One size does not fit all: Selling firms to private equity versus strategic acquirers

Journal of Corporate Finance 18 (2012) 828–848

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Journal of Corporate Finance journal homepage: www.elsevier.com/locate/jcorpfin

One size does not fit all: Selling firms to private equity versus strategic acquirers Jana P. Fidrmuc a,⁎, Peter Roosenboom b, Richard Paap c, Tim Teunissen b a b c

Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom Department of Financial Management, Rotterdam School of Management, Erasmus University, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands

a r t i c l e

i n f o

Article history: Received 6 April 2011 Received in revised form 15 May 2012 Accepted 11 June 2012 Available online 21 June 2012 JEL classification: G32 G34

Keywords: Private equity Takeover premium Auctions

a b s t r a c t This paper investigates the selling process of firms acquired by private equity versus strategic buyers. In a single regression setup we show that selling firms choose between formal auctions, controlled sales and private negotiations to fit their firm and deal characteristics including profitability, R&D, deal initiation and type of the eventual acquirer (private equity or strategic buyer). At the same time, a regression model determining the buyer type shows that private equity buyers pursue targets that have more tangible assets, lower market-to-book ratios and lower research and development expenses relative to targets bought by strategic buyers. To reflect possible interdependencies between these two choices and their impact on takeover premium, as a last step, we estimate a simultaneous model that includes the selling mechanism choice, buyer type and premium equations. Our results show that the primary decision within the whole selling process is the target firm's decision concerning whether to sell the firm in an auction, controlled sale or negotiation which then affects the buyer type. These two decisions seem to be optimal as then they do not impact premium. © 2012 Elsevier B.V. All rights reserved.

1. Introduction In the past decade private equity firms have been a strong driving force in the market for mergers and acquisitions (Cumming et al., 2007). The increased firepower of private equity firms has brought even larger public companies within their reach. Recent studies have shown that private equity bidders offer on average significantly lower takeover premiums than corporate buyers even though, private equity firms often manage to outbid public corporate acquirers in competitive auctions (Bargeron et al., 2008; Officer et al., 2010; Dittmar et al., forthcoming). In this paper we show that premium determination is just one part of a wider and complex selling process that starts with deal initiation. Target companies design the selling process such that it fits their specific firm situation that is reflected in their firm characteristics: we show that different firms are sold in auctions versus controlled sales versus private negotiations and this then impacts on whether they are sold to private equity versus strategic (corporate) buyers. Accounting for the whole selling process that includes the choice of the selling mechanism (auction versus controlled sale versus private negotiation), bidder type (private equity versus strategic buyer) and premium in a simultaneous model, we learn more about interdependencies within the system, premium determination and sequencing of the process. The selling process usually starts by either a prospective buyer approaching a target or by a target management decision to offer their company for sale. In general, the selling company management and its financial advisor could negotiate the deal privately with an exclusive buyer or alternatively negotiate with multiple bidders (Hansen, 2001; Povel and Singh, 2006; Boone and Mulherin, 2007). A negotiation with multiple bidders may either be formally structured in a full-scale formal auction or, ⁎ Corresponding author. Tel.: + 44 24 7652 2210. E-mail addresses: [email protected] (J.P. Fidrmuc), [email protected] (P. Roosenboom), [email protected] (R. Paap), [email protected] (T. Teunissen). 0929-1199/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jcorpfin.2012.06.006

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alternatively, the selling party discretely canvasses demand from a select number of bidders in a controlled sale (Boone and Mulherin, 2009). Boone and Mulherin (2007) are the first to analyze in detail the private selling process that evolves prior to the public announcement of the takeover bid. They show that about half of targets in their sample are auctioned among multiple bidders and that the public takeover activity analyzed in the literature so far reflects only the tip of the iceberg of actual takeover competition. In their further work (Boone and Mulherin, 2009), they distinguish controlled sales versus full-scaled auctions as two possible approaches for competitive bidding and argue that the rule of ‘one size does not fit all’ applies to the selling mechanism choice. After a deal is initiated, the selling firm management has to decide on the best way to conduct the sale taking into consideration the overall firm situation. Management has superior knowledge about the firm, its prospects and potential and should take into account relevant firm characteristics, deal initiation, preferred potential buyer (or at least its type) and also the overall pool of potential bidders. 1 In this sense, the choice of the selling mechanism might be determined endogeneously together with the preferred identity of the buyer and depends on the superior knowledge of the selling firm management. Therefore, we propose that the choice of the selling mechanism reflects extra information on top of information covered by observable target and deal characteristics and its inclusion into the analysis is essential. Choosing the preferred buyer is also an important part of the process. In fact, Elliot Williams of Mirus Capital advises (Williams, 2007, p.1): “… [The selling company] should understand that selling to a private equity firm is not the same as selling to a strategic buyer. Every aspect of the deal [is] affected by the type of buyer including the negotiating process, price, tax and legal implication and most importantly the future prospects of the company.” It is therefore important to recognize the differing nature of the private equity versus strategic buyers. Strategic buyers are usually other firms in the industry who are likely to pay higher premium because they redeploy the assets of the target firms close to their best use (Shleifer and Vishny, 1992; Gorbenko and Malenko, 2009). Strategic buyers can also afford to pay more because they buy specific assets and will benefit from synergies between their organization and the target firm. In contrast, private equity buyers are usually industry outsiders who typically cannot manage the bought targets well themselves and so face agency costs as they have to hire specialists to run the assets for them. They fear overpaying for the target because as outsiders they do not have the knowledge to value the assets precisely (Shleifer and Vishny, 1992) and are expected to pay less than is the value of the target firm's assets in best use. 2 At the same time, sale to a private equity buyer allows the incumbent management to continue to manage and partially own the company and profit from further growth in company value (Lehn and Poulsen, 1989; Lehn et al., 1990; Dittmar et al., forthcoming). In contrast, strategic acquisitions often integrate acquired assets with existing operations of the new owner. These differences in nature between strategic versus private equity buyers highlight the importance of the buyer identity in the selling process. Moreover, these differences may also indicate that different target firms prefer a different type of buyer or vice versa which may result in segmented bidding where private equity and strategic buyers do not compete for the same target. Using a sample of 205 private equity deals of listed US targets over the period from 1997 to 2006 matched with comparable deals by strategic (corporate) buyers this paper makes three important contributions. First, we analyze which targets eventually end up with private equity versus strategic buyers in a single regression setup without analyzing the selling mechanism choice or premium determination. The analysis shows that, everything else keeping constant, the two buyer types end up purchasing different targets. Target initiated deals with low market to book values and high cash levels end up more frequently with private equity buyers. Targets of strategic buyers, in contrast, have higher market to book ratios, more intangible assets and high R&D expenses. These results are in line with Gorbenko and Malenko (2009) who also show that public and financial bidders' valuations depend on target characteristics. Strategic buyers tend to value research and development expenses and intangible assets such as growth options. Our results also suggest that strategic buyers are interested in targets with more specific assets that might potentially result in higher synergies whereas private equity buyers target firms with more generally redeployable assets that they can manage more efficiently (Shleifer and Vishny, 1992). Our second contribution is the analysis of the selling mechanism choice, again in a single regression setup. Our conjecture is that choosing a particular selling mechanism is an important strategic decision that fits a company situation at a given time. To start with, our data show that the selling mechanism choice is indeed not random: more profitable firms with lower leverage are typically sold in auctions rather than in controlled sales or private negotiations. Auctions are also associated with private equity buyers and deals initiated by target firm's management. Buyer initiated deals are most likely to be sold in private negotiations. Higher R&D increases the odds of controlled sales. Moreover, higher M&A activity and lower impediments to takeovers are associated with higher odds of auctions. The two sets of results, however, suggest endogeneity between the selling mechanism and buyer type choices that might also eventually impact takeover premiums. Therefore, as our third and most important contribution, we simultaneously model the whole takeover process taking into account the selling mechanism choice, the buyer type choice and the premium determination. The main advantage of a simultaneous model is that it takes into account potential interdependencies between the dependent variables and empirically determines sequencing of the overall process. We conjecture a mutual interrelation between the choice

1 Target management decides about the sale design also in the case when a potential buyer approaches the target and initiates the deal: management can either negotiate privately, approach a limited number of bidders or organize a full-scale auction. In our data set, auctions constitute 15% of all buyer initiated deals. 2 We would like to note that these general differences between private equity versus strategic buyers do not apply universally. Some private equity firms pursue a strategy of acquiring multiple firms in the same industry, which can lead to synergies. In the premium analysis below, we try to control for this effect using a dummy variable for acquisitions made by portfolio firms of private equity firms.

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of the selling mechanism and the choice of the buyer type and propose that these two choices then impact the premium. However, only by estimating the system, we are able to determine the significant feedback effects and so learn about the sequencing. Our system results suggest the following simple sequencing of the overall selling process. The starting point is the decision on how to sell a firm that best matches the firm's characteristics. That is, when deciding how to sell their firm, managers take into account the firm situation: target initiated deals with better accounting profitability are more likely to be sold in auctions. Target initiation and profitability increase also the odds of controlled sales, but initiation impacts auctions significantly more than controlled sales. The selling mechanism choice then affects the buyer type. In particular, firms sold in auctions are less likely, while those sold in controlled sales are more likely to be sold to private equity buyers (both relative to private negotiations). Taking into account this feedback effect between the selling mechanism and buyer type, the reduced form parameters in the buyer type equation are all insignificant and show that firm characteristics cease to matter for the buyer type in equilibrium. So, firm characteristics affect the choice of buyer type only indirectly through the auction mechanism. Our system results also show that the effects of the selling mechanism and the buyer type on the premium are not significant. In other words, whether a firm is sold in an auction or negotiation, or it is sold to a private equity or strategic buyer is irrelevant for premium determination. A significant coefficient, for example a negative coefficient for the auction variable would indicate that, on average, firms opting for auctions would be better off choosing private negotiations. As a result, our insignificant coefficients indicate optimality of these choices with respect to premium given firm characteristics. Despite these optimal choices, firm characteristics still directly impact the premium: profitable and lower market to book firms get on average higher premiums. Interestingly, target initiation is not significant. As exogenous factors excluded from the other equations, also poor stock performance and analyst coverage increase premiums. In summary, as a part of the overall selling process the firm decision whether to sell in an auction, controlled sale or private negotiation is important as it also affects the buyer type. Our results indicate that the choices of the selling mechanism and buyer type are optimal with respect to the premium received in the deal, but firm characteristics still do matter. Our paper relates to several recent studies that compare takeover premiums between private equity versus public/strategic bidders. Bargeron et al. (2008) attribute the lower takeover premiums to private equity bidders being more selective in the price they are willing to pay for targets than public strategic bidders. They argue that managers of public bidders have an empirebuilding mentality and are willing to overpay for a target firm because they do not bear the full costs of their decisions. At the same time, Bargeron et al. (2008) show that other observable target or transaction characteristics cannot explain the large differences in premiums paid. Officer et al. (2010) show that target shareholders receive a lower premium in case two or more private equity investors join forces to acquire the target firm in consortium deals, although Boone and Mulherin (2011) do not corroborate this finding using a sample that includes smaller private equity firms and longer event windows. Dittmar et al. (forthcoming) analyze bidding competition faced by strategic buyers. Even though premium differences for private equity versus strategic buyers are not the main focus of their paper, in line with previous empirical evidence they show that premiums paid by strategic buyers following competition from financial buyers are on average lower relative to premiums after competition with other strategic buyers. Moreover, they confirm that observable target and deal characteristics cannot explain the difference in the premiums offered. In this paper, we use information on the private selling process and show that the selling mechanism choice is an important firm decision that also affects the type of the eventual acquirer. Using a simultaneous system that empirically determines the sequencing of the whole selling process, we make a wider contribution to the literature beyond explaining differences in premium. The remainder of the paper is organized as follows. Section 2 discusses our data collection and the resulting sample. Section 3 presents our results and Section 4 concludes. 2. Sample 2.1. Sample selection As the main focus of this paper is a comparison of acquisitions by private equity versus strategic buyers, our data collection starts by searching for takeovers by private equity firms in the US. We search through all takeovers of public US targets within the Securities Data Corporation (SDC) database over the period from January 1997 through December 2006 where acquirers seek to fully own the target company. As a first step, we use the ‘acquirer is a leveraged buyout firm’ flag, ‘acquirer is a financial sponsor’ flag and ‘acquirer is an investor group’ flag. Then, for each of the deals we also read the short acquirer description and deal synopsis to check that the acquirer is indeed a private equity firm. We also require that target firms have data available on CRSP and Compustat. This process results in a sample of 205 attempted takeovers by private equity investors of which 197 were completed and 8 were withdrawn. We include withdrawn transactions to avoid biasing our sample in any way. 51 acquisitions involve private equity consortia with two or more private equity firms acquiring the target company. Some private equity firms pursue a buy-and-build strategy of acquiring multiple firms in the same industry and then merging these firms together. We identify 53 deals in which a portfolio firm that is majority owned by a private equity investor acquired the target. The sample of 205 private equity takeovers is then matched firm by firm with takeovers by strategic acquirers based on the target industry, year of announcement and deal size. Our matching procedure involves the following steps: (i) For every transaction in the private equity sample we search for a set of takeovers by strategic buyers where the target company has the same first three SIC code digits as the private equity target. In this list, we attempt to find a matching transaction that was

