Determinants of the Method of Payment in Distressed
Acquisitions.
University of Amsterdam, Amsterdam Business School
Master Thesis
June 2017
MSc Finance, Corporate Finance track
Author: Michal Ratomski
Thesis supervisor: dr. V.N. Vladimir Vladimirov
Statement of Originality
This document is written by Michal Ratomski 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
Using a sample of 679 distressed transactions and 1480 healthy deals involving both public and private targets from 19 European countries over the period 1998-2016, I investigate whether such factors as: target’s level of financial distress, its leverage, cash holdings, industry relatedness or debt enforcement influence whether cash or stock is used more frequently as payment method in acquisitions of financially distressed companies. I find that higher levels of target’s financial distress and leverage have a negative influence on the probability of cash financing in acquisitions. Distressed deals in
Table of Contents
1. Introduction ... 5 2. Literature Review ... 8 3. Methodology and Hypotheses ... 13 4. Data and descriptive statistics ... 23 5. Results ... 29 6. Robustness Checks ... 35 7. Conclusion ... 40 References ... 421. Introduction
Although corporate acquisitions have been analyzed from a variety of angles, is seems like takeovers involving a target in financial distress are the least explored. Jensen (1991) presents an acquisition as one of the means to effectively resolve a company’s financial distress, which can occur both inside and outside of bankruptcy. He argues that takeovers are among three possible ways of reorganizing a troubled firm, next to a general corporate restructuring and liquidation. Acquisitions also allow distressed firms to smoothly redeploy their assets to a company which might put the assets to a more efficient use. Moreover, those kinds of transactions provide target company stakeholders with an attractive exit route from an unfavorable investment. This kind of acquisition, however, is particularly risky, as bidders have to spend significantly more time and resources in order to estimate the target’s true value and avoid a substantial risk transfer that can draw a healthy acquirer closer to bankruptcy. One way of mitigating this risk is to use stock as method of payment in distressed transactions.Existing literature finds that issues related to corporate governance as well as debt financing constraints have significant impact on the acquirer’s decision to finance the acquisition with stock or cash (Faccio and Masulis 2005). Other studies suggest that as the stockholdings of bidder’s management become larger, so does the chance of using shares as the means of payment for an acquisition (Amihud, Lev, and Travlos 1990 and Martin 1996). Further evidence suggests that acquiring companies are not able to successfully turn around a troubled firm, with a downward-sloping market performance of the joined entity. Moreover, the results point to a negative relationship between post-acquisition performance and takeover premiums (Clark and Ofek 1994). Furthermore, Baird and Rasmussen (2003) note that transactions involving financially distressed or
bankrupt targets have increased in frequency during the 2000s, thus stressing the importance of examining the market for distressed acquisitions. Previous studies mostly disregard the unique character of distressed firms that may lead to additional risk and eventual substantial increase in probability of bankruptcy. The purpose of this research is to address this void in the literature by examining a set of potential factors determining what method of payment is used in acquisitions of financially distressed targets. This work thus makes a contribution to the broader area of mergers and acquisitions characteristics, providing a useful insight for corporate managers, executives, and CFOs. Although this study outcome is closely related to the eventual change in capital structure of an acquiring company, it has not been fully investigated yet. This study is the first to analyze a vast number of private targets - their financial characteristics and distress in the context of payment method in mergers and acquisitions. Furthermore, the introduction of a distress measure in the analysis is a natural extension and contribution to the existing literature on the M&A payment method determinants.
Using a sample of 679 distressed transactions and 1480 healthy deals involving both public and private targets from 19 European countries over the period 1998-2016, I investigate whether such factors as: target’s level of financial distress, its leverage, cash holdings, industry relatedness or debt enforcement influence whether cash or stock is used more frequently as payment method in acquisitions of financially distressed companies. I collect and merge deal and company data from 4 different databases (Orbis, Zephyr, Amadeus and Datastream) as well as 2 additional data sources (Heritage website and Djankov et al. (2008) debt enforcement index). I employ a Tobit multivariate model to investigate the influence of several variables on the method of payment decision.
This paper proceeds as follows: Section 2 presents literature overview, section 3 discusses methodology and hypotheses, section 4 includes data and descriptive statistics,
section 5 discusses the results, section 6 details some robustness checks, and, finally,
2. Literature Review
In the existing literature connecting a firm’s financial standing to various deal characteristics, the effects of a target’s financial distress on the structure of corporate transactions received little attention. This comes as a surprise, considering that financial distress is frequently quoted as one of the major reasons for a company to be acquired by a potential bidder (Amit et al. 1989, Jensen 1991, Powell 1997, Powell and Yawson 2007). Other motives of mergers and acquisitions commonly appearing in the literature are: expected synergies, increase in market power (Bradley et al. 1988, Morck et al. 1990), replacement of ineffective target company management (Manne 1965, Martin and McConnell 1991), cutback of agency costs (Jensen 1986), and diversification benefits (Matsusaka 1993). All the above are valid to an even larger extent for distressed transactions. Knowing that the target is in financial distress, potential acquirers have additional motivation to spend substantial time and resources in order to estimate the target’s true value. A distressed company may be attractive as a target if the managers of the acquirer suppose they can improve its performance. Another argument for pursuing a distressed acquisition are the buyer’s managers being attracted to a badly managed target with valuable resources, much to a displease of current managers fearing displacement (Salter and Weinhold 1979). Nevertheless, if a company’s distress is evident and unlikely to be relieved easily, an offer from a potential acquirer might be a valid alternative to a bankruptcy (Jensen 1991). Bidders ought to be cautious, however, when acquiring a troubled firm, because as soon as the transaction is completed, they need to step in and prevent the value of the target from further erosion, or else the joint operations of the newly formed entity are at risk of being severely compromised. If the deal is inadequately structured and executed,
the acquirer might not be able to control, stabilize, and eventually improve the target’s damaged financial condition, which in turn might result in compromising the existing healthy operations. An unsuccessful takeover might place the acquirer in an uncomfortable position, where it might experience a significant transfer of risk from the target, which might increase the acquiring firm’s default risk or even cause financial distress, as a consequence of uncertainty about the target’s current and future profitability. This overall risk can prove to be larger than in classical M&A transactions (Bruyland et al. 2016). Nevertheless, a particular group of potential buyers may have an advantage over other competitors in distressed acquisitions and engage in such deals more frequently. Acquirers with exceptional industry awareness and outstanding sector experience can be better suited for successful redeployment of distressed assets and capitalizing on the risky transaction, while financially strong companies have a potential for adequate funding and restructuring a distressed target (Clark and Ofek 1994). Therefore, a struggling firm might be more likely to be taken over by a bidder from the same industry with particularly strong financials.
In comparatively normal circumstances, the price paid in an acquisition usually is a reflection of the target’s future value (Barney 1988). However, if a bidder fails to accurately estimate the cost of potential synergies, it may end up paying a high premium over the future value of the target. (Jemison and Sitkin 1986, Roll 1986, Salter and Weinhold 1979). Such miscalculations in valuation can result in the acquirer’s successful overestimated bid being significantly higher than the rest of the competitors. This situation is commonly known as the winner’s curse. The winner’s curse can be avoided with higher probability in the case of acquisition of a distressed target which operates in a bidder’s industry. When it comes to potential acquirers, they are arguably more inclined to spend substantial time and resources in order to estimate the target’s true value. It also
happens so that the state of company’s distress discourages many other market participants from placing a bid (Giliberto and Varaiya 1989). The lack of competing bids allows acquirers more time for studying the target. Dundas and Richardson (1982) argue that if both acquirers and targets are in the same industry, there is a higher chance for the acquirer to identify the existing problems and address them accordingly. This in turn opens the door for recognizing potential synergies and identifying undervalued assets. Therefore, if a distress acquisition takes place within the same industry, the acquirer is much less likely to experience the winner’s curse (Barney 1988, Harrisson et al. 1991). Bruyland et al. (2016) and Clark et al. (1994) find that bidders in pursuit of distressed acquisitions tend to acquire companies that are relatively smaller, operate in related industries, and are located domestically. Such bidders often have high initial stake in the target company, as well as more financial flexibility in contrast with bidders for healthy firms. Such transactions are likely to reduce asymmetry of information, funding issues, and be friendly in nature. These studies have been conducted on the U.S. market, and to the best of my knowledge, no similar comparison has been made with regard to the European market. The collective of these results, however, is helpful in defining characteristics and motives that are unique for distressed acquisitions.
