1

Creditor Monitoring versus shareholder monitoring: Evidence

from bank financing of Mergers and Acquisitions

Abstract

We examine a set of bank financed mergers and acquisitions that are either partly or

completely financed by cash. Comparing those mergers that are financed by a bank versus

those that are paid for internal cash, we find that bidder abnormal returns for M&A that are

financed by banks versus other M&A are similar. Next, we find that loans that finance M&A

are significantly different from other sets of loans in terms of having a higher collateral

requirement and higher number of covenants. Lastly, bank financed M&A are more likely to

be completed relatively to other M&A, and value losing M&A are less likely to be financed

by banks. Overall, our results are more consistent with the notion that creditor monitoring is

different from shareholder monitoring. Our results are robust to a variety of controls for the

endogeneity of bank financing.

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1. Introduction

Theories have long suggested that banks screen/monitor borrowers, which

certifies/enhances borrowers’ value (e.g., Diamond, 1984; Ramakrishnan and Thakor, 1984).

Consistent with this, there is a long literature on relationship lending that documents the

specialness of banks (James, 1987). While the earlier studies on the value of bank lending

focus on the event study methodology, later studies focused on loan contract terms (eg

Petersen and Rajan, 1994; Berger and Udell, 1995). Generally, the finding is that firms

benefit in price and non-price terms due to bank lending, and this is interpreted as evidence in

favor of the specialness of banks. On the other hand, recent event study evidence by Maskara

and Mullineaux (2011) calls into question the specialness of banks based on a larger sample

and argues that firms would only announce loans that are likely to generate positive

announcement returns.

So, on one hand, we have several papers with large sample evidence on benefit of

bank lending in terms of loan rates and collateral for small businesses as well as large

publicly traded firms. On the other hand, we have a lack of evidence for the benefits of bank

lending based on a more recent larger sample event study.

To further investigate this question on the value effects of bank loans for the

shareholders of borrowing firms, we examine the impact of bank financing on mergers and

acquisitions. Specifically, we examine the impact of bank financing in cash M&A in terms of

announcement returns, as well as examining the contract terms of the loans used to finance

M&A. Focusing on mergers and acquisitions has several advantages. First, there is a large

literature that documents that mergers often create zero or even negative value for acquiring

firms. Thus, if banks monitoring were to add value to shareholders, then mergers would be

one where the scope for adding value is quite high. Second, an important limitation with the

loan announcement studies is that it is often difficult to identify the loan announcement

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dates.1 In contrast, we have a relatively comprehensive of mergers and acquisitions where

announcement is required due to regulatory reasons. Third, by employing both the event

study methodology as well as the resulting loan contract terms for the same event, we can

link the returns to the acquiring firms to the loan contract terms used for financing the

acquisition, which can more sharply test the value addition from banks.

The hypotheses we test are two fold. On one hand, going by earlier results in the

literature, bank financing should add value, as banks can monitor and potentially block value

reducing acquisitions by withholding financing. Thus, bank financing should be linked to

positive CAR’s for the acquiring firms, which may arise either due to the certification

benefits that a bank is willing to finance the deal, or due to potential monitoring of the

management of the combined firm after the merger. In fact, to the extent that a merger or

acquisition is a large corporate investment, and often a negative value event from an

acquiring firm’s perspective, bank involvement may be viewed as an even positive event

relative to bank financing for other reasons. Henceforth, we refer to this hypothesis as the

‘bank monitoring hypothesis’.

On the other hand, banks may substitute for due diligence and monitoring by

changing the price of loan contract terms. In particular, the model by Manove, Padilla and

Pagano (2001) suggests a model of lazy banks where banks may substitute monitoring by

increasing covenants and collateral whereby they are protected from downside risk by

collateralizing a loan. In this case, banks should not be associated with any positive value

creation for acquiring firm shareholders. Rather, one should observe loan contract terms to be

significantly different for bank loans that finance acquisitions relative to other banks loans.

Further, to the extent that banks may obtain fees for related investment banking services, this

may incentivize them even further to provide financing even if they perceive the merger as

1 This is why even the relatively large sample event study by Maskara and Mullineaux (2011) uses around 1000

observations which is significantly smaller than the universe of loans in the LPC Dealscan data set.

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value reducing from the shareholders perspective. Henceforth, we refer to this as the ‘lazy

bank hypothesis.’

Note that these two hypotheses are not necessarily mutually exclusive. Banks could

be monitoring in ways that increase the value for shareholders, but at the same time, also

changing loan contract terms in a way that protects themselves from downside risk.

Ultimately, it is an empirical question as to whether either or both of these effects has an

economically important effect.

To test these hypotheses, we use a set of loans made to acquiring firms using the LPC

Dealscan database and the Thomson Financial SDC database for mergers and acquisitions.

First, we obtain a base sample of mergers and acquisitions from the SDC database where the

method of payment involves at least some cash payment (thus we exclude all stock deals).

We need this restriction as bank financing would be relevant only if the acquiring firm

requires cash financing. Next, we identify all loans taken by the acquiring firm from one year

prior to the announcement of the loan to the deal completion date. We treat these loans as

being related to finance the acquisition, the underlying economic logic being that cash

borrowed, no matter what the stated purpose, could be used for financing the M&A. We treat

all such loan transactions as acquisition related loans.

Our tests use three approaches. First, we test for impact of bank financing on

abnormal returns for acquiring firms. Second, we test if the price and non-price terms of loan

contract terms (loan rate, collateral and covenants) differ significantly between acquisition

related loans and other loans. Lastly, we examine if the likelihood of completion of the deal

differs significantly across bank financed mergers and other mergers.

Abnormal returns would measure potential benefits to shareholders from bank

monitoring and certification. On the other hand, significant differences in loan contract terms

between acquisition related loans and other loans, in the absence of any abnormal return

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effects, would measure the extent to which the lazy bank hypothesis is valid. Lastly, a higher

likelihood of deal completion may be a positive result if bank financed deals have a positive

wealth effects while a higher likelihood of deal completion may be symptomatic of conflicts

between banks and the firm’s shareholders if such deals have negative value effects on

average. If the wealth effects are zero, then there is no effect of the higher likelihood of deal

completion on shareholders.

Our main results are as follows: First, bank financed mergers and acquisitions do not

have a significant difference from CAR relative to the M&A that are financed using cash

from the firm’s internal funds. Thus, bank involvement in the financing of M&A is not a

positive event from the acquiring firm shareholders perspective.

Next, we examine differences in loan contract terms between the loans we classify as

bank financing of M&A and all the remaining loans in the LPC database. When we examine

loan contract terms, we find several important differences between acquisition related loans

and other loans. Specifically, a loan made for acquisition financing has a significantly higher

likelihood of collateral and higher number of covenants reflecting potential risk shifting

problems due to an increase in leverage post merger. In contrast, the loan rate for acquisition

loans is similar to non-acquisition loans. This evidence is strongly consistent with the fact

that banks protect themselves from events post M&A, rather than screening or monitoring

loans.

We examine a number of subsamples of observations based on firms that are likely to

be credit constrained, banks with high reputation and relationship banks. We find that banks

do not add value in any of these circumstances. We also account for potential endogeneity of

bank financing using a variety of methods such as instrumental variables and propensity

score matching, and generally find that the original results continue to hold, both for

abnormal returns and for loan contract terms.

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The evidence thus far implies that bank financing does not result in any benefits to

the acquiring firm’s shareholders consistent with the recent event study by Maskara nad

Mullineaux (2010) and less consistent with value creation for shareholders from bank

financing. However, the results so far are conditional on the deal being completed.

