Credit ratings and the method of payment in mergers and acquisitions

Credit Ratings and the Method of Payment in

Mergers and Acquisitions

June 19, 2015

Abstract

In this thesis the effect of the credit rating of the bidder on the choice of payment method in an acquisition is investigated. Bidders holding a credit rating before the announcement are found at the 1% significance level to pay (partly) in cash with a higher probability relative to bidders not holding a credit rating. However, there is no relation found between the level of credit rating and the payment method. The results of this thesis are consistent with the peck-ing order theory and not with the tradeoff theory. Furthermore, it is found that when an offer is a tender offer, bidders often choose to pay in cash only, due to the time delay caused by the registration of stock. Finally, private target firms are more often acquired with cash.

Bachelor Thesis Economics & Business Specialization Finance & Organization Name: Mücahid Çolak

Student Number: 10188967

Thesis Supervisor: Dr. Tolga Caskurlu

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Statement of Originality

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This document is written by Student Mücahid Çolak 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 comple-tion of the work, not for the contents.

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Table of Contents

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Abstract………..1 Statement of Originality………...2 Table of Contents………..3 1 Introduction………4 1.1 Background………..4 1.2 Problem Discussion……….5 1.3 Research Question………...5 1.4 Structure………..5 2 Literature………...……….6

2.1 Tradeoff Theory and Payment Method in M&A’s ……….6

2.2 Pecking Order Theory and Payment Method in M&A’s ………...7

2.3 Target and Deal Characteristics and Payment Method in M&A’s…..………..8

2.4 Hypotheses………...9 3 Methodology……….……10 3.1 Main Variables………..10 3.2 Control Variables………..…10 3.3 Dropped Variables………11 3.4 Regression Models……….12 4 Data………...13

4.1 Sample Selection Criteria……….13

4.2 Descriptive Statistics……….13

5 Results………...15

5.1 Rating Existence and Method of Payment………..15

5.2 Rating Level and Method of Payment……….17

6 Robustness………19

7 Conclusion……….20

References………22

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1 Introduction

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1.1 Background

The first publicly available bond ratings focused entirely on railroad bonds and were pub-lished by John Moody in 1909. Moody’s firm was followed by Poor’s Publishing Company in 1916, and the Standard Statistics Company in 1922. After a number of mergers and acquisi-tions these companies became what we now refer to as Moody’s, Standard & Poor’s (S&P), and Fitch. These are called the ‘Big Three.’ Rating agencies, and the Big Three in particular, play an important role in the financial sector by assessing the creditworthiness and assigning a rating to all kinds of firms and of certain types of (debt) securities. The United States (US) fi-nancial regulatory structure propelled the major rating agencies at the center of the US bond markets (White, 2010).

White (2010) states that during the evolvement of the credit rating industry, its interac-tion with financial regulators served as entry barriers. Outsourcing the judgments of financial regulators to the major rating services and requiring financial institutions to use specific infor-mation that was provided by these agencies, resulted in the holding of almost the entire global market by the Big Three. When these rating agencies make mistakes, those mistakes will likely have serious consequences for the financial sector (ibid). Considering the subprime mortgage crisis will suffice to obtain an impression of the contribution of their mistakes to the consequences for the financial sector.

By assigning a credit rating, rating agencies signal information about firms to the mar-ket that is not publicly available and thereby mitigate information asymmetry. In a way, this indirectly affects firms’ financing decisions and investment policies (Tang, 2009). Kisgen (2006) finds that firms issue less debt relative to equity when they are near a credit rating change. Tang (2009) argues that compared with firms with refinement downgrades, firms with upgrades experience an additional decrease in their cost of capital and subsequently also issue more debt relative to equity. These findings are consistent with discrete costs or benefits of rating changes and fill the gap that is left by traditional capital structure theories.

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1.2 Problem Discussion

If we consider the optimal capital structure, what Myers (1984) refers to as ‘the capital struc-ture puzzle’, we can think of the two traditional theories, the tradeoff theory and the pecking order theory. In the tradeoff theory, a firm sets a target debt-to-value ratio and gradually moves towards it. In the pecking order theory, the firm prefers internal financing to external, and issuing debt to issuing equity (Myers, 1984). The more recent studies show how a firm’s capability to access public debt markets can influence capital structure decisions. Lemmon and Zender (2010) measured debt capacity by assessing whether a firm is in possession of a credit rating to proxy for the capability to access public debt markets and Denis and Mihov (2003) by taking the level of credit rating as a proxy.

