Sources of target stock price run-up prior to acquisitions

Journal of Economics and Business 64 (2012) 185–198

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Journal of Economics and Business

Sources of target stock price run-up prior to acquisitions Matthew Brigida a,∗, Jeff Madura b a b

Clarion University of Pennsylvania, 840 Wood St., Clarion, PA 16214, United States Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431, United States

a r t i c l e

i n f o

Article history: Received 14 November 2010 Received in revised form 14 November 2011 Accepted 26 November 2011 JEL classification: G34 Keywords: Informed trading Insider trading Target stock price run-up

a b s t r a c t The anticipation of an acquisition attracts informed trading, which can cause a high run-up in the target stock price prior to an announced acquisition bid. Because research has shown that bidders do not reduce their bid price to compensate for a relatively high run-up, a larger run-up increases the cost of the acquisition to bidders. Our analysis determines that the target stock price run-up before an announced bid is higher for bidders that are not private equity firms, do friendly acquisitions, are from outside the U.S., rely on newly borrowed funds to finance the acquisition, rely on more investment bank advisors to facilitate the acquisition, and did not previously establish a toehold position in the target. It is also higher when targets are smaller, have listed options traded on them, and are in the technology field. Lastly, target run-up is lower since Sarbanes-Oxley. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Studies have documented the existence of an abnormal increase in the target’s stock price prior to the formal acquisition announcement, which is commonly referred to as the target run-up (see Schwert, 1996).1 The target run-up is caused by informed trading, which may be partially due to insider trading or by trading by other investors who use public information to detect which firms may become targets. The target run-up represents a large proportion of the total premium paid by bidders for targets. Barclay and Warner (1993) suggests that it represents about 50% of the total premium,

∗ Corresponding author. E-mail address: [email protected] (M. Brigida). 1 Research by Keown and Pinkerton (1981), Barclay and Warner (1993), Schwert (1996) and others has also documented a significant stock price run-up in acquisitions prior to the formal bid announcement. 0148-6195/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jeconbus.2011.11.003

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while Schwert (1996) estimates that it represents 57%. Such a large price movement motivates some traders to capitalize on private information. Recent insider trading cases suggest that there is a large web of potential informants that have access to information during the acquisition financing and due diligence process. However, there is very limited knowledge of the source of private information that causes the target’s stock price run-up to be more pronounced for some targets than others. Our objective is to identify the underlying factors that cause the stock price run-up to vary among targets.2 To achieve this objective, our focus is on measuring the run-up prior to each announced bid, and then explaining the variation with a multivariate model. Schwert (1996) and Betton, Eckbo, and Thorburn (2008) determine that bidder firms do not fully adjust their bid premium to account for the target’s stock price run-up that occurs before the bid is submitted. Thus, bidder firms partially incur the cost of private information used by traders to invest in the target firms prior to the bid announcement. According to Moeller, Schlingemann, and Stulz (2004), some bidder firms may experience weaker valuation effects than others because they pay an excessive premium for targets. Therefore, our analysis of the characteristics that explain run-up among targets should also offer insight on why the cost of acquisitions varies. Our analysis is also intended to identify characteristics that trigger informed trading prior to an announced bid, and may cause some firms to be subjected to a greater degree of informed trading than others. Our main hypotheses are derived from theories regarding how informed traders can capitalize on high levels of asymmetric information between firms and investors. Specifically, studies by Huddart and Ke (2007) and Haggard, Martin, and Pereira (2008) suggest that when there is a high degree of asymmetric information between firms and investors prior to public earnings guidance, the firms are subject to greater information leakages prior to the public announcement. We adapt the asymmetric information theory to suggest that the degree of transparency varies substantially among target firms because of differences in operations, different degrees of information disclosed by firms, and different degrees of monitoring by analysts. Thus, target firms with less transparency exhibit higher levels of asymmetric information, and there is more potential for traders to acquire private information about these firms that is not already priced. This should cause more pronounced stock price run-ups prior to public bid announcements. The private information that triggers the run-up in the target’s stock price prior to an acquisition announcement could be a profile or behavior of target firms or the corresponding bidding firms that is detected or at least suspected by informed traders. Alternatively, the private information could reflect inside information that is intentionally or accidentally leaked to traders about an impending acquisition. An article on the recent merger deal between Merck and Schering in March 2009 illustrates the challenges in preventing information leakages before public acquisition announcements.3 Much media attention has recently been given to the role of expert networks in detecting potential acquisitions. A recent Bloomberg Businessweek article uses the Galleon case to explain the potential flow of private information from firms (or expert networks) to hedge funds.4 Our objective is not focused on determining whether the private information causing the target’s run-up is legal or illegal, but to determine why the magnitude of the run-up varies among targets. We find the target’s stock price run-up is conditioned on several bidder and target characteristics that reflect the prevailing exposure to private information leakages. In particular, it is higher when the bidder engages in a friendly deal, is from outside the U.S., relies on newly borrowed funds to finance the acquisition, relies on more investment bank advisors to facilitate the acquisition, is not a private equity firm, and did not previously establish a toehold position in the target.

2 The correlation coefficient between run-up and the acquisition premium (the sum of run-up and markup) is 0.71 in our sample. This is consistent with Schwert (1996) which found run-up and markup are uncorrelated, and so a large run-up should imply a large premium. However, a correlation coefficient of 0.71 leaves measurable cross-sectional variation in run-up which is obscured by the addition of markup to get the premium. This is particularly compelling given a 1% variation in the run-up of an average acquisition implies a $10 million cost to the acquirer. 3 ¨ See “Merck-Schering Insider Trading? SEC Should Take a Look,by H.N. Moore, WSJ Blogs: Deal Journal, http://blogs.wsj.com/deals/2009/03/09/merck-schering-insider-trading-sec-should-take-a-look. 4 See “The Inside Guide to Insider Trading,” by C. Winter, D. Glovin, J. Daniel, and D. Yanofsky, Bloomberg Businessweek, March 11, 2011, http://www.businessweek.com/magazine/galleon.