One possible unobservable benefit of bank financing is that banks may prevent the

firm from even embarking on an acquisition that results in a large destruction of value, by

refusing to finance the deal. In particular, does the likelihood of bank financing reduce the

likelihood that the acquiring firm completes a value reducing deal, conditional on the deal

being announced? While the answer to this question is affirmative, once endogeneity of the

bank financing decision is accounted for, there is no incremental difference in the likelihood

of a value reducing deal being less likely to be completed due to bank financing. However, a

benefit to the acquiring firm is that bank financed deals are (in an unconditional sense) more

likely to be completed.

To summarize, our results are consistent with the theoretical arguments in Seward

(1990) and Besanko and Kanatas (1993) who argue that bank monitoring incentives differ

significantly from monitoring incentives of shareholders. Further, banks also change the loan

contract terms of a way that limits their losses due to potential value loss in M&A. We

believe our results highlight an important limitation of the evaluating the benefits of bank

lending using stock price data. Thus, creditor monitoring is significantly different from

shareholder monitoring.

A related paper by Bharadwaj and Shivdasani (2004) examines a similar question in

the context of tender offers. They find that bank financing adds value and interpret this as

evidence that banks monitor and certify the deals. We reexamine this question for all mergers

(not just tender offers) for two reasons. First, the banking industry has undergone a dramatic

change from the time period of their sample (1990-1996). In particular, there has been a large

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change in the competitive landscape in banking, especially in terms of development of the

syndicated loan market. Second, by having a relatively longer time series but with a different

data set, we can reexamine the robustness of their results. We find that we can replicate

several of their results for the time period between 1990 and 1996, but these results do not

continue to hold in later time periods. We attribute this difference to a large change in the

lending market. In particular, this market has seen a large growth in the late 90’s and the first

part of 2000’s with several non banks becoming active lenders in this market.

2. Sample selection

The sample of acquisitions comes from the Securities Data Company’s (SDC) U.S.

Merges and Acquisitions Database. We select domestic mergers and acquisitions with the

announcement date between January 1, 1990 and December 31, 2009. The above time frame

is chosen because the LPC data set does not have very many loans prior to 1990. We consider

only acquisitions in which acquiring firms purchase all the shares of the acquired firm or

subsidiary, and we require the acquiring firm to control less than 50% of the shares of the

target firm before the announcement. We exclude divestitures, repurchases and M&A’s done

by financial firms (SIC code between 6000 and 6999) and utility firms (SIC code between

4900 and 4949). We further require that (1) the deal value is greater than US$ 1million, (2)

the deal value is at least 1% of the acquirer’s market value of equity measured on the 11th

trading day prior to the announcement date, (3) the target firms is a public U.S. firm or a non-

public subsidiary of a US public or US private firm, and (4) the acquirer has annual financial

statement information available from Compustat and stock return data (210 trading days prior

to acquisition announcements) from Center for Research in Security Prices (CRSP).

Our base sample takes all M&A that satisfy the above criteria and one more important

criterion that the merger needs to be paid for either partly or completely by cash. The reason

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is that only these mergers would require banks financing. The total number of mergers with

these sample selection criteria is 2918. Next, we need to identify the mergers that were

financed with bank financing. We define a merger as being bank financed if the acquiring

firm took any loans between one year prior to the announcement of the loan to the completion

date of the loan. The rationale for this choice is that companies in general most loans have a

stated purpose of ‘General Corporate Purposes’ that can be broadly used. Using this

definition of bank financing, 563 M&A are classified as being bank financed and the

remainder are not financed by banks.

2.1 Hypotheses development

As mentioned in the introduction, there are two principal hypotheses that we test – bank

monitoring versus lazy bank. We test the implications of these hypotheses on the following

variables – merger announcement return, loan rates, likelihood of the loan being

collateralized, an index that measures the intensity of covenants in the loan, as well as the

likelihood of completion of the merger conditional on the deal being completed, conditional

on being announced.

2.1.1 Implications on merger announcement returns

The bank monitoring hypothesis implies that merger announcement returns should be

positive if any bank agrees to finance the deal. This can be rationalized based on a long

theoretical literature in banking based on information frictions starting with Diamond (1984)

and Ramakrishnan and Thakor (1984). These theories posit that banks have special

informational advantages relative to capital markets in financing firms. If outside investors

see a bank financing a merger, the resulting certification benefit should result in higher

abnormal returns for bank financed acquisitions relative to other mergers and acquisitions.

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In contrast, the lazy bank hypothesis as in Manove, Padilla and Pagano (2003) implies

that banks would not monitor the mergers and acquisitions, rather they would substitute

monitoring with increasing collateral that would protect themselves in the event of default,

and increasing covenants that would allow them to call the loan if the borrower’s financial

condition worsens. In this case, bank financing should not result in any positive abnormal

return to the acquiring firm shareholders.

2.1.2 Implications on loan contract terms used to finance the merger

The bank monitoring hypothesis implies that loan rates acquisition related loans and other

loans should not differ significantly from each other as banks price each of these loans

consistent with their risk. In this case, there should be no difference between loans used to

finance acquisitions and other types of loans.

In contrast, the lazy bank hypothesis as mentioned above implies that banks should

substitute loan contract terms in place of monitoring. In this case as well, we should observe

that banks increase the collateral requirements as well as the covenants.

2.2.3 Implications on likelihood of completion of merger

To the extent that firms require cash to complete the merger (for example, if target firms do

not want complete stock payment), the availability of bank financing should unconditionally

increase the likelihood of merger completion. However, the two hypotheses do have different

implications for value reducing mergers and acquisitions. To the extent that banks monitor,

value reducing mergers should be less likely to be completed with bank financing. On the

other hand, with the lazy bank hypothesis, the likelihood of value reducing M&A should not

be impacted by the presence of bank financing.

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2.2 Definitions of key variables

We estimate the abnormal percentage returns over the three-day event window (-1, +1)

using market model benchmark returns with the CRSP equally-weighted index returns. The

market model parameters are estimated over the 200-day period from event day -210 to event

day -11, where event day 0 is the acquisition announcement date. Although not provided here,

we also calculate the abnormal returns using (-2, +2) event window as robustness check and it

does not change our result.

We use the all in spread drawn variable from the LPC data as the measure of the

interest rate of the loan. We make a dummy variable for collateral that takes a value of 1 if

the loan is classified as secured and 0 otherwise. We construct an index that sums up all the

covenants present in the loan, and we construct two sub-indices that sum up the total number

of financial and general covenants. These four variables (abnormal return, loan rate, collateral

and covenants) form the principal dependent variables on which the empirical analysis is

done. Other control variables used in the multivariate tests are defined in Appendix A.

3. Summary statistics

Table 1 presents summary statistics for firm characteristics of the acquiring firms in

terms of commonly used accounting and governance variables. In panel B, we present results

for firm characteristics stratified by whether or not the merger is bank financed. Several

differences for the type of firms that undertake bank financed mergers versus remaining firms

are different in a statistical sense, although the differences in magnitude are not economically

significant in all cases. Bank financed firms have a leverage of 40% leverage versus 43%

leverage for non-bank financed firms. Likewise, bank financed acquiring firms have a mean

free cash flow to asset ratio of 4% versus 6% for the converse sample. Bank financed

acquiring firms also have lower tangibility and lower profitability versus non-bank financed

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firms. Bank financed deals also have larger relative deal size (size of the M&A relative to the

market value of the acquiring firm prior to the merger). Thus, the univariate comparison

across acquiring firms that take bank financing for their merger or acquisition versus those

that do not seem consistent with the notion that bank financing is chosen to mitigate possible

cash shortfalls. Corporate governance (number of independent directors, executive ownership,

GIM index) do not appear to differ significantly in economic terms.