According to Kisgen (2009), firms often target a minimum credit rating level and therefore change their capital structure by issuing equity or buying back debt. Given the fre-quency and large sizes of mergers and acquisitions (M&A’s), the capital structure decision also matters for the financing decision and the choice of payment method in M&A deals. Faccio and Masulis (2005) argue that cash offers generally require debt financing. To pay for their targets, acquiring firms use cash, stock, or a mix between those two. This thesis is about the relation between the credit rating of the bidder on the choice of payment method in an M&A deal. The aim of this thesis is to provide further evidence on existing theories about capital structure in general and the choice of payment method in M&A’s in particular, using a unique and recent dataset.

1.3 Research Question

What is the effect of the credit rating of the bidder on the choice of payment method in an M&A deal?

1.4 Structure

This thesis is organized as follows. First theories concerning the determinants of the choice of payment method in M&A deals will be reviewed. Then the methodology will be reviewed. Next the data sources will be documented and some descriptive statistics on deal, acquirer, and target characteristics will be presented. After that, the results will be presented and inter-preted. Finally, the empirical findings will be summarized and the thesis will be concluded.

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2 Literature

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2.1 Tradeoff Theory and Payment Method

As mentioned in the introduction, one of the two traditional theories of capital struc-ture is the tradeoff theory. Miller and Modigliani (1958) stated that in perfect capital markets a firm’s market value is independent of its capital structure. Their assumptions for perfect capital markets include rational behavior, no costs to bankruptcy, no transaction costs, and a tax-free world. These and other oversimplifications were necessary in order to be able to start somewhere to explain capital structure decisions and push the research in the right way. Be-cause firms have to pay corporate taxes and could face financial distress costs, there are no perfect capital markets. This led Kraus and Litzenberger (1973) to relax some of the assump-tions and add corporate taxes and bankruptcy costs into the model.

In this model a firm’s market value is affected by its capital structure in the following way. If the ratio of debt increases, the market value increases with the present value of the taxes that the firm doesn’t have to pay anymore because of the tax deductibility of the costs of debt. However, financial distress costs also increase if the ratio of debt increases and this has to do with the probability of default. Creditors require a higher rate to compensate for the ad-ditional risk. The market value increases until the benefits of the interest tax shield are offset by the increased financial distress costs (ibid). This is the tradeoff theory for the optimal capi-tal structure.

Kisgen (2006, 2009) adds the effect of credit ratings on the capital structure decisions. He finds that managers are concerned with rating-triggered costs or benefits to the firm and the effects of regulations on bond investors. Specifically, in fear of a downgrade, firms near a credit rating change issue less debt relative to equity than firms not near a change. A down-grade could possibly increase the cost of debt (Kisgen, 2006). Managers view ratings as sig-nals of the firm’s quality and target minimum rating levels (Kisgen, 2009). In general, these findings are consistent with the tradeoff theory. Accordingly, Faccio and Masulis (2005) found a negative relation between leverage and the probability of using cash in an M&A deal.

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2.2 Pecking Order Theory and Payment Method in M&A’s

However, in some cases, the level of credit rating and the associated costs make the firms de-viate from their optimal capital structure as implied by the tradeoff theory (Kisgen, 2006). This phenomenon is better explained by the pecking order theory, which is the other tradi-tional theory explaining capital structure decisions. Myers (1984) argues that his modified version of the pecking order theory performs at least as well as the static tradeoff theory in ex-plaining what is known about actual financing choices. According to the pecking order theory, when managers have to make financing decisions, they prefer to use internally generated funds over debt and debt over equity, due to information asymmetries (ibid).

The costs of debt are not always less than the required return on equity and public debt markets are not always accessible. The costs of debt depend on the risk of the firm. Credit rat-ings signal information about firms to the market that is not publicly available and thereby mitigate information asymmetry (Tang, 2007). Additionally, Myers (1977) states that in firms with more tangible assets and less growth opportunities, moral hazard problems are less likely to occur, which leads debt holders to require a lower cost of capital. In combination with the fact that the costs of debt are tax deductible, this makes debt-financing more attractive relative to equity-financing.