Amit et al. (1989) study U.S. targets between 1965 and 1984 to determine whether target’s financial distress has an impact on their shareholder wealth changes around the transaction announcement. The authors discover that distressed targets usually enjoy lower abnormal returns around the takeover announcement. The explanation provides that the reason for the lower returns is the fact that targets in financial distress are usually less attractive to potential bidders compared to companies with better financial performance. The results also suggest that the struggling firms are disadvantaged in terms of negating power in comparison to healthy firms. Amit et al. (1989) do not conduct
any analysis with regard to association between financial distress and other deal characteristics such as such as payment method or takeover competition.
Amihud, Lev, and Travlos (1990) and Martin (1996) conduct empirical analysis on the payment methods in mergers and acquisitions between 1978-1988 and study the characteristics of managerial stockholdings in American acquisitions. The results of both studies suggest that as the stockholdings of bidder’s management become larger, so does the chance of using shares as the means of payment for an acquisition. These findings are in line with a motive of corporate control.
Faccio and Masulis (2005) study payment method determinants in European mergers and acquisitions. Using a sample of 3,667 deals in 16 European countries between 1997 and 2000, the authors find that issues related to corporate governance as well as debt financing constraints have significant impact on the acquirer’s decision to finance the acquisition with stock or cash. Moreover, various target firm characteristics, such as collateral, financial leverage, debt capacity, asymmetric information or bidder’s investment opportunities, as well as some deal features significantly influence the choice of currency used in an acquisition. Clark and Ofek (1994) take a close look at 38 U.S. companies purchasing distressed targets, with a particular focus on their post-merger performance. Financial distress is defined through negative stock returns preceding the acquisition, along with information released by targets pointing to a distress, such as management turnover or employee layoffs. The authors conclude that acquiring companies are not able to successfully turn around a troubled firm, with a downward-sloping market performance of the joined entity. Moreover, the results point to a negative relationship between post-acquisition performance and takeover premiums, which implies overpayment in many instances. However, in this particular study, there is limited evidence on similarities or differences
between healthy and distressed deals with regard to payment method or takeover competition. Moreover, it is important to note that most results are not tested using multivariate setting, which makes the conclusions difficult to interpret with accuracy.
When a company decides to engage in an acquisition of a financially distressed target, it takes a substantial risk of turning the firm around and improving its performance. There is, however, a means of reducing this risk utilized by the acquiring company. By offering an equity consideration, the acquiring firm somewhat protects their existing shareholders by sharing the implied post-takeover risk with the target’s stakeholders. Moreover, Shleifer and Vishny (1992) show that companies are less likely to finance the purchase of illiquid assets with debt, considering an increased uncertainty related to the true value of the acquired assets. Because cash acquisitions usually involve taking some additional debt, these results suggest that acquirers will be less likely to offer cash as transaction currency when targets are experiencing financial distress. While prior literature identifies a number of other potential factors influencing the choice of payment method in regular acquisitions, I identify factors influencing financing decisions in distressed acquisitions.
I therefore draw a prediction that companies wishing to acquire distressed targets will be less likely to offer cash as a method of payment. In the methodology section I will discuss in detail a set of specific hypotheses in terms of the following variables/determinants: level of financial distress, leverage and cash holdings, economic freedom, legal system, debt enforcement, assets, multiple bidders, industry relatedness,
3. Methodology and Hypotheses
As there is no one commonly defined distress measure, previous studies use a variety of definitions, with the most common being Interest Coverage Ratio (ICR) expressed as Interest Expense/EBITDA one or two years prior to an acquisition (Cao and Madura 2011), negative stock returns one or two years prior to an acquisition (Ang and Mauck 2011, Clark and Ofek 1994) and Altman’s Z-Score (Ang and Mauck 2011, Altman 2000). The ICR approach does not appear to be very versatile, as it utilizes only one financial measure for the estimation, which might not yield accurate results across industries because of high variability. For example, an established utility company can have an ICR of 2 and be financially stable, as its revenues and expenses are fairly predictable, while a technology company, on the other hand, cannot. The other measures, such as negative earnings or negative stock returns are arguably even less accurate predictors of financial distress, as they do not capture many financial and operational measures. For this reason, I decided to use Altman’s Z-Score model which estimates the likelihood of bankruptcy using the following equations: ! = 0.012'(+ 0.014'+ + 0.033'- + 0.006'/ + 0.999'1 for public companies, where X1 = Working Capital/Total Assets X2 = Retained Earnings/Total Assets X3 = Earnings Before Interest and Taxes/Total Assets X4 = Market Value of Equity/Total Assets X5 = Working Capital/Total Assets Z = Overall Index
With a Z-Score < 1.81 indicating a public company in distress And ! = 0.717'(+ 0.847'+ + 3.107'- + 0.420'/ + 0.998'1 for private companies, where X1 = Working Capital/Total Assets X2 = Retained Earnings/Total Assets X3 = Earnings Before Interest and Taxes/Total Assets X4 = Book Value of Equity/Total Assets X5 = Working Capital/Total Assets Z = Overall Index With a Z-Score <1.21 indicating a private company in distress. Following Martin (1996) I make a classification of payment method into cash and stock, both of which can range from 0% to 100%, STOCK ONLY represents deals settled only in stock. CASH ONLY captures acquisitions financed by cash only, including cash, assumption of liabilities and newly issued notes. MIXED PYMT represents payments settled by cash and stock.
Following Faccio and Masulis (2005), I employ a Tobit multivariate model to investigate the influence of several variables on the method of payment decision in acquisitions of financially distressed targets. The two-boundary Tobit estimation requires the dependent variable, which in this case is the cash proportion of the total transaction value to be in the interval [0, 100]. This by definition holds true in this research setting. I utilize the following model: 45∗ = ' 578 + 95 where the dependent variable is censored from both left and right, so that
45 = 0 45* 100 <= 45∗ ≤ 0, <= 0 ≤ 45∗< 100 <= 100 ≤ 45∗ where '57 is the vector of explanatory and control variables, and 95 is an independently distributed error term assumed to be normal with zero mean and variance A+. The parameters 8 and A are estimated by maximizing the log likelihood function ℓ 8, A = log F((−I57 5∋KLMN 8/A) + log =((45 − I578 5∋NQKLQ(NN )/A) + log(1 − F((100 − I578)/A)), 5∋KLM(NN
where f and F are the density and cumulative distribution functions, respectively.
