THE LONG-TERM IMPACT OF BANK MERGERS ON SUSTAINABLE GROWTH AND SHAREHOLDER RETURN

Please do not quote or cite without permission.

Gerard T. Olson*

Villanova University College of Commerce and Finance

Department of Finance Villanova PA 19085

(610) 519-4377 Fax: (610) 519-6881

E-Mail: gerard.olson@villanova.edu

Michael S. Pagano Villanova University

College of Commerce and Finance Department of Finance Villanova PA 19085

(610) 519-4389 Fax: (610) 519-6881

E-Mail: michael.pagano@villanova.edu

August, 2003

JEL Classification: G34, G21, G35

Keywords: Banks, Mergers, Acquisitions, Sustainable Growth, Payout Policy, Empirical * Contact author: Michael Pagano. The authors wish to thank Bill Lang and the Office of the Comptroller of the Currency for graciously providing some of the state banking data used in our analysis and David Nawrocki for his assistance in compiling the bank indexes. The views expressed in this paper are those of the authors and do not represent those of the Office of the Comptroller of the Currency or the Department of Treasury. We also thank seminar participants at Villanova University and Temple University for their comments.

THE LONG-TERM IMPACT OF BANK MERGERS ON SUSTAINABLE GROWTH AND SHAREHOLDER RETURN

Please do not quote or cite without permission.

ABSTRACT

We study the mergers of publicly traded bank holding companies during 1987-

1997 and find that the acquiring firm’s sustainable growth rate is an important

determinant of the cross-sectional variation in the merged entity’s long-term operating

and stock performance. The most economically significant determinants of the merged

bank’s abnormal stock return performance are the acquiring bank’s estimated sustainable

growth rate prior to the acquisition, as well as post-acquisition changes in this growth

rate, and the bank’s dividend payout ratio. Improving a bank’s sustainable growth rate

from one standard deviation below the sample mean to one standard deviation above the

mean can increase a merged bank’s cumulative 3-year buy-and-hold abnormal return by

an average of 138.1%. This result is sufficiently large to enable the average bank merger

to significantly out-perform relevant stock market benchmarks over a 3-year post-merger

period. This finding is robust even after controlling for differences in state banking

regulations over the sample period, differences in the relative size and market share of the

acquirer and target banks, managerial and blockholder ownership variables, and the

possible endogeneity of the bank’s sustainable growth rate.

THE LONG-TERM IMPACT OF BANK MERGERS ON SUSTAINABLE GROWTH AND SHAREHOLDER RETURN

I. INTRODUCTION

Mergers and acquisitions represent a widely used technique to increase the rate of

growth in the size of the firm as well as its market share. During the period 1986 to 2000,

merger activity increased in terms of the number of announcements, the total value of the

deals, and the number of mega deals.1 In 1986, there were 3,336 net merger and acquisition

announcements with an estimated total value of $173.1 billion. By the year 2000, net merger

and acquisition announcements increased to 9,566 with a total value of $1,325.7 billion. The

number of mega deals increased at an even faster rate during the period. In 1986, 346 deals

were valued at least $100 million and 27 deals valued at least $1 billion but by 2000 there

were 1,150 deals valued at least $100 million and 206 deals valued at least $1 billion.

According to Mergerstat Review 2000, ninety-eight of the top one hundred transaction values

of all time occurred during this period.

In a study of bank mergers during the period 1980 to 1998, Rhoades (2000) also finds

an increase in the number of mega deals. There were 71 mergers involving target banks with

assets greater than $1 billion during the period 1980 to 1989 but this figure increased to 177

banks during the period 1990 to 1998. Eighteen of the top one hundred mergers of all time

involved banks during the period 1986 to 2000 (per Mergerstat Review 2000).

The mega deals involving banks are the result of deregulation of state and federal laws

limiting intrastate and interstate mergers. The stated motivations of the deregulation and

resulting bank mergers include economies of scale, geographic diversification leading to

1 We define mega deals as any merger where the total value of the purchase price is $1 billion or more.

earnings diversification, sustained revenue enhancement and thus earnings support, and

increases in expected operating performance. The resulting increase in the growth of the

bank’s size and market share due to mergers may not result in gains to shareholder wealth if

these expected benefits cannot be realized.

When mergers result in increases in growth that are sustainable, the shareholders can

expect to observe gains in wealth. Sustainable growth refers to the revenue growth the firm

can achieve given its operating constraints, dividend policy, and capital structure. Sustainable

growth can be defined in terms of operating and financial performance measures such as

return on assets, dividend payout, profit margin, asset turnover, and financial leverage.

