) 0.056 (0.263)  0.040nnn (0.015) 0.004 (0.005) 0.006 (0.017) 0.043n (0.024)  0.039 (0.046) 0.037 (0.038)

 0.054 (0.057)  0.035nnn (0.009) 0.039 (0.263)  0.040nnn (0.015) 0.004 (0.005) 0.007 (0.018) 0.042n (0.023)  0.031 (0.047) 0.043 (0.039)

 0.024 (0.006) 0.109 (0.303)  0.01 (0.009) 0.011n (0.006) 0.003 (0.015) 0.052nnn (0.017)  0.003 (0.039) 0.037 (0.033)

 0.059 (0.013) 0.114 (0.302)  0.009 (0.009) 0.011n (0.006) 0.004 (0.015) 0.052nnn (0.018)  0.003 (0.039) 0.033 (0.032)

0.004 (0.046)  0.019nn (0.009) 0.115 (0.312)  0.009 (0.010) 0.011n (0.006) 0.003 (0.015) 0.051nnn (0.018) o 0.001 (0.040) 0.038 (0.033)

0.039 1,105

0.033 1,105

0.027 1,434

0.032 1,434

0.023 1,434

nnn

nnn

nnn

an increase in the initiation of bad mergers. First, merger waves are characterized by poorer quality of analysts’ forecasts and elevated levels of uncertainty, which may result in reduced monitoring. This in turn may increase the volume of agency-driven acquisitions, which are likely to result in acquisitions of worse quality and performance. Second, managers may be able to share the blame of initiating bad mergers during merger waves since their behavior conforms to their peers. This, too, can lead to more agency-driven acquisitions. However, these results could also be related to managerial herding, unrelated to agency, in which managers rely on information embedded in the actions of their predecessors. This explanation, modeled in Persons and Warther (1997), predicts that managers will continue to follow their predecessors until their experience is poor enough, which implies that late movers will perform poorly. To test these predictions, Section 5.1 tests whether the long-term performance of in-wave mergers differs from that of out-wave mergers. In Section 5.2, we compare

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between the quality of corporate governance of in-wave acquirers and out-wave acquirers. Our evidence suggests that the governance of in-wave acquirers is weaker than the governance of out-wave acquirers. This suggests that the poor performance of in-wave acquisitions is related not only to potential managerial herding but also to an increase in agency-driven acquisitions. 5.1. Long-term performance Our empirical strategy is to compare the long-term performance of in-wave acquirers and out-wave acquirers. We do so by estimating panel regressions that control for deal and firm characteristics, taking into account correlations within each industry and across time. We begin with univariate evidence before turning to the regressions. Fig. 1 compares between the 3-year performances of in-wave and out-wave acquirers following the effective date of the merger.11 We consider the 3-year post-merger performance rather than longer horizons to mitigate the potential for confounding events to influence performance. The results, however, do not change if we consider time horizons of 2–5 years. In particular, Fig. 1 presents a detailed description of the evolution of the cumulative buy-and-hold abnormal returns (BHAR) of in-wave and out-wave acquiring firms. The abnormal returns are calculated against two different benchmark portfolios: a portfolio of propensity scorematched firms (Panel A) and a combined acquirer/target industry portfolio (Panel B). The construction of these measures is explained in Section 2. The results are persistent across both benchmark portfolios: out-wave acquirers lie consistently above inwave acquirers throughout the 36 months following the merger. That is, out-wave acquirers consistently outperform in-wave acquirers. Furthermore, Fig. 1 suggests that acquirers earn negative abnormal returns, and more so when they initiate the merger during merger waves. The magnitudes of the differences in performance stay fairly constant over time for any given benchmark. This suggests that most of the difference materializes in the first year after the merger, which implies that the differences we document are related to the merger itself. The univariate results in Fig. 1 do not control for other factors that could be driving the differences. We next turn to our main performance regressions, in which we control for such factors. Table 5 presents estimates from regressions explaining post-merger performance. The main coefficients of interest are the three proxies for intense merger activity. The control variables include acquirer and deal characteristics commonly found to be correlated with performance in prior literature [these controls are discussed collectively in Masulis, Wang, and Xie (2007)].12 The acquirer characteristics that we control for include Tobin’s Q, firm size, price run-up, leverage, free cash flow (FCF), and CEO age. The deal characteristics 11 These and subsequent results were also estimated in the 2 years following the announcement date rather than the effective date and produced very similar results. 12 See Table 9 for detailed definitions of all variables.

