‘
The impact of credit rating progression on U.S. M&A payment
methods – Pre & Post Global Financial Crisis: A comparative
perspective
Joud Zaumot & Chadwick Dorai
Supervisor: Naciye Sekerci
MSc Corporate & Financial Management
Lund University- Business Administration Department
Acknowledgment
We would like to give our deepest gratitude to our supervisor Naciye Sekerci. Her support
and guidance during the master thesis period were exceptional. We would also like to thank
the Lund University- Business Administration department for all of their support, as well
as our outstanding professors in making this dissertation a reality.
We are also very grateful for the well-constructed feedback given by our opponents, Jannik
and Adam.
2
Abstract
Title: The impact of credit rating progression on U.S. M&A payment methods – Pre &
Post Global Financial Crisis: A comparative perspective
Course: BUSN89 - Degree Project in Corporate and Financial Management. Master Level,
15 ECTS.
Authors: Joud Zaumot & Chadwick Dorai
Advisor: Naciye Sekerci
Purpose: The aim of this study is to investigate impact of 2008 Global Financial Crisis on
the choice of M&A payment methods, more specifically, how M&A payment methods are
influenced by credit rating progression pre and post crisis.
Methodology: The methods used are Ordinary Least Squares and Multinomial probit
regressions. The dependent variables for the OLS model is fraction of cash with credit
rating level as the explanatory variable. The dependent variable for the MNP model is cash,
mixed, or equity with downgrade and upgrade as the main explanatory variables. This
study is based on a similar methodology proposed by Karampatsas et al (2014) and Faccio
and Masulis (2005).
Theoretical perspective: The literature review is fundamentally based upon previous
research on credit ratings and their impact on M&A payment methods. Moreover, a
multitude of theories spanning across capital structure, cost of capital and growth
opportunities were discussed and used in drawing relevant deductions.
Empirical foundation: The research is based on credit ratings of 68 financial firms based
on S&P rating framework across two time periods 1st of January 2003 to 1st of January
2008 and 1st of January 2008 to 1st January 2013
Conclusion: The outcome of this study suggests that there is a negative relationship
between credit rating level and the fraction of cash used in M&A transactions in the U.S.
before and after the Global Financial Crisis. This observation was fundamentally
substantiated by deductions that we derived based on theories and literature analyzed from
previous literature. We also identified a positive significant relationship between credit
rating downgrade and the use of mixed payment method. In essence, the positive
relationship portrays a distinct picture of how U.S. M&A landscape has evolved since the
crisis and it can be concluded that acquirers are now leaning towards a mixture of cash and
equity financing.
Keywords: Global Financial Crisis, mergers and acquisitions, financing decisions, credit
ratings, credit rating agencies, payment method, capital structure.
3
Table of Contents
1.0 INTRODUCTION
6
1.1BACKGROUND
6
1.2 PROBLEM DISCUSSION
7
2.0 LITERATURE REVIEW
9
2.1 THEORETICAL FRAMEWORK OF M&A PAYMENT METHODS
9
2.2 CHARACTERISTICS OF THE ACQUIRER
10
2.2.1 CAPITAL STRUCTURE & COST OF CAPITAL
10
2.2.2 ACQUIRER’S FREE CASH FLOW (FCF) CAPACITY & GROWTH OPPORTUNITIES
13
2.2.4 OWNERSHIP STRUCTURE
14
2.3 THE ROLE OF CREDIT RATINGS AGENCY (CRAS) IN M&A PAYMENT METHOD
14
2.3.1 CREDIT RATING PROGRESSION AND ITS INFLUENCE ON M&A PAYMENT DECISION
15
2.4 CHARACTERISTICS OF THE TARGET
15
2.4.1 SIZE AND AVAILABILITY OF INFORMATION
16
2.4.2 OWNERSHIP STRUCTURE
16
3.0 HYPOTHESIS
17
4.0 METHODOLOGY
18
4.1 RESEARCH METHOD
18
4.2 DATA AND SAMPLE SELECTION
19
4.3 VARIABLES
21
4.3.1 DEPENDENT VARIABLES
21
4.3.2 EXPLANATORY VARIABLES
22
4.3.3 CONTROL VARIABLES
22
4.3.4 EXCLUDED VARIABLES
25
4.3.4 INSTRUMENTAL VARIABLES (IVS)
26
4.4 ECONOMETRICS TECHNIQUES USED
27
4.4.1 ORDINARY LEAST SQUARES REGRESSION (OLS)
28
4.4.2 MULTINOMIAL PROBIT REGRESSION (MNP)
30
5.0 VALIDITY AND RELIABILITY
31
6.0 EMPIRICAL RESULTS
33
6.1 DESCRIPTIVE STATISTICS
33
6.2 ORIDNARY LEAST SQAURES REGRESSION (OLS)
36
6.2.1 FIXED AND RANDOM EFFECTS ESTIMATION
36
4
6.2.2 REGRESSION RESULTS
37
6.2.3 ENDOGENITY CONTROL FOR OLS
39
6.3 MULTINOMIAL PROBIT REGRESSION (MNP)
40
6.3.1 REGRESSION RESULTS
41
7.0 ANALYSIS
43
8.0 LIMITATIONS
46
9.0 CONCLUSION
47
10.0 SUGGESTIONS FOR FUTURE RESEARCH
49
REFERENCES
50
APPENDICES
55
5
1.0 Introduction
Since 2008, the Global Financial Crisis (GFC) has been a topic of great interest with
numerous researches aimed at analyzing its implications on different aspects of the global
economy. Previous researches have subtly touched upon the influence of credit ratings on
the choice of M&A payment method in the United States. However, given the fact that
Credit Rating Agencies (CRAs) were highly regulated as a consequence of the GFC, it is
of paramount importance to further analyze how the progression of credit ratings as a
consequence of the crisis has influenced M&A payment methods in the U.S (U.S. Securities
and Exchange Commision, 2014). Our contribution is therefore aimed at closing this
research gap, more specifically, how the 2008 GFC had affected credit ratings of U.S.
firms and how the progression of credit rating influenced the choice of M&A payment
before and after the crisis. A detailed background of the GFC is discussed in chapter 1.1,
following which; the fundamental problem as well as prior research conducted is reviewed
in section 1.2. Subsequently, the fundamental purpose of our research and research
questions are presented.
1.1 Background
The 2008 GFC was a consequence of the U.S. housing bubble fundamentally driven by
excessive subprime lending, subsequently plummeting the value of Collateralized
Mortgage Obligations (CMOs). The fall of Lehmann Brothers in July 2008 had propelled
economies across the globe into severe recession, raising unending concerns about liquidity
and solvency of financial institutions (The Economist, 2013). The effects of the crisis were
massive and wide-scaled, spanning the entire globe.
Stresses on bank liquidity had led them to taper lending, consequentially, the cost of
corporate and bank borrowing soared drastically along with financial market volatility
rising to levels that were unimaginable (Ivashina & Scharfstein, 2010). Interbank lending
dipped significantly with risk premium soaring as high as up to 5 percent (Mckibbin &
Stoeckel, 2009). Private credit markets froze while balance sheets of major banks
deteriorated (Elliott, 2011). Credit spread widened with consumers and businesses losing
confidence in the economy. Most importantly, loss of confidence amongst investors had
6
plummeted the global stock markets and economies (Elliott, 2011). Emerging markets were
severely crushed when financial institutions hurriedly withdrew vast amounts of funds in
order to cover their exposure (Elliott, 2011). Large CAPEX projects were abandoned since
the corporate sector essentially stopped borrowing (The Economist, 2013). International
credit lines along with private credit markets had frozen, halting imports and exports (The
Economist, 2013). The effects of the GFC had continued to ripple through the world
economy, eventually evolving to what was known as, the Euro Zone Crisis of 2009 (The
Economist, 2013).
1.2 Problem Discussion
The high dependence of M&A on the
400
Figure 1
U.S. Corporate Loans: LBO/ M&A (in billion
USD)
global economic environment triggered a
350
series of fluctuations in M&A activities
300
during the crisis period. As a direct
250
consequence of restricted bank lending,
200
150
total loans issued to the U.S. corporate
100
sector for LBO and M&A activities
50
dipped significantly – from 295.90
0
billion USD in Q4 2007 to 64.35billion
USD in Q42008 (Ivashina & Scharfstein,
U.S. Corporate Loans: LBO/ M&A (in billion USD)
2010) (see figure 1).
10
7
Though M&A activity is still far off its
Figure 2
U.S. M&A Transactions
peak compared to 2007, global M&A
2500
14000
actually rose 23.1 percent in 2010, to a
12000
value worth 2.4 trillion USD while,
10000
M&A activities in the U.S. hiked by
8000
2000
1500
14.2 percent to 822 billion USD
(Merced & Cane, 2011) (see figure 2).
The
renewed
fundamentally
optimism
be
explained
6000
1000
4000
500
2000
can
0
0
by
relatively cheaper credit driven by
exceptionally low interest rates, as well
Number of transactions
Value of transactions (in billion USD)
Source: Merced & Cane, 2011
as companies realizing that acquisitions could be their only growth option given the slow
economic recovery (Merced & Cane, 2011).
As part of the Dodd-Frank reform to minimize the impacts of the crisis, the Securities
Exchange Commission (SEC) stepped up the regulation of CRAs (U.S. Securities and
Exchange Commision, 2014). The SEC introduced new rules that were targeted at
tightening the rating methodologies adopted by CRAs, contributing to less favorable credit
rating outcomes for firms across all industries (Utzig, 2010). Consequentially, raising the
cost of debt and hence impacting the ability of firms sourcing for debt financing.
8
Figure 3
U.S. M&A payment method 1986-2009
200
a significant decrease in the number of
180
M&A
160
Number of transactions
As seen in figure 3, the GFC had led to
transactions
Moreover,
140
it
also
in
the
portrays
U.S..
the
120
changing landscape in the type of
100
financing used by acquirers. It is
80
crucial to note that mixed financing
60
and cash financing has been following
40
an overall upward trend while equity
20
financing has been on a decline since
0
1986.
Cash
Equity
Mixed
Source: Barbopoulos & Wilson, 2013
2.0 Literature Review
This chapter establishes the fundamentals of our study and will provide an in-depth
analysis of previous research related to motivations of M&A payment method,
characteristics of acquirer and target as well as the role of Credit Rating Agencies (CRAs)
and its influence on M&A payment methods.
2.1 Theoretical framework of M&A Payment methods
Following the GFC, rising regulatory framework has significantly altered the landscape
revolving around global M&A transactions. Prior literature has gone far and beyond in
9
identifying factors that affect M&A payment methods in general, however, very few dig
deeper into comparing the effects of the crisis on the motivations of payment methods.
One of the many prominent research papers in M&A payment methods proposed by Martin
(1996), scrutinizes how characteristics of acquirer and target influence the payment
decision. It was concluded that investment opportunities of acquirer and the deal attitude
are the two most crucial characteristics that influences the choice of payment in an M&A
transaction (Martin, 1996). On the other hand, Martynova and Renneboog had categorized
three main drivers of payment decision – (1) cost of capital; (2) agency costs (3) means of
payment (Martynova & Renneboog, 2009).
The devastating implications of GFC have shed light upon researchers given the fact that
prior research were merely based on firm specific factors, more specifically, minimal focus
on the influence of macroeconomic environment on M&A payment methods. The recent
literature by García-Feijóo et al. (2012) had acknowledged the importance of
macroeconomic environment and industry effects in payment decisions. They revealed the
common misconception that stock financing rises during merger waves. A far more crucial
discovery was that the influence of firm characteristics on payment method changes with
changing industry conditions (García-Feijóo, Madura & Ngo, 2012). For example, the
relationship between the level of acquirer’s free cash flow and payment methods is
dependent on the fundamental growth in that particular industry.
2.2 Characteristics of the Acquirer
The following section aims to provide a thorough analysis of how acquirer’s capital
structure and cost of capital, free cash flow and ownership structure influences its M&A
payment decision.
2.2.1 Capital Structure & Cost of Capital
A firm’s capital structure is defined as the financing mix between equity and debt used to
finance real investments (Myers, 2001). Acquirers transact M&As with either stock or
cash, while in some instances a combination of both stock and cash are used. The choice
10
of capital structure is therefore of paramount importance in any corporate financing
decision revolving around M&A, given its direct impact on acquirer’s cost of capital. Thus,
it is crucial to dig deeper into the determinants of acquirer’s capital structure, and how that
influences its cost of capital in an M&A transaction. We will be scrutinizing the following
two theories –– (1) Modigliani and Miller’s tradeoff theory; (2) Pecking Order theory, in
order to establish a holistic understanding of the underlying relationship between acquirer’s
capital structure, cost of capital and M&A payment methods.