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Table 1 Sample by year. This table reports the number of deals in our sample per year of the SDC announcement date. Our sample period covers 1997–2006, but the matching procedure results in 4 strategic deals falling into the year 2007. Year

Private equity buyer

Strategic buyer

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Total

18 28 27 21 11 10 16 17 25 32 0 205

16 32 28 15 14 17 14 20 26 19 4 205

announced in the same year and comes closest in terms of transaction value, using a +/− 25% error range. (ii) If there is no comparable transaction found in the same year and/or with the same transaction value, the same search is applied to the year before and the year after the year of announcement. (iii) If no match is found the year before and the year after in step (ii), we widen the search to two years before and two years after the year of announcement. (iv) If we still do not have a match, we repeat the search in step (i) but looking for strategic buyers with a target company within same two SIC code digits as the private equity target. (v) In a rare occasion that this process still renders no results, we repeat the search in steps (i)-(iv) for transactions with a +/− 50% error range. (vi) As a last resort we repeat step (i) at the first SIC code level. Every strategic deal can be matched to a private equity deal only once. We end up with 205 strategic takeovers that are exclusively matched to our 205 private equity deals based on industry, deal size and deal year. Table 1 shows the distribution of deals over time. We consider the matching procedure to be a key feature of our research design. Matching on industry is important due to the fact that private equity bidders are typically interested in firms coming from particular industries with stable cash flows and substantial fixed assets that can serve as collateral for the loans used to finance the acquisition. Boone and Mulherin (2008b), for example, report that more than half of the private equity takeovers occur in only four industries. Matching on size is also important. Typically, strategic/public buyers are able to target larger companies (Bargeron et al., 2008) and the same is the case for private equity consortium deals (Officer et al., 2010; Boone and Mulherin, 2011). Finally, frequent observations of tougher deal competition in later years of our sample in the popular press highlight the importance of matching in time (Officer et al., 2010). As a result of the matching procedure, our sample consists of 410 takeovers of listed US targets. Table 2 Panel A shows that the mean (median) deal size of the strategic buyers sample is $611 million ($131 million) and is comparable to the deal size of the private equity buyers of $654 million ($139 million). The differences in means are statistically insignificant. Target total assets are also comparable across the two subsamples. The premium offered to the target shareholders relative to the stock price eight weeks before the deal announcement in SDC (SDC premium) is 43% versus 48% for private equity versus strategic buyers, respectively. The difference is however, contrary to previous findings (Bargeron et al., 2008; Officer et al., 2010), not statistically significantly different from zero. This is a direct consequence of our matching procedure based on industry, deal size and time that results in more comparable deals across private equity versus strategic buyers. We have to control for possible biases stemming from the fact that auctions take usually longer to organize, are more likely to have information leakage prior to the formal deal announcement and therefore are more likely to be associated with larger stock price run-ups and smaller premiums (Boone and Mulherin, 2011). We carefully check for possible leakage of information before the announcement reported in SDC. We follow the procedure adopted in Boone and Mulherin (2011). First, for each deal we check the SEC documents for the date when the target firm starts considering a sale (private date). Then, deal by deal we carefully check Factiva for any leakage of information concerning a possible M&A deal (initial public announcement) in the period between the private date and the SDC announcement date. We find that targets of private equity versus strategic bidders are more likely to leak information. On average, the initial public announcement is 122 days before the SDC announcement date for targets eventually sold to private equity buyer relative to 102 days for strategic buyers, a difference of 20 working days. As a result, we benchmark the offer price against the price on the base date that is either the date eight weeks (42 trading days) before the SDC announcement date or one trading day before the Factiva announcement whichever is earlier. The base date is earlier than 8 weeks before the SDC announcement date for 61 private equity and 40 strategic bidder deals. This is our primary premium measure, which we refer to as the adjusted premium or premium. 3

3 We believe that our adjustment for leakage of information is reasonable. Alternatively, we could benchmark the offer price against the stock price 8 weeks before the Factiva initial public announcement date. We however feel that going too far back in time might be associated with other biases. For example a very large lag in time might increase the probability that other important information is communicated to the market and contaminates prices. Also, going back eight weeks from the Factiva announcement date would mean that the time lag between the offer price at the SDC announcement date and the benchmark price would be more than 16 weeks for some deals but only 8 weeks for other deals resulting in larger time inconsistency. Our adjusted premium minimizes this inconsistency while still taking into account the leakage of information.

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Table 2 Private equity versus strategic buyers. This table presents estimation results of a logit model with a dummy variable for a private equity bidder as the dependent variable. Robust standard errors are provided in brackets. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Tangible assets is the net plant and property to total assets for the financial year ending before the SDC announcement. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Auction is a dummy variable equal to one in case the company is sold in a highly organized full-scale auction with pre-set rules and zero otherwise. Controlled sale is a dummy variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target management believes to have a serious interest in acquiring the company and zero otherwise. a, b and c denotes statistical significance at the one-, five- and ten-percent level. Panel A

Transaction value Target total assets Premium (adjusted) SDC premium Factiva premium Profitability Loss Market to book R&D Tangible assets Cash Industry count Stock performance Analyst dummy Liquidity index Anti-takeover state Leverage Fraction sold in Negotiation Controlled sale Auction Bidders contacted % PE bidders Bidders with agreement % PE bidders Target initiated deal Transaction value

Private equity buyer

Difference in means

St.dev.

St.dev. Median

Mean

St.dev.

Median

654 489 45.1% 42.9% 43.2% − 0.03 30.2% 1.11 0.22 0.29 0.15 4.4 6.6% 0.65 0.20 0.69 0.21

1917 1089 45.4% 40.6% 41.1% 0.23 46.0% 0.79 0.42 0.24 0.19 1.0 63.7% 0.48 0.55 0.46 0.26

139 147 36.3% 35.8% 34.9% 0.03 0% 0.96 0.00 0.23 0.07 4.5 − 6.2% 1.00 0.08 1.00 0.14

611 969 49.2% 47.8% 47.3% − 0.09 54.1% 1.92 0.35 0.24 0.14 5.3 1.0% 0.66 0.13 0.75 0.16

1811 7694 62.7% 55.6% 56.6% 0.30 49.9% 1.50 0.48 0.22 0.15 0.9 68.7% 0.47 0.16 0.43 0.22

131 94 38.4% 35.6% 36.8% 0.01 100% 1.41 0.00 0.15 0.07 5.5 − 12.7% 1.00 0.07 1.00 0.06

43 − 480 − 4.1% − 5.0% − 4.1% 0.06b − 23.9%a − 0.81a − 0.13a 0.05b 0.02 − 0.9a 5.6% − 0.01 0.08c − 0.06 0.05b

24.9% 25.4% 49.8% 32 79% 14 93% 63.4% 747

43.3% 43.6% 50.1% 44 27% 19 68% 48.3% 2,121

0% 0% 0% 16 100% 5 100% 100% 141

40.0% 38.5% 21.5% 12 4% 5 3% 40.0% 527

49.1% 48.8% 41.2% 21 15% 9 11% 49.1% 1,581

0% 0% 0% 2 0% 1 0% 0% 133

− 15.1%a − 13.2%a 28.3%a 20a 75%a 9a 90%a 23.4%a 220

Panel B

Target total assets Premium (adjusted) SDC premium Factiva premium Fraction sold in Negotiation Controlled sale Auction Bidders contacted % PE bidders Bidders with agreement % PE bidders

Strategic buyer

Mean

Buyer initiated deal

Target initiated deal

Difference in means

Mean

St.dev.

Median

Mean

St.dev.

Median

555 51.0% 50.1% 51.7%

1518 53.0% 46.5% 50.4%

121 39.2% 40.0% 39.0%

891 43.6% 40.9% 39.4%

7503 56.2% 50.3% 47.9%

128 35.3% 33.3% 32.7%

− 336 7.4% 9.2%c 12.3%b

48.0% 36.9% 15.2% 9 32% 4 30%

50.1% 48.4% 35.9% 17 43% 7 44%

0% 0% 0% 1 0% 1 0%

17.9% 27.4% 54.7% 34 47% 14 55%

38.4% 44.7% 49.9% 44 41% 19 81%

0%a 0% 100% 20 50% 5 50%

30.1%a 9.5%b − 39.6%a − 25a − 15%a − 10a − 25%a

For the whole sample, the average adjusted premium equals 47.2% and as expected due to leakage of information, it is slightly larger than the SDC premium. The difference between the adjusted and SDC premium is, however, not statistically significant. Table 2 reporting averages across private equity versus strategic buyers shows that our adjustment increases the premium for both private equity and strategic buyer groups. Because the increase for the private equity group is slightly larger, the overall difference between private equity versus strategic buyers after adjustment is smaller (4.1% for the adjusted premium versus 5% for the SDC premium) and remains insignificantly different from zero. Even though we believe adjusting premium for leakage of information and associated run-up is very important, these statistics show a limited impact of the adjustment within private equity versus strategic buyer groups. They also highlight that comparing more similar deals due to our matching procedure has a more profound effect.

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Another difference with the literature is that we analyze premiums directly rather than target announcement returns. A potential drawback of cumulative abnormal returns around the deal announcement as a measure of gains to target shareholders is that it reflects other information that may potentially bias the measure such as probability of the deal success and information revealed during the run-up period (Bargeron et al., 2008; Boone and Mulherin, 2011). A potential drawback of our direct premium measure is that it is not adjusted for a benchmark return. However, our industry/size/time matched pairs of private equity and strategic deals should alleviate this problem. Table 2 further shows that private equity targets have low market to book ratio and relatively better performance indicating that they are attractive buys with limited growth opportunities but high resale value. For clarity, all variables are defined in Appendix B. The high fraction of tangible assets and low R&D indicate lower asset specificity and suitability of these firms for private equity buyers. In contrast, strategic buyers are interested to acquire firms with better growth prospects. Despite relatively poor past performance their targets still have high market to book ratio and high fraction of intangible assets and R&D. Moreover, industry count is higher for strategic buyers whereas liquidity index is larger for private equity acquirers. As industry count measures the number of firms in the target's industry with a value greater than the target in the year prior to the takeover announcement, it gauges the potential depth of the takeover market for a target (Boone and Mulherin, 2008a). It shows that if the number of potential acquirers is larger, firms are more likely to be acquired by strategic buyers. In turn, liquidity index is defined as the ratio of value of corporate control transactions in the previous year to the total book value of assets of all firms in the given industry and year (Schlingemann et al., 2002) and measures the actual intensity of corporate control transactions. It shows that hotter takeover markets are associated with higher odds of private equity transactions. All these statistics indicate that the two groups of target firms are significantly different in many aspects.