Denoting R STLUV W , R (NNSTLUV W , Φ STLUV W , and Φ (NNSTLUV
W by the respective symbols
RN, R(NN, ΦN, and Φ(NN, the conditional prediction of 45 given I5 is \ 45 0 ≤ 45∗ ≤ 100 = I578 + A(RN− R(NN)/(Φ(NN− ΦN) and the unconditional prediction of 45 is \ 45 = I578 Φ (NN− ΦN + A RN− R(NN + 1 − Φ(NN 100. Quasi-maximum likelihood (QML) White standard errors are used to correct for heteroskedasticity. A significant improvement of this research over previous studies, including Martin (1996) and Faccio and Masulis (2005), is the inclusion of private target financial
characteristics that combined with public target data allows for simultaneous estimation of equations that capture both bidder and target transaction preferences. Faccio and Masulis (2005) resorted to using a reduced form equation which provides limited insight into the target’s preference influence on the method of payment. When examining potential factors influencing the method of M&A deal financing, I consider the following target, acquirer and deal characteristics (all values are measured as of one fiscal year prior to acquisition announcement): Level of Financial Distress
Nesvold et al. (2012) argue that distressed acquisitions are particularly risky, since the bidders are acquiring a struggling company and therefore must fully understand and quantify all potential financial, operational and legal risks involved. These risks become arguably even more pronounced with the increase of financial distress severity. One way of limiting the bidder’s risk exposure is to offer the target company stock as a method of payment. I hypothesize that with the increase of target’s financial distress level proxied by Altman Z-Score (Altman 2000), the cash offers will be less likely, as acquiring companies will prefer to shift more risk to target’s shareholders. Therefore, within the research setting, (H1) I expect that higher levels of distress (lower values of Altman Z-Score) will be associated with a lower likelihood of cash offers. Variable TARGET Z-SCORE indicates the level of target’s financial distress, with lower values pointing to a higher level of distress. Leverage and Cash Holdings
When a distressed company suffers from a shortage of financial resources, acquirers that exhibit higher levels of financial flexibility might be able to facilitate the target’s recovery by injecting capital. According to a study by Erel et al. (2015),
acquisitions can serve as an effective means for easing the target’s financial frictions. Clark and Ofek (1994) argue that companies with a strong financial position that acquire targets in distress might be able to reduce concerns regarding their high leverage. Financial flexibility uses liquid assets (cash and cash equivalents) and/or spare borrowing capacity to measure how well a firm can respond to new investment opportunities (Graham and Harvey 2001). These firms can better respond to unforeseen variations in cash flows, which results in lowering the financial distress risk (Almeida et al. 2011). In the existing literature two most widely used proxies for financial flexibility are: total liquid assets (measured by cash and cash equivalents) and unused debt capacity (Opler et al. 1999, Almeida et al. 2004). Distressed targets that posses some investment opportunities (positive NPV projects) but are unable to realize them because of lack of financial resources, might experience underinvestment problems (Myers 1977). Healthy companies with abundant resources acquiring less fortunate ones can thus create value (Bruner 1988). Consequently, financially flexible acquirers might manage the distress risk more effectively through several channels. For instance, they can repay the target firm’s financial obligations, finance its losses until resuming profitability or cover other miscellaneous costs related to the restructuring process, allowing the target a smoother route to reducing its leverage and resolving operational concerns. Thus, (H2) I expect bidders with more cash-on-hand to offer stock as a means of payment in anticipation of significant cash spending on the newly acquired distressed target. Variable CASH/ASSETS is constructed by dividing total cash holdings by total assets of the company. Another determinant of financial flexibility is access to external capital through maintaining lower leverage, which translates into unused debt capacity (Almeida et al. 2004). Therefore,
(H3) I hypothesize that acquirers with lower debt levels (LEVERAGE) will offer cash
be critical in turning around a distressed target. Variable LEVERAGE is measured by the acquirer’s total debt plus deal value divided by the book value of total assets plus the deal value. Economic Freedom and Legal System Cao and Madura (2011) find that in certain conditions country risk and corporate governance characteristics are related to the currency used in asset sell-off transactions. More specifically, cash payment in those particular transactions is less likely in countries with lower Economic Freedom Index score and weaker investor protection proxied by a civil law legal system. The intuition in the context of this research is that when firms acquire distressed targets in a country with higher country risk and lower investor protection, they want to shift the risk of the transaction to the sellers’ shareholders. The economic freedom of the world index (Gwartney et al. 2015) is assigned to target firms’ countries based on, among others, government intervention, tax rate, protection of property rights, integrity of the legal system, law enforcement, money growth, trade regulations, capital controls, credit market, labor and business regulations. A higher score is equivalent to less obstructive environment. Using data available on the Heritage website1, each target’s country was assigned a corresponding score in order to analyze
the effect of economic environment on the method of payment. Variable ECONOMIC
FREEDOM measures the Economic Freedom Index for each target’s country. (H4) I expect
a positive relationship between the economic freedom index and cash proportion of the payment, signifying acquirers wanting to shift less of the acquisition risk to target’s shareholders.
Legal system is an indicator that can serve to define investor protection, as it proxies for shareholder rights and the quality of corporate governance (Schleifer and Vishny, 1992). It is widely accepted that common law countries enjoy the highest level of investor protection. Conversely, countries with a legal system based on civil law have the weakest shareholder rights. (H5) I expect cash consideration to be used more frequently for transactions where target company is located in a country governed by common law legal system. A dummy variable COMMON LAW equals one for common law countries. Debt Enforcement Djankov et al. (2008) study debt enforcement in the context of an insolvent firm in 88 different countries. They find that when a company is financially troubled, the debt enforcement inefficiencies can destroy a significant portion of the firm’s value, very often (63% of the time) preventing it from further operations, failing to keep the firm as a going concern. This aspect might play an important role for a bidder in deciding on the method of payment in an acquisition of a distressed company. With lower levels of debt enforcement efficiency comes an increased risk of potential target value destruction in case of post-acquisition complications. Therefore, (H6) I hypothesize that in order to mitigate this risk, bidders will offer cash consideration less frequently to targets located in countries with lower levels of debt enforcement efficiency. Using data from Djankov et al. (2008) I collected all debt enforcement scores and assigned them to each bidder’s and seller’s country in my sample. The score is represented by variable DEBT ENFORCEMENT. Asset Size According to Faccio and Masulis (2005), the size of the bidding firm has an impact on its financing decision. Larger companies tend to be more diversified, which translates
into equivalently lower expected bankruptcy costs. This factor is of extremely high importance in case of acquisitions involving distressed targets, as it might largely lessen the need for risk sharing in favor of the ease of cash use in smaller deals. Moreover, large firms are also characterized by lower flotation costs in addition to improved access to debt markets, which results in debt financing being much more readily available. Therefore, larger firms might be more likely to use cash in their acquisitions. Another argument for using cash is its lower cost in comparison to stock issue, where shareholder approval of preemptive rights exemption is necessary on top of substantial regulatory cost of stock offer and stock authorizations. Considering the above factors, (H7) I expect bidders with higher asset value to use cash offers more frequently. Variable TOTAL
ASSETS measures the bidder and target asset size as of one year prior to acquisition
announcement. Multiple Bidders Mayer and Walker (1996) argue that bidding firms use cash as a means of gaining a competitive edge in the bidding contest, as it increases a chance of winning a bid and allows for potentially quicker deal approval and completion. They found that acquirers use cash more readily in hostile takeovers as well as transactions with several competing bidders. Chapple et al. (2007) find a positive relationship between number of bidders and cash consideration in an acquisition. Hotchkiss (1998) study takeovers of American firms undergoing Chapter 11 bankruptcy and find a positive influence of target’s financial distress on takeover competition, which might stem from increased interest of bidders in distressed targets because of higher potential for improving performance and subsequent profits. Therefore, (H8) I expect to find more frequent cash offers to a target for which multiple competitive bids have been submitted. Variable MULTIPLE BIDDERS
takes a value of 1 if there are multiple bids for the same firm and 0 if there is only one bidder. Industry Relatedness When a distressed firm merges with a healthy one in the same industry, it can be more valuable because of possible synergies (Jory and Madura 2009). Existing literature points to higher synergies when both the target and the acquirer operate in one industry (Servaes 1991). Moreover, relatedness between the two companies may help in facilitating the integration process, which could be of even higher importance in case of distressed acquisitions. Furthermore, bidders that share target’s industry may be in an advantageous situation, as they can utilize their industry-specific information and expertise (Shleifer and Vishny 1992). Acquirer’s expertise can prove helpful not only for determining the target’s value, but also for putting its distressed assets to the best possible use. Additionally, Shleifer and Vishny 1992 show that acquirers of bankrupt companies very often have a history of some previous ties with the target. Overall, bidders from related industries can be better positioned for uncovering the target’s issues and be more effective in finalizing the transaction, which in turn results in potentially higher effectiveness in uncovering the true hidden value of a distress firm. All these aspects lead to a partial, although significant risk reduction for the acquiring firm and its shareholders. Therefore, (H9) I expect cash offers to be more likely in case of intra-industry acquisitions. Variable INTRA-INDUSTRY takes a value of 1 if target’s and acquirer’s 3-digit SIC codes do not overlap and 0 otherwise. Relative Size Hansen (1987) argues that bidders have bigger motivation to use stock financing when there is high asymmetry of information regarding the target assets. As target’s asset
value increases in relation to the acquirer, this information asymmetry is likely to become larger. Moreover, more valuable acquirers face lower potential financing constraints, because of a relatively small influence on its financial heath. Conversely, if the target is relatively larger, a significant part of its financial distress risk might be transferred to the acquirer. Hence, to hedge against excessive risk, (H10) I expect bidders to offer less cash consideration to relatively large targets. Variable RELATIVE SIZE proxies for these changes and is constructed by dividing the deal value by the sum of the deal value and acquirer’s market capitalization. Cross-Border Using shares in cross-border transactions is associated with several issues. Coval and Moskowitz (1999), French and Poterba (1991), and Grinblatt and Keloharju (2001) document that investors are guided by home bias and often have portfolios with little diversification. There are several reasons for this bias, such as bigger costs of trading, foreign exchange risk exposure, lower liquidity and slower, limited access to information about firm financials. Distressed acquisitions magnify those issues even more, increasing the already existing acquisition risk. The collective of these factors make bidder stock less desirable in the eyes of a target, suggesting that (H11) cash consideration will be used more often in cross-border distressed acquisitions. Variable CROSS BORDER equals one when target and bidder countries differ, and zero otherwise.