Consistent with what Myers (1977) found about the relation between growth opportu-nities and the cost of debt, Martin (1996) reported that there exists a positive relation between the acquirer’s growth opportunities and the likelihood to use stock to finance an acquisition. In addition, financing an acquisition with stock could be interpreted by the market as a signal that the management of the acquiring firm thinks the stock is overvalued. This explains why an acquiring firm’s stock typically experiences a negative price reaction in stock-financed ac-quisitions (Travlos, 1987). According to Faccio and Masulis (2005), acac-quisitions that are paid in cash are to a great extent debt-financed. Hovakimian et al. (2001) showed that a firm’s ratio of tangible assets is positively related to its level of debt. They also argued that larger firms are more diversified and have a lower probability of default, which makes them able to issue more debt.

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2.3 Target and Deal Characteristics and Payment Method in M&A’s

When a private target firm is involved in the deal, the seller’s liquidity needs have to be con-sidered. Due to the illiquid and concentrated nature of their portfolio holdings, there is a greater seller preference for cash in privately held firms (Fuller et al., 2002), probably because sellers often try to cash out their wealth opportunities. Bidders of privately owned target firms use cash significantly more often as a means to pay, while bidders of publicly owned firms more often choose stock (Faccio & Masulis, 2005).

Finally, the relation between some deal characteristics and the method of payment in an M&A deal have to be considered. To begin with, tender offers with stock must be made in accordance with the Securities Act of 1933 and the registration statement must be reviewed by the U.S. Securities and Exchange Commission (SEC). This entails a substantial delay for the bidding firm. When a bidding firm desires to close the deal earlier, it could commence a tender offer. In his article, Martin (1996) examines the motives underlying the payment method in corporate acquisitions and he finds that the likelihood of cash financing increases in tender offers.

Furthermore, Berkovitch and Narayanan (1990) investigated among other things the role of the medium of exchange in competition among bidders. They showed that the cash component of the offer as well as the proportion of cash offered increases as competition in-creases. Fishman (1989) argues that cash has the advantage of serving to preempt competition by signaling a high valuation for the target firm and that, when the offer is hostile, the proba-bility of acceptance by the target firm is higher when the bidder offers cash.

Harford et al. (2009) report that the difficulty to raise relatively large amounts of cash decreases the likelihood of a cash payment. The bigger the size of the target firm relative to the bidding firm, the more difficult it is to raise the needed amount of cash to pay for the tar-get firm.

Finally, industry diversification is considered to have an effect on the method of pay-ment. Sellers could be unfamiliar with the industry of the acquirer. In that case, they are not well acquainted with the industry risks and prospects. They risk an overvaluation of the ac-quirer’s stock. For this reason they could be more reluctant to accept a stock payment and pre-fer cash instead to avoid the risk of overvaluation (Faccio and Masulis, 2005).

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2.4 Hypotheses

The main variables of interest in this thesis are the payment method, the credit rating exist-ence and the credit rating level. Lemmon and Zender (2010) showed in the context of capital structure decisions that a publicly traded firm’s concerns over the capability to access public debt markets explain the use of new external equity. They used the existence of a credit rating as a proxy for the debt capacity. Denis and Mihov (2003) took the level of credit rating as a proxy to examine the choice among bank debt, nonbank debt, and public debt. In this thesis the focus is on the relation between the credit rating of the acquirer and the method of pay-ment in an M&A deal.

Based on the reviewed literature, an acquirer holding a (higher) credit rating lacks fi-nancial constraints and therefore the likelihood of using cash as a means of payment in an M&A deal should be higher. Thus, controlling for other effects, the rating existence and rating level are expected to be positively related to the likelihood of using cash in an M&A deal.

H1: An acquirer whose bonds are rated is more likely to use cash as a means of payment in an M&A deal.

H2: An acquirer whose bonds are relatively higher rated is more likely to use cash as a means of payment in an M&A deal.

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3 Methodology

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In this chapter the variables and the regression model that will be used are explained. The first section presents the variables and their definitions. In the second section an explanation will be given about the variables that are derived in a logically way from the theories, but are dropped for some reasons. Finally the models will be introduced.

3.1 Main Variables

The dependent variable PaymentMethod is nominal and equals 0 for all stock transac-tions, 1 for transactions paid with a mix of stock and cash, and 2 for all cash transactions. The independent indicator variable RatingExistence takes the value 1 if the acquirer is holding an S&P domestic long-term issuer credit rating one month prior to the announcement and 0 oth-erwise. The categorical variable RatingLevel represents the level of credit rating one month prior to the announcement and ranges from 0 to 21. Appendix x shows the transformation of the credit ratings into numerical scores.

