• No results found

How do credit ratings impact payment methodes in M and A : consequences of the financial crisis

N/A
N/A
Protected

Academic year: 2021

Share "How do credit ratings impact payment methodes in M and A : consequences of the financial crisis"

Copied!
53
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

How do Credit Ratings impact Payment

Methods in M&A?

Consequences of the Financial Crisis.

By Nikki Krayenoord, 10872043

(2)

Table of Contents

Introduction ... 1

Determinants of the method of payment in M&A ... 2

Data & Methodology ... 12

Analysis ... 14

Robustness check ... 21

Conclusions ... 25

Literature List ... 26

(3)

1

1. Introduction

It’s a well-known phenomenon that the M&A activity in the market is following a wave-pattern and is highly dependent on the economic environment (Eckbo, 2007). The latest financial crisis resulted in a dip of the number of transactions and the total value these transactions represented (see figure 1 in the Appendix). In 2014 both the number and total value of the M&A transactions show some recovery. This might be explained by cheaper credit due to low interest rates and the fact that acquiring other companies might be the only possibility to grow for firms due to the econom ics conditions which recover slowly1. Another trend in the M&A landscape is showed by (Boone, Lieb, & Liuc, 2014), who show that the fraction of cash used in the consideration increased after 1997 while the trend for all-stock deals is the opposite.

These financing options are impacted by a lot of determinants which are quit extensively studied over time. Factors like asymmetric information, corporate control, capital structure etc. are the most well-known amongst these theories. However, despite this attention a lot of the variance regarding this topic remains unexplained and therefore science is constantly exploring new determinants and explanations to fill this gap. Another recently discovered determinant by Karampatsas, Petmezas, & Travlos (2014) focuses on the impact of the credit rating of acquiring U.S. companies on the cash payment for the period 1989 untill the financial crisis. Karampatsas et al. (2014) show that acquiring companies that are assigned a higher rating level are more likely to use cash as their payment medium in a M&A transaction than companies that are assigned a lower rating. The idea behind this finding is related to the business risk profile and debt capacity of the acquiring company. A higher rating is in this sense is related to less credit risk and a better ability to fulfill debt obligations

(considering the acquirer). Credit ratings are therefore a more direct measure of credit safety which is (most of the time) measured by variables as Collateral and Leverage inn earlier research.

During the most recent financial crisis the interest in these credit ratings published by Credit Rating Companies (CRAs) increased as these agencies played an important role in the recent financial subprime crisis. Ratings were far too high during the run-up period which led to a discussion about this industry in the academic literature as well as the media. As a consequence of the large impact of the ratings, the SEC introduced new rules which have to tighten the rating methodologies adopted by CRAs (Securities and Exchange Commission, 2014) and result in less favorable rating levels for firms (Utzig, 2010). Besides the changed CRA-environment some research argues that in stressed economic times, the existence of a rating for a company may be more important due to the credit tightening and trustissues (Kisgen D. (2007); Judge & Korzhenitskaya (2012)).

Taking into consideration the change CRA-environment, credit scarcity and trust issues as a

consequence of the financial crisis, the main objective of this thesis is to test whether credit ratings are a determinant for the use of cash in the consideration in M&A during economic tightening. The question whether there’s a difference between non-crisis and crisis times is the most important one two answer2.

1 Source: Financial Times, http://www.ft.com/cms/s/0/e0c9cbae-45be-11e4-9b71-00144feabdc0.html#axzz3uKzYGum6

2

(4)

2 In chapter 2, the relevant literature on the determinants of the method of payment in M&A are discussed theoretically and empirically. In this chapter the theories on the capital structure of a firm are discussed more briefly as the focus of this thesis is closely related on these developed theories. Furthermore, this chapter first discusses the existence of credit ratings in general, thereafter focuses on the use of these ratings in the case of capital structure decisions and finally discuss the use of credit ratings in the choice for the medium of payment in M&A. The third chapter will discuss the preparation of the data and the methodology which is used in order to answer the main question in this thesis. The focus will lie on the explanation of the hypothesis formulated based on the

determinant mainly described by Karampatsas et al. (2014) and Faccio & Masulis (2005). Chapter 4 will discuss the descriptive analysis and both the outcomes of the (Ordered) Probit and Tobit regressions. Chapter 5 check the results of the first initial regression analysis by doings some extra checks. Firstly, year-fixed effects are added to the regressions, thereafter the same regressions are run by dividing the rating level into two categories which are speculative and investment grade bonds. Last, a check for endogeneity is performed based on methods of

2. Determinants of the method of

payment used in M&A.

As stated in the introduction firms have to make several choices regarding an M&A investment. One of these choices is which medium of payment they’re going to use. Does the firm prefer to pay with cash or is an all-stock payment the better medium?

In the world described and modelled by Modigliani & Miller (1958), the capital structure, also called the financing structure, of a firm doesn’t matter for the actual value of the company. In short this means that it doesn’t matter how many debt a firm has used. The reason for this is that i n perfect, frictionless markets (without tax, bankruptcy costs etc.) a change in the firm’s capital structure by for example taking on more debt only redistributes the cash flows of the firm from the equity holders to the creditors without affecting the total firm value (as the total cash flow doesn’t change). In this M&M world, the use of cash or stock (or a combination) in M&A thus shouldn’t matter as it doesn’t have any effect on the value of the acquisition or merge. A crucial assumption to this outcome is that of a perfect, frictionless market. However, reality diverges much from this assumed world as taxes, bankruptcy costs and transaction costs do exist in our economic reality. This framework created by M&M provided a good benchmark, but do needed an extension to correspond more to reality. One of these extensions is a modified article by Modigliani & Miller (1963) with which they aimed to extend their earlier findings by allowing for the existence of corporate income taxes. They showed that in this case, the capital structure of a firm did matter in a sense that it is increasing in the amount of debt through the tax shield. More debt means a higher interest payment and as interest is tax deductible, the tax shield will increase. The value of the firm was thus an

increasing function of the amount of debt in a firm. In this context, firms aren’t indifferent between debt and equity and prefer to take on as much debt as possible. Robichek & Myers (1966) adds the costs of financial distress to this tax-adjusted framework which include the legal and administrative

(5)

3 costs of bankruptcy, the agency, moral hazard, monitoring and contracting costs. In the case of financial distress, a firm can be forced into reorganization and this isn’t costless at all. Besides such costs, more indirect costs from operating inefficiencies and foregone investments which could’ve been value adding exist in such a situation. Adding more leverage thus increases the risk of such a situation which indeed can erode firm value. Miller (1977) added the personal tax effect to this extended this for tax-corrected world by including a personal tax effect which may partially or wholly offset the corporate tax advantage of a company. With this theory, Miller completed the trade-off theory.