4. Data and descriptive statistics
For the purpose of data collection, I utilized and combined a total of 4 different online databases (Orbis, Zephyr, Amadeus and Datastream) and 2 additional data sources (Heritage website and Djankov et al. 2008 debt enforcement index). The initial sample consists of all acquisitions announced between January 1998 and December 2016 by bidders from 19 European countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. All deals must be included in Zephyr database, which is incorporated in Bureau van Dijk’s Orbis. This database has a coverage of public and private mergers and acquisitions with a particular focus on Europe. In order to narrow the search, the following screening criteria were applied. First, all bidders must be located in one of the European countries specified. Both successful and unsuccessful bids have been included. Second, all deals need to be financed with either cash, stock, or a combination of the two. US SIC codes of regulated industries: 4900-4999 (utilities) and 6000-6999 (financials) were excluded from the search. Next, a pre-deal ownership stake of the target had to be less than 50%, and a post-deal ownership stake higher than 50%. Lastly, only deals valued at more that 1 million euros were included. The application of the above criteria resulted in a sample of 12,278 transactions. In order to analyze financial and accounting data of the sample firms, I require targets to be included in Bureau van Dijk’s Amadeus. This database provides financial data for up to 9 years before the last available year. Information about 11,545 targets was found in Amadeus. However, considering the extensive sample timeframe, information about only 6,472 public and private targets overlapped with the transaction date, of which only 1,287 was sufficient for calculating Altman’s Z-score. To extend the
sample I collected ISIN codes of bidders and targets along with corresponding deal announcement dates (one year prior), both of which were provided by Zephyr. In order to expand the sample, I collected data about another 872 targets and bidders using Datastream. This yielded a final sample of 2,159 deals, for which both target and acquirer financials were sufficient for calculating Altman Z-Score. I then collected data on the Economic Freedom Index from the Heritage website along with debt enforcement efficiency scores from Djankov et al. (2008), both of which were assigned to acquirer and target countries as of one year prior to acquisition announcement. The final sample consists of a total of 2,159 acquisitions (1,480 non-distressed and 679 distressed). There are 1,158 public targets, of which 368 (31.8%) distressed and 1,001 private targets, of which 311 (31.1%) distressed. I applied a cutoff score of 1.81 for public companies and 1.21 for private companies, below which a firm is classified as being financially distressed.
As can be seen in Table I, by far the most popular form of payment in both distressed and non-distressed acquisitions is cash (67.3% and 72.84% respectively), followed by pure stock acquisitions, followed by a mixture of cash and stock. The entire sample of distressed acquisitions contains 457 cash-only deals, 171 stock-only deals, and 51 deals with a mixed payment. United Kingdom is the country with the largest number of acquisitions of both kinds, followed by France, Germany, Sweden and Italy. The table also points to a higher percentage of stock financing in distressed acquisitions (25.18%) compared to (17.43%) acquisitions of non-distressed targets as well as the whole sample of all deals (19.87%). Another important point to note is that in none of the countries, with the sole exception of Sweden, the proportion of stock-financed deals is larger that 50%, which is in contrast with the findings of Andrade et al. (2001), who report that the majority of U.S. acquisitions (58%) is settled in stock only. The sub-sample of distressed
acquisitions is fairly consistent in proportion, making up around 30% of the entire sample across most countries. Table II presents selected target characteristics. Interestingly, distressed targets in my sample have a larger mean asset value in comparison to non-distressed targets. Financially distressed targets also experience substantially lower sales relative to healthy targets, in addition to negative earnings before interest and taxes one year prior to being acquired. Moreover, distressed targets in my sample are characterized by noticeably higher debt levels and lower working capital in comparison to healthy targets and the whole sample. Finally, cash holdings of companies in distress appear to be on a similar level as the ones of healthy companies. This observation might point in the direction of precautionary savings that a struggling company is making in order to be able to fund critical positive NPV projects despite severe financial constraints (Han and Qui 2007). Table III presents summary statistics of the dependent variable PERCENTAGE OF CASH as well as several other continuous and binary explanatory variables. All variables capture the preference of both bidders and targets in acquisitions of distressed and non-distressed companies. It appears that cash constitutes a lesser portion of the deal payment structure in distressed acquisitions with a mean of 71% and higher standard deviation in comparison to healthy deals with a 77.77% mean. Additionally, there is a slightly lower mean debt enforcement efficiency in countries where a distressed target is acquired. While acquirers in both types of acquisition are similarly leveraged, distressed target firms’ leverage is over twice as high compared to non-distressed targets. On the other hand, when it comes to relative size of the target and its cash holdings, both groups of targets depict resemblance.
Table I. Descriptive statistics by payment method STOCK ONLY represents deals settled only in stock. CASH ONLY captures acquisitions financed by cash only, including cash, assumption of liabilities and newly issued notes. MIXED PYMT represents payments settled by cash and stock.
