Re-examination of industry effects due to withdrawnmergers

Jeff Madura & Thanh Ngo

Published online: 17 April 2010# Springer Science+Business Media, LLC 2010

Abstract We find that the announcement of amerger withdrawal elicits negative industryeffects on average, which reflect a partial reversal of the favorable industry effects that hadpreviously occurred at the time of the merger proposal. The mean reversal is about 35% ofthe original favorable industry effect at the time of the merger announcement. This resultfor the mean effect is opposite of that found by Akhigbe et al. (2000). When we break oursample into sub-periods, we find that industry effects are substantially weaker in a morerecent sub-period beyond the sample period used by Akhigbe et al. (2000). We also findthat the industry effects are more negative when the share price response of the target atthe time of the announced withdrawal is weaker. Whether the negative impact on thetarget is attributed to either a reduced likelihood of takeover or weaker industryprospects, it carries over to industry rivals.

Keywords Merger Industry Effects . MergerWithdrawals

JEL Classification G34 . G14

1 Introduction

Studies by Eckbo (1983, 1985), Akhigbe and Madura (1999), and Song andWalkling (2000) find that merger announcements create a favorable signal for therespective industries. Common reasons offered for the favorable signal are that thebidder recognizes opportunities within the industry, or that the merger bid mayprecipitate consolidation within the industry, which should increase valuations ofprospective targets.

J Econ Finan (2012) 36:613–633DOI 10.1007/s12197-010-9133-z

We wish to thank two anonymous reviewers and James Payne (Editor) of Journal of Economics andFinance for their valuable suggestions.

J. MaduraFlorida Atlantic University, Boca Raton, FL, USAe-mail: jeffmadura@bellsouth.net

T. Ngo (*)Pan-American, The University of Texas, Edinburg, TX, USAe-mail: ngotn@utpa.edu

More than one in every ten merger proposals is withdrawn. During the period 1980–2005, 1,901 merger proposals (for both publicly and privately-held targets) have beenwithdrawn, representing about 11.1% of all proposals. Akhigbe et al. (2000) determinedthat withdrawn mergers resulted in negative valuation effects for the target firms, butfavorable valuation effects for the rivals of these target firms on average. They concludethat the termination of a merger signals a higher probability that rivals within theindustry will be acquired. Since about 45% of their events elicited unfavorable industryeffects, it appears that the distribution of the industry effect is disbursed, and that theindustry effects may vary with specific underlying conditions.

Our goal is to build on the study by Akhigbe et al. (2000), using a larger samplebecause of the additional merger withdrawals that have occurred since 1996, whichis the end of their sample period. We find that the withdrawal announcement causesnegative and significant industry effects on average, which differs from the positiveindustry effects found by Akhigbe et al. (2000). This is primarily attributed tonegative industry effects detected in an earlier sub-period and in a more recent sub-period than the sample period they assessed. Overall, our mean industry effectsreflect a partial correction (estimated to be 35%) of the initial industry response atthe merger announcement. Just as the merger announcement could signal an increasein merger activity within the industry or more favorable industry prospects, thewithdrawal of the merger could signal either a decrease in possible merger activitywithin the industry or unfavorable industry prospects.

Since our results vary among our total sample, we also assess the cross-sectionalvariation of industry effects at the time of merger withdrawal. We find that theindustry effects at the time of the announced withdrawal are worse when the targetexperiences a weaker stock price response. This suggests that the negative surpriseto the target’s corresponding industry at the time of the merger withdrawal is relatedto the negative surprise to the target itself. A closer examination shows that thisrelationship is especially pronounced when stock is the planned method of paymentfor the merger proposal. We also conduct a cross-sectional analysis of individualrival firms (within the corresponding industry), and find that the individual rivaleffects are more negatively affected upon withdrawal when the share price responseof the corresponding target is weaker, especially when stock is the planned mediumof payment. This result is consistent with cross-sectional analysis of industry effects.Whether the underlying negative impact on the target firm is due to a reducedlikelihood of a future takeover or a signal about weaker industry prospects, theimpact on the target carries over to the industry rivals.

2 Literature review

2.1 Related literature on industry effects of merger announcements

Eckbo (1983, 1985) finds that merger announcements elicit a favorable share priceresponse for industry rivals. He offers collusion as a possible reason for thefavorable effect, but finds no evidence to support this reason. Akhigbe and Madura(1999) suggest that the effects on rivals reflect the probability of a future takeover.Based on an analysis of 245 large mergers during the 1980–1996 period, they find

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that industries (and individual rival firms) with a higher level of free cash flow, ahigher level of tangible assets, and a smaller market value experience more favorableeffects in response to the merger announcement. They also find that individual rivalswith weak performance prior to the merger announcement experience morefavorable effects. Song and Walkling (2000) suggest that industry rivals shouldexperience a stronger positive valuation effect at the time of a merger announcementif they exhibit a profile that makes them more likely to be acquired in the future.They assess 141 acquisition bids over the 1982–1991 period and find that theindustry effect is stronger for rivals that are more likely to be acquired.

2.2 Related literature on merger withdrawals

Several studies have assessed how withdrawn mergers affect the bidder or targetfirms. Studies by Dodd (1980), Asquith (1983) Bradley et al. (1983), Chang and Suk(1998) measure the impact of merger withdrawals on bidder firms. Sullivan et al.(1994), Davidson et al. (1989), and Madura and Ngo (2008) focus on the impact ofmerger withdrawals on target firms. Overall, the results are mixed depending on thetime period assessed, the method of financing, and whether private targets wereincluded in the study.

Akhigbe et al. (2000) find that withdrawn mergers resulted in favorable valuationeffects for the rivals of these target firms on average. Their results suggest that thetermination of a merger signals a higher probability that rivals within the industrywill be acquired. Using a large sample of merger withdrawals over a long time span,we attempt to build on the study of Akhigbe et al. (2000) as explained above.

3 Hypotheses

We offer hypotheses for the variation in industry effects due to the merger proposals thatwere ultimately withdrawn. Then we offer hypotheses on industry effects of mergerwithdrawal announcements and reasons for the variation in these industry effects.

In addition to the above hypothesized factors, we also include control variables assuggested by Akhigbe and Madura (1999). They argue that the industry effects ofthe merger announcements are related to the expected financial synergy of themergers, industry free cash flow, industry tangible assets, size of rivals within theindustry, industry Q ratio, efficiency of the rival firms and the market power.