Modigliani and Miller (MM) capital structure irrelevance proposition in 1958, affirmed
that in a perfect economy, the market value of the firm is independent of its capital structure
(Modigliani and H. Miller, 1958). On the contrary, given the tax-deductible nature of debt
financing, MM Proposition 1 with taxes argues that a firm’s value rises with increasing
proportions of debt (Modigliani and H. Miller, 1958). Kraus and Litzenberger (1973) had
debated against this asserting that optimal leverage reflects a trade-off between tax benefits
arising from debt and the deadweight costs of bankruptcy – traditional tradeoff theory
(Kraus & Litzenberger, 1973).
The theory contends that as firm increases its debt relative to equity, expected costs of
future financial distress and bankruptcy rises, eventually sufficient to fully offset the
benefit arising from tax shield. More specifically, the traditional tradeoff theory suggests:
(a) For a given firm; there exists a unique optimal capital structure that entails a
finite level of leverage
(b) The optimal amount of leverage varies across firms since
1. Corporate taxes varies across firms
2. The rate at which expected costs of future financial distress and
bankruptcy increases with leverage also varies across firms. (Ogden, Jen
and O'Connor, 2003).
Prior M&A studies have indicated that cash financed acquisitions are to a large extent
funded by debt (Faccio & Masulis (2005); Karampatsas, Petmezas & Travlos (2012)).
11
Consequently, as suggested by the traditional trade off theory, increasing leverage beyond
a firm’s optimal capital structure results in a higher risk of bankruptcy. Therefore, Harford
et al. (2009) identified that when acquirer’s leverage increases above its target level, there
is a higher likelihood of financing the deal with equity instead of cash (i.e. debt). However,
a study by Leary and Roberts (2005) and Kisgen (2007) suggested that not all firms follow
the tradeoff theory. More specifically, investment grade firms tend not to use equity but
instead employ more debt even after reaching their optimal capital structure. This is
consistent with the pecking order theory.
Myers (1984) establishes that a firm should finance itself first with its retained earnings,
subsequently debt, and equity as the last resort – more commonly referred to as the pecking
order theory. The pecking order theory suggests that there exist no defined optimal debt
ratio; however, a firm’s debt ratio is dependent on changes in internal cash flow, net of
dividends, and real investment opportunities. Changes in debt ratios are fundamentally
driven by the need for external funds and not by the mere attempt to reach an optimal
capital structure (Shyam-Sunder & Myers, 1999). Moreover, interest tax shields arising
from debt financing and the threat of financial distress are presumed second-order in the
pecking order theory (Shyam-Sunder & Myers, 1999).
Researchers on “managerial capitalism” have construed firm’s reliance on internal finance
as a result of separation and control, that is fundamentally driven by information
asymmetry in external financing (Myers, 1984). Another possible argument that supports
the pecking order hypothesis is that, internal financing mitigates substantial issuance costs
in contrast to external financing. However, if external financing proves necessary, debt
financing takes priority since debt issuance cost is comparably lower than an equity issue.
However, what lies most intriguing is the fact that debt issuance costs tend to be not large
enough to outweigh the implicit costs and benefits arising from increasing firm’s leverage.
The pecking order theory is essentially established on the information asymmetry problem
(Myers, 1984). A firm’s management tends to possess information superiority about the
firm’s performance than investors, leading to adverse selection. Rising uncertainty about
12
an acquirer’s asset value further contributes to adverse selection between cash and equity
payments in M&A (Faccio and Masulis, 2005). Jensen (1986) acknowledges that when
managers of acquiring firm have superior information, they are more likely to fund the
M&A with stocks, on the condition that the stock is overvalued (Jensen, 1986). On the
contrary, the use of cash in an M&A transaction tend to signal an undervaluation of the
firm, essentially reducing the information asymmetry between debtholders and
shareholders (Jensen, 1986).
2.2.2 Acquirer’s Free Cash flow (FCF) Capacity & Growth Opportunities
Jensen and Meckling (1976) proposed the free cash flow hypothesis – managers have the
tendency to not distribute surplus funds to shareholders, instead, invest in projects to
maximize their own benefits. This is more commonly referred to as the principle agent
conflict. Manager’s goal of empire building might consequently give rise to over
investment in negative NPV projects, thereby eroding the fundamental value of the
acquirer.
To further substantiate the influence of acquirer’s cash availability on M&A payment
methods, Owen and Yawson (2010) had identified a significant relationship between
acquirer’s life cycle, its performance and the M&A deals it undertakes (Owen & Yawson,
2010). The results depicted that matured firms with high amounts of cash are less likely to
engage in profitable deals. This is primarily because matured firms generally tend not to
have many profitable opportunities, instead, possess surplus of cash stemming from their
core business (Owen & Yawson, 2010). As a result, such cash rich matured firms prefer
financing M&As with cash.
Contrary, the investment opportunities theory – acquiring firms with high growth
opportunities tend to possess high levels of debt, however, they will prefer equity if they
are able to avoid the underinvestment problem (Jung, Kim & Stulz, 1996). Moreover,
market overvaluation theory states that acquirers tend to prefer equity financing when it is
13
comparatively overvalued than target’s equity due to lower acquisition costs (Shleifer &
Vishny, 2003).
2.2.4 Ownership Structure
Prior M&A studies have gone far and beyond in examining the relationship between
corporate ownership structure and M&A payment methods. They have dwelled on the
question, if entrenched managers prefer increasing debt levels or use internal funds to
finance M&A so as to preserve their voting rights.
Amihud et al. (1990) had acknowledged a negative relationship between managerial
ownership and the probability of equity financing. To illustrate, greater probability for
acquisition to be financed by cash instead of equity when managerial ownership of
acquiring firm is high. This is further ascertained by Faccio and Masulis (2005), where
acquirer firms’ management that possesses a substantial proportion of shares (i.e. between
15.79% and 61.67% of total shares), would prefer to transact M&A deals by cash rather
than equity so as to prevent the dilution of control.
On the contrary, Martin (1996) had argued a nonlinear relationship between managerial
ownership and the probability of equity financing in M&A. To emphasize, managers tend
not to be affected by the dilution of their voting rights at exceptionally low and high levels
of ownership (Martin, 1996). He further concluded that the probability of equity financing
decreases when acquirer has a large number of institutional shareholders (i.e. block
ownership exceeding 5%).
2.3 The role of Credit Ratings Agency (CRAs) in M&A payment method
CRAs are of a great importance in the financial world, as to what they offer in evaluating
the creditworthiness of firms, as well as assigning a credit rating score (Securities and
Exchange Commission, 2003). Moreover, Schwarcz (2004) had reasoned that the role of
14
CRAs are far and beyond. The existence of CRAs aids in increasing transparency,
simultaneously reducing information asymmetry in the capital markets by assessing the
credit quality of a firm (Fulghieri, Strobl & Xia, 2013).
As previously mentioned, cash financed acquisitions are to a large extent funded by debt.
A firm’s debt capacity is highly dependent on its creditworthiness; the credit rating of firm
is therefore of paramount importance in determining the amount of leverage a firm can
employ in its capital structure (Faulkender & Petersen, 2005). In a study by Graham and
Harvey (2001), it was acknowledged that credit ratings are ranked second in order of
importance for CFOs when determining firm’s capital structure – 57.1% of CFOs claimed
that credit ratings are exceptionally crucial in deciding the appropriate amount of debt for
their firm (Graham & Harvey, 2001).
2.3.1 Credit rating progression and its influence on M&A payment decision
As previously discussed, the relationship between credit rating and capital structure
suggests a tradeoff between the cost of equity financing and the effect of change in credit
rating (e.g. credit rating downgrade increases cost of debt). Kisgen (2006) concluded that
such a tradeoff would exist most strongly for firms that are on the brink of a credit rating
change – either a credit rating upgrade or downgrade, contradictory to the pecking order.
Firms that are near a credit rating upgrade might transact acquisition via equity instead of
debt (cash) in order to leverage the benefits of a better credit rating. While, firms near a
downgrade might avoid issuing debt to avoid the explicit costs resulting from a credit rating
downgrade (Tang, 2009).
2.4 Characteristics of the Target
The following section aims to provide an in-depth analysis of how target specific
characteristics influences acquirer’s M&A payment decision.
15
2.4.1 Size and availability of information
In every M&A deal, complete availability of information regarding the target firm (i.e.
public firm) plays a crucial role in deciding the form of payment method. Hansen (1987)
discusses the choice of payment method under circumstances of asymmetric information
between acquirer and target. Fishman (1989) and Hansen (1987) had hypothetically
reasoned that if costs associated with gathering information as well as information
asymmetry regarding target’s intrinsic value are high; acquirers tend to favor equity as the
payment method. This forces the target to take a stake in any post acquisition revaluation
effects. Hansen further concluded a positive relationship between information asymmetry
and target’s size (i.e. information asymmetry increases with increasing target size). Hence,
if the target can create significant synergies for the acquirer, the acquirer would be more
likely to use equity or a mixture of equity and cash to finance the transaction.
Fishman (1989) proposed the risk sharing hypothesis – acquirer favor cash payment, only
if they are confident about the profitability of the deal since it discourages excessive
competition. Fuller, Netter and Stegemoller (2002) had further reinforced that if an acquirer
is uncertain about the value of the target and hence is hesitant to overpaying, they are then
less likely to finance acquisition by cash.
2.4.2 Ownership structure
Faccio and Masulis (2005) had identified that shareholders of private targets are less likely
to accept equity payment in an M&A transaction. This is primarily because the sale of
target’s assets is often driven by liquidity issues and/or restructuring purposes. As a result,
shareholders of private target are highly in favor of cash payment so as to meet their short
term liquidity demands.
The ownership of a private target tends to be highly concentrated with each blockholder,
typically owning at least five percent of equity in the firm. The acquirer’s primary
shareholders tend to prefer debt financing relative to equity, given the fact that issuing new
16
shares will lead to ownership dilution arising from external influence and ultimately a loss
of control (Stulz, 1988; Jung, Kim & Stulz, 1996). Therefore, blockholders are more prone
to choose cash as a financing method, as argued by Martin (1996) and Faccio and Masulis
(2005).
3.0 Hypothesis
Based on the abovementioned literature review, we are interested in the following
hypotheses that lay the grounds of our research and analysis. There are as follows:
Hypothesis 1:
Null Hypothesis: Credit rating progression of acquirers’ pre and post crisis affects the
fraction of cash used in M&A transaction
Alternative Hypothesis: Credit rating progression acquirers’ pre and post crisis does not
affect the fraction of cash used in M&A transaction
Hypothesis 2:
17
Null Hypothesis: Acquirers that experienced a credit rating downgrade pre and post crisis
are more likely to transact M&A with equity
Alternative Hypothesis: Acquirers that experienced a credit rating downgrade pre and post
crisis are less likely to transact M&A with equity
Hypothesis 1 is tested using the Ordinary Least Squares (OLS) regression model while
hypothesis 2 is tested via the Multinomial Probit model (MNP). This is primarily because
of the two different dependent variables used in each of the hypothesis.
Detailed
information regarding the two models will be discussed in section 4.4.
4.0 Methodology
In this section, we will first discuss the methodological approach used to provide us with
the appropriate techniques to test our hypotheses and reach adequate results. We will then
discuss the applied sample selection criteria, as well as the variables employed (dependent
and explanatory). And lastly, we will scrutinize the econometrics techniques and the results
obtained.
4.1 Research Method
For our research method, we used a deductive approach where we made use of existing
theories to generate our hypothesis. Following which, the relevant data was gathered in
order to test our hypotheses (Hyde, 2000). We will then examine the findings and conclude
on the confirmation or rejection of the hypotheses (Hyde, 2000). Finally, based on these
18
results previous theories can either hold or rejected (Hyde, 2000). In addition to a deductive
approach, we have employed a quantitative techniques given the parameters of our data.
4.2 Data and Sample Selection
Due to time constraints, we were not able to conduct any first hand surveys and
quantitative/qualitative analysis, therefore our research was solely based on secondary data
engines, books, surveys, online journals and dissertations.
According to the Institute of Mergers, Acquisitions and Alliances, the United States has
the highest number of announced M&As throughout 1985-2014 than the rest of the world
(Imaa-institute.org, 2015). Therefore, we chose the U.S. given the availability of
information as well as the coverage it receives.
The period used was from 1st of January 2003 till 1st of January 2013 (i.e. 10 years) so as
to capture the progressive change in the characteristics of our variables. Taken into
consideration that 2008 was the focal point for the financial crisis, we divided the period
into two sub periods; from 1st of January 2003 to 1st of January 2008 and 1st of January
2008 to 1st of January 2013. The reason behind including the year 2008 in our time frame
is primarily because our main explanatory variable of interest – credit rating level, changes
gradually over a long period of time.
For our sample selection, we employed a variety of databases – Thomson Reuters Eikon,
DataStream and Compustat. We first started off with Thomson Reuters Eikon, where we
limited our M&A sample to 100% acquired and completed deals within the United States
for both the target and the acquirer. We further restrained our sample by choosing the
acquirer to be a public company only. We also placed restrictions on the type of transaction
to include only mergers and acquisition deals (excluding acquisition of assets, acquisition
of partial interest, acquisition of majority of assets, buyback, acquisition of remaining
interest, acquisition of certain assets, exchange offer and recapitalization). This eventually
resulted in a sample size 5,663 (3,501 from 2003 to 2008 and 2,162 from 2008 to 2013).