2.2. Company sale process For all takeovers in our sample we are able to retrieve the proxy or solicitation statements from the EDGAR database of the SEC. These filings usually contain a ‘background to the merger’ section that describes the initiator of the takeover (target management or an interested buyer) and whether the company was sold in a private negotiation with one buyer or in a competitive process with multiple bidders competing for the target. In general, competitive bidding involves either a full-scale formal auction or a controlled sale where the selling firm negotiates with a limited number of interested bidders. Limiting the number of bidders in a controlled sale is a strategic decision by the target firm (Boone and Mulherin, 2009). Auctions with a large number of bidders increase competitive pressures among bidders and therefore increase premiums (Bullow and Klemperer, 2009). At the same time, however, bidders in auctions collectively bear high search and evaluation costs that together with lower probability of winning for each individual bidder may eventually result in less aggressive bidding and so lower premiums (French and McCormick, 1984). For some firms, then, auctions might be very costly and the optimal choice might involve limited competition in form of controlled sales (Boone and Mulherin, 2009). Moreover, selling firms might opt for controlled sales with a limited number of competing bidders in situations when they are confident that they are able to attract the most suitable bidders. Usually, shortly after the decision to sell is made, the selling firm retains a financial advisor. If the firm decides for a full-scale auction the advisor serves as the ‘auctioneer’. Drawing on knowledge of the selling company, the advisor draws up a preliminary list of potential bidders and contacts these bidders to obtain information on their interest of making a potential bid. The contacted parties who show interest receive a very cursory description of the selling company and are offered a more in depth information memorandum provided they sign a confidentiality agreement. Then the number of bidders is further reduced in submission of preliminary non-binding offers (‘letters of intent’) and final sealed binding bids. The final bids are then considered by the selling company and the best bid is chosen depending on valuation, financing structure and future plans of the bidder. For illustration, to highlight the distinguishing features of auctions versus controlled sales Appendix A describes two deals. The first deal involves a full-scale auction with multiple rounds and a large number of bidders. The second deal involves a controlled sale where the selling firm is explicit in listing reasons for limiting the bidding competition. In the whole population of deals, we have slightly more auctions (36%) and less controlled sales (32%) and private negotiations (32%). Once we partition by buyer type (Panel A of Table 2), the frequencies become more tilted towards auctions for private equity deals (50% auctions versus 25% each in negotiation and controlled sales) and towards private negotiations or controlled sales for strategic buyer deals (40% and 38% negotiation and controlled sale, respectively, versus 22% auction). 4 From Table 2 we further see that private equity buyers face fiercer competition both in terms of number of bidders contacted (32 versus 12) and bidders with confidentiality agreement (14 versus 5). In addition, the fraction of private equity bidders is remarkably high for the targets eventually bought by private equity buyers and low for targets bought by strategic buyers. 5 This fragmentation increases from 79% (4%) of bidders contacted to 93% (3%) of bidders signing the confidentiality agreement for the private equity (strategic) group. As invitation to participate in bidding is a decision of the target firm, but agreement signing is buyer driven, these numbers indicate that the market segmentation is preferred by both parties. 4 These frequencies are slightly different comparing to (Boone and Mulherin, 2009) whose data favor private negotiations with 50%, followed by controlled sales and auctions with 25% each, but they focus on 400 large corporate takeovers during the 1990s. 5 Please note that we have fewer observations for bidders contacted and bidders with confidentiality agreement (359 and 364 out of 410, respectively). The fragmentation data coverage is even lower (239 and 255, respectively).

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Private equity deals are more often target rather than buyer initiated. Panel B of Table 2 shows that the adjusted premium is larger for buyer (51%) versus target initiated deals (44%) which suggests that it might be important to control for deal initiation throughout our analysis (Simsir, 2008; Xie, 2010; Macias et al., 2011). Buyer initiated deals are slightly larger and more often use negotiations whereas target initiated deals are mostly organized in full-scaled auctions. Overall, all selling mechanisms are relatively populated across both initiator and buyer types. The most frequent are target initiated deals sold in auctions (116 deals). The least populated are auctions for buyer initiated deals (30 deals).

Table 3 Selling mechanism summary statistics. This table presents summary statistics per selling mechanism type. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Bidders with agreement is the number of bidders that signed a confidentiality agreement. This data is available only for 112 controlled sales and 119 auctions. Bidders with non-binding bid is the number of bidders that submit a preliminary non-binding offer (available only for 118 controlled sales and 119 auctions). Bidders with final bid is the number of bidders that submit a final binding bid (available only for 125 controlled sales and 129 auctions). Non-binding (final) bids to agreement is the ratio of the number of non-binding (final) bids to the number of all bidders that signed a confidentiality agreement. Transaction value is the total value of consideration in million USD paid by the acquiror, excluding fees and expenses. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Loss is a dummy variable equal to one in case of negative profitability and zero otherwise. Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Tangible assets is the net plant and property to total assets for the financial year ending before the SDC announcement. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Stock performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes. Antitakeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. We test for differences in means using a t-test. a b , and c denote statistical significance at the one-, five- and ten-percent level.

Premium

#obs.

Premium

#obs.

Premium

#obs.

Neg. versus contr.

Neg. versus auction

Contr. versus auction

45% 55% 39% 50% 59% 34% 40%

133 51 82 21 30 17 65

54% 44% 61% 45% 44% 54% 65%

131 52 79 28 24 30 49

42% 40% 47% 40% 41% 42% 68%

146 102 44 81 21 35 9

− 9% 11% − 22%b 5% 15% − 20% − 25%b

3% 15%c − 8% 10% 17% 8% − 27%

12%c 4% 14% 5% 2% 12% 3%

Panel A:

Negotiation

All deals PE deals Strategic buyer deals Target initiated PE deals PE initiated deals Target initiated strat.-buyer deals Strategic buyer initiated deals

Controlled sale

Panel B:

Controlled sale

Bidders with agreement Bidders with non-binding bid Bidders with final bid Non-binding bids to agreement Final bids to agreement Panel C:

Transaction value Profitability Loss Market to book R&D Tangible assets Cash Industry count Stock performance Analyst dummy Liquidity index Anti-takeover state Leverage Target initiated deal

Auction

Negotiation

Auction

Mean

St.dev.

Mean

St.dev.

4.43 2.32 1.38 75% 56%

0.60 0.16 0.06 3% 3%

21.96 5.86 1.90 39% 20%

1.92 0.41 0.11 3% 2%

Controlled sale

Auction

Total

PE buyer

Strat. buyer

Total

PE buyer

Strat. buyer

Total

PE buyer

Strat. buyer

761 − 0.08 50% 1.57 0.23 0.30 0.13 4.86 1% 0.68 0.12 0.78 0.20 29%

555 − 0.06 37% 0.94 0.16 0.33 0.15 4.45 − 13% 0.69 0.13 0.71 0.22 41%

890 − 0.09 59%a 1.96a 0.27a 0.27b 0.13 5.12a 10%a 0.67 0.11c 0.83a 0.18c 21%a

716 − 0.07 42% 1.64 0.38 0.23 0.15 5.02 3% 0.70 0.19 0.69 0.19 44%

1,051 − 0.03 31% 1.28 0.33 0.26 0.17 4.27 12% 0.71 0.28 0.69 0.26 54%

496a − 0.10b 49%a 1.87a 0.42c 0.21b 0.15 5.51a − 4%b 0.70 0.14a 0.70 0.15a 38%a

441 − 0.02 35% 1.35 0.26 0.26 0.14 4.77 7% 0.60 0.18 0.68 0.18 79%

502 − 0.01 26% 1.10 0.21 0.28 0.15 4.52 14% 0.61 0.20 0.68 0.18 79%

301c − 0.06a 55%a 1.92a 0.39a 0.23b 0.13 5.34a − 7%a 0.59 0.14 0.70 0.16 80%

Contr. versus auction 17.53a 3.54a 0.52a − 36%a − 36%a Neg. versus contr.

Neg. versus auction

Contr. versus auction

45 − 0.01 8%c − 0.07 − 0.16a 0.06a − 0.02 − 0.16 − 1% − 0.03 − 0.08b 0.09b 0.01 −16%a

320c − 0.06b 15%a 0.22b − 0.03 0.03 − 0.01 0.09 − 6% 0.07 − 0.07c 0.10b 0.02 −51%a

275c − 0.05c 7% 0.29b 0.12a − 0.03 0.01 0.25b − 5% 0.10b 0.01 0.01 0.02 −35%a

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Panel A of Table 3 suggests the importance of the sale process as a determinant of bidding premiums for private equity versus strategic buyers. Overall, the adjusted premium is the highest (54%) for controlled sales and the difference with respect to premium in auctions (42%) is significant at the ten-percent level. This highlights that it is important to distinguish auctions from controlled sales. In (Boone and Mulherin, 2007), the premium differences are not significant when comparing auctions (including both full-scale auctions and controlled sales) versus negotiations. When considering also the buyer type dimension, we see that the generally high premium in controlled sales is due to a very high premium paid by strategic buyers (61%) with private equity buyers paying significantly less (44%). In turn, strategic buyers tend to pay relatively little in private negotiations: the average premium is 39% and significantly smaller (at the ten-percent level) relative to 55% paid by private equity buyers. The bottom of Panel A shows average takeover premiums across both initiator and buyer types. Interestingly, when controlling for buyer type, initiator identity does not matter. Overall, this part of the table indicates that takeover premiums are affected by the selling mechanism and buyer type rather than initiator identity. In Panel B of Table 3, we demonstrate that target firms opt for controlled sales in situations when they are confident in identifying the right bidders. Panel B reports the number of bidders with confidentiality agreement, the number of non-binding bids and the number or formal final bids across auctions and controlled sales. 6 We see that auctions are more competitive according to all three measures relatively to controlled sales. Panel B, however, also reports ratios of non-binding and final bids to the total number of bidders signing a confidentiality agreement. These ratios show that controlled sales are indeed more successful in identifying bidders that are more willing to make bids. As a ratio of all bidders that sign confidentiality agreement, 75% make a non-binding bid in controlled sales versus only 39% do so in auctions. The difference is significant at the one-percent level. For final bids, the corresponding ratios are 56% versus 20%. So far, Table 3 suggests that the selling mechanism strongly affects premiums paid for both types of buyers and therefore may provide some additional important information that is not reflected through the other target and deal characteristics. To check this conjecture, Panel C of Table 3 reports the characteristics provided in Table 2 through an additional layer of selling mechanism. It shows averages of the observable characteristics across negotiations, controlled sales and auctions, but for each of these groups it also provides average values for the two buyer types. The first observation is that the three selling mechanisms are associated with different firm characteristics. Target firms sold in auctions are smaller, more profitable and have smaller market to book ratios. Firms sold in controlled sales have higher market to book, R&D and intangible assets. Private negotiations are associated with the largest firms, poorest profitability, smallest liquidity index and happen in anti-takeover states. Second, we see that the buyer type matters even when we control for the selling mechanism. Across all three selling mechanisms, strategic buyers tend to buy less profitable targets with higher market to book, more R&D, fewer tangible assets and higher number of potential strategic bidders (industry count). 3. Results 3.1. Targets of private equity versus strategic buyers As a first step, we estimate logistic models for a private equity buyer. Our results in Table 4 confirm the univariate results that private equity buyers acquire targets with characteristics that are different relative to the targets acquired by strategic buyers. Targets of private equity buyers are more profitable, less research intensive, have lower market to book ratio and higher tangible assets. These characteristics are significant even when we control for the fact that private equity deals are more often target initiated and more frequently sold in formal auctions. Private equity targets also tend to have more cash and smaller number of larger firms in the industry as potential strategic bidders. 7 Overall, the results confirm predictions based on Shleifer and Vishny (1992) that private equity buyers buy targets with more generally redeployable assets, with tangible assets and low R&D expenses, whereas strategic buyers are after more specific assets with high potential synergies. 3.2. Selling mechanism choice As a next step, we model the selling mechanism choice using a multinomial logistic model. As private negotiation is the omitted reference category, in Table 5 we report two sets of coefficients: for auctions and controlled sales. These coefficients show the effect of our explanatory variables on the likelihood of being sold through an auction or controlled sale relative to a private negotiation. We also report differences in the two coefficients that indicate the effect of auctions relative to controlled sales. The results in Table 5 confirm our conjecture that observable target and deal characteristics affect the selling mechanism choice and that buyer type is also important. Firms sold to private equity buyers are more likely to be sold in auctions. Deal initiation also matters: target initiated deals are most likely to be sold in auctions and then in controlled sales whereas buyer initiated deals are most likely to be sold in private negotiations. Higher profitability increases the likelihood of auctions relative to both controlled sales and private negotiations. Targets with more R&D are more likely to be sold in controlled sales, which indicates higher interest of particular individual buyers who would like to exploit the specific assets. It also indicates higher potential costs of full-scale auctions for these firms (Boone and Mulherin, 2009). 6 They are all equal to one for private negotiations. We have fewer observations for these variables: 231 for bidders with agreement, 237 for bidders with nonbinding agreement and 254 for bidders with final bid out of total 277 auctions and controlled sales. 7 Cash and industry count are later used to identify the private equation in the simultaneous system.