Defined in this way, sustainable growth represents a comprehensive summary measure that

captures the bank’s ability to manage the composition, credit quality, and pricing of its assets

and liabilities, as well as the bank’s degree of operating and financial leverage.

The actual growth the bank may attain, however, is dependent upon the growth of the

markets in which it operates and the actions and reactions of its competitors. Given the

constraints of the growth of its markets and competitive pressures, mergers might represent

the only way for the bank to grow at its sustainable growth rate. In particular, the mega deals

of banks might be an important way for large banks to realize their sustainable growth.

Historically, banks have operated in a highly regulated environment that has affected

the size and number of firms in the industry. With recent deregulation of the industry, banks

have responded with increased merger activity. The purpose of this paper is fourfold. First,

we develop a model of sustainable growth that incorporates the bank’s return on assets,

dividend payout, and equity capital ratio. These factors can be used to determine whether the

stated motivations of bank mergers are realized. Second, we link the bank’s sustainable

growth to the value of the bank. Given an optimal operating performance, dividend policy,

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capital structure, and nonbinding market constraints, we demonstrate the resulting sustainable

growth rate will maximize the value of the bank.

Third, we empirically test whether the increased rate of growth in the size of banks

and the reduced competition resulting from mergers has resulted in an increase in the long run

performance of the acquiring bank’s stock price. Prior research on bank mergers has focused

on the short run effects of merger announcements on both the acquiring and target firms’

returns. The results indicate wealth transfers from the acquiring to target shareholders as

demonstrated by zero or negative average abnormal returns for acquiring banks and positive

average abnormal returns for target banks on the announcement of the merger.

Announcement effects reflect investor’s expectations concerning the prospects of mergers at

the time of the announcement. Due to information asymmetries between management and

shareholders, these expectations may not be realized. Thus, the success or failure of bank

mergers should not be judged solely on the announcement effects on stock price but rather on

the long-term performance of the acquirer’s stock subsequent to the merger.2 Fourth, we

determine the impact of changes in the bank’s sustainable growth resulting from mergers on

the long-term performance of the acquirer’s risk-adjusted return. By focusing on the

movements of the variables through time we can get a better understanding of their linkages

and relationships. A better understanding of these relationships can permit banks to improve

the target selection process and improve the management of the post-combination firm.

Our empirical results indicate that the acquiring bank’s estimated sustainable growth

rate prior to the acquisition, the post-acquisition changes in this growth rate, and the bank’s

dividend payout ratio are economically significant determinants of the merged bank’s

2 Note that we focus on returns to common shareholders because they are the owners of the residual claim on the bank’s cash flows and assets. For example, we do not focus on changes in accounting measures or estimated cost efficiency measures because these are, at best, indirect measures of an acquisition’s effect on shareholder wealth.

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abnormal stock return performance over the three years following the merger. Our findings

are robust after controlling for differences in state banking regulations over the sample period,

differences in the relative size and market share of the acquirer and target banks, managerial

and blockholder ownership variables, and the possible endogeneity of the bank’s sustainable

growth rate.

II. REVIEW OF LITERATURE

Whether a merger is ultimately found to be successful is dependent upon the price

paid in order to gain control. If the target firm’s stock is not mispriced in the capital markets,

the premium paid to gain control must be less than the expected benefits from the acquisition

for the merger to be characterized as successful. Several studies on bank mergers have

focused on the size of the merger premium and typically relate this premium to the target’s

and acquirer’s financial conditions and state regulatory controls.3 For example, Adkisson and

Fraser (1990) find bank merger premiums are significantly larger in states that permit

interstate banking. They also find that premiums are greater in states that allow unlimited

intrastate holding company acquisitions. Hakes, Brown, and Rappaport (1997) investigate the

relationship between state legislation that limits the percent of total deposits a bank may hold

within a state (referred to as deposit caps) and merger premiums. Hakes et al. (1997) contend

that deposit caps substantially reduce the size of the potential merger premium because, once

reached, a bank cannot exceed the caps either through expansion or mergers. They find that

the size of the premium will be larger for stock transactions compared to cash deals and that

acquirers pay a premium for off-balance sheet income. They find no evidence that a premium

is paid to enter a more concentrated market.

3 We do not attempt to catalogue all merger-related papers in this section. Instead, we focus on those papers most closely related to our goal of understanding the factors affecting long-term post-merger stock performance.

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Hunter and Wall (1989) examine the financial characteristics of the target banks in

559 U.S. bank mergers during the 1981 to 1986 period. Their results, which were stable

across time and geographic region, indicate that the most valued merger partner is likely to

have faster growth in core deposits and total assets, higher than average profitability, a higher

ratio of loan-to-assets, and rely on more financial leverage than the typical bank in the sample.