that we control for are target ownership status (public vs. private), method of payment, relative deal size, and degree of diversification. A few comments about these controls are noteworthy. First, to mitigate endogeneity concerns, we measure Tobin’s Q at the industry level (Industry Tobin’s Q).13 Second, we control for overvaluation to distinguish our hypothesis from the findings in Bouwman, Fuller, and Nain (2009) that lower long-term performance follows acquisitions during periods of high stock-market valuation. We use two controls for overvaluation. The first is a dummy variable representing industry overvaluation (High valuation). This variable is equal to one if the industry median market-to-book ratio is above its 5-year moving average. Our second control is the premerger change in the value of the acquirer (Price run-up). This variable is intended to control for the possibility that wave acquirers are different in valuation compared to out-wave acquirers. Third, we control for CEO age because of recent evidence that younger managers are more likely to make acquisitions, which tend to be worse (Yim, 2010). We set missing CEO age values to zero and include a missing variable indicator (Missing CEO age). Table 5 reports the regression estimates for the 2-year post-merger performance using BHAR abnormal returns. All regressions include year fixed effects and report standard errors that are robust to clustering by industry. Columns 1–3 report the results when the benchmark portfolio comprises propensity score-matched firms. Columns 4–6 correspond to the combined industry benchmark portfolio. Columns 1–6 show that across both benchmark portfolios, all three measures of merger intensity predict lower long-term buy-and-hold returns. The magnitude of the effects is economically nontrivial: the coefficient on the wave dummy varies from  9.3% to  12.5%, whereas the effects of a one-standard deviation increase in the two continuous proxies range from  3.76% (column 6) to  6.85% (column 2). The results are statistically significant at the 5% level or better. The effects of the control variables are consistent with previous findings. Cashonly acquisitions earn higher abnormal returns, whereas diversifying acquisitions with higher price run-up earn lower abnormal returns. The acquiring firm’s size is positively correlated with returns, and so is CEO age. These effects persist across the different specifications. Prior literature raises a number of issues that potentially affect long-term event studies (e.g., Barber and Lyon, 1997; Fama, 1998; Brav, 2000; Mitchell and Stafford, 2000). Perhaps the most salient is that they assume stock market efficiency and a model of market equilibrium. Moreover, many long-term event studies assume that abnormal returns are independent across firms. However, mergers happen in industry waves, and

13 Prior studies find mixed results for the relation between an acquirer’s Tobin’s Q and announcement returns. Lang, Stulz, and Walkling (1991) and Servaes (1991) find a positive relation for tender offer acquisitions and public firm acquisitions, respectively. In contrast, Moeller, Schlingemann, and Stulz (2004) find a negative relation in a more comprehensive sample of acquisitions.

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0.05 Outside waves

Inside waves

Cumulative BHAR

0.00

−0.05

−0.10

−0.15

−0.20

0

4

8

12

16 20 Months after merger

24

28

32

36

0.05 Outside waves

Inside waves

Cumulative BHAR

0.00

−0.05

−0.10

−0.15

−0.20

0

4

8

12

16 20 Months after merger

24

28

32

36

Fig. 1. Long-term performance inside and outside waves. This figure shows the post-merger, long-term performance of acquirers relative to different benchmarks. For each acquirer i, we compute buy-and-hold abnormal returns from one to 36 months following the acquisition. In Panel A, raw returns are adjusted using a portfolio of propensity score-matched nonacquirers, in which each acquirer is matched to the three nonacquiring firms in the same industry and size decile with the closest propensity scores. We use Harford’s (1999) probit model to estimate the propensity to acquire. In Panel B, we adjust returns using a weighted-average of the acquirer’s and target’s industry returns (excluding companies involved in acquisitions during the 3-year window surrounding the event), where the weights correspond to the relative sizes of the acquirer and target, respectively. The graph plots, for each event month from 1 to 36, the average of the abnormal returns of all acquirers that merged inside and outside a wave. Sample characteristics and a detailed description of each variable are included in Table 9. Panel A: Returns on matched firms as benchmarks. Panel B: Combined industry returns as benchmarks.

therefore, they cluster through time by industry. This clustering leads to positive cross-correlation of abnormal returns, which biases upward test statistics that assume independence. We address these issues in several ways. First, we consider different benchmark portfolios. Second, we include in all specifications year fixed effects while clustering standard errors at the industry level.14 To further mitigate these concerns, Table 6 reports the results for operating performance, as measured by the

14 In unreported tests, we estimate the coefficients using a FamaMacBeth procedure, with very similar results.

change in (industry-adjusted) ROA over the 2 years following the merger, and for calendar time portfolios. Panel A focuses on ROA. Previous operating performance studies attempt to determine whether the expected gains at announcement are ever realized. The findings are mixed. For example, Ravenscraft and Scherer (1989) examine target firm operating profitability and find that firms suffer a loss in profitability following the merger. In contrast, Healy, Palepu, and Ruback (1992) examine post-merger operating performance relative to the industry median and find that merged firms have higher operating cash flows relative to industry peers. Their results highlight the importance of selecting an appropriate expected performance benchmark in the absence of a merger. To account for the change in the

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Table 5 Long-term performance inside and outside waves. This table presents estimates from panel regressions explaining post-merger long-term market performance using our measures of merger waves. In each case, the dependent variable is a measure of post-merger performance during the 24 months following the event. We use two different benchmarks to adjust the returns of the acquirers. Matched firms is a portfolio of propensity score-matched nonacquirers, in which each acquirer is matched to the three nonacquiring firms in the same industry and size decile with the closest propensity scores (columns 1–3). We use Harford (1999)’s probit model to estimate the propensity to acquire. Combined industry is a weighted-average of the acquirer’s and target’s industry returns (excluding companies involved in acquisitions during the 3-year window surrounding the event), where the weights correspond to the relative sizes of the acquirer and target, respectively (columns 4–6). The independent variables of interest are our three measures of merger waves. Sample characteristics and a detailed description of each variable are included in Table 9. All regressions include year fixed effects and robust standard errors (clustered at the industry level) in parentheses. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively.

