Acquirer Financial Constraints, Takeover Characteristics, and Short-Term Performance of Distressed Target Takeovers

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Acquirer Financial Constraints, Takeover Characteristics, and Short‐term Performance of Distressed Target Takeovers University of Amsterdam, Amsterdam Business School Master Thesis June 2015

Student: F. L. Gelens Student number: 10034714 Email: [email protected] Supervisor: dr. V. Vladimirov Faculty: Economics and Business Program: Business Economics, Finance Track Field: Corporate Finance, Mergers & Acquisitions

Statement of Originality:

This document is written by Student F. L. Gelens who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Abstract Many motives for corporate acquisitions have been investigated, however the takeovers of financially distressed targets are less well explored. The papers that investigate distressed target takeovers examine the takeover characteristics and their wealth effects for the target and acquirer shareholders. These papers however, do not investigate the possible influence of acquirer financial constraints on these factors. This study examines whether acquirer financial constraints are related to the premiums, payment method, and short‐term performance of distressed target takeovers. Despite inconsistent findings across the different financial distress measurements used, the results suggest that acquirers of financially distressed targets are more likely to pay with equity, and this likelihood is increased when the acquirer is financially constrained. Additionally, the results of this research suggest that mergers and acquisitions in which distressed targets are combined with a financially unconstrained acquirer, lead to the highest wealth effects for the target company.

Table of Contents 1. Introduction ...... 4 2. Literature Review ...... 7

2.1 Motives for distressed target takeovers...... 7 2.2 Fire sales ...... 8 2.3 Distressed target takeovers ...... 9 2.4 Method of payment ...... 12 2.5 Determinants of short‐term performance in M&A ...... 13 2.5.1 Determinants of short‐term wealth effects for acquiring companies...... 14 2.5.2 Determinants of short‐term wealth effects for target companies ...... 14 2.6 Acquirer financial constraints ...... 15 2.7 Hypothesis ...... 16 3. Research Design ...... 18 3.1 Data and sample selection ...... 18 3.1.1 Data ...... 18 3.1.2 Sample selection ...... 18 3.2 Methodology ...... 19 3.2.1 Financial constraints and Financial distress ...... 19 3.2.2 Financial distress and the takeover premium ...... 20 3.2.3 Financial distress and the method of payment ...... 22 3.2.4 Short‐term M&A performance and its relationship to financial distress and takeover characteristics ...... 24 4. Results ...... 28

4.1 Descriptive statistics ...... 28 4.2 Regression results ...... 31 4.2.1 Premium Regression ...... 31 4.2.2 Method of payment Tobit regressions ...... 32 4.2.3 CAR Regressions ...... 34 4.3 CAAR t‐tests...... 35 4.3.1 Full cash versus Full stock CAARs ...... 35 4.3.2 CAARs differences by acquirer constraints and target distress ...... 36 4.4 Robustness Checks ...... 37 4.4.1 Premium robustness check ...... 37 4.4.2 Method of payment robustness check ...... 37 4.4.3 CAAR differences robustness check...... 38 5. Discussion ...... 40 6. Conclusion ...... 45 References ...... 46 Appendix ...... 50

1. Introduction

Many studies investigate the main motives for corporate takeovers. All the motives are based on creating value for the acquirer in one way. In case of acquiring a financially distressed company (a company that has difficulties paying off its financial obligations to its creditors) a number of attractive motives can be found. The acquisition of a distressed company is potentially attractive in the means of market share, efficiency enhancement, cost reduction, and diversification (Jensen, 1986; Bradley, Desai & Kim, 1988; Amit et al. 1989; Morck et al. 1990, Jensen, 1991). Little existing literature on Mergers and Acquisitions (M&A) focusses on the acquisitions of distressed target companies. Companies facing financial distress or even bankruptcy might see an acquisition as the most preferable exit path (Jensen, 1991). The stock and assets of these distressed companies often trade at prices that reflect the difficulties they face and may therefore be under pressure to sell assets or securities quickly to raise capital or to pay down debt. Acquirers may have an opportunity to acquire attractive assets or securities at a discount (Watchell et al, 2013). Attractive targets usually have interesting assets or high growth and improvement opportunities. The bidder might redeploy the distressed assets and realize post‐acquisition synergies (Bruton et al. 1994; Hotchkiss and Mooradan, 1998). However, a distressed acquisition is a risky investment due to the distress itself. The bidding company is taking a risk by acquiring such a firm since it is not sure it will be able to turn the distressed position around. If the bidder is improperly prepared, it might not be able to eliminate the target’s distress and might be subjected to a risk transfer from the distressed target (Bruyland and de Maeseneire, 2011). Bidding companies face a number of choices in their deal consideration. An acquiring company has to make choices regarding the takeover premium and the method of payment (a payment in cash, stock, or a mixture of both). The choices of these deal characteristics will have an influence on the post‐merger capital‐ and ownership structure. In case of a distressed target takeover, a number of different factors, like information asymmetry and the availability of funds, might influence these choices compared to non‐distressed target takeovers. Another, rather unexplored factor that might influence earlier mentioned takeover characteristics is the level of financial constraints faced by the acquiring company. The scarce existing literature on distressed

target takeovers mainly focuses on a distressed position of the target but does not investigate constraints of the acquiring company. Many of the studies focus on the post‐ merger performance of financially distressed target takeovers but do not take into account the possible financial constraints the acquiring company is facing. Alshwer et al. (2011) find that financially constrained bidders are more likely to use stock in acquisitions but do not examine financial distress for the target companies. Amit et al. (1989) find that distressed targets earn lower abnormal returns than non‐distressed targets. Hotchkiss and Mooradian (1998) focus on acquisitions of bankrupt and reorganizing firms and find positive abnormal returns for both the bidder and the target. Both these studies do not investigate the effect of acquirer constraints and target distress on the takeover premium and method of payment choice. Clark and Ofek (1994) examine distressed target takeovers and find that bidders are unsuccessful at restructuring targets. They show that the post‐merger performance of the bidding companies is negatively related to the takeover premium. Their study provides limited evidence on the takeover premium and payment choice due to testing in a non‐ multivariate way. The research of Ang and Mauck (2010) focuses on the existence of fire sales and their relation to the takeover premium. They find that distressed targets receive higher takeover premiums compared to non‐distressed targets. Furthermore they find that acquirers do not gain from these transactions both over the short‐ and the long run. Both the research of Clark and Ofek (1994) and that of Ang and Mauck (2010) do not examine the effect of acquirer financial constraints on the payment characteristics and the post‐merger performance. This study will investigate the relationships between acquirer financial constraints, the takeover characteristics, and the post‐merger performance of both the acquirer and the target in distressed target takeovers. The research question of this study is: How do acquirer financially constraints affect the takeover premium, payment choice, and post‐ merger performance in distressed target takeovers? This research will use a sample of 3,889 U.S. mergers and acquisitions between 1985 and 2014. The level of financial constraints for the acquirer is measured by the HP‐ index that was constructed by Hadlock and Pierce (2010). To check for robustness the Whited and Wu (2006) index of financial constraints will be used. In order to measure target financial distress a number of proxies, based on financials for the one or two years prior to the takeover announcement, will be examined. These proxies include

5 negative income, negative equity, the Altman‐, and Ohlson bankruptcy models (Ang and Mauck, 2011). An OLS‐regression will be used to find a relationship between the financial constraints, target distress, and the takeover premium. Following Faccio and Masulis (2005), Tobit and Ordered Probit models will be used to find evidence on the relation between financial constraints, target distress, and the method of payment. Abnormal returns to the bidding and target firm around the announcement will be used to measure short‐term performance. OLS regressions and multiple t‐tests on these abnormal returns will be used to find relationships between financial constraints, target distress, transaction characteristics, and the short‐term performance. The remainder of this study will look as follows. The next section will discuss the existing literature on financial distressed takeovers, takeover premium, method of payment, and short‐term post‐merger performance. In this section the hypothesis, based on the existing literature, will be formulated. The third section will discuss the sample selection and the methodology that is used. The fourth section describes and presents the results of the regression models that were performed including the robustness checks. The fifth section discusses the found results and compares these findings to the related existing literature and analyzes its consequences for the constructed hypothesis. Finally, the sixth section presents the conclusion and possible implications for further research.

2. Literature Review

This section discusses the existing literature on distressed target M&As. First the motives for distressed target takeovers are stated. Second, the existing literature on fire sales and financially distressed takeovers is reviewed. Next the existing literature on takeover premium and payment choice is discussed. Furthermore, the short‐term wealth effects to acquirers and its determinants are reviewed. Finally, the hypotheses are constructed.

2.1 Motives for distressed target takeovers

Many studies investigate the main motives for corporate takeovers. In case of acquiring a distressed company, a number of attractive motives can be found. The achievement of economies of scale might be attractive to an acquirer, regardless of the targets past performance. When combining business that shares resources, economies of scope can be achieved (Seth, 1990). Some firms are distressed not because they lack the resource combinations but because of inefficient management. The change of management might improve the companies’ strategy (Jensen, 1988; Schleifer & Vishny, 1989; Hayward & Hambrick, 1997). Other motives for a distressed takeover could be the enhancements of market power, by increasing market share through the acquisition. Some takeovers can create value by facilitating tax savings (Devos et al., 2009). In summary, the acquisition of a company in distress is potentially attractive in the means of increasing market share, the achievement of synergies (Bradley, Desai & Kim, 1988; Morck et al. 1990), the reduction of costs (Jensen, 1986), diversification (Amit et al. 1989; Jensen, 1991), and more efficient use of existing assets (Jensen 1991; DePamphils, 2010). Jensen (1991) states that mergers and acquisitions are an effective way of resolving financial distress. Seth (1990) states that in general M&As increase the wealth of the stockholders that are involved in the takeover. Most of this wealth is transferred to the target firm stockholders, while the effect of M&As on the wealth of the acquiring firm vary (Martynova & RenneBoog, 2008).

2.2 Fire sales

Optimal capital structure theories suggest that firms choose their debt levels such that tax and agency benefits of debt are balanced with expected costs of financial distress. One of the indirect financial distress costs is the sale of assets at deep price discounts, the so‐called fire sales (Pulvino, 1998). In his paper Pulvino (1998) investigates if financial constraints cause firms to liquidate assets at discounts to fundamental values. He uses a sample including commercial aircraft transactions between 1978 and 1991. He shows that financially distressed airlines sell their planes at a significant discount. They are more likely to sell assets to industry out‐siders at low prices, especially during market recessions when the capital constraints limit inside buyers from paying full value for distressed firm’s assets. Pulvino does not provide evidence of fire sales from the bidders’ viewpoint. Eckbo and Thorburn (2008) test for fire‐sale tendencies in case of automatic bankruptcy auctions. Their sample exists of 258 mandatory auctions of entire Swedish firms in bankruptcy. They find evidence that fire‐sale discounts exist when the auction lead to piecemeal liquidation, but not when the bankrupt firm is acquired as a going concern. They find that neither industry‐wide distress nor the industry affiliation of the buyer affect prices in going‐concern sales. Ang and Mauck (2011) provide empirical evidence on the conjecture that in economic crises, firms could be forced to sell at deep discounts, or fire sale prices. They use a sample of 5,794 transactions between 1977 and 2008 of which 2,012 transactions are identified as distressed. They identify financial distress by four proxy variables: two years of negative income, negative equity, the Altman Z‐score and the Ohlson O‐score. Furthermore they use proxies for crisis periods. They state that fire‐sales occur in several conditions: non‐efficient market pricing or in crisis periods, the distressed firm is in a weakened bargaining position, there are inadequate numbers of bidders once the target receives the first bid, bidders do not commit behavioral errors, such as overconfidence and overpay, bidders do not overestimate the target’s value, or target managers who want to retain control may be willing to sacrifice interest of claimholders to sell at a discount in order to raise cash to continue operations. They state there are three types of fire sales: A transfer of wealth from the sellers to the buyers (in case of positive synergy gains for the bidder), a lose‐lose situation, and a win‐win situation in

which target firms are able to obtain funds and bidders gain from improving target performance. They find that distressed target firms receive a 30% higher offer premium in crisis period than distressed firms in normal periods. They also receive higher premium than non‐distressed firms in crisis periods. Furthermore their evidence suggests that acquirers do not gain over both the short‐ and long‐run.

2.3 Distressed target takeovers

In their research Amit et al. (1989) use a sample of 151 targets and 141 bidder firms on US mergers and acquisitions. They divide the mergers and acquisitions into three target subgroups. The first group is financially distressed targets that choose M&A as an alternative to bankruptcy. The second group consists of highly liquid target firms. The third group consists of the remainder of M&As. They expect that highly liquid targets are more able to obtain bids with high premium above market prices. They expect that the targets that choose M&A as an alternative to bankruptcy are less likely to receive a high premium since they are unlikely to have large free cash flows. They compare the target abnormal returns around the takeover announcement between the three subgroups. To identify the three subgroups they use the Altman bankruptcy score or Z‐ score (1968) that is based on business ratios, weighted by coefficients. They find statistically significant abnormal gains for target stockholders and negative abnormal returns for the bidding stockholders for the same period. Consistent with their expectations, stockholders of distressed target firms earned the lowest abnormal returns and stockholders with highly liquid positions earned the highest returns. The abnormal returns for the bidding firms are different. Both the bidders acquiring financially distressed or highly liquid companies did not demonstrate abnormal returns that were statistically different from zero. The remainder of the M&As however showed negative abnormal returns. In their research they control for premium, size, method of payment, and tender offers but they do not investigate these relationships in further detail. Clark and Ofek (1994) study the effectiveness of mergers in restructuring distressed firms and examine determinants of the success of these restructurings by analyzing post‐merger performance using abnormal returns. They investigate 38 takeovers of financially distressed firms that occurred between 1981 and 1988. Financially distressed firms are identified by negative share returns prior to the announcement of

the acquisition. They find that the market shows some ability to forecast the restructuring’s success. Abnormal announcement period returns on the bidder’s shares are positively and significantly related to the post‐merger returns earned by the bidders on their investments in the targets. They find a positive relationship between the post‐ acquisition performance and the announcement abnormal returns. This indicates that the market identifies restructuring attempts that are not going to be successful the moment they are initiated. Furthermore, Clark and Ofek (1994) find that distressed target acquisitions involve fewer hostile takeovers, more bidders, and more targets in the same industry than acquisitions in general. They find a similar proportion of contested bids to the general population of acquisitions. They expected the premiums paid to the target shareholders of distressed targets to be different from the average target premiums. They state that the premiums will be higher if there is more to gain from combining the operations of a distressed target with the bidding company. On the other hand they state that premiums could be lower if distressed target companies have less bargaining power due to a weak condition or in case of low bidding competition. By using several factors that determine the success of the restructuring, they find a negative relationship between the size of the premium that was paid and post‐merger success of the combined firms. This implies that overpaying reduces the potential success of the restructuring. The findings for the premium, method of payment and the level of contested mergers are similar for distressed targets to those find in prior research examining non‐distressed targets. Since all tests on takeover premium, method of payment and the level of contested bidders are all tested separately, the conclusions that can be drawn from the research of Clark and Ofek (1994) are limited. Furthermore, their research does not compare regular acquisitions with financially distressed target acquisitions. Hotchkiss and Mooradian (1998) investigate a sample of 55 acquisitions involving targets that are subjected to a Chapter 11 bankruptcy. They compare firms acquired in Chapter 11 to firms that are reorganized as independent companies. They use several ex‐merger measures of success of the transaction and the abnormal returns for both the bidder and target shareholders. Their analysis shows that there is no evidence that the post‐bankruptcy performance of firms reorganizing as independent companies differs from those acquired in Chapter 11. They find that firms in the same industry most often acquire bankrupt targets. Furthermore they find that bankrupt targets are on average

purchased at a 45% discount relative to prices paid for non‐bankrupt targets in the same industry. Consistent with the idea that acquisitions of bankrupt firms create value, they find that firms merged with bankrupt targets show significant improvements in operating performance, while matching non‐bankrupt transaction show no significant improvement. They find positive significant abnormal returns for the bidder and bankrupt target at the announcement of the acquisition. For the reorganizing matching transactions, they find positive abnormal returns to the target but not to the bidding firms. One of their possible explanations for this difference in bidder’s stock price reaction is that empire‐building managers find acquiring bankrupt firms less desirables because they require more complex negotiations with creditors and the courts. Since both the bankrupt and the non‐bankrupt companies are financially distressed, their research does not compare distressed takeovers with non‐distressed takeovers. In relatively efficient markets, competing bids for a target firm often reflect the future value generated by an acquisition (Barney, 1988). However, sometimes the acquirer pays even more than the future value for a target because of the underestimation of the costs of exploiting potential synergies (Jemison & Sitkin, 1986, Roll, 1986; Salter & Weinhold, 1979). These valuation errors might lead to one overestimating bidder placing the highest offer. This phenomenon is the so‐called the winner’s curse (Oster, 1990). The chance of avoiding the winner’s curse may increase in case of distressed target acquisition in a business related to that of a potential acquirer. In case of financially distressed targets, the potential bidders will be taking more time to estimate the true value of firm. The distress of a target discourages the interest from some potentially competing bidders (Varian, 1988). In this case the acquirers who are interested in the distressed firm have more time to study the target. In case the industry of the acquirer and the target company are in the same industry, the bidding company can more easily uncover targets’ hidden problems and can more easily uncover opportunities for synergy and thus are more likely to discover undervalued assets (Dundas & Richardson, 1982). Thus, in case of a related distressed target takeover, the acquiring firm is less likely to suffer from the winner’s curse (Barney, 1988; Harrisson et al., 1991).