3.1 Industry effects of merger withdrawal announcements

Akhigbe and Madura (1999) and Song and Walkling (2000) argue that an acquisitionannouncement might signal the onslaught of additional acquisitions in the industry,and should therefore be a favorable signal about target industry. Favorable industryeffects could also occur because of favorable signals about opportunities within theindustry. The withdrawal of a merger proposal could possibly eliminate or reduce thefavorable industry signal that was previously emitted by the merger proposal.Therefore, we hypothesize that merger withdrawal announcements cause negativeindustry valuation effects.

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An alternative to our hypothesis is that if a proposed merger might createmonopoly power, it could adversely affect rival firms. Therefore, a withdrawnmerger might signal the elimination of potential monopoly power and couldtherefore result in favorable industry effects.

3.2 Variation in industry effects of the withdrawal announcements

We hypothesize that the industry effects could be affected by the following characteristics.

Target valuation effects upon the merger withdrawal (TCAR) The impact of thewithdrawal announcement on the target may be partially due to a signal about thecorresponding industry. Just as a target and its rivals can benefit from an acquisitionannouncement due to either the likelihood of a takeover or a signal of favorableindustry prospects, the withdrawal of a proposed merger might emit a reversedsignal. Consequently, the signal regarding the target can carry over to the industry. Awithdrawal announcement that results in weaker abnormal returns for targets mayreflect more negative signals about the corresponding industry. Therefore, wehypothesize a positive relationship between the target abnormal returns and theindustry effects at the time of the withdrawal announcement.

We also control for several other characteristics that could influence industry effects.

Multiple bids (MULTBID) When there are multiple bidders for the target, awithdrawal announcement may occur simply because bidders do not want tocompete for a target, rather than because of new information about the target’scorresponding industry. Thus, negative industry effects in response to a withdrawalannouncement may be attenuated when there are multiple bidders.

Hostile bids (HOSTILE) Hostile bids may lead to a merger withdrawal if the targetcounters with strategies that make a takeover costly, even if industry prospects areunchanged. Thus, negative industry effects in response to a withdrawal announce-ment may be attenuated when there hostile bids.

Withdrawn bids by related firms (RELATED) Withdrawals by firms in the sameindustry may reduce the probability of the rival firms to be subsequently acquired.Therefore, industry effects at the time of merger withdrawals may be worse when thebidder is in the same industry as the target.

Stock payment (STOCK) Stock bids by bidders may reflect less confidence in thetarget and its corresponding industry. Therefore, a merger withdrawal may reinforceconcerns about industry prospects and cause more pronounced adverse industryeffects.

Duration from merger proposal to announced withdrawal (DURATION) The moretime that elapses between the announcement of the merger proposal and the mergerwithdrawal announcement, the bigger is the surprise once a withdrawal occurs.Therefore, the industry effect may be more negative when the duration is longer. Weapply a control variable that accounts for the number of days from the merger bid untilthe withdrawn bid.

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Takeover volume within the industry (COMPDEALS) The more mergers that aremade in the industry, the stronger the signal about the future prospects of theindustry, and thus the less pronounced are adverse industry effects in response to awithdrawal announcement.

Market and industry performance prior to the bid Industry effects in response towithdrawal announcements are expected to be worse when recent market andindustry performance levels are relatively weak.

Source of withdrawal (REJECT) When withdrawals are initiated by targets, thereason may be because the target wants to remain independent or has concerns aboutthe bidder firm, rather than because of concerns about the target’s industry. We createan interaction term called REJECT that represents stock bids that are rejected by thetarget. We expect a positive relationship between the interaction term and theindustry effects of the withdrawal.

Bidder debt (BIDDERDEBT) When the bidder has a high level of debt, a withdrawalmight signal that the bidder can not afford to acquire the target. In this case, theadverse industry signal due to a withdrawal announcement should be reduced.

Premium offered (PREMIUM) When the bidder offers a relatively high premium forthe target, a withdrawal might signal that the bidder recognizes its risk ofoverpayment for the target. In this case, the adverse industry signal due to awithdrawal announcement should be reduced.

Estimated probability of withdrawal (WITHPROB) Some withdrawals of merger bidsmay be more expected than others. Therefore, we hypothesize that characteristicsknown at the time of the merger bid could influence the expected likelihood of awithdrawal, and therefore influence the industry effect at the time of the withdrawalannouncement. One argument is that if a merger proposal is anticipated, its effects on thecorresponding industry will be attenuated upon confirmation of the withdrawal.

However, consider that industry effects may come from new information aboutthe target firm, its corresponding industry, and the interaction between the twosources. Recall that the key conclusion by the analysis of Akhigbe et al. (2000) wasthat a merger withdrawal may signal a higher probability of mergers in the target’scorresponding industry. However, to the extent that a higher probability ofwithdrawal for a merger also signals less favorable conditions for mergers, a highprobability of a specific merger withdrawal may cause less favorable (or moreunfavorable) industry effects at the time the merger withdrawal is confirmed.

4 Data

Our sample is compiled from the Securities Database Corporation (SDC) Mergersand Acquisitions database. First, we identify all takeovers with the proposalannouncements made in the 1980–2005 period for which the deal value, the bidder’s

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and target’s market value prior to the merger announcement and the premium dataare available from SDC. We include all mergers in which both the bidder and thetarget are publicly-traded companies and can be identified from CRSP database. Weinclude complete mergers and mergers that are ultimately withdrawn so that we candirectly compare results between the two subsamples.

Based on our criteria, there are 2,434 completed mergers and 533 withdrawnmergers public targets that qualify for our sample. Our focus is on the subsample ofmergers that are ultimately withdrawn and the subsample of completed mergers isthe control sample. Table 1 provides descriptive statistics about the sample. Oursample is larger than the study by Akhigbe et al. (2000), as their sample was smallerbecause of the earlier time period in which their study was conducted. In addition,Akhigbe et al. (2000) obtain their sample of 176 cancelled deals from theCancellation Roster in Mergerstat Review (as opposed to our sample of 269withdrawn deals from SDC within that same period). They also impose therequirement that the target firm stocks have to be listed on NYSE or AMEX and thatthe target rival firms have to be included in the Value Line Index.

Panel A provides the sample distribution by the year of the merger proposalannouncement. In general, the withdrawn mergers were especially high in the 1985–1989 and 1994–2000 periods during which the number of complete mergers washigher. Panel B provides the sample distribution by other characteristics. Theproportion of non-related business deals is higher for withdrawn deals thancompleted deals. The proportion of deals involving stock is higher for withdrawndeals than complete deals. Hostile deals are more frequently observed amongwithdrawn deals versus completed deals.

Panel C of Table 1 also provides the descriptive statistics of some keycharacteristics of the bidder and the target. On average, the dollar value of thecompleted deals is smaller than the dollar value of the withdrawn deals. The marketvalue of the bidders in completed deals is larger than those of the bidders inwithdrawn deals. However, the market value of the targets in completed deals islarger than those of the targets in withdrawn deals.