Other literature extended the M&M framework by focusing on the existence of an agency problem in companies. Jensen & Meckling (1976), for example, introduce a framework where agency costs exist as a consequence of the risk-taking incentives of the equity holders (management) of a company. In this framework equity can be seen as a (Merton (1974)) call option. As general theory implies (Black & Scholes (1973)), the value of an option is increasing in its volatility. As the equity of a company is a call option, the value of the equity increases in the volatility of the firms’ cash flows following from the assets. More risky assets can generate higher cash flows relative to more safe assets, but are also more risky in a sense that bigger losses are more probable too. However, as equity holders are protected by limited liability, they only gain the high profits but don’t have to bear the losses caused by the risky assets. For this reason, shareholders have the incentive to engage in high risk taking as they are protected at the expense of the creditors even when the Net Present Value of a project is negative3. Secondly, Myers(1977) explains a theory of debt overhang which predicts that for a certain debt burden that is so large a company can’t take on any additional equity capital to finance future projects, even if their NPV is positive and thus are value-adding. The reason for this is that in a situation of debt overhang the earnings of a new investment would go mostly to the existing debt holders and would leave too less advantage to the investors, resulting in a situation in which it’s difficult for the entitiy to better it’s situation.

So summarized, Modigliani & Miller initialized a theory about the capital structure of a firm which stated that this structure doesn’t have any impact on its value. However, real-world frictions like taxes, bankruptcy costs and asymmetric information do have impact on firm value through the debt-equity. Therefore, the choice for financing with debt or equity does matter for firm value and therefore should matter in the context of an M&A transaction. The following chapter will discuss all these determinants like asymmetric information, taxes and others in order to make clear through which channels these determinants have effect on the choice bidders make regarding the financing structure in M&A transactions in order to get the optimal outcome of such a deal.

2.1. Firm-specific determinants.

2.1.1. Asymmetric information & relative deal size

Fishman (1989) states that, ‘’with no asymmetric information, no transaction costs and no taxes, the

medium of exchange is irrelevant’’. However, this statement isn’t applicable in the real world as

asymmetric information does exists which will cause a biased valuation of the acquiring/target’s company stock. A few theoretical papers deal with this asymmetric information and provide a framework for it. Myers & Majluf (1984) and Hansen (1987) consider a situation where the acquiring firm knows more about its own real value than the target does. It will therefore use stock as payment

(6)

4 medium when the market (and thus the target) will overestimate (overvalue) its stock as in this situation it has to pay ‘’less’’ and the wealth transfer will be the least4. The market predicts this situation to happen and will drive down the value of the bidders shares which comes at the cost of the insiders. Because of this consequence the bidding firm will use cash to avoid this ‘’’downgrading’’ of its value by he market. Using cash will avoid this problem. Hansen (1987) also spends some attention to this asymmetric information for the target and he predicts that stock payment will be offered if the acquirer’s stock experiences a run-up before the M&A transaction.

As Amihud, Lev, & Travlos (1990) state, a lot of research has examined the relation between the method of financing and the abnormal returns on bidders’ stock at the announcement date. Abnormal returns at this date are assumed to indicate the existence of private information. In general, results of several articles suggests that bidder’s abnormal returns are lower for stock financing relative to cash financing which indicates that financing with stock is a negative signal to the market and thus causes a less positive (or even negative) abnormal return at the announcement date. Fishman (1989) and (Savor & Lu, 2009) focus on the asymmetric information advantage of the bidder and find that bidders are more likely to use stock when they’re overvalued. In this context, when the fixed costs of collecting information about the target is high, cash financing is more like ly than stock as a mean to signal high valuation in order to deter competing offers for the target firm. Also Houston & Ryngaert (1997) find support on the adverse selection argument in the context of mergers. They test the idea of asymmetric information by comparing fixed exchange-ratio stock deals, fixed value stock deals and cash deals and find that for the last category less negative

abnormal returns arise. The variable ‘’Sensitivity’’ was included to indicate the target’s compensation sensitivity to the stock price of the acquirer (which is biggest in the situation of a fixed exchange-ratio stock deal). Eventually, the results indicate a negative significant Sensitivity coefficient and thus a more negative abnormal return when the sensitivity is bigger which can be explained due to private information. Furthermore more recent research of Faccio & Masulis (2005) also find evidence consistent with the asymmetric information argument. Earlier arguing by Korajczyk, Lucas & MacDonald (1991) says that a bidder that has recently experienced a sizable stock price gain, then existing shareholders experience lower dilution of their voting power when stock financing is employed. Following from this argument, (Faccio & Masulis, 2005) also find a positive relationship between their variable, the acquirer’s run-up5 1 year before the announcement date, and the likelihood of stock payment.

The effect of asymmetric information can also work the other way in the case that the acquirer seeks to purchase the assets of another firm but doesn’t know its true value. Hansen (1987) describes a model in this situation where the target will only accept the offer when it’s higher than its true value and thus deals won’t always get closed6. The acquirer might consider offering stock in this situation as this payment medium has a contingent-pricing effect that induces the target to accept the offer in all states for which it would’ve accepted an equal costs cash offer. The relative size of the deal is also important in this context as it’s quite straightforward that the effect of paying in cash (and the risk on a loss therefore) increases in the size of the target. Combining both theories creates a situation

4

As the management is acting in the interest of the existing ‘’old’’ shareholders.

5 A stock price run-up is a sudden and unexpected rise of the stock price. Run-up in this analysis is a proxy for the bidder’s

over(under)valuation calculated from the bidder’s buy-and-hold cumulative stock return over the year preceding the M&A announcement month.

6 The target company will always only accept offers which are e qual or higher than the real value of the company. As the bidder doesn’t

(7)

5 where the acquiring firm will not offer stock when the target underestimates its value, but on the other had will offer stock because of the unknown target value. An equilibrium will develop whereby the acquirer offer stock under some conditions and cash under others. The relative size of the deal is important

2.1.2. Corporate control.

Another theory which has been showed to explain a part of the financing preferences of companies focuses on corporate governance issues, more specifically, on the manager’s incentive to maintain control over the company. This theory and hypothesis predicts that management with a significant ownership in the firm wishes to retain their control (Harris & Raviv (1988); Stulz (1988)). Therefore, management will prefer using cash or debt rather than equity/stock as the last method will dilute their ownership stake. By using cash, management won’t risk this dilution (Amihud, Lev, & Travlos, 1990).

Amihud, Lev, & Travlos (1990) examine the relationship between corporate control and cash payment by studying corporate acquisitions of the Fortune 500 companies from 1981 to 1983. The eventual results show that a higher managerial ownership fraction makes it more likely that the acquisition is financed with cash. Even after controlling for an endogeneity problem caused by asymmetric information7 they find this result. A study done by Martin (1996) focuses on the same topic and finds the same results for a medium ownership stake8. A European study of Faccio & Masulis (2005) focusses on a more general form of corporate control as their focus is on blockholders in general. They find that control incentives of the bidder’s management leads to a situation in whch cash is the preferred payment medium. Most of the evidence is in favor of the Corporate Control

Hypothesis. But there’s also some evidence against it. One example of such a paper is written by

Zhang (2001). He studies the 103 UK listed companies from 1990 to 1999 and finds no significant relationship between the acquirer’s ownership structure and payment method. Concluding, the reluctance of the management and other blockholders to lose control leads to a preference for a cash payment in general.