3.2 Control Variables

The variable Leverage will capture the effect of the tradeoff between the consequences of issuing debt and issuing equity. This variable is measured by the ratio of the bidder’s total financial debt (long-term debt plus debt in current liabilities) to the book value of total assets at the fiscal year-end before the announcement of the acquisition. Faccio and Masulis (2005) found a negative relation between leverage and the probability of using cash in an M&A deal, which is consistent with the tradeoff theory.

The variable Cashflows-to-Assets represents the income before extraordinary items plus depreciation minus dividends on common and preferred stock divided by the firm’s book value of total assets at the fiscal year-end prior to the announcement. This variable controls for the effect of the availability of cash. Martin (1996) showed that the likelihood of a cash payment increases with the acquirer’s availability of cash. This is consistent with the pecking order theory. A firm’s debt capacity is also expected to be positively related to the probability of using cash in an M&A deal (Hovakimian et al., 2001). To proxy for the debt capacity, the

Page | 11 variable Collateral is used. Collateral is measured by the ratio of property, plant and equip-ment (PPE) to the book value of total assets at the year-end prior to the announceequip-ment.

Growth opportunities are measured by the variable Book-to-Market, which is the ratio of the bidder market value of equity four weeks prior to the announcement to the book value of total assets at the fiscal year-end prior to the announcement. The relation between the book-to-market ratio and the likelihood of paying cash is expected to be positive (Myers, 1977). By contrast, the variable RelativeSize should be negatively related to the likelihood of using cash, because of the difficulty to raise relatively large amounts of cash (Harford et al., 2009). The variable RelativeSize controls for this effect and is measured by the ratio of the transaction value to the acquirer’s market value of equity four weeks prior to the announce-ment (Harford et al., 2009).

Finally, three indicator variables are included in the model. The indicator variable

Ten-derOffer takes the value 1 if the offer is a tender offer and 0 otherwise. This variable should

be positively related to the likelihood of using cash in an M&A deal (Martin, 1996). To ac-count for the effect of the target firm’s shareholder ownership structure, the indicator variable

Private will be used, which is an indicator variable taking the value 1 for private target firms

and 0 for public target firms. The effect of inter-industry deals is captured by the indicator variable DiversifyingDeal taking the value 1 for inter-industry deals and 0 otherwise. Indus-tries are defined by the acquirer primary Standard Industrial Classification (SIC) codes from Thomson One.

3.3 Dropped Variables

As mentioned in the literature review, larger firms are more diversified and have a lower probability of default, which makes them able to issue more debt (Hovakimian et al., 2001). The variable BiddersSize would control for this effect and is measured by the natural loga-rithm of the bidder’s market value of equity one month prior to the announcement date. Un-surprisingly, it appeared to be strongly correlated to the variables RatingExistence (r=0.5542) and RatingLevel (r=0.7933). Although this control variable derive logically from theory, it is dropped from the model.

Furthermore, two indicator variables are excluded from the model. One of them is the variable Hostile, which takes the value 1 for hostile acquisitions and 0 otherwise. The sample contains two hostile bids. It is impossible to make inferences based on such a small number. The other one is the variable Competition, which takes the value 1 when more than one bidder

Page | 12 entered the bidding contest and 0 otherwise. Here again, it is impossible to make inferences on such a small number of cases. The sample contains just 20 observations where competition is the case.

3.4 Regression Models

Because the dependent variable is nominal, the appropriate regression model to test the hy-potheses is a multinomial logistic regression. This regression model uses maximum likelihood estimation to evaluate the probability of categorical membership. Multicollinearity will be evaluated with correlations between the independent variables (Appendix 4). The requirement of 10 cases per independent variable is abundantly met.

Figure 1 Distribution of Payment Method

The first model makes use of the whole sample and tests whether the existence of a credit rating has an effect on the payment method in an M&A deal. The second model uses the part of the sample for which credit ratings are available and tests whether the credit rating level of the bidder is related to the payment method.