We identify 160,083 rival firms (in the same 4-digit SIC codes) corresponding tothe 2,434 targets in completed mergers, and 27,541 rival firms corresponding to the533 targets in withdrawn mergers. The average number of rivals per merger is 59;the median is 26; the minimum is 5 and the maximum is 418.

5 Methodology

5.1 Method of computing abnormal returns

Our main methodology is similar to other studies that measured the industry effectsin response to corporate events, including Foster (1981), Hertzel (1991), Slovin et al.(1991), Szewczyk (1992), Lang and Stulz (1992), Howe and Shen (1998), and Lauxet al. (1998). To test whether the merger withdrawals elicit industry effects, wemeasure abnormal returns surrounding the announcement of the withdrawn bids forthe portfolio of all industry rivals that share the same 4-digit SIC code as the targetedfirms.

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Table 1 Descriptive statistics

Panel A—sample distribution by year

Year Number of withdrawn deals Percent Number of complete deals Percent

1980 0 0.00% 4 0.16%

1981 2 0.38% 17 0.70%

1982 0 0.00% 15 0.62%

1983 2 0.38% 20 0.82%

1984 7 1.31% 24 0.99%

1985 20 3.75% 46 1.89%

1986 24 4.50% 60 2.47%

1987 35 6.57% 71 2.92%

1988 52 9.76% 73 3.00%

1989 36 6.75% 61 2.51%

1990 15 2.81% 42 1.73%

1991 15 2.81% 48 1.97%

1992 8 1.50% 50 2.05%

1993 17 3.19% 77 3.16%

1994 40 7.50% 124 5.09%

1995 31 5.82% 144 5.92%

1996 29 5.44% 162 6.66%

1997 37 6.94% 212 8.71%

1998 29 5.44% 237 9.74%

1999 40 7.50% 236 9.70%

2000 34 6.38% 193 7.93%

2001 19 3.56% 140 5.75%

2002 11 2.06% 83 3.41%

2003 8 1.50% 102 4.19%

2004 13 2.44% 97 3.99%

2005 9 1.69% 96 3.94%

Total 533 100.00% 2,434 100.00%

Panel B—sample distribution by deal characteristics

Deal type Number of withdrawn deals Percent Number of complete deals Percent

Non-horizontal 355 66.60% 1,498 61.54%

Horizontal 178 33.40% 936 38.46%

Total 533 100.00% 2,434 100.00%

Stock 468 87.80% 2,032 83.48%

Cash-only 65 12.20% 402 16.52%

Total 533 100.00% 2,434 100.00%

Non-hostile 479 89.87% 2,410 99.01%

Hostile 54 10.13% 24 0.99%

Total 533 100.00% 2,434 100.00%

Panel C—deal statistics summary

Withdrawn M&As Complete M&As

Deal Statistics Mean Median Mean Median

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We measure the average daily abnormal stock returns (ARs) in response to themerger bid announcement and withdrawal date for each firm i and for each day t inthe event period t−10 to t+10 as:

ARit ¼ Rit � a þ bRmtð Þ ð1Þwhere ARit is the daily abnormal return for firm i, Rit is the daily return for firm i, Rmt

is the daily return on the CRSP equally-weighted index, and the parameters α and βare obtained from the market model, estimated with daily returns from the periodt−180 to t−11 relative to the event date.

For each announcement, we form an equally weighted portfolio of all rival firms thatshare the same four-digit SIC code as the target firm. This portfolio method accounts forpotential cross-sectional correlation of returns in the industry. The returns of the rivalportfolio are used to estimate the market model parameters from the period t−180 to t−11relative to the announcement date of the merger proposal. The abnormal return of eachrival portfolio p and for each day t in the event period t−10 to t+10 is computed as:

ARpt ¼ Rpt � a þ bRmtð Þ ð2Þ

where ARpt is the daily abnormal return of the rival portfolio and Rpt is the daily returnof the rival portfolio We then accumulate these ARpt over several different eventwindows. We apply the procedure of Mikkelson and Partch (1988) to compute z-statistics are computed and test for statistical significance of cumulative standardizedaverage ARs of both the revised firms and of the rival portfolios.

5.2 Model for estimating the probability of a merger withdrawal

To estimate the withdrawal probability of the bid, we employ the Heckman two-stepprocedure (1979) as explained here. In the first step, we estimate the probability ofthe bid to be withdrawn using the following logistic regression:

WITHDRAWi ¼ ai þ b1MULTBIDi þ b2HOSTILEi þ b3RELATEDi þ b4STOCKi

þ b5COMPDEALSi þ b6MKTRET þ b7INDUSTRYRET

þ b8BIDDERDEBTi þ b9PREMIUMi þ "i

MUTLBID is a dummy variable for deals in which there are multiple bidders.When there are multiple bidders, a firm that announces a bid may withdraw its bid asit realizes that it may have to raise its price in order to compete for the target.

HOSTILE is the dummy variable for hostile deals. A hostile bid may cause thetarget to take a defensive position, which could increase the cost of an acquisition.Therefore, a bidder is more likely to withdraw a hostile bid.

Deal value 1,819.64 177.20 1,425.25 206.97

Target market value 7,402.28 700.25 13,337.66 1,686.30

This table provides some descriptive statistics of the sample. The sample distribution in each of the sampleyear is reported in Panel A. In Panel B, we report the number of deals by some deal characteristics. InPanel C, we report the mean and the median of deal value, target’s and acquirer’s market capitalization (in$ million) in the month prior to the announcement date of the merger proposal

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RELATED is a dummy variable for deals in which the bidder and the target are inthe same 4-digit SIC codes. The planned merger between two parties in the sameindustry may be subject to less suspicion and criticism than a planned mergerbetween parties in different industries. Therefore, the likelihood of a mergerwithdrawal should be greater when it involves parties in different industries.

STOCK is a dummy variable for stock-swap deals. When a bidder plans tofinance a merger with stock, target shareholders must accept ownership of the biddershares. Thus, the target owners share the benefits or adverse effects of a merger.Bidders may be less concerned about the chance of overpayment when using stockas opposed to when using cash. Consequently, withdrawals may be less likely whenthe planned medium of payment is stock.

COMPDEALS is the ratio of the number of completed deals to the total numberof bid announcements in the year. COMPDEALS is included because a firm’sdecision to pursue a merger may be affected by the consolidation trend in itsindustry. In an industry that is experiencing consolidation, firms are more prone tomerge with others to survive and stay competitive. We also measure the industryconsolidation trend by the natural logarithm of the total deal values of all completemergers in the bidder’s industry in the same year as a deal in consideration; however,we do not report the results for this alternative measure since they are qualitativelysimilar to results when using number of deals as the proxy.