2.1.3. Free cash flows and growth opportunities

The third theory explained in this chapter is considering the effect of the free cash flows (hereafter: FCF) of the firm which are cash flows in excess of the required amount of cash to fund all

investments that have a positive net present value. These FCF can create a conflict of interest in the sense that managers need to make a choice on what they spend this money and thus can choose to spend in their own interest9. Jensen’s (1986) theory shows the FCF Hypothesis in an M&A context and describes that firms with unused borrowing power and large FCFs are more likely to enter in value-destroying mergers as there’s simply more cash available. Fishman (1989) uses the variable

Liquidity to indicate the free cash flow of a company (by defining it as the difference between the

acquirer’s FCF and the average FCF of the industry). Also Hayn (1989) defined a Liquidity measure as a proxy for the FCF’s of a company. His definition measured the FCF by dividing the FCF of the

7 The theory about asymmetric information tells us that insiders know more about the company than outsiders. It may be that

management holds a large amount of stock as they know the equity is undervalued by the market. As a stock issue is often associated by negative abnormal returns and Amihud et al. find that financing with stock by companies with high managerial ownership isn’t

accompanied by these significant negative returns, it can be concluded that the corporate governance aspect is more important than the stock financing itself. A dilution of the high stake in the firm is perceived to be important.

8

For low and high amounts of managerial ownership this study doesn’t find any significant relationship between ownership stake and the likelihood to pay in stock/cash.

(8)

6 acquirer to the acquirer’s equity. Both articles find the same result: a positive relationship between FCF’s and the likelihood of a cash payment in M&A. Faccio & Masulis (2005) find the opposite of earlier results, namely a negative relationship between the variable Cash Holding and the amount of cash used in the transaction.

As FCF give companies the opportunity to invest it’s quite straightforward that the investment opportunities of a firm are linked to this theory. Adam & Goyal (2008) use the Market-to-Book ratio (Tobin’s Q) as a proxy for the investment/growth opportunities as they find that this ratio on a relative scale has the highest information content with respect to investment opportunities. Jung, Kim, & Stulz (1996) find that their market-to-book ratio variable has a positive coefficient that’s highly significant in explaining an equity issue. This finding is based on the combination of two theories which are the agency costs of debt and asymmetric information. Managers with growth objectives prefer to use equity financing as this gives them more discretion over the funds raised as debt financing requires management to pay out cash flows Following from this, debt financing maximizes firm value for firms with poor (value destroying) investment opportunities as these funds can’t be used anymore to invest in poor projects. In the opposite case, equity financing is more attractive for firms with good investment opportunities as they can consequently take full advantage of these opportunities (there are no fund restrictions due to debt). Therefore, low-growth firms will issue debt under the monitoring of capital providers. Martin (1996) and Faccio & Masulis (2005) find the same results and thus evidence for the hypothesis that a firm with bigger investment

opportunities is more interested in paying in stock. 2.1.4. Debt capacity and capital structure.

Following the pecking order theory (Myers, 1984) the bidder prefers to finance a transaction with internal cash as this is the cheapest source of funding. However as such transactions always involve a large amount of money, companies must borrow to finance this as there’s not enough cash available. The amount a company can borrow depeds on a few firm-specific factors like the current capital structure and debt capacity of the firm. The higher the leverage or the lower the collateral, the less a firm can borrow as it’s expected to be ‘’less safe’’ by the lender. Besides this, not only the safety of a company for its lenders, but also the cost of debt for the company itself matters. Black & Scholes (1973) show that te firms equity is a call option and that the value of this equity can increase as the firm takes on more risky projects. On the other hand, taking on a more risky profile leads to an increase in the probability of bankruptcy and with that an increase in the costs of borrowing. Therefore it’s more likely that ‘’safer’’ bidders will finance their transaction with cash as they can borrow against more favorable costs and terms. Hovakimian, Opler, & Titman (2001) find that firms with more tangible assets are more likely to have a higher debt level because they are considered to be safer. The explanation for this lays in the size of the company. Bigger companies tend to be more diversified (so less risky) and have more bargaining power as they are well-known (results in a better bargaining position). In accordance with this statement is the finding of Sengupta (1998) which finds a significant negative relationship between the size of a firm (measured in the book value of assets) and the debt yield. Another study done by Uysal (2011) finds that the target capital structure of a company is taken into account when planning an acquisition. When a firm is overleveraged

compared to its target structure (debt-to-equity ratio), the bidder is less likely to offer cash. Harford, Klasa, & Walcott (2009) focus on this same topic, which is the target capital structure of companies. They investigate whether such a target exists in the light of M&A done by US companies. In

(9)

7 affects the way how bidders choose to finance their acquisitions. A higher leverage than their set target allows (overleveraged in a sense) results in a payment which is more likely to occur in equity (less likely in debt, thus cash). A study by Faccio & Masulis (2005) focuses on a European dataset in order to identify the impact of the capital structure and find that certain variables representing the financial condition of the bidder, like collateral, financial leverage and asset value, are significant in predicting the method of payment.

2.1.5. Size

The bidder size can affect the method of payment through different channels. It can be argued that a larger bidders will choose cash financing more easily in smaller deals as it’s more likely that they own sufficient cash, debt capacity or liquid assets. The descriptive analysis in the article of Faccio & Masulis (2005) show results in favor of this theory. They found that bidders making pure cash acquisitions own a larger amount of total assets compared to pure stock-offering bidders. The regression analysis confirmed the descriptives as their TOTAL ASSETS variable showed a positive significant sign which is consistent with larger firms offering cash more often than smaller firms.

2.2. Deal-specific Determinants.

Besides these firm-specific characteristics which have influence on the way there’s paid for an M&A transaction, some deal-specific characteristics have been showed to be of importance too. The following paragraphs will explain some of these identified determinants.

2.2.1. Country and industry

The CAPM model is a model which offsets the expected return on a share against the risk of the share. This risk is undiversifiable and also called systematic risk represented by beta. In order to optimize the expected return, given the risk, investors should hold the world market portfolio of risky assets. However, investors prefer domestic shares and hold too much of them as these asset bear less liquidity risk, trading costs, exposure to exchange rate risk etc. 10 (French & Poterba, 1991). For cross-country M&A transactions this may hold too resulting in a preference for cash. Rossi & Volpin (2004 ) test this assumption and find a positive significant coefficient for the dummy variable

CROSS-BORDER which indicates that a cross-border transaction indeed results in the payment medium being

cash more frequently. Faccio & Masulis (2005) also find this positive relationship between cross-border transactions and the percentage of cash used in the financing of European M&A transactions. Besides this cross-border effect, Faccio & Masulis (2005) argue that the industry of both parties might matter too. Between two parties operating in different industries may exist an information gap (asymmetric information). A target in the Chemicals Industry might know not too much about the industry of the acquirer. This gap may result in the fact that the preferred payment method is cash -based (like explained before with asymmetric information). With their empirical analysis they find evidence for their ‘’intra-industry theory’, represented by a negative significant coefficient. This indicates that the likelihood of stock payment is significantly higher for transaction within an industry than for transactions between two different industries11.In contrast to this finding (Martynova & Renneboog, 2009) find no evidence for an existing relationship between the means of payment and the industries of the different parties in a transaction.