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We also find that the target’s stock price run-up is higher when targets are smaller and have listed options traded on them. Finally, we find that it is significantly lower since the Sarbanes-Oxley Act, which may be due to more stringent reporting requirements concerning unscheduled option grants, as well as the generally heightened regulatory environment. Overall, our results offer substantial evidence that prospective target firms with higher levels of asymmetric information experience more pronounced information leakages prior to a public acquisition bid. The stock price run-up of target firms is higher when conditions regarding the bidder, the target, or the regulatory environment result in less transparency and more potential for leakages of private information. 2. Hypothesized characteristics that explain the variation in target run-up We hypothesize that the target’s stock price run-up is related to several characteristics identified below that influence the potential leakage of private information. We classify our characteristics according to whether they reflect the bidder, the target, or the regulatory environment. 2.1. Bidder characteristics Bidder specializing in acquisitions. Since information leakage about the impending bid for the target can encourage speculative positions in the target stock before the acquisition and therefore cause bidder overpayment, bidders prefer to prevent information leakage. Because private equity (PE) firms specialize in acquisitions, they should be particularly adept in mitigating information leakage. We hypothesize that the run-up may be smaller for bidders that specialize in acquisitions, such as PE firms. Hostile bidder. In a hostile bid, the target’s management is less likely to know that a bid for the firm is imminent than if a bid was negotiated with target management. Accordingly, there is a smaller probability that target managers would leak the information intentionally or accidentally. This leaves the most likely sources of inside information regarding the imminent bid as the bidder or someone working closely with the bidder. Since the bidder is motivated to keep information on the forthcoming bid from leaking, and the target’s management cannot leak the bid for lack of knowledge, we would expect that the target’s stock price run-up would be lower for hostile bids. Management participation. Given the result of Schwert (1996) that there is little substitution between run-up and markup, it is clear that run-up is a cost to the bidder. If management is participating in the bidding for the target, it has an economic incentive to reduce information leakage and therefore reduce target run-up before the acquisition announcement. Therefore, we expect that targets with greater managerial ownership will be less exposed to information leakages. Foreign bidder. The extent to which a foreign bidder can hide its acquisition intentions in the U.S. may differ from a U.S. bidder. Advisors or employees of the foreign firm may feel more free to divulge inside information if they are subject to less restrictive laws on insider trading. La Porta, Lopez-deSilanes, Shleifer, and Vishny (1998) show that investor protection and the enforcement of anti-insider trading laws are weaker in some foreign countries than in the U.S. Bidder financing. Some bidders that pursue acquisitions must secure financing. Accordingly, the bidder retains an investment bank responsible for placing the securities before submitting the bid to the board of directors of the target. Thus, by the time the bid is submitted, much work has been done regarding the placement of the securities. This opens another avenue for an information leakage. Acharya and Johnson (2007) find evidence that investment banks underwriting firm debt hedge their exposure in credit derivatives markets, and this hedging unintentionally leaks private information into other markets. In addition, Acharya and Johnson (2010) find that more debt and equity participants in a deal’s financing implies more information leaked to the debt and equity markets respectively. This information leakage cannot be wholly explained through hedging activity. Thus, we expect that when a bidder borrows to finance its deal that it will result in more pronounced information leakages, and will cause greater run-up in the target’s stock price prior to a publicized bid. Bidder cash percentage. When bidders are in a position that they can use a relatively high proportion of cash to acquire a target, they may be closely monitored by informed traders. Even if they did not rely

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on debt financing, their efforts to build a high cash balance (through retaining earnings) may cause informed traders to anticipate a takeover. Harford (1999) documented that firms with excess cash were more likely to pursue acquisitions. We hypothesize that the target’s stock price run-up is higher for acquisitions in which bidders used a high percentage of cash as payment. Bidder ownership of target before bid. Informed traders may be particularly focused on prospective targets in which a toehold equity position has already been established, since such a position might signal an imminent acquisition (see Betton et al., 2008). This may cause run-up to be increasing in bidder ownership prior to the bid. However, Song, Stulz, and Walkling (1990) find that a larger proportion of the target owned by the bidder before the initial takeover offer tends to decrease the target’s takeover gain. Thus, bidders may buy a controlling stake in the target at less cost, and this might encourage traders to focus on alternative prospective targets where the gains from a future acquisition may be greater. Bidder’s advisors. When bidder firms that have more financial and legal advisors, there is a greater potential for information leakage. A recent article illustrates how firms engaged in merger discussions are exposed to the leakage of private information due to advisory services.5 Therefore, we expect that the target’s stock price run-up is positively related to the number of advisors used by the bidder. We test this factor while attempting to control for the quality of advice. We also expect that the stock price run-up is inversely related to the quality of advice from the bidder’s financial advisors. We measure the quality of advice as the amount of money paid by the bidder to the financial advisor fees as a percentage of the deal value. 2.2. Target characteristics Target size and liquidity. Targets that are larger are likely to have less asymmetric information. It may be more difficult for informed traders to detect any information about larger targets that is not already known by the market. Therefore, we hypothesize that the stock price run-up is smaller for targets that are larger (see Meulbroek, 1992). Listed target stock options. Some studies argue that the existence of options on a firm’s stock will increase the price informativeness of that firm’s stock. Among them, Cao (1999) argued that the existence of options on a stock allows informed agents to more effectively trade future contingencies, which improves informational efficiency. Roll, Schwartz, and Subrahmanyam (2009) find the volume of options trading in a stock is positively correlated with information production. If options on the target’s stock does increase price informativeness, then run-up in the target’s stock should be greater. Of stocks for which there are listed options, Jayaraman, Frye, and Sabherwal (2001) find that there is a significant increase in trading of both call and put options in firms involved with a takeover, prior to the announcement of the takeover. They also find that a significant amount of the options trading is being done by informed traders. Further, Jayaraman et al. (2001) find evidence that the trading in the options market leads the trading in the underlying stock. Ni, Pan, and Poteshman (2008) detect the presence of private information in the options market before earnings announcements. Brent, Morse, and Stice (1990), Chen and Singal (2003), and Senchak and Starks (1993), all show that the level of short interest in a stock is affected by whether there are options available to trade on that stock. An informed trader will likely prefer to exploit her information using options rather than stock, because of the leverage offered by the former. A Wall Street Journal article on September 24, 2009 uses this reasoning by stating that options may be a better investment than stocks for betting on takeover targets, because they allow a cheaper form of betting.6 It follows information regarding targets with listed options should be more valuable, and more sought after, than information on targets without listed options. Therefore, stock price run-up should be greater if there are listed options on the target.