19
Additionally, our sample was also selected based on having the same acquirer for both sub
periods. To illustrate, the acquirer should have one transaction before the crisis and one
after, so that we are able to construct a balanced comparison pre and post crisis. This
restriction had reduced our sample to 531 deals (266 deals for the first sub period and 265
for the second sub period). Taking into account, that some acquirers had multiple
transactions, we had to limit to only one by choosing the transaction with the highest deal
size.
Subsequently, we employed the use of Compustat to gather the credit ratings for all selected
firms. We entered each company’s ticker symbol and gathered the information from
January 2003 to January 2013. The credit ratings chosen were S&P Domestic Long Term
Issuer Credit Rating. Given the fact that limited firms have credit ratings in the desired
time period, it further reduced our sample size to 68 deals per sub period (136 deals for the
entire duration). Moreover, the payment method (i.e. cash, mixed or equity) used in
financing each M&A deal was extracted from
Figure 3
Credit rating progression of U.S.
acquirers pre and post GFC
Thomson Reuters Eikon.
Figure 3 illustrates the credit rating progression of U.S.
acquirers for the two sub periods, pre and post GFC. It
21%
is observed that, in our sample of 68 acquirers
51%
28%
measured over the crisis period, 51% had maintained
the same credit rating, 21% were upgraded while 28%
Upgraded
Downgraded
No change
were downgraded. Standard and poor’s states
Standard & Poor’s upgraded or downgraded roughly 20% of its corporate credit
ratings each year from 1981 through 2007, compared to about 10% for structured
finance from 1978 through 2007 (standardandpoors.com, 2015). However, these
percentages can increase during periods of significant and unexpected changes in
the credit markets or the business environment. (standardandpoors.com, 2015).
20
This illustrates that the frequency of change in credit rating is exceptionally slow,
supporting our findings that 51% had remained constant throughout the entire period.
Figure 4 depicts M&A payment methods pre
Figure 4
80%
M&A Payment method pre and post GFC
and post the financial crisis in our sample.
70%
Before the crisis, the use of cash as a
60%
50%
2003-2008
payment
method
was
67.6%,
mixed
40%
2008-2013
payment was 26.5% and equity was 5.9%.
30%
After the crisis, the use of cash had
20%
decreased by 7.3% and the mixed payment
10%
increased by the same amount, while the use
0%
Cash
Mixed
Equity
of equity remained constant.
4.3 Variables
We identified three highly relevant journals – Faccio and Masulis (2005), Karampatsas et
al. (2012) and Karampatsas et al. (2014) to establish the foundation of our research. We
acknowledged that most of the variables used in these studies are relevant to our research.
4.3.1 Dependent Variables
As previously discussed, the main purpose of this dissertation is to investigate the effect of
credit rating progression on the choice of M&A payment method in the U.S.. Hence, two
dependent variables – (1) fraction of cash; (2) payment method (i.e. cash, mixed or equity),
are highly relevant in identifying this relationsip.
Fraction of cash is a continuous variable that takes on the value between 0% and 100%,
hence the OLS model is applied. The other dependent variable, payment method, is a
discrete variable that takes the value of 1 for cash, 2 for mixed and 3 for equity. With regard
to this variable, the MNP model is used. The reason why we used payment method (i.e.
21
cash, mixed or equity) is to capture the qualitative nature as well as to establish an in-depth
analysis of financing methods.
4.3.2 Explanatory variables
According to Brooks (2008), an explanatory variable is defined as any variable that
explains the dependent variable. The explanatory variables for our regression are
Credit_Ratinglevel, Ln_size, Collateral, FinancialLeverage, BookToMKT, Rel_dealsize,
Num_analysts, Private_Public, Cashflow_assets, IntraIndustry, Runup, Blockownership.
In our study, we propose Credit_ratinglevel, as the explanatory variable for the two
different dependent variables; fraction of cash and payment method. Credit rating level is
the main independent variable of interest in the OLS regression model. It represents the
credit rating of the acquirer that ranges from 1 to 22. The credit rating DDD- takes the
value of 1, and AAA takes the value of 22 (See appendix B). We employed Compustat to
gather each acquirer’s credit rating, more specifically; we took the credit rating of each
acquirer one month prior to the announcement of the acquisition. The credit rating scale
was adopted from S&P Domestic Long Term Issuer Credit Rating.
With regards to the MNP model, two dummy variables – upgrade and downgrade, were
introduced. The dummy variable upgrade that takes the value of one if acquirers were
upgraded and the value of zero if they were downgrade or stayed constant. The dummy
variable downgrade that takes the value of one if acquirers were downgraded, and the value
of zero if they were upgraded or stayed constant.
4.3.3 Control Variables
Ln_size – as defined in Karampatsas et al. (2014), this variable takes into account the size
of the acquirer by taking the natural logarithm of its market value of equity one month
22
before the announcement of the acquisition. Each acquirer in our sample differed
substantially in size; hence, we took the natural logarithm of their market capitalization to
allow for better distribution by converting absolute distances to relative distances (Brooks,
2008). This variable is taken into account because according to Karampatsas et al. (2014),
the greater the firm size, the more diversified it will be, thus a lower probability of default.
A lower probability of default implies that firms will have greater access to debt capital
markets, hence cash financing is more likely.
Collateral – according to Faccio and Masulis (2005), this variable is another measure of
debt capacity. A study by Hovakimian et al (2001) shows a strong and a positive influence
between the company’s tangible assets and its level of debt. Collateral is calculated by
dividing the property, plant and equipment by the book value of total assets at the end of
fiscal year before the acquisition announcement.
FinancialLeverage – according to Faccio and Masulis (2005), this variable is taken into
account to measure the financial condition of the acquirer since it was found to have a
substantial influence on M&A payment method. Faccio and Masulis (2005) had argued
that highly leveraged firms tend to use more equity financing instead of cash since they are
more restricted in issuing debt. On the contrary, Karampatsas et al (2014) had argued a
positive relationship between acquirer’s financial leverage and cash as a payment method.
Their motivation behind this is that acquirers that are more susceptible to paying with
equity typically have better growth opportunities (Karampatsas, Petmezas & Travlos,
2012). FinancialLeverage is calculated by dividing the total debt over the book value of
total assets at the end of the fiscal year preceding acquisition announcement (Faccio &
Masulis, 2005).
In order to account for acquirer’s growth opportunities, Karampatsas et al. (2012) justified
the use of BookToMKT as a valid proxy. Book to market value of equity is calculated by
taking the ratio of the book value of equity at the fiscal year end before the announcement
of the acquisition over the market value of equity one month preceding the announcement
of the acquisition (Karampatsas, Petmezas & Travlos, 2012).
23
Rel_dealsize – the relative deal size is used as a proxy for information asymmetry since
according to Faccio and Masulis (2005), information asymmetry is expected to increase
when target’s assets increases relative to acquirer’s assets. To illustrate, the greater the
target’s assets compared to acquirer, the higher the probability the use of equity in payment
method. Additionally, the use of equity in large amounts will lead to a greater dilution of
ownership, especially so for controlling shareholders (Faccio & Masulis, 2005). This
variable is calculated using the value of M&A deal divided by the acquirer’s market value
of equity one month before the acquisition announcement.
Num_analysts – is used to take into account the information asymmetry problem that is
associated with acquirers attempting to transact acquisition with overvalued stocks. Target
firms however, are more likely to reject this offer if the actual value of the acquirer’s stock
is uncertain. As such, Karampatsas et al (2014) argued that the higher number of analysts
monitoring the deal, the lower the information asymmetry associated with it, thereby
increasing the likelihood of acquirers financing acquisition via cash.
Another variable that takes into consideration the information asymmetry problem is the
status of the target firm. Private_Public is used as a proxy for that. According to
Karampatsas et al. (2012), the smaller the target (i.e. nontransparent private firm) the higher
the information asymmetry. Private targets are thus more likely to favor cash over equity
offers, because of possible liquidity problem. Private_Public is a dummy variable that
takes the value of one if target is unlisted and zero otherwise.
Cashflow_assets – is taken into consideration to control for the pecking order theory as
substantiated by Karampatsas et al. (2012).
The pecking order theory explains the
motivations why managers follow a financing order, starting from internal funds, followed
by debt and lastly equity (Myers, 1984). Jensen (1986) argues that companies are more
prone to engage in value destroying mergers and empire building activities when they have
a surplus of free cash flow. Therefore, we deduced a positive relationship between
Cashflow_assets and the use of cash in M&A. Cashflow_assets is the proportion of
24
acquirer’ earnings before interest and taxes (excluding unusual items, plus depreciation,
less total dividends) and its total assets at fiscal year end preceding the acquisition
announcement.
IntraIndustry – taking different industries into consideration is essential, since the target
firm will favor cash as a payment method when acquirer is from a different industry (Faccio
& Masulis, 2005). This is due to the fact that targets are usually less acquainted about the
risks associated with the acquirer’s industry (Faccio & Masulis, 2005). IntraIndustry is a
dummy variable that takes on the value of one if acquirer and target are from the same
industry (i.e. interindustry) and the value of zero if they are intraIndustry. We used
Thomson Financial SDC to get the data for this variable.
To account for the acquirer’s overvaluation and undervaluation, the variable Runup is used.
In accordance with the market overvaluation theory, acquirers opt to take equity offers
when its equity is considered overvalued to the target’s equity (Shleifer & Vishny, 2003).
This variable is calculated by taking the cumulative return on stock one month prior the
acquisition date over the year prior to the acquisition announcement.
The variable Blockownership is used as a proxy for acquirer’s blockholder ownership. As
argued in Karampatsas et al. (2014), if the number of blockholders is high, the use of cash
as a payment method should be favored since equity dilutes ownership. The variable
Blockownership is calculated by summing up the total number of blockholders who own
at least 5.0% of the company’s stock.
4.3.4 Excluded Variables
Some variables were used in Faccio and Masulis (2005) and Karampatsas et al. (2014) but
were not included in our study due to multiple reasons. For example, the variable InterLock
was not used due to the difficulty of obtaining information about the directors of acquirers.
Competition and Interest rate spread were excluded on the basis that they were
insignificant as well as the difficulty in obtaining information. Additionally, hostile deals
25
and cross border were excluded because all deals in our sample were friendly and are based
in the U.S. Furthermore, the variable tender was disregarded because of the lack of data
even though it was significant in Karampatsas et al. (2014).
4.3.4 Instrumental Variables (IVs)
To take into account possible endogenity in our OLS regression model, we identified
Karampatsas et al. (2014) particularly relevant in our analysis. The source of endogenity
in our regression fundamentally stems from omitted variable bias in our variable of interest
– credit rating level. According to Angrist and Pischke (2009), in order for an IV to be
valid, it has to fulfil the relevance condition, that is:
(a) IV has to be correlated with the endogenous variable – credit rating level
(b) IV has to be uncorrelated with the error term.
It is however crucial to note that point (b) is difficult to assess given the fact that the error
terms are unobservable Angrist & Pischke, 2009). Hence, when introducing the IVs we
have to be sure that they are not directly correlated with the dependent variable, fraction of
cash. This allows us to make a fair assumption that the IVs would be uncorrelated with the
error term (Angrist & Pischke, 2009).
We therefore propose three IVs as follows:
AltmanZScore – considered a relevant IV primarily because it determines a firm’s
likelihood of bankruptcy which is paramount in defining its credit quality. Since
AltmanZScore is independent of our dependent variable, fraction of cash, it fulfils the
relevance condition required of an IV. It is therefore a valid IV and can be used as a proxy
for acquirer’s credit quality. It is calculated via 4 specific ratios – (1) Working capital/ total
assets; (2) Retained earnings/ total assets; (3) EBIT/ Total assets; (4) Book value of equity/
Book value of total liabilities (Karampatsas, Petmezas & Travlos, 2012). All required data
was extracted from Compustat.
26
Profitability – According to Karampatsas et al (2012), a firm’s profitability has a positive
linear relationship with its credit rating, that is, higher profitability is accompanied with
lower debt ratios and higher credit rating levels. Therefore, Profitability is used as a proxy
for determining if a firm’s credit rating is to be upgraded, downgraded or kept constant. It
fulfils the relevance condition required of an IV since it does not affect our dependent
variable, fraction of cash, directly, but only through the explanatory endogenous variable
of interest, credit rating level. Profitability is calculated by the fraction of acquirer’s
earnings before interest, taxes, depreciation and amortization (EBITDA) and total assets of
acquirers. All required data was extracted from Datastream.