10 Definition given by Investopedia (http://www.investopedia.com/terms/h/homebias.asp) 11 As their dependent variable represents the likelihood of cash payment.

(10)

8 2.2.2. Competition, tender offers & hostility

Another important deal characteristic to keep in mind is the number of bidders for the specific target company. In the acquisition process, the offer/consideration doesn’t only rely on the value of the target. It’s possible that after the first initial bid competition enters the bidding process and therefore it’s very important for the initial bidder to offer such a consideration structure to discourage other parties to start bidding. Malgrom & Roberts (1982) develop a model in which a bidder with private information has the incentive to set a high initial price for the target to deter potential other competing companies. In this way, the bidder signals a high valuation and therefore a low possible profit for competing parties as the fixed entry costs are now very high. Another model develop by Baron (1983) also focuses on this situation. He allows for the fact that the bidder can only bid once, and competing parties only after this initial bid has been rejected. In this light, the initial bidder will set the price so that the target management accepts the offer and the bidding stops. Like these other studies, a study of Fishman (1988) has shown that a high bid signals a high valuation and preempts competition from bidding. Combining this knowledge with asymmetric information theory (Fishman, 1989), leads to the conclusion that a cash bid is offered by higher valuing bidders a serves to preempt potential competition by signaling a high valuation. This leads to the expectation that transactions with more competing bidders were initially bid for in cash more frequently as the need for winning is bigger.

Another determinant, somewhat related to competition, is the fact if the deal is a tender offer. Just as in competitive bids, using cash in a tender offer increases the probability of the bid’s success (Fishman, 1989). Therefore, in such transactions, the bidding company may prefer to use this payment medium in favor of stock. A tender offer is a public open offer to all the shareholders of a publicly traded target to tender their stock for sale at a prespecified price during a specified time. In such a case the bidding company contacts the shareholders of the target company directly and the management in such an offer may not be included in this offer at all. (Schwert, 2000) suggests that most multiple-bidder auctions (bidding competition) tend to involve tender offers. However, he doesn’t find any clear prove for this in the statitisc and therefore these 2 variables are included seperately in this thesis. Just as for tender offers, Fisman (1989) finds an increasing chance of success for hostile bids when using cash. Also, Schwert (2000) finds that the bidder is more likely to offer cash as the medium of payment reflected by the fact that he finds a significant, positive relationship between hostility and the use of cash.

2.3. Target-specific Determinants.

2.3.1. Listing status and liquidity.

The fifth firm-specific determinant is the listing status of the target. The relationship between the listing status of a company and the payment medium lies within the bidders’ liquidity. Liquidity is determined by a combination of speed and price effect. An investor which owns a certain financial security wishes to sell at a moment in time. Liquidity, in this context, refers to the ease with which this security is sold with minimum loss and is represented by the bid-ask spread. When the security is difficult to sell as the bid-ask spread is large, this security is called to be illiquid. This implies that liquidity is a desirable characteristic for a financial security. Firms thus wish like this ’’liquid status’’ and try to accomplish this by listing on a stock exchange (when a stock is publicly available it’s more easy to trade and thus more liquid). The desirability of liquidity is also observable in the M&A market. Fuller, Netter, & Stegemoller (2002 ) examine whether the listing status of the target firm matters.

(11)

9 The underlying reasoning is that it’s harder for private targets to sell their shares as these are illiquid. The bidder has more bargaining power in this situation and pay a lower price. This exact reasoning is proven as they find that bidders buying private targets receive better prices compared to public targets. In terms of returns, they find positive returns for a bidder buying a subsidiary or private target (regardless of the method of payment) compared to negative returns for stock based considerations in public transactions. Also (Officer, 2007) finds such acquisition discounts of about 15% to 30% for unlisted targets relative to publicly traded targets, affected by liquidity. In case of an acquisition, the listing status of the target matters as well as the consumption needs of the target are important when it’s a private company. Shareholders of a private target can sell their shares less easy even when they want to cash out. Therefore, it’s logical that these shareholders prefer a payment in cash considering their selling motivation. Another reason in advantage of cash is the risk of losing control as private targets are often highly concentrated firms. Martin (1996) firstly includes the private target in his study but finds no difference between the situation with a private and public target. Faccio and Masulis (2005) however, analyze the impact of the private or subsidiary target on the choice of payment method too and find that cash payment is preferred to purchase both private and subsidiary targets.

In line with the liquidity of the target is a situation where a subsidiary is being sold by its ‘’mother’’ which is often restructuring-driven. This situation indicates a strong preference for cash to realize the goals aimed for by restructuring. Faccio & Masulis (2005) find a positive significant coefficient for their SUBSIDIARY dummy which indicates proof for the relationship described before. Another determinant following the same reasoning is the occurrence of a target going bankrupt. As Faccio & Masulis (2005) argue, a target in financial distress is often controlled by its creditors. These creditors are willing to reduce the risk of the company in order to be surer of getting their money back and therefore prefer cash over stock.

2.4. Credit ratings pre- and during the financial crisis.

2.4.1. The history of Credit Rating Agencies.

The first publicly available bond credit rating was published in 1909 by John Moody. Moody’s was followed by Poor’s Publishing Company in 1916, by the Standard Statistics Company in 1922 and by Fitch Publishing Company in 1924 (White, 2010). Before the Securities and Exchange Commission (SEC) was established, the corporations earned their revenue by selling their assessment of the creditworthiness of certain public securities by assigning a letter-based rating to them. At that time, the use of credit ratings on the part of investors was entirely voluntary. After the Great Depression of the 1930s the SEC was established and a major change in the responsibility of the credit rating agencies occurred following from a couple of regulatory policy changes. The first initial set of these regulations included the fact that banks could only invest in safe (investment grade) securities and prohibited banks from investing in speculative investment securities according to ‘’recognized rating manuals’’. More regulation followed and CRAs became more crucial everyday business as the use of their official credit ratings became obligatory and banks were forced to use the judgements of the CRAs (Sylla (2001); White (2010)). C RAs started to have guaranteed audience for their ratings (White, 2013). Besides this, the upcoming globalization which led to a bigger need for outside

(12)

10 information about an unknown transaction party was another important factor making credit ratings impossible to remove from the economic landscape12 (Sylla, 2001).

Today, credit ratings are, just like in earlier times, judgements about the quality of the bonds issued by corporations, nations and local governments. A rating summarizes the opinion about the ability and willingness of a bond/debt issuer to meet its financial obligations and tells something about the credit quality of a bond issue and its likelihood to default13. The existence of CRAs is thus to solve the problem of asymmetric information in the market as company insiders know more about the

creditworthiness of their company than outside investors. Credit ratings solve this asymmetry in a way that the credit quality of a company becomes public to the market which results in lower interest rates and smaller risk premiums.