2.4 Method of payment

Many existing literature has been written about the determinants of the method of payment choice in M&A. Amihud et al. (1990), Chaney et al. (1991) and Martin (1996) study the determinants of payment choice in the US. Later, Faccio and Masulis (2005) and Schwieringa and Schauten (2008) focus on European countries. The determinants that are found include firms‐, deal‐ and target‐ specific factors. The choice of payment method has important implications for both the acquirer and target, including post‐ merger ownership structure, risk profile, and allocation of gains from the transaction (Faccio and Masulis, 2005; Golubov Petmezas and Travlos, 2012). According to Hansen (1987) there is an asymmetric information problem in the real market, and this problem will cause misvaluation between the acquirer and target firms. Shleifer and Vishny (1992) argue that the purchase of illiquid assets is more likely to be financed with debt in case there is more uncertainty about the value of the acquired assets. Myers and Majluf (1984) and Hansen (1987) predict that if acquirer’s stock price experiences a run‐up for a certain period before the M&A transaction, stock payment will be offered because of overvaluation. They find significant evidence that there is a positive relationship between acquirer’s stock run‐up 1 year before the announcement date and the likelihood of stock payment. Martin (1996), and Faccio and Masulis (2005) find consistent evidence for European countries. Due to limited cash and liquid assets, the payment in cash is usually financed by the use of debt. Hansen (1987) who takes into account the importance of firm size when attracting debt finds a negative relationship between the size of the acquiring firm and the probability of financing the takeover with stock. Martin (1996) uses financial leverage as a proxy for the ability to attract debt and finds a similar relationship. Faccio and Masulis (2005) and Swieringa and Schauten (2008) also find a negative relationship between the debt capacity of the bidding company and equity transaction. In their research Jensen (1986) and Fishman (1989) find a positive relationship between the liquidity, measured by the available free cash flow, of an acquirer and a payment in cash. In contrast, Faccio and Masulis (2005) find a negative relationship between the availability of cash and cash payments in European M&As. Jung, Kim and Stulz (1996) find that an acquirer with higher investment opportunities prefers to pay with stock. They define the investment opportunity

12 available as the market‐to‐book ratio and find a positive relationship with the likelihood of payment in stock. A higher market‐to‐book ratio is related to a higher level of tax‐ deductible expenditures and lower cash dividends, which make a cash payment less attractive. This evidence is consistent with the findings of Martin (1996) and Faccio and Masulis (2005). Faccio and Masulis (2005) conclude that when the acquirer and target firm are in the same industry, they can get more up‐to‐date information and reduce the risk of asymmetric information. Therefore, the target firms are willing to accept a stock payment from the acquirer in the same industry. They find a positive relationship between cross‐industry M&As and a payment in cash. Another widely studied determinant is the relative deal size. The existing literature is not in line with each other but significant relationships have been found. Grullon et al. (1997) find evidence of a positive relationship between the deal size and a payment in stock. Faccio and Masulis (2005) and Schwieringa and Shouten (2008) support this conclusion. The bigger the target firm’s assets relative to the acquirer’s assets, the more likely is a payment in equity.

2.5 Determinants of short‐term performance in M&A

In general, mergers and acquisitions create positive, statistically significant short‐term abnormal returns for targets and slightly negative abnormal returns for the acquirer. Andrade et al. (2001) and Georgen and Renneboog (2004) find large, statistically significant positive abnormal returns for the targets using an event study around the announcement. Jensen and Ruback (1983) and Jarrel et al. (1988) find slightly negative abnormal returns for the acquirers. A number of studies have sought to identify the underlying determinants of abnormal returns, including the method of payment, the relative size of the target company to the bidding company, the type of the acquisition, the growth prospects of the companies, market‐to‐book value ratios, and the number of bidding companies contesting the bid for the target company. Dennis et al. (2002) and Freund et al. (2007, 2008) argue that abnormal returns are negative for acquirers in diversifying acquisitions. Martynova and Renneboog (2006) show evidence of higher abnormal returns for targets in diversifying deals but negative abnormal returns for the acquirers. They find higher abnormal returns for targets involved in hostile deals compared to friendly deals. The opposite is found for the acquiring companies.

Furthermore Martynova and Renneboog (2006) find that abnormal returns are higher for targets in tender offers (negotiated deals) compared to mergers. By analyzing of the effect of method of payment on short‐term performance Faccio et al. (2006) find that abnormal returns are higher for cash offers compared to stock offers. Servaes (1991), Schwert (2000), and Moeller et al. (2005) report a positive relation between the acquirers’ abnormal return and their Tobin’s Q. However, Freund et al. (2008) argue that this relationship should be negative because overvalued firms are poor acquirers.

2.5.1 Determinants of short‐term wealth effects for acquiring companies Pettyway and Yamada (1986) and Scanlon Trifts and Pettyway (1989) found that acquiring firm shareholders lost significantly when relatively large firms were acquired. Asquith, Brunner and Mullins (1983), and Jarrel and Poulsen (1989) provided contrary evidence by finding a significant positive relationship between relative sizes and returns to acquiring firms. They explained this relationship by arguing that small acquisitions would move with the performance of the acquirer whereas the acquisition of a relatively larger target would not. Walker (2000) found significant associations between abnormal returns and the method of payment, the relative size of the transaction, and contesting bidders. Travlos (1987) showed that, in pure share exchanges, bidding companies experienced significant negative abnormal returns during takeover announcements, while for cash acquisitions, bidding companies returns were normal. Honert, Barr, Affleck‐Graves & Smale 1988 found that for cash acquisitions the bidding firm’s cumulative abnormal returns (CARs) would decline rapidly after the announcement. For share‐based acquisitions, bidding companies exhibited random behavior. Travlos and Papaioannou (1991) examined impacts of method of payment on bidding firms’ stock returns at the initial announcement of the takeover bids. They found that the abnormal return of bidding firms on the announcement day were negative, and 0.5% higher for cash offers.

2.5.2 Determinants of short‐term wealth effects for target companies Wansley, Lane and Yang (1983) indicated that target company shareholders earned around 16% more on average in case of cash payment acquisitions compared to stock payments. Huang and Walkling (1987) confirmed that abnormal returns to target companies associated with cash offers were significantly higher than those associated with share offers. Davidson and Cheng (1997) explained the difference in target’s

abnormal returns between cash and stock financed takeovers by the bid premium. They argued that large bid premiums are positively related to the abnormal returns, and showed that after controlling for bid premiums, there was no significant difference between cash and stock acquisitions. Suk and Sung (1997) looked at the effects of methods of payment, form of acquisition and type of offer on target firm’s abnormal returns around the takeover announcement. They showed that there is no difference in premiums between a stock and a cash offer. Georgen and Renneboog (2004) investigate European takeover bids an find that cash offers produce an abnormal announcement return to targets that is 3% higher than those offered to abnormal returns to targets in stock offers. In their research Wansley et al. (1983) investigate the relationship between abnormal returns by looking at the acquisition type and payment method. They find that the shareholder of poorly performing targets earn larger abnormal returns than shareholder of well performing targets in takeover over announcements but do not investigate what the factors are that determining this abnormal return. Furthermore they find that because of tax effects, premium should be larger for cash mergers than for stock mergers. These findings are consistent with earlier literature and can be explained by the fact that, in case of a stock transaction, the capital gains taxes may be deferred until the new securities are sold while in cash mergers, any capital gains are taxed in the year of the acquisition (Gorden and Yagil, 1981; Amihud et al. 1990; Brown and Ryngaert, 1991).

2.6 Acquirer financial constraints

Financial constraints affect the investment and cash policies of some firms. The constraints increase the costs of attracting external capital and thus make the use of internally generated funds, like cash and cash holdings, more attractive (Myers and Majluf, 1984; Greenwald et al., 1984). The financial constraints might lead to underinvestment, which reduces future growth and destroys firm value. To prevent these negative impacts and to fund these expenditures, some companies use the available internal resources (Denis and Sibilkov, 2010; Alshwer, 2011). For financially constrained companies, the changes in capital expenditures are mainly determined by the changes in cash flow. Companies facing financial constraints tend to accumulate and save more cash (Almeida et al., 2004; Denis and Sibilkov, 2010). A company facing

15 financial constraints has to restrict its investments to the most valuable ones in order to preserve its liquidity and ability to finance future growth opportunities (Almeida et al., 2004). The companies will limit their investment to the most profitable and less risky investments, and have to pass up profitable opportunities (Stein, 2003). This could suggest that financially constrained firms are more careful in making acquisitions and are therefore less likely to overvalue a company. Existing literature states that the choice between stock and cash payment in M&As depends on the relative benefits and costs of issuing stock and using cash. In case the acquiring company is facing financial constraints this has several consequences for the payment choice. As earlier mentioned, the financial constraints increase the costs of issuing equity and result into a preference for, and higher value of, internal resources including cash and cash holdings. These two distinctions likely generate the main difference in the method of payment for constrained versus unconstrained acquirers. The cost of a payment in stock, and thus the issuance of equity, is higher for constrained firms than for unconstrained. This would suggest that the use of stock payment by a financially constrained acquirer is less likely. However, when the financially constrained acquirer has other available growth opportunities, he will be more likely to save cash to reduce the uncertainty about their future financing of investments. This would indicate that the availability of growth opportunities is positively related to the likelihood of a payment in cash. In case of an unconstrained acquirer, the availability of internal resources does not determine capital investments and thus, growth opportunities should not affect the method of payment (Denis and Sibilkov, 2010; Alshwer et al. 2011). The financially constrained companies are more likely to have many unexploited investment opportunities and growth options. An acquisition can reduce financial constraints if the acquirer’s financial condition and ability to attract capital allow the target firm to engage in more profitable investments (Erel et al. 2014).

2.7 Hypothesis Based on the findings of Ang and Mauck (2011), the takeover premium is expected to be higher in case of a distressed target takeover. When the acquiring company is financially constrained, the company will be more careful in their investments and will only invest in the most profitable and less risky investments (Stein, 2003). The acquirer is therefore

less likely to overpay. The takeover premium is expected to be lower in case the acquiring company is financially constrained. The following hypothesis is constructed:

H1: There is a negative relationship between acquirer financial constraints and the takeover premium in distressed target takeovers.

In case of a financially distressed target, the existing literature suggests that the acquirer is less likely to offer cash due to the information asymmetry problem (Hansen, 1987; Shleifer and Vishny 1992; Martin, 1996; Faccio and Masulis, 2005). The evidence provided by Alschwer (2011) suggests that financially constrained acquirers are more likely to offer cash due to the increase in the cost of issuing equity and their preference to save valuable cash. The existing literature leads to believe that financially constrained acquirers are more likely to pay with stock in a merger or acquisition, and this probability is increased in case the financially constrained acquirer is involved in a distressed target takeover. The following hypothesis is constructed:

H2: There is a positive relationship between acquirer financial constraints and the likelihood of a stock payment in distressed target takeovers.

In case a company is in a distressed position, it is likely that has unexploited investments and thus has large growth potential. These opportunities might lead to high synergies, if the acquirer is able to turn the distressed position of the target and its assets around. In order to do so, the availability of capital might be beneficial (Bruton et al. 1994; Hotchkiss and Mooradan, 1998). Financially constrained acquirers have less access to capital, and therefore might be less able to exploit the investment and growth opportunities of the distressed target (Myers and Majluf, 1984; Greenwald et al., 1984). Therefore, acquisitions in which a non‐distressed acquirer is combined with a distressed target are expected to yield the highest wealth effects for both the target and the acquiring company. The following hypothesis is constructed:

H3: Mergers and acquisitions in which a non‐distressed acquirer is combined with a distressed target lead to the highest wealth effects for both the target and acquirer shareholders.

3. Research Design

In this paragraph, the conducted research is described. First, the data and sample selection method used in this study are described. Then, the research methodology is discussed and explained in detail.

3.1 Data and sample selection

3.1.1 Data For this thesis, a sample of U.S. mergers and acquisitions is collected from Thomson One Banker. This database contains an M&A section in which mergers and acquisitions occurring from 1979 and on can be found. This database includes the M&A announcements, deal characteristics including method of payment, takeover premium, deal value, and several financials about the acquiring and target companies. In order to complete the data, the financials found in Thomson One Banker are further completed with data from DataStream and Compustat. In gathering the necessary daily stock returns to calculate the abnormal returns, the CRSP database is used. In this study, the CRSP value‐weighted market index is used as the market proxy and its daily returns are collected from the CRSP database.

3.1.2 Sample selection A number of data filters are applied: (1) Completed acquisitions are selected and must be announced between 1985 and 2014. The year 1985 is chosen as the starting year because only limited data can be found on the years before. (2) Acquisitions that do not involve U.S. corporate bidders and targets are removed. In addition, acquisitions that involve government owned companies, joint‐ventures and mutual funds are removed from the sample. (3) For complete acquisitions, the bidder needs to own more than 50% of the target after the transaction and has to acquire at least 50%. This way partial or remaining interest in the target is excluded. (4) Self‐tenders, repurchases, recapitalizations, and buybacks are removed from the sample and carve‐outs, spin‐offs, split‐offs, and transactions that are announced to the public after they became effective are dropped. (5) Financial firms (SIC codes 6000 to 6999) and regulated firms (SIC codes 4900 to 4999) are excluded. The final sample consists of 3,889 acquisitions.

3.2 Methodology

3.2.1 Financial constraints and financial distress proxies In order to measure the financial constraints for the acquiring company the HP‐(or SA‐) index proposed by Hadlock and Pierce (2010) is used. They find that firm size and age are particularly useful predictors of financial constraint levels and they construct a measure of financial constraints that is based on these firm characteristics. The HP‐ index is calculated as follows:

0.737 0.043 0.040

Where Size is defined as the natural logarithm of the acquirer’s total asset capped at $4.5 billion. Firms are sorted into terciles based on their index values. Firms in the top tercile are coded as constrained. In order to test for robustness of the findings the Whited Wu (2006) index of financial constraints is used, which is calculated as follows:

0.091 0.062 0.021 0.044 0.102 0.035

Where is the acquirer’s available cash flow divided by total assets, is a dummy variable indicating if the company paid dividends to its shareholders, is the long‐term debt divided by total assets, is the natural logarithm of the total assets, is the industry sales growth, and is the company’s sales growth. Firms are sorted into terciles based on their index values. Firms in the top tercile are coded as constrained. Since there is no universally accepted measure of financial distress, several measures of financial distress are used. Based on work of Ang and Mauck (2011), in order to identify if a target company is financially distressed the following proxies are used: (1) negative income in the last twelve months; (2) two successive years of negative income; (3) negative equity in the last 12 months; (4) the Altman Z‐score (Altman, 1968); and the Ohlson O‐score (Ohlson, 1980). Both the Altman‐score and the Ohslon‐score are designed to estimate the likelihood of bankruptcy. The Altman Z‐score is calculated as follows:

1.2 1.4 3.3 0.6 0.99

Where is the target’s working capital scaled by its total assets, are the target’s

retained earnings scaled by its total assets, are the target’s earnings before interest 19

and taxes scaled by its total assets, is the target’s market value of equity scaled by

the book value of total liabilities, and are the target’s sales scaled by its total assets. Altman coded a company as distressed when the Z‐score was below a value of 1.81. The Ohlson O‐score is similar to the Altman Z‐score in the sense that it is meant to predict corporate failure by the event of bankruptcy. Ohlson criticized the model of Altman and used a logit approach that was based on a larger sample than the sample used by Altman. The Ohlson O‐score is calculated as follows:

1.32 0.407 6.03 1.43 0.0757 1.72 2.37 1.83

0.285 0.521

Where are the target’s total assets adjusted for inflation, are the target’s total

liabilities scaled by its total assets, is the target’s working capital scaled by its total

assets, is the current ratio, is a dummy variable taking the value of 1 if the total

liabilities exceed total assets and 0 otherwise, is the targets net income scaled by the

total assets, are the funds from operations scaled by the total assets, is a dummy variable taking the value of 1 if the net income was negative for the last two years and 0 | | otherwise, and is the ( )/ ||), where is the net income. The value of the Ohslon O‐score is than transformed into a probability using a logistic transformation. If the probability is exceeding 50%, the target is coded as constrained.