PaymentMethod = α + ß1RatingExistence + ß2BookToMarket + ß3Leverage + ß4Collateral +

ß5RelativeSize + ß6CashflowsToAssets + ß7TenderOffer + ß8Diversifying + ß9Private + ℇ

PaymentMethod = α + ß1RatingLevel + ß2BookToMarket + ß3Leverage + ß4Collateral +

ß5RelativeSize + ß6CashflowsToAssets + ß7TenderOffer + ß8Diversifying + ß9Private + ℇ

Stock only 10% Mix 25% Cash only 65%

Payment Method

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4 Data

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4.1 Sample Selection Criteria

The sample of the deals is downloaded from ThomsonOne and included 21,022 completed or withdrawn M&A deals announced between January 1, 2008 and December 12, 2014. Acquir-ers are public firms listed in the U.S. and targets are either public or private firms. Deals clas-sified as repurchases, leveraged buyouts, privatizations, spinoffs, recapitalizations, minority stake purchases, self-tenders, exchange offers and deals with an undisclosed transaction value are dropped and 6,250 deals left over. Furthermore, the sample is restricted to deals where the bidders owned less than 10% of the target shares before the deal and sought to own more than 50% after the deal. This criterion left us with deals that represent a transfer of control. Finally, to avoid noise deals with a transaction value of less than one million dollars and less than 1% of the acquirer market value are dropped.

The acquirer’s Standard & Poor’s (S&P) domestic long-term issuer credit ratings and the company fundamentals are collected from Compustat. The domestic long-term issuer credit ratings are used because the acquirers in the sample are listed firms in the U.S. and therefore no foreign comparison is needed. The acquirer CUSIP codes in Thomson One are first linked to the PERMNOs and then to the GVKeys via CRSP. Eventually 2,085 deals left over.

The reliability of this thesis depends on the quality of the data. There appeared to be severe outliers in the dataset. Some outliers were obviously the result of typos. Comparing the same data from both sources made it possible to correct some outliers.

4.2 Sample Description

The sample consists of 2,085 deals. Table 1 presents some descriptive statistics. Table 2 pre-sents the distribution of the payment method for rated and unrated acquirers. The distributions of the payment method of rated and unrated firms are approximately the same as for the total sample. This doesn’t indicate a relation between rating existence and method of payment, while it should be expected that the distributions do. The lowest rating in the sample is 4 (CCC) and the highest is 21 (AAA). Appendix 2 and 3 show the distribution of the ratings in this sample and the transformation of the ratings into numerical scores. The distribution of the deals by year is shown in Appendix 1.

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Table 1 Descriptive Statistics

mean median SD N min max

%RatingExistence 30.88 - - 2,085 - - Rating 11.55 11 3.098 644 4 21 Ln Bidders Size 20,62 20,60 1.812 2,085 13.92 26.60 Leverage .373 .355 .209 1,658 .000 1.198 BookToMarket 1.440 .949 1.340 2,083 -.000 4.670 RelativeSize .192 .083 .254 2,085 .010 .964 CashflowsToAssets .038 .060 .155 2,027 -1.192 .524 Collateral .163 .079 .206 1,958 .000 .947 %Diversifying 64.17 - - 2,085 - - %Private 72.76 - - 2,085 - - %TenderOffer 5.37 - - 2,085 - -

Table 2 Distribution of Payment Method against Rating Existence

Payment Method Unrated Rated Total

Stock only 155 53 208

Mix 397 126 523

Cash only 889 465 1354

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5 Results

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5.1 Rating Existence and Method of Payment

To let the models fit correctly, only meaningful variables are included and also all meaningful variables are included. A stepwise method is used to estimate the multinomial logistic regres-sions. The model as a whole fits significantly better than an empty model at the 1% signifi-cance level in all steps. This means that at least one of the regression coefficients in the model is not equal to zero. The variables Leverage and Diversifying are excluded from the model, because they were not significant and thus have no meaning. Based on the literature it was ex-pected that the level of leverage in a firm is negatively related to a cash payment. However, the models do not consider whether a firm has maximized and/or wants to maximize its debt level. This is a possible explanation for the insignificance of the variable Leverage.