This risk is expressed in some qualitative and quantitative criteria which are; Political & Country risk, Industry risk, Company-Specific Business risk, Management factors and Financial risk Ganguin & Bilardello (2005). The Big 3 Credit Rating Agencies use an alphabet-orientated rating scale in order to make these criteria measurable. The most well-known scale is used by Standard & Poor’s which uses ratings from AAA down to D, where AAA is the best possible rating and tells an investor that the bond issuer has an extremely strong capacity to meet financial commitments. Besides this, pluses and minuses are added which ‘’show relative standing within the major rating categories’’14. All these alphabetic quantifications are relative to each other, which means that A-bonds are less likely to default than BBB-bonds. Bonds with a rating equal to/higher than BBB- are considered to be ‘’investment grade’’ bonds, while those with a rating of BB- and lower are said to be speculative or junk bonds (White, 2013). When comparing credit ratings across different rating agencies, one should know that the meaning of these ratings differ across agencies as they are based on different analytical, subjective approaches. An example of these different approaches can be the fact that some CRAs aim for stability in their ratings and therefore assume a longer time horizon in their analysis while other agencies prefer to focus on short-term risks and events15.

Currently, there are 3 main CRAs in the United States which are Moody’s, Standard & Poor’s an d Fitch. Furthermore, the SEC introduced the ‘’nationally recognized statistical rating organizations (NRSRO)’’ category in the mid-90s to make it more clear which ratings are the appropriate ones to be used by security firms. As of 2013, besides the Big 3, just 7 CRAs have been added to this category since 200316. The situation in Europe isn’t quite different from that of the United States as the Big 3 major CRAs also control the rating market there and cover around 90.44% of the total market share in Europe based on the annual turnover for the calendar year 2013. In this European market, Standard & Poor’s Group has the largest share equal to 36.69% followed by Moody’s which covers around 34.53% of the total rating market (ESMA, 2014). As in the U.S. the ESMA recognizes 13 smaller CRAs.

2.4.2. Credit ratings and capital structure

A measure of corporate quality is the rating assigned to a firm. The importance of this measurement of risk has evolved dramatically over the last years due to a number of reasons stated by Altman &

12

As most business was local in the early years of the United States of America, transactions occurred between people which knew each other and credit ratings weren’t necessary as business was confidential and without information gaps.

13 Definition provided by Standard & Poors. 14

https://www.spratings.com/about/about-credit-ratings/ratings-definitions-faqs.html

15 http://www.sec.gov/investor/alerts/ib_creditratings.pdf 16 https://www.sec.gov/rules/final/2014/34-72936.pdf

(13)

11 Saunders (1998) like (1) a-world-wide structural increase in the number of bankruptcies, (2) a trend towards disintermediation by the highest quality and largest borrowers, (3) more competitive margins on loans and (4) a declining value of real assets in many markets and a dramatic growth of off-balance sheet instruments. The origination of these off-balance sheet instruments may be the most important development which made credit ratings even more important (Saunders 1997) . Off-balance sheet instruments like Collateralized Debt Obligations were very hard to understand package of mortgages without a clear risk-profile (Brooks, 2008) and were traded very frequently before the most recent financial crisis. This trade created a more risky and vague environment which increased the need for clarity by a centralized credit assessment.

Many studies focused on this information providing role of credit ratings. Whited (1992), Almeida, Campello, & Weisbach (2004) and Himmelberg (1995) find that the existence of a rating reduces the amount of asymmetric information in the market and lowers constraints. Kerwer (2005) tests a more qualitative approach, the credit rating level, and finds that an increase in this level results in lower costs of debt and a bigger debt capacity. This exact outcome is found by Billet et al (2011) and also other studies of Frank & Goyal (2009) and Rauh & Sufi (2010) find that high credit quality firms experience less negative consequences of asymmetric information and rely more frequently on more favorable forms of debt. Besides the more favorable conditions and lower constraints will a high quality credit rating have easier access to liquid capital bond markets while it will be more difficult for lower rated bond issuers to enter these liquid markets (Kerwer, 2005). In general, it seems that a higher credit rating is advantageous in terms of less asymmetric information, lower costs of debt and less tight covenants resulting in more advantageous borrowing capacity.

2.4.3. Credit ratings and the financial crises

The earlier paragraph made clear that both the existence and height of credit ratings do have serious impact on capital constraints and conditions for firms which need to borrow. However, standards may be different in times like the Asian crisis of the 1990s and the latest global financial crisis. Before the 2007 crisis, CRAs weren’t able to spot the upcoming problems and kept publishing high ratings. At the time these crises struck, CRAs quickly downgraded their ratings which resulted in huge capital flights which had a pro-cyclical effect (Kerwer, 2005). This credit scarcity combined with trust issues during a financial crisis creates an environment in which it’s very difficult for companies to obtain public financing. Ivashina & Scharfstein (2010) find evidence for the fact that the most recent credit dry-up during the financial crisis indeed had a negative impact on the lending amount supplied and more specifically on M&A financing in the form of debt. Judge & Korzhenitskaya (2012) find that not all firms are affected by these market conditions equally. Their results imply that hav ing a rating during normal times is associated with higher leverage ratios, thus more easy entrance to the public debt market. However, they in addition find that this effect is even greater in those years when market conditions are the tightest, during a financial crisis. Similarly Kisgen D. (2007) that the existence of a credit rating helps firms to raise funds during adverse economic conditions. Bacon et al. (2009) highlight the importance of a rating during a crisis as they state that many firms which didn’t have a rating were seeking to obtain one. Besides this, they find that the possession of a rating and the corresponding access to the public debt market were especially important during the latest financial crisis. Last, Leary (2009) found that the leverage difference between firms with and without access to the public debt market (represented by a credit rating) becomes greater in crisis periods. These results imply that being credit ratings is more important during a crisis as the differences, In

(14)

12 terms of the quantity and quality of financing, between rated and unrated firms seem to be bigger during these times.

2.4.4. Credit ratings and M&A

After the last paragraphs it’s quite clear that as well as the existence as the height of a credit rating do have an impact on the financing structure a company can obtain. Therefore, credit ratings might be important in the case of M&A transactions as well as in most of these transactions debt has to be attracted in order to finance the mostly large deal values (Bharadwaj & Shivdasani (2003); Harford, Klasa, & Walcott (2009) for example). A study of Karampetsas et al. (2014) focuses for the first time on this subject and find that the rating level of the bidder is of positive significant impact on the amount of cash used in a M&A transaction while the mere existence of a credit rating isn’t found to be of significant impact17.

Stable times are quite different from times of economic tightening characterized by credit scarcity and trust issues, sketched by the observations of the last paragraph. Following from results of Judge & Korzhenitskaya (2012) and Kisgen (2007) it can be argued that a (higher) rated bidder will

experience easier access to debt markets and therefore pay more frequently by cash in comparison to non-rated/lower rated bidders. Summarizing, a (higher) credit rating leads to a more frequent use of a cash medium in M&A transactions during financial crises in comparison to normal times.