3.2.2 Financial distress and the takeover premium In order to test for the relationship between the financial distress and the takeover premium, an OLS regression is used including the different dummy variables for target distress (DISTRESS). In order to calculate the premium the definition of Ang and Mauck (2011) is used. The premium is defined as:

∗ 100

The first independent variable of interest is the dummy variable indicating target financial distress (DISTRESS) which takes on the value of 1 in case the target company is in financially distress. The earlier mentioned financial distress measurements are used in separate regressions. The other variable of interest included is the variable indicating the HP‐index (HPINDEX). In a separate regression the dummy variable for the top tercile of the HP‐index is included (HPINDEXTOP). In order to examine the effect of a takeover

involving a financially constrained acquirer and a distressed target on the takeover premium, interaction terms between the HP‐index Top variable and the financial distress dummies are included. Besides the variables of interest, a number of control variables can be derived to control for other factors affecting the level of takeover premium. Baker et al. (2009), Walkling and Edmister (1985) state that the premium depends on the bargaining power of the parties. For this reason a higher competition, in terms of multiple bidders, leads to a higher takeover premium. For this reason a dummy control variable for multiple bidders is included (COMP). Furthermore, an acquisition involving a hostile takeover is expected to lead to a higher takeover premium. In order to control for this effect dummy variable indicating a hostile takeover (HOSTILE) is included in the regression. The size of a company is expected to influence its negotiation power. Although previous studies were unable to find a relationship between the size of the target firm and the takeover premium, the regression controls for this effect by including the logarithm of the total value of the market capitalization (TSIZE) in the regression. The same is done for the acquiring company (ASIZE). The theory of Grondhalekar et al. (2004) suggests that firms with a higher internal cash generation and low market‐to‐book ratios tend to pay higher premiums. The Free Cash Flow theory of Jensen (1986) supports suggests the findings because these companies generally face fewer available investment opportunities and therefore look for external growth through acquisitions. For the free cash flow available (AFCF), a control variable is included. Prior literature states that premiums should be higher when the transactions is a financed with cash (Huang and Walkling, 1987; Da Silva et al. 2000). A control variable for full cash acquisitions (CASHOFFER) is therefore included. Israel (1991) argues that more leverage leads to more concentrated ownership and therefore it will lead to a higher premium. A control variable for Acquirer firm leverage (ALEVERAGE) is included which is the debt‐to‐equity ratio of the acquirer. The same is done for the target company (TLEVERAGE). Offenberg (2012) finds higher takeover premiums for tender offers. For this reason the control variable indicating a tender offer (TEND) is included in the resgression. In order to control for entity‐fixed effects in premiums, dummy variables for the different industries (INDUSTRY) are included. The following regression model is tested using OLS:

Where indicates the HP‐Index in the first regressions, HP‐index Top in the second regressions, and the HP‐index Top and the interaction terms in the third regressions. In order to check for robustness of the results, the same regression is performed but instead the WW‐index is used to identify financially constrained acquirers.

3.2.3 Financial distress and the method of payment In order to test for the relationships between financial distress and the method of payment, two models similar to Faccio and Masulis (2005) are used. These models are the Tobit and Ordered Probit models. Both the models will include a dummy variable indicating if the target company is financially distressed. Furthermore, both the models will include a variables for the HP‐index by which the effect of acquirer financial constraints on the method of payment can be measured. The setup of the Tobit and Ordered Probit will now be described.

A. Tobit regression In the Tobit regression the dependent variable is the proportion of cash that is used in the M&A transaction. The proportion is a value between 0 and 100. For this reason a two‐boundary Tobit estimator is used. The following general model is used:

∗ ,

Where indicates the variables of interest, is an independently distributed error term assumed to be normal with zero mean and variance. The dependent variable has both left and right censoring so that:

∗ 0 0, ∗ ∗ ∗ 0 100 ∗ 100 100

Where 0 and 100 are the censoring points. The parameters are estimated by the maximization of the log likelihood function. The first independent variable of interest is the dummy variable indicating target financial distress (DISTRESS) which takes on the value of 1 in case the target company is

in financially distress. The earlier mentioned financial distress measurements are used in separate regressions. The other variable of interest included is the variable indicating the HP‐index (HPINDEX). In a separate regression the dummy variable for the top tercile of the HP‐index is included (HPINDEXTOP). This dummy variable takes on the value of 1 in case the company is in the top tercile of the HP‐index distribution. In order to examine the effect of a takeover involving a financially constrained acquirer and a distressed target on the percentage of cash, interaction terms between the HP‐index Top variable and the financial distress dummies are included. The remaining independent variables in the regression are used to control for deal characteristics and target and bidder firm financial characteristics based on existing literature. The first control variable is the takeover premium (PREMIUM). Existing literature states that in case of a cash payment in M&As, the premium should be larger due to tax effects (Wansley et al., 1983; Gorden and Yagil, 2001; Amihud et al. 1990; Brown and Ryngaert, 1991). Hansen (1987),Myers and Majluf (1984), Schleifer and Vishny (1992), Martin (1996), and Faccio and Masulis (2005) all find because of asymmetric information the acquiring firm is less likely to offer cash. A high market‐to‐book ratio usually indicates that a company is overvalued. For this reason a control for the market‐ to‐book value (TMTB) of the target company is included. Hansen (1987), Martin (1996), Faccio and Masulis (2005), and Swieringa and Schauten (2008) all find that debt capacity is positively related to a payment in cash. Since studies use different indicators of debt capacity, for both acquirer firm size (ATA) and leverage (ADEBT) a control variable is included in the regression. Jensen (1986) and Fishman (1989) both find a positive relationship between the available cash to the acquiring company and a payment in cash. In contrast, Faccio and Masulis (2005) find a negative relationship. Since they both find a relationship, to control for the free cash flow to the company a variable indicating the acquirer free cash flow (AFCF) is included. Jung, Kim and Stulz (1996), Martin (1996), and Faccio and Masulis (2005) all find that an acquirer with higher investment opportunities is preferring a payment stock. This indicates a negative relationship between investment opportunities and a payment in cash. Investment opportunity available can be measured by the Tobin’s Q. For this reason the acquirer Tobin’s Q (ATOBINQ) is included as a control variable in the regression. A dummy variable (DIVERSIFY) indicating if both the target and acquirer are in the same industry is included as Faccio and Masulis 2005 found a positive relationship between cross‐

industry M&As and a payment in cash. Grullon et al. (1997), Faccio and Masulis (2005), and Schwieringa and Schouten (2008) all find that there is a positive relationship between the relative deal size and stock transaction. For this reason a control variable for the relative size of the deal (DEALSIZE) is included. Similar to Amit et al. (1989) to control for tender offers and the characteristics of the takeover, dummy variables (TENDER, HOSTILE) are included in the regression. Industry dummies are used to control for entity‐fixed effects.

B. Ordered Probit regression In line with Faccio and Masulis (2005), additional to the Tobit Model, an Ordered Probit estimation is used to check for robustness. The benefits of this model are that it allows us to focus on the qualitative decision in payment method. In the Ordered Probit model the dependent variable is 0 for pure cash deals, 1 for mixed stock and cash, and 2 for all stock deals. The same control variables as for the Tobit regression will be used. Although the magnitude of the coefficients in the Ordered Probit model cannot be interpreted directly due to scaling, the sings on the coefficients indicate the relationship with the method of payment. If the coefficient shows a positive sign, a payment in stock is more likely, if the coefficient shows a negative sign, a payment in cash is more likely.

3.2.4 Short‐term M&A performance and its relationship to financial distress and takeover characteristics First the measurement of the short‐term takeover performance will be explained. Secondly, the regression model testing for the determinants of this performance will be constructed.

A. CAR model In order to measure the short‐term performance of an acquisition the effects of the M&A on the announcement returns for both the target and the acquirer are examined using an event study. The cumulative abnormal returns (CARs) earned by both parties are measured over different event windows. The model to calculate the CARs looks as follows:

, ,

Where , denotes the cumulative abnormal return over the event window , , where 0 indicates the day of the announcement. ∑ , denotes the sum of the abnormal returns over the event window. These abnormal returns

(,) capture the effect of the announcement on the returns earned on the company’s stock. The following model is used to calculate the abnormal returns:

, ,,

Where , indicates the abnormal returns for stock “i” on day “t”. , denotes the return for stock “i” on day “t”. The , is the return of the chosen benchmark for stock “i” on day “t”. In order to calculate this benchmark return the market model is used. The market model looks as follows:

Where denotes the estimated constant, denotes the estimated sensitivity of the

return on stock “i” on day “t”, denotes the return on the market index on day “t”,

and denotes the error term of stock “i” on day “t”. In order to get the most accurate estimation of this benchmark model an estimation window is set starting 250 days prior to the takeover announcement until 50 days prior to the announcement. This estimation window will be used to determine the “normal” return the stock would have generated if the takeover announcement would not have taken place. Using OLS the coefficients of the market model can now be determined and the benchmark returns can be calculated. Performing separate regressions for each firm using data within the estimation window and save the intercepts and coefficients of the independent variables and implementing these in the market model, the “normal” return can now be predicted. After calculating these normal returns, the cumulative abnormal returns are calculated over different event windows: 5, 5, and 1, 1. These CARs are then used in an OLS regression to test for relationships with the variables of interest. The regression model will be discussed in the next subsection. Additionally, the cumulative average abnormal returns (CAAR) for the total of companies are calculated as follows:

1 , ,

Where , is the CAAR over the event window ,, N denotes the number of companies, and ∑ , denotes the sum of the CARs for each stock “i” over the

event window ,. These CAARs are calculated separately for constrained acquirers versus non‐constrained acquirers; and for financially distressed versus non‐distressed takeovers. Furthermore separate CAARs are calculated for cash offers and stock offers. By calculating these CAARs separately, a t‐test can be performed to test for a significant difference between constrained acquirers versus non‐constrained acquirers and distressed versus non‐distressed takeovers. In order to check for robustness of the results, similar t‐tests on the differences are performed using the WW‐index to identify financially constrained acquirers.

B. CAR Regressions In order to test for the relationships between the CARs, financial constraints, financial distress, and the takeover characteristics, an OLS regression is used. The independent variable in this model is the CAR for either the acquiring or target company. The variables of interest are the variable for the HP‐index (HPINDEX), the dummy variable indicating if the company is in the top tercile of the HP‐index distribution (HPINDEXTOP), the dummy variable indicating a financially distressed takeover (DISTRESS). In order to examine the effect of a takeover involving a financially constrained acquirer and a distressed target on the takeover CARs, interaction terms between the HP‐index Top variable and the financial distress dummies are included. A number of variables affecting the short‐term performance can be found in the existing literature. These variables are included in the regression to control for other factors that might relate to the CARs. Dennis et al. (2002), Martynova and Renneboog ( 2006), and Freund et al. (2007, 2008) all find a relationship between a diversifying deal and the abnormal returns for both the acquirer and target company. Martynova and Renneboog (2006) further find relationships between the CARs for both parties involved in hostile deals and tender offers. For these reasons a control variables indicating a diversifying deal (DIVERSIFY), hostile takeover (HOSTILE), and tender offer (TEND) are included in the regression. Faccio et al. (2006) find that abnormal returns are higher for cash offers compared to stock offers. For this reason a dummy control variable indicating the percentage of cash is included in the regression (PERCCASH). Servaes (1991), Schwert (2000), and Moeller et al. (2005) Freund et al. (2008) find a relationship between the

CAR and the Tobin’s Q of the acquirer. For this reason this variable (ATOBINQ) is added to the regression. In addition controls for the transaction size relative to the size (RELSIZE) of the bidder and competing bidders (COMP) are included. The following regression model is tested using OLS:

4. Results

In this paragraph, the results from the conducted tests are stated. First, the summary statistics for the takeover characteristics, takeover premium, method of payment, and abnormal returns for the sample will be presented. Then, the regression results for the determinants of the takeover premium and method of payment are stated. Hereafter, the regression results of the abnormal returns for both the target and acquirer will be presented. Next, the results of the t‐tests performed to assess whether there is a difference in the abnormal returns between financially constrained and financially unconstrained acquirers in distressed target takeovers are listed. Finally, the results of the tests using the WW‐index, in order to check for robustness of the results, will be presented.

4.1 Descriptive statistics Table 1a in the Appendix presents the summary statistics for the different proxies for the different financial constraints (NII1, NNI2, NEQ, ALTMAN, and OHLSON) and financial distress proxies (HPTOP, WWTOP). Looking at the different distress proxies, the portion of financially constrained target companies in the sample varies between 4.84% and 42.27%. The proportion of financially constrained companies indicated by negative equity seems low compared to the other proxies while the proportion indicated by one year of negative income seems rather high. Leaving out these proportions, the sample contains between 21.25% and 39.17% constrained target takeovers. Looking at transactions for which both the acquirer and the target are in a constrained or distressed position the sample contains between 1.60% and 18.06% distressed target takeovers by financially constrained acquirers. Leaving out the proportions of the negative equity and Altman Z‐score, which are relatively low compared to the other proxies, these proportions vary between 11.00% and 18.06% in our sample. Table 1b in the Appendix presents the correlation coefficient between the variables in the sample. Looking at the proxies for the acquirer financial constraints, the HP‐index and the WW‐index, a high positive correlation is found that is significant at a 1% significance level. These findings are consistent with the findings of Farre‐Mensa and Ljungqvist (2013). All proxies for financial distress are positively correlated with

coefficients varying between 0.14 and 0.95. The correlations coefficients are statistically significant a 1% level. There is a strong, positive correlation between the variable indicating negative income in the last year and the variable indicating negative income for the last two years. This indicates that companies that are experiencing negative income two years prior to the takeover announcement, are likely to experience this negative income the year after. Both the correlation coefficients between the, negative income proxies and the variable indicating financial distress according to the Ohlson O‐ score are positive and around 0.62. The correlation coefficients between the variable indicating a negative equity value and the other proxies are all below 0.33. Table 2a in the Appendix shows the takeover characteristics by the financial constraints and financial distress proxies. The sample contains 5.22% competing bids, 1.77% hostile takeovers, and 21.37% tender offers. Comparing the proportions by the different proxies of financial distress for the target companies. It is found that all distressed target takeovers in our sample include less competitive bids, hostile takeovers, tender offers, and more diversifying deals compared to non‐distressed target takeovers. The findings on the proportions of hostile takeovers are consistent with the findings of Clark and Ofek (1994). Looking at these proportions for the acquirer financial constraints, it is found that bidding companies that are identified as financially constrained involve fewer competitive bids, hostile takeovers, tender offers, and diversifying takeovers. These findings for the HP‐index are consistent with the findings for the WW‐index. Looking at the distribution of the method of payment choice in the sample, the proportion of takeovers involving a full stock transaction is the largest with around 45.11%, followed by 41.75% full cash transactions. Comparing the method of payment proportions between distressed and non‐distressed targets it is found that, for all proxies, the percentage of full cash payments is lower in financially distressed target takeovers. These findings are the same for the proportion of takeovers involving a mixture of cash and equity payment, except for the negative equity distress indicator. The percentage of stock payments increases as the target is coded as financially distressed by all proxies, except for the negative equity proxy. When looking at acquirer financial constraints, the acquirers labeled as constrained, on average use more cash and less stock. The percentage of cash payments is lower for both acquirers in the top tercile of the HP‐index and the WW‐index while the percentage of stock is higher. Table