Cheng, Gup, and Wall (1989) attempt to identify acquirer and target financial characteristics

that explain the ratio of purchase price-to-book value paid for target banks. They find that the

growth rates of assets and core deposits, return on equity, as well as the loan quality of the

target banks are positive determinants of merger premiums. They also report premiums are

negatively related to the return on assets and asset growth of the acquiring banks and

positively related to market-to-book value.

Houston and Ryngaert (1994) find wealth transfers from bidding to target shareholders

using a “leakage date” rather than the announcement date. By combining the average

residuals from a market model regression of the returns related to the acquiring and target

firms, however, they show that the five-day average residuals are not significantly different

than zero. They also find the average residuals are higher when both firms have above-

average return on assets, significant overlap in operations, the relative size of the target is

larger, and the payment method is not in the form of stock.

Frame and Lastrapes (1998) also find wealth transfers from bidding to target

shareholders on the announcement of the transaction. In a study of 52 acquiring banks during

the period 1990 to 1993, they find, however, that interstate acquisitions using the purchase

method of accounting actually increased shareholder wealth for acquirers on the

announcement. Over a longer event horizon of 11 days, Frame and Lastrapes find the relative

size of the transaction and the method of accounting to be the most important determinants of

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acquirer abnormal returns. Shawky, Kilb, and Staas (1996) report that higher merger

premiums are paid for smaller size targets, targets with higher return on equity and lower

leverage, interstate transactions, and stock acquisitions.

Palia (1993) identifies financial, regulatory, and managerial factors that impact the

level of bank merger premiums. The managerial factors include the equity ownership of the

acquiring bank’s management and the equity ownership of the target bank’s management.

Palia finds financial factors related to loan quality and market power are positively related to

merger premiums and negatively related to the relative size of the participants, suggesting a

lack of opportunities to realize economies of scale or scope when acquiring a relatively large

bank. All three regulatory factors are statistically significant and are positively related to

merger premiums. This suggests that a protective regulatory environment for the target bank

is potentially more valuable to the acquirer. Palia also identifies a non-linear relationship

between the management ownership values and merger premiums. For acquirers, the results

exhibit a U-shaped relationship between merger premiums and the acquirer’s management

ownership, implying potential agent-owner conflicts. For targets, an inverted U-shaped

relationship exists between merger premiums and the target’s management ownership.

Consistent with an earnings diversification hypothesis related to mergers, Benston,

Hunter, and Wall (1995) find that bid premiums are negatively related to the variances and

covariances of the bidder’s and target’s returns on assets and relative size, as well as

positively related to the capital-to-assets and market-to-book value ratios.4 Demsetz and

Strahan (1997) support the earnings diversification hypothesis of Benston, Hunter, and Wall

(1995). Demsetz and Strahan (1997) find that large bank holding companies can diversify

4 The earnings diversification hypothesis predicts that acquirers will pay a higher bid premium for targets that are less risky. The earnings diversification hypothesis is based on the assumption that a merger decreases the combined variability of the combined entity’s cash flows and therefore enables the banks to increase their financial leverage and after-tax cash flow subsequent to the merger.

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more than their smaller counterparts via geography, product scope, and by the sheer number

of customers. The larger bank holding companies can increase their financial leverage to

compensate for the natural decrease in business risk afforded by size. As a result, the large

bank’s total risk can remain unchanged while increasing its after-tax cash flows.

In contrast to prior research that focuses on short-term performance and merger

premiums, Piloff (1996) studies 48 bank mergers during 1982-1991 using both stock market

and accounting data and finds that both sets of performance measures do not show any

significant changes during the 2-year post-merger period. Not surprisingly, Piloff does not

find a consistent set of factors that explain the observed insignificant changes in post-merger

performance. Using an earlier data set, Rhoades (1987) finds corroborating evidence for

Piloff’s results in that both post-merger cost and profit measures are unaffected by bank

acquisition activities. Similarly, Peristiani (1997) measures post-merger efficiency using

accounting data during 1980-1990 and finds that acquirers failed to improve efficiency after

the acquisitions have been completed. Madura and Wiant (1994) focus on stock market

reactions to bank acquisitions and find evidence supportive of the weak results identified by

the above research. They note that acquiring banks experience negative stock price reactions

that persist for 3 years after the merger. Those mergers that perform better than average are

those that have poor pre-acquisition performance and are made within the acquirer’s existing

operating region.