No. Stock Only Cash Only Mixed PYMT No. Stock Only Cash Only Mixed PYMT No. Stock Only Cash Only Mixed PYMT
Number 2159 429 1535 195 679 171 457 51 1480 258 1078 144 Austria 36 8% 86% 6% 10 10% 70% 20% 26 8% 92% 0% Belgium 55 7% 84% 9% 13 23% 69% 8% 42 2% 88% 10% Bulgaria 21 19% 81% 0% 8 13% 88% 0% 13 23% 77% 0% Czech Republic 7 0% 100% 0% 2 0% 100% 0% 5 0% 100% 0% Denmark 30 10% 87% 3% 13 15% 85% 0% 17 6% 88% 6% Finland 83 25% 60% 14% 18 11% 83% 6% 65 29% 54% 17% France 341 18% 75% 7% 109 19% 75% 6% 232 17% 75% 8% Germany 169 16% 80% 4% 49 37% 63% 0% 120 8% 88% 5% Greece 51 25% 73% 2% 21 29% 71% 0% 30 23% 73% 3% Ireland 33 24% 52% 24% 11 27% 45% 27% 22 23% 55% 23% Italy 194 15% 80% 5% 82 21% 73% 6% 112 11% 85% 4% Netherlands 105 10% 79% 10% 24 17% 71% 13% 81 9% 81% 10% Norway 121 17% 65% 17% 40 18% 75% 8% 81 17% 60% 22% Poland 107 36% 58% 7% 23 17% 83% 0% 84 40% 51% 8% Portugal 22 23% 77% 0% 6 33% 67% 0% 16 19% 81% 0% Spain 140 29% 62% 9% 72 25% 65% 10% 68 34% 59% 7% Sweden 209 26% 61% 13% 46 52% 41% 7% 163 18% 67% 15% Switzerland 79 8% 87% 5% 23 13% 78% 9% 56 5% 91% 4% United Kingdom 356 22% 65% 12% 109 32% 54% 14% 247 18% 70% 12% Whole Sample 100% 19.87% 71.10% 9.03% 100% 25.18% 67.30% 7.51% 100% 17.43% 72.84% 9.73% Acquisitions of Non-Distressed Targets Whole Sample Panel A: Method of Payment Choice for the 1998-2016 Period Acquisitions of Distressed Targets
Table II. Target characteristics The sample includes 679 acquisitions of distressed targets and 1480 acquisitions of non-distressed targets announced by European bidders between January 1998 and December 2016. Targets are classified as distressed if their calculated Altman Z-Score is less that 1.21 for privately held firms and less than 1.81 for publicly listed companies one year prior to deal announcement. The table presents number of observations, mean, median, standard deviation, minimum and maximum of target accounting ratios as well as market characteristics one year prior to deal announcement.
Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max
Total Assets (€ mil.) 2159 610.76 1,244.36 90.30 4,874.54 679 773.58 1,385.29 90.30 4,874.54 1480 554.42 1,170.25 90.30 4,874.54
Sales/Assets 2159 1.14 0.91 0.00 4.88 679 0.62 0.58 0.00 4.88 1480 1.38 0.93 0.00 4.88 EBIT/Assets 2159 0.01 0.26 -2.10 0.53 679 -0.16 0.36 -2.10 0.29 1480 0.08 0.15 -1.22 0.53 Debt/Assets 2159 0.16 0.19 0.00 1.05 679 0.25 0.24 0.00 1.05 1480 0.12 0.15 0.00 1.04 Working Capital/Assets 2159 0.17 0.24 -0.48 0.82 679 0.07 0.24 -0.48 0.82 1480 0.21 0.22 -0.48 0.82 Cash/Assets 2159 0.14 0.17 0.00 0.79 679 0.13 0.18 0.00 0.79 1480 0.14 0.16 0.00 0.79 Z-Score 2159 2.93 4.53 -7.53 27.10 679 -0.34 2.54 -7.53 1.81 1480 4.43 4.46 1.21 27.10
Table III. Summary statistics for dependent and explanatory variables The table presents summary statistics of the dependent variable PERCENTAGE OF CASH, which measures the cash proportion of the entire transaction offer, as well as several other continuous and binary explanatory variables. All variables capture the preference of both bidders and targets in acquisitions of distressed and non-distressed companies. Targets are classified as distressed if their calculated Altman Z-SCORE is less that 1.21 for privately held firms and less than 1.81 for publicly listed companies one year prior to deal announcement. Target and acquirer ECONOMIC FREEDOM Index scores are assigned to each country based on Heritage website’s data. DEBT ENFORCEMENT information is collected from Djankov et al. (2008) and also assigned to each respective country of target and acquirer. TARGET LEVERAGE is calculated by dividing total debt by total assets. ACQUIRER LEVERAGE is calculated by dividing the sum of debt and deal value by the sum of total assets and deal value. RELATIVE SIZE is constructed by dividing the deal value by the sum of the deal value and acquirer’s market capitalization. Deal is classified as INTRA-INDUSTRY if the 3-digit SIC codes of target and acquirer firms do not match. Deal is classified as CROSS BORDER if countries of bidder and target firms differ. MULTIPLE BIDDERS takes a value of one if there is more than one bidder for a target. COMMON LAW is a dummy variable indicating countries that follow a common law legal system.
Variable Obs Mean Std. Dev Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev Min Max
Percentage of Cash 2159 75.65 40.86 0.00 100.00 679 71.02 43.81 0.00 100.00 1480 77.77 39.28 0.00 100.00 Target Z-Score 2159 2.93 4.53 -7.53 27.10 679 -0.34 2.54 -7.53 1.81 1480 4.43 4.46 1.21 27.10 Acquirer Z-Score 2159 3.16 3.99 -1.37 30.09 679 3.17 4.98 -1.37 30.09 1480 3.16 3.44 -1.37 30.09 Target Economic Freedom 2159 68.80 6.79 46.10 89.40 679 68.78 6.96 47.30 89.40 1480 68.81 6.71 46.10 88.00 Acquirer Economic Freedom 2159 69.31 6.43 53.20 82.60 679 68.86 6.49 53.20 82.20 1480 69.52 6.39 53.20 82.60 Target Debt Enforcement 2159 70.89 19.82 6.60 96.10 679 69.70 20.76 11.00 96.10 1480 71.44 19.36 6.60 96.10 Acquirer Debt Enforcement 2159 73.56 18.06 40.70 94.90 679 72.02 18.36 40.70 94.90 1480 74.26 17.88 40.70 94.90 Target Leverage 2159 0.16 0.19 0.00 0.86 679 0.25 0.23 0.00 0.86 1480 0.12 0.15 0.00 0.86 Acquirer Leverage 2159 0.36 0.21 0.02 0.97 679 0.37 0.22 0.02 0.97 1480 0.35 0.21 0.02 0.97 log Acaquirer Assets 2159 13.38 2.55 6.44 17.95 679 13.35 2.56 7.29 17.95 1480 13.39 2.55 6.44 17.95 log Target Assets 2159 11.31 2.35 4.55 15.83 679 11.50 2.40 4.98 15.83 1480 11.22 2.32 4.55 15.83 Relative Size 2159 0.22 0.25 0.00 1.01 679 0.22 0.25 0.00 1.00 1480 0.22 0.25 0.00 1.01 Target Cash/Assets 2159 0.14 0.17 0.00 0.79 679 0.13 0.18 0.00 0.79 1480 0.14 0.16 0.00 0.79 Acquirer Cash/Assets 2159 0.14 0.14 0.00 0.74 679 0.15 0.16 0.00 0.74 1480 0.13 0.13 0.00 0.74 Deal Value (€ th.) 2159 516,646 1,370,399 1,000 7,384,616 679 388,641 902,959 1,000 4,081,056 1,480 524,808 1,356,874 1,000 7,384,616
Variable Obs Mean Std. Dev Min Max Obs Mean Std. Dev. Min Max Obs Mean Std. Dev Min Max
Intra-Industry 2,159 0.33 0.47 0 1 679 0.36 0.48 0 1 1,480 0.32 0.47 0 1
Cross Border 2,159 0.47 0.50 0 1 679 0.47 0.50 0 1 1,480 0.47 0.50 0 1
Multiple Bidders 2,159 0.37 0.48 0 1 679 0.74 0.44 0 1 1,480 0.20 0.40 0 1
Common Law 2,159 0.20 0.40 0 1 679 0.22 0.42 0 1 1,480 0.19 0.39 0 1
Acquisitions of Distressed Targets
Whole Sample Acquisitions of Non-Distressed targets Summary statistics for continuous variables
5. Results
In the regression (1) of Table IV I estimate the model for the distressed target sample. I find that target’s distress level has a significant bearing on the bidder’s decision regarding the method of payment. The coefficient TARGET Z-SCORE is significant and positive, which suggests that bidders prefer to shift more risk to target’s shareholders by offering less cash and more stock as the target distress level rises. This finding is in line with the argument of Nesvold et al. (2012) that potential financial, operational and legal risks become even more pronounced with the increase of financial distress severity. With stock offers, those risk can be mitigated to some extent. Variable ACQUIRER Z-SCORE is also positive and significant at 1% confidence level, which leads to a conclusion that healthier bidders are able to choose to finance their acquisitions with cash for the ease of use, as their risk of bankruptcy is relatively low. TARGET LEVERAGE is another decisive factor for a bidder in choosing a payment method, with a positive and significant coefficient. With the rise of the target’s financial leverage, the bidders choose cash consideration less readily, as they might prefer to save the valuable cash in order to repay the target firm’s financial obligations or finance its losses until resuming profitability. Moreover, with the increase of the target’s leverage, the company faces greater bankruptcy risk, which might materialize post-acquisition. For this reason, the bidders prefer to transfer minimal amount of the risk to themselves by offering stock consideration. ACQUIRER LEVERAGE also plays an important role, with a negative and significant coefficient suggesting that highly leveraged bidders lack the necessary debt capacity to finance a cash acquisition and they have to resort to the costly stock issuance. In the context of distressed acquisitions, it translates on one hand into more risk shifting