Panel B presents differences in the loan contract terms for M&A loans (recall M&A

loans are loans made in a time window better one year prior to the announcement date and

the completion date of the merger) and the remaining loans to public firms in the LPC

Dealscan database. There are a total of 1556 M&A loans, thus, there are around 3 loans per

bank financed merger. In terms of loan rates, M&A loans have a much lower spread relative

to non M&A loans with the difference being almost 30 basis points relative to the average

loan spread of around 192 basis points over LIBOR. This may possibly be due to a reduction

in credit risk associate with diversifying mergers, on the other hand, several mergers also

result in an increase in leverage which should result in higher spreads for M&A loans. Other

loan contract terms also show large differences – the average number of covenants in a non

M&A loan is 1.15 versus 3.15 for an M&A loan. Thus, on average, an M&A loan has two

more covenants, which is large economically significant difference between these two sets of

loans. However, loan maturity of M&A loans and non-M&A loans do not differ significantly

from one another.

Thus, banks appear to recognize that the loan given is likely for a merger and are

protecting themselves from potential downside risk by increasing covenants which allow

them to call back the loan in the event of a covenant violation. M&A loans are also

significantly more like to be collateralized (39% versus 25% for non M&A loans). Thus, the

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univariate results on loan contract terms are consistent with the notion that banks change both

the price and non-price terms of loan contract terms for M&A loans.

Panel C reports the CAR (-1, +1) for the deals in our sample. The literature

documents positive CARs for the cash deal. Similarly, in our sample, both the bank financed

and non-financed deals on average have positive and statistically significant CAR, but the

difference in mean CAR’s between these two groups is statistically insignificant with a p-

value 0.59. The univariate analysis shows the bank financed deals are not favored by the

market.

In summary, the univariate results of CAR and loan terms suggest that there is no

significant association between bank involvement in financing the deal and the quality of the

deals. Although the banks don’t charge higher spread for M&A loans, they secure themselves

by imposing collateral and more covenants. Next we proceed with multivariate analyses to

isolate the effect of bank involvement on CAR and the effect of the M&A purpose on the

loan contract.

4. Multivariate tests

In the previous section, we examined differences in merger returns across acquiring

firms that used bank financing versus those that did not, and found no significant difference.

However, loan contract terms for M&A loans differed significantly from loan contract terms

for non M&A loans. Since the acquiring firm characteristics are significantly different for

M&A and non M&A loans, some of these differences could be driven by differing borrowing

firm characteristics. In this section, we examine if these differences continue to persist after

accounting for such differences. We also spend significant time accounting for potential

endogeneity in the bank financing decision.

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4.1 Bidder Announcement Returns

In Table 3, we present regressions with the acquirer abnormal return on the bank

financed dummy variable and other variables that have been shown to be significant in

explaining bidder returns. The key independent variable of interest is the bank-financed

dummy. Other control variables, motivated by prior studies include size (natural log of

bidder market capitalization at 11 trading days before the announcement, Asquith et al.,

1983), target status (private, public, subsidiary, Fuller et al., 2002), a dummy for diversifying

acquisition (target is outside of the bidder’s 48 Fama-French industries, Morck et al., 1990),

equity or cash payment, relative size of the acquisition (total value of consideration paid by

the bidder divided by the bidder’s equity market capitalization at 11 trading days before the

announcement), bidder Tobin’s Q, bidder book leverage, and bidder profitability (operating

cash flow scaled by assets). In model 1 of Table 2, we find that bank financing has no effect

on abnormal returns, similar to the univariate results. In all the regressions henceforth, we

control include year and industry fixed effects to account for potential differences in the time

period and industry on acquiring firm returns.

In model 2, we, we control the asymmetric information of the bidder using the earning

residual, defined as the standard deviation of all three-day cumulative abnormal returns

around earnings announcements from I/B/E/S using the market model over the 5-year period

preceding the acquisition announcement (Moeller, Schlingemann, and Stulz (2007)). This

does not make any difference to the results on the irrelevance of bank financing.

Next, in model 3, following Masulis, Wang, and Xie (2007), we also include

corporate governance measure (GIM index) to control for the possibility that public scrutiny

of some firms provides sufficient monitoring to ensure that these firms make better

acquisitions. Datta, Iskandar-Datta, and Raman (2001) find a significantly positive relation

between bidder managers’ equity-based compensation (EBC) and bidder announcement-

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period abnormal returns. Similar to Datta et al. (2001), we define EBC as the percentage of

equity-based compensation in a CEO’s annual compensation package, with equity-based pay

defined as the value of stock options and restricted stock grants. None of these make much

difference to the main result that we observe, namely that bank financing is not relevant for

acquiring firm returns.

In Table 4, we further investigate this question. We test if there are any differences

between acquisition loans given by relationship versus outside banks. This is motivated by

theories and empirical studies of relationship banking (Boot and Thakor, 1993; Berger and

Udell, 1995; Boot, 2000) that postulate that relationship banks have more information relative

to outside banks on their borrowers. Thus, if relationship banks monitor the firms more,

perhaps, a positive result should only be observed if relationship banks provide the

acquisition financing. To test this, in model 1, we include an interaction between bank-

financed and relationship bank, an indicator that equals one if the deal is financed by

relationship bank. The interaction term is insignificant which suggests that the involvement

of relationship bank in financing M&A also does not add value to the borrowing firm’s

shareholders.

Next, we investigate if more reputable banks have a greater incentive to monitor,

where a reputable bank is defined as a bank that has a market share greater than the median in

the previous year. The result shows that no marginal effect from bank-financed dummy and

reputable bank dummy, which suggest no difference between normal bank and reputable

bank in the M&A deal.

Thus, far, we investigated if the merger is impacted by the bank or deal characteristics

and find no significant difference in the basic result that bank monitoring does not have an

effect on bidder returns. Next, we investigate if the bidder characteristics have any impact on

abnormal returns. In particular, bank financing might be a valuable certification device only

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for bidders that are capital constrained or for bidders that have a high degree of information

asymmetry with respect to the capital market. In Table 4B, we investigate this by interacting

the bank financed dummy variable with dummy variables for firms that are unrated, firms

below investment grade as well as dividend paying firms. However, interactions of the bank-

financed dummy with each of these variables is consistently insignificant from 0, suggesting

that firms that are more informationally opaque or firms that have greater credit constraints

also do not benefit from bank financing of their mergers or acquisitions.

4.2 Loan contract term regression

Given the earlier results that bank financing have no wealth effects of average, the

next question that we examine is whether the loan contract terms of acquisition related loans

differ significantly from other loans in price (interest rate) and non-price terms. With the

bank monitoring hypothesis, there should be no difference from acquisition related and other

loans, after adjusting for other borrower and loan characteristics. On the other hand, with the

lazy bank hypothesis, loan contrast terms for acquisition related loans may have higher

interest rate and/or higher collateral requirement and/or higher covenant number of covenants,

the last two which the bank uses to protect itself from downside risk, while at the same time,

not exerting effort on monitoring.

We use several firm-specific characteristics to control for their impact on loan spread.