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Table 4 Private equity versus strategic buyers. This table presents estimation results of a logit model with a dummy variable for a private equity bidder as the dependent variable. Robust standard errors are provided in brackets. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Tangible assets is the net plant and property to total assets for the financial year ending before the SDC announcement. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Auction is a dummy variable equal to one in case the company is sold in a highly organized full-scale auction with pre-set rules and zero otherwise. Controlled sale is a dummy variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target management believes to have a serious interest in acquiring the company and zero otherwise. a, b and c denote statistical significance at the one-, five- and ten-percent levels. 1 Coef. Constant Target initiated deal Profitability Market to book R&D Tangible assets Cash Industry count Auction Controlled sale Number of observations χ2

2 s.e.

Coef. c

− 0.310 0.985 0.803

(0.160) (0.207)a (0.427)c

− 0.546

(0.233)b

410 32.1a

3 s.e.

Coef. a

− 0.669 0.989 0.881

(0.191) (0.206)a (0.432)b

0.774

(0.446)c

410 29.4a

4 s.e. a

0.847 0.873 1.580 − 0.842 − 0.163

(0.321) (0.219)a (0.575)a (0.214)a (0.260)

410 43.3a

Coef.

s.e.

6.864 0.566 0.871 − 1.072 − 0.200

(1.105)a (0.276)b (0.723) (0.291)a (0.328)

5.273 − 1.387 1.262 0.149

(0.999)a (0.193)a (0.335)a (0.323) 408 95.0a

Higher takeover activity increases and takeover impediments decrease the odds of both auctions and controlled sales, which highlights that competitive bidding correlates with more liquid takeover market and lower costs of auctions. Finally, auctions attract less levered firms. 8 Taking into account that they attract also more profitable firms, it seems that firms organize full-scale auctions when they are more sure of their future prospects and they are able to communicate this to the bidders. Higher leverage, as suggested by Aktas et al. (2010) might also measure cost of auctions. Aktas et al. (2010) argue that more levered firms might be more eager to sell and therefore consider lengthier auctions as more costly and prefer private negotiations. In short, these results show that the target management decision about how to sell their company reflects each firm's particular situation including their firm characteristics, deal initiation, preferred buyer type and the potential buyer pool.

3.3. Premium Table 6 shows that even though the univariate difference in premium for private equity versus strategic buyers is insignificant, after controlling for observable target and deal characteristics in a multivariate setting, private equity buyers tend to pay 11 percentage points less. Relatively speaking, strategic buyers pay on average 25% higher premium. The private equity premium discount increases once we control for consortium deals and private equity portfolio firms, but neither of the two dummies is significant. This is in line with the results of Boone and Mulherin (2011). As for other explanatory variables, more profitable targets that are approached by a buyer and have high market to book ratio tend to get higher takeover premium. Poor past stock performance, smaller size and lesser firm visibility (measured through analyst following) are also associated with larger takeover premiums. 9 The negative correlation between recent stock performance and premium is puzzling and has not yet been much discussed in the literature. Bargeron et al. (2008) show a similar relationship between deal announcement cumulative returns and target past stock performance but do not provide an explanation. Baker et al. (forthcoming) suggest that target firms engage in anchoring and show that recent stock price highs are very significant in determining offer prices. To test for this hypothesis, we check for the minimum and maximum price within the last year ending on the base date. We scale these prices by the price on the base date so that they could be interpreted as relative distance from the maximum/minimum price. Results in Panel B of Table 6 that include the minimum and maximum price show that target shareholders get compensated in the offer price for recent stock price declines. However, this effect disappears once we control for stock performance regardless of the period over which the stock performance is measured. 10 The negative coefficient for stock performance shows that poorly performing targets get higher premiums. Part of this effect is compensation for recent price decreases: the effect of maximum becomes insignificant once stock performance is included. However, as the stock performance effect is particularly strong and 8

In the simultaneous model, liquidity index, anti-takeover state and leverage are used for identification of the selling process equations. We use these latter three variables for identifying restrictions in the simultaneous system. 10 We check stock performance over the last half year just before the base date, but also one, one and half and two years. All are significant at the one percent level. For consistency with previous results, Table 6 reports results with the stock performance over one and half years before the base date. 9

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Table 5 Choice of the selling mechanism. This table presents estimation results for a multinomial logistic regression that models the choice of the selling mechanism. Dependent variable is the selling mechanism that could be a full-scale auction, controlled sale or private negotiation. Private negotiation is the omitted category. Robust standard errors are provided in brackets. Private equity is a dummy variable equal to one in case a private equity firm was the winning bidder and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. a b , and c denote statistical significance at the one-, five- and ten-percent level. 1 Auction

Constant Private equity Target initiated deal Profitability Market to book R&D Liquidity index Anti-takeover state Leverage Number of obs. χ2

2

Controlled sale

Coef.

s.e.

Coef.

s.e.

− 1.662 1.016 2.172 0.923 0.041

(0.346)a (0.287)a (0.291)a (0.454)b (0.112)

− 0.374 0.012 0.720 0.178 0.067

(0.279) (0.275) (0.263)a (0.447) (0.101)

410 89.0a

Coeff. diff.

− 1.288a 1.004a 1.452a 0.744c − 0.026

Auction

Controlled sale

Coef.

s.e.

Coef.

s.e.

− 1.404 0.998 2.287 1.094 − 0.005 0.521 1.600 − 0.522 −1.052

(0.433)a (0.301)a (0.310)a (0.457)b (0.113) (0.351) (0.636)a (0.319)c (0.546)c

− 0.332 − 0.068 0.803 0.444 − 0.040 0.973 1.547 − 0.482 0.015

(0.373) (0.286) (0.278)a (0.447) (0.099) (0.311)a (0.637)b (0.293)c (0.543)

Coef. diff.

− 1.071a 1.066a 1.484a 0.650 0.035 − 0.452 0.053 − 0.040 −1.066c

406 101.5a

pertains even when we measure stock performance over two years, it seems to capture more than just temporary undervaluation of the target firm. Further analysis indicates that premium is even larger for poorly performing firms that are likely to bring along higher postdeal synergies. First, we partition between private equity and strategic buyers. For strategic buyer deals that are more likely to rely on post-deal synergies, the distance from minimum and maximum is insignificant when controlling for stock performance. Second, strategic targets with more intangible assets like growth prospects or research and development that are more likely to bring larger post-deal synergies are associated with higher premium: the coefficient for asset tangibility for strategic buyers is significantly negative. Moreover, an interaction term for past stock performance with asset tangibility turns out to be positive and significant indicating that the negative effect of stock performance is less pronounced for targets with more tangible assets but more pronounced for targets with more intangible assets. So, poorly performing targets get even higher premium when they have many intangible assets that potentially bring larger synergies. This in our view indicates that the low stock performance effect reflects not only a temporary mispricing but also longer-term underlying issues in the target firms as for example underutilization of assets in place. The new buyer might be able to employ the assets to the best use and therefore gain high synergies. High synergies then mean that the buyer is able to pay a higher premium. Similar analysis for private equity buyers in the last column of Panel B shows that (i) the minimum price remains significant even after controlling for stock performance indicating that the closer the price on the base date remains to a yearly minimum, the higher the premium, (ii) the tangibility of assets does not affect premium and (iii) the interaction term between asset tangibility and stock performance is also not statistically significant. Overall, the impact of stock performance on premium is less strong. It seems that private equity bidders pay more for poorly performing targets but the correlation between stock performance and premium is weaker and less likely to reflect synergies. 3.4. System regressions Our results so far for modeling the buyer type, selling mechanism choice and takeover premium indicate a certain degree of endogeneity in the system. When a company management team decides about how to sell the firm, it considers the overall company situation and naturally it would also consider potentially interested buyers and buyers the firm is interested in. This process might eventually also affect the premium offered by the winning bidder. Indeed, our analysis so far suggests that target initiated company sales tend to be organized through auctions and often are acquired by private equity investors. Typically, firms sold in auctions are more profitable, less levered and have low market to book ratios, low R&D intensity and high tangible assets. This suggests that the potential buyer type and selling mechanism choice are interlinked and together they then affect the premium paid by the bidder. As a result, we propose a simultaneous model that consists of a linear regression model for premium, a binary probit model for the buyer type and a multinomial probit model for the selling mechanism choice. The joint model incorporates a possibility for feedback effects within the system: between the buyer type and the selling mechanism choice and from the buyer type and

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Table 6 Premium and target characteristics. This table presents OLS estimation results. Dependent variable is the adjusted premium defined as the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Premium is winsorized at the 1st and 99th percentile. Robust standard errors are provided in brackets. Private equity is a dummy variable equal to one in case a private equity firm was eventually the winning bidder and zero otherwise. Consortium deal is a dummy variable equal to one in case the private equity acquisition is made by two or more private equity investors and zero otherwise. PE portfolio firm is a dummy variable equal to one in case the target firm is acquired by a firm that is majority owned by a private equity investor and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. Stock performance is the return over one and half years ending at the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Target total assets is log of the book value of total assets. Minimum price is the log of the ratio of the price on the base date to the minimum price over one year ending on the base date. Maximum price is the log of the ratio of the maximum price over one year ending on the base date to the price on the base date. a, b and c denote statistical significance at the one-, five- and ten-percent level. Panel A

Constant Private equity Consortium deal PE portfolio firm Target initiated deal Profitability Market to book Stock performance Analyst dummy Target total assets Number of observations R2

1

3

Coef.

s.e.

Coef.

s.e.

Coef.

s.e.

0.738 − 0.115

(0.073)a (0.065)c

− 0.083 0.105 − 0.106

(0.056) (0.126) (0.021)a

0.737 − 0.140 0.090 0.006 − 0.080 0.112 − 0.106

(0.074)a (0.070)b (0.071) (0.069) (0.055) (0.126) (0.021)a

1.059 − 0.112 0.062 0.057 − 0.092 0.295 − 0.080 − 0.183 − 0.199 − 0.045

(0.125)a (0.067)c (0.074) (0.070) (0.054)c (0.140)b (0.019)a (0.045)a (0.066)a (0.018)b

410 5.6

Panel B

Constant Private equity Consortium deal PE portfolio firm Target initiated deal Profitability Market to book Tangible assets Stock performance Stock perf. × tangible Analyst dummy Target total assets Minimum price Maximum price Number of observations R2

2

410 5.9

Pooled sample

409 14.2

Strategic buyer

PE buyer

Coef.

s.e.