(1) Wave

Benchmark¼ Matched firms (2)

 0.125nnn (0.037)

Industry leverage (  100) Price run-up Relative deal size (  100) Acq size Public tgt Cash only High valuation Public tgt  Cash only Free cash flows CEO age (  100) Missing CEO age

R-squared Observations

Benchmark¼ Combined industry (5)

(6)

 0.093nn (0.043)  0.739nnn (0.189)

 0.078nnn (0.016) 0.001 (10.060)  0.086 (0.118)  0.081nnn (0.010) 0.061 (0.661) 0.055nnn (0.006)  0.008 (0.024) 0.048n (0.028)  0.081nn (0.030)  0.008 (0.037)  0.101 (0.078) 0.102nn (0.046)  0.049n (0.028)

 0.080nnn (0.017) 1.800 (10.320)  0.084 (0.113)  0.082nnn (0.010) 0.033 (0.679) 0.055nnn (0.006)  0.016 (0.023) 0.050n (0.028)  0.083nnn (0.029)  0.005 (0.037)  0.100 (0.079) 0.100nn (0.046)  0.050n (0.027)

 0.323nn (0.122)  0.080nnn (0.017) 3.378 (10.190)  0.091 (0.124)  0.080nnn (0.010) 0.135 (0.689) 0.055nnn (0.006)  0.013 (0.023) 0.050n (0.028)  0.079nnn (0.028)  0.008 (0.037)  0.104 (0.080) 0.100nn (0.046)  0.052n (0.027)

0.102 9,103

0.102 9,103

0.101 9,103

% Deal value

Industry Tobin’s Q (  100)

(4)

 1.033nnn (0.297)

% N deals

Diversifying

(3)

industry composition of acquiring firms, we benchmark ROA against the combined industry portfolio discussed in Section 2. Panel A of Table 6 reports the regression estimates for ROA and combined industry-adjusted ROA. The dependent variable in columns 1–3 is the change in the firm’s ROA over the two years following the merger. Columns 4–6 correspond to the change in the combined industry-adjusted ROA. We include the same control variables as in Table 5 (unreported for brevity), and once again include year fixed effects and use robust standard errors in all regressions. The results show that the change in ROA of in-wave acquirers is lower than that of outwave acquirers. The results for the change in the combined industry-adjusted ROA reveal a similar pattern. In Panel B we report the alphas of the calendar time portfolios from a three-factor model that includes the market portfolio and the Fama and French (1992) size

 0.065nnn (0.022)  4.796 (8.524) 0.003 (0.026)  0.051nnn (0.010) 0.885 (0.882) 0.046nnn (0.008)  0.049nn (0.021) 0.047n (0.024)  0.051nn (0.024) 0.049 (0.037)  0.030 (0.082) 0.098nn (0.042)  0.083nnn (0.024)

 0.067nnn (0.022)  3.479 (8.328) 0.004 (0.022)  0.052nnn (0.010) 0.860 (0.893) 0.046nnn (0.008)  0.055nnn (0.021) 0.050nn (0.024)  0.052nn (0.024) 0.052 (0.037)  0.029 (0.083) 0.097nn (0.041)  0.084nnn (0.024)

 0.229nnn (0.053)  0.066nnn (0.021)  2.287 (8.533) 0.001 (0.027)  0.051nnn (0.010) 0.944 (0.904) 0.047nnn (0.008)  0.053nn (0.020) 0.049nn (0.023)  0.050nn (0.024) 0.049 (0.037)  0.031 (0.084) 0.096nn (0.041)  0.085nnn (0.024)

0.087 8,914

0.087 8,914

0.086 8,914

(SMB) and book-to-market (HML) portfolios. We consider three samples: (i) all deals; (ii) cash-only deals (i.e., deals in which the method of payment was cash only); and (iii) public targets deals. We consider the subsample of cash-only deals to test the alternative hypothesis that in-wave mergers have worse long-term performance because they are driven by stock market overvaluation (Shleifer and Vishny, 2003). This hypothesis corresponds to mergers in which the method of payment is stock, and it is therefore important that we show that our results hold when we exclude stock-paid acquisitions. We consider the subsample of public target companies because previous studies have shown that bidder returns are systematically higher in acquisitions of private targets than of public targets (e.g., Fuller, Netter, and Stegemoller, 2002; Moeller, Schlingemann, and Stulz, 2004). To the extent that there are more acquisitions of public targets during merger

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Table 6 Long-term performance inside and outside waves: Operating performance. This table presents additional evidence on the long-term performance of in-wave acquisitions. Panel A contains estimates from panel regressions explaining post-merger long-term operating performance using our measures of merger waves. In each case, the dependent variable is a measure of postmerger performance during the 24 months following the event. We use two different measures of operating performance. In columns 1–3, we use DROAt,t þ 2, defined as the change in return on assets from time t to tþ 2 (ROAt þ 2  ROAt). In columns 4–6, we use DInd adj ROAt,t þ 2, defined as the change in industry-adjusted return on assets from time t to time t þ 2 (Ind adj ROAt þ 2  Ind adj ROAt). The independent variables of interest are our three measures of merger waves. In Panel B, we use calendar time portfolios to measure the difference in performance inside and outside waves. This portfolio buys any acquirer that merged outside a wave and sells any acquirer that merged inside a wave. The portfolios are rebalanced monthly and each stock is held for either 24 or 36 months (corresponding to columns 2-Year Holding and 3-Year Holding). Panel B presents estimates from the Fama and French three-factor model. The alpha represents the abnormal return earned on this trading strategy. Sample characteristics and a detailed description of each variable are included in Table 9. All regressions include year fixed effects and robust standard errors (clustered at the industry level) in parentheses. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Panel A: ROA regressions

DROAt,t þ 2 (1) Wave

DInd adj ROAt,t þ 2

(2)

(3)

(4)

(5)

 0.750 (0.462)