We use the logarithm of market capitalization Log(assets) as a measure of firm’s size. Larger

firms tend to be more established and thus have easier access to both internal and external

financing. In addition, since a firm’s age and size are positively correlated, larger firms are

likely to have developed a reputation over time. Therefore, larger firms are likely to borrow

from banks on better terms. Another important firm-level characteristic for loan pricing is

profitability, since firms with higher current profits should be able to borrow at better terms

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from the banks. We define profitability as a ratio of EBITDA to Sales. We include the firm’s

leverage ratio in our model since highly levered firms face a higher probability of default, all

else remaining equal. We expect a positive association between leverage and loan spread. We

use Book to Market, the ratio of the book value of assets to market value of assets (market

value of equity plus book value of debt) to proxy for a firm's growth opportunities. All else

equal, a firm recognized as having better growth opportunities can have a lower borrowing

cost. On the other hand, growth firms may be vulnerable to financial distress or subject to

information asymmetry, thus subject to higher costs of loan. Thus, the expected effect of

market to book on loan rates is ambiguous. Tangibility is defined as the ratio of tangible

assets to total assets. Because lenders may recover tangible assets should the firm default, we

expect firms with more tangible assets to have lower borrowing costs. Cash flow volatility,

measured as the standard deviation of quarterly cash flows from operations over the 16 fiscal

quarters prior to the loan initiation year, scaled by total debt, is used to proxy for a firm's

earnings risk and is expected to be positively correlated with the cost of debt.

We control for Log(Maturity) the natural logarithm of loan maturity in months,

because the lender requires a liquidity premium for longer term debt and this liquidity

premium translates into a higher loan spread. We also include Log (Loan Size), the natural

logarithm of the amount of a loan, which may capture economies of scale in bank lending and

thus is expected to be inversely related to the loan rate. Alternatively, this same negative

relation might occur if riskier borrowers are granted smaller loans with higher interest rates.

Performance pricing is a dummy variable equal to one if a loan contract has the performance

pricing feature.2 This is to control for the possibility that lenders price loans differently if they

contain performance pricing clauses.

2 The performance pricing feature makes the loan interest rate step up in the event of adverse shocks to the

borrower’s credit risk. This feature has been shown to have a significant effect on the loan interest rate (Asquith

et. al., 2005).

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The results also show that larger loans and loans with shorter maturity have smaller

spreads, while performance pricing is not significantly related to the loan spread.

Macroeconomic conditions can affect debt pricing. We use three different variables to control

for macroeconomic cycles. Credit spread is the difference between the yields of BAA and

AAA corporate bonds. Term spread is the difference between the yields of 10 year treasury

bonds and 2 year treasury bonds. The literature suggests that credit spread and term spread

are good proxies of macroeconomic conditions and help explain stock and bond returns

(Chen, Roll, and Ross, 1986; Fama and French, 1993). Specifically, credit spreads tend to

widen in recessions and to shrink in expansions (Collin-Dufresne, Goldstein, and Martin,

2001). This is because investors require more compensation for increased default risk in bad

economic times. High (low) term spreads are often used as an indicator of good (bad)

economic prospects. In the regressions, we measure credit spread and term spread one month

before the time the loan becomes active. The results show that credit spread is positively

related to loan spread, suggesting that market-wide default risk is reflected in the individual

loan rate. Finally, we control for loan type, loan purpose, and industry effects. Loans are of

different types, such as 364-day loans, term loans, and revolving loans. Loans can also be

declared for different uses like corporate purposes, debt repayment, takeovers, working

capital, etc. Because loans with different types and purposes are associated with different

risks, they may be priced differently. In addition, we employ 2 digit SIC dummies to control

for the potential differences in risks and debt pricing structures across industries.

Table 5 presents our regression analyses on the loan contract terms for loan in M&A

deals. Model 1 shows the results for the loan rate. Most of the control variables yield a

similar result to Graham, Li, and Qiu (2008). Larger firms have lower loan rates. In addition,

firms with high ebitda/sales pay lower interest rate. However, unlike the univariate results,

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bank financing has no impact on the loan rate. Thus, bank loans that finance mergers are not

different from other loans in terms of loan interest rate.

Non-price terms of the loan contract are equally important in explaining the cross

section of loan spreads. The performance pricing dummy is negative and significant in

explaining the variation in loan spreads. Interestingly, the negative (and significant)

coefficient for maturity and the positive (significant) coefficient for collateral are inconsistent

with the notion that these non-price terms can be used as trade-off features for price terms.3

We find that the banks don’t charge higher spread for M&A loans which are

conceptually considered as higher risk. However, the banks do protect themselves by

imposing collateral and more covenants. In Table 6, we investigate the subsamples for the

bidders are no ratting firms, small firms and dividend paying firm. These kinds of firms are

more likely to be bank-dependent and banks might have more inside information so that they

don’t need to impose more restrictions. Overall, the results partial validate our conjectures.

The interaction term are all negative but insignificant, which means the bank-dependent firms

get less spread, less collateral, less covenant comparing to other firms.

4.3 Endogeneity problem in bank-financing decision

The results so far imply that M&A loans are different from other loans in non-price terms

such as collateral and covenants, and that the CAR of bank financed M&A is not significantly

different from CAR of M&A’s not financed by banks. However, to the extent that such bank

financing decision are correlated with other observable or unobservable firm characteristics

that lead firms to choose bank financing in the first place, then this coefficient may be biased.

3 Paper such as Stultz and Johnson (1985) and others model the collateral decision as one where higher quality

borrowers signal their decision by pledging collateral. On the other hand, models of collateral based on risk

shifting have riskier borrowers pledging collateral. Empirically, several papers starting with Berger and Udell

(1990) and Jimenez et. al. (2006) find that collateral has a positive impact on loan rate, more consistent with the

risk shifting model rather than the signaling models of collateral.

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To account for this several approaches have been proposed in the literature which we employ

here.

4.3.1 Instrumental variable estimation

A potential source of endogeneity is that there may be a common unobserved factor

that drives both the bank financing decision as well as the CAR. For example, only the value

creating deal can be financed by the banks and these deals will get a positive CAR. One

solution is to use the instrument variables that are correlated with bank financing decision but

not correlated with the CAR. We use three instrumental variables which we justify

economically: Our first instrumental variable is a bank availability dummy which equals 1 if

the bidder has obtained any loan that is recorded in the LPC database from any bank in the (-

4, -1) years prior to the merger. The idea here is that a bidder which has accessed this market

before is more like to get a bank loan subsequently, and this should not be related to the

likelihood of positive or negative abnormal returns on the acquisition. A second related

instrument we use is relationship bank dummy which equals 1 if the bidders have a

relationship bank prior to the acquisition, which capture the idea that the bidders can get the

bank funding easier from relationship bank; Our last instrumental variable is credit bubble

period dummy which equals 1 if the merger happened during year 2005 to 2007. Since this

was a time period when banks had access to low cost credit, they should unconditionally be

more likely to make loans, including acquisition loans. However, there is no a-priori reason

to believe that this should be correlated with merger announcement returns for such bidders.

Table 7 gives the results of the IV regressions for the CAR. These 3 IVs yield similar

results that the first stage F-statistics are all larger than 10 and reject the null that the

coefficient on the instruments are insignificantly different from 0 at the 1% level. Also the

second step estimation all get the insignificant coefficient for the bank-financed dummy.

20

Overall, the IV regression result is consistent with the OLS and suggest that banks don’t

screen or monitor the M&A deal.

4.3.2 Treatment effect model

An alternative way of accounting for endogeneity is via the treatment effect model

proposed by Heckman (1979). Here the endogeneity arises because of the binary nature of the

bank-financed dummy which could result in a biased estimate of this variable on the

dependent variables. In this model, the method of adjusting for this bias would be to add the

Heckman’s λ as an additional second stage independent variable in the control regression.