MKRET is the return on the S&P 500 index in the month preceding theannouncement date of the bid. INDUSTRYRET is the equally-weighted returns ofall stocks in the target industry in the month preceding the announcement date of thebid. Since withdrawal probability may be affected by market and industryconditions, we in include market return (MKRET) and industry return (INDUS-TRYRET) variables to capture performance in the month preceding the announce-ment date of the bid. Withdrawal is expected to be more likely when recent marketand industry conditions are relatively weak.

We also account for the bidder’s financial leverage and the bid premiumproposed. The variable BIDDERDEBT is the bidder’s industry-adjusted debt-to-asset ratio in the year of the announcement of the merger proposal. A mergerproposal is more likely to be withdrawn when the bidder financial leverage isrelatively high. PREMIUM is the percentage difference between the deal value andthe target market capitalization the day preceding the bid announcement. PREMIUMis included because when the premium bid for the target is higher, the bidder is morelikely to withdraw the merger proposal to avoid overpayment and shareholders’criticism.

The logistic model above is estimated by maximizing the log-likelihood function.To avoid in-sample biasedness in our estimation of the probability for a deal to beterminated, we estimate the model using not only the 2,434 successful mergers and533 withdrawn mergers of publicly-traded targets in our sample, but also another7,763 out-of-sample successfully and withdrawn acquisitions of privately-heldtargets We refer to the estimation of the withdrawal probability by using the 10,730observations as non pair-matched design since the number of successful mergers isnot exactly matched with the number of withdrawn mergers. We also estimate themodel using a pair-matched design in which samples of equal numbers of successfulmergers and withdrawn mergers are employed. From our non-pair matched sample

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of successful mergers and withdrawn mergers, we match the withdrawn mergersubsample with five different subsamples of successful mergers of the same size,alternatively, which are randomly selected without replacements. By applying thepair-matching analysis five times (generating a separate matching sample for eachiteration), we can determine whether results are robust or are conditioned on thespecific matched sample that was chosen. To save space, we only report the resultsfrom the logistic regressions of non-pair matched sample.

In the second step of the Heckman two-step procedure, we obtain the predictedprobabilities of withdrawing a merger proposal from the logistic regression aboveand use it to calculate the self-selection correction term. The Heckman self-selectioncorrection term for WITHDRAW=1 and WITHDRAW=0 can be written as follows.

l ¼ WITHDRAW : 1þ eð�Xk bkΛÞ

� �ln 1þ eXkb

Λ

k � Xk bkΛ

� �� �þ

ð1�WITHDRAW Þ: eXkbΛ

k :Xk bkΛ� 1þ eXkb

Λ

K

� �ln 1þ eXk bk

� �� �

To avoid losing observations in the cross-sectional analyses of the withdrawalannouncement valuation effects, we estimate the probability of withdrawal (calledPROB) based on the estimated λ when applying the refined version of the logisticregression model to the non pair-matched sample.

5.3 Cross-sectional models measuring industry effects of merger withdrawals

To test the effects of the hypothesized factors that influence the industry effects ofmerger withdrawal announcements, we test the following two models:

WRPCARi ¼ a þ b1WTCARi þ b2MULTBIDþ b3HOSTILE þ b4RELATEDþ b5STOCKþb6COMPDEALS þ b7MKTRET þ b8INDUSTRYRET þ b9REJECT þ b10BIDDERDEBTþb11PREMIUM þ "i

ð1Þ

WRPCARi ¼ a þ b1WTCARi þ b2WITHPROBþ b3FINSYN þ b4CFLOW þ b5TANGþb6SIZE þ b7QRATIOþ b8ROE þ b9CONC þ "i

ð2ÞWTCAR is the abnormal return to the target rival portfolio stock in the window

(−1,+1) around the withdrawal announcement date, REJECT is a dummy variablefor merger proposals which are rejected by the target, and the other variables werealready defined. Model (1) tests for the impact of deal characteristics on industryeffects of merger proposal announcements. Model (2) tests for the impact of theprobability that the deal will ultimately be withdrawn on industry effects, whilecontrolling for other characteristics.

In Model (2), we include the variable WITHPROB that captures the probabilitythat a bid will ultimately be withdrawn, along with control variables. In this model,the dependent variable, WITHDRAWi, represents proposed mergers that wereultimately withdrawn.

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In Model (2), we include several control variables considered by Akhigbe andMadura (1999) in their analysis of factors that could affect the probability that a firmbecomes a target. These same variables could affect the likelihood of a mergerwithdrawal. FINSYN is the product between the relative size of the bidder and thedummy variable for horizontal mergers, which captures the potential financialsynergy expected from the merger. CFLOW is the median of the post-tax free cashflows of all rivals in the target industry, in which the post-tax free cash flow iscalculated using the following formula: OIBD�TAX�INTEXP�PFDDIV�COMDIV

EQUITY . OIBD isthe operating income before depreciation, TAX is the total income taxes minuschange in deferred taxes from the previous year to the current year, INTEXP is thegross interest expense on short and long-term debt, PFDDIV is the total dollaramount of preferred dividends and COMDIV is the total dollar amount of commonstock dividends.

TANG is the median ratio of net fixed assets to total assets of all rivals in thetarget industry. SIZE is the ratio of the median market capitalization of all rivals inthe target industry to the market capitalization of the target. QRATIO and ROE arethe median Tobin Q’s ratio and return on equity, respectively, of all rivals in thetarget industry. CONC is the dummy variable for horizontal mergers. When thecorresponding industry has a high level of monopoly power, the rivals are expectedto surrender more value when there is a higher industry concentration of collusionamong existing rivals, so that the coefficient of this variable is expected to bepositive. To the extent that a proposed merger increases the potential for collusion,the withdrawal of a merger should reduce the potential for collusion. Therefore, weanticipate weaker industry effects in response to a withdrawal announcement whenthe industry exhibits a greater degree of monopoly power. The variables CFLOW,TANG, SIZE, QRATIO, and ROE are calculated for the year preceding theannouncement of the bid in consideration.

6 Results

6.1 Estimated probability of merger withdrawal

As explained in the methodology section, to estimate the probability of a bid to beultimately withdrawn, we use the Heckman two-step procedures. The results of thelogistic regressions from which we estimate the withdrawal probability are presentedin Table 2. Results for four model specifications are provided to check forrobustness. The withdrawal probability is more likely for mergers that have multiplebidders, are hostile, and involve parties in the same industry. The withdrawalprobability is less likely when the deals are financed with stock (significant in threemodels) and when there are more completed mergers in the same year.