Regulated Industry – used as a proxy for acquirer’s reliance on public debt, more
specifically, firms in regulated industry are more reliant on public debt since they face
lower agency and monitoring costs (Karampatsas, Petmezas & Travlos, 2012). This
variable fulfils the relevant condition given the fact that it is correlated with the endogenous
variable, credit rating level, but does not directly impact the dependent variable fraction of
cash. This particular variable is a dummy variable that takes on the value of one if the
acquirer is a financial institution or a utility (i.e. oil, gas and water) firm and zero otherwise.
All required data were extracted from Thomson Reuters Eikon.
4.4 Econometrics techniques used
The sample we have is a panel data set (i.e. longitudinal or cross-sectional time-series data),
since, we have different observations for the same entities n, at different time periods T
(Stock & Watson, 2007). As stated by Stock and Watson (2007), panel data is represented
as:
(𝑋𝑖𝑡 , 𝑌𝑖𝑡 ), 𝑖 = 1, … , 𝑛 𝑎𝑛𝑑 𝑡 = 1, … , 𝑇
i: entity being observed
t: the date of the observation
27
Subsequently, our regressions are chosen based on a balanced panel data specifications,
since there are no missing observations for any of the firms for the two sub periods (Stock
& Watson, 2007).
In order to close our research gap, we tested our hypotheses with two different regression
models – (1) OLS model; (2) MNP model. This is primarily because we have two different
dependent variables – fraction of cash and payment method. For the dependent variable,
fraction of cash, the OLS regression was used primarily because it is a continuous variable
that takes the value between 0% and 100%.
With regards to the other dependent variable, payment method, there are three different
outcomes with no natural ordering; cash, equity or a mix of both. For this variable, there
are two models that can be used (1) Multinomial Probit model; (2) Multinomial Logit
model. Although the MNP model takes into account the possibility of correlated error
terms, especially if there are similarities in the characteristics of the choices, both models
generate very similar results (Brooks, 2008). Additionally, Train (2003) stated that “[probit
models] can handle random taste variation, they allow any pattern of substation, and they
are applicable to panel data with temporally correlated errors”. Therefore, this supports the
use of MNP model in our regression.
4.4.1 Ordinary Least Squares Regression (OLS)
The Ordinary Least Squares (OLS) estimation method is one of the most important
techniques in econometrics. Gauss-Markov assumptions suggested that OLS coefficients
(β and α) are the Best Linear Unbiased Estimators – BLUE (Brooks, 2008). The general
equation stated in Brooks (2008) is
𝑦𝑖𝑡 = 𝛼 + 𝛽𝑋𝑖𝑡 + 𝑢𝑖𝑡
According to Brooks (2008), the five fundamental assumptions underlying the OLS are as
follows:
1) The errors should have a zero mean, E(ut ) = 0
28
2) The variance of errors should be constant and finite for the values of xt .
3) The errors should be statistically independent from each other, COV(ui , uj ) = 0
4) The errors are normally distributed, ut ~ N(0, σ2 )
5) There should be no relationship between the error term and its corresponding
independent variable, COV(ut , xt ) = 0.
With reference to our OLS regression model, there is no necessity to test for assumption
(1) since the error term is included in the regression (Brooks, 2008). Therefore, the
violation of this assumption is inapplicable in our study. To account for assumption (2), we
ran the White Diagonal test in order to mitigate any form of heteroscedasiticity across
cross-sectional and period dimensions. Furthermore, to account for assumption (3) we ran
the Durbin Watson test. The results showed that the Durbin Watson test is close to 2 and
therefore we do not reject the null hypothesis of no autocorrelation. In order to validate
assumption (4), we ran the Ramsey RESET test and found that skewness and kurtosis were
very close to 0 and 3 respectively, indicating a normal distribution. To account for
assumption (5), we had performed the Hausman test for endogenity. The procedures and
results of the Hausman test will be further explained in section 6.2.3. Lastly, we plotted a
correlation matrix of all variables in order to identify the presence of collinearity. With
regards to the general rule of thumb, mentioned in Brooks (2008), of not having a
correlation higher than 0.8, the correlation matrix indicated no form of multicollinearity
(See table 1). Moreover, table (2) shows the collinearity of the MNP model, which also
expresses no form of multicollinearity.
Our dependent variable in the OLS regression is fraction of cash and it is a continuous
variable that takes on any value from 0% to 100%. The OLS regression equation is as
follows:
Fraction of cash2003-2013= α + β1Credit_Ratinglevel + β2ln_size + β3FinancialLeverage +
β4Rel_dealsize
+
β5Private_Public
+
β6IntraIndustry
+
β7BookToMKT
+
β8Cashflow_assets + β9Num_analysts + β10Collateral + β11Runup + β12Blockownership +
uit.
29
The error term uit is assumed to be normally distributed.
Since our sample is a balanced panel dataset, we have to take into account fixed and random
effects in our panel estimator when running the OLS regression. This will be further
discussed in the section 6.2.1.
4.4.2 Multinomial Probit Regression (MNP)
The general equation for the Multinomial Probit Model (MNP) according to Antunes et al.
(2011) is:
𝑗
𝑢 𝑖𝑡 = 𝛾𝑗 𝑥𝑖𝑡 + 𝜀𝑗𝑖𝑡
where,
xit : Represents relevant characteristics of the firm i in the year t.
γj : Regression coefficient
j : is the option of choosing from 3 different payment methods. j = 1, 2, 3
εjit : Random error term with a normal distribution N (0, Σ).
In this particular regression model, our dependent variable is, Payment Method and it takes
on three specific unordered values: 1 for cash only payment, 2 mixed payment (cash and
equity) and 3 for equity only payment. It is also crucial to note that the choices between
cash, mixed or equity payment are mutually exclusive and comprehensive, that is, only one
payment method can be selected. Based on the general MNP model, our specific equations
are as follows:
Mixed2003-2013
= α + β1Credit_Ratinglevel + β2ln_size + β3FinancialLeverage +
β4Rel_dealsize
+
β5Private_Public
+
β6IntraIndustry
+
β7BookTOMKT
+
β8Cashflow_assets + β9Num_analysts + β10Collateral + β11Runup + β12Blockownership +
ui
30
Equity2003-2013
= α + β1Credit_Ratinglevel + β2ln_size + β3FinancialLeverage +
β4Rel_dealsize
+
β5Private_Public
+
β6IntraIndustry
+
β7BookTOMKT
+
β8Cashflow_assets + β9Num_analysts + β10Collateral + β11Runup + β12Blockownership +
υi
The error terms ui and υi are assumed to have a multivariate normal distribution and can be
correlated (Brooks, 2008).
As stated by Antunes et al. (2011), the MNP model is established on k-1 equations, where
k in our regression is defined by the three payment methods. To illustrate, the use of cash
as a payment method is not included in a third equation. This is because, if the two above
mentioned equations equals to zero, the payment method is cash. As such, cash payment is
defined as the base case or reference point in our MNP model. Moreover, Stata chooses
cash only payment as the base case since it occurs most frequent in our sample.
5.0 Validity and Reliability
According to Bryman and Bell (2011), the validity of a study is defined as ensuring that
the research methodology is conducted in a manner that allows it to measure what it
proposes to measure. With regards to our study, prior to choosing the appropriate
methodology approach, we had thoroughly analyzed a wide spectrum of previous research
that includes published journals, books and articles. Although our paper closely follows
Karampatsas et al. (2014) and Faccio and Masulis (2005), it is crucial to note that we do
not adopt their econometric techniques since we aim to establish a comparative perspective.
Therefore, additional research was conducted to identify appropriate econometrics
techniques that were more suitable for our assumptions and regressions.
In addition, external validity and internal validity should also be taken into account when
analyzing the consistency of any study (Bryman & Bell, 2011). External validity
emphasizes the generalization of the research that one uses in his/her study which can be
applicable to other studies (Bryman & Bell, 2011). For example, in this research, only the
31
U.S. was used and no other country was accounted for. Hence, the fundamental problem
of the generalization of our study lies in differences in criteria and behaviors that are only
focused in the U.S. market. This might lead to conflicting results and inaccurate deductions
in comparison with that of the above mentioned studies. In order to account for such issues,
a comparison of results between our study and the above mentioned two should be
conducted.
With regards to verifying the internal validity of a study, Bryman & Bell (2011) argued
that the underlying correlation between measured variables should be taken into
consideration. We thus employed the use of control variables to aid in the internal
validation of our study. Furthermore, we identified statistically significant explanatory
variables and compared its influence on the dependent variables so as to draw a clear
picture of the fundamental relationship underlying U.S. M&A payment decisions pre and
post crisis.
Moreover, another crucial element to analyze in any study is its reliability. Bryman & Bell
(2011) mentioned that the reliability of any research is explained as providing similar or
compatible results in different statistical trials. With reference to our paper, if the
methodology was adopted in a systematic manner, it should result in accurate results. The
databases we used to retrieve all relevant data for this study – CompuStat, ThomsonReuters
Eikon and Datastream, are considered highly reliable, since they are well established data
providers in the finance society. Furthermore, in order to ensure consistency, all financial
statements extracted from Thomson Reuters Eikon were counter checked with those
published by the SEC. It is far more crucial to keep in mind that if the data selection
procedure was systematically followed and if appropriate tests were conducted; the
replication of the research will be easy to realize.
32
6.0 Empirical results
The following chapter presents the descriptive statistics of our study, following which; the
regression results for both, MNP and OLS regressions, along with model assumptions will
be discussed thoroughly.
6.1 Descriptive statistics
Table (3) presents the descriptive statistics for the overall sample and is further segregated
into three highly specific characteristics (i.e. credit rating upgrade, downgrade and
constant). The proportion of acquirers that did not experience a change in credit rating as
a result of the crisis stands highest (51.5%) while (28.0%) faced a credit rating downgrade
and (20.5%) experienced an upgrade.
Panel A exhibits acquirer specific characteristics and it can be concluded that these
characteristics differed across the three different credit rating situations. Acquirers that
experienced a credit rating upgrade have the highest average Ln_size (9.595m USD), while
acquirers that experienced a downgrade have the lowest average Ln_size (8.469m USD).
This finding is consistent with theoretical motivations analyzing the relationship of credit
rating changes on a firm’s market value. Acquirers that experienced a downgrade exhibit
the highest level of collateral (0.250) while acquirers that experienced an upgrade have the
33
lowest level of collateral (0.138). Moreover, acquirers that were downgraded reveal the
highest level of leverage (0.290) while those that experienced an upgrade have the lowest
level of leverage (0.227). These 2 findings are exceptionally relevant and are in line with
theoretical standpoint of the influence of credit rating changes on a firm’s leverage and
collateral value.
BookToMKT ratio is significantly higher in acquirers that were downgraded (0.531) as
opposed to those that were upgraded (0.384) and those that retained the same rating (0.392)
– indicating that acquirers who were downgraded faces higher future growth opportunities
(Jung, Kim & Stulz, 1996). Runup is significantly higher in acquirers that were
downgraded (0.254) implying that stocks of firms that were downgraded were overvalued
during its post-crisis acquisition. With regards to acquirer’s blockownership, it was
observed that those who were downgraded have the highest number of blockholders
(2.553). Cashflow_assets appears to be relatively higher amongst firms that were upgraded
(0.132), as opposed to firms that were downgraded (0.118) and those that remained
constant (0.113).
Panel B displays the statistics for deal specific characteristics and likewise, they appear
rather different across the three different credit rating situations. What lies most intriguing
is the fact that the average Rel_dealsize of the M&A deal is higher for firms that
experienced a credit rating downgrade (0.238). However, based on the earlier mentioned
deduction that downgraded acquirers have higher future growth opportunities, we can
conclude that it is justifiable for them to have higher average Rel_dealsize.
Table (4) and (5) presents the descriptive statistics for the three different choices of M&A
payment methods (cash, equity, or mixed) for period 1 and 2 respectively.
Table (4) shows that firms that used cash as a payment method had the highest mean in
Credit_ratinglevel (14.854) and Cashflow_assets (0.137). The mean for Ln_size was the
highest for cash deals (8.948), while lowest for all equity deals (8.384). The mix of cash
and equity as a financing source has the highest mean in FinacialLeverage (0.334),
34
Collateral (0.311), Blockownership (2.43) and Runup (0.271) in comparison with complete
cash and complete equity deals. The variable BookToMKT ratio has the highest value in
cash deals (0.374), which is supported by the growth opportunity theory. The Rel_dealsize
was found to be the highest for mixed payment method (0.337), and lowest for equity deals
(0.101). IntraIndustry was also the highest for mixed payment method (0.739), yet it was
the lowest for cash deals (0.390). Num_analysts had the highest mean for mixed payment
(0.957).
Table (5) indicates the change in the financing method after the crisis (i.e. period 2). The
use of mixture of equity and cash in the payment method has the highest mean in the
majority of variables – Collateral (0.210), FinancialLeverage (0.336), Blockownership
(2.560), Rel_dealsize (0.467), Num_analysts (1.278) and IntraIndustry (0.556). The use of
equity in financing M&As, has the highest mean Credit_ratinglevel (15.000), Ln_size
(9.793) and BookToMKT (0.690). The remaining variables, Cashflow_assets (0.130) and
Runup (0.157) stood highest for cash only deals.