 2.138 (0.479)  11.740nnn (2.256)

% N deals

 6.557nn (3.124)  4.516nnn (1.047)

% Deal value

R-squared Observations

(6)

nnn

0.036 8,261

0.043 8,261

 1.124 (2.399)

0.041 8,261

0.024 7,893

0.022 7,893

0.021 7,893

Panel B: Calendar time

All Alpha Market SMB HML

R-squared Observations

0.0034nn (0.0016) 0.4830nnn (0.0391) 0.0067 (0.0524) 0.1710nnn (0.0602) 0.3300 348

Cash only 0.0027nn (0.0014) 0.3810nnn (0.0331) 0.0584 (0.0437) 0.1850nnn (0.0518) 0.3300 295

2-Year holding Public only 0.0026n (0.0014) 0.3340nnn (0.0342) 0.0349 (0.0452) 0.2400nnn (0.0535) 0.2580 295

waves, differences in performance might be driven by the ownership status of the target rather than by the merger wave itself. Panel B of Table 6 reveals similar results when we estimate the differences in performance between in-wave and out-wave acquirers using calendar time portfolios that sell in-wave acquirers and buy out-wave acquirers. For holding periods of 2 and 3 years, the intercepts (alphas) from a three-factor model are positive and different from zero at the conventional significance levels. These results suggest that a trading strategy that buys out-wave acquirers and sells in-wave acquirers generates positive abnormal returns over a 2-to-3-year horizon. Taken together, the results in Tables 5 and 6 suggest that both stock return and operating performance of inwave acquiring firms are lower than those of out-wave acquirers. These results are consistent with our hypothesis that in-wave acquisitions are worse, possibly due to agency conflicts and managerial herding. The next subsection seeks to provide more direct evidence about

All 0.0036nn (0.0015) 0.4780nnn (0.0356) 0.0194 (0.0478) 0.1210nn (0.0549) 0.3860 348

Cash only 0.0034nnn (0.0012) 0.3910nnn (0.0284) 0.0592 (0.0370) 0.1680nnn (0.0440) 0.4050 307

3-Year holding Public only 0.0027nn (0.0012) 0.3640nnn (0.0293) 0.0270 (0.0382) 0.1660nnn (0.0454) 0.3480 307

agency conflicts by studying the corporate governance of in-wave versus out-wave acquirers. 5.2. Corporate governance This subsection studies the relation between merger waves and corporate governance. Ideally, we would like to identify managers who initiate agency-driven acquisitions. Data limitations prevent us from observing this directly. We therefore take an indirect approach and attempt to identify acquisitions more likely to be driven by agency problems due to weaker governance mechanisms. That is, our empirical approach is to compare the average quality of corporate governance in acquiring firms that initiate acquisitions inside and outside merger waves. Our hypothesis suggests that in-wave or late-wave acquirers have weaker governance than out-wave acquirers and are therefore more prone to agency problems. In contrast, alternative hypotheses that do not tie between merger waves and agency do not necessarily

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predict systematic differences between the corporate governance of in-wave and out-wave acquirers.15 We use a number of internal and external corporate governance measures to test whether merger waves are negatively related to the quality of governance. These measures include board attributes, CEO compensation, the degree of managerial entrenchment owing to takeover defenses, and the presence of large shareholder monitoring. We consider two attributes of the board of directors. The first is CEO/chairman duality, that is, whether the CEO is also the chairman of the board. Prior work shows that CEO/chairman duality is associated with higher CEO compensation (Core, Holthausen, and Larcker, 1999) and lower sensitivity of CEO turnover to firm performance (Goyal and Park, 2002). The second board attribute we consider is the degree of board independence. Following Weisbach (1988), we construct a dummy variable representing a majority of independent directors (Independent board). Although there is no consensus on whether a more independent board leads to better overall firm performance (Bhagat and Black, 1999; Hermalin and Weisbach, 2003), evidence does exist that firms with a majority of independent directors make major corporate decisions in the best interests of shareholders. Relevant to our study, Byrd and Hickman (1992) find in a sample of tender offers that independent boards are associated with higher bidder returns. Data on boards of directors are obtained from the Investor Responsibility Research Center (IRRC) and BoardEx. We employ two measures of CEO compensation: CEO equity ownership and equity-based compensation (EBC). Higher levels of CEO ownership and EBC can help align the interests of managers with those of shareholders. We obtain CEO compensation and ownership data from ExecuComp. Lewellen, Loderer, and Rosenfeld (1985) find that bidder returns are increasing in bidder managers’ stock ownership. Datta, Iskandar-Datta, and Raman (2001) find a significantly positive relation between bidder managers’ EBC and bidder announcement-period abnormal returns. Similar to Datta, Iskandar-Datta, and Raman (2001), we define EBC as the percentage of equitybased compensation in a CEO’s annual compensation package, with equity-based pay defined as the value of stock options and restricted stock grants. To measure the degree of managerial entrenchment as a result of antitakeover provisions, we use the E-index developed in Bebchuk, Cohen, and Ferrell (2009). This index is a modification of the corporate governance index introduced by Gompers, Ishii, and Metrick (2003). It uses only six provisions that Bebchuk, Cohen, and Ferrell (2009) show have the greatest impact on firm value. The data for 15 The relation between corporate governance and the intensity of merger activity may be nonlinear. Merger waves may play a smaller role in fostering agency when other corporate governance mechanisms are either extremely weak or extremely strong. When governance is weak enough, highly entrenched or powerful managers do not need merger waves to initiate agency-driven acquisitions. In contrast, when governance is too strong, the mechanisms through which waves facilitate agency may not be strong enough to allow agency-driven acquisitions. If such effects do exist, they may offset the relation between governance and waves discussed above.