While the treatment effect model does not specifically require an excluded variable, as

identification comes from the non-linearity of the first stage regression, most estimations

would include some excluded variables in the first stage regression. We use the same

variables we used before namely credit bubble period dummy, relationship bank dummy, and

bank availability dummy. As shown in Table 7B, the treatment effect models also gives

insignificant results for bank-financed dummy.

4.3.3 Propensity score matching model

A third method of accounting for selection on observables that has been commonly

used in the literature is propensity score matching, the key econometric difference being from

the instrumental variables and the treatment effect models being that the difference in the

treatment group, here the acquiring firms that obtained bank financing, and the control group,

those acquirers that did not get bank financing, is based on a set of observable characteristics.

Thus, the key econometric methodology here would be to match each treatment observation,

an acquiring firm that obtained bank financing with another acquiring firm that was equally

likely to have obtained bank financing, but in fact did not. The differences between these two

21

samples would reflect the additional value created from bank financing, and the differences in

loan contract terms between these two samples would reflect the incremental effect of

acquisition related loans. We estimates the average treatment effect on the treated (ATT)

using nearest neighbor matching; The nearest neighbors are not determined by comparing

treated observations to every single control, but by first sorting all records by the estimated

propensity score and then searching forward and backward for the closest control unit(s); if

for a treated unit forward and backward matches happen to be equally good, this program

randomly draws either the forward or backward matches; The ATT is computed by averaging

over the unit-level treatment effects of the treated where the control(s) matched to a treated

observation is/are those observations in the control group that have the closest propensity

score; if there are multiple nearest neighbors, the average outcome of those controls is used.

Table 7C shows the result. We find that the average effect of being bank-financed is

insignificantly different from 0.

4.4 Deal completion regression

Lastly, we examine the impact of bank financing on the likelihood of merger

completion. To the extent that bank financing mitigates any credit constraints, and viewing

the merger as a large investment decision, one should expect bank financing to increase the

likelihood of merger completion, conditional on being announced, independent of whether

banks are monitoring or banks are lazy. A secondary test that will distinguish the above two

would be the interaction of bank financing with value losing acquisitions. If banks were

monitoring, then bank financing should reduce the likelihood of value reducing acquisitions.

We conduct a probit analysis of bid completion, where the dependent variable is a

dummy variable equal to 1 if the announced deal is completed, and 0 otherwise. The control

variables are same as that in table 3. The independent variables of interest are the bank-

22

financed dummy, value-losing dummy and the interaction term of bank-financed and value-

losing. The value-losing dummy equals 1 if the CAR is less than 0. In Table 8, we report the

results that the bank-financed deals are more likely to complete. Also when there is a bank-

involvement and the stock price reaction upon initial announcement is negative, the bidding

firm is under pressure to call off the deal. Thus, in this sense, we do find evidence for bank

financing being associated with lower likelihood of completion of value reducing deals,

which benefits the shareholders of the acquiring firm.

5. Conclusion

Our study started from a large base literature that banks add value to borrowing firms and

examined the impact of bank financing on bidder value creation in M&A. There were strong

theoretical and empirical reasons to believe that bank financing should add value to bidding

firm shareholders. Nevertheless, we found scant evidence for this, in fact, to the contrary, we

found that bank loan contracts react in ways that are consistent with bank’s protecting their

own interests by changing loan contract terms in terms of more stringent covenants and more

strict collateral requirements. Further research would examine the circumstances under which

bank financing would add value for shareholders and where it does not add value.

23

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25

Table 1: Summary statistics of mergers

A bank financed deal is defined as a loan made to an acquiring firm between 1 year prior to the announcement

date of the merger to the completion date of the merger. See Appendix A for a detailed definition of all variables.

Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Panel A: Acquirer characteristics

Mean Median Standard

Deviation

Number of

observations

Total assets

($ million) 6812 698.49 21486 2783

Tangibility 0.25 0.18 0.22 2779

Profitability 0.13 0.14 0.12 2783

Current ratio 2.75 1.99 2.62 2683

Asset maturity 7.51 5.53 7.05 2663

Book to market 0.48 0.39 0.36 2779

Leverage 0.4 0.41 0.19 2783

Free Cash Flow 0.04 0.06 0.12 2733

GIM index 9.14 9.00 2.62 1450

No. of directors 9.19 9.00 2.75 1044

Managerial

Ownership 0 0.000006 0 1507

Independent board 0.79 0.00 0.4 1039

Z score (modified) 6.27 4.02 8.32 2774

26

Table 1: Summary statistics of mergers (continued)

Panel C: Target type

Bank-

financed Joint Venture Private Public Subsidiary Deal size Total

0 24 909 1,152 270 0.358 2,355

1 2 191 306 64 0.458 563

Total 26 1,100 1,458 334 2,918

Panel D: Diversifying deal

Diversifying Deal

Bank-financed 0 1 Total

0 1383 972 2355

1 334 229 563

Total 1717 1201 2918

Panel B:Differnce of acquirer characteristics

Bank financed mergers

Mergers not financed

by banks

mean median mean median

Difference of

Means

Difference of

Medians (chi2)

Total asset 6950 628 624 1042 705.90 16.60***

Tangibility 0.24 0.17 0.28 0.18 -0.034** 1.34

Profitability 0.12 0.14 0.16 0.16 -0.036*** 28.29***

Current ratio 2.90 2.04 2.14 1.75 0.75*** 18.73***

Asset maturity 7.51 5.48 7.52 5.68 -0.01 1.07

Book to

market 0.48 0.40 0.46 0.37 0.03 7.38***

Leverage 0.40 0.40 0.43 0.43 -0.034*** 5.99**

Free Cash

Flow 0.04 0.06 0.06 0.07 -0.025*** 16.86***

GIM index 9.17 9.00 9.04 9.00 0.12 0.9

No. of

directors 9.22 9.00 9.07 9.00 0.15 0.27

Managerial

Ownership 3.45e-5 5.72e-6 3.21e-5 5.05e-6 2.4e-6 0.18

Independent

board 0.265 0.000 0.357 0.000 -0.092*** 18.79***

Z score

(modified) 6.520 3.968 5.232 4.108 1.29** 1.85

27

Table 2: Summary statistics of loan facilities This table presents summary statistics of loan facilities in the loan sample. An M&A loan is defined as a loan

that is taken by an acquiring firm between 1 year prior to the announcement date to the completion date of the

merger. A Bank financed merger is a merger paid for partly or completely by cash, and the acquiring firm took a

loan from the LPC database that was in a time window from 1 year prior to the announcement date of the

merger to the completion date of the merger. CAR(-1,1) is the abnormal return computed between -1 to +1 days

relative to the merger for the acquiring firm. Standard errors in parentheses. See Appendix A for detailed

definitions of variables. *, ** and *** represent significance at 10%, 5% and 1% respectively.

Panel A

M&A

loan Frequency Percent

0 32,350 95.41

1 1,556 4.59

Total 33,906 100

Panel B: Loan characteristics.