3. Data & Methodology

3.1. Data Selection

The data collection is based on earlier mentioned research of Faccio & Masulis (2005) and

(Karampatsas, Petmezas, & Travlos, 2014), the article of interest.The data on the M&A transactions will be obtained from the Thomson One M&A Database for transactions announced between the period January 1998-March 2015 in order to be able to compare pre-crisis with post-crisis. All bidders must be listed on a US stock exchange while on target companies no restrictions in terms of listing status and location is imposed. Both, successful and unsuccessful bids are included in the analysis by using the announcement date as a selection criteria for the period included in the research and including both completed and withdrawn transactions in the analysis. Furthermore, only deals which result in an ownership share of more than 50% for the acquirer are included in the sample as Swieringa & Schauten (2008) state in their paper that these deals only are successful and the transactions have to be paid for by either cash, stock or a combination. All the observations of which the consideration structure is unknown or ‘’other’’ are deleted. Last, both hostile and friendly deals are included in the analysis.

3.1.1. Dependent variables

Deals are classified in three categories which are all-cash (CASHO), all-stock (SHARES) or a combination of both (HYBRID). The 3 dependent variables in the analysis, dominated by cash

(CASHDOM), the faction of cash used (CASHFRAC) & the consideration structure (STRUC), are derived

from these classifications and an explanation can be found in the Appendix.

17 The reason Karampetsas et al. (2014) give is that the mere existence of a credit rating doesn’t say anything about the credit quality of a

(15)

13 3.1.2. Independent variables

In chapter 2 of this thesis some theories about firm-, deal- and environment-specific determinants of the method of payment used in M&A were described. Most of the tested determinants in these studies have been proved to have a significant effect on the payment medium and therefore proxies for these theories need to be included in the analysis in order to control for them. Important

variables like balance sheet related proxies (leverage, amount of cash etc.), variables regarding the deal (hostile, tender offer) and target characteristics (listing status, subisidary)are included and used as control variables in the regression analysis (for a specification of the different control variables & variables of interest, see the Appendix). The balance sheet-related variables are subtracted from the Compustat database and will be obtained at year-end before the announcement. Furthermore, Compustat is used to get S&P 500 historical credit ratings18. Furthermore, the CRSP database is used to get the daily stock prices from the acquirers in the dataset of 100-5 days before the

announcement in order to create the ‘’Run-Up’’ variable introduced by Faccio & Masulis (2005). All deals for which the wanted information is incomplete are deleted from the dataset. Some

inconsistencies existed in the dataset regarding the method of payment and the fraction of the consideration paid in either cash stock or a consideration. Finally, after all these actions, there were 1841 transactions left in the dataset

3.1.3. Hypotheses

As stated earlier, a lot of control variables were included in order to run the analysis correctly. However, only the credit rating related variables are of importance for the thesis results. As stated in the Introduction chapter, the relationship between the Credit Rating Existence/Level variable and the use of cash as the payment medium is estimated for stable and crisis times (February 2007-June 200919). The first set of hypotheses is focused on the rating existence and level during stable times:

CR1: Rated bidders find it relatively easier to attract debt financing due to solved asymmetric information and as a consequence are more likely to use cash as the method of payment in M&A transactions. This indicates that a positive significant relationship is expected between the Credit Rating Existence and the payment in cash.

CR: Bidders with a higher credit rating can attract debt financing more easy due to their more

favorable risk profile and are thus more likely to pay the M&A consideration in cash relative to lower rated bidders. This implicates that higher rated companies will be more likely to pay for their M&A transaction/deal in cash and thus a positive significant relationship can be found between these two variables.

As literature shows, in situations of a more constraint capital market credit ratings become an important determinant of the access to debt capital. Therefore, a stronger relationship between the credit rating variables and the use of cash is expected to be found summarized by the fo llowing hypotheses:

CR3: During a financial crisis, the positive relationship between the Credit Rating Existence and a cash payment will be stronger in comparison to economic stable times resulting in a bigger probability of a cash payment during the financial crisis.

18

Using only S&P credit ratings shouldn’t be a problem as the S&P 500 covers a market share of about more than 40% of the total credit rating industry.

(16)

14

CR4: The positive relationship between a higher credit rating and a cash payment will be even stronger during a financial crisis in comparison to economic stable times which will result in a bigger probability of a cash payment during the financial crisis.

4. Analysis

4.1. Descriptive analysis.

Table 1 shows a summary of the mean and median for different independent variables used in the regression analysis grouped in two categories: more than 50% cash and less than 50% cash. The table includes the total of 1841 observations of which 645 deals are paid by with less than 50% cash w hile the other 1196 deals are paid with more than (or precisely) 50% cash. The results of the descriptive statistics analysis may indicate that there’s a possible relationship between the variables and the use of cash in the deal consideration. However, to really be able to conclude that there’s a significant relationship between the used variables and the use of cash in a deal consideration, the relationship must become more concrete by running multiple regression analyses.

4.1.1. Variables of interest

The descriptive statistics seem to be consistent with the idea of the asymmetric information being solved by credit ratings. In this case it’s expected that for bidders, of which information is publicly available through a credit rating, it’s easier to get debt financing and pay the transaction value in cash. Therefore, in order to back-up this statement, the results should show a significant difference between the more than & less than 50% groups where the rated companies represent a higher percentage. That’s actually what the descriptive statistics show: 40.3% of the bidders who use less than 50% cash are rated relative to 48.7% of the bidders who use more than 50% cash20. Having a credit rating seems to be positively related to the amount of cash used in the conside ration.

The dummy variable RxC2 (representing the existence of a rating for deals during crisis times) shows a negative sign for both cash-dominated and less than 50% cash deals. The opposite of what was expected following the results of Judge & Korzhenitskaya (2012) and Kisgen (2007) which indicate a stronger relationship between the existence of a rating and debt financing during a financial crisis. However, the percentage of bidders being rated during crisis times is higher for cash -dominated deals (27% compared to 18.6%) (19).

Besides the theory about the effect of the existence of a rating, the more qualitative relationship between the credit rating level of the bidder and the payment medium is being tested. The results of the descriptive statistics seem to be consistent with the positive relationship between the credit rating level and the proportion of cash used. The mean of the rating level for the cash-dominated deals is positive (+8.877). However, the difference between this group and the ‘’less than 50% cash’’-group is minimal (0.158) and insignificant. The relationship between a higher credit rating level and the amount of cash used can’t be observed following these results.

More important is the rating level during crisis times. This variable shows the additional effect of the rating level during crisis times for both groups ((not) cash-dominated). For example, when the coefficient of the variable RATINGLEV (representing the rating level before the crisis) is 4.345 and the

(17)

15 variable RxC (representing the rating level after the crisis) is 1.234, the overall rating level during a crisis is equal to 4.345+1.234. As can be seen in table 1, the coefficient of the RxC variable is significantly higher for cash acquisitions and therefore it seems that the effect of a higher rating on the cash amount is more severe during crisis times.