the index are reported by the IRRC.16 Gompers, Ishii, and Metrick (2003), Cremers and Nair (2005), and Bebchuk, Cohen, and Ferrell (2009) show that more antitakeover provisions have a negative impact on firm value. Finally, we also consider large shareholder monitoring. Our measure looks at the total proportion of the firm in the hands of institutions holding at least 5% of the company. We call this variable Block ownership. We collect holdings data from the 13-F filings by Thomson Financial. Table 7 investigates the relation between acquirers’ corporate governance and merger intensity. Specifically, it tests the hypothesis that the governance of in-wave acquirers is weaker than the governance of out-wave acquirers. Panel A reports difference-in-means estimates that compare the corporate governance of in-wave and out-wave acquirers. In Panel A, merger waves are defined based on Harford (2005). In Panel B, we estimate regressions in which the dependent variable is a measure of corporate governance and the independent variable is a measure of the intensity of merger activity. All regressions include industry fixed effects, and the standard errors are clustered at the industry level. For brevity, we report only the regression coefficients for the merger intensity variables. The estimates in Panel A suggest that the corporate governance of in-wave acquirers is indeed poorer compared to that of out-wave acquirers. These findings are consistent across all measures, and the difference between in-wave and out-wave acquirers is statistically significant at the 1% level in four out of six cases (and at the 10% level in another case). In particular, the results in Panel A suggest that CEO/chairman duality is more frequent for in-wave acquirers and that board independence is lower for in-wave acquirers. Further, both CEO ownership and EBC are lower for in-wave than out-wave acquirers, suggesting poorer governance quality for in-wave acquisitions. Finally, block ownership is also lower for in-wave acquirers. The differences in the Eindex between in-wave and out-wave acquirers, however, are not statistically significant at conventional levels. One potential concern is that the mean corporate governance of in-wave acquirers is similar to the population-wide average. Since the participation rate of firms in mergers increases during merger waves, it is possible that the mean values reported in Panel A for inwave acquirers are simply moving toward the populationwide means. In this case, the differences between in-wave and out-wave acquirers reflect differences between outwave acquirers and the general population rather than inwave mergers being fundamentally different. To test this hypothesis, Panel A also reports the population-wide means of the various governance measures. The numbers suggest that the population-wide means are substantially closer to the out-wave means than the in-wave means. These findings suggest that consistent with our 16 When we use data for years in which the IRRC does not report scores, we assume, similar to Gompers, Ishii, and Metrick (2003) and Bebchuk, Cohen, and Ferrell (2009), that the index remains unchanged in the year following the most recent report.

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Table 7 Governance during merger waves. This table compares different measures of governance inside and outside waves. The first column of Panel A (All) presents simple averages of each measure, whereas columns Out wave and In wave contain the averages for mergers outside and inside waves, respectively. The difference between these two columns is presented in column Out–In. For the first three columns, simple t-tests are run for each average, where the null is an average of zero. For the last column, a mean difference test is performed, where the null represents no difference between the averages outside and inside the wave. In Panel B, we regress each measure of governance on industry fixed effects and our merger wave proxies. Sample characteristics and a detailed description of each variable are included in Table 9. Standard errors are clustered at the industry level. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Panel A: Governance measures (averages)

All

Out wave

In wave

Out–In

Board characteristics CEO/chairman Independent board

0.1190nnn 0.7350nnn

0.0993nnn 0.7510nnn

0.1570nnn 0.7050nnn

 0.0575nnn 0.0455nnn

CEO incentives CEO ownership Equity-based compensation

0.8800nnn 0.8200nnn

0.8890nnn 0.8320nnn

0.8620nnn 0.7990nnn

0.0273nnn 0.0326nnn

Managerial entrenchment E-index

2.2880nnn

2.3070nnn

2.2420nnn

0.0645

Shareholder monitoring Block ownership

0.4300nnn

0.4380nnn

0.4160nnn

0.0213n

Panel B: Governance measures (regressions) Wave

% N deals

% Deal value

Board characteristics CEO/chairman Independent board

0.0648nnn  0.0470nnn

0.8270nnn  0.3440nnn

0.3190nnn  0.1150nnn

CEO incentives CEO ownership Equity-based compensation

 0.0270nnn  0.0385nn

 0.2530nnn  0.3630nnn

 0.0346  0.1050nnn

Managerial entrenchment E-index

 0.0165

 1.0150

 0.5130nn

Shareholder monitoring Block ownership

 0.0145

 0.0841

 0.0126

hypothesis, the governance of in-wave acquirers is indeed poorer than the governance of the average acquirer. The regression results in Panel B portray a similar picture. Across our merger wave proxies, in-wave acquirers are more likely to have a CEO/chairman duality and are less likely to have an independent board. Further, CEO ownership and EBC are both lower for in-wave acquirers. The regression coefficients are statistically significant at the 1% level in ten out of the 12 cases. These findings suggest that corporate governance is poorer for in-wave acquirers, implying that in-wave acquisitions are more likely to be agency-driven. We note, however, that the results for the E-index and Block ownership are not statistically significant in most cases. Interestingly, the regression coefficients for the E-index imply that in-wave acquirers have fewer takeover provisions. 6. ‘‘Mergers of necessity’’ and target firms’ merger waves Another possibility is that in-wave mergers are fundamentally different from out-wave mergers. In particular, out-wave mergers may be ‘‘elective mergers,’’ whereas inwave mergers may be nonelective ‘‘mergers of necessity.’’ Under this scenario, the higher levels of uncertainty are caused by industry shocks, which create a new economic environment and trigger merger waves. The new