6.2 Industry effects due to merger withdrawal announcements

The valuation effects of the merger withdrawal announcements on the target and thetarget rival portfolios are reported in Table 3 and 4, respectively. Table 3 shows thattargets experience a three-day (−1, +1) CAR of −7.29% on average, which is

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significant. Panel A of Table 4 shows that the rival portfolios experience a three-dayCAR of −.19% on average, which is significant. This result differs distinctly fromthe study by Akhigbe et al. (2000), which found positive and significant results forrival portfolios. We more closely review the difference between our results and thoseof Akhigbe et al. (2000) by segmenting our sample into three sub-periods in Panel Bof Table 4. The sub-period 1987–1996 represents the sample period for Akhigbe etal. (2000), although our sample in that subperiod is larger than theirs for reasonsexplained earlier. For that sub-period, the rival portfolio CAR in the (−1, +1) daywindow is positive but not significant. However, in our earliest sub-period, the rivalportfolio CAR (−1, +1) is −.49% (significant at the.01 level), while in our mostrecent subperiod, the rival portfolio is −.23% (significant at the.01 level).

Overall, our results support our hypothesis that the merger withdrawal not onlyadversely affects the target, but also affects the target’s industry. This result impliesthat a withdrawal announcement contains an adverse signal about the target’scorresponding industry. The negative signal might be a reduction in the likelihood ofsubsequent takeovers within the industry, or a downward revision in the economicprospects of the industry. The mean negative industry effect in our sample reflects a

Table 2 Logistic regressions of the probability of a bid to be withdrawn

Independent variables Model 1 Model 2 Model 3 Model 4

Intercept 0.02 0.01 0.02 0.02

MULTBID 2.23**** 2.23**** 2.24**** 2.02****

HOSTILE 2.06**** 2.07**** 2.16**** 1.84****

RELATED 2.62**** 2.62**** 2.65**** 2.36****

STOCK −0.13* −0.12* −0.12 −0.25**COMPDEALS −0.0005*** −0.0004*** −0.0005*** 0.00

MKTRET −1.04 −0.28 −0.28 −1.90*INDUSTRYRET −0.70*BIDDERDEBT −0.06PREMIUM 0.00

Pseudo-R-squared 0.15 0.15 0.15 0.18

Percent of correct classification 64.40% 64.50% 63.50% 68.50%

Number of observations 10,730 10,730 9,363 4,386

This table reports the results from the logistic regressions to estimate the probability of a bid to beultimately withdrawn. The estimation of the model employs all complete and withdrawn mergers ofpublicly-traded targets and privately-held targets in SDC from 1980–2005 of which information about dealvalue is available. The dependent variable is a dummy variable for deals that are ultimately withdrawn.MUTLBID is a dummy variable for deals in which there are multiple bids. HOSTILE is a dummy variablefor hostile deals. RELATED is a dummy variable for deals made by firms in the same 4-digit SIC codes.STOCK is a dummy variable for deals in which stock is a form of payment. COMPDEALS is the ratio ofnumber of complete deals to the total number of deals announced in the year. MKTRET is the rate ofreturn on the S&P 500 index in the month preceding the announcement month. INDUSTRYRET is thereturn on the equally-weighted portfolios of all stocks in the same industry as the targets. BIDDERDEBTis the industry-adjusted ratio of total debt to total assets of the bidder at the end of fiscal year preceding theannouncement of the bid. PREMIUM is the percentage difference between the deal value and the target’smarket value the day before the bid announcement. *, **, *** and **** indicate the significance level of10%, 5%, 1% and 0.1%, respectively

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reduction of about 35% of the original positive industry effect at the time of theannounced merger proposal. Thus, an important contribution of our analysis is that theindustry signal at the time of the merger announcement is partially offset at the time ofthe withdrawal. It appears that the same underlying source of the signal at the time of theproposed merger is also influential at the time that the proposal is withdrawn.

However, our analysis of sub-periods clearly indicates that the specific resultsvary among withdrawal announcements. Therefore, we analyze the cross-sectionaldispersion in results in order to explain the variation in industry effects.

6.3 Cross-sectional analysis of the industry effects due to merger withdrawals

Results from a cross-sectional analysis of industry effects due to the mergerwithdrawal announcements are reported in Table 5. The CAR of the rival portfoliosupon merger withdrawal is positively related to the target’s CAR upon the

Table 3 CARs of the targets upon merger withdrawal announcements

Days N CARs Pos:Neg Patell Z-stat Signed-Rank Z-stat

−10 533 0.06% 246:287 1.06 0.67

−9 533 0.29% 248:285 2.48** 0.85

−8 533 −0.01% 231:302 1.26 −0.66−7 533 −0.16% 227:306 0.17 −1.01−6 532 0.31% 217:315 1.89* −1.86*−5 533 −0.07% 261:272 0.82 2.00**

−4 533 −0.68% 229:304 −3.47**** −0.83−3 532 −0.37% 231:301 0.36 −0.62−2 533 −0.48% 218:315 −3.14*** −1.81*−1 533 −0.18% 233:300 −1.26 −0.480 532 −4.49% 174:358 −24.44**** −5.69****1 525 −2.67% 204:321 −14.81**** −2.76***2 524 0.48% 228:296 1.53 −0.573 523 0.34% 234:289 1.27 0.00

4 523 −0.18% 223:300 −1.40 −0.985 522 −0.33% 202:320 −2.46** −2.83***6 522 −0.10% 226:296 −0.58 −0.677 521 −0.27% 229:292 −1.77* −0.368 522 0.07% 224:298 0.31 −0.859 519 0.06% 237:282 0.69 0.43

10 518 −0.12% 234:284 −0.56 0.20

(−2,+1) 533 −7.77% 166:367 −21.79**** −6.43****(−1,+1) 533 −7.29% 163:370 −23.37**** −6.70****

This table reports the cumulative abnormal returns (CARs) on the target upon the announcement of themerger withdrawals. CARs are estimated from the market model with CRSP equally-weighted index asthe market benchmark. The estimation period is from (−11,−180) days prior to the announcement date.Pos:Neg indicates the number of positive versus the number of negative CARs. *, **, *** and ****indicates the significance level of 10%, 5%, 1% and 0.1%, respectively

J Econ Finan (2012) 36:613–633 625

withdrawal announcement, which implies that the degree of the negative surprise tothe target’s corresponding industry is related to the degree of negative surprise to thetarget itself. This suggests that a portion of the adverse effects to the target at thetime of the withdrawal are attributed to a revision of the industry valuation. Whenconsidering this result along with the results in Table 4 described above, thewithdrawal announcement carries an industry signal for both the target and for itscorresponding rivals.