4.1.2. Control variables

Other variables included in the table are the control variables. For completeness, the descriptive statistics of these variables will be shortly described below. The bidders Run-Up shows the same number for both the cash-dominated and stock-dominated groups and thus doesn’t seem to be related to the payment method. The bidder’s market-to-book ratio is significantly higher for cash dominated acquisitions (mean 2.644; median 2.047) than for deals financed with less than 50% cash (mean 1.574; median 1.574) which is consistent with the growth opportunity-theory. The theory about debt capacity is backed up by the leverage variable which shows a lower leverage for acquisitions financed with more than 50% cash compared to transactions for which cash is lower than half of the value. However, the same theory is contradicted by another important indicator for this theory which is collateral, this variable shows a significantly lower number for cash acquisitions at a 1% level. According to the theory, a competitive process should result in a preference for cash as a quick process is preferable for a bidder when this company wants to be one who acquires the target. The results can’t confirm this as the percentage of competing deals is smaller for cash-dominated deals. Considering the insider ownership of shares, a significant difference between cash and stock dominated deals is only found when using the median and is in accordance with the theory which states that cash is the dominant factor when the insider ownership is higher. A larger cash balance should make it easier to pay with this medium and therefore should result in it to be more frequently used in deal considerations. The absolute results clearly show a difference between the bidder’s cash balance in cash-dominated and other deal, however the difference between both means isn’t significant. Last but not least, Mean bidder size is surprisingly larger for stock dominated deals and finally, the amount of cash the bidder owns doesn’t appear to be significantly different. Besides the bidder’s specific characteristics, some deal dependent variables are taken into account in the analysis as well. These deals-specific characteristics are presented by Panel B. Again, these variables appear to differ between the two financing categories. The RELATIVE SIZE of the target shows a bigger mean for ‘’less than 50% cash deals’’ (0.227 – 0.134) which is significant and supports the theory about the effect of the relative size of the target on the risk on losses due to asymmetric information about the target. The variables CROSSBORDER and CROSSINDUSTRY show positive variables and the difference in mean for these variables is significantly different and bigger for cash-dominated deals. These descriptives support the fact that it’s more likely that cash is used in the case of these deals and thus indicate that asymmetric information and liquidity considerations in these deals might be important in the choice for cash. It seems that a larger deal value makes it more likely that a significant amount of stock is used in the consideration. This supports the expectation that a bigger deal value is more difficult to finance with cash (as it’s less easy to borrow this much, becomes less likely that there’s enough cash in the company etc.). Both theories of a listed target and a

subsidiary target which both are related to liquidity considerations are supported by the descriptive statistics as both variables are larger for cash-dominated deals which means that it’s more likely that there’s a positive relationship between deals considering liquidity and the amount of cash used in the consideration. As the use of cash in a tender offer increase the probability of the bid’s success, it’s expected that the mean percentage of tender offers is larger for cash-dominated deals. 13.5% of the

(18)

16 deals are tender offers in the case of a cash-dominated deal while just 2.9% of the non-cash

dominated form are tender offers and supports the earlier expectation. The only deal-specific theory which is contradicted by the outcomes of the descriptive analysis is the hypothesis about hostile deals. The percentage of hostile acquisitions is higher in cash deals (0.3%) than for the other group (0.5%) which indicates that the statement that cash is preferred because of the need for a quick process in hostile deals may not matter at all.

4.2. Regression analysis.

4.2.1. Applied regression techniques

In order to establish a more concrete statistical relationship between the method of payment and the credit rating variables, this section describes and explains the results of the multivariate regression analysis. Following Faccio & Masulis (2005) & Karampatsas, Petmezas, & Travlos (2014), Probit and Tobit regressions are used in order to establish this relationship.

(Ordered) Probit regressions

The first version uses the variable Cash Dominated as the dependent variable. Because this variable is a binary variable a Probit regression model is used as such a model is specially designed for these kind of dependent variables. The parameters of the Probit model are computed with the method of the Maximum Likelihood Estimator (MLE) which consists of the values of the coefficients that maximize the likelihood function. The MLE chooses the values of the parameters to maximize the probability of drawing the data that are actually observed (Stock & Watson, 2011). A Probit Model looks the following:

Where:

- is the dependent variable which is equal to either 0 or 1 dependent on the consideration used (cash-dominated or not).

- is a k-vector of all the independent variables used in the analysis.

- is the error term normally distributed with mean 0 and variance , conditional on Faccio & Masulis argue in their article that it’s not always just the bidder who determines the amount/percentage of cash in the consideration in the case of mixed deals. It’s often the truth that the target gets to choose whether it’s more interested in stock or cash and in what proportion. Therefore, it’s more accurate to specify the consideration as a choice between cash, stock or a combination of both. Therefore the three options each get a classification where the dependent variable will be equal to 0 for all stock deals, 1 for a mixture and 2 for all -cash deals. Another version of the Probit model is the ordered Probit model which is a generalization of the Probit analysis in the case of a situation where more than 2 outcomes of an ordinal dependent variable exist. The model consists of more than 1 intercepts (k-1, where k is the number of categories) which represent the different categories of the dependent variable. An Ordererd Probit Model in general is of the following form:

(19)

17 Where:

- is the dependent variable which is equal to either 0, 1 or 2 dependent on the consideration used (stock, a mixture or cash).

- is a k-vector of all the independent variables used in the analysis.

- is the error term normally distributed with mean 0 and variance , conditional on

Tobit regressions

The third dependent variable used to test the relationship between the amount of cash used in a transaction and the rating existence/level is the fraction of cash, the amount of cash used as a

percentage of the total consideration. In order to establish this relationship a Tobit regression model is used. This model is also called a censored regression model as it was developed to estimate linear relationships between variables which are either left- or right-censored in the dependent variable. This means that the Y-variable is X for all observations, but the true value of Y is only known for a restricted range of observations. Thus, the dependent variable is constrained & there exists some clustering of this variable at this constraint. Since the dependent variable cashfraction must be between 0 and 100% by definition, it’s constrained/censored and therefore a two-boundary Tobit estimator is used (Maddala, 1991).

Where:

- is the dependent variable which is equal to the portion/percentage of cash used in the consideration between 0% and 100% .

- is a k-vector of all the independent variables used in the analysis.