environment creates lower returns for firms because the mergers are done by necessity, in contrast to elective mergers done outside waves. This is so because if a firm has to merge to respond to external shocks, it is less likely to have high returns compared to elective mergers. This hypothesis may also explain why CEO turnover is less sensitive to performance in merger waves. If investors are rational and recognize that these were mergers of necessity initiated during a period of high uncertainty, they might be more reluctant to dismiss the CEO following bad post-merger performance. To test this alternative explanation, we reestimate our tests focusing only on merger waves in the industry of the target firms. That is, we restrict attention to the subsample of merger waves that were concentrated in industries of acquired rather than acquiring firms. This way, our measures of merger intensity do not correspond to the acquirer’s industry but rather represent intensified merger activity in the target’s industry. We argue that this approach can be used to distinguish our hypothesis from the hypothesis of ‘‘necessity mergers’’ for the following reason. If the intense merger activity corresponds to the target’s industry, it means that the acquiring firms, which come from multiple industries, were not subject to an industry-specific shock that forced them to initiate nonelective mergers. Therefore,

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to the extent that we observe lower performance-turnover sensitivity, worse performance, and poorer governance in these mergers, the cause is unrelated to the hypothesis of ‘‘necessity mergers.’’ It is possible, however, that even if the acquirer’s industry is not undergoing a merger wave, acquirers still merge out of necessity as part of a vertical consolidation wave. Under this scenario, firms may acquire their suppliers or customers because their peers do so. Such mergers may still be mergers of necessity, even though the acquirers come from multiple industries. Our merger wave measures do not account for such a possibility. To do so, we construct measures of industry dependence following Ahern (2012) and Ahern and Harford (2011). In particular, we collect data from the U.S. Bureau of Economic Analysis (BEA), which produces input–output (IO) tables of product market relations for roughly 500 unique industries. We use the 2002 IO report to create matrices from the Use and Make tables that record flows of inputs and outputs between industries. The Make table records the dollar value of each commodity produced by each industry, whereas the Use table defines the dollar value of each commodity that is purchased by each industry or final user.17 From the Make table, we compute an industry-by-commodity matrix, which records the percentage of each commodity produced by each industry. We combine this matrix with the Use (commodityby-industry) table to create an industry-by-industry matrix, called RevShare, of dollar flows from each customer industry to each supplier industry. We then create the Customer matrix by dividing RevShare by the sum of all sales for an industry (both in producers’ prices). Finally, we construct the Supply matrix as the RevShare divided by the sum of all purchases by industry (both in purchasers’ prices). These two matrices describe the relative trade flows between all industries in the economy. We define two industries as connected when either the percentage of the buyer’s input purchased by the seller, or the percentage of the seller’s sales purchased by the buyer exceeds 5%. We then exclude mergers where the acquirer and target industries are connected based on the flows of inputs and outputs. Table 8 reports the results of reestimating our tests in the subsample of target merger waves, excluding mergers across connected industries. For brevity, we report a subset of the array of tests employed throughout the paper. Our key hypothesis is that target wave acquisitions are accompanied by elevated levels of uncertainty and worse analysts’ forecasts due to the limited monitoring capacity in the target industry. This, in turn, implies that they may still foster agency-driven acquisitions. Panels A and B test this hypothesis directly. When we use our continuous measures of merger intensity, both the implied volatility and GARCH-based tests suggest that the uncertainty surrounding target merger waves is higher than the

17 As in Ahern and Harford (2011), we modify the Make table to include employee compensation as a commodity that is solely produced by the employee compensation industry.

uncertainty surrounding out-wave acquisitions. In both sets of regressions, the relevant coefficients on the % N deal and % Deal value measures are positive and statistically significant at the 1% level. These findings suggest that the implied and GARCH-based volatilities are higher during merger waves. We note, however, that the coefficients on the wave dummy are statistically insignificant and have the opposite signs in two of the three cases, possibly because this variable is relatively sparse when only target waves are included. Panel C reports the performance-turnover sensitivity results. Consistent with our previous findings, this sensitivity is lower for in-target-wave mergers than it is for out-wave mergers when waves are proxied by our continuous variables. In this case, results are statistically significant at the 5% level or better in five out of six regressions. Panels D and E show that the long-term performance of in-target wave acquisitions is also worse than that of out-wave performance. These results are consistent across the calendar time alphas and the benchmarked BHARs. The coefficient estimates all point at the same direction, and the results are statistically significant at the 5% level or better in ten of the 12 cases. Finally, Panel F reports the results of the corporate governance tests. These results are also similar to the ones obtained for the full sample of merger waves. We therefore conclude that our findings are robust to the hypothesis that the differences in uncertainty, performance, and turnover– performance sensitivity between in-wave and out-wave acquisitions are driven by the difference between ‘‘necessity’’ and ‘‘elective’’ mergers (Table 9). 7. Concluding remarks Using a large sample of 9,854 mergers from 1980 to 2009, we examine the differences between mergers initiated during and outside merger waves. We find evidence of higher levels of uncertainty and a poorer quality of analysis surrounding acquisitions initiated during waves relative to acquisitions initiated outside merger waves. We also test whether managers of inwave acquiring firms are less likely to be terminated following bad performance compared to managers of out-wave acquiring firms. We find that this is indeed the case and argue that this is because they can ‘‘share the blame’’ with peers who also initiated mergers during the wave. We posit that the above findings may lead to worse post-merger performance of in-wave mergers relative to out-wave mergers via different channels. One channel is the higher costs of external monitoring, which may allow agency-driven managers to ‘‘get away’’ with bad mergers. Another channel is managerial herding, where career concerns may push managers to follow their peers and initiate mergers of deteriorating quality. To test these predictions, we compare between the performances of in-wave and out-wave acquirers. Our findings suggest that in-wave acquirers have lower abnormal returns over the years immediately following the merger. We also compare the quality of internal and