Coef.

s.e.

Coef.

s.e.

Coef.

s.e.

1.028 − 0.128 0.072 0.058 − 0.105 0.296 − 0.101

(0.147)a (0.070)c (0.070) (0.075) (0.054)c (0.149)b (0.021)a

0.996 − 0.108 0.059 0.061 − 0.094 0.346 − 0.079

(0.145)a (0.066)c (0.069) (0.075) (0.055)c (0.155)b (0.019)a

1.125

(0.193)a

0.934

(0.203)a

− 0.169

(0.055)a

− 0.198 − 0.042 0.038 0.069

(0.066)a (0.020)b (0.086) (0.097)

− 0.068 0.750 − 0.069 − 0.526 − 0.385 0.787 − 0.318 − 0.038 0.174 0.058

(0.083) (0.148)a (0.021)a (0.166)a (0.104)a (0.360)b (0.102)a (0.028) (0.117) (0.106)

− 0.059 − 0.186 − 0.106 0.092 − 0.175 0.174 − 0.079 − 0.049 − 0.150 0.017

(0.068) (0.194) (0.042)b (0.125) (0.089)c (0.170) (0.078) (0.020)b (0.089)c (0.173)

(0.065)a (0.019)b (0.079) (0.094)c

− 0.182 − 0.045 − 0.026 0.160 410 11.9

410 14.5

204 26.7

205 17.1

selling mechanism choice to takeover premium. To account for these feedback system effects is very important for efficient parameter estimation and their interpretation. Our model is described in detail in Appendix C. 3.4.1. System identification We have to make sure that the system is properly identified. In a system of simultaneous equations, identification of the model parameters is based on exclusion restrictions (Judge et al., 1988, Chapter 14). Loosely speaking, the variables involved in the exclusion restrictions can be seen as instruments. The role of these variables is, however, not exactly the same as the role of instruments. It is challenging to find good exclusion restrictions. Therefore, even though we formally test for validity of all our exclusion restrictions, we also carefully provide proper economic justification and intuition for validity of the exclusion restrictions (Roberts and Whited, 2011). 11 In particular, in our setting we need to justify why the particular variables (exclusion restrictions) have a direct effect on one endogenous variable in the system but not a direct, only indirect, effect on the other endogenous variables. 11 We consider several candidate variables based on similar papers in the literature (Schlingemann et al., 2002; Bebchuk et al., 2002; Boone and Mulherin, 2007, 2008a; Aktas et al., 2010). We could not include several potential variables as they do not fit our setting with private equity buyers. For example, we are not able to include the payment consideration, bidder size, relative size or bidder ownership structure as all private equity deals are paid for in cash and bidder size or ownership structure are not applicable to private equity buyers.

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Table 7 Test results for the validity of the exclusion restrictions. The table displays individual likelihood ratio test results for the presence of the variables in the corresponding equations. More detailed discussion of the tests is provided in Appendix C. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for target incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. Stock performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Target total assets is log of the book value of total assets. a, b and c denote statistical significance at the one‐, five‐ and ten‐percent level. Variable

Validity test LR

Industry count Cash

44.335 75.864

Liquidity index Anti-takeover state Leverage

22.292 8.127 14.417

Stock performance Analyst dummy Target total assets

22.379 6.981 3.272

dof Private equity equation 1 1 Selling mechanism equations 2 2 2 Premium equation 1 1 1

Exogeneity test p-value 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.07

LR

dof

p-value

Premium & selling mechanism equations 0.171 3 0.98 0.248 3 0.97 Private equity & premium equations 0.426 2 0.81 1.372 2 0.50 0.265 2 0.88 Private equity & selling mechanism equations 1.489 3 0.68 4.180 3 0.24 2.308 3 0.51

Table 7 shows the results of our statistical tests for identification. The first set of tests corresponds to the validity (significance) of the variables involving the exclusion restrictions in the relevant equation, while the second set of tests corresponds to the exogeneity of these variables by adding them to the other equations. In this way, the tests show whether all explanatory variables necessary to identify the system are only significant in one of the equations (validity tests) but not in the other equations (exogeneity tests). These two sets of tests account for similar properties as the test for weak instruments and the Sargan test. For more details see Appendix C. We identify the private equity equation by excluding target cash level and industry count from the other equations. Industry count is measured as the number of firms in a given industry that are larger than the target and therefore it proxies for the depth of the takeover market. Intuitively, a larger pool of potential strategic buyers increases the likelihood that the target firm is eventually sold to a strategic buyer (Boone and Mulherin, 2008a). The validity test in Table 7 shows that industry count is highly significant in explaining the likelihood of private equity buyer, but the exogeneity test indicates that industry count does not contribute to explaining the selling mechanism choice or the premium. Aktas et al. (2010) and Boone and Mulherin (2008a) show that industry count is positively correlated with auctions in their samples of deals by public (strategic) buyers. The correlation matrix in Table 8 shows that, on the univariate level, industry count is also in our sample associated with higher probability of controlled sales, which contradicts the exogeneity test in Table 7. However, this positive correlation between industry count and controlled sales is likely to indicate that industry count captures the depth of the strategic bidding market rather than the private equity market. Once we pool strategic and private equity deals together and control for R&D, liquidity index, anti-takeover state and leverage, industry count is not any more significant in explaining the selling mechanism choice. Premium is not, in our data, correlated with industry count even on the univariate level. We are not aware of any academic papers that find a strong correlation between industry count and takeover premium. However, partially supporting our assumption that industry count does not affect premium, Boone and Mulherin (2008a) find insignificant effect of industry count on bidder returns and exclude it from the bidder return regression when using instruments. One of the trademarks of private equity is to keep idle cash to a minimum (Pozen, 2007) and to offer a solution to the agency costs of free cash flow (Lehn and Poulsen, 1989). Target firms with high cash levels on the balance sheet for precautionary or other reasons are therefore seen as attractive targets for private equity buyers. Private equity investors can have the target firm pay out the cash (Lehn and Poulsen, 1989). Moreover, more cash on the balance sheet may also be associated with higher asset tangibility that attracts private equity bidders (Shleifer and Vishny, 1992). The validity test for cash in Table 7 confirms these conjectures: target's cash level contributes significantly to explaining the buyer type. The exogeneity test in Table 7 indicates that cash is exogenous for explaining premium and the selling mechanism choice. This is also in line with the correlation matrix in Table 8 that shows that cash is not correlated with premium, auctions or controlled sales. We are not aware of any academic papers that link cash levels to the choice of the selling mechanism. Pinkowitz (2000), however, shows that cash levels are not correlated with takeover premium even though they predict higher probability of takeover offers. Concerning the selling mechanism choice, the selling firm management considers costs and benefits of all alternatives for selling their firm. Assuming no transaction costs, selling firms should favor auctions as more bidders generally translate to higher premiums (Bullow and Klemperer, 2009). But auctions are costly for sellers to organize and to search for potential bidders as well as for bidders in terms of investigation and bid preparation costs (French and McCormick, 1984; Aktas et al., 2010; Rogo, 2010).

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Premium Private equity Auction Controlled sale Target initiated deal Profitability Market to book Stock performance Analyst dummy Target total assets R&D Cash Industry count Liquidity index Anti-takeover state Leverage

− 0.038 − 0.064 0.091c − 0.068 0.006 − 0.192a − 0.221a − 0.184a − 0.116b − 0.055 − 0.026 0.027 − 0.042 − 0.075 − 0.009

Private equity 0.295a − 0.141a 0.234a 0.114b − 0.322a 0.042 − 0.010 0.086c − 0.140a 0.050 − 0.420a 0.097c − 0.071 0.101b

Auction

− 0.51a 0.413a 0.095c − 0.096c 0.039 − 0.088c − 0.036 − 0.045 − 0.005 − 0.080 0.033 − 0.057 − 0.037

Contr. sale

Target initiated

− 0.102b − 0.037 0.067 − 0.013 0.063 0.017 0.142a 0.042 0.094c 0.048 − 0.038 0.009

− 0.032 − 0.139a 0.018 − 0.109b − 0.027 − 0.022 0.013 − 0.022 − 0.088c 0.016 0.068

Profitability

0.151a 0.284a 0.030 0.270a − 0.214a − 0.273a − 0.343a 0.068 − 0.089c 0.119b

Market to book

Stock perform.

Analyst dummy

Total assets

0.260a 0.110b − 0.114b 0.214a 0.155a 0.105b 0.003 0.001 − 0.005

− 0.093c 0.025 − 0.080 − 0.085c − 0.166a − 0.008 0.016 0.037

0.275a 0.094c 0.006 0.055 − 0.025 − 0.015 0.019

− 0.166a − 0.268a − 0.304a 0.125b 0.157a 0.263a

R&D

Cash

0.479a 0.381a − 0.081 − 0.011 − 0.250a

0.344a − 0.053 0.059 − 0.351a

Industry count

Liquid. index

− 0.112b − 0.026 − 0.211a

− 0.015 0.102b

Anti-takeover

0.054

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Table 8 Correlation matrix. This table presents the correlation matrix. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Premium is winsorized at the 1st and 99th percentile. Auction is a dummy variable equal to one in case the company is sold in a highly organized full-scale auction with pre-set rules and zero otherwise. Controlled sale is a dummy variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target management believes to have a serious interest in acquiring the company and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. Stock performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Target total assets is natural logarithm of the book value of total assets. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. a, b and c denote statistical significance at the one-, five- and ten-percent level.