2b in the Appendix takes a more detailed look at the distribution of the method of payment choice by examining differences in method of payment by both target distress and acquirer financial constraints. Panel A looks at the distribution of the method of payment for the whole sample. Panel B looks at the distribution of the method of payment comparing financially constrained with financially unconstrained acquirers. In case the acquirer is financially unconstrained, the proportion of cash used in a distressed target takeover is typically larger in the sample while the proportion of stock and mixed transactions is lower. In case the acquirer is financially constrained, the proportion of stock is typically larger in the sample while the proportion of cash and mixed transactions is lower. Table 2c in the Appendix shows the summary statistics for both the bidder and target divided grouped according to the different proxies for target distress and acquirer financial constraints. Column 1 reports the summary statistics for the full sample. The following columns show the summary statistics by the proxies for distress and constraints. The highest number of observations is 3,628. Comparing the statistics for the targets labeled as constrained to full sample average it is found that the distressed targets are smaller than their non‐distressed counterparts, in terms of, market value, total assets, net income, and sales. Looking at the statistics for acquirer it appears that the average Tobin’s Q is lower for acquirers facing financial constraints. This could indicate that these acquirers have less investment opportunities than unconstrained acquirers (Jung, Kim, and Stulz 1995; Martin 1996). Furthermore the Debt‐to‐Equity ratio is lower for financially constrained acquirers, this could indicate less concentrated ownership (Israel, 1991). On average the financially constrained acquirers in the sample are smaller in terms of market value, total assets, net income, and sales. On average, the takeover premium is higher when the target company is financially distressed. This finding is consistent between all proxies for financial distress. This finding is consistent with the evidence found by Ang and Mauck (2010). Furthermore, the average takeover premiums paid by financially constrained acquirers are higher than those paid by unconstrained acquirers in the sample. Table 2d takes a more detailed look at the differences in takeover premiums by examining the differences in the takeover premium for both target distress and acquirer financial constraints. No significant differences between the takeover premiums of constrained and unconstrained acquirers are found. For distressed target companies the takeover

premium on average is higher than for those that are non‐distressed. Three of the five distress proxies show positive significant differences. This indicates that the distressed targets in our sample receive higher premiums than the non‐distressed targets. The differences in premiums for the negative equity and Altman proxies are insignificant. Table 2e Average abnormal returns around the takeover announcement

Panel A: CAARs Acquirer Event Window Mean Q1 Median Q3 [‐5, 5] ‐0.93%(‐3.82)*** ‐5.98% ‐0.84% 3.89% [‐1, 1] ‐0.68%(‐3.57)*** ‐4.06% ‐0.47% 2.60% Panel B: CAARs Target Event Window Q1 Median Q3 [‐5, 5] 25.36%(42.53)*** 7.60% 21.00% 38.05% [‐1, 1] 22.23%(40.02)*** 4.98% 17.39% 33.70% *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 2e shows the average abnormal returns around the takeover announcement earned by the acquiring and target firm. Panel A shows the average abnormal announcement returns for the acquirer for the different event windows. The found abnormal returns for the acquirer are slightly negative and significant at a 1% level. These findings are consistent with the work of Ruback (1983), Jarrel et al. (1988) and Amit et al. (1989). Panel B shows the average abnormal announcement returns for the target companies for the different event windows. Large, statistically significant abnormal returns are found for the target companies. These findings are consistent with the evidence found by Andrade et al. (2001), Georgen and Renneboog (2004), Amit et al. (1989), and Hotchkiss and Mooradian (1998). Table 3b in the Appendix shows the acquirer and target average abnormal announcement returns by the target distress and acquirer financial constraints.

4.2 Regression results

4.2.1 Premium Regression Table 3a in the Appendix shows the OLS regression results of the takeover premium on the different financial constraints and financial distress proxies and the control variables as described in the methodology paragraph. Due to high correlations with the HP‐index and HP‐index Top variables, the ASIZE variable is dropped from the model.

The main reason for this correlation (‐0.63) is caused by the indirect inclusion of the Size variable that is used in order to calculate the HP‐index. In the results of the regressions including the financial distress proxies and the HP‐ index (models 1‐5), significant coefficients on three of the five distress proxies are found. For the NNI1, NNI2, and NEQ variables positive coefficients are found, which are statistically significant at the 1%, 1%, and 10% levels respectively. The coefficients on these significant variables vary between 4.84 and 12.62. These significant coefficients indicate that there is a positive relationship between target distress and the takeover premium. The other financial distress variables have the expected positive sign, but the coefficients show non‐significant relationships. The coefficients on the HP‐Index variable show the expected negative sign but are all statistically insignificant except for the coefficients in the NNI1 and NNI2 regressions, which are significant at the 10% significance level. When instead of the HP‐index variable the HP‐Index Top variable is included in the regressions (models 6‐10) similar coefficients are found on the financial distress variables. All the coefficients on the HP‐index Top variable are negative and not statistically significant. When interaction terms between the financial distress proxies and the HP‐index Top variable are added to the regression (models 11‐15), similar significant coefficients for the NNI1 and NNI2 regressions are found. The NEQ variable no longer shows a statistically significant result. All coefficients on the interaction terms are insignificant and thus show no relation between a combination of a constrained acquirer and distressed target and the takeover premium. The coefficients on the HOSTILE, TSIZE, and TEND control variables are all significant at least at the 5% significance level and have the expected signs based on the existing literature .

4.2.2 Method of payment Tobit regressions Table 3b in the Appendix shows the Tobit regression results of the percentage of cash on the different financial constraints and financial distress proxies and the control variables as described in the methodology paragraph. The TMTB variable is dropped due to vary low observations. The ATA variable is dropped due to the same reason as for which the ASIZE variable is dropped. In the results of the regressions including the financial distress proxies and the HP‐ index (model 1‐5) significant negative coefficients are found on four of the five financial distress dummies. The NNI1 variable shows a coefficient of ‐14.41, the NNI2 variable shows a coefficient of ‐15.11, the Altman variable shows a coefficient of ‐19.63%, and

the Ohlson variable shows a coefficient of ‐32.23. These variables are significant at the 10%, 10%, 5%, and the 1% significance level respectively. The coefficient on the NEQ variable shows an insignificant relationship. The significant negative coefficients indicate a strong negative relationship between target distress and the percentage of cash financing, as was expected. The coefficients on the HP‐Index variable are negative and significant at the 1% significance level for the first five models. The coefficients vary between ‐49.85 and ‐45.10. This indicates a negative relationship between level of the HP‐index and the percent of cash financing in the takeover deals. When instead of the HP‐index variable the HP‐Index Top variable is included in the regressions (models 6‐ 10) similar coefficients are found on the financial distress variables only the get slightly more negative and more significant, except for the NEQ variable. The coefficients on the HP‐index Top variable are all negative and significant at the 1% significance level. The coefficients vary between ‐29.87 and ‐25.62. These findings indicate that there is strong negative relationship between the companies that are in the top tercile of the distribution of the HP‐index, and thus are coded as financially distressed, and the percent of cash financing in the takeover deals. These findings are consistent with the expected relationship. In the last five models (11‐15) the interaction terms between the financial distress proxies and the HP‐index Top variable are included. The inclusion of this interaction terms increases the level of correlation between the variables, increasing the standard errors of the distress variables and the HP‐index Top variable. The coefficients on the distress proxies all become statistically insignificant and so does the HP‐index Top variable in the NEQ and Altman model. Three out of five interaction terms are significant. The coefficients on the interaction terms that are significant are negative and vary between ‐60.51 and ‐52.51. These significant coefficients suggest that there is a strong negative relationship between a takeover in which a financially constrained acquirer and a financially distressed target are combined and the percent of cash financing in the deal. This effect is larger than the two separate effects that were found in models 6‐10. The coefficient on the control variables TDEBT and TSIZE are both significant a negative. This could indicate that the company has more bargaining power and more debt capacity and thus might prefer a payment in stock. The coefficient on the AFCF control variable is positive, this consistent with the findings of Jensen (1986) and Fishman (1989). The coefficient on the AQ variable is negative and significant, this is consistent with the findings of Jung, Kim and Stulz (1996), Martin

(1996) and, Faccio and Masulis (2005). The coefficient on the DEAL control variable is significant and negative which is consistent with the findings of Grullon et al. (1997), Faccio and Masulis (2005), and Schwieringa and Schouten (2008).

4.2.3 CAR Regressions Table 3c and 3d in the Appendix shows the results of the OLS regressions of the acquirer abnormal returns on the different financial constraints and financial distress proxies and the control variables as described in the methodology paragraph. Table 3c shows the results for the 11‐day event window (indicated by [‐5, 5]) and Table 3d shows the results for the 3‐day event window (indicated by [‐1, 1]). The results of the OLS regressions in Table 3c and Table 3d both show insignificant coefficients on the distress dummies, HP‐index, HP‐index Top and interaction terms (the significant relationships found are non‐consistent). This indicates that no significant relationship was found between acquirer financial constraints, target distress and a combination of the two and the abnormal announcement returns of the acquirer. The coefficients on the PERCCASH and AQ control variables are significant and the signs of the coefficients are consistent with the existing literature. Table 3e and 3f in the Appendix shows the results of the OLS regressions of the target abnormal returns on the different financial constraints and financial distress proxies and the control variables as described in the methodology paragraph. Table 3c shows the results for the 11‐day event window (indicated by [‐5, 5]) and Table 3d shows the results for the 3‐day event window (indicated by [‐1, 1]). The findings for the regressions on both the 11‐ and 3‐day window show very similar results in both event windows. Looking at the first five regressions (model 1‐5), three out of five financial distress proxies show positive, at the 5% level, significant coefficients. This indicates a positive relationship between target distress and the abnormal returns for the target, both for the 11 and 3 days around the takeover announcement. All the five regressions show negative, at least at the 5% level, significant coefficients on the HP‐index variable. This indicates a negative significant relationship between the HP‐index and the target abnormal returns. The regressions including the HP‐index Top variable show negative, at least at the 5% level, significant coefficients. This indicates a negative significant relationship between the fact that the company is coded as constrained and the abnormal returns for the target company both 11 and 3 days surrounding the takeover

announcement. Models 6‐11 shows similar coefficients on the financial distress dummies. The last five regression models, including the interaction terms, do not show significant coefficients and thus no significant relations are found between the combination of a financial constrained acquirer and a financially distressed target company involved in a takeover, and the target abnormal return, and the target’s abnormal return.

4.3 CAAR t‐tests

4.3.1 Full cash versus Full stock CAARs Table 4 in the Appendix shows the differences in the cumulative average abnormal returns by method of payment, financial constraints, and financial distress. Comparing the returns for full stock with full cash transactions, it appears that both targets and acquirers experience a higher return in full cash transactions, irrespective of the financial constraints faced by the acquirer. This is similar to the findings of Faccio and Masulis (2006). Looking at the acquirer’s CAARs it is found that, in case the acquirer is financially unconstrained, the difference in CAARs between full cash and full stock transactions for the acquiring company are 2.80% and 2.55%, for the 11‐ and 3‐day event window respectively. In case the acquirer is identified as constrained, these differences are 5.24% and 5.06%. These differences are all significant at the 1% level. In case of a full cash transaction, the 3‐day target CAAR is 0.76% lower in case that the acquirer is financially constrained. This difference is significant at the 5% significance level. This difference is insignificant for the 11‐day event window. Looking at the target's CAARs it appears that, in case the acquirer is financially unconstrained, the difference in CAARs between full cash and full stock transactions for the target company are 14.35% and 9.41%, for the 11‐ and 3‐day event window respectively. In case the acquirer is identified as constrained, these differences are 12.66% and 6.46%. These differences are all significant at the 5% level. In case of a full cash transaction, the 3‐day target CAAR is 6% lower in case that the acquirer is financially constrained. This difference is significant at the 1% significance level. This difference is insignificant for the 11‐day event window.

4.3.2 CAARs differences by acquirer constraints and target distress The top tables in Table 4 show the acquirer CAARs by event window. It appears that the acquirers CAARs are the highest in case of an unconstrained acquirer. For all distress proxies, except for the NEQ variable, it appears find that the CAARs are the highest (less negative) when the HP‐Index Top variable is 0 in both the 11‐ and 3‐day event window. In case the acquirer is coded as non‐constrained, the highest acquirer CAARs are experienced when the target company is identified as distressed. The CAARs of financially unconstrained acquirers involved in a takeover with a financially distressed target, are between a loss of ‐0.35% and a gain of 0.22% on average, 11 days around the announcement. Looking at the 3‐day event window, these gains vary between a loss of ‐ 0.52% and a gain of 0.05%. These findings indicate that acquiring companies experience the least negative wealth effect in case they are financially constrained their selves and take over, or involve in a merge with a target company that is financially distressed. These differences are significant at the 5% level for the NNI1, NNI2, and Ohlson variable. In case the acquirer is financially constrained, no consistent significant differences in CAARs are found between the acquisitions of a distressed versus a non‐distressed targets. The bottom tables in Table 4 in the Appendix show the target CAARs by event window. It is found that the targets CAARs are the highest in case of an unconstrained acquirer. For all distress proxies, except for the NEQ variable, it appears that the CAARs are the highest when the HP‐Index Top variable is 0 in both the 11‐ and 3‐day event window. In case the acquirer is coded as non‐constrained, it appears that the highest target CAARs are experienced when the target company is identified as distressed. The CAARs of distressed targets involved in a takeover with a financially constrained acquirer, gain between 30.57% and 34.75% on average, 11 days around the announcement. Looking at the 3‐day event window, these gains vary between 27.56% and 30.43%. These findings indicate that target companies experience the highest announcement wealth effect in case a distressed target is taken over by, or is involved in a merger with, a financially unconstrained acquirer.

4.4 Robustness Checks

In order to check for robustness a number of different robustness checks, as described in the methodology paragraph, will be performed on the earlier regressions. For the premium and the CAAR differences, the WW‐index of financial constraints is used to check for robustness of the results. In order to check for robustness of the findings of the method of payment regression, an Ordered Probit model is used, which is similar to the robustness test performed by Faccio and Masulis (2005).

4.4.1 Premium robustness check Table 5a in the Appendix shows the results for a similar premium regression as described in the methodology paragraph, only now the HP‐index and HP‐index Top variables are replaced by the WW‐index and the WW‐index Top variable. For convenience, only the variables of interest are presented. For the NNI1 and NNI2 distress dummies similar positive coefficients, which are significant at least at the 10% level, are found. For the other distress proxies, no significant coefficients were found. Similar insignificant coefficients are found on the WW‐index, WW‐index Top and interaction terms. Similar to the first premium regression, no significant relationships between acquirer financial constraints and the takeover premium were found.

4.4.2 Method of payment robustness check Table 5b in the Appendix shows the same model as used in the Tobit regression, only now an Ordered Probit regression is used. For convenience, only the variables of interest are presented. The coefficients in the Ordered Probit model show similar results to the Tobit model. Although the coefficients of the Ordered Probit model cannot be interpreted, a positive coefficients for the NNI1, NNI2, and Ohlson variables is found indicating that, in case the target is financially distressed, a payment in stock is more likely (this is similar to the results of the Tobit model in which the percentage of cash is decreased if the target is financially distressed). These coefficients are all significant at the 10% level. In the Ordered Probit model the coefficients on the NEQ variable show positive coefficients, where no significant coefficients where found in the Tobit model. The coefficient on the Altman variable is insignificant in the Ordered Probit model. The coefficients on the HP‐index variable are all positive and significant at the 1% level, indicating a positive relationship between the HP‐index level and the likelihood of

a payment in stock. These findings are similar to findings in the Tobit model. In models 6‐10 in the Ordered Probit model the HP‐index Top variable is used. Positive and significant coefficients on these variables are found, indicating a positive relationship between the acquirer being in the top of the distribution of the HP‐index, and thus being coded as constrained, and the likelihood of a stock payment. These findings are similar to the negative relationship between the HP‐index Top variable and the percentage of cash that was found in the Tobit model. The last five models in the Ordered Probit model (10‐15) show the estimation models including the interaction terms. The coefficients on the NNI1, NNI2, and Ohlson variables are positive and significant. These coefficients indicate a positive relationship between a takeover in which a distressed target is combined with a financially constrained acquirer and the likelihood of a payment in stock. These findings are similar to the outcomes of the Tobit regression model.

4.4.3 CAAR differences robustness check. In order to check for the robustness of the results of the t‐tests on the CAARs, the same t‐tests are performed using the WW‐index. The HP‐index, HP‐index Top and the interactions between HP‐index Top and the financial distress dummies are replaced by the WW‐index variable, the WW‐index Top variable, and interaction terms between WW‐ index Top and the distress dummies. The results of these t‐tests can be found in Table 5c in the Appendix.

Full cash versus full stock CAARs Comparing the returns for full stock with full cash transactions, it appears that both the acquirers and targets experience the highest returns in case of a full cash transaction compared to full stock transactions, irrespective of the financial constraints of the acquirer. These findings are similar to the results for the CAAR t‐tests using the HP‐ index. Looking at the target’s CAARs it is found that, in case the acquirer is financially unconstrained, the difference in CAARs between full cash and full stock transactions for the target company are 12.39% and 10.66%, for the 11‐ and 3‐day event window respectively. In case the acquirer is identified as constrained, these differences are 10.03% and 8.84%. These differences are all significant at the 1% level. In case of a full

cash transaction, the 3‐day target CAAR is 2.43% lower in case that the acquirer is financially constrained. This difference is 2.15% in the 11‐day event window. These differences however, are both not statistically different from zero. Looking at the acquirer’s CAARs it appears that, in case the acquirer is financially unconstrained, the difference in CAARs between full cash and full stock transactions for the acquiring company are 2.44% and 2.44%, for the 11‐ and 3‐day event window respectively. In case the acquirer is identified as constrained, these differences are 5.02% and 4.78%. These differences are all significant at the 1% level. In case of a full cash transaction, the 3‐day target CAAR is 0.80% lower in case that the acquirer is financially constrained. This difference is 0.98% in the 11‐day event window. However, these differences are both not statistically different from zero. The findings for the robustness checks on the differences in CAARs support the findings that the largest CAARs are experienced in case of full cash transactions for both the acquiring and the target company irrespective of the financial constraints faced by the acquirer.