Table 3 presents the results of the first model, which tested the relationship between credit rating existence and the method of payment in M&A deals. The coefficients of all of the control variables in the first model are significant at the 1% significance level. A one-unit increase in an independent variable is associated with an increase or a decrease, depending on the sign of the coefficient, in the relative log odds of being in mix or cash only versus stock only. So based on this model, if the bidder in an acquisition is holding a credit rating, the like-lihood of paying with a mix of stock and cash or in cash only increases relative to paying in stock only. The same holds for the situation where the target firm is privately owned. When the offer is a tender offer, only the likelihood of paying 100% in cash increases relative to paying in stock only. This is consistent with the expectation based on the literature. The time delay caused by the registration of the stock is often the reason for a tender offer.

The coefficient of the ratio of the transaction value to the size of the bidder has a nega-tive sign. This meets the expectation that it would be difficult to raise relanega-tively large amounts of cash to pay for an acquisition. The coefficient of the book-to-market ratio is also negative. A higher book-to-market ratio indicates that the management of the bidding firm thinks its stock is undervalued with an increasing probability. This makes it more attractive to pay in stock. The coefficient of the ratio cashflows-to-assets is positive. Consistent with the pecking order theory, a higher availability of cash increases the likelihood of including cash as a means to pay for the target.

Page | 16 Finally, the sign of the coefficient of the variable Collateral is negative. Based on the literature it was expected to be positive. This variable proxies for an acquirer’s debt capacity and according to Faccio and Masulis (2005), most transactions paid for in cash are debt-fi-nanced. It could be the case that most of the bidders already maximized their debt level or even want to reduce it. The pseudo R2 is .1760.

Table 3 Results of the model estimating the relation between Rating Existence and Payment Method

Payment Method (1) (2) (3) (4) (5) (6) (7)

Stock only Base Outcome Mix Constant .940*** (.095) 1.253*** (.128) 1.885*** (.172) 2.031*** (.182) 2.238*** (.199) 1.039*** (.250) 1.020*** (.258) Rating Existence -.074 (.189) -.056 (.191) -.080 (.194) -.175 (.202) .087 (.217) .446** (.228) .442* (.228) Relative Size -.934*** _(.239) -.903*** _(.247) -.898*** _(.256) -.786*** _{(.266) (.285)} -.353 -.370*** _(.286) Book to Market -.322*** _(.052) -.357*** _(.054) -.394*** _(.055) -.307*** _(.057) -.293*** _(.058) Cashflows to Assets 1.440*** (.389) 1.502*** (.388) 1.899*** (.408) 1.852*** (.409) Collateral -1.262*** _(.405) -1.347*** _(.421) -1.304*** _(.422) Private 1.547*** _(.202) 1.516*** _(.205) Tender Offer .326 (.717) Cash only Constant 1.746*** .(087) 2.613*** (.119) 3.090*** (.142) 3.499*** (.178) 3.589*** (.195) 2.320*** (.243) 1.907*** (.255) Rating Existence .425** (.169) .458** (.179) .392** (.184) .253 (.193) .565*** (.208) .934*** (.221) .915*** (.224) Relative Size -3.894*** (.276) -3.826*** (.283) -3.829*** (.301) -3.550*** (.311) -3.015*** (.324) -2.979*** (.329) Book to Market -.208*** _(.025) -.503*** _(.053) -.554*** _(.055) -.460*** _(.057) -.405*** _(.059) Cashflows to Assets 3.856*** (.503) 3.993*** (.510) 4.364*** (.525) 4.224*** (.525) Collateral -1.179*** (.391) -1.292*** (.410) -1.192*** (.415) Private 1.605*** _(.199) 1.906*** _(.206) Tender Offer 2.361*** _(.640) Pseudo R2 _{.0062 .0885 .1094 .1378 .1400 .1624 .1760} *** P<.01, ** P<.05, * P<.10

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5.2 Rating Level and Method of Payment

Table 4 presents the results of the second model, which tested the relation between credit rat-ing levels and the method of payment in M&A deals. Here again, the model as a whole fits sig-nificantly better than an empty model at the 1% significance level in all steps, except for col-umn 1 in table 4 where it is significant at the 5% significance level. The pseudo R2_{is .2276.}

The variable TenderOffer is dropped from this second model, because it had no meaning.

The coefficient for the credit rating level is not significant. This means that it is impossible to reject that, among firms holding a rating, the level of the rating does not affect the choice of payment method. In other words, based on this model it is impossible to conclude that a higher credit rating level corresponds to an increased likelihood of using cash in M&A deals. The significant coefficients of the control variables have the same sign as in the first model, only the significance levels differ for some. Hence, the same explanations apply here.