Overall sample Non M&A

loan M&A loan

Test for

difference

of Means

Loan Spread 192.2 194.4 163.4 31.07***

Collateral 0.249 0.242 0.389 -0.147***

Maturity in months 47.24 47.29 46.24 1.055

Number of Financial

Covenants 0.185 0.173 0.416 -0.243***

Number of General

Covenants 1.059 0.978 2.743 -1.765***

Total number of

covenants 1.244 1.152 3.159 -2.008***

Panel C: Announcement return for acquiring firms

Number of observations CAR (-1,+1)

Not bank financed merger 2355 0.9%

(0.1%)

Bank financed merger 563 0.8%

(0.3%)

Total 2918 0.9%

(0.1%)

Difference of means 0.1%

(0.4%)

28

Table 3 Effect of bank-financing on Acquiring firm return The dependent variable in all specifications is the abnormal return of the acquiring firm in a time

window from -1 to +1 day relative to the announcement date of the merger. A Bank financed merger is

a merger paid for partly or completely by cash, and the acquiring firm took a loan from the LPC

database that was in a time window from 1 year prior to the announcement date of the merger to the

completion date of the merger. A detailed definition of all variables is reported in Appendix A. All

models have year and industry fixed effects that are not reported in the table. Standard errors are

reported in parentheses. Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***,

respectively.

(1) (2) (3) (4)

Bank financed 0.0027 -0.00034 0.00037 -0.0048

(0.0039) (0.0042) (0.0047) (0.0056)

Deal size 0.0042* 0.0023 -0.020*** -0.0061

(0.0023) (0.0030) (0.0060) (0.0096)

Target is Private 0.018*** 0.022*** 0.014*** 0.0053

(0.0039) (0.0045) (0.0051) (0.0062)

Target is a Subsidiary 0.023*** 0.024*** 0.015** 0.0034

(0.0052) (0.0060) (0.0071) (0.0081)

Tender Offer 0.0070 0.0084* 0.0043 -0.0023

(0.0043) (0.0046) (0.0049) (0.0057)

Diversifying merger 0.0015 0.00013 -0.0024 -0.00014

(0.0032) (0.0036) (0.0041) (0.0048)

Log (Market Capitalization) -0.0036*** -0.0034*** -0.0034** -0.0025

(0.00097) (0.0012) (0.0015) (0.0018)

Tobin’s q -0.000071 -0.00027 0.00054 0.00020

(0.0011) (0.0015) (0.0018) (0.0020)

Leverage 0.0019 0.0022 0.011 0.017

(0.0086) (0.011) (0.013) (0.016)

Free Cash Flow -0.011 0.016 0.051* 0.073**

(0.013) (0.023) (0.028) (0.036)

Earnings residual -0.11* -0.034 0.012

(0.057) (0.071) (0.088)

Independent Board -0.0015 0.0034

(0.0048) (0.0058)

GIM index -0.00077 -0.00069

(0.00079) (0.00096)

Managerial ownership 20.4 -27.7

(21.0) (40.3)

Constant 0.091*** 0.073*** 0.076** 0.040

(0.023) (0.026) (0.036) (0.042)

Number of observations 2709 1652 1052 674

adj. R-sq 0.047 0.064 0.035 0.011

29

Table 4: Subsample analysis of acquiring firm abnormal return

The dependent variable in all specifications is the abnormal return of the acquiring firm in a time

window from -1 to +1 day relative to the announcement date of the merger. A Bank financed merger is

a merger paid for partly or completely by cash, and the acquiring firm took a loan from the LPC

database that was in a time window from 1 year prior to the announcement date of the merger to the

completion date of the merger. A detailed definition of all variables is reported in Appendix A. All

models have year and industry fixed effects that are not reported in the table. Standard errors are

reported in parentheses. Other control variables in Table 3 are also used in the empirical estimation but

not reported to conserve space. Significance at the 10%, 5%, and 1% level is indicated by *, **, and

***, respectively.

Panel A: Effect of bank reputation and bank relationship

(1) (2) (3)

Bank financed 0.0030 -0.00077 -0.0089

(0.0092) (0.0048) (0.0097)

Bank Financed * Reputable bank

dummy -0.0033

(0.0100)

Bank Financed * Relationship

Bank Dummy 0.012

(0.014)

Bank financed * Earnings

Residual 0.14

(0.13)

N 1052 1052 1052

adj. R-sq 0.035 0.035 0.036

30

Table 4 (continued)

Panel B: Subsample analysis of CAR

(1) (2) (3) (4) (5) (6) (7) (8)

Bank-financed 0.0036 -0.0011 0.0011 -0.0019 -0.00059 -0.0025 0.00043 0.0036

(0.0055) (0.0065) (0.0049) (0.0059) (0.0055) (0.0068) (0.0049) (0.0051)

No rating 0.0013 -0.0016

(0.0052) (0.0046)

Bank financed * No rating 0.0094 0.005

(0.0083) (0.0076)

Not investment grade -0.0034 -0.011**

(0.0055) (0.0051)

Bank financed * Not Investment Grade 0.0012 0.0064

(0.0084) (0.0082)

Small Firm 0.012** 0.0019

(0.01) (0.01)

Bank financed * Small firm -0.0057 0.0044

(0.01) (0.01)

Dividend payer 0.0041 0.0091**

0 0

Bank financed * Dividend payer 0.0026 -0.0048

(0.01) (0.01)

N 1652 1652 1652 1652 2575 2575 2575 2575

adj. R-sq 0.065 0.063 0.066 0.064 0.053 0.053 0.053 0.055

31

Table 5: Differences of M&A loans from other loans in contract terms The dependent variable in each regression is reported at the top of the column. A detailed definition of all variables is reported in Appendix A. All models have

year, industry, loan type and loan purpose fixed effects that are not reported in the table. Standard errors are reported in parentheses.. Significance at the 10%, 5%,

and 1% level is indicated by *, **, and ***, respectively.

Loan Spread

(1)

Collateral

(2)

Log (maturity)

(3)

Financial Covenants

(4)

General Covenants

(5)

All covenants

(6)

M&A loan 0.0036 0.69*** -0.037* 0.071 0.11*** 0.11***

(0.019) (0.11) (0.021) (0.070) (0.027) (0.025)

Collateral 0.25*** 0.039** 0.93*** 1.03*** 1.02***

(0.017) (0.017) (0.055) (0.025) (0.023)

Log(asset) -0.12*** -0.67*** 0.017 -0.11*** -0.023* -0.031**

(0.014) (0.043) (0.015) (0.026) (0.013) (0.013)

Book to Market 0.17*** 0.67*** -0.026 -0.15*** 0.042* 0.0071

(0.017) (0.080) (0.018) (0.046) (0.022) (0.020)

Leverage 0.68*** 2.14*** -0.045 0.36** 0.16** 0.16**

(0.060) (0.24) (0.063) (0.15) (0.071) (0.067)

Profitability -0.799 -1.02 0.23 0.365 0.194 0.168

(0.103)*** (0.356)*** (0.101)*** (0.234) (0.124) (0.115)

Log(maturity) -0.060*** 0.19*** -0.015 -0.0093 -0.014

(0.012) (0.067) (0.043) (0.019) (0.018)

Log(loan size) -0.060*** -0.060 0.040*** 0.00070 0.085*** 0.064***

(0.0070) (0.039) (0.0071) (0.025) (0.011) (0.010)

Performance pricing -0.059*** 2.34*** 0.087*** 0.95*** 1.14*** 1.10***

(0.015) (0.100) (0.016) (0.053) (0.023) (0.022)

Relationship Dummy -0.015 0.32*** -0.031** 0.18*** 0.11*** 0.10***

(0.013) (0.082) (0.014) (0.048) (0.020) (0.018)

Credit spread 0.17*** 0.14 -0.054 0.076 0.057 0.098**

(0.041) (0.22) (0.041) (0.095) (0.057) (0.050)

Term spread 0.037 0.22 -0.030 -0.16 0.00090 -0.0011

(0.023) (0.14) (0.024) (0.13) (0.034) (0.033)

Constant 6.16*** -0.79 3.98*** -0.37 -0.75*** -0.32

(0.19) (0.89) (0.30) (0.42) (0.24) (0.21)

adj. R-sq 0.57 0.246

32

Table 6: Loan contract analysis - subsample The dependent variable in each regression is panel at the top of the column. A detailed definition of all

variables is reported in Appendix A. All models have year, industry, loan type and loan purpose fixed

effects that are not reported in the table. In addition, all control variables for firm and loan characteristics in

Table 5 are used in the estimation but not reported to conserve space. A detailed definition of all variables

is reported in Appendix A. Standard errors are reported in parentheses. Significance at the 10%, 5%, and

1% level is indicated by *, **, and ***, respectively.