DeLong (2003) uses 54 bank mergers during 1991-1995 to examine whether

“focusing” strategies (i.e., concentrating on a geographic area or generating economies of

scale) or “diversifying” strategies (e.g., diversifying geographically or generating economies

of scope) affect the long-term performance of measures such as return on assets, stock return,

and efficiency (as measured by the non-interest expense-to-revenue ratio). She finds that the

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only consistently significant variable that explains cross-sectional differences in long-term

performance is the relative volatilities of earnings of the target and acquirer, thus suggesting

that banks with similar earnings streams might be effectively using an earnings diversification

strategy.

Contrary to the numerous studies described above that have found negative or

insignificant changes in post-merger performance measures, there are two papers that have

found positive post-merger results. Using a set of 30 large bank mergers, Cornett and

Tehranian (1992) find positive post-merger gains in accounting- and stock-based performance

measures and that these gains are correlated with increased growth rates in profits,

productivity, assets, and deposits. Houston, James, and Ryngaert (2001) examine a set of 41

bank mergers during 1985-1996 and find positive changes in the combined market value of

bidders and targets. They find that these positive results are primarily driven by cost savings

rather than increased post-merger revenues. These two papers suggest that the post-merger

performance of banks is not clear-cut and that there may be some winners in this area.

Further, these results indicate that changes in growth rates might be an important determinant

that distinguishes between the winners and losers.

In the corporate finance literature, there are a couple of notable papers that are relevant

to our analysis. Franks, Harris, and Titman (1991) examine the post-acquisition stock price

reaction of 399 takeovers during 1975-1984 and suggest that a multi-factor approach of

measuring performance removes a large portion of the negative reactions to corporate

mergers. Loughran and Vijh (1997) study 947 corporate mergers during 1970-1989 and find

that the form of payment matters in terms of determining the post-merger stock performance

of takeovers. They find that stock-based acquisitions experience negative 5-year excess

returns while cash-based acquisitions result in strong positive 5-year excess returns.

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The above literature suggests that earnings diversification-related and interstate

mergers are rewarded by investors. Also, there is some evidence that regulation and

managerial ownership can play an important role in affecting merger premiums. The vast

majority of the above studies are primarily focused on short-term stock market reactions

related to merger premiums and announcement effects. In addition, other papers that do focus

on long-term performance such as Rhoades (1982, 2000), Focarelli, Panetta, and Salleo

(2002), and Schranz (1993) typically concentrate on long-term changes in financial statement

variables such as return on assets, return on equity, financial leverage, etc., and do not

examine long-term stock performance. In particular, our focus on the effect of sustainable

growth and other factors on the bank’s post-merger long-term stock performance provides a

new way of studying the performance of bank mergers.

III. SUSTAINABLE GROWTH AND FIRM VALUE

Assuming the firm has shares that are publicly and actively traded as well as positive

earnings, the P/E model, as depicted in equation (1), can be a useful model of valuation of the

firm’s common stock.

V0 = [(P/E)t Et] / (1+K)t = (P/E)t E0 (1+g1) (1+g2)…(1+gt) / (1+K)t (1)

where,

V0 = the stock’s value at the present time,

P/Et = the expected price-earnings ratio as of period t,

Et = expected earnings in period t,

K = the discount rate on the firm’s common stock, and

gt = the expected growth in earnings in period t.

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The value of P/Et is dependent upon the expected growth and risk of the firm in period

t. The value of K is dependent upon the risk free rate and risk of the firm. The value of Et is

dependent upon the firm’s current earnings and the expected growth in earnings, gt, over the

forecast horizon. In summary, we can present the P/E model in stylized form as follows:

V0 = f1 (Eo, Γ, g) (2)

where,

Eo = current earnings,

Γ = a measure of risk of the firm, an increase in Γ will increase K and decrease P/Et, and

g = expected growth of the firm over the forecast period.

The expected growth of the firm over the forecast period is dependent upon market or

industry conditions, operating performance, use of leverage, and dividend policy. One

measure of the firm’s expected growth that incorporates these factors is its sustainable

growth. Numerous models of sustainable growth have been presented in the literature that

attempt to measure what growth the firm can sustain in the long run given its operating and

financial constraints.5 Sustainable growth refers to the growth in sales the firm can achieve

given its operating constraints and without altering its dividend or financial policies. The

sustainable growth models usually assume, either explicitly or implicitly, that,

1. Depreciation is sufficient to maintain the value of existing assets,

2. The profit margin remains constant during the planning period,

3. The firm maintains a target debt to equity ratio,

4. The firm has a target dividend payout ratio that it maintains,

5 For more details on sustainable growth, see Babcock (1970), Higgins (1977, 1981), Fruhan (1979), Kyd (1981), Johnson (1981), Higgins and Kerrin (1983), Varadarjan (1983), Eiseman (1984), Olson, Clark and Chiang (1986), Clark, Chiang, and Olson (1989), Clark, Clark, and Olson (1990), Hempel and Simonson (1991), Olson and Clark (1993), Platt, Platt, and Chen (1995).