Moreover, the coefficients of the variables RelativeSize and CashflowsToAssets are not signifi-cant in the part of the model that compares M&A deals paid for in a mix of stock and cash and deals paid for in stock only.

Table 4 Results of the model estimating the relation between Rating Level and Payment Method

Payment Method (1) (2) (3) (4) (5) (6)

Stock only Base Outcome Mix Constant 1.292** (.632) 1.657** (.696) 2.518*** (.786) 2.522*** (.801) 3.375*** (.961) 1.162 (1.088) Rating Level -.038 (.055) -.050 (.055) -.0751 (.0562) -.082*** (.057) -.106* (.062) .001 (.068) Relative Size -.605*** (.482) -.550 (.491) -.642 (.496) -.368 (.530) .742 (.617) Book to Market -.322*** _(.123) -.292** _(.129) -.383*** _(.136) -.389*** _(.144) Cashflows to Assets 1.759 (1.955) 1.257 (1.940) .760 (2.031) Collateral -1.288* (.693) -1.250* (.735) Private 3.177*** _(.774) Pseudo R2 .0086 .1481 .1629 .1686 .1777 .2276 *** P<.01, ** P<.05, * P<.10

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Table 4 Results of the model estimating the relation between Rating Level and Payment Method (continued)

Payment Method (1) (2) (3) (4) (5) (6)

Stock only Base Outcome Cash only Constant 1.540 (.561) 3.315*** (.656) 4.388*** (.739) 4.256*** (.758) 5.642*** (.923) 2.860*** (1.054) Rating Level .055 (.048) -.009 (.052) -.0350 (.0528) -.050 (.055) -.105* (.059) .026 (.065) Relative Size -4.828*** (.566) -4.755*** (.577) -4.879*** (.589) -4.306*** (.617) -2.796*** (.679) Book to Market -.454*** _(.115) -.388*** _(.121) -.478*** _(.129) -.472*** _(.138) Cashflows to Assets 4.075** (1.962) 4.538** (2.087) 4.154* (2.176) Collateral -2.664*** _(.685) -2.674*** _(.746) Private 3.548*** _(.762) Pseudo R2 _{.0086 .1481 .1629 .1686 .1777 .2276} *** P<.01, ** P<.05, * P<.10

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6 Robustness

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To test in another way whether credit rating levels affect the choice of payment method, the indicator variable InvestmentGrade is used instead of RatingLevel. This variable equals 1 if the acquiring firm is holding a rating of BBB- or higher and 0 otherwise. The results are al-most exactly the same as the results for the model that tested the relationship between the level of credit ratings and the choice of payment method. Table 5 presents them.

Table 5

Results of the model estimating the relation between Investment Grade and Payment Method

Payment Method (1) (1)

Stock only Base Outcome

Mix Cash only Constant 1.364** (.610) 3.289*** (.595) Investment Grade -.208 (.401) -.063 (.391) Relative Size .669 (.602) -2.897*** (.665) Book to Market -.399*** (.142) -.483*** (.136) Cashflows to Assets .806 (2.052) 4.259* (2.207) Collateral -1.355* (.737) -2.786*** (.745) Private 3.118*** (.770) 3.476*** (.758) Pseudo R2 .2277

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8 Conclusion

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The research question of this thesis is: what is the effect of the credit rating of the bidder on the choice of payment method in an M&A deal? The sample consists of 2,085 acquisitions an-nounced between 2008 and 2014, where the bidders are listed in the U.S.. Two models are used to investigate the effect of credit ratings on the choice of payment method in acquisi-tions. The first model used the credit rating existence and the second model used the level of credit rating

Significant at 1% and controlling for other effects, a positive relation is found between the bidder in an acquisition holding a credit rating and an increase in the likelihood of paying in cash only, relative to paying in stock only. At the 10% significance level the rating exist-ence is also found to be positively related to the likelihood of paying in a mix of stock and cash, relative to paying in stock only. This effect is attributed to the fact that an acquirer hold-ing a credit rathold-ing has less financial constraints relative to an acquirer that does not. Therefore, the likelihood of using cash as a means of payment in an acquisition is higher when a firm’s bonds are rated.