from the acquirer, but on the other hand it limits their further options of acting on the target’s distress through managing its financial obligations. Further, higher TARGET
ASSETS make the bidder more hesitant to offer cash consideration, perhaps because the
target’s assets might still provide some form of collateral, which might enable for debt financing. Combined with a healthy acquirer firm, the target company might be able to eventually emerge from financial distress and resume its operational profitability. Positive and significant coefficient by the variable ACQUIRER ASSETS proves the hypothesis about larger acquirers being generally more diversified and thus facing a lower bankruptcy risk, therefore being able to finance their acquisitions with cash with a minimal exposure to the potential consequences of the target’s financial distress. Such companies also have an improved access to debt markets, which results in debt financing being much more readily available (Faccio and Masulis 2005). As indicated by a negative and significant coefficient by the variable ACQUIRER CASH/ASSETS, acquirers with more cash-on-hand are more likely to offer stock as a means of payment in anticipation of significant cash spending on the newly acquired distressed target. This comes from financially flexible acquirers potentially managing the distress risk more effectively through several channels. For instance, they can repay the target firm’s financial obligations, finance its losses until resuming profitability or cover other miscellaneous costs related to the restructuring process, allowing the target a smoother route to reducing its leverage and resolving operational concerns. Target cash holdings are not statistically significant in this model and do not have any causal interpretation. Variable
CROSS BORDER is positive and significant, indicating that if a distressed transaction is
Table IV. Tobit regressions explaining cash proportion in distressed M&A deal financing
The regressions are estimated based on a two-boundary Tobit model reflecting lower and upper bound constraints on the dependent variable. The t-Stats are based on QML heteroskedasticity-consistent standard errors. The dependent variable PERCENTAGE OF CASH measures the cash proportion of the entire transaction offer. All variables capture the preference of both bidders and targets in acquisitions of distressed and non-distressed companies. Targets are classified as distressed if their calculated Altman Z-SCORE is less that 1.21 for privately held firms and less than 1.81 for publicly listed companies one year prior to deal announcement.
Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat
(St. Error) (St. Error) (St. Error)
TARGET Z-SCORE 32.02*** (2.82) -2.05 (-0.89) 6.64*** (2.95) (11.35) (2.29) (2.25) ACQUIRER Z-SCORE 12.17*** (2.65) 0.23 (0.08) 2.99 (1.32) (4.59) (2.86) (2.27) TARGET LEVERAGE 198.20** (2.18) 63.25 (0.90) 94.88* (1.88) (91.12) (70.06) (50.35) ACQUIRER LEVERAGE -461.94*** (-3.94) -233.08*** (-4.40) -317.95*** (-6.35) (117.25) (52.99) (50.11) TARGET DEBT ENFORCEMENT -1.19 (-0.71) 0.27 (0.32) -0.12 (-0.15) (1.68) (0.87) (0.78) ACQUIRER DEBT ENFORCEMENT -0.65 (-0.36) -4.35*** (-4.21) -3.25*** (-3.67) (1.81) (1.03) (0.89) TARGET ECONOMIC FREEDOM 7.77 (1.28) -2.86 (-1.04) 0.22 (0.09) (6.06) (2.75) (2.55) ACQUIRER ECONOMIC FREEDOM -7.48 (-1.36) 12.08*** (4.19) 6.26** (2.52) (5.50) (2.88) (2.48) LOG TARGET ASSETS -29.75** (-2.17) -21.82*** (-3.10) -19.31*** (-3.18) (13.73) (7.03) (6.06) LOG ACQUIRER ASSETS 53.08*** (3.67) 43.52*** (6.02) 40.57*** (6.43) (14.47) (7.23) (6.31) TARGET CASH/ASSETS -36.96 (-0.28) -131.88** (-2.06) -145.89** (-2.56) (131.61) (64.08) (56.90) ACQUIRER CASH/ASSETS -251.29* (-1.74) 27.45 (0.34) -144.89** (-2.09) (144.70) (80.32) (69.17) MULTIPLE BIDDERS 89.98* (1.65) 153.27*** (5.34) 56.06*** (2.75) (54.67) (28.71) (20.42) INTRA-INDUSTRY 75.98* (1.73) -38.81** (-1.97) -12.56 (-0.68) (43.95) (19.67) (18.51) RELATIVE SIZE 0.14** (2.17) 0.05 (1.38) 0.08*** (2.81) (0.06) (0.03) (0.03) CROSS BORDER 77.82* (1.63) 113.89*** (4.95) 106.55*** (5.00) (47.73) (23.03) (21.30) COMMON LAW -10.11 (-0.14) -37.86 (-1.18) -60.36** (-1.98) (72.73) (32.10) (30.50) CONSTANT 48.25 (0.12) -353.84** (-2.21) -179.37 (-1.16) (393.77) (160.15) (154.43) NUMBER OF OBS. 679 1,480 2,159 LOG LIKELIHOOD -723.44 -1640.13 -2409.37 *, **, *** denote significance at the 10%, 5% and 1% levels, respectively
costs of trading, foreign exchange risk exposure, lower liquidity and slower, limited access to information about firm financials in cross border transactions, as argued by Coval and Moskowitz (1999), French and Poterba (1991), and Grinblatt and Keloharju (2001). Positive and significant coefficient by the variable RELATIVE SIZE indicates that as target’s asset value increases in relation to the acquirer, cash bids are more likely to placed. This finding contradicts the argument of Hansen (1987) that bidders have bigger motivation to use stock financing when there is high asymmetry of information regarding the target assets. One possible explanation for this finding is the one proposed by Faccio and Masulis (2005) who posit that there exists a higher probability of corporate control loss by the bidding firm if it acquires a relatively large target using shares. In order to prevent the loss of corporate control, bidding firms might use a higher portion of cash in their deal structure when acquiring a financially distressed target. Further, a positive and significant coefficient by the variable MULTIPLE BIDDERS signifies that when there is a competition between bidders, they are more likely to offer cash to the target in order to gain a competitive edge in the bidding contest, as it increases a chance of winning the bid and allows for potentially quicker deal approval and completion. This finding is in line with Mayer and Walker (1996) and Chapple et al. (2007). It also confirms the findings of Hotchkiss (1998) who describes an increased interest of bidders in distressed targets because of higher potential for improving performance and subsequent profits. Further, acquisitions of distressed companies in the same industry are associated with a higher probability of cash financing (positive and significant coefficient). This implies that bidders express a smaller necessity for risk transfer, as they are better positioned for uncovering the target’s issues and be more effective in finalizing the transaction, which in turn results in potentially higher effectiveness in uncovering the true hidden value of
variables DEBT ENFORCEMENT are not statistically significant in this empirical setting. One of the reasons might be that bidders put significant time and resources into researching the target company and are truly confident that they will be able to turn around the target that do not take the debt enforcement measure into consideration. This is a very interesting finding, because most of the time when a company is financially troubled, the debt enforcement inefficiencies can destroy a significant portion of the firm’s value, preventing it from further operations, failing to keep the firm as a going concern, as found by Djankov et al. (2015). Finally, coefficients by the variables