Panel A: Loan Spread

M&A loan 0.76 1.34 -0.081

(4.40) (4.21) (4.41)

No rating -1.53

(4.71)

M&A loan* no rating -2.77

(6.47)

Small firm 5.48

(5.29)

M&A loan*small firm -4.56

(6.66)

Dividend payer -3.55

(4.89)

M&A loan* dividend payer -0.87

(6.55)

Panel B: Collateral

M&A loan 0.82*** 0.63*** 0.66***

(0.17) (0.16) (0.13)

No rating -0.28**

(0.13)

M&A loan* no rating -0.24

(0.22)

Small firm 0.43***

(0.14)

M&A loan* small firm 0.14

(0.22)

Dividend payer -0.81***

(0.11)

M&A loan* dividend payer -0.0046

(0.24)

33

Table 6: Loan contract analysis – subsample (continued)

Panel C: All covenants

M&A loan 0.13*** 0.086** 0.14***

(0.04) (0.03) (0.03)

No rating -0.021

(0.03)

M&A loan* no rating -0.05

(0.05)

Small firm 0.0058

(0.04)

M&A loan*small firm 0.051

(0.05)

Dividend payer -0.00097

(0.03)

M&A loan* Dividend Payer -0.085

(0.05)

34

Table 7: Controlling endogeneity of bank financing Bank Available is defined as 1 if the firm has any loan outstand in the year (-4,-1) to the announcement. Relationship is defined as 1 if the deal gets funding at

least partially from relationship bank. Credit bubble is defined as 1 if the loan is issued between years 2005 and 2007. A detailed definition of all variables is

reported in Appendix A. Standard errors are reported in parentheses. Significance at the 10%, 5%, and 1% level is indicated by *, **, and ***, respectively.

Panel A: IV regression

First Stage

Bank financed

Second Stage

CAR

First Stage Bank

financed

Second stage

CAR

First Stage

Bank Financed

Second stage

CAR

Bank Available 0.51***

(0.023)

Relationship Bank 0.53***

(0.022)

Credit Bubble 0.14***

(0.023)

Bank financed 0.0016 0.0070 -0.0037

(0.0088) (0.0082) (0.028)

Deal size 0.044*** 0.0022 0.040*** 0.0020 0.052*** 0.0026

(0.016) (0.0030) (0.015) (0.0030) (0.018) (0.0034)

Target is private -0.019 0.022*** -0.013 0.022*** -0.0036 0.022***

(0.023) (0.0045) (0.023) (0.0045) (0.027) (0.0044)

Target is a subsidiary 0.014 0.024*** 0.022 0.024*** 0.057 0.024***

(0.031) (0.0060) (0.031) (0.0060) (0.036) (0.0062)

Tender offer 0.038 0.0082* 0.034 0.0079* 0.042 0.010**

(0.024) (0.0046) (0.024) (0.0046) (0.027) (0.0045)

Diversifying Merger 0.0056 0.00011 -0.0011 0.000068 0.0055 0.00023

(0.019) (0.0036) (0.018) (0.0036) (0.022) (0.0036)

Log (market capitalization) 0.0025 -0.0034*** 0.0037 -0.0034*** 0.0094 -0.0034***

(0.0060) (0.0012) (0.0059) (0.0012) (0.0067) (0.0012)

Tobin’s q 0.000027 -0.00026 -0.0000047 -0.00025 -0.0036 -0.00022

(0.0078) (0.0015) (0.0076) (0.0015) (0.0087) (0.0015)

Leverage 0.010 0.0021 -0.037 0.0016 0.067 0.0015

(0.055) (0.011) (0.054) (0.011) (0.062) (0.011)

35

Table 7 (continued)

First Stage

Bank financed

Second Stage

CAR

First Stage Bank

financed

Second stage

CAR

First Stage

Bank Financed

Second stage

CAR

Free Cash Flow 0.35*** 0.015 0.33*** 0.013 0.47*** 0.018

(0.12) (0.023) (0.12) (0.023) (0.13) (0.026)

Earnings Residual 0.074 -0.11* 0.077 -0.11* 0.0052 -0.12**

(0.30) (0.057) (0.29) (0.057) (0.32) (0.054)

First stage residual -0.0025 -0.010 0.0032

(0.010) (0.0096) (0.028)

Constant -0.19 0.073*** -0.22 0.073*** -0.082 0.074***

(0.14) (0.026) (0.13) (0.026) (0.15) (0.024)

N 1652 1652 1652 1652 1652 1652

adj. R-sq 0.305 0.064 0.333 0.064 0.083 0.066

F-value in first stage 479.985 565.912 36.7211

P-value for the IV test 0.0000 0.0000 0.0000

36

Panel B: Heckman treatment-effect model

Bank financed CAR Bank financed CAR Bank financed CAR

Bank financed 0.003 0.001 0.006

(0.07) (0.008) (0.007)

Bank Available 0.453

(0.076)***

Relationship Bank 1.554

(0.087)***

Credit Bubble 1.626

(0.085)***

Deal size 0.002 0.002 0.002

(0.002) (0.002) (0.002)

Target is private 0.021 0.021 0.021

(0.004)*** (0.004)*** (0.004)***

Target is a subsidiary 0.024 0.024 0.023

(0.005)*** (0.005)*** (0.005)***

Tender offer 0.008 0.008 0.008

(0.004)* (0.004)* (0.004)*

Diversifying Merger 0.000 0.000 0.000

-0.003 (0.003) (0.003)

Log (market

capitalization) 0.019 -0.003 0.009 -0.003 0.007 -0.003

(0.020) (0.001)*** (0.022) (0.001)*** (0.022) (0.001)***

Tobin’s q -0.043 -0.000 -0.005 -0.000 0.000 -0.000

(0.032) (0.001)*** (0.034) (0.001) (0.034) (0.001)

Leverage 0.592 0.001 0.384 0.001 0.193 0.000

(0.209)*** (0.015) (0.228)* (0.010) (0.234) (0.010)

Free Cash Flow 1.566 0.015 1.389 0.015 1.484 0.013

(0.512)*** (0.034) (0.546)** (0.022) (0.560)*** (0.022)

37

Panel B (continued)

Earnings Residual -0.595 -0.108 0.822 -0.108 0.956 -0.107

(1.080) (0.056)* (1.144) (0.055)* (1.161) (0.055)*

Constant -1.434 0.072 -1.630 0.072 -1.623 0.071

(0.321)*** (0.025)*** (0.351)*** (0.025)*** (0.358)*** (0.025)***

Lambda -0.001 -0.000 -0.004

(0.040) (0.005) (0.004)

Panel C: Propensity score matching method

Here we estimates the average treatment effect on the treated (ATT) using nearest neighbor matching; The nearest neighbors are not determined by comparing

treated observations to every single control, but by first sorting all records by the estimated propensity score and then searching forward and backward for the

closest control unit(s); if for a treated unit forward and backward matches happen to be equally good, this program randomly draws (hence the letters "nd" for

Nearest neighbor and random Draw) either the forward or backward matches; The ATT is computed by averaging over the unit-level treatment effects of the

treated where the control(s) matched to a treated observation is/are those observations in the control group that have the closest propensity score; if there are

multiple nearest neighbors, the average outcome of those controls is used.