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5. The firm will not issue new equity during the planning period,

6. The ratio of total assets to sales is constant throughout the planning period,6

7. The firm operates in a perfectly competitive market and no constraint from the

external environment is binding,

8. The only binding constraints of the internal environment are financial.

Given these assumptions, sustainable growth can be derived from the identity that a firm’s

uses of funds must equal its sources. By placing the uses of funds on the left hand side and

sources of funds on the right hand side results in equation (3):

(∆s / S0) A = ∆s / T = P (1 - d) L (S0 + ∆s) (3)

where,

∆s = the change in sales (S),

A = total assets,

T = total asset turnover = sales / total assets,

P = profit margin = earnings / sales,

(1 - d) = the retention ratio, and d = dividend payout = dividends / earnings,

L = measure of leverage = assets / common equity = 1 / (equity / assets) = 1 / ECAP, and

S0 = sales from last period.

Solving equation (3) for ∆s / S0, we obtain the firm’s sustainable growth, gs, which is depicted

in equation (4):

gs = ∆s / S0 = [P (1 – d) L T] / [(1 – P (1 – d) L T)]

= [F (1 – d) L] / [(1 – F (1 – d) L)] (4)

where,

F = return on assets = ROA = earnings / assets. 6 Note that this assumption is equivalent to assuming that a simplified percentage-of-sales forecasting method is used to estimate a firm’s financing requirements.

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Equation (4) has several implications for bank managers. First, the growth the bank

can sustain in the long run is dependent upon its long run operating performance as measured

by its return on assets, its dividend policy, and leverage as measured by its equity capital ratio,

ECAP. Second, changes in the bank’s return on assets, dividend policy, and equity capital

ratio will result in a change in the bank’s sustainable growth. Changes in the bank’s growth

will be sustainable in the long run only if the changes in the components can be maintained.

For example, if the return on assets increases due to an increase in riskier loans, any spike in

ROA in the current period may not be sustained due to the possibility of higher defaults

occurring in the future. Third, the bank can grow faster than its sustainable growth only if it

increases its return on assets, reduces its dividend payout, decreases its equity capital ratio, or

issues new equity. Fourth, sustainable growth can be viewed as an optimal long run growth if

the bank can achieve a simultaneous optimal operating performance, dividend policy, and

capital structure. Fifth, an optimal sustainable growth can lead to maximization of the value

of the bank.

IV. EMPIRICAL METHODOLOGY

We examine the long-term performance of bank mergers by analyzing the cumulative

buy-and-hold abnormal stock returns (BHAR) associated with each deal on a cross-sectional

basis. We can use the above models to examine how BHARs vary on a cross-sectional basis

by looking at the change in the market value of a firm’s common equity, ∆V, and how

changes in risk, ∆Γ, and expected growth, ∆g, affect the firm’s change in stock value. In

effect, the ∆V over the 3 years following a merger (adjusted for movements in broad stock

indexes) is the BHAR for an acquiring bank. This idea can be summarized as:

∆V = BHAR = f2(∆Γ, ∆g) (5)

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where,

BHAR = the cumulative difference between the stock’s return and the return on a relevant

stock index benchmark (over 1-3 years).7

Since we are studying long-term market-adjusted performance, we can calculate the

changes in the above variables over 1-, 2-, and 3-year time horizons after the merger is

consummated. Based on Equation (5), we expect the cross-sectional difference in BHARs to

be due to changes in risk, ∆Γ, and the expected growth of the bank, ∆g.

Changes in the relative riskiness, ∆Γ, or stated alternatively, changes in the relative

safety of the firm’s equity cash flows will be due to numerous firm-specific and market-wide

factors. Since the choice of asset pricing model to use for estimating Γ is contentious, we

focus on a firm-specific factor that is under the control of management as our proxy for

changes in risk. Namely, we use the change in the bank’s common equity-to-assets capital

ratio (∆ECAP) as a measure of the firm’s financial leverage and, hence, risk because bank

management (and bank regulators) view this ratio as an important metric in assessing the

relative safety and soundness of a financial institution. Further, as Hamada (1972)

demonstrated, financial leverage is also directly related to a firm’s beta. Thus, ∆ECAP can

act as a reasonable proxy for changes in the firm’s total risk and systematic risk sensitivities.8