5 ¨ H.N. Moore, WSJ Blogs: Deal Journal, See “Merck-Schering Insider Trading? SEC Should Take a Look,by http://blogs.wsj.com/deals/2009/03/09/merck-schering-insider-trading-sec-should-take-a-look. 6 See “Voracious Risk Appetite Drives M&A Guessing Game” by Geoffrey Rogow, Wall Street Journal, September 24, 2009, p. C6.

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Target’s technology. The target’s run-up may be affected by whether the target is in a hightechnology industry. There is more asymmetric information in the tech industry, which might allow more opportunities for informed traders to capitalize on information. 2.3. Regulatory environment Within Section 403 of the Sarbanes-Oxley (SOX) Act is the requirement that option grants to senior executives be reported to the SEC within two days of the grant date. Possibly due to this reason, Fich, Cai, and Tran (2011) find that unscheduled option grants to target CEOs during the negotiation period of an ultimately successful merger is 57% less likely after SOX (though such unscheduled option grants persist). These unscheduled option grants may be interpreted by market participants as increasing the probability that the granting firm will be acquired. Consistent with this idea, Fich et al. (2011) find that average cumulative abnormal returns over the 30 days following the option grant were a significant 3.6%. It follows that unscheduled option grants in the may signal an increase in the granting firm’s probability of being acquired, thereby increasing target run-up. If there are fewer unscheduled option grants to target CEOs before successful acquisitions, then there may be fewer such signals of an increased acquisition probability. We hypothesize this will cause average target run-up over a cross-section of acquisitions to decrease after the passage of SOX. Further, while SOX was not directly intended to reduce illegal leakages of information, it may have indirectly led to this effect by requiring executives to be more accountable for the information that is disclosed by their firm. Thus, we expect that the target’s stock price run-up is reduced since SOX. 3. Data and methodology The sample analyzed in this study is a set of acquisitions of publicly traded US firms announced from 1995 to 2007 inclusive. There are 28,362 acquisitions meeting these criteria in Thompson’s SDC Mergers and Acquisitions database (SDC). We further limit the sample to targets that were traded on either the New York Stock Exchange or NASDAQ leaving a sample of 21,310 acquisitions. Because we are concerned with full acquisitions, we require the bidder to complete the acquisition with a controlling stake in the target firm. This requirement reduces the sample to 4911 acquisitions. Requiring acquisitions to have a deal value greater than $10 million reduces the sample to 4450. We then exclude all deals with multiple bidders to ensure the information leakage is not due to competing bidders leaving 4252. Lastly, we exclude all targets in the financial or utility industries because both of these are highly regulated (SIC codes 6000-6799 and 4900-4991) affording an initial SDC sample of 3071 acquisitions. After excluding deals where there were not sufficient target stock data to calculate the target’s stock price run-up, the sample size is 2512 deals. All data for independent variables are drawn from the SDC database. 3.1. Dependent variable: run-up We measure the stock price run-up of the target using an event window of (−42, −1), where each integer represents a trading day and with t = 0 being the day of the bid announcement, as defined in Schwert (1996). To choose the event window, Schwert created a daily plot of the cumulative average abnormal returns (CARs) over the window (−126, 253) for his entire sample of 1814 mergers and tender offers between 1975 and 1991. The plot showed that the CARs started to rise at approximately t = −42, which established the start of the event window. Run-up is defined in the literature as the sum of the target’s abnormal return for some event window before the announcement of the acquisition or tender offer (Schwert, 1996). Throughout the announcement date is t = 0. Therefore, for a target firm i, we must first estimate: Rit = ˛i + ˇi Rmt + ıit

t = −320, . . . , −65

(1)

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where Rit is the day t return for target i, Rmt is the return on the market portfolio on day t, and t ranges from 320 to 65 days before the announcement of the acquisition. We use the estimated parameters from (1) to calculate abnormal returns, eit , for the periods. Run-up is then: Runupt =

−1 

eit

(2)

t=−42

where t = 0 is the announcement date. It is possible the event time at which CARs become positive has changed since 1991, if more recent bidders have a shorter or lengthier planning process before the initial bid, or the time required to secure the financing may have changed. To ensure that our results are robust to the choice of event window used to measure run-up, we also estimate run-up over alternative (−60, −1) and (−30, −1) event windows. Reported CARs were calculated using the market model with the CRSP equally weighted index. However, the results are robust to using the CRSP value-weighted index, as well as using the FamaFrench 3-factor model.7 Stock price data are from the Center for Research in Security Prices (CRSP). 3.2. Multivariate model We apply multivariate models, in which the dependent variable in these regressions is the runup for each target firm i. The independent variables are proxies for the characteristics identified in hypotheses. The full model8 is specified as: Runupi = ˇ1 PEi + ˇ2 HOSTi + ˇ3 MGMTi + ˇ4 FRGNi + ˇ5 BORROWi + ˇ6 CASHPCTi + ˇ7 PCTOWNi + ˇ8 BIDADVi + ˇ9 BIDFEEi + ˇ10 TSIZEi + ˇ11 OPTIONi + ˇ12 HTECHi + ˇ13 SOXi + εi