The effects of the GFC were apparent on acquirer specific, target specific and deal specific
characteristics in U.S. M&A. To illustrate, Ln_size had increased from period 1 to period
2, moreover, the use of cash as a payment method had also increased across period 1 and
2. FinancialLeverage of acquirers had increased across period 1 and 2, and this is
consistent with the increasing number of acquirers using cash payment. This observation
supports the theory mentioned above, that if acquirer’s size increases, more debt will be
used to finance the acquisition.
Acquirers acquiring Private_Public targets had increased from period 1 to 2 with an
increasing number of firms using cash as a payment method. Moreover, acquirer’s
Credit_ratinglevel had decreased from period 1 and 2, which is entirely consistent with
theories mentioned above. Cashflow_assets had remained the highest for both periods for
cash deals, supporting our assumption that the relationship between cash flow to assets and
the use of cash in M&A is positive. The BookToMKT of acquirers had increased
substantially across the three payment methods, indicating that acquirers on average were
35
less undervalued before the financial crisis and more undervalued after it. This implies that
the crisis had actually led to increasing growth opportunities for acquirers. In summary,
when comparing the total samples of period 1 and period 2, on average; Ln_Size,
FinancialLeverage, Blockownership, Private_Public and Rel_dealsize increased, while,
Collateral, Cashflow_assets, Runup and IntraIndustry, had decreased.
6.2 OLS Regression
This section provides the interpretation of the OLS regression that was fundamentally
based on the dependent variable – fraction of cash. Prior to discussing the results, we will
first outline the various assumptions of the model. Subsequently, we will draw relevance to
previous studies and literature so as to provide a holistic understanding of how M&A
payment decision is influenced by credit rating progression prior to and after the crisis.
6.2.1 Fixed and random effects estimation
Given the fact that our data involves the use of a panel data set, we have to account for
fixed and random effects across both cross-sectional and period dimension. In order to
identify the type of effect we have on the cross-sectional dimension, we first ran a regular
OLS on the structural equation. We also accounted for random effects in the cross-sectional
dimension as well as the heteroskedasticity-robust standard errors (i.e. White Diagonal test)
to account for acquirer clustering because of repeated acquirers in our sample.
Subsequently, we ran the Hausman random effects test and based on the p-value (0.923),
we do not reject the null hypothesis that the random effects model is well specified. We
therefore employed the random effects model specification across cross-sectional
dimension. Moreover, to identify the type of effect our panel dataset have on the period
dimension, we followed the same steps as mentioned, but this time, we accounted for
random effects in the period dimension. However, upon running the Hausman random
effects test, we were prompted with a dialogue stating that the number of cross section is
36
greater than the number of coefficients for between estimators. As mentioned by
Wooldridge (2009), in such situation, we can simply assume fixed effects across period
dimension.
6.2.2 Regression results
We first scrutinize the relationship between acquirer’s credit rating progression on the
fraction of cash they employ in an M&A transaction. We controlled for acquirer, target and
deal specific characteristics so as to observe how credit rating level would impact fraction
of cash used in M&A under the three different characteristics. Specification (1) controls
for target specific characteristic, specification; (2) controls for deal specific characteristics,
specification; (3) controls for acquirers specific characteristics and specification; (4)
controls for target, deal and acquirer specific characteristics. Refer to table (6) for detailed
information on regression results for all 4 specifications.
The regression results in specification (1) depict Credit_ratinglevel significance at 5%
significance level with a coefficient of (0.021). This indicates that a 1-unit credit rating
downgrade (upgrade) leads to a 0.021 decrease (increase) in fraction of cash used in an
M&A transaction before and after the GFC. Though this particular finding is consistent
with literature as well as previous studies, an exceptionally low R-square (0.034) questions
the goodness of fit of this model to a large extent. Taking into account only target specific
characteristics do not explain much about how credit rating progression affects M&A
payment decision prior to and after the crisis.
Regression results in specification (2) show that Credit_ratinglevel is significant at 10%
level of significance with a coefficient of (0.012). This indicates that a 1-unit credit rating
downgrade (upgrade) results in a 0.012 decrease (increase) in fraction of cash used in
transaction M&A before and after the crisis. Num_analyst monitoring M&A deal is
significant at 5% level, with a coefficient of (-0.103), implying a negative and significant
relationship with fraction of cash used in M&A payment decision. Moreover, IntraIndustry
is significant at 5% level, with a coefficient of (-0.132) indicating a significant and negative
37
relationship with fraction of cash used in M&A. A substantially higher R-square (0.142) in
specification (2) in comparison with specification (1) indicates a far better goodness of fit
in this model.
Specification (3) illustrates Credit_ratinglevel significance at 10% level with a coefficient
of (0.011), suggesting that a 1-unit credit rating downgrade (upgrade) results in a 0.011
decrease (increase) in fraction of cash employed in transaction an M&A across period 1
and 2. Blockownership depicts a significant negative relationship at a 10% significance
level with a coefficient of (-0.038). Moreover, Cashflow_assets depicts a highly significant
positive relationship at 1% significance level with a coefficient of (1.863).
FinancialLeverage appears to be significantly negatively correlated with fraction of cash
at a 5% significance level with a coefficient of (-0.437). Lastly, the variable Runup has a
significant positive correlation with fraction of cash at 10% significance level with a
coefficient of (0.077). Specification (3) appears to have an R-Square of (0.240), which is
relatively higher than specification (2), indicating a slightly more accurate goodness of fit.
Results in specification (4) indicate that Credit_ratinglevel is insignificant in explaining
the fraction of cash used in transaction M&A. However, given the fact that specification
(4) takes into account all control variables across target, bidder and deal specific
characteristics, the existence of endogenity is applicable and this will be accounted for in
section 6.2.3. FinancialLeverage is significant at a 10% significance level with a
coefficient of (-0.375), indicating an inverse relationship with fraction of cash.
Blockownership is also negatively correlated with fraction of cash at a 5% significance
level with a coefficient of (-0.045). Furthermore, Num_analyst is negatively correlated with
fraction of cash at a 1% significance level with a coefficient of (-0.115). On the contrary,
Cashflow_assets appeared to have positive relationship with fraction of cash with a
coefficient of (1.717).
Further scrutinizing the goodness of fit of all four specifications mentioned above, the Fstatistic of specification (1) was observed to be insignificant. Therefore, we do not reject
38
the null hypothesis that all regression coefficients are equal to zero, indicating an overall
weak goodness of fit.
6.2.3 Endogenity control for OLS
The issue of endogenity stands paramount in many empirical studies and accounting for
this goes far and beyond in establishing an unbiased and consistent coefficient estimates.
Endogenity is defined as the correlation between explanatory variables and the error term
in a regression, consequently leading to unreliable inferences and conclusions of the
hypothesis (Roberts & Whited, 2012).
There is two primary sources of endogenity in our OLS regression – (1) omitted variable
bias; (2) simultaneity. Omitted variables bias is defined as biased coefficient estimates that
result from excluding explanatory variables (Roberts & Whited, 2012). While, simultaneity
occurs when an independent and dependent variable in a given model influences each other
instantaneously (Brooks, 2008). As argued by Karampastas et al. (2014), a firm’s credit
rating level could be influenced by firm specific characteristics and failure to consider these
would lead to biased coefficient estimates in our regression.
Prior controlling for endogenity, we first have to identify if our explanatory variable,
Credit_ratinglevel, statistically possess endogenity via the Hausman test. In order to run
the Hausman test, we first have to identify which of the above three listed IVs, identified
in section 4.3.4, are statistically significant. We hence ran an OLS regression with the IVs
and found that Profitability was the only IV that was statistically significant at a 5%
significance level. As a result, Profitability is the only IV we can use to control for possible
endogeneity. We then conducted the Hausman test for endogenity manually in Eviews by
first regressing Credit_ratinglevel as a function of all our control variables and
instrumental variable, Profitability. The equation is as follows:
39
Credit_ratinglevel = β0 + β1Ln_size + β2FinancialLeverage + β3Rel_dealsize +
β4Private_Public + β5IntraIndustry + β6BookToMKT + β7Cashflow_assets +
β8Num_analysts + β9Collateral + β10Runup + β11Blockownership + β12Profitability + uit.
Following which, we used the fitted values obtained from the above regression and ran a
separate regression with the structural equation. The fitted values were statistically
significant at 5% significance level and thus we reject the null hypothesis of no endogenity.
To control for endogenity in our OLS regression, we then employed the Two Stage Least
Squares (2SLS) regression technique with all control variables (i.e. acquirers
characteristics, target characteristics, deal characteristics) and the IV, Profitability (see
Table 7 for regression output). The equations employed in the 2SLS model are as follows:
1st stage equation:
Credit_ratinglevel = 𝛼̂0 + 𝛼̂1 Ln_size + 𝛼̂2 FinancialLeverage + 𝛼̂3 Rel_dealsize +
𝛼̂4 Private_Public + 𝛼̂5 IntraIndustry + 𝛼̂6 BookToMKT + 𝛼̂7 Cashflow_assets +
𝛼̂8 Num_analysts + 𝛼̂9 Collateral + 𝛼̂10 Runup + 𝛼̂11 Blockownership +𝛼̂12 Profitability +
𝜐it.
2nd stage equation:
Fraction of cash2003-2013= 𝛽0 + β1Credit_ratinglevel + β2Ln_size + β3FinancialLeverage
+ β4Rel_dealsize + β5Private_Public + β6IntraIndustry + β7BookToMKT +
β8Cashflow_assets + β9Num_analysts + β10Collateral + β11Runup + β12Blockownership +
β13 Credit_ratinglevel + uit
After accounting for endogeneity, the Credit_ratinglevel is significant at 5% level. This
finding provides the fundamental argument that coincides and supports the story defined
by literature, that is, a credit rating downgrade affects the fraction of cash used by acquirer
in an M&A transaction.
6.3 MNP Regression
This section provides the interpretation of the MNP regression that was fundamentally
based on the dependent variable – Cash, mixed or equity. Subsequently, we will draw
40
relevance to previous studies and literature so as to provide a holistic understanding of
how M&A payment decision is influenced by credit rating progression prior to and after
the GFC.
6.3.1 Regression results
Interpreting the MNP regression differs from the OLS since it takes into account a
reference point (i.e base case) used to compare between three different possible outcomes
– cash, mixed and equity payment. Refer to table (8) for detailed information of the MNP
regression results.
Based on the regression results, Credit_ratinglevel was insignificant which led us to
conclude that credit rating progression had no significant influence on M&A payment
decision during period 1 and 2.
To further our analysis, we performed a second MNP regression with the segregation of
the explanatory variable, credit rating level, into two independent dummy variables – (1)
Upgrade, (2) Downgrade (see table 9). More specifically, the dummy variable, upgrade,
takes on a value of one when acquirers were upgraded and take on a value of zero when
they were downgrade or had retained the same credit rating across period 1 and 2. The
dummy variable, downgrade takes on a value of one when acquirers were downgrade and
takes on a value of zero when they were upgrade or had retained the same credit rating
across period 1 and 2.
Interestingly enough, the variable downgrade was significant at 10% level of significance
while upgrade was insignificant. We will hence focus our analysis on the variables that
were significant. Since the coefficient of downgrade in mixed payment is higher than
equity, a credit rating downgrade over the crisis period is associated with a lower likelihood
of equity payment and a higher likelihood of mixed payment in an M&A transaction, in
comparison to cash. More specifically, 1-unit credit rating downgrade pre and post crisis
is associated with acquirer’s cash financing being 12.4% less likely, mixed financing being
12.9% more likely and equity financing being 0.55% less likely (see table 10).
41
Cashflow_assets displayed significance across both equity and mixed payment with
reference to the base case (i.e cash payment) (see table 9). Since its coefficient is negative
in both mixed and equity payment method, we can conclude that acquirers with increasing
Cashflow_assets is associated with a lower likelihood of mixed and equity payment in
comparison to cash. More specifically, a 1-unit increase in Cashflow_assets of acquirers
before and after the crisis is associated with acquirer’s cash financing being 20.5% less
likely and mixed financing being 23.2% more likely (see table 10).
With regards to acquirer specific control variables – FinancialLeverage and BookToMKT,
they illustrate significance across mixed payment while insignificance across equity
payment (see table 9). The coefficient of FinancialLeverage is positive in mixed payment,
indicating that it is associated with a higher likelihood of mixed payment in comparison to
cash. On the contrary, the coefficient of BookToMKT is negative in mixed payment,
implying that it is associated with a lower likelihood of mixed payment in comparison to
cash. The level of FinancialLeverage of acquirers before and after the crisis is associated
with cash financing being 55.7% less likely and mixed financing being 55.6% more likely.