Notice that when we segment our results for cash and stock subsamples, theresults show a strong positive relationship between the target effect and industryeffect when stock was the planned method for financing the merger. However, therelationship between the target and industry effect is not significant when cash wasthe planned method of payment. Thus, the negative industry signal resulting from awithdrawal announcement is triggered when the merger proposal being withdrawnwas to be financed with stock. The other variables used in Model 1 are typically notsignificant. Thus, the key condition driving the variation in industry effects amongwithdrawn merger announcements is a relatively weak share price response of thetarget upon that announcement, and when stock was the planned method of paymentto finance the proposed merger.

When applying Model 2 to the sample of mergers that were ultimately withdrawn,the CARs of the rival portfolios are positively and significantly related to the target

Table 4 CARs of the rival portfolios of the targets upon merger withdrawal announcements

Year 1980–1986

Windows N CAR Pos:neg Patell Z-stat Signed-Rank Z-stat

(−2,+1) 49 −0.41% 20:29 −1.39 −1.31(−1,+1) 49 −0.49% 15:34 −1.90* −2.74***Year 1987–1996

Windows N CAR Pos:neg Patell Z-stat Signed-Rank Z-stat

(−2,+1) 269 0.08% 131:138 0.48 0.68

(−1,+1) 269 0.12% 135:134 0.98 0.19

Year 1997–2005

Windows N CAR Pos:neg Patell Z-stat Signed-Rank Z-stat

(−2,+1) 215 −0.14% 100:115 −1.38 −1.17(−1,+1) 215 −0.23% 88:127 −1.99** −2.81***Whole sample period

Windows N CAR Pos:neg Patell Z-stat Signed-Rank Z-stat

(−2,+1) 533 −0.14% 251:282 −1.64 −1.63(−1,+1) 533 −0.20% 238:295 −2.57** −2.76***

This table reports the cumulative abnormal returns (CARs) on the rival portfolios of the targets upon theannouncement of the bid withdrawal. The rival portfolios are equally-weighted portfolios of the rivals inthe same 4-digit SIC codes as the targets. CARs are estimated from the market model with CRSP equally-weighted index as the market benchmark. The estimation period is from (−11,−180) days prior to theannouncement date. We report the results separately for the subsample of rival portfolios of targets inwithdrawn mergers and for the subsample of rival portfolios of targets in complete mergers. Pos:Negindicates the number of positive versus the number of negative CARs. *, **, *** and **** indicates thesignificance level of 10%, 5%, 1% and 0.1%, respectively

626 J Econ Finan (2012) 36:613–633

CAR. However, when assessing subsamples, the relationship is not significant forthe subsample of cash mergers. This result is consistent with that found whenapplying Model 1, and reinforces the conclusion that negative industry effects aremore pronounced when the target experiences a weaker share price response andwhen stock was the planned method of payment to finance the proposed merger.

When applying Model 2, we also find that the FINSYN variable is negative andsignificant. This finding suggests that negative industry effects are more pronouncedwhen the proposed merger offered a higher degree of potential financial synergy.Thus, the greater potential at the time of the merger announcement, the morenegative shock is at the time that the proposed merger is withdrawn. The cash flowand ROE variables were also significant in one model specification, but notconsistently across alternative model specifications.

6.4 Cross-sectional analysis of effects on individual rivals

The cross-sectional analyses of the valuation effects are reported for individual rivalsin Table 6. The goodness of fit of the cross-sectional models for individual rivalvaluation effects is lower than that of the models for rival portfolio valuation effects,but the models are significant based on the F-statistic. When applying Model 1, thevaluation effects of the merger proposal announcements on individual rivals arepositively related to the target’s CARs, (especially when stock is planned as themedium of payment), which reflects more pronounced negative individual rivaleffects when the target experiences a weaker share price response to the announcedmerger withdrawal. There is also some evidence of a positive relationship(significant in three of five models) between the premium offered to the target andthe individual rival effect at the time of the announcement.

When applying Model 2, the results again reinforce the more pronouncednegative individual rival effects when the target experiences a weaker share priceresponse to the announced merger withdrawal. In addition, the negative individualrival effects are less pronounced for rivals that have relatively high Q ratios.

7 Summary

We analyze industry effects in response to announcements of withdrawn mergers,using a large sample (533 announcements) that are spread over a large time span,and find negative and significant industry effects. Our result differs distinctly fromthat of the study by Akhigbe et al. (2000), who found positive industry effects. Wemore closely compare our results to Akhigbe et al. (2000) by separating our sampleinto three sub-periods, one of which overlaps their sample. We find that positive (butnot significant) industry effects in that period (1987–1996). However, we find strongnegative industry effects in an earlier subperiod (1980–1986) and during a morerecent sub-period (1997–2005).

We find that the industry effects reflect a partial correction (estimated to be 35%on average) of the initial industry response at the merger announcement. Thus, animportant contribution of our analysis is that the industry signal at the time of themerger announcement is partially offset at the time of the withdrawal. Just as the

J Econ Finan (2012) 36:613–633 627

Tab

le5

CSanalyses

ofCARof

rivalportfolio

supon

mergerwith

draw

alannouncement

Indep.

Vars

Whole

sample

Horizontalmergersample

Non-horizontalmergersample

Cashsample

Stock

sample

Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2

Par.

Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

Intercept

0.04

0.29

0.03

0.15

0.01

0.38

0.01

−0.29

0.01

0.73

0.01

0.2

0.01

−1.06

0.01*

1.66

0.01

0.11

0.03

−0.60

TARGETCAR

0.29***

2.74

0.23****

4.39

0.31**

2.06

0.21*

1.81

0.32***

2.55

0.22***

3.06

−0.03

−0.15

0.23

1.26

0.29***

2.59

0.24***

3.45

MULT

BID

0.00

−0.01

−0.02

−0.19

0.01

0.11

0.11

0.69

0.00

−0.03

HOSTILE

0.08

1.13

−0.02

−0.14

0.05

0.65

0.01

0.08

0.09

1.14

RELATED

−0.04

−0.51

0.03

0.06

−0.03

−0.23

−0.19**

−2.21

−0.01

−0.17

0.05

0.93

STOCK

−0.03

−0.6

0.03

0.32

−0.17***

−2.46

−0.16

−1.23

DURATIO

N−0

.04

−0.75

−0.09

−1.22

0.08

1.13

0.21**

2.05

−0.03

−0.48

COMPDEALS

0.08

0.94

−0.08

−0.66

0.18**

1.97

−0.06

−0.42

0.05

0.58

MKTRET

−0.01

−0.15

−0.08

−0.66

0.09

1.02

−0.56****

−4.47

0.00

−0.05

INDUSTRYRET

0.12

1.11

−0.04

−0.41

0.19

1.52

−0.03

−0.19

0.16**

2.33

REJECT

−0.06

−0.98

0.06

0.71

−0.23****

−6.22

0.29***

3.24

−0.05

−0.71

BID

DERDEBT

−0.03

−0.41

−0.09

−0.64

−0.02

−0.19

−0.39***

−2.61

−0.06

−0.70

PREMIU

M−0

.02

−0.19

0.09

0.58

−0.12

−1.01

0.00

−0.03

WITHPROB

−0.01

−0.21

0.01

0.19

0.01

0.07

−0.04

−0.46

0.00

0.04

FIN

SYN

−0.07**

−1.98

−0.34****

−4.53

−0.03***

−2.54

628 J Econ Finan (2012) 36:613–633

Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2

Par.

Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.

Est.

t-stat

Par.Est.

t-stat

CFLOW

0.08

1.25

−0.01

−0.09

0.13**

2.4

0.34

1.33

0.02

0.34

TANG

0.06

0.05

0.13*

1.77

−0.06

−0.96

−0.14

−0.96

0.03

0.59

SIZE

0.04

0.84

0.06

0.59

0.03

0.7

0.06

0.31

0.04

0.89

QRATIO

−0.03

−0.45

0.02

0.26

−0.05

−0.59

−0.15

−1.08

−0.01

−0.12

ROE

−0.10

−1.48

−0.19

−1.17

−0.09

−1.22

−0.31**

−2.01

−0.07

−0.81

F-statistics

2.87**

3.95***

0.98

3.04**

2.71***

2.39**

2.93**

2.40**

3.93***

375***

Adj-R

squared

0.06

0.05

0.01

0.05

0.17

0.04

0.21

0.06

0.06

0.05

N533

533

178

178

355

355

5965

451

468

Thistablereportstheresults

from

thecross-sectionalanalyses

ofthecumulativeabnorm

alreturns(CARs)

oftherivalportfolio

sof

thetargetsupon

themergerwith

draw

alannouncement.CARsareestim

ated

from

themarketm

odelwith

CRSPequally

-weightedindexas

themarketb

enchmark.

The

estim

ationperiod

isfrom

(−11,−18

0)days

priorto

theannoun

cementdate.W

erepo

rttheresults

separately

forthewho

lesample(inPanelA),forthesubsam

pleof

completemergers(inPanelB)andforthesubsam

pleof

mergers

thatareultim

atelywith

draw

n(inPanelC).Wealso

furtherpartition

theresults

bywhether

thedealsinvo

lvefirm

sin

thesameindu

stry

(horizontald

eals)andby

whether

thedeals

arepaid

with

cash

only

ineach

ofthepanels.T

hedependentv

ariableistheCARsof

therivalp

ortfoliosof

thetargetsin

the(−1,+1)

daywindo

waround

theanno

uncemento

fthe

bid.

TARGETCARistheCARof

thetargetin

the(−1,+1)

daywindowaround

theannouncemento

fthebid.

MUTLBID

isadummyvariablefordealsin

which

therearemultip

lebids.HOSTILEisadummyvariable

forhostile

deals.RELATED

isadummyvariable

fordealsmadeby

firm

sin

thesame4-digitSIC

codes.STOCK

isadummyvariable

for

dealsin

which

stockisaform

ofpaym

ent.DURATIO

Nisthenu

mberof

days

sincetheannoun

cementof

thebid.

COMPDEALSistheratio

ofnu

mberof

completedealsto

the

totaln

umberof

dealsannoun

cedin

theyear.M

KTRETistherateof

return

ontheS&P50

0indexin

themon

thprecedingtheannoun

cementm

onth.INDUSTRYRETisthereturn

ontheequally

-weigh

tedpo

rtfolio

sof

allstocks

inthesameindu

stry

asthetargets.REJECTistheinteractionterm

betweenthedu

mmyvariablefordealspaid

with

stockandthe

dummyvariablefordealsrejected

bythetarget.B

IDDERDEBTistheratio

ofthebidder’stotaldebt

tototalassetsattheendof

thefiscalyear

precedingtheanno

uncementd

ate.

PREMIU

Mis

thepercentage

difference

betweenthedeal

valueandthetarget’s

marketcapitalizationthedaypriorto

theannouncementdate

ofthebid.

WITHPROB

isthe

estim

ated

probability

ofthedeal

tobe

ultim

atelywith

draw

nthat

isobtained

from

model

1in

Table2.

FIN

SYN

istheprod

uctbetweentheratio

ofthebidd

er’s

market

capitalizationto

thedealvalueandthedu

mmyvariableforno

n-ho

rizontaldeals.CFLOW

isthemedianpo

st-tax

free

cash

flow

ofallrivalsin

theindu

stry

correspo

ndingto

the

target.T

ANGisthemedianratio

ofnetfixedassetsto

totalassetsof

allrivalsin

theindu

stry

correspo

ndingto

thetarget.S

IZEistheratio

ofthemedianmarketcapitalizationof

allrivalsin

thetargetindu

stry

tothetarget’smarketcapitalization.QRATIO

isthemedianTo

bin’sQratio

ofallrivalsin

thetargetindu

stry.R

OEisthemedianreturn

onequity

ofallrivalsin

thetargetindu

stry.*

,**,

***and**

**indicatethesign

ificance

levelo

f10

%,5

%,1

%and0.1%

,respectively.The

t-statisticsarecalculated

usingheteroskedasticity

-consistent

standard

errors

J Econ Finan (2012) 36:613–633 629

Tab

le6

CSanalyses

ofCARof

individual

rivalsupon

mergerwith

draw

alannouncement

Indep.