7 Market-adjusted returns are computed for our definition of BHAR rather than attempting to estimate an expected return via an asset pricing model. Due to the estimation problems noted in Lyon, Barber, and Tsai (1999), we view the market- and custom portfolio-adjusted BHARs as potentially more accurate than estimating expected returns with an asset pricing model. 8 It should be noted that these relations between financial leverage, risk, and the firm’s required discount rate assume that we are holding all other variables constant. For example, a merger that is motivated by earnings diversification might lead a firm to have a lower level of operating risk but might also lead the firm to take on more financial leverage so that the overall level of total firm risk is unchanged (or even higher). We allow for the possibility of leverage’s multiple influences by leaving the parameter for financial leverage unconstrained. Thus, according to our prediction, we expect financial leverage to be negatively related to a bank’s BHAR. If, on the other hand, the bank is adjusting its operating risk to offset changes in financial leverage, then we expect the leverage parameter in our model to be insignificant or positively related to the bank’s BHAR. The empirical results of our model can then be used to verify which of these competing effects is best supported by our sample.

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Based on the above discussion and Equations (1) – (5), we can specify a more detailed

version of Equation (5) that includes variables to control for cross-sectional variations in: a)

state banking regulations, b) concentrations of banking activity within each state, c) the asset

size of the target relative to the acquirer, d) the prior history of an acquiring bank’s

sustainable growth rate, and e) differences in managerial incentives. Similar to Healy, Palepu,

and Ruback (1992), we compute long-term changes in operating and stock performance in

order to gauge the lasting impact of the merger rather than focus on short-term merger

premiums and short-term abnormal stock returns. However, in contrast to Healy et al., we do

not aggregate the target’s and acquirer’s pre- and post-merger operating performance because

we wish to isolate and examine the separate effects of target- and acquirer-specific variables.9

Thus, our model is more closely aligned to the ones specified in Hakes et al. (1997) and

Cheng et al. (1989) where the disaggregated variables for both targets and acquirers are used.

The more descriptive version of Equation (5) is presented below:

∆V = BHAR = f2(∆d, ∆K, ∆g) = f3( ∆ECAP, ∆gs, Controls) (6)

where,

∆d = CHDIV = change in the acquiring firm’s dividend pay-out ratio (over 1, 2, and 3 year

horizons),

∆ECAP = CHECAP = change in the acquiring firm’s book value equity-to-total assets capital

ratio (over 1-3 year horizons),

∆ gs = CHSGR = change in the acquiring firm’s sustainable growth as defined in Equation (4)

(over 1-3 year horizons), and

9 Clearly, aggregating the two parties’ results would hinder our ability to identify which of the target’s or acquirer’s variables are the most important in determining long-term abnormal stock performance of these bank mergers.

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Controls = 13 variables based on the five control factors: state banking regulations, bank

concentration, relative size of the target, pre-acquisition sustainable growth, and differences in

managerial incentives.

Our empirical model also specifies “change” variables (∆d, ∆ECAP, ∆SGR) for only

the acquiring banks because there are, by definition, no equivalent data for the target banks’

change variables.10 In addition, initial empirical tests were performed using the target banks’

data for pre-merger sustainable growth rates (as well as financial leverage and dividend pay-

out ratios). The target banks’ data were initially included based on the findings of Cheng et

al. (1989) that suggested target-specific financial variables could help explain short-term

merger premiums. However, our initial results showed no significance for these additional

target-specific variables and, due to the reduction in the number of observations caused by the

inclusion of these variables and our interest to conserve space, we report our results without

including these target-specific financial variables.11

The first set of control variables are related to variations in state banking regulations

during 1987-1997. Two dummy variables are constructed (CAPi and BRANCHi, where i =

either the Target or Acquirer) to capture state-specific differences in restrictions on within-

state banking activities. For example, as Hakes et al. (1997) points out, several states during

our sample period had caps on the amount of statewide deposits any one banking entity could

10 We omit the change in ROA (CHROA) from Equation (6) because the inclusion of this variable in a model that already contains changes in SGR (CHSGR) as well as the two other sub-components of SGR (i.e., CHDIV and CHECAP) creates a high degree of multi-collinearity between these four variables. This finding is not surprising given that Equation (4) demonstrates that SGR is computed using the d, ECAP, and ROA variables. We address this multi-collinearity issue, as well as the potential endogeneity of CHSGR, in the empirical results section (Section V.B). 11 In this way, we can analyze a larger sample of mergers and obtain empirical results that are qualitatively similar to the ones derived from the smaller data set that includes both acquirer- and target-specific financial variables. As we discuss below, we still include target-specific variables related to differences in state banking regulations.