Furthermore, the BookToMKT value of acquirers before and after the crisis is associated
with cash financing being 37.0% more likely and mixed financing being 37% less likely
(see table10).
With regards to deal specific control variables – Num_analyst, it appears significant across
mixed payment while insignificant across equity payment. We can therefore conclude that
increasing number of analyst monitoring an M&A deal over the crisis period are associated
with a higher likelihood of transacting deal via mixed payment in comparison to cash. More
specifically, Num_analyst covering the M&A deal before and after the crisis is associated
with cash financing being 26.7% less likely and mixed financing being 26.7% more likely.
Further scrutinizing the goodness of fit of our MNP regressions, the Prob > Chi (2) in both
regressions is significant at 5% significance level. Therefore, we reject the null hypothesis
42
that there is no relationship between explanatory variables and the outcome (i.e. cash,
mixed or equity).
7.0 Analysis
The following section establishes a comprehensive analysis of both the MNP and OLS
regressions. We will draw relevance of our results to the above discussed theories in
chapter 2, and subsequently present constructive deductions.
Based on our OLS regression, Cashflow_assets had a positive and significant correlation
with fraction of cash, indicating that acquirer’s cash flow positively impacts the fraction of
cash used in an M&A transaction. This is supported by Myers’s (1984) pecking order
theory, which emphasizes the use of internal funds (i.e. retained earnings) prior to external
funds (i.e debt and equity). Moreover, Jensen and Meckling’s free cash flow hypothesis is
relevant with our results, implying that managers with surplus of free cash flow are more
prone to financing M&A deals with cash. Our results are also consistent with having
positive and significant Cashflow_assets as identified in Karampatsas et al. (2014).
Furthermore, our analysis shows that FinancialLeverage had a significant negative
correlation with fraction of cash, indicating that increasing leverage decreases the fraction
of cash used in transacting M&A. This is in line with M&M preposition II; increasing
financial leverage increases probability of financial distress and bankruptcy, thereby
reducing acquirer’s access to debt capital markets (Kraus and Litzenberger, 1973).
Moreover, under Standard & Poor’s credit rating framework (2015), increasing leverage
results in lower credit rating score assigned to a firm’s business risk profile.
Blockownership depicts a significant negative correlation with fraction of cash, indicating
that increasing concentration of blockholders results in decreasing fraction of cash used in
43
M&A. Our results contradict that of Karampatsas et al. (2014) of having a positive
relationship. This anomaly could be explained by looking in-depth on the acquirer’s
ownership characteristics. More specifically, concentrated blockholders identified in our
sample might not possess major influence on decision-making (e.g. voting rights).
Number_analyst is significantly negatively correlated with fraction of cash, indicating that
increasing number of analyst covering an M&A deal, results in decreasing fraction of cash
used in transacting the deal. Theoretically, increasing number of analyst decreases
information asymmetry. Karampastas et al. (2014) had argued that the lower the
information asymmetry, the higher the use of cash in an M&A. However, our results
indicate otherwise.
After accounting for endogeneity, Credit_ratinglevel was now observed to be significant
(see table 7). Contrary to Karampastas et al (2012), our study shows a negative relationship
between Credit_ratinglevel and fraction of cash. This is supported by Tang (2009) where
he identified that firms who issue high amounts of debt face the possibility of being
downgraded. Therefore, financing M&A deals with increasing debt (i.e cash) might lead
to a downgrade of acquirer’s credit rating.
In the MNP regression, we observed that only two variables – (1) downgrade; (2)
Cashflow_assets were significant across both equity and mixed payment with reference to
the base case (i.e cash payment).
The regression output indicated a lower likelihood of equity payment and a higher
likelihood of mixed payment in comparison to cash when acquirers experience a credit
rating downgrade during the crisis period. Theoretically, credit rating downgrade will lead
to lower accessibility to debt capital markets (i.e. cash). On the other hand, equity is
considered the most expensive financing method (Watson & Head, 2007). A balance of
both cash and equity is required; therefore, mixed payment is the appropriate financing
method for M&A deals in such situations. Moreover, significant reduction in bank lending
44
during the crisis had placed acquirers in a situation of having to balance debt financing (i.e
cash) with equity financing.
Furthermore, Cashflow_assets is significant, indicating a lower likelihood of equity
payment and a lower likelihood of mixed payment in comparison to cash. This is in line
with the pecking order theory and Jensen (1986) study as discussed in section 4.4.3.
FinancialLeverage is significant across mixed while insignificant across equity payment,
indicating a higher likelihood of transacting M&A via mixture of cash and equity in
comparison to cash payment. This is once again consistent with M&M preposition II;
increasing financial leverage increases probability of financial distress and bankruptcy
thereby reducing acquirer’s access to debt capital markets (i.e. increase cost of debt) (Kraus
and Litzenberger, 1973). Increasing cost of debt tightens acquirer’s ability in financing the
acquisition via cash only; instead, a balance of both cash and equity aids in reducing overall
cost of capital and the risk of being downgraded.
BookToMKT is significant across mixed while insignificant across equity payment,
indicating a lower likelihood of transacting M&A via mixture of cash and equity in
comparison to cash payment. This is in line with the market overvaluation theory argued
by Shleifer and Vishny (2003), acquirers tend to prefer cash financing when it is
comparatively undervalued than target’s equity.
Number_analyst is significant across mixed while insignificant across equity payment,
indicating a higher likelihood of transacting M&A via mixture of cash and equity in
comparison to cash payment. Karampatsas et al (2014) argued that the higher number of
analysts covering an M&A deal, the lower the information asymmetry associated with it,
thereby increasing the likelihood of cash financing. Our results are not entirely consistent
with previous literature; however, historically seen, mixed payment has been following an
upward trend compared to cash and equity payment method (see figure 3).
45
8.0 Limitations
The limitations for MNP model are numerous, since very few studies have dwelled upon
it and even fewer studies have utilized it. This is due to multiple reasons, most importantly,
the MNP model is exceptionally difficult to estimate given the need to evaluate multiple
integrals (Brooks, 2008). Moreover, the MNP model is computationally demanding,
especially when large number of alternatives are considered (Daganzo, 1979). The errors
in an MNP model follow a multivariate normal distribution, where each error term has a
mean of zero and are allowed to be correlated (Daganzo, 1979).
The OLS model is fundamentally based on the five assumptions as discussed in section
4.4.1. These assumptions are highly stringent and must be followed strictly. If any one of
the assumptions is violated, the OLS estimation will result in a biased coefficient estimates
eventually leading to inaccurate deductions (Brooks, 2008).
Another crucial limitation in our study is the use of a panel data set. Kasprzyk et al. (1989)
argued that there are fundamental problems associated with the design of panel data as well
as data collection and management. They further argued that panel data are susceptible to
distortions arising from measurement errors. Even though, these issues are present in crosssectional data, they are more apparent and extensive in panel data sets (Kasprzyk et al.,
1989).
Additionally, the relatively small sample size could be a possible flaw in our study.
However, attempts to increase sample size proved impossible given the lack of access to
Moodys and Fitch rating databases. Nonetheless, the current sample size of 136 is
considered a fair representation of S&P credit rating progression in U.S. M&A payment
46
methods, since minimal restrictions were included in the data gathering process.
9.0 Conclusion
This paper provided a comprehensive analysis measuring the influence of credit rating
progression on the fraction of cash and payment methods in U.S. M&A before and after
the GFC. This has been done using U.S. based companies for a two period regression before
and after the crisis (i.e. 2003-2008 and 2008-2013). Previous studies were highly focused
on the determinants of payment with no consideration of the GFC taken into account. This
study supplements previous literature by establishing further evidence on how credit rating
progression of U.S. acquirers since the onset of the crisis had influenced their M&A
payment decisions. Our study goes far and beyond in exhibiting complete relevance of the
current research gap. Additionally, this paper extended the research by using different
empirical analysis and econometric techniques.
The GFC had led to restricted bank lending in the U.S., resulting in significant decline in
corporate loans to firms for LBO/ M&A activities (see figure 1), consequently,
revolutionizing the landscape of M&A payment methods in the U.S.. One would expect
the percentage of cash transaction to dip as a result of the crisis, however, it was surprising
to note that our descriptive statistics painted a positive trend for cash only, negative trend
for mixed and a constant trend for equity only financing.
In our OLS regression, we had noticed a negative significant relationship between
acquirer’s Credit_ratinglevel and the fraction of cash used in transacting M&A. This
observation was fundamentally substantiated by deductions that we derived based on
theories and literature as analyzed in section 7.0. We therefore concluded that hypothesis
“Credit rating progression of acquirers’ pre and post crisis affects the fraction of cash
47
used in M&A transaction”, is an accurate representation of the changing M&A landscape
in the U.S. since the GFC. Hence, we do not reject the null hypothesis.
However, distinct evidence was identified in our MNP regression while taking into account
the downgrade and upgrade of acquirers across period 1 and 2. We had identified a positive
significant relationship between credit rating downgrade and the use of mixed payment
method. In essence, the positive relationship portrays a distinct picture of how U.S. M&A
landscape has evolved since the crisis and it can be concluded that acquirers are now
leaning towards a mixture of cash and equity financing. The increasing sentiment amongst
U.S. acquirers in employing mixed financing was further substantiated by historical trends
as highlighted by Barbopoulos & Wilson (2013) (see figure 3). We therefore concluded
that hypothesis 2 – “Acquirers that experienced a credit rating downgrade pre and post
crisis are more likely to transact M&A with equity” is not an accurate representation of the
changing M&A landscape in the U.S. since the GFC. Hence we rejected the null
hypothesis.
48
10.0 Suggestions for future research
As mentioned above, due to the lack of access to Moodys and Fitch credit rating databases,
our analysis was limited to acquirers that were rated only by S&P. We propose expanding
the research by further including acquirers that were rated by these two credit rating
agencies. Moreover, this research could be further enriched by also focusing on the
European M&A market, given the severe impact of the GFC on European economies. A
comparative perspective between U.S. and European acquirers could then be established
to allow for a far more comprehensive analysis of credit rating progression on M&A
payment decisions.
Given the fact that our explanatory variable, credit rating level, only captures general
macroeconomic factors, we propose including highly specific macroeconomic influences
such as interest rate risks, inflation risks and political risks. This allows for a far more
holistic analysis when taking into account the influence of market specific factors on M&A
payment decisions.
In addition, the research could be further extended to cross border M&As, more
specifically, U.S. acquirers engaging in international acquisitions. This allows for a
comparison between factors that influence acquirer’s domestic M&A payment decisions
and those that influence their cross border M&A payment decisions. Lastly, acquirer’s
qualitative behavior such as CEO and employee characteristics could also be introduced to
account for possible influences in M&A payment decisions.
49
References
Amihud, Y., Lev, B. and Travlos, N. (1990). Corporate Control and the Choice of
Investment Financing: The Case of Corporate Acquisitions. The Journal of
Finance, 45(2), p.603 -616.
Angrist, J. and Pischke, J. (2009). Mostly harmless econometrics. Princeton: Princeton
University Press.
Antunes, A., Mata, J. and Portugal, P. (2011). Debt and Extinction of Firms. Banco De
Portugal | Financial Stability Report.
Barbopoulos, L. and Wilson, J. (2013). The Valuation Effects of Earnouts in Mergers and
Acquisitions of US Financial Institutions. SSRN Journal.
Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge: Cambridge
University Press, pp: 24-100
Bryman, A., Bell, E., (2011). Business Research Methods. Third Edition. New York:
Oxford University Press Inc
Daganzo, C. (1979). Multinomial Probit: The Theory and Its Application to Demand
Forecasting. New York: Academic Press Inc.
Elliott, L. (2011). Global financial crisis: five key stages 2007-2011. The Guardian.
[online] Available at: http://www.theguardian.com/business/2011/aug/07/globalfinancial-crisis-key-stages [Accessed 14 Apr. 2015].
Faccio, M. and Masulis, R. (2005). The Choice of Payment Method in European Mergers
and Acquisitions. The Journal of Finance, 60(3), pp.1345-1388.
Faulkender, M. and Petersen, M. (2005). Does the Source of Capital Affect Capital
Structure?. Review of Financial Studies, 19(1), pp.45-79.
Fishman, M. (1989). Preemptive Bidding and the Role of the Medium of Exchange in
50
Acquisitions. The Journal of Finance, 44(1), pp.41-57.
Fulghieri, P., Strobl, G. and Xia, H. (2013). The Economics of Solicited and Unsolicited
Credit Ratings. Review of Financial Studies, 27(2), pp.484-518.
Fuller, K., Netter, J. and Stegemoller, M. (2002). What Do Returns to Acquiring Firms Tell
Us? Evidence from Firms That Make Many Acquisitions. The Journal of Finance,
57(4), pp.1763-1793.
García-Feijóo, L., Madura, J. and Ngo, T. (2012). Impact of industry characteristics on the
method of payment in mergers. Journal of Economics and Business, 64(4), pp.261274.