Vars

Whole

sample

Horizontalmergersample

Non-horizontalmergersample

Cashsample

Stock

sample

Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Intercept

0.01

0.52

0.00

0.16

0.01

−1.16

0.00

0.56

0.01*

1.75

0.00

−0.38

0.01

0.83

0.01

1.55

0.01

0.51

0.01

0.24

TARGETCAR

0.03***

2.87

0.02**

2.21

0.02**

2.44

0.00

0.10

0.05***

2.60

0.03**

2.42

0.01

0.28

0.05**

2.16

0.03***

2.72

0.02**

2.15

MULT

BID

0.00

−0.05

0.02

1.24

−0.02*

−1.69

−0.02

−0.35

0.00

0.39

HOSTILE

0.01

1.42

−0.01

−1.17

0.01

0.77

−0.03

−0.57

0.01

1.15

HORIZONTA

L−0

.01

−1.19

0.00

0.52

−0.01

−0.35

−0.03*

−1.76

−0.01

−0.86

0.01

0.80

STOCK

0.00

0.34

−0.01

−1.30

−0.02*

−1.82

DURATIO

N−0

.01

−0.70

−0.04***

−3.26

0.04***

3.34

−0.09*

−1.71

0.00

0.21

COMPDEALS

−0.01

−1.00

0.01

0.77

−0.02

−1.05

0.00

0.01

−0.01

−0.90

MKTRET

−0.02

−1.02

−0.03

−1.61

0.03

1.41

−0.14***

−3.10

−0.01

−0.42

INDUSTRYRET

0.02**

2.06

0.00

−0.15

0.04**

2.04

−0.07

−1.44

0.02

1.45

REJECT

−0.01

−1.32

0.00

−0.16

−0.05****

−4.10

−0.04

−1.04

−0.01

−1.16

BID

DERDEBT

0.00

0.29

0.02

1.44

−0.01

−0.71

0.04

0.82

0.00

−0.19

PREMIU

M0.02**

2.3**

0.06****

4.18

−0.01

−0.71

−0.11*

−1.77

0.03**

2.36

WITHPROB

0.01**

2.12

0.01

1.43

0.01

0.90

0.01

0.68

0.01

1.48

FIN

SYN

0.01

0.32

−0.01

−0.49

630 J Econ Finan (2012) 36:613–633

Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2Model

1Model

2

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

Par.Est.

t-stat

CFLOW

0.02

0.14

0.15

0.58

0.02

1.03

0.04

0.70

0.09

0.53

TANG

0.02**

2.09

0.01

1.55

−0.01

−1.50

−0.03**

−2.07

0.02***

2.78

SIZE

−0.02*

−1.86

−0.02**

−2.34

0.00

0.36

0.00

−0.11

−0.02**

−2.27

QRATIO

0.01****

26.86

0.01****

24.46

0.01

0.86

−0.03

−1.38

0.01****

24.81

ROE

0.02

0.15

0.15

0.59

−0.03

−1.13

−0.07

−1.04

0.09

0.54

F-statistics

1.94*

2.29**

2.82***

1.49

2.90***

2.47**

4.75****

1.81*

1.86**

2.78***

Adj-R

squared

0.01

0.02

0.03

0.01

0.05

0.1

0.13

0.12

0.01

0.02

N23,085

26,704

13,839

13,727

9,246

12,977

2,823

4,552

20,262

22,152

Thistablereportstheresults

from

thecross-sectionalanalyses

ofthecumulativeabnorm

alreturns(CARs)

oftheindividual

rivals

ofthetargetsup

onthemergerproposal

announcementin

Panel

Aandupon

with

draw

alannouncementin

Panel

B.CARsareestim

ated

from

themarketmodel

with

CRSPequally

-weightedindexas

themarket

benchm

ark.

The

estim

ationperiod

isfrom

(−11,−18

0)days

priorto

theanno

uncementdate.The

depend

entvariable

istheCARsof

therivalsof

thetargetsin

the(−1,+1)

day

windo

warou

ndtheanno

uncementof

thebid(inPanel

A)andarou

ndthewith

draw

alannoun

cement(inPanel

B).TA

RGETCAR

istheCARof

thetarget

inthe(−1,+1)

day

window

around

theannouncementof

thebid.

MUTLBID

isadummyvariable

fordealsin

which

therearemultip

lebids.HOSTILEis

adummyvariable

forhostile

deals.

RELATEDisadummyvariablefordealsmadeby

firm

sin

thesame4-digitS

ICcodes.STOCKisadummyvariablefordealsin

which

stockisaform

ofpaym

ent.DURATIO

Nisthenu

mberof

days

sincetheannoun

cemento

fthebid.

COMPDEALSistheratio

ofnu

mberof

completedealsto

thetotaln

umberof

dealsannoun

cedin

theyear.M

KTRETis

therate

ofreturn

ontheS&P500indexin

themonth

precedingtheannouncementmonth.IN

DUSTRYRETisthereturn

ontheequally

-weightedportfolio

sof

allstocks

inthe

sameindu

stry

asthetargets.REJECTis

theinteractionterm

betweenthedu

mmyvariable

fordealspaid

with

stockandthedu

mmyvariable

fordealsrejected

bythetarget.

BID

DERDEBTistheratio

ofthebidder’stotald

ebttototalassetsattheendof

thefiscalyear

precedingtheannoun

cementd

ate.PREMIU

Misthepercentage

difference

between

thedeal

valueandthetarget’s

marketcapitalizationthedaypriorto

theannouncementdate

ofthebid.

WITHPROB

istheestim

ated

probability

ofthedeal

tobe

ultim

ately

with

draw

nthatisobtained

from

model1in

Table2.

FIN

SYNistheproductbetweentheratio

ofthebidder’smarketcapitalizationto

thedealvalueandthedummyvariablefor

non-horizontaldeals.CFLOW

isthemedianpost-tax

free

cash

flow

ofallrivalsin

theindustry

correspondingto

thetarget.T

ANG

isthemedianratio

ofnetfixedassetsto

total

assets

ofallrivals

intheindustry

correspondingto

thetarget.SIZEis

theratio

ofthemedianmarketcapitalizationof

allrivals

inthetarget

industry

tothetarget’s

market

capitalization.

QRATIO

isthemedianTo

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**indicate

thesignificance

levelof

10%,5%

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ely.

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J Econ Finan (2012) 36:613–633 631

announced merger proposal could have signaled either a possible increase in mergeractivity within the industry or more favorable industry prospects, the withdrawal ofthe merger could have signaled either a decrease in possible merger activity withinthe industry or unfavorable industry prospects.

Since our results vary among our total sample, we also assess the cross-sectionalvariation of industry effects at the time of merger withdrawal.. We find that theindustry effects at the time of the announced withdrawal are worse when the targetexperiences a weaker stock price response. This suggests that the negative surpriseto the target’s corresponding industry at the time of the merger withdrawal isrelated to the negative surprise to the target itself. A closer examination shows thatthis relationship is especially pronounced when stock is the planned method ofpayment for the merger proposal. We also conduct a cross-sectional analysis ofindividual rival firms (within the corresponding industry), and find that theindividual rival effects are more negatively affected upon the withdrawalannouncement when the share price response of the corresponding target isweaker, especially when stock is the planned medium of payment. This result isconsistent with cross-sectional analysis of industry effects. Whether the underlyingnegative impact on the target firm is due to a reduced likelihood of a futuretakeover or a signal about weaker industry prospects, the impact on the targetcarries over to the industry rivals.

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