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Lower auction costs are then associated with a higher probability of auctions and higher number of bidders. We use three variables to proxy for these costs: (i) liquidity index, (ii) a dummy for anti-takeover state and (iii) target firm leverage. Liquidity index captures buoyancy of takeover activity within an industry (Schlingemann et al., 2002). We conjecture that higher takeover activity is associated with lower search costs for the selling company looking for potential bidders and therefore increases the likelihood of auctions and controlled sales. This conjecture is confirmed in Table 7 as the validity test for liquidity index in the selling mechanism equations is highly significant. Similarly, search costs might be relatively high for selling firms headquartered in states with strong anti-takeover laws. Bidding for firms with higher takeover impediments involves also higher costs of making bids. Comment and Schwert (1995) argue that more stringent anti‐takeover law is associated with greater bargaining power of the target. Therefore, we argue that anti‐takeover law lessens the likelihood of a target being sold in an auction. Boone and Mulherin (2007) also show that targets in anti-takeover states are more likely to choose negotiations. The importance of high anti-takeover state in our data set is confirmed by the validity test in Table 7. Aktas et al. (2010) use leverage as a proxy for auction costs and argue that levered firms are more eager to sell and as auctions usually take longer to complete, levered firms prefer private negotiations. Also, more levered target firms are more difficult to price (Jandik and Makhija, 2005) but their value, ceteris paribus, does not vary much across different bidders and therefore the value of the firm would not increase with number of bidders (French and McCormick, 1984). So we conjecture that higher leverage is associated with higher odds of negotiations, which is again confirmed by the validity test in Table 7. Even though high takeover activity and leverage have also been associated with premium (Aktas et al., 2010; Bargeron et al., 2008), previous literature has not controlled for the selling process using the system of equations we use in this paper. We argue that any possible correlation between takeover premium and high takeover activity, anti-takeover provisions or leverage is only indirect through the selling process. For example, Aktas et al. (2010) argue that as leverage increases the cost associated with organizing an auction for the selling firm, highly levered firms prefer not to sell in auctions. As they suffer fewer outside options to sell, they also suffer lower bargaining power. These firms prefer faster private negotiations and so are willing to accept smaller premiums. Also in our support, Boone and Mulherin (2007) show that even though anti-takeover state is positively correlated with negotiations, it is not correlated with announcement returns for the selling firms. On a univariate level, liquidity index and leverage in our sample are also correlated with private equity buyers (Table 8). However, as private equity buyers are also highly correlated with auctions, it is likely that the relationship is indirect. The exogeneity test in Table 7 confirms that liquidity index, anti-takeover state and leverage do not significantly contribute to explaining the premium and the type of buyer within our system. Premium is likely to be associated with the bargaining power of the two parties involved and value (synergy) created in the deal. As shown in Section 3.3, poor prior stock performance is correlated with higher premium and this correlation is, at least partially, associated with higher potential for synergy opportunities. Bargeron et al. (2008) also find negative correlation between target announcement returns and past stock performance. It might also be argued that firms that are smaller and lack analyst coverage are associated with higher potential synergies to acquirers. Higher analyst scrutiny and investor visibility mean that potential synergies are spotted and exploited sooner. 12 Moreover, Greenwood and Schor (2009) show that smaller firms without analyst coverage are often targeted by activist shareholders who consequently manage to get high takeover premiums. Larger firms are associated with higher institutional ownership (O'Brien and Bhushan, 1990). This might translate into the effect of visibility just discussed above. It might also translate into lower bargaining power of large firms with larger stakes by institutional owners who are more inclined to sell and realize high returns. Stulz et al. (1990) document a negative relation between target announcement abnormal returns and institutional ownership and argue that institutional owners are willing to accept lower premium due to their very low capital gains tax rates. 13 Also, larger firms might be more difficult to sell and therefore their bargaining power might be lower relative to smaller firms. Betton et al. (2008) show a negative relationship between premium and target size, while Boone and Mulherin (2011) and Bargeron et al. (2008) show a negative correlation between target announcement returns and target size. In fact, the validity tests in Table 7 show that stock performance, analyst dummy and target total assets each significantly contribute to explaining the premium. The target total assets is significant only on the ten-percent level, but this is not a large concern as our system is overidentified. It has been documented in the literature that private equity buyers acquire smaller targets relative to strategic buyers (Bargeron et al., 2008). However, we control for this effect through our matching procedure and so do not expect any correlation between target size and buyer type. Table 2 shows that in our data set, private equity versus strategic buyer targets are comparable in term of transaction value and total assets. 14 Analyst coverage and past stock performance are also comparable across the two buyer types. Table 7 confirms the exogeneity tests for the private equity equation. Concerning the relationship between premium and the selling mechanism choice, the correlation matrix in Table 8 shows no correlation between target total assets and auctions or controlled sales, which is further confirmed by the exogeneity test in Table 7. This is in contrast with, for example, Boone and Mulherin (2007) who show that target size proxied by total assets is

12 We find that the negative correlation between premium and past stock performance is more negative for targets without analyst coverage. This indicates that the synergy effect is larger. 13 Blouin et al. (2011), however, show that only a subset of institutional investors is tax sensitive. 14 Table 8 shows a weak correlation between log of total assets and the private equity dummy, but this correlation is not confirmed when we add log of total assets as an additional regressor in specifications in Table 5.

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significantly negative in predicting auctions. We believe this is because of our data collection set up where we start with all private equity deals that are then matched with strategic deals. As private equity deals are not particularly large, the firms in our data set are smaller. Smaller variation in total assets then results in total assets not being significant in explaining the selling mechanism choice. The correlation matrix in Table 8 also indicates that lack of analyst coverage is weakly correlated with auctions. However, the endogeneity test for the analyst dummy in Table 7 suggests that the analyst dummy is not significant in explaining the selling mechanism. So, the univariate correlation between analyst coverage and auctions must be indirect: for example, sales of firms covered by analysts are less frequently initiated by target management and target initiation is in turn strongly associated with auctions. In summary, we provide intuition for why our exogenous variables have a direct effect on particular endogenous variables but no direct effect on the other endogenous variables in such a way that they identify the parameters of our simultaneous system. Moreover, we also provide test results that show that indeed all explanatory variables necessary to identify the system are only significant in one of the equations (validity tests) and not in the other equations (exogeneity tests). Speaking in terms of instruments, the relevant variables are not weak and are exogenous. As a result, the tests confirm industry count and cash as exogenous variables in the private equity equation, liquidity index, anti-takeover state and leverage in the selling process equation and stock performance, analyst coverage and total assets in the premium equation.

3.4.2. System results Table 9 reports the results. The individual equations for the premium, buyer type (private equity buyer) and selling mechanism (auction and controlled sale) are reported in corresponding columns. For each equation, at the top we report structural parameters that are dependent variables of the other equations. Significance of these structural parameters shows interdependencies throughout the model and therefore indicates sequencing of the whole selling process. For each equation, we report in separate columns both estimated and reduced form parameters. An estimated parameter of a particular exogenous variable measures the effect of changing this variable conditional on keeping everything else constant/ unchanged. However, in a system with feedback effects, this does not provide much information as a change of one exogenous variable necessarily impacts other dependent variables and causes adjustments throughout the system. Given the feedback effects, we are interested in an overall effect of a change in a particular variable that is expressed through the reduced form parameter. The reduced form parameters, however, only make sense in the equations with significant structural parameters because only then we can talk of significant feedback effects. Also, by definition, the reduced form parameters are different from the estimated coefficients only for the exogenous explanatory variables that are present in more than one equation. For the exclusion restrictions, the reduced form parameters are equal to the estimated ones as they are not present elsewhere in the model. Considering the structural parameters, Table 9 shows that the selling mechanism choice significantly affects the buyer type but not the other way round. Moreover, the coefficients for the buyer type and selling mechanism are not significant in the premium equation. So, Table 9 shows a simple triangular system with only one significant feedback effect from the selling mechanism choice to the buyer type that suggests sequencing of the selling process that starts with the selling mechanism choice. The starting point is, therefore, the decision on how to sell a firm. In the auction and controlled sale equations, several of the estimated coefficients are significant, which shows that the decision reflects firm and deal characteristics. 15 Target initiated deals with better accounting profitability are more likely to be sold in auctions. Target initiation and profitability increase also the odds of controlled sales. Still, the initiation coefficient is significantly higher for auctions. The effect of profitability is slightly larger for auctions, but the difference is not significant. Industry count that proxies for the depth of the takeover market also increases the odds of auctions and controlled sales relative to private negotiations. So in short, when deciding on how to sell their firm, managers take into account the firm situation. The selling mechanism choice then affects the buyer type. In particular, firms sold in auctions are less likely, while those sold in controlled sales are more likely, to be sold to private equity buyers (all relative to private negotiations). Interestingly, this is in contrast to the observed correlations reflected in the univariate and single equation results (Tables 2, 4 and 8), where we see that private equity deals are more likely in auctions. The negative structural coefficient for auctions in the private equity equation in Table 9, however, indicates that the positive observed correlation between private equity buyers and auctions is due to common effects of target initiation, profitability, market to book ratio and R&D in the two equations. These variables impact the odds of a private equity buyer and the odds of auctions in the same direction, which causes the positive observed correlation between private equity buyers and auctions. However, once we keep these common factors constant and observe exogenous effects through the exclusion restrictions of liquidity index, anti-takeover state and leverage, we see that higher odds of auctions, coming from purely exogenous factors, decrease the odds of private equity buyers. Similarly at odds with observed correlations, the structural coefficient for controlled sales in the private equity equation is positive: firms that decide to sell through a controlled sale rather than a private negotiation ceteris paribus also increase the likelihood of being sold to a private equity buyer. The two structural parameters clearly show that for a buyer type determination it is important to distinguish between full-scale auctions and controlled sales with a limited number of bidders that with exception of Boone and Mulherin (2009) has not been done in the related literature so far. 15 We focus here on the estimated parameters rather than the reduced form parameters as the private equity coefficient is not significant indicating that the feedback effect is zero.

Premium equation Estimation Coef. Structural parameters Private equity 0.028 Auction − 0.095 Controlled sale 0.056 Direct effect parameters Constant 0.886 Target initiated deal 0.022 Profitability 0.360 Market to book − 0.070 Stock performance − 0.186 Analyst dummy − 0.095 Target total assets − 0.041 R&D Cash Industry count Liquidity index Anti-takeover state Leverage

s.e.

Private equity equation

Reduced form Coef.

s.e.

(0.118) (0.191) (0.162) (0.169)a (0.282) (0.180)b (0.041)c (0.055)a (0.044)b (0.028)

0.930 − 0.119 0.290 − 0.065 − 0.186 − 0.095 − 0.041 0.004 − 0.010 0.002 − 0.052 0.006 0.066

(0.204)a (0.056)b (0.103)a (0.034)b (0.055)a (0.044)b (0.028) (0.067) (0.134) (0.029) (0.127) (0.037) (0.136)

Estimation

Auction equation

Reduced form

Coef.

s.e.

Coef.

− 0.428 0.678

(0.211)b (0.090)a

1.333 0.598 0.332 − 0.228

(1.014) (0.309)c (0.328) (0.135)c

1.183 0.196 0.160 − 0.189

− 0.358 1.371 − 0.294

(0.218)c (0.849) (0.177)c

− 0.028 1.109 − 0.237 0.246 − 0.123 0.267

s.e.

Estimation Coef.

s.e.

Controlled sale equation

Reduced form Coef.

s.e.

Estimation Coef.

s.e.

− 0.166

(0.428)

Reduced form Coef.

s.e.

0.290

(0.544)

(0.908) (0.163) (0.160) (0.142)

− 0.745 1.856 0.965 − 0.069

(0.320)b (0.228)a (0.579)c (0.141)

− 0.402 1.912 1.011 − 0.123

(0.552) (0.237)a (0.575)c (0.105)

− 0.279 0.645 0.410 − 0.051

(0.266) (0.227)a (0.431)c (0.122)

− 0.476 0.613 0.383 − 0.019

(0.515) (0.215)a (0.415) (0.093)

(0.078) (0.867) (0.182) (0.223) (0.095) (0.172)

0.387

(0.280)

(0.262)

(0.670)c (0.242) (0.577)

(0.286)a (0.546) (0.117) (0.663)c (0.245)b (0.576)

0.720

1.270 − 0.293 − 0.696

0.379 0.321 − 0.069 1.341 − 0.329 − 0.618

1.250 − 0.409 0.048

(0.650)b (0.202) (0.489)

0.725 − 0.184 0.039 1.209 − 0.389 0.004

(0.263)a (0.456) (0.098) (0.635)c (0.197)b (0.478)

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Table 9 System estimation results. This table presents system estimation results with the adjusted premium, buyer type and selling mechanism being determined together in one system as defined in Appendix C. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Premium is winsorized at the 1st and 99th percentile. Private equity is a dummy variable equal to one in case a private equity firm was eventually the winning bidder and zero otherwise. Auction is a dummy variable set equal to one if the firm was sold in a full-scale formal auction and zero otherwise. Controlled sale is a dummy variable equal to one in case the firm was sold in competitive bidding with several bidders but not is a full-scale formal auction and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. Stock performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Target total assets is log of the book value of total assets. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Cash is defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target as of previous December. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. a, b and c denote statistical significance at the one-, five- and ten-percent level.