Graham, J. and Harvey, C. (2001). The theory and practice of corporate finance: Evidence
from the field. Journal of Financial Economics, 60(2-3), pp.187-243.
Hansen, R. (1987). A Theory for the Choice of Exchange Medium in Mergers and
Acquisitions. Journal of BUS, 60(1), p.75.
Harford, J., Klasa, S., Walcott, N. (2009). Do Firms Have Leverage Targets? Evidence
From Acquisitions. Journal of Financial Economics, (93), pp.1-14.
Hovakimian, A., Opler, T. and Titman, S. (2001). The Debt-Equity Choice. The Journal of
Financial and Quantitative Analysis, 36(1), p.1-25.
Hyde, K. (2000). Recognising deductive processes in qualitative research. Qualitative
Market Research: An International Journal, 3(2), pp.82-90.
Imaa-institute.org, (2015). Statistics on Mergers & Acquisitions (M&A) - M&A Courses |
Company Valuation Courses | Mergers & Acquisitions Courses. [online] Available
at:http://www.imaa-institute.org/statistics-mergersacquisitions.html#MergersAcquisitions_United States of America [Accessed 19
Apr. 2015].
Ivashina, V. and Scharfstein, D. (2010). Bank lending during the financial crisis of 2008.
Journal of Financial Economics, 97(3), pp.319-338.
Jensen, M. (1986). Agency Cost Of Free Cash Flow, Corporate Finance, and Takeovers.
American Economic Review, 76(2), p.323.
51
Jensen, M. and Meckling, W. (1976). Theory of the firm: Managerial behavior, Agency
costs and Ownership structure. Journal of Financial Economics, 3(4), pp.305-360.
Jung, K., Kim, Y. and Stulz, R. (1996). Timing, investment opportunities, managerial
discretion, and the security issue decision. Journal of Financial Economics, 42(2),
pp.159-186.
Karampatsas, N., Petmezas, D. and Travlos, N. (2012). Credit Ratings and the Choice of
Payment Method in Mergers and Acquisitions. SSRN Journal.
Karampatsas, N., Petmezas, D. and Travlos, N. (2014). Credit Ratings and the Choice of
Payment Method in Mergers and Acquisitions. SSRN Journal.
Kasprzyk, D., Duncan, G., Kalton, G. and Singh, M. (1989). Panel surveys. New York:
Wiley.
Kisgen, D. (2006). Credit Ratings and Capital Structure. The Journal of Finance, 61(3),
pp.1035-1072.
Kisgen, D. (2007). The Influence of Credit Ratings on Corporate Capital Structure
Decisions. Journal of Applied Corporate Finance, 19(3), pp.65-73.
Kraus, A. and Litzenberger, R. (1973). A State-Preference Model of Optimal Financial
Leverage. The Journal of Finance, 28(4), p.911.
Leary, M. and Roberts, M. (2005). Do Firms Rebalance Their Capital Structures?. The
Journal of Finance, 60(6), pp.2575-2619.
Martin, K. (1996). The Method of Payment in Corporate Acquisitions, Investment
Opportunities, and Management Ownership. The Journal of Finance, 51(4),
p.1227.
Martynova, M. and Renneboog, L. (2009). What determines the financing decision in
corporate takeovers: Cost of capital, agency problems, or the means of payment.
Journal of Corporate Finance, 15(3), pp.290-315.
Mckibbin, W. and Stoeckel, A. (2009). The Global Financial Crisis: Causes and
Consequences. Working Papers in International Economics, 2(9), p.1.
Merced, M. and Cane, J. (2011). Confident Deal Makers Pulled Out Checkbooks in 2010.
52
DealBook
-
The
New
York
Times.
[online]
Available
at:
http://dealbook.nytimes.com/2011/01/03/confident-deal-makers-pulled-outcheckbooks-in-2010/?_r=1 [Accessed 25 Apr. 2015].
Modigliani, F. and Miller, M. (1958). The Cost of Capital, Corporation Finance and the
Theory of Investment. American Economic Association, [online] 48(3), pp.261297. Available at: http://www.jstor.org/stable/1809766 [Accessed 25 Mar. 2015].
Myers, S. (1984). The Capital Structure Puzzle. The Journal of Finance, 39(3), pp.574592.
Myers, S. (2001). Capital Structure. Journal of Economic Perspectives, 15(2), pp.81-102.
Ogden, J., Jen, F. and O'Connor, P. (2003). Advanced corporate finance. Upper Saddle
River, N.J.: Prentice Hall
Owen, S. and Yawson, A. (2010). Corporate life cycle and M&A activity. Journal of
Banking & Finance, 34(2), pp.427-440.
Roberts, M., Whited, T. (2012). Endogeneity in Empirical Corporate Finance. SSRN
Journal.
Schwarcz, S. (2004). Private Ordering of Public Markets: The Rating Agency Paradox.
Treasury Affairs, 1(2), pp.18-25.
Shleifer, Andrei, and Robert W. Vishny, 2003, Stock market driven acquisitions, Journal
of Financial Economics, 70, pp.295-311.
Shyam-Sunder, L. and C. Myers, S. (1999). Testing static tradeoff against pecking order
models of capital structure. Journal of Financial Economics, 51(2), pp.219-244.
Standardandpoors.com, (2015). Standard & Poor's | About Credit Ratings. [online]
Available
at:
http://www.standardandpoors.com/aboutcreditratings/RatingsManual_PrintGuide.
html [Accessed 12 May 2015].
Stock, J. and Watson, M. (2007). Introduction to econometrics. Boston: Pearson/Addison
Wesley.
Stulz, R. (1988). Managerial control of voting rights: Financing policies and the market for
53
corporate control. Journal of Financial Economics, 20, pp.25-54.
Tang, T. (2009). Information asymmetry and firms’ credit market access: Evidence from
Moody's credit rating format refinement. Journal of Financial Economics, 93(2),
pp.325-351.
The Economist, (2013). The origins of the financial crisis. [online] Available at:
http://www.economist.com/news/schoolsbrief/21584534-effects-financial-crisisare-still-being-felt-five-years-article [Accessed 16 Mar. 2015].
Train, K. (2003). Discrete choice methods with simulation. New York: Cambridge
University Press.
U.S. Securities and Exchange Commision, (2014). Dodd-Frank Act Rulemaking: Credit
Rating Agencies. [online] Available at: https://www.sec.gov/spotlight/doddfrank/creditratingagencies.shtml [Accessed 14 Apr. 2015].
Utzig, S. (2010). The Financial Crisis and the Regulation of Credit Rating Agencies: A
European Banking Perspective. SSRN Journal, (188).
Watson, D. and Head, A. (2007). Corporate finance: Principles & Practice. 4th ed.
London: Pitman.
Wooldridge, J. (2009). Introductory econometrics. Mason, OH: South Western, Cengage
Learning.
54
Appendices
Appendix A. Definitions
Variable
Definition
Panel A: Payment Form
Fraction Of cash
Percentage of cash used to finance the
acquisition deal - Thomson Reuters Eikon.
Payment Method (Cash, mixed
A dummy variable taken the value of one if the
&Equity)
deal is financed with 70% or more cash and
zero otherwise - Thomson Reuters Eikon.
Panel B: Credit Rating Variables
Credit_ratinglevel
The Acquirer credit rating, taking the score
from 1 to 22 (1 being DDD- and 22 being A) Compustat.
Upgrade
Dummy variable, taking the value of one if its an
upgrade, and zero if it is a downgrade and
constant.
Downgrade
Dummy variable, taking the value of one if it’s a
downgrade and zero if it is an upgrade and
constant.
Panel C: Bidder Characteristics
Ln_Size
The natural Logarithm of the acquirer's market
equity value one month prior the announcement
- DataStream
FinancialLeverage
Total debt over the book value of total assets in
the end of fiscal year preceding to acquisition
announceent - DataStream
55
Collateral
PPE divided by the book value of total assets at
the end of fiscal year before the acquisition
announcement - Comupstat
BooktoMKT
Book value of equity at the fiscal year end
before the annoncement of the acquisition over
the market value of equity one month preceding
the announcement of the acquisition DataStream
Blockhownership
Total number of blockholders who own at least
5% of the company's stock - Thomson Reuters
Eikon
Cashflow_assets
Earnings before interest and taxes excluding
unusual items plus depreciation minus total
dividends, divided by total assets in fiscal year
end prior the acquisition – Compustat
Runnup
The cumulative return on stock one month prior
the acquisition over the year prior to the
acquisition announcement - Thomson Reuters
Eikon
Panel D: Deal Characteristics
Rel_dealsize
The value of the transaction divided by the
acquirer’s market value of equity one month
before the acquisition announcement - Thomson
Eikon
Num_analysts
Number of financial analysts following the M&A
deal - Thomson Reuters Eikon
56
IntraIndustry
Dummy variable, takes the value of one if the
acquirer and the target are from the same
industry (interindustry) and the value of zero if
they are from a different industry (intraindustry)
- Thomson Reuters Eikon
Panel E: Target Characteristic
Private_Public
Dummy variable, takes the value of one if the
target is unlisted and zero otherwise - Thomson
Reuters Eikon
Panel F: Instrumental Variables
AltmanZScore
Z =6.56 (Working Capital/Total Assets) +3.26
(Retained Earnings/Total Assets) + 6.72
(EBIT/Total Assets) + 1.05 (Book Value of
Equity/Book Value of Total Liabilities) –
Compustat
Profitability
EBITDA to total Assets – Compustat
Regulated Industry
Dummy variable, takes the value of one if the
acquirer is a utility firm or a financial
institution, and zero otherwise - Compustat
57
Appendix B. S&P credit rating score
S&P Credit Rating
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
BCCC+
CCC
CCCDDD+
DDD
Numerical Score
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
58
Table 1: OLS Correlation Matrix
Rel_dealsize
Runup
Private_Public
Num_analysts
Ln_size
Intraindustry
FinancialLeverage
Credit_ratinglevel
Collateral
Cashflow_assets
BookToMKT
Blockownership
Fraction of cash
Fraction of cash
1.000
Blockownership
-0.215
1.000
BookToMKT
-0.095
0.199
1.000
Cashflow_assets
0.379
-0.089
-0.414
1.000
Collateral
-0.127
0.126
0.099
0.073
1.000
Credit_ratinglevel
0.195
-0.423
-0.013
0.205
-0.150
1.000
FinancialLeverage
-0.244
0.190
-0.246
0.056
0.348
-0.419
1.000
IntraIndustry
-0.179
0.135
0.079
-0.090
0.072
-0.086
0.142
1.000
Ln_size
0.167
-0.423
-0.103
0.097
-0.236
0.650
-0.345
-0.087
1.000
Num_analysts
-0.205
-0.024
0.054
-0.050
-0.011
-0.105
-0.031
0.044
-0.043
1.000
Private_Public
-0.021
-0.061
-0.045
-0.072
-0.046
-0.329
0.017
0.198
-0.281
-0.169
1.000
Runup
-0.023
0.101
0.163
-0.097
0.611
-0.116
0.189
-0.067
-0.156
-0.066
-0.007 1.000
Rel_dealsize
-0.205
0.228
-0.089
-0.040
0.210
-0.268
0.351
-0.004
-0.324
0.224
-0.143 0.233 1.000
59
Table 2: MNP Correlation Matrix
Upgrade
Runup
Rel_dealsize
Private_Public
Num_analysts
ln_size
IntraIndustry
Blockownership
BookToMKT
Collateral
Cashflow_assets
Downgrade
FinancialLeverage
Fraction of cash
Fraction of cash
1.000
FinancialLeverage -0.244
1.000
Downgrade
-0.091
0.090
1.000
Cashflow_assets
0.379
0.056
0.000
1.000
Collateral
-0.127