CAARs differences by acquirer constraints and target distress Similar to the earlier found results, it appears that the acquirers CAARs are the highest in case the acquirer is financially unconstrained. For all distress proxies, except for the NEQ variable, it is found that the CAARs are the higher when the WW‐index Top variable is 0, in both the 11‐ and 3‐day event window. However these differences seem less significant compared to the findings of the model that uses the HP‐index. In case the acquirer is coded as non‐constrained, the highest acquirer CAARs are experienced when the target company is identified as distressed, except for the Ohlson distress dummy in the 11‐day event window. However, these differences are not statistically different from zero. These findings are not consistent with the earlier found results. Similar to the earlier found results, it appears that the targets CAARs are the highest in case the acquirer is financially unconstrained. For all distress proxies, except for the NEQ and Altman variables, the CAARs are the higher when the WW‐index Top variable is 0, in both the 11‐ and 3‐day event window. In case the acquirer is coded as non‐ constrained, the highest target CAARs are found when the target company is identified as distressed. The differences between financial distressed and non‐distressed targets

for the NNI, NNI2, and Ohlson variables are all significant at the 1% level. The CAARs of distressed targets involved in a takeover with a financially constrained acquirer, gain between 35.22% and 38.62% on average, 11 days around the announcement. Looking at the 3‐day event window, these gains vary between 30.84% and 33.83%. These findings indicate that target companies experience the highest announcement wealth effect in case a distressed target is taken over by, or is involved in a merger with, a financially unconstrained acquirer. These findings are similar to the earlier found results.

5. Discussion In this section the results as described in the previous paragraph are analyzed and compared with the results of previous research. Furthermore, the what consequences of the found results are for the constructed hypotheses.

Ang and Mauck’s financial distress proxies In this research, the financial distress proxies proposed by Ang and Mauck (2011) are used. Their financial distress proxies however show some weaknesses. The NNI1 variable, indicating one year of negative income, might not be a good indicator of financial distress. A company facing one year of negative income does not necessarily have to be financially distressed. The same holds for the NEQ variable, indicating negative equity one year prior to the announcement. The Altman Z‐score also shows some weaknesses. The Altman Z‐score has been criticized by many scholars, for example for the small sample that was used to construct it. Therefore the outcomes on the other, more reliable variables (NNI2 and Ohlson) are valued the most.

Acquirer constraints and the takeover premium in distressed target takeovers Table 2d in the Appendix shows that distressed target companies in the sample receive higher takeover premiums compared to non‐distressed target companies. These results support the findings of Ang and Mauck (2011). These findings are supported by the evidence from the OLS regressions explaining the takeover premium in Table 3a in the Appendix. The regressions support the positive relationship, however the evidence is limited. Only 2 out of 5 distress dummies (NNI1 and NNI2) show a significant positive relationship, where in the regressions of Ang and Mauck (2011) all the distress proxies showed significant results. In order to test for the expected negative relationship between the acquirer financial constraints and takeover premium, the HP‐index and a top tercile dummy were included in the premium regression. No significant relationship was found between acquirer financial constraints and the takeover premium. Robustness checks using the WW‐index as a measure of acquirer financial constraints neither showed significant relationships. The coefficients on the financial constraints all show the expected negative sign, however the tests on this sample lacks power to produce significant

results. The insignificant results fail to reject the first hypothesis. However, the first hypothesis cannot be accepted either. Since these relationships have not been tested before, the results cannot be compared to earlier found evidence. A possible explanation for the insignificant results is that their might not be a relationship between acquirer financial constraints and the takeover premium in distressed target takeovers, although the existing literature leads to believe so.

Acquirer constraints and the method of payment in distressed target takeovers Table 2b in the Appendix shows the proportions of method of payment used in the takeovers in our sample. The proportion of stock payments is highest in case the target company is in financial distress. 4 out of 5 of our distress proxies (NNI1, NNI2, Altman, and Ohlson) show an increase in the proportion of stock used if the target is labeled as distressed. These findings are consistent with the theory on existing literature. In the Tobit regression model, presented in Table 3b in the Appendix, a negative relationship between the percentages of cash used is found in takeovers and target financial distress. 4 out of 5 distress dummies (NNI1, NNI2, Altman, and Ohlson) show significant results. These findings support the theories that suggest that due to information asymmetry between the target and the acquirer, an acquirer is more likely to offer cash. This likelihood is increased in case the target company is in distress because the estimation of the value of the company and the value of the possible synergies that can be realized are harder to find. By including a dummy variable indicating if the acquirer is financially constrained in the Tobit regression, a negative relationship between financial constraints and the percentage of cash used in the M&A deals in our sample is found. All the coefficients on the HP‐index Top variable are negative and significant. These findings are consisted with the evidence found by Alshwer et al. (2011) who find that financially constrained bidders are more likely to use stock in acquisitions. In addition to the investigated relationships by Alshwer et al. (2011) this research tests for the relationship between an M&A in which a financially constrained acquirer is combined with a distressed target and the proportion of cash used in the transaction. By including an interaction term between the dummy for acquirer constraints and target distress, a negative relationship for 3 out of 5 of our financial distress proxies (NNI1, NNI2 and Ohlson) is found. The effect of the combination of a financially constrained acquirer with a distressed target on the percentage of cash is larger than the separate effects. This

42 indicates that the relationship is even more negative in case both companies are facing problems. In order to check for robustness of the results an Ordered Probit model is used which shows similar results as the Tobit model. Using the NNI1, NNI2, and Ohlson indicators as proxies for financial distress, the findings of the regression analyses support the second hypothesis.

Announcement returns for the acquiring and bidding company in distressed target takeovers In Table 2d the average abnormal returns earned by both the target and the acquirer are presented. Consisted with the existing literature it is found that the target companies on average experience positive wealth effects, while the acquirers experience small losses (Amit et al., 1989). In Tables 3c‐3f in the Appendix our regressions results on the determinants for short‐ term wealth effects for both the acquirer and the target are presented. This study fails to find consistent significant relationships between acquirer constraints, target distress and the acquirer announcement returns. By examining the determinants of the target abnormal returns positive relationships between 3 out of 5 of the financial distress proxies (NNI1, NNI2 and Ohlson) and the target abnormal return are found, indicating that financial distress leads to higher short‐term wealth effects for the target stockholders. A negative relationship is found between the financial constraints of the acquirer and the targets abnormal returns which would indicate that acquirer financial constraints lead to lower returns to the target stockholders. No significant relationships were found for the acquirer abnormal returns. Table 4 in the Appendix present differences in CAARs for different takeover characteristics. Full cash offers provide the highest abnormal returns for both the acquiring an target firms. These findings are consistent with the findings of Faccio and Masulis (2006). Furthermore, it is found that acquisitions in which a financially unconstrained acquirer is combined with a distressed target, the highest abnormal returns are experienced by both the acquirer and the target. The differences in the returns are significant and robust for the target companies. The differences in returns for the acquirers are not consistently significant and not robust for the t‐test using the WW‐index. Our findings on the target abnormal returns contradict the findings of Amit et al. (1989) who find that distressed target firms earned the lowest abnormal returns.

Similar to our research they find that the abnormal returns for the acquirer are no statistically different from zero. The research of Amit et al. however use a, more than ten times, smaller sample. Furthermore they only use one variable for financial distress, the Altman Z‐score (1968), which does not show significant results in our sample. When the NNI1, NNI2, and Ohlson dummy variables are used in order to determine target financial distress, our findings support the third hypothesis in which is stated that the highest abnormal returns are earned in case a distressed target company taken over by, or involved in a merger with, a financially non‐distressed acquirer.

6. Conclusion

The main subject of this research is the influence of acquirer financial constraints on the takeover premium, the method of payment, and the short‐term wealth effects in distressed target takeovers. More specifically, this study examines whether takeover deal characteristics are associated with acquirer financial constraints and target financial distress in M&As. It investigates whether there are differences in the abnormal returns earned by shareholders of financially constrained acquirers and by shareholders of non‐constrained acquirers involved in distressed target takeovers. Furthermore it examines whether these differences appear for distressed targets stakeholders and non‐distressed targets. This research examines 3,889 U.S. mergers and acquisitions that occurred between January 1985 and December 2014. The impact of financial distress on the takeover premium appears to be positive, indicating that acquirers are willing to pay more for distressed companies which have large growth and performance enhancement opportunities. The evidence found however is limited. No statistical evidence is found on the impact of acquirer financial constraints on the takeover premium. Stock offerings seem to be the preferred method of payment in distressed target takeovers and this preference is increasing as the acquirer is financially constrained. Bidders involved in acquiring a distressed target require target shareholders to share post‐merger risks by offering equity. In case the bidder is financially constrained, the preference of holding valuable cash increases the probability of a stock offering. No statistically significant evidence of differences in abnormal returns experienced by non‐constrained acquirers compared to constrained acquirers in distressed target takeovers is found. M&As in which a distressed target are taken over by non‐ constrained acquirers appear to generate the most positive average abnormal announcement returns for the target shareholders, and thus create the greatest short‐ term wealth effects for the target company. This research is subjected to limitations. The different measures used for financial distress show inconsistent findings to the research question. This inconsistency reduces the strength of our results and further research might therefore focus on different proxies for financial distress.

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Appendix

Table 1a Financial Distress Proxies by Acquirer Financial Distress Indicators HP indicates if the acquiring company is financially constrained according to the HP‐index (1 indicates financially constrained and 0 indicates financially unconstrained). WW indicates if the acquiring company is financially constrained according to the WW‐index. NNI1 indicates if the company is facing negative income in the last year. NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. Distress and Constraint Proxies Total Sample HP 0 HP1 WW0 WW1 Total 100% 66.69% 33.31% 66.69% 33.31% N 3,889 1,944 971 1,702 850 NNI1 0 56.73% 46.31% 10.42% 43.62% 23.41% N 2,020 1,649 371 1,077 578 NNI1 1 43.27% 28.48% 14.80% 14.90% 18.06% N 1541 1014 527 368 446 NNI2 0 60.83% 49.37% 11.46% 45.48% 21.55% N 2,166 1,758 408 1,123 532 NNI2 1 39.17% 25.41% 13.76% 16.28% 16.69% N 1395 905 490 402 412 NEQ 0 95.16% 71.76% 23.40% 64.48% 3.45% N 3,693 2,785 908 1,458 78 NEQ 1 4.84% 3.25% 1.60% 29.68% 2.39% N 188 126 62 671 54 Altman 0 78.75% 64.83% 18.76% 56.01% 11.79% N 2,360 2,516 728 1,221 257 Altman 1 21.25% 10.18% 6.24% 23.85% 8.35% N 637 395 242 520 182 Ohlson 0 71.86% 57.14% 14.72% 54.77% 12.90% N 2,319 1,844 475 1,269 299 Ohlson 1 28.14% 17.14% 11.00% 18.77% 13.55% N 908 553 355 435 314

Table 1b Correlations between Financial Constraints and Target Distress Proxies HP indicates the Hadlock and Pierce index (HP‐index) of financial constraints. WW indicates the Whited Wu index (WW‐index) of financial constraints. HPT is a dummy variable that takes the value “1” if a company is the top tercile of the HP‐index distribution. WWT is a dummy variable that takes the value “1” if a company is the top tercile of the WW‐index distribution. NNI1 is a dummy variable that take the value “1” if the company faced a negative income in the last year. NNI2 is a dummy variable that take the value “1” if the company faced a negative income in the last 2 years. NEQ is a dummy variable that take the value “1” if the company faced a negative equity in the last year. Altman is a dummy variable that take the value “1” if the probability of bankruptcy predicted by the Altman Z‐score is higher than 50%. Ohlson is a dummy variable that take the value “1” if the probability of bankruptcy predicted by the Ohlson O‐score is higher than 50%. Variable HP WW HPT WWT NNI1 NNI2 NEQ Altman Ohlson HP 1.00 WW 0.71 1.00 HPT 0.84 0.57 1.00 WWT 0.63 0.76 0.54 1.00 NNI1 0.21 0.21 0.19 0.21 1.00 NNI2 0.22 0.20 0.20 0.21 0.95 1.00 NEQ 0.00 ‐0.03 0.02 0.01 0.14 0.14 1.00 Altman 0.15 0.07 0.14 0.09 0.32 0.33 0.33 1.00 Ohlson 0.27 0.22 0.23 0.25 0.62 0.63 0.25 0.37 1.00 All correlations are significant at the 1% significance level.

Table 2a Takeover Characteristics by Target Financial Distress and Acquirer Financial Constraints NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index (1 indicates financially constrained and 0 indicates financially unconstrained). WW indicates if the acquiring company is financially constrained according to the WW‐index.

Full Sample NNI1 0 NNI1 1 NNI2 1 NNI2 1 NEQ 0 NEQ 0 Characteristics % N % N Percentage N % N % N % N % N Full Cash (%) 41.75% 1,414 44.80% 853 38.03% 518 44.32% 897 38.16% 474 42.03% 1176 41.06% 62 Mixture (%) 13.14% 445 14.08% 268 12.04% 164 13.88% 281 12.16% 151 13.44% 376 15.89% 24 Full Stock (%) 45.11% 1,528 41.12% 783 49.93% 680 41.80% 846 49.68% 617 44.53% 1246 43.05% 65 Competitive (%) 5.22% 203 6.43% 130 4.47% 69 6.27% 136 4.51% 63 5.93% 176 4.26% 8 Non‐competitive (%) 94.78% 3,686 93.57% 1,893 95.53% 1,474 93.73% 2,033 95.49% 1,334 94.07% 2,793 95.74% 180 Hostile (%) 1.77% 69 2.92% 59 0.65% 10 2.81% 61 0.57% 8 1.99% 59 0.53% 1 Friendly (%) 98.23% 3,820 97.08% 1,964 99.35% 1,533 97.19% 2,108 99.43% 1,389 98.01% 2,910 99.47% 187 Tender (%) 21.37% 831 26.79% 542 18.02% 278 26.00% 564 18.32% 256 23.81% 707 12.77% 24 Merger (%) 78.63% 3,058 73.21% 1,481 81.98% 1,265 74.00% 1,605 81.68% 1,141 76.19% 2,262 87.23% 164 Diversify (%) 22.29% 867 20.42% 413 22.29% 344 20.38% 442 22.55% 315 20.28% 602 21.81% 41 Same Industry (%) 77.71% 3,022 79.58% 1,610 77.71% 1,199 79.62% 1,727 77.45% 1,082 79.72% 2,367 78.19% 147 All differences are significant for at least the 5% significance level, except for the negative equity target distress variable for which the differences are insignificant.

Table 2a continued Takeover Characteristics by Target Financial Distress and Acquirer Financial Constraints

Altman Altman Ohlson 0 Ohlson 1 HP 0 HP 1 WW 0 WW1 Characteristics % N % N % N % N % N % N % N % N Full Cash (%) 43.55% 975 35.88% 211 43.56% 957 38.81% 314 37.06% 667 57.63% 487 34.10% 549 54.50% 412 Mixture (%) 13.71% 307 13.61% 80 14.61% 321 11.00% 89 11.83% 213 16.33% 138 13.11% 211 15.08% 114 Full Stock (%) 42.74% 957 50.51% 297 41.83% 919 50.19% 406 51.11% 920 26.04% 220 52.80% 850 30.42% 230 Competitive (%) 6.60% 156 3.61% 23 6.60% 153 4.07% 37 6.33% 123 2.99% 29 5.46% 93 3.76% 32 Cooperative (%) 93.40% 2,208 96.39% 615 93.40% 2,166 95.93% 871 93.67% 1,821 97.01% 942 94.54% 1,609 96.24% 818 Hostile (%) 2.24% 53 0.31% 2 2.54% 59 0.22% 2 1.95% 38 0.82% 8 1.76% 30 0.59% 5 Friendly (%) 97.76% 2,311 99.69% 636 97.46% 2,260 99.78% 906 98.05% 1,906 99.18% 963 98.24% 1,672 99.41% 845 Tender (%) 25.13% 594 14.89% 95 25.27% 586 17.51% 159 27.73% 539 11.12% 108 27.97% 476 14.47% 123 Merger (%) 74.87% 1,770 85.11% 543 74.73% 1,733 82.49% 749 72.27% 1,405 88.88% 863 72.03% 1,226 85.53% 727 Diversify (%) 19.92% 471 21.16% 135 19.79% 459 22.47% 204 21.45% 417 20.29% 197 20.33% 346 19.76% 168 Same Industry (%) 80.08% 1,893 78.84% 503 80.21% 1,860 77.53% 704 78.55% 1,527 79.71% 774 79.67% 1,356 80.24% 682 All differences are significant for at least the 5% significance level, except for the negative equity target distress variable for which the differences are insignificant.