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Taking into account the significant feedback effect between the selling mechanism and buyer type, the reduced form parameters in the private equity equation are all insignificant and show that firm characteristics cease to matter for the buyer type in equilibrium. So, firm characteristics affect the choice of buyer type only indirectly through the selling mechanism. The effect of the selling mechanism and buyer type on premium, as estimated through the structural parameters in the premium equation, is not significant. We see that the observed correlation between the premium and buyer type (selling process) is only due to the correlations of both premium and buyer type (premium and selling mechanism) with the common explanatory variables of target initiation, profitability and market to book ratio. The fully exogenous effects that control for this common correlation are insignificant. As a result, we can say that whether a firm is sold in an auction or negotiation, or it is sold to a private equity or strategic buyer is irrelevant for premium determination. A significant coefficient, for example a negative coefficient for the auction variable would indicate that, on average, firms opting for auctions would get higher premium if they opted for a private negotiation instead. So they would do better by making a different choice. Insignificant coefficients, therefore, indicate optimality of the choices: by changing their choices, firms would not improve the takeover premium. The premium remains unaffected. This also means that private equity buyers do not pay less for their target firms. In fact, the premium differences only reflect differing situations of firms sold to the two types of buyers. Neither do auctions and controlled sales versus negotiations result in differing premiums. This is in line with Aktas et al. (2010) who show that takeover premiums in private negotiations reflect potential competition and so should not be smaller relative to auctions. Despite these optimal choices, firm characteristics still directly impact the premium. Profitable and lower market to book firms get on average higher premiums. Interestingly, target initiation is not significant. 16 It enters the system primarily through the selling mechanism choice. The link between initiation and selling mechanism choice is documented also in Aktas et al. (2010). Xie (2010) also highlights that target initiation is a very important factor but they do not focus on the buyer type. Also, poor stock performance and analyst coverage significantly increase premiums. 4. Conclusions In this paper we analyze the selling process of firms eventually sold to private equity versus strategic buyers. On a data set of 205 private equity deals of US listed targets over the period from 1997 to 2006 matched with comparable deals by strategic buyers we show that the selling mechanism choice is an important strategic decision that reflects their observable firm characteristics. Our data also indicate that private equity versus strategic buyers tend to bid for different types of targets. This suggests that the selling mechanism and buyer type choices are endogenous and both potentially affect takeover premiums. Therefore, we estimate a simultaneous system with the takeover premium modeled together with the buyer type and selling mechanism choice. Our results show that the selling mechanism choice is a very important corporate decision that constitutes the beginning of the selling process and consequently also determines whether the firm is sold to a private equity or strategic buyer. In addition, the system results show that takeover premium paid by private equity versus strategic buyers is not significantly different. The effect of the selling process on the premium is also insignificant. Acknowledgements We would like to thank the editor (J. Harold Mulherin) and an anonymous referee for their very helpful comments. Our thanks also go to Antonio Macias, Mary Anne Majadillas, Manuel Vasconcelos, and participants of the 2010 EFM Symposium on Entrepreneurial Finance & Venture Capital Markets in Montreal, Canada and the 2010 FMA European Conference in Hamburg, Germany. Appendix A. Auctions versus controlled sales: two examples PlayCore: example of a full-scale auction In 1998, PlayCore, Inc., a manufacturer of playground equipment expressed concern that its common stock price on the American Stock Exchange did not adequately reflect the financial performance or prospects of the Company. Based on these concerns the Company interviewed four international investment banking firms from March to May 1999 to obtain their views on value enhancing strategies for the Company, including the sale of the Company. On September 20, 1999, the Company publicly announced the engagement of DLJ as their financial advisor and began the process of identifying candidates that might be interested in acquiring or making a strategic investment in the Company. Over the next month, DLJ contacted 119 potential buyers comprised of 90 financial buyers and 29 strategic buyers. Of the parties contacted, 51 financial buyers and 5 strategic buyers executed confidentiality agreements, 12 parties submitted preliminary indications of interest in early November 1999. Beginning in late November and concluding in early December 1999, 8 interested parties attended management presentations and were provided access to a data room, which included the Company's material agreements and other financial and due diligence information. These parties were asked to submit proposals by December 3, 1999. On December 6, 1999, the Board reviewed the proposals. DLJ reported that the values associated with the proposals were at the lower end of the value range and recommended that four of the interested parties, including Chartwell, be selected to 16 Again, as the structural parameters for private equity and selling mechanism are insignificant, we disregard their feedback effects and focus on the estimated coefficients rather than reduced form parameters.

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continue in the process, which would include facility tours, additional due diligence and providing comments to the draft Merger Agreement previously distributed. Between December 6, 1999 and January 14, 2000, Chartwell and one other party (the Other Party) conducted additional due diligence, visited the Company's key manufacturing facilities and reviewed the Merger Agreement. The two other parties declined to move forward in the process. As requested by the Company, Chartwell provided a proposed letter of intent on January 14, 2000. Pursuant to this letter of intent, Chartwell proposed a transaction consisting of a offer at $10.00 per share subject to satisfactory completion of its due diligence and a number of other conditions. On January 17, 2000, the Other Party provided its proposed letter of intent to the Company. Although unclear from the language of the letter of intent, the Other Party's per share acquisition price was less than $10.00. On January 17 and 18, 2000, DLJ had numerous discussions with Chartwell and the Other Party regarding their respective proposals. On January 20, 2000, the Company's financial advisors reviewed with the Executive Committee of the Board the proposed letters of intent and provided an update on the progress made with these parties. DLJ indicated that the Other Party was unwilling to commit to a fixed per share price without additional due diligence. DLJ further indicated that the Other Party had completed substantially less accounting, business and legal due diligence than had been completed by Chartwell. Chartwell, in discussions with DLJ, had agreed to increase its offer to $10.10 per share in cash. Chartwell had also completed substantial due diligence and had obtained committed financing. Accordingly, the Company began negotiating an agreement with Chartwell on January 30, 2000, whereby the Company would agree to deal exclusively with Chartwell and not solicit additional acquisition proposals for a specified period of time (the “no-shop” agreement). On April 13, 2000, legal counsel to Chartwell and the Company executed the Merger Agreement. On April 14, 2000, the Company issued a press release announcing the execution of the Merger Agreement.

Physicians Health Services: example of a controlled sale On January 14, 1997, the Board of Directors of Physicians Health Services, Inc. (PHS) authorized a subcommittee to explore the company's strategic alternatives. This subcommittee directed management to prepare analyses of the potential values that might be achieved for stockholders through a sale of the company, through continued implementation of the company's strategic plan or through other possible strategies. In early November 1996, representatives of Foundation Health Systems, Inc. (FHS) had advised the company on an unsolicited basis that FHS might have an interest in acquiring the company. On February 23, 1997, FHS reiterated the interest of acquiring the company. Management presented an analysis of the company's strategic alternatives to the members of the subcommittee on February 25, 1997. Particular attention was focused upon the results that might be obtained, alternatively, from a continued implementation of the company's strategic plan, and from a possible sale of the company. The subcommittee authorized management to explore further the possibility of a sale. The premium that the subcommittee believed the company could obtain in connection with a sale during the period of market consolidation could be jeopardized once the New York metropolitan market became more stabilized. It was recognized that any such exploration of a sale would have to be conducted on a discreet basis since a more extensive exploration process, such as a public sale or auction of the company, was likely to expose the company to substantial business risks. In this connection, the subcommittee was advised by management that if the company were to undertake a public sale or auction process, its competitors could and would be expected to use the process to undermine the company's marketing efforts. Also, insurance brokers and consultants would be reluctant to recommend the company to current and potential clients in light of its uncertain future. Moreover, some of the company's competitors might profess an interest in acquiring the company in order to draw out the exploration process, during which the company's ability to compete for business would be impaired. At the same time, the subcommittee recognized that unless indications of interest were solicited from at least some potential acquirors, it would be difficult to assess whether there was any interest in an acquisition at values that would warrant a departure from the company's existing strategic plan. At the conclusion of the meeting, the subcommittee authorized management to approach four potential acquirors whom it believed were the most likely to have the capability and interest to make an acquisition proposal that would be acceptable. These candidates were selected from the possible acquirors identified by management, based on management's familiarity with the consolidation occurring in the industry. Other possible acquirors were judged unlikely to be able to acquire the company in the near term because they were engaged in the process of consolidating other acquisitions into their operations, because they were pursuing other significant opportunities or because they did not appear to have the financial resources to acquire the company at values that might warrant consideration. Members of senior management approached the four possible acquirors in late February and March of 1997 to ascertain whether discussions with any of them might prove fruitful. Of the four possible acquirors, two indicated that they did not have any interest in making an acquisition proposal that would involve the payment of a substantial premium. A meeting with the president of one of these potential acquirors was eventually scheduled for May 13, 1997. A third potential acquiror did not express any interest in pursuing a possible acquisition. None of these approaches led to either ongoing discussions or the prospect of fruitful discussions. The fourth potential acquiror, FHS, indicated that it might have such an interest, and, on March 3, 1997, FHS and PHS entered into a confidentiality agreement.

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On March 18, 1997, representatives of PHS management advised FHS that PHS would entertain an acquisition proposal from FHS only if the offering price equaled or exceeded $30 per share. On April 1, 1997, FHS indicated that it was prepared to consider an acquisition in the range of such an offering price, subject to its review of the possible cost savings that could be generated in a combination. On April 3, 1997, PHS retained Morgan Stanley as its financial advisor. On May 2, 1997, FHS advised the company that it would be prepared to offer to acquire all of the outstanding common stock of the company at a price of $30 per share, subject to certain conditions. An industry publication dated May 5, 1997, reported that FHS had made an informal offer to purchase the Company for about $27.00 per share but that PHS wanted at least $30 per share. A reporter from The Wall Street Journal advised the company and FHS on May 5, 1997, that he had obtained a copy of FHS's merger proposal and intended to publish a story with respect to the proposed merger on May 6. Accordingly, FHS and PHS issued a joint press release announcing that they were engaged in merger discussions, involving a merger consideration of approximately but not more than $30 per share." On May 7, 1997, PHS was then presented with FHS' firm offer for a merger consideration of $29.25 per share. Morgan Stanley advised the Board that following the announcement of merger negotiations at a price of approximately but not more than $30 per share" a number of institutional shareholders of FHS had reacted negatively to the possible price being offered, creating pressure on FHS to secure a price as much below $30 per share as possible. The Board was further advised that FHS was unwilling to offer more than $29.25 per share. Morgan Stanley rendered its opinion that the merger consideration of $29.25 was fair to the holders of the company's common stock from a financial point of view. The full Board then met, and upon further discussion and consideration of such factors, concluded that the merger agreement and the transactions were in the best interests of the stockholders of the company and, by the unanimous vote of the directors present, approved the merger agreement. Appendix B. Variable definitions Variable

Definition

Analyst dummy Anti-takeover state

Dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Dummy equal to one for targets incorporated in Delaware or states determined by (Bebchuk et al., 2002) to have strong takeover impediments (Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin). Dummy variable equal to one in case the company is sold in a highly organized auction with pre-set rules and zero otherwise. 42 trading days before the SDC announcement or one trading day before the first mention of the deal in Factiva, whichever is earlier. Cash and marketable securities to total assets for the financial year ending before the SDC announcement. Dummy variable equal to one in case the private equity acquisition is made by two or more private equity investors and zero otherwise. Dummy variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target management believes to have a serious interest in acquiring the company and zero otherwise. Price offered less price 42 trading days before the first mention of the deal in Factiva over the price 42 trading days before the first mention of the deal Factiva. Natural logarithm of the number of firms in the same FF49 industry that have larger market capitalization than the target (as of previous December) as in (Boone and Mulherin, 2008a). Long-term debt to total assets for the financial year ending before the deal announcement. Ratio of the value of corporate control transactions in a year to the total book values of assets of all the firms in the same industry during that year, using the primary SIC code of each firm and 3-digit SIC codes as in etschlingemann02. Dummy variable equal to one in case return on assets as defined below is negative and zero otherwise. Target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. Natural logarithm of the ratio of the maximum price over one year ending on the base date to the price on the base date. Natural logarithm of the ratio of the price on the base date to the minimum price over one year ending on the base date. Dummy variable equal to one in case the company is sold in a privately negotiated sale and zero otherwise. Dummy variable equal to one in case the target firm is acquired by a firm that is majority owned (≥50%) by a private equity investor and zero otherwise. Price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Return on assets (net income to total assets) for the financial year ending before the SDC deal announcement. Dummy variable equal to one for all Fama and French (49) industries that are top 7 industries with respect to the average industry R&D ratio (R&D expense to total assets) and zero otherwise. Price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement. Abnormal return over one and half years before the base date. We also check specifications with stock performance over two years, one year and half of a year ending on the base date. Net plant and property to total assets for the financial year ending before the SDC announcement. Dummy variable equal to one in case the selling firm initiates the sale of their company and equal to zero in case the buyer approaches the company with a proposal to buy it. Book value of total assets; in regressions included as a natural logarithm. Total value of consideration paid by the acquiror, excluding fees and expenses. The dollar value includes the amount paid for all common stock, common stock equivalents, preferred stock, debt, options, assets, warrants, and stake purchases made within six months of the announcement date of the transaction. Liabilities assumed are included in the value if they are publicly disclosed. Preferred stock is only included if it is being acquired as part of a 100% acquisition. If a portion of the consideration paid by the acquiror is common stock, the stock is valued using the closing price on the last full trading day prior to the announcement of the terms of the stock swap. If the exchange ratio of shares offered changes, the stock is valued based on its closing price on the last full trading date prior to the date of the exchange ratio change. For public target 100% acquisitions, the number of shares at date of announcement is used.