ECONOMIC FREEDOM as well as COMMON LAW are statistically insignificant, suggesting that country risk and corporate governance characteristics proxied by the legal system are not directly related to the currency used in distressed transactions. It appears that when firms acquire distressed targets in a country with higher country risk and lower investor protection, they are not particularly concerned with shifting the potential risk of the transaction to the sellers’ shareholders. Regression (2) estimates the model for the non-distressed target sample. To briefly summarize the table, the findings are different than in case of distressed acquisitions. Firstly, both Z-SCORE variables lost statistical significance. This suggests that when a target is relatively healthy, the acquiring company does not consider further variations in the target’s probability of bankruptcy to be an important factor in making a decision regarding the payment method. The same holds true for TARGET LEVERAGE. Conversely, ACQUIRER LEVERAGE continues to be positive and significant. Furthermore, the variable TARGET CASH/ASSETS became statistically significant and negative, which indicates that bidders acquiring healthy companies are less likely to make a cash offer when the target is relatively large, perhaps because the acquired firm is well diversified with lower probability of bankruptcy and is willing to accept stock consideration, as it is less likely
to contaminate their healthy operations. Moreover, TARGET ECONOMIC FREEDOM and
TARGET DEBT ENFORCEMENT are not significant and do not have an influence over the
payment method decision. On the other hand, ACQUIRER ECONOMIC FREEDOM variable coefficient is positive and significant, which points to acquirers valuing their own country’s economic conditions and regulations, as the risk of the target’s country dissipates because the target company is not in a state of financial distress and the acquiring firm is focusing on the local conditions. The bidders reward better economic conditions by offering more cash in acquisitions. Further, the coefficients by BIDDERS and
INTRA-INDUSTRY are both positive and significant, which translates into the bidders for
healthy companies offering more cash if there is a bidding competition as well as when the deal involves a target from a different industry than the acquirer. The latter finding is in line with Faccio and Masulis (2005) and Cao and Madura (2011). The remaining variables either lost statistical significance or continue to be insignificant just like in regression (1) which estimates the model for the non-distressed target sample.
In summary, my Tobit regression estimates are in line with the target company’s level of distress, its leverage and the number of competing bidders being strongly influential over the financing decisions in acquisitions of financially distressed targets. On the other hand, country’s debt enforcement and economic freedom do not seem to influence whether acquisitions are financed with stock or cash.
6. Robustness Checks
I asses the robustness of my findings by introducing additional variables with regard to deal announcement timing as well as target listing status. Moreover, I conduct additional Probit estimations to verify the robustness of my findings. The following variables have been included in the Tobit model in Table V and Probit model of Table VI:
Economic Downturn
The findings of Rhodes-Kropf and Viswanathan (2004), Goel and Thakor (2005) and Bouwman et al. (2009) indicate that acquirers benefit from announcing an acquisition during a major financial crisis, but only if the acquisition concerns a distressed or bankrupt target firm. Healthy firm acquisitions are not so advantaged. During a crisis period access to debt markets is particularly difficult, but perhaps not so challenging as finding investors willing to purchase newly issued shares. I therefore expect that during a financial crisis, acquiring firms will be more likely to offer cash considerations as opposed to stock, utilizing their cash reserves or partial debt financing. Variable CRISIS takes a value of 1 if the acquisition announcement date is between January 1st 2008 and December 31st 2009.
Target Listing Status
For acquisitions involving private targets, some important aspects need to be taken under consideration are target’s liquidity needs. Such firms usually prefer cash, as their portfolio tend to be more concentrated and illiquid compared to listed companies. Moreover, since in case of distressed acquisitions the target’s managerial body is often replaced, this factor further amplifies their strong preference for cash (Faccio and Masulis
2005). Variable PRIVATE TARGET takes a value of 1 for private targets and 0 for publicly listed targets. I anticipate a greater use of cash in case of acquisitions involving private targets.
Table V estimates the Tobit model for the distressed target sample with additional control variables. Regression (1) is the original estimation, mirroring the Tobit regression (1) in Table IV. Regression (2) includes CRISIS variable in the estimation, which is not statistically significant. It also does not change the specification of the remaining variables. One conclusion that can be drawn from this result is that the time of acquisitions coinciding with a major financial crisis does not influence the choice of payment method in distressed acquisitions. This finding is somewhat complementing the results of Rhodes-Kropf and Viswanathan (2004), Goel and Thakor (2005) and Bouwman et al. (2009). Regression (3) includes PRIVATE TARGET variable, which is also not statistically significant. According to Faccio and Masulis (2005), the listing of the target proxies for its ownership structure. They found that bidders for private target use cash more extensively as a precaution against potential control loss, while bidders for public targets use stock financing more readily, as the risk of losing control rights is lower. However, in my regression design, variable capturing target’s listing status is insignificant, which might be specific to acquisitions of companies in financial distress. Perhaps the bidding firms rule out the possibility of losing corporate control rights in this particular kind of transactions as a result of extensive due diligence. Regression (4) includes both CRISIS and PRIVATE TARGET. The findings are largely unchanged in comparison to regression (1), which provides evidence for the robustness of the original findings.
Table V. Robustness tests for Tobit regressions explaining cash proportion in distressed M&A deal financing
The regressions are estimated based on a two-boundary Tobit model reflecting lower and upper bound constraints on the dependent variable. The t-Stats are based on QML heteroskedasticity-consistent standard errors. The dependent variable PERCENTAGE OF CASH measures the cash proportion of the entire transaction offer. All variables capture the preference of both bidders and targets in acquisitions of distressed and non-distressed companies. Targets are classified as distressed if their calculated Altman Z-SCORE is less that 1.21 for privately held firms and less than 1.81 for publicly listed companies one year prior to deal announcement.
Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat
(St. Error) (St. Error) (St. Error) (St. Error)
CRISIS -67.13 (-1.11) -65.88 (-1.09) (60.33) (60.32) PRIVATE TARGET -85.91 (-1.44) -84.98 (-1.42) (59.71) (59.69) TARGET Z-SCORE 32.02*** (2.82) 32.29*** (2.85) 35.69*** -3.02 35.91*** (3.04) (11.35) (11.35) (11.81) (11.80) ACQUIRER Z-SCORE 12.17*** (2.65) 12.47*** (2.71) 12.59*** -2.72 12.87*** (2.77) (4.59) (4.59) (4.64) (4.64) TARGET LEVERAGE 198.20** (2.18) 200.51** (2.20) 177.47* -1.95 180.08** (1.98) (91.12) (91.12) (91.15) (91.16) ACQUIRER LEVERAGE -461.94*** (-3.94) -462.29*** (-3.95) -454.74*** (-3.89) -455.18*** (-3.90) (117.25) (117.17) (116.87) (116.79) TARGET DEBT ENFORCEMENT -1.19 (-0.71) -1.25 (-0.75) -0.85 (-0.50) -0.92 (-0.54) (1.68) (1.68) (1.69) (1.69) ACQUIRER DEBT ENFORCEMENT -0.65 (-0.36) -0.71 (-0.39) -0.82 (-0.45) -0.87 (-0.48) (1.81) (1.81) (1.82) (1.82) TARGET ECONOMIC FREEDOM 7.77 (1.28) 8.43 (1.38) 6.97 -1.15 7.61 (1.25) (6.06) (6.10) (6.07) (6.11) ACQUIRER ECONOMIC FREEDOM -7.48 (-1.36) -7.42 (-1.35) -6.96 (-1.26) -6.90 (-1.25) (5.50) (5.49) (5.52) (5.50) LOG TARGET ASSETS -29.75** (-2.17) -29.55** (-2.15) -37.01** (-2.48) -36.74** (-2.47) (13.73) (13.72) (14.91) (14.89) LOG ACQUIRER ASSETS 53.08*** (3.67) 52.48*** (3.64) 54.93*** -3.75 54.32*** (3.71) (14.47) (14.43) (14.66) (14.62) TARGET CASH/ASSETS -36.96 (-0.28) -25.66 (-0.19) -61.79 (-0.47) -50.44 (-0.38) (131.61) (131.86) (132.66) (132.90) ACQUIRER CASH/ASSETS -251.29* (-1.74) -258.37* (-1.78) -256.01* (-1.77) -262.86* (-1.81) (144.70) (145.22) (144.71) (145.23) MULTIPLE BIDDERS 89.98* (1.65) 90.70 (1.63) 117.03* -1.96 118.43** (1.98) (54.67) (55.67) (59.74) (59.74) INTRA-INDUSTRY 75.98* (1.73) 74.63* (1.70) 76.98* -1.75 75.64* (1.72) (43.95) (43.90) (43.95) (43.91) RELATIVE SIZE 0.14** (2.17) 0.13** (2.14) 0.14** -2.29 0.14** (2.27) (0.06) (0.06) (0.06) (0.06) CROSS BORDER 77.82* (1.63) 75.44 (1.58) 77.65 -1.63 75.30 (1.58) (47.73) (47.67) (47.67) (47.62) COMMON LAW -10.11 (-0.14) -13.69 (-0.19) -48.44 (-0.62) -51.45 (-0.66) (72.73) (72.77) (77.64) (77.67) CONSTANT 48.25 (0.12) 21.56 (0.05) 144.38 -0.36 117.84 (0.29) (393.77) (394.23) (400.63) (401.06) NUMBER OF OBS. 679 679 679 679 LOG LIKELIHOOD -723.44 -722.82 -722.37 -721.76 *, **, *** denote significance at the 10%, 5% and 1% levels, respectively (1) (2) (3) (4)
Table VI. Ordered Probit regressions explaining cash proportion in distressed M&A deal financing In all models, the dependent variable takes a value of 2 for all cash acquisitions, 1 for mixed payment transactions and 0 for all stock acquisitions. The z-Stats are based on QML heteroskedasticity-consistent standard errors. The dependent variable PERCENTAGE OF CASH measures the cash proportion of the entire transaction offer. All variables capture the preference of both bidders and targets in acquisitions of distressed and non-distressed companies. Targets are classified as distressed if their calculated Altman Z-SCORE is less that 1.21 for privately held firms and less than 1.81 for publicly listed companies one year prior to deal announcement.
Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat
(St. Error) (St. Error) (St. Error) (St. Error)
CRISIS -0.19 (-1.15) -0.19 (-1.13) (0.17) (0.17) PRIVATE TARGET -0.12 (-0.74) -0.12 (-0.70) (0.16) (0.16) TARGET Z-SCORE 0.09*** (3.26) 0.10*** (3.29) 0.10*** (3.34) 0.10*** (3.36) (0.03) (0.03) (0.03) (0.03) ACQUIRER Z-SCORE 0.04*** (2.97) 0.04*** (3.04) 0.04*** (3.00) 0.04*** (3.06) (0.01) (0.01) (0.01) (0.01) TARGET LEVERAGE 0.65** (2.57) 0.66*** (2.60) 0.62** (2.42) 0.63** (2.46) (0.25) (0.25) (0.26) (0.26) ACQUIRER LEVERAGE -0.99*** (-3.47) -0.99*** (-3.47) -0.98*** (-3.43) -0.98*** (-3.44) (0.29) (0.29) (0.29) (0.29) TARGET DEBT ENFORCEMENT -0.00 (-0.88) -0.00 (-0.93) -0.00 (-0.78) -0.00 (-0.84) (0.00) (0.00) (0.00) (0.00) ACQUIRER DEBT ENFORCEMENT 0.00 (0.29) 0.00 (0.28) 0.00 (0.25) 0.00 (0.24) (0.01) (0.01) (0.01) (0.01) TARGET ECONOMIC FREEDOM 0.01 (0.84) 0.02 (0.96) 0.01 (0.79) 0.02 (0.90) (0.02) (0.02) (0.02) (0.02) ACQUIRER ECONOMIC FREEDOM -0.01 (-0.94) -0.01 (-0.93) -0.01 (-0.89) -0.01 (-0.89) (0.02) (0.02) (0.02) (0.02) LOG TARGET ASSETS -0.08** (-2.19) -0.08** (-2.18) -0.09** (-2.31) -0.09** (-2.29) (0.04) (0.04) (0.04) (0.04) LOG ACQUIRER ASSETS 0.15*** (4.07) 0.15*** (4.03) 0.15*** (4.12) 0.15*** (4.07) (0.04) (0.04) (0.04) (0.04) TARGET CASH/ASSETS -0.13 (-0.36) -0.10 (-0.27) -0.16 (-0.44) -0.13 (-0.35) (0.37) (0.37) (0.37) (0.37) ACQUIRER CASH/ASSETS -0.49 (-1.25) -0.51 (-1.30) -0.49 (-1.25) -0.51 (-1.30) (0.39) (0.39) (0.39) (0.39) MULTIPLE BIDDERS 0.28* (1.81) 0.28* (1.84) 0.32* (1.95) 0.32** (1.97) (0.15) (0.15) (0.16) (0.16) INTRA-INDUSTRY 0.21* (1.76) 0.21* (1.72) 0.22* (1.78) 0.21* (1.75) (0.12) (0.12) (0.12) (0.12) RELATIVE SIZE 0.00* (1.87) 0.00* (1.84) 0.00* (1.93) 0.00* (1.90) (0.00) (0.00) (0.00) (0.00) CROSS BORDER 0.23* (1.74) 0.22* (1.67) 0.23* (1.74) 0.22* (1.68) (0.13) (0.13) (0.13) (0.13) COMMON LAW -0.03 (-0.13) -0.04 (-0.17) -0.08 (-0.37) -0.09 (-0.40) (0.21) (0.21) (0.22) (0.22) CONSTANT -0.26 (-0.23) -0.34 (-0.30) -0.12 (-0.11) -0.21 (-0.18) (1.11) (1.11) (1.13) (1.13) NUMBER OF OBS. 679 679 679 679 LOG LIKELIHOOD -330.98 -330.33 -330.71 -330.08 (1) (2) (3) (4)
Table VI estimates a Probit model for the distressed target sample. Regression (1) is the original estimation, mirroring the Tobit regression (1) in Table IV. All coefficients remain statistically significant and keep theirs signs with the exception of ACQUIRER
CASH/ASSETS. This result points to acquirers’ cash holdings not being a decisive factor in
choosing a payment method in distressed acquisitions, which is different from the results in Regression (1) in Table IV and also different from the findings of Faccio and Masulis (2005), Martin (1996), and Cao and Madura (2011). Regression (2) estimates the model using additional CRISIS variable. The findings are identical as in case of regressions (2), (3) and (4) in Table V. The collective of the above results is convincing about the robustness of the findings. As an additional robustness check, I estimated both Tobit and Probit regressions using Interest Coverage Ratio (ICR) one year prior to an acquisition announcement as a proxy for financial distress of the target. However, the distressed target sample turned out to be larger than that of non-distressed targets (1,171 distressed versus 988 non-distressed), which provides dubious evidence with regard to the appropriateness of the ICR as a measure of distress. Therefore, I decided not to report and interpret the results of the above-mentioned regressions.