(3)

where: • PE: An indicator variable taking the value 1 if the bidder is a private equity firm and 0 otherwise. The data are from SDC entitled ‘Acquirer is a Leveraged Buyout Firm (Y/N)’ which has the SDC code ‘ALBOFIRM’. • HOST: An indicator variable taking the value 1 if the deal is hostile and 0 otherwise. The data are from SDC entitled, ‘Deal Started as Unsolicited Flag (Y/N)’ which has the SDC code ‘UNSOLICITED’. • MGMT: An indicator variable taking the value 1 if there is management participation in the acquisition and 0 otherwise. The data are from SDC entitled ‘Management Participation Flag (Y/N)’ which has the SDC code ‘MGMT’. • FRGN: An indicator variable taking the value 1 if the bidder’s ultimate parent is a foreign firm and 0 otherwise. • BORROW: An indicator variable taking the value 1 if the deal is financed through borrowing and 0 otherwise. The data are from SDC entitled ‘Borrowings Flag (Y/N)’ which has the SDC code ‘SFB’. • CASHPCT: The percent of the deal value paid in cash. The data are from SDC entitled, ‘Consideration: Percentage of Cash’ which has the SDC code ‘PCT CASH’.

7 Using data from 2002 to 2007 inclusive we estimated CARs using a variety of expected returns (Fama-French 3-factor, value-weighted and equally-weighted market models, the market model with Scholes-Williams betas, and market adjusted returns). All of these methodologies afforded very similar run-ups, often only basis points apart, and gave identical results in the cross-sectional analysis. For example, over 2002–2007 the average CARs are: Equally weighted market model: (−60, −1) 5.04%; (−42, −1) 4.91%; (−30, −1) 4.07%; Equally weighted Fama-French 3-factor: (−60, −1) 4.81%; (−42, −1) 4.87%; (−30, −1) 4.04%. 8 Alternative models were estimated which included a book-to-market variable, an ex-ante probability of acquisition variable, and various ownership measures from proxy statements. The ex-ante probability of a firm being acquired was calculated in the same fashion as Song and Walkling (2000). The inclusion of these variables meant matching our sample of targets with the Compustat database and thereby markedly reduced our sample size. Moreover, the coefficients were insignificant, and so their inclusion did not warrant the sample size reduction.

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Table 1 Run-up characteristics by year. Year

Average run-up

Median run-up

Run-up standard deviation

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Number of deals 148 165 246 299 338 288 202 129 129 121 139 157 151

6.1% 11.2% 11.5% 14.6% 13.9% 12.5% 15.7% 9.7% 4.8% 3.5% 5.0% 4.2% 5.2%

6.3% 9.5% 9.3% 11.7% 10.8% 14.1% 8.6% 4.0% 3.9% 5.4% 5.0% 1.6% 5.4%

22.7% 22.9% 23.5% 30.4% 31.4% 40.5% 37.4% 35.7% 25.1% 20.7% 24.2% 22.8% 20.4%

Total

2512

10.3%

7.8%

29.7%

This table summarizes by year the number of deals, average target run-up, median target run-up, and target run-up standard deviation. Estimates for the entire sample are below the yearly estimates. The total sample size is 2512 mergers or acquisitions. Target run-up is calculated over the event window (−42, −1) using CRSP equally weighted daily returns.

• PCTOWN: The percentage of the target that the bidder owns at announcement. The data are from SDC entitled ‘Percent of Shares Held By Acquiror at Announcement’ which has the SDC code ‘PHDA’. • BIDADV: The number of bidder financial and legal advisors. This is the sum of SDC codes ‘AACOUNT’ and ‘NUMALEG’ which are ‘Number of bidder Financial Advisors’ and ‘Number of bidder Legal Advisors’ respectively. • BIDFEE: The amount the bidder spent on financial advisory fees as a percent of the deal value. The data are from SDC entitled ‘Acquiror Financial Advisor Fees, Total Amount as % of Deal value’ which has the SDC code ‘BIDFEE’. • TSIZE: The natural logarithm of the target’s market capitalization four weeks prior to the acquisition announcement. The data are from SDC entitled ‘Target Market Value 4 Weeks Prior to Announcement’ which has the SDC code ‘MV’. • OPTION: An indicator variable taking the value 1 if there are listed stock options traded on the target’s stock and 0 otherwise. The data are from SDC entitled, ‘Acquiror Stock Options Exist on Target Stock Flag (Y/N)’ which has the SDC code ‘OPTIONS YN’. • HTECH: An indicator variable taking the value 1 if the target is in a high-tech industry and 0 otherwise. The data are from SDC entitled ‘Target High Tech Industry Code’ which has the SDC code ‘THTECHC’. If the target has a high-tech code we map the value to 1, and otherwise to 0. • SOX: An indicator variable taking the value 1 if the acquisition was announced after July 2002 and 0 otherwise.

Table 1 summarizes our sample of 2512 acquisitions by year. Notice that the acquisition activity was especially high in the 1997–2000 period. In these years both the mean run-up, and standard deviation of the run-up among targets, were relatively high. Table 1 also shows that the mean run-up9 over the entire sample is 10.29% over (−42, −1). As a basis of comparison, the mean run-up is 10.84% over the (−60, −1) event window, and 9.16% over (−30, −1). Each mean run-up is significantly different from zero at the 1% level of significance.10 The mean run-up in our analysis is smaller than in many studies that focused on earlier sample periods. We offer an explanation for this difference within our forthcoming analysis.