Table 2b Method of Payment by Acquirer Financial Constraints and Target Distress NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index (1 indicates financially constrained and 0 indicates financially unconstrained). WW indicates if the acquiring company is financially constrained according to the WW‐index. Method of Payment Panel A: Full Sample Total NNI1 0 NNI1 1 NNI2 0 NNI2 1 NEGE 0 NEGE 1 Altman 0 Altman 1 Ohlson 0 Ohlson 1 Full Cash 41.75% 44.80% 38.03% 44.32% 38.16% 42.03% 41.06% 43.55% 35.88% 43.56% 38.81% N 1,414 853 518 897 474 1,176 62 975 211 957 314 Mixture 13.14% 14.08% 12.04% 13.88% 12.16% 13.44% 15.89% 13.71% 13.61% 14.61% 11.00% N 445 268 164 281 151 376 24 307 80 321 89 Full Stock 45.11% 41.12% 49.93% 41.80% 49.68% 44.53% 43.05% 42.74% 50.51% 41.83% 50.19% N 1,528 783 680 846 617 1,246 65 957 297 919 406 Panel B: HP 0 Total NNI1 0 NNI1 1 NNI2 0 NNI2 1 NEGE 0 NEGE 1 Altman 0 Altman 1 Ohlson 0 Ohlson 1 Full Cash 51.11% 49.78% 52.95% 49.96% 52.87% 50.16% 55.41% 50.23% 50.77% 49.14% 56.57% N 920 569 332 597 304 787 41 667 132 661 185 Mixture 11.83% 12.86% 10.05% 12.55% 10.43% 11.98% 16.22% 12.50% 10.77% 13.16% 8.56% N 213 147 63 150 60 188 12 166 28 177 28 Full Stock 37.06% 37.36% 37.00% 37.49% 36.70% 37.86% 28.38% 37.27% 38.46% 37.70% 34.86% N 667 427 232 448 211 594 21 495 100 507 114 HP 1 Total NNI1 0 NNI1 1 NNI2 0 NNI2 1 NEGE 0 NEGE 1 Altman 0 Altman 1 Ohlson 0 Ohlson 1 Full Cash 44.35% 28.08% 23.33% 26.58% 24.31% 25.83% 31.25% 26.52% 25.46% 28.95% 22.65% N 220 98 108 101 105 179 15 131 55 130 70 Mixture 27.82% 18.91% 14.69% 19.47% 13.89% 16.45% 16.67% 17.81% 14.35% 18.71% 13.27% N 138 66 68 74 60 114 8 88 31 84 41 Full Stock 27.82% 53.01% 61.99% 53.95% 61.81% 57.72% 52.08% 55.67% 60.19% 52.34% 64.08% N 138 185 287 205 267 400 25 275 130 235 198

Table 2c Summary Statistics

Full Sample NNI1 NNI2 NEQ Variable Mean Median N Mean Median N Mean Median N Mean Median N Value Trans. 720.80 174.20 3,592 445.10 90.00 1,450 444.40 90.00 1,321 636.80 110.00 171 Premium (%) 45.83 38.25 2,677 48.92 39.70 1053 49.55 40.03 972 51.20 38.79 102 HP‐index (A) ‐4.06 ‐4.27 2,915 ‐3.91 ‐4.06 1,195 ‐3.91 ‐4.06 1,102 ‐3.88 ‐4.08 143 WW‐index (A) ‐0.34 ‐0.34 2,552 ‐0.32 ‐0.32 1,024 ‐0.32 ‐0.32 944 ‐0.33 ‐0.33 132 Z‐score (T) 10.56 3.66 3,002 10.15 2.75 1,266 9.97 2.58 1,182 2.98 0.39 174 O‐Probability (T) 0.32 0.11 3,227 0.60 0.73 1,375 0.62 0.78 1,284 0.73 0.90 174 Net Income (A) 304.90 29.10 3,621 266.40 7.80 1,463 271.50 7.92 1,333 274.40 8.10 180 Net Income (T) 8.28 1.10 3,566 ‐19.42 ‐8.58 1,543 ‐20.29 ‐9.41 1,397 ‐16.93 ‐7.20 188 Net Income last year (T) 8.41 1.08 3,502 ‐16.95 ‐8.06 1,506 ‐19.21 ‐9.41 1,397 ‐18.40 ‐8.23 179 Equity (A) 2969.38 387.25 3,362 3100.77 245.76 1,360 3139.75 253.68 1,239 4809.38 240.05 167 Equity (T) 294.10 54.49 3,157 111.20 26.40 1,339 95.23 26.01 1,219 ‐124.90 ‐16.23 188 Relative Size 6.19 0.15 3,212 4.51 0.10 1,331 4.41 0.09 1,227 3.54 0.05 161 Relative Deal Size 22.41 0.29 3,367 15.45 0.22 1,366 15.08 0.22 1,251 16.72 0.31 162 Market Value (A) 7540.00 757.50 3,544 7764.00 452.40 1,427 7941.00 495.40 1,302 8336.00 651.40 173 Market Value (T) 450.20 101.10 3,406 257.90 44.68 1,415 251.60 44.03 1,297 291.00 16.42 171 Size (A) 7.29 7.28 3,048 7.00 6.79 1,217 7.03 6.82 1,115 6.91 6.98 149 Size (T) 4.46 4.40 3,400 3.85 3.77 1,426 3.82 3.73 1,314 4.17 4.19 133 Tobins Q (A) 1.64 1.21 3,427 1.77 1.36 1,390 1.80 1.37 1,271 1.49 1.06 169 Tobins Q (T) 4.46 4.40 3,400 3.85 3.77 1,426 3.82 3.73 1,314 4.17 4.19 133 Market‐to‐Book ratio (A) 4.45 2.61 2,932 4.64 2.63 1,186 4.64 2.64 1,091 3.30 1.65 146 Market‐to‐Book ratio (T) 3.29 1.92 2,134 2.66 1.05 909 2.61 1.01 854 ‐0.12 ‐0.52 118 Debt‐to‐Equity (A) 0.26 0.13 3,047 0.22 0.09 1,217 0.22 0.09 1,115 0.34 0.15 149 Debt‐to‐Equity (T) 0.33 0.27 3,322 0.36 0.26 1,436 0.37 0.26 1,328 0.70 1.03 174 Total Assets (A) 4872.00 861.90 3,628 4411.00 516.70 1,467 4492.00 533.00 1,335 5809.00 635.80 179 Total Assets (T) 423.30 102.30 3,584 248.90 54.53 1,540 242.40 54.77 1,394 406.90 49.36 188 Total Debt (A) 1127.00 173.10 3,106 975.50 57.44 1,250 1007.00 58.40 1,147 1570.00 223.50 154 Total Debt (T) 150.60 12.36 3,339 102.70 5.27 1,440 99.80 5.20 1,330 273.90 34.14 176 Sales (A) 4313.00 714.90 3,364 3589.00 357.00 1,360 3663.00 362.50 1,241 4408.00 496.50 167 Sales (T) 432.10 111.10 3,164 225.00 47.75 1,341 220.80 44.94 1,221 339.50 67.81 188

Table 2c continued Summary Statistics

Altman Ohlson HP WW Variable Mean Median N Mean Median N Mean Median N Mean Median N Value Trans. 663.10 109.70 618 259.00 56.73 866 412.50 83.34 904 189.50 72.00 817 Premium (%) 46.15 36.18 468 49.49 40.93 607 46.19 36.75 677 48.57 40.00 628 HP‐index (A) ‐3.89 ‐3.99 511 ‐3.81 ‐3.93 706 ‐3.32 ‐3.57 971 ‐3.66 ‐3.72 705 WW‐index (A) ‐0.33 ‐0.32 439 ‐0.31 ‐0.29 613 ‐0.25 ‐0.25 641 ‐0.23 ‐0.24 850 Z‐score (T) 0.72 0.33 638 10.22 1.90 821 11.85 3.45 764 13.07 4.16 702 O‐Probability (T) 0.65 0.77 636 0.88 0.97 908 0.45 0.29 830 0.43 0.24 749 Net Income (A) 286.60 8.89 616 240.40 4.35 870 37.37 1.00 947 6.25 4.69 850 Net Income (T) ‐11.03 ‐6.81 638 ‐17.08 ‐8.98 908 ‐2.34 ‐1.27 898 ‐4.61 ‐0.90 814 Net Income last year (T) ‐12.26 ‐8.11 637 ‐15.38 ‐8.26 908 ‐1.63 ‐1.25 885 ‐4.51 ‐0.95 802 Equity (A) 3969.53 318.50 569 2884.43 164.54 807 488.61 77.62 906 146.15 95.51 850 Equity (T) 93.06 14.60 638 21.17 14.41 842 118.30 31.01 799 60.28 26.55 725 Relative Size 4.02 0.11 592 4.13 0.08 793 3.24 0.23 833 4.50 0.20 777 Relative Deal Size 13.61 0.26 596 16.24 0.21 811 13.57 0.40 869 18.26 0.37 809 Market Value (A) 8500.00 657.10 613 7040.00 324.50 843 1793.00 262.20 919 585.00 234.20 840 Market Value (T) 370.50 50.30 616 138.60 28.94 842 260.00 46.44 868 128.10 38.92 784 Size (A) 7.07 6.99 523 6.73 6.38 725 5.83 5.68 809 5.47 5.43 833 Size (T) 4.02 3.75 596 3.35 3.29 839 3.81 3.75 850 3.59 3.61 771 Tobins Q (A) 1.63 1.24 598 1.81 1.36 824 2.08 1.42 910 1.94 1.37 840 Tobins Q (T) 4.02 3.75 596 3.35 3.29 839 3.81 3.75 850 3.59 3.61 771 Market‐to‐Book ratio (A) 3.72 2.34 510 4.70 2.46 707 4.44 2.28 768 4.38 2.18 835 Market‐to‐Book ratio (T) 2.61 1.00 436 2.47 0.79 530 3.03 1.62 597 2.75 1.33 515 Debt‐to‐Equity (A) 0.28 0.13 523 0.23 0.08 725 0.24 0.06 809 0.21 0.05 833 Debt‐to‐Equity (T) 0.51 0.53 617 0.40 0.26 871 0.31 0.19 845 0.28 0.14 760 Total Assets (A) 5533.00 733.60 613 4133.00 365.50 870 1212.00 136.40 951 270.20 176.00 850 Total Assets (T) 419.40 58.81 638 116.40 37.19 908 243.20 52.12 900 110.20 49.65 815 Total Debt (A) 1369.00 196.00 531 901.90 36.91 751 328.50 8.02 855 59.33 8.02 848 Total Debt (T) 213.30 16.17 617 62.48 3.17 872 88.54 3.51 850 28.13 2.79 761 Sales (A) 3933.00 402.10 570 3270.00 248.10 809 818.90 94.31 904 271.60 138.90 850 Sales (T) 211.80 42.29 638 114.10 25.40 843 221.10 46.49 802 116.00 46.53 726

Table 2d Differences in Method of Payment by Acquirer Constraints and Financial Distress NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP 1 (0) indicates if the acquiring company is in the top (not in the top) tercile of the HP‐ index distribution of the sample. Average takeover premiums Premium Full Sample HP 0 HP 1 Difference Total 45.83% 45.71% 46.19% 0.49% N 2677 2000 677 NNI 0 43.89% 44.20% 42.58% ‐1.62% N 1602 1297 305 NNI 1 48.92% 48.55% 49.64% 1.09% N 1053 688 365 Difference 5.03%*** 4.34%*** 7.06%** NNI2 0 43.77% 44.11% 42.36% ‐1.75% N 1683 1357 326 NNI2 1 49.55% 49.16% 50.27% 1.12% N 972 628 344 Difference 5.78%*** 5.05%*** 7.92%** NEGE 0 45.51% 45.40% 45.81% 0.41% N 2409 1798 611 NEGE 1 51.20% 48.22% 59.08% 10.85% N 102 74 28 Difference 5.70% 2.82% 13.26% Altman 0 45.53% 45.17% 46.73% 1.56% N 1971 1518 453 Altman 1 46.15% 44.99% 48.30% 3.31% N 468 304 164 Difference 0.62% ‐0.18% 1.57% Ohlson 0 44.93% 45.22% 43.83% ‐1.39% N 1933 1527 406 Ohlson 1 49.49% 47.32% 52.79% 5.47% N 607 366 241 Difference 4.57%** 2.10%* 8.96%***

Table 3a OLS regression Explaining the Takeover Premium NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 4.84 4.60 4.88 (1.94)** (1.93)** (2.28)** NNI 2 5.32 5.14 5.83 (2.01)*** (2.00)** (2.34)** NEQ 12.62 12.68 9.37 (6.78)* (6.76)* (7.64) Altman 0.21 0.06 1.03 (3.00) (3.00) (3.58) Ohlson 0.49 0.19 ‐0.99 (2.71) (2.69) (3.34) HP‐Index ‐4.04 ‐4.02 ‐2.84 ‐2.85 ‐2.47 (2.29)* (2.28)* (2.29) (2.31) (2.29) HP‐Index Top ‐2.09 ‐2.15 ‐1.41 ‐1.29 ‐0.96 ‐1.69 ‐1.22 ‐1.76 ‐0.78 ‐1.75 (2.12) (2.12) (2.14) (2.17) (2.13) (2.41) (2.39) (2.16) (2.35) (2.29) NNI1#HPT ‐0.88 (4.07) NNI2#HPT ‐2.12

(4.15) NEQ#HPT 11.49 (14.11) ALT#HPT ‐2.47 (5.49) OHLS#HPT 2.79 (5.02) Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3a continued OLS regression Explaining the Takeover Premium