Treatment (bank financed = 1) Control Average Treatment effect Std. Err. T - statistic

354 291 -0.004 0.006 -0.773

38

Table 8: Likelihood of Deal completion

The dependent variable (completion dummy) is a dummy variable that takes a value of 1 if the merger is

successfully completed and 0 otherwise. . A Bank financed merger is a merger paid for partly or completely

by cash, and the acquiring firm took a loan from the LPC database that was in a time window from 1 year

prior to the announcement date of the merger to the completion date of the merger. A detailed definition of

all variables is reported in Appendix A. All models have year and industry fixed effects that are not

reported in the table. Standard errors are reported in parentheses. Significance at the 10%, 5%, and 1%

level is indicated by *, **, and ***, respectively.

Completion

Dummy

Completion

Dummy

Completion

Dummy

Bank financed 0.22* 0.22* 0.56***

(0.11) (0.11) (0.19)

Value losing -0.15* -0.066

(0.083) (0.090)

Bank financed* value losing -0.56**

(0.24)

Deal size -0.14*** -0.14*** -0.13**

(0.051) (0.051) (0.051)

Target is private 1.16*** 1.14*** 1.16***

(0.12) (0.12) (0.13)

Target is a subsidiary 0.88*** 0.87*** 0.88***

(0.16) (0.16) (0.16)

Log (market capitalization) 0.090*** 0.095*** 0.099***

(0.027) (0.027) (0.027)

Tobin’s q -0.0092 -0.010 -0.012

(0.032) (0.032) (0.032)

Leverage 0.13 0.13 0.13

(0.24) (0.24) (0.24)

Free Cash flow 0.34 0.30 0.28

(0.37) (0.37) (0.37)

Tender offer 0.18* 0.18* 0.19*

(0.10) (0.10) (0.10)

Constant -0.56 -0.57 -0.63

(0.55) (0.55) (0.55)

N 2579 2579 2579

39

Appendix A: Variable definition

Variable Names Variable Definitions and Corresponding Compustat Data Items

Firm Characteristics

Leverage (Long Term Debt + Debt in Current Liabilities)/Total

Assets=(data9+data34)/data6

Log(Assets) Natural log of Total Assets=log(data6)

Tangibility Net Property, Plant and Equipment/Total assets=data8/data6

Profitability EBITDA/Total Assets=data13/data6

Market to Book (Market value of equity plus the book value of debt)/Total

Assets=( data25*data199+data6-data60)/data6

Z-score Modified Altman’s (1968) Z-score=(1.2working capital+1.4retained

earnings+3.3EBIT+0.999Sales)/Total

Assets=(1.2data179+1.4data36+3.3data170+0.999data12)/data6

Asset Maturity Asset maturity is the book value-weighted maturity of long-term assets

and current assets, where the maturity of long-term assets is computed as

gross property, plant, and equipment divided by depreciation expense, and

the maturity of current assets is computed as current assets divided by the

cost of goods sold (see Barclay and Smith (1995) and Billet, King, and

Mauer (2007)). In other words, asset maturity = [PPE

/(CA+PPE)]×[PPE/Depreciation] + [CA/(CA+PPE)]×[CA/COGS] =

[data7/(data4+data7)]×[data7/data14] +

[data4/(data4+data7)]×[data4/data41], where PPE=gross property, plant,

and equipment, CA=current assets, and COGS=cost of goods sold

Small firm Dummy variable equal to one if the acquirer has a deflation total asset

equal to or less than that of the median of all firms in the sample.

Earnings Residual

The standard deviation of all three-day cumulative abnormal returns

around earnings announcements from I/B/E/S using the market model

over the 5-year period preceding the acquisition announcement.

Idiosyncratic Volatility

the standard deviation of the market-adjusted residuals of the daily stock

returns measured during the period starting from 205 to six days prior to

the acquisition announcement.

Free cash flow

Operating income before depreciation (item13) – interest expenses

(item15) – income taxes (item16) – capital expenditures (item128), scaled

by book value of total assets (item6).

Market capitalization Number of shares outstanding multiplied by the stock price at the 11th

trading day prior to announcement date.

Managerial equity ownership Bidder managers' percentage ownership of the firm, including both stock

and stock options.

Board size Number of directors on bidder’s board.

Independent board Dummy variable: 1 if over 50% of bidder directors are independent, 0

otherwise.

GIM index Taken from GIM (2003), based on 24 antitakeover provisions. Higher

index levels correspond to more managerial power.

40

Deal Characteristics

Bank financed Dummy variable: 1 for there is at least one loan issued during (-12 month,

deal completion) M&A event window.

Public target Dummy variable: 1 for public targets, 0 otherwise.

Target is private Dummy variable: 1 for private targets, 0 otherwise.

Target is a subsidiary Dummy variable: 1 for subsidiary targets, 0 otherwise.

All-cash deal Dummy variable: 1 for purely cash-financed deals, 0 otherwise.

Diversifying merger Dummy variable: 1 if bidder and target do not share a Fama–French

industry, 0 otherwise.

Relative deal size Deal value (from SDC) over bidder market value of equity defined above.

CAR(–1,+1) Denotes the three-day cumulative abnormal return (in percent) measured

using market model residuals.

Loan Characteristics

M&A loan Dummy variable: 1 for the loan is issued during (-12 month, deal

completion) M&A event window.

Loan Spread Loan spread is measured as all-in spread drawn in the Dealscan database.

All-in spread drawn is defined as the amount the borrower pays in basis

points over LIBOR or LIBOR equivalent for each dollar drawn down.

(For loans not based on LIBOR, LPC converts the spread into LIBOR

terms by adding or subtracting a differential which is adjusted

periodically.) This measure adds the borrowing spread of the loan over

LIBOR with any annual fee paid to the bank group.

Log(Maturity) Natural log of the loan maturity. Maturity is measured in months.

Log(Loan Size) Natural log of the loan facility amount. Loan amount is measured in

millions of dollars.

Collateral dummy A dummy variable that equals one if the loan facility is secured by

collateral and zero otherwise.

Covenant Number of Covenants is the total number of covenants included in the

debt agreement. Number of General (Financial) Covenants is the total

number of general (financial) covenants in the debt agreement

Log (Maturity) Natural logarithm of debt maturity measured in months.

Performance Pricing dummy A dummy variable that equals one if the loan facility uses performance

pricing.

Number of Lenders Total number of lenders in a single loan.

Loan Type Dummies Dummy variable for loan types, including term loan, revolver greater than

one year, revolver less than one year, and 364 day facility.

Loan Purpose Dummies Dummy Variable for loan purposes, including corporate purposes, debt

repayment, working capital, etc.

Macroeconomic Factors

Credit Spread The difference between BAA corporate bond yield and AAA corporate

bond yield. (Data source: Federal Reserve Board of Governors.)

Term Spread The difference between the 10 year treasury yield and the 2 year treasury

yield. (Data source: Federal Reserve Board of Governors.)