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Table 2 Sample sizes and proportions. Proportion of the sample with attribute

Binary variable

n

Proportion of the sample with attribute

754 89 62 84

30.0% 3.5% 2.5% 3.3%

Borrow Foreign Funds Line of Credit Tender Offer

349 19 261 682

13.9% 0.8% 10.4% 27.1%

433 124 52 40

17.2% 4.9% 2.1% 1.6%

Fin Options High Tech Flat Fee

327 2269 1359 547

13.0% 90.3% 54.1% 21.8%

Binary variable

n

SOX Private Equity Hostile Management Participation Foreign Bidder Debt Issue Equity Issue Bridge Loan

This table summarizes the sample sizes, and the proportion of the sample with a particular attribute, for each binary explanatory variable. SOX signifies the acquisition was announced after Sarbanes-Oxley became law (July 2002). Private Equity signifies that the bidder is a private equity firm. Hostile signifies that the deal was hostile. Mgmt and Foreign signify whether there was management participation in the acquisition and the bidder was foreign, respectively. Debt Issue and Equity Issue denote that the bidder issued debt and equity securities, respectively, to assist in financing the acquisition. Bridge Loan signifies that the bidder receives bridge financing for the acquisition. Borrow denotes whether the bidder borrowed to finance the acquisition. Foreign Funds denotes whether the bidder financed the acquisition using funds borrowed abroad. Line of Credit denotes whether the bidder opened a line of credit to assist in financing the acquisition. Tender Offer signifies that the deal was a tender offer. Fin signifies that the bidder is a financial firm. Options signifie that there are listed options on the target’s stock. High Tech signifies whether the target was a high-tech firm. Lastly, Flat Fee signifies that the bidder’s advisory fees were flat-rate. The sample size is 2512 mergers or acquisitions.

Table 2 summarizes the proportion of the deals which have a given attribute, for each binary variable. The proportions range from 0.8% of the deals using foreign funds, to 90% of the targets having listed options on their stock. The median attribute is present in 11.7% of the acquisitions. 4. Results We first provide results from a univariate analysis of the variables used to test our hypothesis. Then, we disclose results from applying a multivariate analysis. 4.1. Results of univariate analysis Results from conditioning the mean run-up on each binary explanatory variable are disclosed in Table 3 (panel A).11 Target run-up is reduced by 8.4% if the bidder is a private equity firm (significant at the 0.1% level), and by 16.8% if the deal is hostile (significant at the 0.1% level). These results imply that being adept at acquisitions, and not allowing the target to know of the imminent bid, are both effective means of reducing run-up acquisition costs. For acquisitions announced post-SOX, target runup was 7.9% lower (significant at the 0.1% level) possibly showing the efficacy of heightened regulatory oversight, or reduced signaling through unscheduled option grants. Target run-up decreases by 3.9% if management is participating in the acquisition, but this variable is not significant. Target run-up is 4.8% greater if the bidder is a foreign firm (significant at the 0.1% level), 3.5% greater if the bidder borrows to finance the acquisition (significant at the 5% level), 5.5% greater if the target has listed options on its stock (significant at the 5% level), and 2.9% greater if the target is a high tech firm (significant at the 5% level). These results are evidence that cross-border deals affect run-up costs.

9 These run-ups were calculated using the equally weighted market index. The average run-up using the value-weighted index is: 11.57% over (−60, −1); 10.95% over (−42, −1); 9.65% over (−30, −1). 10 Each average run-up is significant at 2e−16, far less than the 1% level. 11 We had planned to test the impact of some additional financing characteristics that reflected junk bond, preferred stock, mezzanine, and rights offering financing. However, each of these characteristics existed for less than 10 observations. Therefore, we elected not to include them in our analysis.

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Table 3 Univariate analysis. Panel A: Binary variables Binary variable

n

Mean (if = 1)

PE**** Hostile**** Mgmt Foreign**** DebtIssue EquityIssue Bridge Borrow**

89 62 84 433 124 52 40 349

2.2% −6.1% 6.5% 14.3% 9.1% 7.8% 8.2% 13.3%

Mean (if = 0)

Diff.

p-Value

Binary variable

n

Mean (if = 1)

Mean (if = 0)

Diff.

p-Value

10.6% 10.7% 10.4% 9.5% 10.4% 10.3% 10.3% 9.8%

−8.4% −16.8% −3.9% 4.8% −1.3% −2.5% −2.1% 3.5%

0.00 0.00 0.14 0.00 0.50 0.38 0.50 0.02

ForeignFunds** LineCredit Tend**** Fin** Options** HTech** Flatfee SOX****

19 261 682 327 2269 1359 547 754

3.7% 10.5% 14.2% 7.7% 10.8% 11.6% 9.9% 4.8%

10.3% 10.3% 8.8% 10.7% 5.3% 8.7% 10.4% 12.7%

−6.6 0.2% 5.4% −3% 5.5% 2.9% −0.5% −7.9%

0.03 0.90 0.00 0.05 0.01 0.01 0.74 0.00

Two tailed t-tests for significantly different mean target run-up over the event window (−42, −1). Each variable is a binary explanatory variable taking the value 1 if the deal has the particular characteristic and 0 otherwise. PE signifies that the acquirer is a private equity firm. Hostile signifies that the deal was hostile. Mgmt and Foreign signify whether there was management participation in the acquisition and the acquirer was foreign, respectively. DebtIssue and EquityIssue denote that the acquirer issued debt and equity securities, respectively, to assist in financing the acquisition. Bridge signifies that the acquirer receives bridge financing for the acquisition. Borrow denotes whether the acquirer borrowed to finance the acquisition. Foreign Funds denotes whether the acquirer financed the acquisition using funds borrowed abroad. Line Credit denotes whether the acquirer opened a line of credit to assist in financing the acquisition. Tend signifies that the deal was a tender offer. Fin signifies that the acquirer is a financial firm. Options signifies that there are listed options on the target’s stock. Htech signifies whether the target was a high-tech firm. Flatfee signifies that the acquirer’s advisory fees were flat-rate. SOX signifies the acquisition was announced after Sarbanes-Oxley became law (July 2002). The sample size is 2512. *, **, ***, **** denote significance at the 10%, 5%, 1%, and 0.1% levels respectively. Panel B: Continuous and integer-valued variables Explanatory variable Cashpct** Pctown**** Bidadv** Bidfee Tadv Tsize****

Max.