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

COMP 4.60 4.59 5.09 5.17 4.53 4.52 4.51 5.05 5.11 4.48 4.52 4.53 5.21 5.15 4.53 (4.03) (4.01) (4.20) (4.20) (4.16) (4.02) (4.00) (4.19) (4.19) (4.15) (4.02) (4.00) (4.19) (4.19) (4.16) HOST 18.95 18.85 19.18 19.36 19.68 19.14 19.04 19.35 19.51 19.84 19.17 19.08 19.32 19.60 19.74 (6.78)*** (6.78)*** (7.11)*** (7.03)*** (7.01)*** (6.79)*** (6.79)*** (7.10)*** (7.01)*** (6.99)*** (6.78)*** (6.76)*** (7.10)*** (7.01)*** (7.02)*** TSIZE ‐4.60 ‐4.54 ‐4.56 ‐4.86 ‐4.88 ‐4.40 ‐4.34 ‐4.41 ‐4.70 ‐4.77 ‐4.40 ‐4.33 ‐4.39 ‐4.71 ‐4.79 (0.66)*** (0.67)*** (0.67)*** (0.69)*** (0.71)*** (0.65)*** (0.66)*** (0.66)*** (0.68)*** (0.70)*** (0.65)*** (0.66)*** (0.66)*** (0.68)*** (0.70)*** AFCF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** ALEV 0.83 0.80 1.41 2.09 1.14 0.93 0.91 1.50 2.17 1.20 0.92 0.86 1.67 2.20 1.24 (3.22) (3.22) (3.24) (3.28) (3.24) (3.22) (3.22) (3.23) (3.27) (3.24) (3.22) (3.22) (3.24) (3.28) (3.24) TLEV 3.58 3.46 0.41 4.58 4.62 3.78 3.64 0.53 4.82 4.85 3.75 3.53 0.31 4.73 4.99 (3.69) (3.70) (3.89) (4.22) (3.79) (3.69) (3.70) (3.89) (4.22) (3.79) (3.69) (3.69) (3.91) (4.22) (3.79) Cash ‐3.48 ‐3.49 ‐3.45 ‐3.20 ‐3.21 ‐3.05 ‐3.07 ‐3.14 ‐2.87 ‐2.93 ‐3.09 ‐3.15 ‐3.15 ‐2.88 ‐2.85 (2.00)* (2.00)* (2.03)* (2.04) (2.02) (1.99) (1.99) (2.02) (2.03) (2.01) (1.99) (1.99) (2.02) (2.03) (2.01) DIV 0.40 0.33 0.70 0.15 0.36 0.40 0.33 0.69 0.15 0.36 0.40 0.33 0.67 0.13 0.36 (2.26) (2.26) (2.28) (2.30) (2.27) (2.26) (2.26) (2.28) (2.30) (2.27) (2.25) (2.25) (2.28) (2.30) (2.27) TEND 7.31 7.33 7.44 7.13 6.94 7.46 7.47 7.55 7.24 7.05 7.47 7.50 7.56 7.21 7.03 (2.07)*** (2.07)*** (2.14)*** (2.14)*** (2.10)*** (2.07)*** (2.07)*** (2.14)*** (2.14)*** (2.10)*** (2.08)*** (2.08)*** (2.14)*** (2.14)*** (2.10)*** Cons. 56.95 56.75 63.13 63.40 65.24 73.13 72.87 74.35 74.55 74.97 73.11 72.81 74.36 74.43 75.30 (18.38)* (18.43)** (18.07)** (18.04)** (18.19)** (16.53)** (16.59)** (16.22)** (16.15)** (16.26)** (16.52)** (16.56)** (16.16)** (16.24)** (16.15)** ** * * * * * * * * * * * * * * Ind. f.e. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Obs. 1,947 1,947 1,886 1,850 1,909 1,947 1,947 1,886 1,850 1,909 1,947 1,947 1,886 1,850 1,909 R2 0.0643 0.0648 0.0604 0.0576 0.0592 0.0628 0.0634 0.0597 0.0568 0.0586 0.0629 0.0636 0.0603 0.0570 0.0588 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3b Tobit Regressions Explaining the Percent Cash Financing in M&A Deals NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 ‐14.41 ‐16.47 ‐0.20 (7.74)* (7.78)** (9.12) NNI 2 ‐15.11 ‐16.47 1.70 (7.87)* (7.93)** (9.37) NEQ 17.82 19.98 12.73 (18.93) (19.06) (22.96) Altman ‐19.63 ‐20.54 ‐15.34 (9.95)** (10.01)** (12.37) Ohlson ‐32.23 ‐36.07 ‐11.44 (9.66)*** (9.68)*** (11.94) HP‐Index ‐46.48 ‐46.74 ‐49.86 ‐48.56 ‐45.10 (8.61)*** (8.60)*** (8.79)*** (8.79)*** (8.72)*** HP‐Index Top ‐28.88 ‐28.93 ‐29.87 ‐28.63 ‐25.62 ‐4.87 ‐4.71 ‐30.68 ‐25.77 ‐8.81 (8.52)*** (8.52)*** (8.62)*** (8.65)*** (8.53)*** (11.03) (10.79) (8.75)*** (9.52)*** (9.74) NNI1#HPT ‐52.51 (15.80)*** NNI2#HPT ‐56.39 (15.95)*** NEQ#HPT 21.93 (38.96) ALT#HPT ‐13.77 (19.32) OHLS#HPT ‐60.51 (17.79)*** Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3b continued Tobit Regressions Explaining the Percent Cash Financing in M&A Deals

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Premium ‐0.07 ‐0.07 ‐0.06 ‐0.05 ‐0.05 ‐0.05 ‐0.05 ‐0.05 ‐0.04 ‐0.04 ‐0.05 ‐0.05 ‐0.05 ‐0.04 ‐0.03 (0.09) (0.09) (0.10) (0.10) (0.09) (0.09) (0.09) (0.10) (0.10) (0.09) (0.09) (0.09) (0.10) (0.10) (0.09) ADEBT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) TDEBT ‐0.08 ‐0.08 ‐0.09 ‐0.08 ‐0.08 ‐0.08 ‐0.08 ‐0.09 ‐0.08 ‐0.08 ‐0.08 ‐0.08 ‐0.09 ‐0.08 ‐0.08 (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.02)*** (0.02)*** (0.01)*** (0.01)*** (0.01)*** (0.02)*** (0.02)*** (0.01)*** AFCF 0.03 0.03 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** AQ ‐23.62 ‐23.61 ‐23.69 ‐24.68 ‐24.30 ‐24.68 ‐24.68 ‐24.82 ‐25.82 ‐25.32 ‐24.83 ‐24.77 ‐24.79 ‐25.96 ‐25.60 (2.72)*** (2.72)*** (2.76)*** (2.79)*** (2.74)*** (2.74)*** (2.74)*** (2.77)*** (2.80)*** (2.75)*** (2.74)*** (2.74)*** (2.77)*** (2.81)*** (2.76)*** DIV ‐2.56 ‐2.37 ‐4.67 ‐7.05 ‐3.65 ‐2.27 ‐2.08 ‐4.57 ‐6.85 ‐3.38 ‐2.54 ‐2.25 ‐4.62 ‐7.05 ‐3.48 (9.20) (9.20) (9.35) (9.35) (9.20) (9.25) (9.25) (9.41) (9.40) (9.25) (9.21) (9.22) (9.41) (9.40) (9.21) DEAL ‐0.20 ‐0.20 ‐0.19 ‐0.17 ‐0.20 ‐0.21 ‐0.20 ‐0.20 ‐0.17 ‐0.21 ‐0.21 ‐0.21 ‐0.20 ‐0.18 ‐0.20 (0.09)** (0.09)** (0.09)** (0.09)* (0.09)** (0.09)** (0.09)** (0.09)** (0.09)* (0.09)** (0.09)** (0.09)** (0.09)** (0.09)* (0.09)** TEND 151.73 151.72 149.78 148.51 148.88 154.52 154.57 152.88 151.67 151.88 153.96 154.19 152.96 151.36 151.10 (9.98)*** (9.98)*** (10.18)*** (10.15)*** (9.96)*** (10.09)*** (10.09)*** (10.31)*** (10.28)*** (10.07)*** (10.05)*** (10.05)*** (10.31)*** (10.27)*** (10.02)*** HOST ‐12.15 ‐11.87 ‐10.08 ‐12.06 ‐9.65 ‐10.29 ‐9.87 ‐6.56 ‐8.75 ‐6.43 ‐9.09 ‐8.76 ‐6.29 ‐8.33 ‐5.18 (29.84) (29.84) (31.10) (30.83) (30.78) (30.07) (30.08) (31.40) (31.13) (31.04) (30.02) (30.03) (31.41) (31.12) (30.95) TSIZE ‐7.32 ‐7.41 ‐5.04 ‐6.99 ‐9.83 ‐5.06 ‐5.04 ‐2.36 ‐4.46 ‐7.91 ‐5.24 ‐5.02 ‐2.35 ‐4.50 ‐7.59 (3.20)** (3.20)** (3.16) (3.28)** (3.35)*** (3.16) (3.17) (3.12) (3.25) (3.33)** (3.16)* (3.16) (3.12) (3.25) (3.33)** Cons. ‐126.85 ‐127.82 ‐153.60 ‐137.60 ‐109.61 64.70 64.10 49.85 60.54 76.28 62.99 61.73 49.98 60.30 69.18 (70.09)* (70.03)* (70.02)** (69.59)** (69.99) (63.31) (63.33) (63.20) (62.70) (62.78) (63.46) (63.50) (63.20) (62.67) (62.52) Ind. f.e Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Obs 1,965 1,965 1,904 1,868 1,927 1,965 1,965 1,904 1,868 1,927 1,965 1,965 1,904 1,868 1,927 Pseudo R2 0.095 0.095 0.0923 0.0924 0.0948 0.0927 0.0927 0.0896 0.0897 0.0925 0.094 0.0942 0.0942 0.0898 0.094 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3c OLS Regressions Explaining the Acquirer Abnormal Returns for the 11‐day event window NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 0.01 0.01 0.01 (0.01) (0.01) (0.01) NNI 2 0.01 0.01 0.00 (0.01) (0.01) (0.01) NEQ 0.03 0.03 ‐0.00 (0.02) (0.02) (0.02) Altman 0.01 0.01 0.01 (0.01) (0.01) (0.01) Ohlson 0.01 0.01 0.01 (0.01) (0.01) (0.01) HP‐Index ‐0.02 ‐0.02 ‐0.01 ‐0.01 ‐0.01 (0.01)* (0.01) (0.01) (0.01) (0.01) HP‐Index Top ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) NNI1#HPT ‐0.00 (0.02) NNI2#HPT 0.01 (0.02) NEQ#HPT 0.10 (0.04)** ALT#HPT 0.00 (0.02) OHLS#HPT ‐0.01 (0.02) Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3c continued OLS Regressions Explaining the Acquirer Abnormal Returns for the 11‐day event window

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Premium 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) DIV ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) HOST ‐0.04 ‐0.04 ‐0.04 ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.04 ‐0.03 ‐0.03 ‐0.03 ‐0.03 ‐0.04 ‐0.03 ‐0.03 (0.01)*** (0.01)*** (0.01)*** (0.01)** (0.01)*** (0.01)** (0.01)** (0.01)*** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)*** (0.01)** (0.01)** TEND 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Perc. Cash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** AQ ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** RELSIZE ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) COMP ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 ‐0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Constant ‐0.09 ‐0.08 ‐0.08 ‐0.05 ‐0.05 ‐0.02 ‐0.02 ‐0.02 ‐0.02 ‐0.01 ‐0.02 ‐0.02 ‐0.02 ‐0.02 ‐0.02 (0.04)** (0.04)* (0.04)* (0.04) (0.03) (0.01)** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)**

Observations 1,529 1,529 1,457 1,420 1,479 1,529 1,529 1,457 1,420 1,479 1,529 1,529 1,457 1,420 1,479 R‐squared 0.0729 0.0724 0.0806 0.0750 0.0743 0.0699 0.0696 0.0784 0.0744 0.0738 0.0699 0.0698 0.0854 0.0745 0.0742 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3d OLS Regressions Explaining the Acquirer Abnormal Returns for the 3‐day event window NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 0.00 0.00 0.00 (0.00) (0.00) (0.00) NNI 2 0.00 0.00 0.00 (0.00) (0.00) (0.00) NEQ 0.02 0.02 0.02 (0.01)* (0.01)* (0.01) Altman 0.01 0.01 0.01 (0.01) (0.01) (0.01)* Ohlson 0.00 0.00 0.01 (0.01) (0.01) (0.00)** HP‐Index ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 (0.01) (0.01) (0.01) (0.01) (0.01) HP‐Index Top ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.00 ‐0.00 ‐0.01 ‐0.00 ‐0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) NNI1#HPT ‐0.01 (0.01) NNI2#HPT ‐0.01 (0.01) NEQ#HPT 0.00 (0.03) ALT#HPT ‐0.00 (0.01) OHLS#HPT ‐0.02 (0.01) Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3d continued OLS Regressions Explaining the Acquirer Abnormal Returns for the 3‐day event window

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Premium ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 (0.00) (0.00) (0.00) (0.00)* (0.00)** (0.00) (0.00) (0.00) (0.00)* (0.00)** (0.00) (0.00) (0.00) (0.00)* (0.00)** DIV ‐0.00 ‐0.00 ‐0.00 0.00 0.00 ‐0.00 ‐0.00 ‐0.00 0.00 0.00 ‐0.00 ‐0.00 ‐0.00 0.00 0.00 (0.00) (0.01) (0.01) (0.00) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00) HOST ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) TEND 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ‐0.00 0.00 0.00 0.00 0.00 ‐0.00 0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Perc. Cash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** AQ ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** RELSIZE ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) COMP ‐0.00 ‐0.00 0.00 0.00 ‐0.00 ‐0.00 ‐0.00 0.00 0.00 ‐0.00 ‐0.00 ‐0.00 0.00 0.00 ‐0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) Constant ‐0.05 ‐0.05 ‐0.06 ‐0.04 ‐0.03 ‐0.01 ‐0.01 ‐0.02 ‐0.02 ‐0.01 ‐0.02 ‐0.02 ‐0.02 ‐0.02 ‐0.01 (0.03)* (0.03)* (0.03)** (0.02) (0.02) (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)***

Observations 1,529 1,529 1,457 1,420 1,479 1,529 1,529 1,457 1,420 1,479 1,529 1,529 1,457 1,420 1,479 R‐squared 0.0814 0.0814 0.0890 0.0855 0.0830 0.0808 0.0808 0.0884 0.0858 0.0836 0.0816 0.0812 0.0884 0.0859 0.0853 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3e OLS Regressions Explaining the Target Abnormal Returns for the 11‐day event window NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 0.04 0.04 0.04 (0.01)*** (0.01)*** (0.02)** NNI 2 0.04 0.03 0.04 (0.01)*** (0.01)** (0.02)** NEQ 0.03 0.03 0.05 (0.05) (0.05) (0.05) Altman 0.01 0.01 0.01 (0.02) (0.02) (0.02) Ohlson 0.06 0.06 0.06 (0.02)*** (0.02)*** (0.02)*** HP‐Index ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.05 (0.01)*** (0.01)*** (0.01)** (0.01)** (0.01)*** HP‐Index Top ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.04 ‐0.03 ‐0.03 ‐0.03 ‐0.03 ‐0.03 (0.01)*** (0.01)*** (0.01)** (0.01)** (0.01)*** (0.01)** (0.01)** (0.01)** (0.01)** (0.01)*** NNI1#HPT ‐0.02 (0.03) NNI2#HPT ‐0.01 (0.03) NEQ#HPT ‐0.06 (0.13) ALT#HPT ‐0.01 (0.04) OHLS#HPT ‐0.01 (0.04) Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3e continued OLS Regressions Explaining the Target Abnormal Returns for the 11‐day event window

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Premium 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** DIV ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.01) (0.01) (0.01) (0.02) (0.01) (0.01) (0.01) (0.01) (0.02) (0.01) (0.01) (0.01) (0.01) (0.02) (0.01) HOST ‐0.04 ‐0.04 ‐0.05 ‐0.05 ‐0.04 ‐0.04 ‐0.04 ‐0.05 ‐0.05 ‐0.04 ‐0.04 ‐0.04 ‐0.05 ‐0.05 ‐0.04 (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) TEND 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.02 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Perc. Cash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** AQ ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** RELSIZE ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 (0.00)* (0.00)** (0.00)* (0.00)* (0.00)* (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** COMP ‐0.11 ‐0.11 ‐0.11 ‐0.12 ‐0.12 ‐0.11 ‐0.11 ‐0.11 ‐0.12 ‐0.12 ‐0.11 ‐0.11 ‐0.11 ‐0.12 ‐0.12 (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** Constant ‐0.15 ‐0.14 ‐0.09 ‐0.10 ‐0.16 0.05 0.05 0.06 0.06 0.04 0.05 0.05 0.06 0.06 0.04 (0.06)** (0.06)** (0.06) (0.06) (0.06)*** (0.01)*** (0.01)*** (0.01)*** (0.02)*** (0.01)*** (0.01)*** (0.02)*** (0.01)*** (0.02)*** (0.01)***

Observations 1,756 1,756 1,724 1,694 1,732 1,756 1,756 1,724 1,694 1,732 1,756 1,756 1,724 1,694 1,732 R‐squared 0.3599 0.3595 0.3566 0.3560 0.3722 0.3583 0.3580 0.3557 0.3551 0.3703 0.3584 0.3580 0.3559 0.3551 0.3704 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3f OLS Regressions Explaining the Target Abnormal Returns for the 3‐day event window NNI1 indicates if the company is facing negative income in the last year (“1” indicates financially distressed, “0” indicates financially non‐distressed). NNI2 1 indicates two years of successive negative income. NEQ indicates negative equity. Altman indicates a probability of bankruptcy above 50% according to the Altman Z‐score. Ohlson indicates a probability of bankruptcy above 50% according to the Ohlson O‐score. HP indicates if the acquiring company is financially constrained according to the HP‐index) (“1” indicates financially constrained and “0” indicates financially unconstrained). HP‐index Top indicates if the acquiring company is in the top tercile of the HP‐index distribution of the sample. “#” indicates an interaction term between the HP‐index Top dummy variable and the financial distress dummy variable. Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 0.04 0.04 0.04 (0.01)*** (0.01)*** (0.02)** NNI 2 0.03 0.03 0.03 (0.01)** (0.01)** (0.02)** NEQ ‐0.01 ‐0.01 0.00 (0.05) (0.05) (0.05) Altman 0.01 0.01 0.00 (0.02) (0.02) (0.02) Ohlson 0.05 0.04 0.04 (0.02)*** (0.02)** (0.02)* HP‐Index ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.04 (0.01)*** (0.01)*** (0.01)** (0.01)** (0.01)*** HP‐Index Top ‐0.04 ‐0.04 ‐0.03 ‐0.04 ‐0.04 ‐0.03 ‐0.04 ‐0.03 ‐0.04 ‐0.04 (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)** (0.01)*** (0.01)*** (0.01)*** (0.01)*** NNI1#HPT ‐0.02 (0.03) NNI2#HPT ‐0.01 (0.03) NEQ#HPT ‐0.06 (0.12) ALT#HPT 0.01 (0.03) OHLS#HPT ‐0.00 (0.03) Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 3f continued OLS Regressions Explaining the Target Abnormal Returns for the 3‐day event window