0.348
0.167
0.073
1.000
BookToMKT
-0.095 -0.246
0.191
-0.414
0.099
1.000
Blockownership
-0.215
0.190
0.201
-0.089
0.126
0.199
1.000
IntraIndustry
-0.179
0.142
0.084
-0.090
0.072
0.079
0.135
Ln_size
0.167
-0.345 -0.168
0.097
-0.236 -0.103 -0.423 -0.087
Num_analysts
-0.205 -0.031 -0.008 -0.050 -0.011
Private_Public
-0.021
0.017
Rel_dealsize
-0.205
Runup
-0.023
Upgrade
0.054
1.000
1.000
-0.024
0.044
-0.043
0.112
-0.072 -0.046 -0.045 -0.061
0.198
-0.281 -0.169
0.351
0.050
-0.040
0.210
-0.089
0.228
-0.004 -0.324
-0.143
1.000
0.189
0.107
-0.097
0.611
0.163
0.101
-0.067 -0.156 -0.066 -0.007
0.233
-0.052 -0.097 -0.317
0.089
-0.140 -0.069 -0.142
0.051
0.191
1.000
0.224
0.090
1.000
1.000
-0.082 -0.085 -0.229 1.000
60
Table 3: Sample descriptive statistics by credit rating progression pre & post crisis
Variable
Panel A: Acquirer
Characteristics
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Panel B: Deal Characteristics
Rel_dealsize
Num_analysts
IntraIndustry
Panel C: Target Characteristics
Private_Public
Total Sample (N=136)
Mean
Median
Upgrade (N=28)
Mean Median
Downgrade (N=38)
Mean
Median
Constant (N=70)
Mean Median
8.940
0.195
0.263
0.429
2.007
0.118
0.104
8.493
0.117
0.231
0.357
2.000
0.115
0.089
9.595
0.138
0.227
0.384
1.536
0.132
-0.288
9.463
0.084
0.213
0.343
1.000
0.154
-0.202
8.469
0.250
0.290
0.531
2.553
0.118
0.254
7.858
0.140
0.255
0.421
2.000
0.091
0.132
8.933
0.187
0.262
0.392
1.900
0.113
0.180
8.934
0.140
0.226
0.350
2.000
0.117
0.098
0.204
0.772
0.485
0.066
1.000
0.000
0.134
0.893
0.536
0.052
1.000
1.000
0.238
0.763
0.553
0.075
1.000
1.000
0.214
0.729
0.429
0.066
1.000
0.000
0.287
0.000
0.214
0.000
0.368
0.000
0.271
0.000
61
Table 4: Sample descriptive statistics by M&A Payment Method (2003 – 2008)
Variable
Panel A: Bidder Characteristics
Credit_ratinglevel
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Panel B: Deal Characteristics
Rel_dealsize
Num_analysts
IntraIndustry
Panel C: Target Characteristics
Private_Public
Total Sample (N = 68)
Mean
Median
Cash (N = 41)
Mean Median
Mixed (N = 23)
Mean Median
Stock (N = 4)
Mean Median
14.529
8.909
0.204
0.250
0.345
1.897
0.124
0.143
15.000
8.240
0.141
0.214
0.307
2.000
0.125
0.152
14.854
8.948
0.160
0.208
0.374
1.829
0.137
0.101
15.000
8.230
0.129
0.178
0.320
2.000
0.135
0.143
14.391
8.930
0.311
0.334
0.298
2.043
0.123
0.271
15.000
8.534
0.247
0.323
0.250
2.000
0.094
0.229
12.000
8.384
0.038
0.205
0.322
1.750
-0.009
-0.172
10.500
8.182
0.039
0.239
0.327
1.500
-0.012
-0.025
0.201
0.853
0.515
0.065
1.000
1.000
0.134
0.805
0.390
0.065
1.000
0.000
0.337
0.957
0.739
0.126
1.000
1.000
0.101
0.750
0.500
0.108
1.000
0.500
0.279
0.000
0.293
0.000
0.217
0.000
0.500
0.500
62
Table 5: Sample descriptive statistics by M&A Payment Method (2008 – 2013)
Variable
Panel A: Bidder Characteristics
Credit_ratinglevel
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Panel B: Deal Characteristics
Rel_dealsize
Num_analysts
IntraIndustry
Panel C: Target Characteristics
Private_Public
Total Sample (N = 68)
Mean
Median
Cash (N = 46)
Mean
Median
Mixed (N = 18)
Mean
Median
Stock (N = 4)
Mean
Median
14.353
8.971
0.186
0.275
0.513
2.118
0.113
0.066
15.000
8.934
0.104
0.231
0.439
2.000
0.112
-0.080
14.978
9.194
0.191
0.246
0.506
1.913
0.130
0.157
15.000
9.056
0.117
0.216
0.392
2.000
0.122
-0.054
12.611
8.219
0.210
0.336
0.493
2.556
0.089
-0.048
13.000
8.013
0.083
0.308
0.439
2.000
0.074
-0.326
15.000
9.793
0.010
0.329
0.690
2.500
0.019
-0.465
15.500
10.050
0.008
0.316
0.692
2.000
0.012
-0.421
0.207
0.691
0.456
0.067
1.000
0.000
0.121
0.435
0.435
0.049
0.000
0.000
0.467
1.278
0.556
0.216
1.000
1.000
0.025
1.000
0.250
0.022
1.000
0.000
0.294
0.000
0.261
0.000
0.389
0.000
0.250
0.000
63
Table 6: OLS regression of credit rating progression and fraction of cash
The table depicts the results of the OLS regression of credit rating progression of acquirers on
fraction of cash used in our sample of 136 U.S. M&A transaction before and after the global
financial crisis (i.e. 2 time periods). Refer to Appendix A for definition of variables. All regressions
are controlled for year fixed effects where the coefficients are suppressed. The symbols *,** and
*** represents statistical significance at 10%, 5% and 1% levels respectively. The t-statistics
displayed in parentheses are adjusted for bidder clustering and heteroskadisticity.
Constant
(1)
0.4519***
Total sample
(2)
(3)
0.7637*** 0.8341***
(2.71)
(4.09)
0.0211**
(2.00)
0.0123*
(1.68)
Ln_size
Credit_ratinglevel
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Rel_dealsize
IntraIndustry
Private_ Public
N
R-Square
F-Statistic
(3.48)
0.0119
(0.40)
0.0110*
(1.89)
-0.2969
(-1.50)
-0.4365**
(-2.33)
0.0231
(0.16)
-0.0384*
(-1.80)
1.8629***
(4.10)
0.0772*
(1.68)
(3.68)
0.0098
(0.32)
-0.0174
(-1.06)
-0.2628
(-1.27)
-0.3749*
-1.85
0.0175
(0.13)
-0.0447**
(-1.97)
1.7170***
(3.58)
0.0672
(1.47)
-0.0878
(-0.89)
-0.1153***
(-2.77)
-0.0543
(-0.92)
-0.0594
(-0.83)
136
0.240
(4.42)
136
0.293
(3.89)
-0.1357
(-1.52)
-0.1030**
(-2.47)
-0.1318**
(-2.21)
Num_analyst
0.04368
(0.62)
136
0.034
(1.54)
136
0.142
(4.29)
(4)
1.1046***
64
Table 7: Endogenity control for credit rating progression via 2SLS
The table illustrates the results of the instrumental variable regression to take into account possible
endogenity of credit rating progression of acquirers on U.S M&A transaction before and after the
crisis. Refer to Appendix A for definition of variables. All regressions are controlled for year fixed
effects where the coefficients are suppressed. The symbols *,** and *** represents statistical
significance at 10%, 5% and 1% levels respectively. The t-statistics displayed in parentheses are
adjusted for bidder clustering and heteroskadisticity.
Constant
1st Stage
2nd Stage
2.4365
(1.31)
1.1046***
(3.68)
-0.0174**
(-1.97)
Credit_ratinglevel
Profitability
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Rel_dealsize
Num_analyst
IntraIndustry
Private_ Public
N
Adjusted R-Square
2.4570***
(4.37)
1.2756***
(7.77)
0.0576
(0.04)
-2.540*
(-1.94)
0.9984
(1.41)
-0.2325***
(-2.58)
-1.1156**
(-2.47)
-0.2624*
(-1.91)
0.5765
(1.09)
-0.3450**
(-2.13)
0.2227
(0.96)
-0.5233
(-1.31)
0.0098
(0.32)
-0.2628
(-1.27)
-0.3749*
(-1.85)
0.0175
(0.129)
-0.0447*
(-1.97)
1.7170***
(3.58)
0.0672
(1.47)
-0.0878
(-0.88)
-0.1153***
(-2.77)
-0.0543
(-0.92)
-0.0594
(-0.83)
136
0.638
136
0.218
65
Table 8: MNP regression of credit rating progression and M&A Payment method
The table depicts the results of the MNP regression of credit rating progression of acquirers on
M&A payment method (i.e. cash, mixed or equity) used in our sample of 136 U.S. M&A transaction
before and after the global financial crisis (i.e. 2 time periods. Refer to Appendix A for definition
of variables. The symbols *,** and *** represents statistical significance at 10%, 5% and 1% levels
respectively. The z-statistics displayed in parentheses are adjusted for bidder clustering and
heteroskadisticity.
Number of obs = 136
Wald chi2(24) = 39,09
Prob > chi2 = 0,0267
Constant
Credit_ratinglevel
Ln_size
Collateral
FinancialLeverage
Blockownership
Cashflow_assets
Runup
Rel_dealsize
Num_analyst
IntraIndustry
Private_ Public
Mixed
-4.3939
-3.917**
(-1.10)
(-2.17)
0.1906
0.0324
(0.61)
(0.31)
0.1180
0.1374
(0.23)
(0.67)
-2.4871
3.412***
-(0.41)
(2.52)
4.4004
2.3342*
(1.21)
(1.79)
-2.0328
-1.365*
(-0.96)
(-1.71)
0.4235
0.1103
(1.16)
(0.82)
-41.1505***
-7.848***
(-2.52)
(-2.63)
-0.5192
-0.6541**
(-0.73)
(-2.04)
-0.2193
0.9504
(-0.05)
(1.32)
0.8065
1.0899***
(1.04)
(3.40)
-1.0742
0.7749*
(-1.16)
(1.89)
0.2211
0.4017
(0.17)
(0.84)
Cash
-----BASE CASE-----
BookToMKT
Equity
66
Table 9: MNP regression of credit rating upgrades and downgrade on M&A
Payment method
The table depicts the results of the MNP regression of credit rating upgrade and downgrade of
acquirers on M&A payment method (i.e. cash, mixed or equity) used in our sample of 136 U.S.
M&A transaction before and after the global financial crisis (i.e. 2 time periods. Refer to Appendix
A for definition of variables. The symbols *,** and *** represents statistical significance at 10%,
5% and 1% levels respectively. The z-statistics displayed in parentheses are adjusted for bidder
clustering and heteroskadisticity.
Number of obs = 136
Wald chi2(24) = 39,09
Prob > chi2 = 0,0267
Upgrade
Downgrade
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Rel_dealsize
Num_analyst
IntraIndustry
Private_ Public
Mixed
Cash
-2.8706
(-0.63)
-0.7492
(-0.55)
-0.4180*
(-1.80)
0.3540
(0.78)
-3.6890
(-0.53)
2.8140
(0.83)
-2.2786
(-0.93)
0.3833
(1.07)
-43.8721**
(-2.17)
-0.4356
(-0.63)
-1.0060
(-0.14)
1.0935
(1.18)
-0.5636
(-0.62)
-0.6223
(-0.43)
-3.6351**
(-2.02)
0.8113
(1.53)
0.6492*
(1.89)
0.1387
(0.94)
3.2689**
(2.43)
2.3452*
(1.80)
-1.5601*
(-1.92)
0.1056
(0.8)
-8.6344***
(-2.95)
-0.6141**
(-1.90)
0.8750
(1.28)
1.1273***
(3.45)
0.7211*
(1.75)
0.3705
0.77
-----BASE CASE-----
Constant
Equity
67
Table 10: Marginal effects of MNP regression of credit rating upgrades and
downgrade on M&A Payment method
The table depicts the marginal effects of the MNP regression of credit rating upgrade and
downgrade of acquirers on M&A payment method (i.e. cash, mixed or equity) used in our sample
of 136 U.S. M&A transaction before and after the global financial crisis (i.e. 2 time periods. Refer
to Appendix A for definition of variables. The symbols *,** and *** represents statistical
significance at 10%, 5% and 1% levels respectively. The z-statistics displayed in parentheses are
adjusted for bidder clustering and heteroskadisticity.
Number of obs = 136
Wald chi2(24) = 39.09
Prob > chi2 = 0.0267
Upgrade
Downgrade
Ln_size
Collateral
FinancialLeverage
BookToMKT
Blockownership
Cashflow_assets
Runup
Rel_dealsize
Num_Analyst
IntraIndustry
Private_Public
Cash
-0.2072
(-1.48)
-0.1242*
(-1.80)
-0.0329
(-0.94)
-0.7755**
(-2.41)
-0.5565*
(-1.81)
0,3702*
(1,93)
0,0251
(-0,79)
-0,2053***
(-2,91)
0,1457*
(1,87)
-0,2076
(-1,26)
-0,2675***
(-3,52)
-0,1706*
(-1,79)
-0,0905
(-0,75)
dy/dx
Mixed
0.2072
(1.48)
0.1293*
(1.86)
0.0329
(0.94)
0.7757**
(2.41)
0.5564*
(1.81)
-0,3701*
(-1,93)
0,0251
(0,79)
0,2321***
(2,95)
-0,1457*
(-1,87)
0,2076
(1,26)
0,2675***
(3,52)
0,1706*
(1,79)
0,0905
(0,75)
Equity
0.0000
(-0.15)
0.0055
(-0.12)
0,0000
(0.14)
0.0002
(-0.15)
0.0001
(0.13)
-0,0001
(-0,15)
0,0000
(0,14)
0,0268
(0,14)
0,00
(-0,12)
0,0000
(-0,13)
0,0000
(0,15)
0,0000
(-0,13)
0,0000
(-0,15)
68