100% 98.2% 17.0% 6.2% 10.0% 11.4%

Min.

Mean

Median

Run-up if variable > mean

Run-up if variable < mean

Difference

p-Value

0.0% 0.0% 0.0% 0.0% 0.0% 0.6%

52.6% 4.5% 2.4% 0.2% 2.7% 6.0%

64.3% 0.0% 2.0% 0.0% 3.0% 5.2%

11.6% 4.2% 8.5% 9.0% 9.6% 3.3%

8.9% 11.0% 11.5% 10.6% 11.0% 11.8%

2.7% −6.8% −3.0% −1.6% −1.4% −8.5%

0.03 0.00 0.01 0.23 0.21 0.00

Two tailed t-tests for significantly different mean target run-up over the event window (−42, −1). Each variable is either integer valued or ranges from 0% to 100%. Cashpct represents the amount of the deal value paid in cash by the acquirer, and Pctown the percent of the target the acquirer owned at announcement of the acquisition. Bidadv is the number of acquirer financial and legal advisors. Bidfee is the amount the acquirer paid in advisory fees as a percent of the deal value. Tadv is the number of target financial and legal advisors. Tsize is the natural logarithm of the target’s market capitalization (in millions). The maximum, minimum, mean, and median are listed for each variable. Mean target run-up is conditioned on whether the variable is greater or less than its mean. The sample size is 2512. *, **, ***, **** denote significance at the 10%, 5%, 1%, and 0.1% levels respectively.

Further, the results support that securing financing, the presence of leverage increasing derivatives (options) on the stock, and target asymmetric information all increase acquisition costs. Table 3 (panel B) provides univariate results for either continuous or integer valued variables. If the bidder pays more than 52.6% of the acquisition price in cash (the mean percentage paid in cash over the entire sample of acquisitions), then target run-up increases by 2.7% (significant at the 5% level). Target run-up decreases by 6.8% if the bidder owns more than 4.5% of the target prior to the acquisition announcement (significant at the 0.1% level). If the number of bidder advisors is greater than 2, then target run-up decreases by 3% (significant at the 5% level). If the bidder pays more than the mean 0.2% of the deal value in advisory fees, then target run-up is reduced by 1.6% (not significant). If the natural logarithm of the target’s market capitalization (in millions) is greater than the median of 1039, then target run-up decreases by 8.5% (significant at the 0.1% level). Many of these results are consistent with our hypotheses, and suggest that characteristics that reflect a higher degree of asymmetric information allow more potential for informed trading. Target

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firms experience higher run-ups when there are higher levels of asymmetric information. In addition, target firms experience higher run-ups when there are more parties that may be privy to inside information prior to the bid announcement, or when informed traders may be more able to hide their positions. While the results of the univariate analysis offer insight, they do not control for other factors that should also be considered simultaneously. We provide results of our multivariate analysis next. 4.2. Results of multivariate analysis We estimate the coefficients of the full set of explanatory variables using target run-up over the windows (−60, −1), (−42, −1), and (−30, −1) in three regression models using ordinary least squares (OLS). We used the Breusch-Pagan test for heteroscedasticity in the residuals of each linear regression model. In each case, we were able to reject the null hypothesis of no heteroscedasticity at the 1% level. Given this heteroscedasticity, we report White’s (1980) heteroskedasticity-consistent standard errors. Alternatively, we estimate the model using weighted least-squares and find similar results. We also find similar results by estimating a robust regression using iteratively reweighted least squared, ensuring the OLS estimates are not affected by outliers. We checked our set of independent variables for multicollinearity using both variance inflation factors and scaled condition numbers. The variance inflation factor for each independent variable was below 2, indicating little multicollinearity. The condition number was also well below the threshold for multicollinearity.12 We conclude there is little instability in the model’s estimated parameters induced my multicollinearity. Results of our multivariate analysis are disclosed in Table 4.13 The results are partitioned into columns based on the window used to measure the dependent variable. Overall, the results of the multivariate analysis reinforce the results of the univariate analysis. The Private Equity (PE) variable is negative and significant over the (−42, −1) and (−30, −1) run-up windows, which supports the hypothesis that the target’s stock price run-up is lower when the bidder is a private equity firm. Given the average market capitalization of PE firm targets is $1482 million, and over the window (−42, −1) run-up is 6.4% lower for PE firms, this suggests that PE firms are able to lower the dollar cost of run-up by $95 million on average. The hostile bidder variable (Host) is negative and significant for all three run-up windows, which supports the hypothesis that the target stock price run-up prior to the announcement is lower when the takeover is hostile. Given the average market capitalization of a target of a hostile acquisition is $1266 million, and that run-up is 14.1% over the window (−42, −1), hostile bids are able to lower the dollar cost of run-up by $179 million on average. The management participation (Mgmt) variable is negative for all three run-up windows, and significant (at the 10% level) for two of the three windows, offering some support for the hypothesis of a lower target stock price run-up when there is management participation. In economic terms, management participation is able to lower the dollar cost of run-up by $35 million on average. The foreign bidder (Foreign) variable is positive for all three event windows and significant (at the 5% level) for two of them, providing support for the hypothesis that the target’s stock price run-up is higher when foreign bidders are involved. On average, foreign bidders incur an additional $34 million in costs. The Borrow variable is significant over (−60, −1) (at the 10% level), and significant over (−42, −1) (at the 5% level), supporting the hypothesis that the target’s stock price run-up is higher when the bidder borrows funds to finance the acquisition. This result is consistent with information leaking through the process of the bidder securing funds. On average, bidders that borrow to finance the acquisition incur an additional $28 million in costs because of the higher run-up in the target stock price. The Cashpct variable is positive and significant for all three windows, which is consistent with high cash

12 The condition number of the scaled data matrix (as in Belsley, Kuh, & Welsch, 1980) is 15, which is well below the 30 threshold which signifies strong multicollinearity. The condition number is the ratio of the largest to smallest eigenvalue of the scaled data matrix premultiplied by its transpose. 13 Table 5 checks the robustness of the multivariate analysis, over the standard (−42, −1) event window, by including all variables which were significant in the univariate analysis and subsets of these variables. The results are robust with respect to the set of independent variables.