- is the error term normally distributed with mean 0 and variance , conditional on

Considerations beforehand

In order to be able to identify the relationships between the 4 credit rating variables and the method of payment, various bidder- and deal-related variables are included in the analysis in order to control for the theories as excluding them can cause the endogeneity problem to have influence on the coefficients and standard errors of the variable(s) of interest. Heteroscedasticity -robust standard errors are also adjusted for the possibility of clustering due to the fact that some bidders are represented in the sample more than once. Besides this, as the data involves a panel data set, there must be accounted for period fixed and random effects by including both crisis years and year-fixed effects in the regressions. Before the regression analysis can be run, there must be checked for multicollinearity. Taking into account the general rule of thumb for ‘’dangerous’’ multicollinearity (>0.8)21 no serious form of collinearity between the predictive variables in the correlation matrix (Table 2)22. Therefore, we can safely incorporate all independent variables in the regression analysis without worrying that coefficients and standard errors will be affected. Last, in order to double check

21

A lecture of the University of San Francisco is used as a source for this:

http://www.sfu.ca/~pendakur/teaching/buec333/Multicollinearity%20and%20Endogeneity.pdf 22

The only dangerous multicollinearity exists between the Rating Level & Rating Existence and RxC & RxC2 variables (before and post-crisis) as they show a value above 0.8. However, these two combinations are never used together in one regression analysis.

(20)

18 the results of the different models, opposite regressions will be done where a stock payment will be the dependent variable.

4.2.2. Regression results; credit rating existence

Firstly, the relationship between the bidder’s credit rating exi stence and the method of payment is examined. As described in the literature review before, a variety of studies have been done to check for this relationship in crisis times as in such times asymmetric information and distrust are more explicitly present. In this context, having a credit rating may be more important as it solves these problems and creates certainty about the situation of a financing needing company. Judge & Korzhenitskaya (2012) and Khanga & Kingb (2015) indeed find evidence for this.

Different regression models have been run in order to check which model fits the population the best. Tables 3-7 show the (Ordered) Probit and Tobit regression models which check for the

relationship between the existence of a rating and the use of cash during normal and crisis times. The first regressions presented in table 3-5, show rather low pseudo R-squared values (an indicator of the goodness of fit) between 6% and 12%. When all control variables are included this ratio improves to around 20%-30%. Besides the R-squared value, the Wald chi-square and F-tests (test which model fits the population the best) show significant p-values. Based on both the outcomes of the R-squared and the goodness-of-fit tests, the last models will be used in order to estimate the relations between the rating existence and the payment medium (Table 6).

Specification (1) presents the estimates for the Probit, specification (2) for the Ordered Probit & specification (3) for the Tobit model. All the specifications show somehow the same effect of the existence of a bidder’s credit rating on the method of payment used. The significant, positive coefficients show that the existence of a credit rating makes it more likely that cash is used as a severe part of the consideration. The coefficients of respectively 0.0936, 0.0840 and 0.0749 show that the existence of a credit rating makes it 7.5%-9.6% more likely that the acquisition is paid for in cash23. As earlier stated in the article of Karampatsas, Petmezas, & Travlos (2014) this finding is somewhat strange in the sense that the mere existence of a credit rating can’t prove the superior debt capacity of rated firms. It can be the case that a firm with a credit rating is actually less

creditworthy than a firm without a rating and in this case the relationship between the existence of a rating and the ability of attracting debt shouldn’t hold. However, the outcomes of the regression analysis don’t support this theory as they all are significantly positive and thus state that having a credit rating indeed makes it easier to lend and therefore pay in cash.

The variable RxC2 represents the existence of a credit rating during the most recent financial crisis. On the relationship between this variable and the payment medium exist some contracting views. Firstly, as discussed earlier in the theoretical framework, it may be more important for a firm to be rated during a financial crisis due to uncertainty and credit scarcity. A credit rating may help solving this uncertainty as the lender of credit will be informed about the likelihood that he will get his money back and therefore can make a well-considered decision on whether to lend and under which conditions (Korzhenitskaya (2012), Khanga & Kingb (2015)). The other view is already explained above; as the mere existence of a credit rating says nothing about the credit quality of a firm, no relationship is expected. As the results show, the relationship between the bidder’s credit rating

(21)

19 during the financial crisis and the use of cash in the consi deration isn’t economically significant and moreover decreases the likelihood of paying in cash by 2.43%24.

Table 7 shows same regression models as before which now consider the relationship between the credit rating existence and the likelihood of paying with stock. The coefficients show the exact opposite of the initial analysis and thus confirm these outcome

24

This percentage is estimated by calculating the marginal effects for the Ordered Probit model using Stata. The marginal effect of the Probit model is -1% and the Tobit model shows a negative coefficient of -1.4%. These result indicate the same finding as described by the marginal effects of the Ordered Probit model.

(22)

20 4.2.3. Regression results; Credit rating level

In this section the more qualitative relationship between the rating level and the use of cash wil l be examined by applying the same set of regression models as in paragraph 4.2.2. Just like before, the largest model shows the best fit (highest R-square; significant Chi-square/F-test results).

First, specification (1) shows the Probit model where the dependent variable is the dummy variable

CASHDOM. The variable of interest, RATINGLEV, has a negative but insignificant coefficient at all

significance levels. The sign is thus consistent with the expectance that a higher credit rating level is related to a cash payment, but this relationship doesn’t seem to be strong enough to be of real economics importance (a lower rating level (one unit up) makes a cash payment it 0.0703% less likely). Specifications (2) & (3) show the same negative, insignificant results and thus doesn’t change the conclusion (Table 11).

The variable RxC represents the bidder’s credit rating level during the financial crisis of 2007-2009. As discussed are trust and credit scarcity characteristics of a financial crisis and lead to stricter

borrowing standards for getting new credit. A higher credit rating may resolve this asymmetric information issue which is even more visible during financial crises, information may be more important. For this reason, it’s expected to find a negative coe fficient which indicates a larger additional effect of the credit rating level during a crisis. The coefficient of this variable is indeed found to be negative in all regression models which supports the expectations of additional importance during a crisis. However, again, the outcomes aren’t statistically significant so that no strict conclusion can be formulated.

Table 12 shows the opposite regression analysis for the credit rating level: these regressions show the effect of the rating level on the likelihood of using stock. The coefficients show the exact opposite of the initial analysis and thus confirm these outcomes.

Referenties

GERELATEERDE DOCUMENTEN

Besides this, I also found that independence of the board of the parent has no significant effect on the relationship between foreign subsidiaries financial reporting quality and

The results confirmed the expected relation between the market value (measured using the market price to book ratio) and the credit rating, as well as relations between the CR

o Colours & pictures o Placement External Sources Prior Knowledge Product Choice Information Search Problem Recognition Evaluation of Alternatives Post- purchase

As low dose aspirin use and calcium supplementation lower the risk of hypertensive disorders of pregnancy, proper screening and selection of women who may benefit is of

Furthermore, a safety related need was found based on 1% of the participants from the questionnaire and two observations. End users visit patients alone and dangerous situations

Although most of the research efforts have been performed to analyse the effect of degradation mechanisms, very limited research has been carried out on the countermeasures

To analyze the multilayer structure we combined the Grazing Incidence X-ray Reflectivity (GIXRR) technique with the analysis of the X-rays fluorescence from the La atoms excited

Om echter goed te kunnen begrijpen hoe goedwillende mensen zich voortdurend proberen aan te passen aan alle eisen die aan hen gesteld worden, moet u ook iets weten over hoe