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Premium 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** DIV ‐0.02 ‐0.02 ‐0.01 ‐0.01 ‐0.01 ‐0.02 ‐0.02 ‐0.01 ‐0.01 ‐0.01 ‐0.02 ‐0.02 ‐0.01 ‐0.01 ‐0.01 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) HOST ‐0.03 ‐0.03 ‐0.04 ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.04 ‐0.04 ‐0.04 ‐0.03 ‐0.03 ‐0.04 ‐0.04 ‐0.04 (0.03) (0.03) (0.04) (0.04) (0.04) (0.03) (0.03) (0.03) (0.03) (0.04) (0.03) (0.03) (0.03) (0.03) (0.04) TEND 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.02 0.01 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Perc. Cash 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** (0.00)*** AQ ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 ‐0.01 (0.00)** (0.00)** (0.00)* (0.00)* (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** (0.00)** RELSIZE ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 ‐0.00 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) COMP ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 ‐0.11 (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** (0.02)*** Constant ‐0.13 ‐0.13 ‐0.08 ‐0.09 ‐0.14 0.04 0.05 0.05 0.05 0.04 0.04 0.05 0.05 0.05 0.04 (0.06)** (0.06)** (0.06) (0.06) (0.06)** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)*** (0.01)***

Observations 1,756 1,756 1,724 1,694 1,732 1,756 1,756 1,724 1,694 1,732 1,756 1,756 1,724 1,694 1,732 R‐squared 0.3150 0.3142 0.3125 0.3153 0.3276 0.3148 0.3141 0.3126 0.3157 0.3273 0.3149 0.3141 0.3129 0.3157 0.3273 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 4 T‐tests testing for differences in CAAR by Method of Payment, Financial Constraints, and Financial Distress Acquirer [‐5, 5] Acquirer [‐1, 1] HP 0 HP 1 Difference HP 0 HP 1 Difference Full Stock ‐2.76% ‐4.46% ‐1.69% Full Stock ‐2.42% ‐4.17% ‐1.75% Full Cash 0.03% 0.79% 0.75% Full Cash 0.13% 0.90% 0.76%** Differences 2.80%*** 5.24%*** Differences 2.55%*** 5.06%*** NNI 0 ‐1.02% ‐3.00% ‐1.98%*** NNI 0 ‐0.78% ‐1.69% ‐0.91%** NNI 1 ‐0.01% ‐2.43% ‐2.42%** NNI 1 ‐0.40% ‐2.44% ‐2.04%*** Differences 1.01%** 0.57% Differences 0.38% ‐0.75% NNI2 0 ‐0.85% ‐2.91% ‐2.06%*** NNI2 0 ‐0.70% ‐1.54% ‐0.84% NNI2 1 ‐0.35% ‐2.46% ‐2.11%** NNI2 1 ‐0.52% ‐2.63% ‐2.11%*** Differences 0.50%** 0.45% Differences 0.18% ‐1.09% NEQ 0 ‐0.86% ‐3.63% ‐2.77%*** NEQ 0 ‐0.80% ‐2.57% ‐1.77%*** NEQ 1 1.58% 4.31% 2.73% NEQ 1 1.91% ‐1.86% ‐3.77% Differences 2.44%** 7.94%** Differences 2.71%*** 0.71% Altman 0 ‐0.89% ‐3.06% ‐2.17%*** Altman 0 ‐0.82% ‐2.26% ‐1.44%*** Altman 1 ‐0.04% ‐1.99% ‐1.95% Altman 1 ‐0.01% ‐2.22% ‐2.21%*** Differences 0.85% 1.07% Differences 0.81% 0.04% Ohlson 0 ‐0.99% ‐2.49% ‐1.50%*** Ohlson 0 ‐0.86% ‐1.80% ‐0.94%* Ohlson 1 0.22% ‐3.03% ‐3.25%*** Ohlson 1 0.05% ‐2.53% ‐2.58%*** Differences 1.21%*** ‐0.54% Differences 0.91%** ‐0.73%

Target [‐5, 5] Target [‐1, 1] HP 0 HP 1 Difference HP 0 HP 1 Difference Full Stock 21.55% 19.06% ‐2.49% Full Stock 19.61% 16.56% ‐3.05%*** Full Cash 35.90% 31.72% ‐4.18% Full Cash 29.03% 23.02% ‐6.00%*** Differences 14.35%*** 12.66*** Differences 9.41%*** 6.46%** NNI 0 26.15% 20.93% ‐5.22%*** NNI 0 23.30% 18.07% ‐5.23%*** NNI 1 33.28% 24.71% ‐8.57%*** NNI 1 29.74% 20.93% ‐8.81%*** Differences 7.13%*** 3.81** Differences 6.44%*** 2.86%* NNI2 0 26.18% 20.69% ‐5.49*** NNI2 0 23.48% 17.82% ‐5.66%*** NNI2 1 33.89% 25.12% ‐8.77%*** NNI2 1 29.94% 21.31% ‐8.63%*** Differences 7.71%*** 4.43%* Differences 6.46%*** 3.49%* NEQ 0 28.48% 22.62% ‐5.87%*** NEQ 0 25.64% 19.78% ‐5.86%*** NEQ 1 30.46% 31.38% 0.92% NEQ 1 23.20% 15.41% ‐7.79% Differences 1.98% 4.43% Differences ‐2.44% ‐4.37% Altman 0 28.35% 23.33% ‐5.02%*** Altman 0 25.41% 20.26% ‐5.15*** Altman 1 30.57% 23.47% ‐7.10%* Altman 1 27.56% 19.29% ‐8.27%*** Differences 2.22% 0.14% Differences 2.15% ‐0.97% Ohlson 0 27.10% 21.09% ‐6.01%*** Ohlson 0 24.34% 18.22% ‐6.12*** Ohlson 1 34.75% 27.22% ‐7.53%** Ohlson 1 30.43% 22.78% ‐7.65** Differences 7.65%*** 6.12%*** Differences 6.09%*** 4.56%*

Table 5a Robustness Test for OLS Regression Explaining the Takeover Premium in M&A Deals

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 3.59 3.57 5.37 (1.97)* (1.97)* (2.33)** NNI 2 4.25 4.22 6.26 (2.05)** (2.05)** (2.40)*** NEQ 9.58 9.62 9.62 (6.98) (6.99) (8.23) Altman ‐2.10 ‐2.11 ‐0.71 (3.12) (3.12) (3.49) Ohlson ‐1.22 ‐1.12 ‐0.30 (2.71) (2.71) (3.47) WW‐Index ‐11.29 ‐11.13 ‐3.04 ‐3.63 ‐3.73 (15.04) (15.02) (15.26) (15.38) (15.15) WW‐Index Top ‐2.96 ‐2.93 ‐1.67 ‐2.11 ‐1.96 ‐0.45 ‐0.38 ‐1.67 ‐1.41 ‐1.42 (2.36) (2.36) (2.41) (2.42) (2.39) (2.72) (2.67) (2.43) (2.54) (2.64) NNI1#WWT ‐5.86 (4.30) NNI2#WWT 0.00 (13.98) NEQ#WWT ‐3.73 (6.02) ALT#WWT ‐1.81 (5.19) OHLS#WWT 86.67 86.34 90.41 91.78 91.89 91.74 91.35 92.49 94.36 94.30 91.53 91.12 92.49 94.33 94.13 (12.18)*** (12.17)*** (12.49)*** (12.32)*** (12.48)*** (11.63)*** (11.63)*** (11.93)*** (11.76)*** (11.91)*** (11.62)*** (11.62)*** (11.93)*** (11.75)*** (11.92)*** Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry f.e. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Obs. 1,860 1,860 1,792 1,757 1,818 1,860 1,860 1,792 1,757 1,818 1,860 1,860 1,792 1,757 1,818 R‐squared 0.0662 0.0668 0.0636 0.0632 0.0631 0.0667 0.0674 0.0639 0.0636 0.0635 0.0685 0.0685 0.0639 0.0639 0.0636 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 5b Robustness Test for Tobit Regression Explaining the Percent Cash Financing in M&A Deals

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

NNI 1 0.11 0.11 ‐0.03 (0.07)* (0.07)* (0.08) NNI 2 0.12 0.13 ‐0.03 (0.07)* (0.07)* (0.08) NEQ ‐0.45 ‐0.46 ‐0.45 (0.17)*** (0.17)*** (0.21)** Altman 0.01 0.01 ‐0.07 (0.09) (0.08) (0.11) Ohlson 0.17 0.20 ‐0.07 (0.08)** (0.08)** (0.11) HP‐Index 0.39 0.39 0.42 0.41 0.39 (0.07)*** (0.07)*** (0.07)*** (0.07)*** (0.07)*** HP‐Index Top 0.27 0.27 0.28 0.28 0.25 0.08 0.07 0.28 0.23 0.09 (0.07)*** (0.07)*** (0.07)*** (0.07)*** (0.07)*** (0.09) (0.09) (0.07)*** (0.08)*** (0.08) NNI1#HPT 0.42 (0.13)*** NNI2#HPT 0.45 (0.13)*** NEQ#HPT ‐0.04 (0.33) ALT#HPT 0.21 (0.16) OHLS#HPT 0.61 (0.15)*** Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry f.e. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,955 1,955 1,894 1,858 1,917 1,955 1,955 1,894 1,858 1,917 1,955 1,955 1,894 1,858 1,917 Robust standard errors are in parentheses. *, **, and *** denote significance at the 1%, 5%,and 10% levels, respectively.

Table 5c Robustness Test for Differences in CAARs Acquirer [‐5, 5] Acquirer [‐1, 1] WW 0 WW 1 Difference WW 0 WW 1 Difference Full Stock ‐2.53% ‐4.13% ‐1.59% Full Stock ‐2.53% ‐4.08% ‐2.05%*** Full Cash ‐0.09% 0.89% 0.98% Full Cash ‐0.09% 0.70% 0.80% Differences 2.44%*** 5.02%*** Differences 2.44%*** 4.78%*** NNI 0 ‐0.88% ‐2.44% ‐1.55%** NNI 0 ‐0.67% ‐1.62% ‐0.94%* NNI 1 ‐0.60% ‐1.94% ‐1.34% NNI 1 ‐0.44% ‐2.38% ‐1.94%*** Differences 0.28% 0.49% Differences 0.24% ‐0.76% NNI2 0 ‐0.89% ‐2.06% ‐1.20%** NNI2 0 ‐0.63% ‐1.56% ‐0.92%** NNI2 1 ‐0.57% 2.28% ‐1.31%* NNI2 1 ‐0.51% ‐2.51% ‐2.00%*** Differences 0.32% 0.22% Differences 0.13% ‐0.95% NEQ 0 ‐0.85% ‐2.98% ‐2.13%*** NEQ 0 ‐0.65% ‐2.44% ‐1.79%*** NEQ 1 ‐0.32% 4.88% 5.20%* NEQ 1 0.42% ‐0.07% ‐0.49% Differences 0.53%*** 7.86%*** Differences 1.06% 2.37% Altman 0 ‐0.96% ‐2.44% ‐1.48%*** Altman 0 ‐0.69% ‐2.15% ‐1.46%*** Altman 1 ‐0.34% ‐2.11% ‐1.77% Altman 1 ‐0.21% ‐2.29% ‐2.08%** Differences 0.62% 0.32% Differences 0.48% ‐0.14% Ohlson 0 ‐0.79% ‐3.16% ‐2.37%*** Ohlson 0 ‐0.65% ‐2.33% ‐1.68%*** Ohlson 1 ‐0.90% ‐1.22% ‐0.32% Ohlson 1 ‐0.34% ‐1.88% ‐1.58%* Differences ‐0.11% 1.94%* Differences 0.31% 0.46%

Target [‐5, 5] Target [‐1, 1] WW 0 WW 1 Difference WW 0 WW 1 Difference Full Stock 21.13% 21.34% 0.21% Full Stock 18.98% 18.37% ‐0.60% Full Cash 33.52% 31.37% ‐2.15% Full Cash 29.64% 27.21% ‐2.43% Differences 12.39%*** 10.03%*** Differences 10.66%*** 8.84%*** NNI 0 26.38% 22.36% ‐4.02%** NNI 0 23.46% 20.08% ‐3.38%** NNI 1 35.33% 27.24% ‐8.08%** NNI 1 30.84% 23.50% ‐7.34%** Differences 8.95%*** 4.89%* Differences 7.37%*** 3.41%* NNI2 0 26.25% 22.69% ‐3.56%** NNI2 0 23.44% 20.50% ‐2.93%* NNI2 1 36.46% 27.18% ‐9.28%*** NNI2 1 31.59% 23.25% ‐8.34%*** Differences 10.21%*** 4.49%* Differences 8.15%*** 2.74%* NEQ 0 29.19% 24.41% ‐4.78%*** NEQ 0 25.92% 22.21% ‐3.72%*** NEQ 1 33.04% 38.42% 5.83% NEQ 1 27.19% 13.26% ‐13.92% Differences 3.85% 14.01%* Differences 1.26% ‐8.95% Altman 0 29.25% 24.60% ‐4.64%*** Altman 0 26.10% 21.93% ‐4.17%*** Altman 1 30.92% 26.42% ‐4.50% Altman 1 26.86% 22.17% ‐4.70% Differences 1.67% 1.82% Differences 0.76% 0.24% Ohlson 0 27.37% 23.30% ‐4.07%** Ohlson 0 24.39% 21.17% ‐3.21%** Ohlson 1 38.62% 27.44% ‐11.18%*** Ohlson 1 33.38% 22.85% ‐10.54%*** Differences 11.25%*** 4.15%* Differences 9.00%*** 1.68%*

Table 6 Variable Definitions

Variable Definition ADEBT The total debt of the acquirer AFCF Acquirer Free Cash Flow Age The age of the acquiring company, capped at 37 years ALEV Acquirer Debt‐to‐Equity ratio A variable indicating that the target company is not labeled as financially distressed according Altman 0 to the Altman Z‐score A variable indicating that the target company is labeled as financially distressed according to Altman 1/ Altman the Altman Z‐score AQ The acquirers Tobin's Q ASIZE Acquirer Size measured by market capitalization A dummy variable taking the value of 1 in case the transaction is fully financed with cash and Cash 0 otherwise A dummy variable taking the value of 1 in case multiple bidders are involved in the takeover COMP bid and 0 otherwise DEAL The relative size of the transaction to the size of the acquirer A dummy variable taking the value of 1 in case the acquirer and target are in different DIV industries and 0 otherwise HOST A dummy variable taking the value of 1 the merger or acquisitions is hostile and 0 otherwise HP The Hadlock and Pierce index of financial constraints A variable indicating that the acquiring company is not labeled as financially constrained HP 0 according to the HP‐index A variable indicating that the acquiring company is labeled as financially constrained HP 1 according to the HP‐index HP index The Hadlock and Pierce index of financial constraints A dummy variable indicating if the acquirer is in the top tercile of the distribution of the HP‐ HPT index in the sample A variable indicating that the company has a positive equity the in the last twelve months and NEQ 0 is therefore labeled as non‐distressed A variable indicating that the company has a negative equity the in the last twelve months and NEQ 1/ NEQ is therefore labeled as non‐distressed A variable indicating that the company has a positive net income the in the last twelve months NNI1 0 and is therefore labeled as non‐distressed A variable indicating that the company has a negative net income the in the last twelve NNI1 1 / NNI 1 months and is therefore labeled as non‐distressed A variable indicating that the company has a positive net income the in the last twentyfour NNI2 0 months and is therefore labeled as non‐distressed A variable indicating that the company has a negative net income the in the last twentyfour NNI2 1 / NNI2 months and is therefore labeled as distressed A variable indicating that the target company is not labeled as financially distressed according Ohlson 0 to the Ohlson O‐score A variable indicating that the target company is labeled as financially distressed according to Ohlson 1/ Ohlson the Ohlson O‐score RELSIZE The relative size of the target compared to the acquirer Size The log of the acquirers total assets, capped at 4.5 billion dollars TDEBT The total debt of the target company TEND A dummy variable taking the value of 1 in case of tender offer and 0 otherwise TLEV Target Debt‐to‐Equity ratio TSIZE Target Size measured by market capitalization

Variable Definition WW The Whited Wu index of financial costraints A variable indicating that the acquiring company is not labeled as financially constrained WW 0 according to the WW‐index A variable indicating that the acquiring company is labeled as financially constrained WW 1 according to the WW‐index WW index The Whited Wu index of financial costraints A dummy variable indicating if the acquirer is in the top tercile of the distribution of the WW‐ WWT index in the sample