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Table 4 Results of multivariate analysis.

Intercept PE Host Mgmt Foreign Borrow Cashpct Pctown Bidadv Bidfee Tsize Option Htech SOX F-Statistic p-Value Adj.-R2

CAR (−60, −1)

CAR (−42, −1)

CAR (−30, −1)

0.20606 (0.03824)**** −0.05486 (0.03912) −0.11709 (0.04180)*** −0.05392 (0.03512) 0.03623 (0.01700)** 0.03219 (0.01722)* 0.00040 (0.00017)** −0.00103 (0.00053)* 0.00885 (0.00567)* −0.00438 (0.01986) −0.03261 (0.00539)**** 0.05089 (0.02747)* 0.02654 (0.01427)* −0.08162 (0.01590)**** 9.6 (0.00)**** 0.04246

0.18943 (0.03177)**** −0.06442 (0.02462)*** −0.14068 (0.03049)**** −0.04634 (0.02742)* 0.03334 (0.01456)** 0.02867 (0.01421)** 0.00040 (0.00014)*** −0.00081 (0.00041)** 0.00810 (0.00445)** −0.02188 (0.01434) −0.02983 (0.00453)**** 0.05127 (0.02213)** 0.03071 (0.01164)*** −0.08380 (0.01328)**** 13.3 (0.00)**** 0.06015

0.18642 (0.02801)**** −0.08394 (0.02438)**** −0.11580 (0.02477)**** −0.04181 (0.02299)* 0.01923 (0.01207) 0.01545 (0.01218) 0.00032 (0.00012)*** −0.00041 (0.00036) 0.01018 (0.00380)** −0.01160 (0.01262) −0.02985 (0.00387)**** 0.03867 (0.01991)* 0.03376 (0.00990)**** −0.07994 (0.01107)**** 15.8 (0.00)**** 0.07120

Ordinary Least Squares regressions with White’s (1980) heteroskedasticity-consistent standard errors (in parentheses below the coefficients). For each explanatory variable and regression the estimated coefficient is listed above and the robust standard error is below in parentheses. PE signifies that the bidder is a private equity firm. Host signifies that the deal was hostile. Mgmt signifies whether there was management participation in the acquisition. Foreign indicates whether the bidder was outside of the U.S. Borrowed denotes whether the bidder borrowed to finance the acquisition. Cashpct represents the amount of the deal value paid in cash by the bidder, and Pctown the percent of the target the bidder owned at announcement of the acquisition. Bidadv is the number of bidder financial and legal advisors. Bidfee is the amount the bidder paid in advisory fees as a percent of the deal value. Tsize is the natural logarithm of the target’s market capitalization. Option represents whether there are listed options on the target’s stock. Htech signifies whether the target was a high-tech firm. SOX signifies the acquisition was announced after Sarbanes-Oxley became law (July 2002). *, **, *** and **** denotes significance at the 10%, 5%, 1%, and 0.1% level respectively. t-Tests are two-tailed. There are 2512 observations and 2498 degrees-of-freedom. Variance inflation factors for the coefficients of each regression are all below 2, indicating multicollinearity is not unduly affecting the least squares estimates.

balances signaling acquisitions. An increase of one standard deviation in Cashpct (42%) increases in run-up costs by $19 million. The Pctown variable is negative for all three windows, and is significant for two of them, supporting the hypothesis of a lower stock price run-up for acquisitions in which the bidder previously established a toehold position. A one standard deviation increase in Pctown (15%) decreases run-up costs by $12 million. The Bidadv variable is positive and significant for all three windows, offering support for the hypothesis that the target’s stock price run-up is positively related to the number of bidder advisors. A one standard deviation increase (1.7 advisors) causes run-up to on average increase by $15 million. The target size coefficient (Tsize) is negative and significant, indicating there is less asymmetric information regarding larger firms and thus target stock price run-up is decreasing in target size. A one standard deviation increase in target size decreases run-up by 5.1%, implying on average a $53 million decrease in costs. The Option variable is positive and significant for all three windows, which supports the hypothesis that the target’s run-up is higher for targets on which listed stock options

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Table 5 Multivariate analysis: robustness.

Intercept PE Host Mgmt Foreign Borrow Cashpct Pctown Bidadv Bidfee Tsize Option Htech SOX ForeignFunds Fin Tend Deg. of freedom F-Statistic p-Value Adj.-R2

CAR (−42, −1)

CAR (−42, −1)

CAR (−42, −1)

0.18443 (0.03143)**** −0.06270 (0.02951)** −0.14102 (0.03064)**** −0.04289 (0.02914) 0.03303 (0.01429)** 0.02546 (0.01452)* 0.00039 (0.00016)** −0.00086 (0.00041)** 0.01652 (0.00927)* −0.02377 (0.01454) −0.02855 (0.00414)**** 0.05229 (0.02238)** 0.03194 (0.01167)*** −0.08019 (0.01352)**** 0.00643 (0.03283) −0.00170 (0.01968) 0.00908 (0.014814) 2495 10.87 (0.00) 0.05916

0.17663 (0.03086)**** −0.05791 (0.02850)** −0.14529 (0.03017)****

0.17685 (0.03068)**** −0.06400 (0.02463)*** −0.14293 (0.02997)****

0.03493 (0.01425)** 0.02225 (0.01448) 0.00039 (0.00016)** −0.00097 (0.00040)** 0.01343 (0.00903)

0.03670 (0.01395)*** 0.02387 (0.01420)* 0.00041 (0.00014)*** −0.00096 (0.00040)** 0.01336 (0.00901)