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Table 8 Target waves. This table reestimates our tests for the subset of merger waves where: (i) the target company industry is undergoing a merger wave but the acquiring company industry is not, and (ii) the acquiring and target companies are not connected via the supply chain. In Panel A, we regress implied volatility on industry fixed effects and our proxies for merger waves. Panel B presents estimates from GARCH models similar to those in French, Schwert, and Stambaugh (1987), except that we allow for the variance to depend on our proxies for merger waves. Panel C presents estimates from probit regressions explaining CEO turnover using pre- and post-merger performance similar to those in Lehn and Zhao (2006). We only include mergers announced on or after 1990 due to data limitations. Panel D presents estimates from panel regressions explaining post-merger long-term market performance (BHAR) using our measures of merger waves. Panel E uses a calendar time portfolio to measure the difference in performance between in-wave and out-wave mergers. This portfolio buys any acquirer that merged outside a wave and sells any acquirer that merged inside a wave. The specifications in all panels follow those in the previous tables. Sample characteristics and a detailed description of each variable are included in Table 9. Standard errors are clustered at the industry level. Throughout the table, n, nn, and nnn represent significance at the 10%, 5%, and 1% level, respectively. Panel A: Implied volatility (regressions)

Wave % N deals % Deal value

Implied vol (t)

Implied vol (t 30, t)

0.0207 1.3190nnn 0.3020nnn

0.0441 1.2340nnn 0.2880nnn

Panel B: GARCH estimates Mean

a

0.0005nnn 0.8867nnn 0.1449nnn 0.0757 0.6458nnn 0.0393nn 14.3270nnn 0.5062 14.5712nnn 1.3203nnn 14.4420nnn 0.6884nnn

b c1 c2

b y g1 g2 g1 g2 g1 g2

Std. dev.

(W¼ Wave) (W¼ Wave) (W¼ % N deals) (W¼ % N deals) (W¼ % Deal value) (W¼ % Deal value)

0.0006 0.1963 0.0439 0.7749 0.1337 0.0870 2.1317 1.4810 1.2052 1.6687 1.1348 0.9005

25th

Median

75th

0.0004 0.9051 0.1337 0.1112 0.5618 0.0175 14.7543 0.3548 15.1373 0.2297 15.1325 0.0412

0.0004 0.9304 0.1531  0.0801 0.6548 0.0205  14.0853 0.3362  14.5444 0.8894  14.5317 0.5284

0.0005 0.9476 0.1611  0.0591 0.7261 0.0864  13.5813 0.6253  14.1113 1.7956  13.9782 1.2271

Panel C: Post merger BHAR in turnover regressions

Post-merger BHAR  Wave Post-merger BHAR  % N deals Post-merger BHAR  % Deal value

Full sample

Largest acquisition

Largest relative deal values

0.010 (0.039) 0.272nnn (0.095) 0.027 (0.024)

0.121 (0.075) 0.467nnn (0.116) 0.123nnn (0.034)

0.002 (0.033) 0.384nnn (0.089) 0.073nn (0.031)

Panel D: Effects of waves on long term performance

Wave Coefficient

R-squared Observations

Benchmark¼ Matched firms % N deals

% Deal value

Wave

Benchmark ¼Combined industry % N deals % Deal value

 0.093n (0.053)

0.842nn (0.318)

 0.352nnn (0.118)

 0.013 (0.060)

 0.611nn (0.229)

0.237nnn (0.066)

0.098 9,103

0.100 9,103

0.101 9,103

0.084 8,914

0.086 8,914

0.086 8,914

Public only

All

Panel E: Calendar time alphas 2-Year holding Cash only

All Alpha

R-squared Observations

nnn

nnn

nnn

3-Year holding Cash only nnn

nnn

Public only

0.0060 (0.0012)

0.0058 (0.0014)

0.0047 (0.0012)

0.0063 (0.0012)

0.0062 (0.0012)

0.0047nnn (0.0011)

0.7780 348

0.7710 279

0.7940 279

0.7960 348

0.7940 291

0.8360 291

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Panel F: Governance measures (regressions)

CEO/chairman Independent board CEO ownership Equity-based compensation E-index Block ownership

Wave

% N deals

% Deal value

0.1470nnn 0.0226  0.0398  0.0548n 0.0697  0.0647nn

0.8640nnn 0.3250nnn 0.2530nnn 0.3720nnn 1.0860 0.0721

0.3270nnn 0.1150nn 0.0381 0.1050nnn 0.5080nn 0.0155

Table 9 Variable definitions. The sample consists of all mergers from 1980 to 2008 in which: (i) the acquirer is a publicly traded company with common stock data available on the CRSP tapes at the time of the announcement, (ii) the acquirer gained control over the target company, (iii) the deal value, as reported by SDC, was at least $10 million, and at least 5% of the value of the acquirer at the time of the announcement, (iv) the deal was completed, and (v) the acquirer had data available from Compustat for the fiscal year preceding the merger.