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control. These authors found that the presence of a deposit cap in the target bank’s state

lowers the short-term merger premium paid by an acquiring bank. Given this evidence, we

control for the cap’s effect on our long-term BHARs by setting CAPi equal to 1 if the bank

(either as a target or acquirer) is based in a state that has deposit cap. Also, several states had

restrictions on interstate branching prior to the enactment of the Reigle-Neal Act in 1994. As

Hughes, Lang, Mester, and Moon (1996) and Hakes et al. (1997) have noted, these interstate

branching restrictions can have important effects on bank profitability, riskiness, efficiency,

and merger activity. Thus, BRANCHi is set to 1 if the bank (either as a target or acquirer) is

based in a state that had interstate branching restrictions at the time of the merger. We expect

mergers where the target banks operate in states with deposit caps and / or interstate

branching restrictions to have lower BHARs, ceteris paribus, because the acquiring bank will

be investing in a state where its growth potential is limited by bank regulations. Conversely,

mergers where the acquiring banks have state-mandated deposit caps might have higher

BHARs, especially if the firm acquires a bank in a state that does not have such a cap. In this

case, the acquiring bank may become more valuable by side-stepping its home state’s deposit

limits in order to continue to grow.

We control for the effects of within-state mergers by assigning a 1 to the dummy

variable called STATE if the target and acquirer are both based in the same state. There is a

growing banking literature, as noted in Evanoff and Ors (2001), that studies the differences in

the effects of intra- vs. inter-state bank mergers in terms of bank efficiency, profitability, and

riskiness. Our dummy variable, STATE, is one way to control for the effects of a within-state

merger. The sign on this variable is indeterminate because an intra-state merger can lead to

greater market power (and a higher BHAR) but it can also lead to greater limitations on future

growth (and thus a lower BHAR), especially if the banks are in a state that has a deposit cap.

16

According to Hakes et al. (1997), among others, another factor that might influence

the value of a merger premium is the level of concentration within the banking industry of the

target bank’s state. In our sample, we use state-specific rankings from the Office of the

Comptroller of the Currency for the top 3 banks (in terms of the book value of total assets)

within each state to form a variable, RANKi, that assumes the integers 1-4, where a value of 1

denotes the bank is the largest bank in its home state, 2 denotes the second-largest bank, 3

represents the third-largest bank, and 4 signifies all other banks within the state that are not

within the top 3. This variable is included to control for both the target’s and acquirer’s

relative ranking within their respective home states. The sign on this control variable is

indeterminate because BHAR might be greater or smaller depending on whether the bank’s

standing in terms of its home state market share is beneficial (e.g., if a target’s ranking is high

and an out-of-state firm wants to “buy market share” via a merger) or detrimental (e.g., if a

potential target’s high ranking decreases the chances of a merger because of the bank’s large

size, then it might deter some banks from entering the target’s state in the first place).

Another factor that can affect merger premiums noted by Cheng, Gup, and Wall

(1989) and Palia (1993), among others, is the size of the target relative to the acquirer

(typically measured by the book value of total assets). We use the variable, RELSIZE, to

control for this effect. RELSIZE is defined as the book value of the target’s assets divided by

the book value of the acquirer’s assets. As Cheng et al. (1989), Palia (1993), and others have

observed, RELSIZE exhibits an inverse relation with short-term bank merger premiums.

Similarly, we expect RELSIZE to have an inverse relation with our sample of BHARs.12

12 Initially, we also considered including a dummy variable for stock- versus cash-based mergers but nearly all of the mergers in our sample (87%) used stock as their form of compensation to the target bank’s shareholders. Not surprisingly, the lack of variability in this potential independent variable led to statistically insignificant parameter estimates across all specifications when such a dummy was included in the model. Thus, we have omitted this variable from the final results reported here.

17

One other variable to consider is whether or not the acquirer’s previous operating

history provides some insight into how the merged entity will perform over the subsequent 1-

3 years. For example, does the acquirer’s past sustainable growth rate (referred to as LSGR)

affect the bank’s BHAR? We use the acquirer’s sustainable growth rate for the year prior to

the merger’s effective date as our proxy variable for the bank’s past ability to manage

growth.13 We expect LSGR to be positively related to BHAR because a higher LSGR

suggests the acquirer has been successful in the past in terms of managing growth and that

this can represent a favorable signal of the bank’s managerial talent and might indicate that

the bank is more likely to generate positive BHARs after the merger is completed.14