Auction Base date Cash Consortium deal Controlled sale Factiva premium Industry count Leverage Liquidity index Loss Market to book Maximum price Minimum price Negotiation PE portfolio firm Premium (adjusted)

Profitability R&D SDC premium Stock performance Tangible assets Target initiated deal Target total assets Transaction value

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Appendix C. Simultaneous model specification The system of equations consists of a linear regression model for premium (premi), a binary probit model for the potential buyer type (PEi = {0, 1}) and a multinomial probit model for the selling mechanism choice (SMi = {1, 2, 3} that represent the three choices of a full-scale auction, controlled sale and private negotiation, respectively). The model is given by the equations ¼ ¼ ¼ ¼

premi pei sm1i sm2i

α 1 pei þ α 2 sm1i þ α 3 sm2i þ β′ 11 X i þ β′ 12 Z i þ ε1i α 4 sm1i þ α 5 sm2i þ β′ 21 X i þ β′ 22 W i þ ε2i α 6 pei þ β′ 31 X i þ β′ 32 V i þ ε3i α 7 pei þ β′ 41 X i þ β′ 42 V i þ ε4i

ð1Þ

where αj (j = 1, …, 7) are parameters and βjk (j = 1, …, 4, k = 1, 2) are parameter vectors measuring the effect of the exogenous explanatory variables Xi, Zi, Wi and Vi. PEi is determined using the standard probit rule
PEi ¼

1 if 0 if

pei > 0 pei ≤0

ð2Þ

and SMi is defined using the conventional restrictions on utility differences in an identified multinomial probit model with three outcomes (auction, controlled sale and private negotiation) 8 < 1 if SMi ¼ 2 if : 3 if

sm1i ≥sm2i ∧sm1i > 0 sm2i > sm1i ∧sm2i > 0 sm1i ≤0∧sm2i ≤0

ð3Þ

The vector of error terms εi = (ε1i, ε2i, ε3i, ε4i)′ is assumed to be normally distributed with mean 0 and (4 × 4) covariance matrix Σ. For parameters identification we impose the standard identification restrictions of probit models Σ22 = 1, Σ33 = Σ44 = 2 and Σ34 = Σ43 = 1. To estimate the model parameters θ = (α1, …, α7, β11, β12, …, β14, β24, Σ) we use the full information maximum likelihood [FIML] approach. Let's denote yi = (premi, pei, smi) the data of observation i. To derive the likelihood function we use that the joint distribution of (premi, pe*i , sm*1i, sm*2i) is multivariate normal. The joint density can be written as the product of marginal density of * , sm2i * ) given premi which is a multivariate normal premi (univariate normal density) and the conditional density of (pei*, sm1i density           f premi ; pei ; sm1i ; sm2i ; θ ¼ f ðpremi ; θÞf pei ; sm1i ; sm2i premi ; θÞ: Hence, the likelihood contribution of observation i is given by f ðyi ; θÞ ¼ f ðpremi ; θÞPr ðPEi ¼ pei ∧SMi ¼ smi jpremi ; θÞ; where the latter term is        ∫∫∫f pei ; sm1i ; sm2 i premi ; θÞdpei dsm1i dsm2i : The limits of the integral depend on the restrictions (2) and (3) imposed by the observations pei and smi. The FIML estimator is obtained by maximizing the log-likelihood function

Lðy; θÞ ¼

N X

ln f ðyi ; θÞ

i¼1

with respect to θ. Standard errors of the parameters can be obtained by minus the inverse of the second-order derivative of this log-likelihood functions evaluated in the maximum likelihood estimates. Parameter identification, validity and exogeneity tests As the model is a simultaneous equation model, we cannot include the same explanatory variables in all equations as this would lead to an unidentified system. To identify all αj parameters we need to exclude explanatory variables in a proper way, see for example, Judge et al. (1988, Chapter 14). We impose that the explanatory variables contained in Zi, Wi and Vi are unique and are not allowed to be part of Xi. To achieve identification the number of exogenous variables in Zi, Wi and Vi has to be larger than

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the number of endogenous variables in the corresponding equation minus 1. Note that due to the restrictions on (sm*1i, sm*2i), sm*1i and sm*2i correspond to one endogenous variable. Testing for proper exclusion restrictions (identification) is only possible in case of overidentification as we have to perform individual likelihood ratio tests and keep the system identified when excluding the tested variable. Therefore, we take in each equation more exclusion restrictions than strictly necessary to identify the parameters and the number of exogenous variables in Zi, Wi and Vi is then larger than the number of endogenous variables in the corresponding equation minus 1. We perform two set of tests: First, we test for the validity (significance) of the exclusion restrictions. In particular, we check whether the individual parameters contained in β12, β22 and (β32, β42) are significant using individual likelihood ratio tests. Second, we test for exogeneity of the exclusion restrictions. In particular, we test whether the individual explanatory variables contained in Zi, Wi and Vi are significant in the other equations using several likelihood ratio tests. Exogeneity of all variables in Zi, Wi and Vi is confirmed only if all likelihood test statistics are insignificant. Otherwise, the corresponding exclusion restriction would not be correct. If all individual parameters in βj2 (for j = 1, …, 4) are significant and the explanatory variables in Zi, Wi and Vi are not significant in the other equations the exclusion restrictions are correct and the system is properly identified. References Aktas, N., de Bodt, E., Roll, R., 2010. Negotiations under the threat of an auction. J. Financ. Econ. 98, 241–255. Baker, M., X. Pan, and J. Wurgler, forthcoming: The effect of reference point prices on mergers and acquisitions. J. Financ. Econ. Bargeron, L., Schlingemann, F.P., Stulz, R.M., Zutter, C., 2008. Why do private equity acquirers pay so little compared to public acquirers? J. Financ. Econ. 89, 375–390. Bebchuk, L.A., Cohen, A., Ferrell, A., 2002. Does the evidence favor state competition in corporate law? Calif. Law Rev. 90, 1775–1821. Betton, S., Eckbo, B.E., Thorburn, K.S., 2008. Corporate Takeovers. In: Eckbo, B.E. (Ed.), Handbook of Corporate Finance, Empirical Corporate Finance, Vol. 2. Elsevier, North-Holland, pp. 291–429. Blouin, J.L., Bushee, B.J., Sikes, S.A., 2011. Tax-sensitive institutional investors. Tech. rep., American Accounting Association Annual Meeting — Tax Concurrent Sessions. . Boone, A.L., Mulherin, J.H., 2007. How are firms sold? J. Finance 62, 847–875. Boone, A.L., Mulherin, J.H., 2008a. Do auctions induce a winner's curse? Evidence from the corporate takeover market. J. Financ. Econ. 89, 1–19. Boone, A.L., Mulherin, J.H., 2008b. Do private equity consortiums impede takeover competition. Working paper, University of Georgia. Boone, A.L., Mulherin, J.H., 2009. Is there one best way to sell a company? Auctions versus negotiations and controlled sales. J. Appl. Corp. Finance 21 (3), 28–38. Boone, A.L., Mulherin, J.H., 2011. Do private equity consortiums facilitate collusion in takeover bidding? J. Corp. Finance 17, 1475–1495. Bullow, J., Klemperer, P., 2009. Why do sellers (usually) prefer auctions? Am. Econ. Rev. 99, 1544–1575. Comment, R., Schwert, W., 1995. Poison or placebo? evidence on the deterrence and wealth effects of modern anti-takeover measures. J. Financ. Econ. 39, 3–43. Cumming, D., Siegel, D.S., Wright, M., 2007. Private equity, leveraged buyouts and governance. J. Corp. Finance 13, 439–460. Dittmar, A. K., D. Li, and A. Nain, forthcoming: It pays to follow the leader: Acquiring targets picked by private equity. J. Financ. Quant. Anal. French, K.R., McCormick, R.E., 1984. Sealed bids, sunk costs, and the process of competition. J. Bus. 57, 417–441. Gorbenko, A.S., Malenko, A., 2009. Strategic and Financial Bidders in Takeover Auctions. Working paper, Stanford University. Greenwood, R., Schor, M., 2009. Investor activism and takeovers. J. Financ. Econ. 92, 362–375. Hansen, R., 2001. Auctions of companies. Econ. Enq. 39, 30–43. Jandik, T., Makhija, A.K., 2005. Leverage and the complexity of takeovers. Financ. Rev. 40, 95–112. Judge, G.G., Hill, R.C., Griffiths, W.E., Lutkepohl, H., Lee, T.-C., 1988. Introduction to the theory and practice of econometrics, 2d ed. John Wiley & Sons, New York. Lehn, K., Poulsen, A., 1989. Free cash flow and stockholder gains in going private transactions. J. Finance 44, 771–787. Lehn, K., Netter, J., Poulsen, A., 1990. Consolidating corporate control: dual-class recapitalizations versus leveraged buyouts. J. Financ. Econ. 27, 557–580. Macias, A., Cain, C.A., Sanchez, J.M., 2011. Can targets benefit from negotiations? Evidence from auctions and negotiations. Working paper, Texas Christian University. O'Brien, P., Bhushan, R., 1990. Analyst following and institutional ownership. J. Account. Res. 28, 55–65. Officer, M.C., Ozbas, O., Sensoy, B.A., 2010. Club deals in leveraged buyouts. J. Financ. Econ. 98, 214–240. Pinkowitz, L., 2000. The market for corporate control and corporate cash holdings. Working paper, Georgetown University. Povel, P., Singh, R., 2006. Takeover contests with asymmetric bidders. Rev. Financ. Stud. 19, 1399–1431. Pozen, R.C., 2007. If private equity sized up your business. Harv. Bus. Rev. 11, 78–87. Roberts, M.R., Whited, T.M., 2011. Endogeneity in Empirical Corporate Finance. Financial research and policy working paper No.FR 11–29. The Bradley Policy Research Center, University of Rochester. Rogo, R., 2010. The effect of buyers' valuation uncertainty on the seller's choice of selling mechanism. Working paper. Sauder School of Business, University of British Columbia. Schlingemann, F.P., Stulz, R.M., Walkling, R.A., 2002. Divestitures and the liquidity of the market for corporate assets. J. Financ. Econ. 64, 117–144. Shleifer, A., Vishny, R., 1992. Liquidation values and debt capacity: a market equilibrium approach. J. Finance 47, 1343–1366. Simsir, S.A., 2008. The Information Content of Deal Initiation in Mergers and Acquisitions. Working paper, Cornell University. Stulz, R.M., Walkling, R.A., Song, M.H., 1990. The distribution of target ownership and the division of gains in successful takeovers. J. Finance 45, 817–833. Williams, E., 2007. Choosing the right buyer: Private equity versus strategic acquirer. Viewpoint 20. Xie, K., 2010. The deal process, asymmetric bidders and target premia. Working paper. Olin Business School, Washington University.