There is no evidence found that a higher level of credit rating corresponds to a higher likelihood of using cash in an acquisition. The leverage ratio is also not found to be related to the choice of payment method. The debt capacity, however, is found to be negatively related (α=.01) to the likelihood of using cash as a means of payment, which is the opposite of the ex-pected. The reason for this could be that it is not considered in the model whether the bidders have maximized or want to maximize their level of debt.

Furthermore, the ratio of cash flows to assets is found to be positively related at the 1% significance level to the probability of using cash relative to stock only. The book-to-mar-ket ratio is negatively related to the likelihood of using cash at the 1% significance level. These findings are consistent with the pecking order theory. Based on this thesis, however, there is no further evidence for the tradeoff theory. So in this case, the pecking order theory seems superior to the tradeoff theory.

The relative size of the target and the bidder are also found to be significantly related at the 1% level to the choice of payment method in an acquisition. The likelihood of using cash as a means of payment decreases when the target firm is large relative to the bidder. The economic intuition behind this is the difficulty to raise large amounts of cash. Additionally,

Page | 21 target firms that are privately owned are more often paid for in cash. This is probably the re-sult of the poor diversified portfolios of small investors who attempt to cash out their wealth. Finally, when the offer is a tender offer the likelihood of using cash only as a means of pay-ment increases relative to using stock only at the 1% significance level. The reason for this is presumably the time delay caused by the registration of the stock.

Further research on this topic have to take into account whether firms did or want to maximize their debt level. One way to do this could be to take into account whether the credit rating is changed shortly after the acquisition. Moreover, in this thesis only the ratings of one rating agency is used. The firms in the sample for which there are no ratings could be rated by another agency.

Page | 22

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Appendices

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Appendix 1 Distribution of the deals by year

Appendix 2 Distribution of the credit rating levels

0 50 100 150 200 250 300 350 400 2008 2009 2010 2011 2012 2013 2014 Frequency 0 20 40 60 80 100 AAA AA A+ A-BBB BB+ BB-B CCC+ CCC-SD Frequency Rat in g L ev el

Page | 25 Appendix 3 S&P Domestic Long-Term Issuer Credit Ratings

Category Indicator Definition

Investment Grade

AAA 21

An obligor rated 'AAA' has extremely strong capacity to meet its financial commitments. 'AAA' is the highest issuer credit rat-ing assigned by Standard & Poor's.

AA+ 20 An obligor rated 'AA' has very strong capacity to meet its

finan-cial commitments. It differs from the highest-rated obligors only to a small degree.

AA 19

AA- 18

A+ 17 An obligor rated 'A' has strong capacity to meet its financial

commitments but is somewhat more susceptible to the adverse effects of changes in circumstances and economic conditions than obligors in higher-rated categories.

A 16

A- 15

BBB+ 14 An obligor rated 'BBB' has adequate capacity to meet its

finan-cial commitments. However, adverse economic conditions or changing circumstances are more likely to lead to a weakened capacity of the obligor to meet its financial commitments.

BBB 13

BBB- 12

Speculative Grade

BB+ 11 Obligors rated 'BB', 'B', 'CCC', and 'CC' are regarded as having

significant speculative characteristics. 'BB' indicates the least degree of speculation and 'CC' the highest. While such obligors will likely have some quality and protective characteristics, these may be outweighed by large uncertainties or major expo-sures to adverse conditions.

BB 10 BB- 9 B+ 8 B 7 B- 6 CCC+ 5 CCC 4 CCC- 3 CC 2

SD 1 An obligor rated 'SD' (selective default) or 'D' is in default on

one or more of its financial obligations.

Page | 26 Appendix 4 Table of the correlation coefficients

Variable R at ing E xis te n ce R at ing L ev el R elat iv e Si ze Book to Mar ke t C as hf lo w s t o A ss ets C o llat eral P ri vat e Te nde r O ffe r Rating Existence 1 - - - - Rating Level - 1 - - - - Relative Size -0.0361 -0.1802 1 - - - - - Book to Market 0.0302 -0.1557 0.1628 1 - - - - Cashflows to Assets 0.1348 0.1585 -0.1182 -0.0724 1 - - - Collateral 0.2312 -0.2058 0.0969 -0.0757 0.0825 1 - - Private -0.2116 -0.2342 -0.2811 -0.2328 -0.0219 -0.0588 1 - Tender Offer 0.1481 0.2241 -0.0108 -0.0968 0.0730 0.0042 -0.3817 1