DInd adj ROA DROAt,t þ 2

t,t þ 2

Acq size Block ownership CAR[ 1,1]

CAR[ 3,3]

CEO age CEO ownership CEO/chairman Cash only Deal value Diversifying E-index Equity-based compensation Forecast error Forecast std Free cash flows

High valuation Implied vol (t)

Implied vol (t  30, t) Ind adj ROA

Independent board Industry Tobin’s Q Industry leverage Missing CEO age Post-merger BHAR Pre-Merger BHAR Price run-up Public tgt ROA Relative deal size

is the change in industry-adjusted return on assets from time t to time t þ2 (Ind adj ROAt þ 2  Ind adj ROAt). is the change in return on assets from time t to tþ 2 (ROAt þ 2  ROAt). is the logarithm of total assets. is the total percentage institutional ownership in the firm by block holders. represents cumulative abnormal returns during the one-day window surrounding the announcement date. Estimates from the market model are used as the benchmark, with the CRSP value-weighted market portfolio serving as a proxy for market returns. represents cumulative abnormal returns during the 3-day window surrounding the announcement date. Estimates from the market model are used as the benchmark, with the CRSP value-weighted market portfolio serving as a proxy for market returns. represents the age of the CEO (from ExecuComp and BoardEx). is the proportion of the firm owned by the CEO at the end of the fiscal year preceding the acquisition announcement (from ExecuComp). It excludes options. is a dummy variable that is equal to one when the CEO is also the chairman of the board. is a dummy variable that is equal to one when the acquisition is financed entirely with cash. is the value of the deal as reported by SDC (in millions). is a dummy variable that is equal to one when the target and the acquirer are in different four-digit SIC code industries. is the entrenchment index of Bebchuk, Cohen, and Ferrell (2009). is the equity-based compensation measure used by Datta, Iskandar-Datta, and Raman (2001). It is defined as the sum of the value of new stock options (using the modified Black-Scholes method) granted to the CEO as a percentage of total compensation. is the analysts’ average forecast error (normalized by assets) from 3 months before to the announcement date. represents the standard deviation of analysts’ forecasts (normalized by assets) from 3 months before to the announcement date. is defined as in Dittmar and Mahrt-Smith (2007). Each year, the following regression is estimated (using all companies in         Compustat): ln cashi =NAi ¼ b0 þ b1 lnðNAi Þ þ b2 FCF i =NAi þ b3 NWC i =NAi þ b4 ðIndSigmai Þ þ b5 MV i =NAi þ ei where Cash is cash and equivalents (item 1), NA represents net assets (item 3  item 1), FCF is operating income (item 13) minus current liabilities (item 5) minus cash (item 1), IndSigma is the industry average of prior 10-year standard deviation of FCF/NA, MVi represents past 3-year sales growth and is used as an instrument for market-to-book. The residuals are used to compute excess cash at time tþ 1. is a dummy variable representing industry overvaluation. We classify the industry as overvalued if the industry median market-to-book ratio is above its five-year moving average. is the implied volatility of the bidder’s option at the time of the acquisition. This measure is computed as the average between the implied volatility of the 91-day at-the-money call and put options on the bidder’s stock. Data on implied volatility were obtained from the estimated volatility surface in the Optionmetrics database. is the median implied volatility from 22 trading days before to the announcement date. See Implied vol (t). is the difference between the firm’s ROA and a weighted-average of the acquirer’s and target’s median industry ROA (excluding companies involved in acquisitions during the three-year window surrounding the event), where the weights correspond to the relative sizes of the acquirer and target, respectively. is a dummy variable that is equal to one when at least 50% of the board consists of independent directors. is the acquirer’s industry median Tobin’s Q across all Compustat firms (using four-digit SIC codes) divided by 100. See Tobin’s Q. represents the acquirer’s industry median leverage across all Compustat firms (classified using four-digit SIC codes). Leverage is defined as the sum of long-term debt (dltt) and debt in current liabilities (dlc) over common equity (ceq). dummy representing missing CEO age. represents buy-and-hold abnormal returns from the announcement date to either three years after the announcement or the month in which the acquiring firm’s CEO is replaced, whichever comes first. is the buy-and-hold abnormal returns from 3 years to 1 month before the announcement. is the bidder’s buy-and-hold abnormal return from 230 to 11 days before the announcement. The CRSP value-weighted index is used as the benchmark. is a dummy variable that is equal to one when the target firm is publicly traded. is computed as income before extraordinary items (ib) divided by total assets (at). is the value of the deal as reported by SDC over the market value of the acquirer measured one year preceding the announcement.

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Table 9 (continued ) Tobin’s Q

Wave % Deal value % N deals

is the sum of the market value of book assets (at) and the market value of common equity (csho  prcc), minus the sum of common equity (ceq) and deferred taxes (txdb), all over the sum of 0.9  book value of assets (at) and 0.1  market value of assets. is a dummy representing a merger wave. Merger waves are identified following the procedure discussed in Harford (2005). is computed as the moving sum of the value of all deals (in an industry) over the prior 12 months normalized by the sum of value of all deals in that industry over the entire decade. is computed as the moving sum of all deals (in an industry) over the prior 12 months normalized by the sum of all deals in that industry over the entire decade.

Note: Compustat variable names are in parentheses.

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