−0.02698 (0.00402)**** 0.04851 (0.02221)** 0.03218 (0.01168)*** −0.07751 (0.01336)**** 0.00168 (0.03322) −0.00521 (0.01867) 0.00754 (0.01460) 2497 12.07 (0.00) 0.05812

−0.02708 (0.00400)**** 0.04931 (0.02204)** 0.03223 (0.01166)*** −0.07948 (0.01273)****

2500 15.35 (0.00) 0.05913

Ordinary Least Squares regressions with White’s (1980) heteroskedasticity-consistent standard errors (in parentheses below the coefficients). These models include every variable which was significant in the univariate analysis. For each explanatory variable and regression the estimated coefficient is listed above and the robust standard error is below in parentheses. PE signifies that the bidder is a private equity firm. Host signifies that the deal was hostile. Mgmt signifies whether there was management participation in the acquisition. Foreign indicates whether the bidder was outside of the U.S. Borrowed denotes whether the bidder borrowed to finance the acquisition. Cashpct represents the amount of the deal value paid in cash by the bidder, and Pctown the percent of the target the bidder owned at announcement of the acquisition. Bidadv is the number of bidder financial and legal advisors. Bidfee is the amount the bidder paid in advisory fees as a percent of the deal value. Tsize is the natural logarithm of the target’s market capitalization. Option represents whether there are listed options on the target’s stock. Htech signifies whether the target was a high-tech firm. SOX signifies the acquisition was announced after Sarbanes-Oxley became law (July 2002). Foreign Funds denotes whether the bidder financed the acquisition using funds borrowed abroad. Fin signifies that the bidder is a financial firm. Tend signifies that the deal was a tender offer. *, **, *** and **** denotes significance at the 10%, 5%, 1%, and 0.1% level respectively. t-Tests are two-tailed. There are 2512 observations. Variance inflation factors for the coefficients of each regression are all below 2, indicating multicollinearity is not unduly affecting the least squares estimates.

are traded. Over the (−42, −1) window run-up is 5.1% higher for targets with listed options, and since the average market capitalization of a target with listed options is $1084 million, this implies a $55 million increase in run-up costs on average. The high-tech target variable (Htech) is positive and significant, which supports the hypothesis that the target’s run-up is increasing in its information asymmetry. On average run-up adds $33 million to the price of a high-tech target. The SOX variable is negative and significant for all three windows

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indicating the passage of SOX has significantly reduced the target’s stock price run-up. Using the estimates from the (−42, −1) event window, SOX decreased the target stock price run-up by 8.4%, which reflects an average $87 million reduction in costs to the bidder. Overall, results of the multivariate analysis are consistent with our hypotheses, and help explain the variation in target stock price run-up prior to the bid announcement. Several characteristics that reflect information asymmetries are related to the degree of run-up, implying that informed trading prior to the bid announcement is more pronounced in the presence of a higher level of asymmetric information. In addition, the stock price run-up is more pronounced when more parties are privy to the planned takeover, and when listed options on the target stock are available. Table 5 checks the robustness of the multivariate analysis, over the standard (−42, −1) event window, by including all variables which were significant in the univariate analysis and subsets of these variables. The results are robust with respect to the set of independent variables. 5. Conclusion Previous studies have clearly documented that the target’s stock price experiences a substantial run-up before announced acquisitions. However, little is known about the variation in run-up among acquisitions, and it is our objective to explain the determinants of this variation. Since target run-up is a cost to the bidder (Schwert, 1996), the bidder would prefer as small a target run-up as possible. To this end, our analysis offers implications on characteristics that affect the cost incurred from acquiring other companies. We adapt theories by Huddart and Ke (2007), Haggard et al. (2008), and Dasgupta, Gan, and Gao (2010) to propose why prospective target firms with higher levels of asymmetric information should experience more pronounced information leakages prior to a public acquisition bid. There may be more opportunities for traders to acquire useful private information about these firms prior to a publicized bid, because there is very limited public information available. Thus, prospective targets with higher levels of asymmetric information are more exposed to potential private information leakages prior to public bid announcements, and therefore should experience more pronounced stock price run-ups. The private information that triggers the run-up in the target’s stock price prior to an acquisition announcement could represent a profile or behavior of target firms or the corresponding bidding firms that is detected or at least suspected by informed traders, or could represent inside information that is intentionally or accidentally leaked to traders about an impending acquisition. Recent insider trading cases, such as the Galleon case, suggest that informed (or insider) trading can result from many different sources of information. We identify several possible sources representing bidder, target, or regulatory characteristics that could expose target firms to different levels of exposure to private information leakages. We find that the target stock price run-up before the publicly announced bid is influenced by bidder characteristics. In particular, the run-up is higher when the bidder is not a private equity firm, is friendly, is foreign, or borrows to finance its acquisition. We also find that the target stock price run-up is related to some target-specific characteristics. It is higher for targets that are smaller, have listed stock options, and are in the technology sector. Finally we find that target run-up before all deals has decreased significantly since SOX, which may be attributed to greater accountability and regulatory oversight of bidder managers and board members involved in acquisitions due to SOX. Managers may be more careful in selecting targets and in preventing leakages since they have become more accountable for the process they use in making such key decisions as a result of SOX. This result may also be due to the lower frequency of unscheduled option grants to CEOs during the merger or acquisition negotiation period after the passage of SOX. Overall, our results offer substantial evidence that targets with higher levels of asymmetric information are more exposed to private information leakages prior to public bid announcements, and therefore experience more pronounced stock price run-ups. Further, the stock price run-up is higher when conditions regarding the bidder, the target, or the regulatory environment result in less transparency. Since a higher stock price run-up can increase the cost of an acquisition (see Schwert, 1996), our sources of stock price run-up also add to existing explanations of why acquisition costs vary among bidders. Furthermore, since higher costs of acquisitions can destroy bidder value (see Moeller et al.,

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