As noted in the previous section, Palia (1993) observed that the managerial incentives

of both the target’s and acquirer’s senior managers can affect short-term merger premiums. In

addition, large external investors known as “blockholders” can also exert influence over bank

merger activity. We include four variables to control for the effects of managerial incentives

and outside blockholders (MGMTOWNi, MGMTOWN2i, OPTGRANTi, and BLOCKi). The

MGMTOWNi variable is defined as the percentage of the bank’s total amount of equity shares

owned by senior management. As Palia (1993) showed, managerial equity ownership can

have a non-linear effect on the short-term merger premium and can work in opposite

directions depending on the level of the equity stake and whether the managers represent the

acquiring or target bank. We therefore also include the square of MGMTOWNi in order to

account for this potential non-linearity (MGMTOWN2i). In addition to the two managerial

equity ownership variables, stock options granted to senior management (measured as a

13 LSGR is formally defined as the acquiring bank’s sustainable growth rate lagged one year from the merger’s effective date. 14 Note that LSGR can serve as a reasonable proxy for managerial talent and that this variable can also have predictive power in our model if LSGR can reduce informational asymmetries between the bank’s insiders and external investors.

18

percentage of total shares outstanding and denoted as OPTGRANTi) are included to account

for the incentives these grants might provide to senior managers when formulating a bank’s

merger strategy. For example, higher levels of OPTGRANTi might cause managers to choose

a riskier merger strategy because, as is well known, greater risk in the underlying stock of the

bank is positively related to the value of the managers’ stock options.

The blockholder variable, BLOCKi, represents the total percentage of total equity

shares that is held by independent, external investors who own 5% or more of the bank’s

shares. We expect BLOCKi to have a direct relation with BHAR because, in theory, these

large independent blockholders are working to improve their investors’ welfare by investing

in stocks that out-perform specific stock market benchmarks. Thus, these blockholders have

an incentive to push the management of the merged bank to improve its operating and

financial performance.

In sum, Equation (6) forms the basis of our empirical tests using cross-sectional OLS

regressions. To check the robustness of our results, we use three different time horizons (1-3

years) and four different stock index benchmarks to compute the BHARs, as well as a two-

stage least squares specification to account for the possible endogeneity of the SGR

variable.15, 16

15 Note that we do not include in our model specific components of the bank’s asset portfolio, deposit mix, or the related credit quality of the bank’s assets because these are variables that are summarized in the bank’s sustainable growth rate variables. That is, we view the bank’s choices related to assets, liabilities, credit quality, etc., as consequences of the firm’s policies related to sustainable growth, the dividend payout ratio, as well as the degree of financial and operating leverage. 16 We also estimated the model with two additional dummy variables to control for variations in BHARs: 1) over time due to the introduction of the FDIC Improvement Act (FDICIA) in 1991 and 2) across acquirers that performed multiple acquisitions during 1987-1997. For example, we tested our model with a dummy variable set to 1 if the merger’s effective date was after FDICIA’s approval in 1991. In addition, we included another dummy variable that was set to 1 if the bank engaged in multiple acquisitions during the sample period. This second dummy variable attempts to control for possible confounding effects on our long-term BHAR estimates when a bank engages in acquisitions that overlap with each deal’s 3-year post-acquisition evaluation periods. It should also be noted that 65% of the acquisitions in our sample were done by banks that made two or more acquisitions during the sample period. Thus, the use of a dummy variable to control for these multiple acquirers

19

V. SAMPLE

We use the Securities Data Corporation (SDC) U.S. Merger database to identify

publicly-traded, highest-level bank holding companies that successfully acquired other

publicly-traded commercial banks during January, 1987 – December, 1997.17 This provided

us with an initial set of 516 mergers. We then used Standard & Poor’s Compustat service to

obtain financial data for both the acquirer and target companies. For the acquirers, we

gathered annual financial data for a five-year window surrounding the merger event (referred

to as annual dates t-1 to t+3). That is, we gathered acquirers’ data for the year before the

merger (e.g., 1986 data for a merger with a 1987 effective date) through the third year after

the merger’s effective date (e.g, 2000 data for a 1997 merger). We examine the merged

firm’s performance during the three years after the merger because it can take several years

for management to complete a merger and begin realizing potential gains in efficiency and

profitability.18

We use calendar year-end annual data from the Compustat and Office of the

Comptroller (OCC) databases in conjunction with the SDC data set. We obtained data from

the OCC related to the ranking of the three largest banks in each state (in terms of total assets) is preferred to restricting our analysis to only those banks that engaged in only one merger (due to econometric problems related to limited degrees of freedom). To conserve space, we do not report the results of these additional tests here because the inclusion of these extra control variables does not materially affect the results reported in Tables 2-4 and Tables A1-A2. 17 Publicly-traded, highest-level bank holding companies are used in our analysis because, as noted in Pagano (2001), these entities control the majority of assets in the U.S. banking industry and therefore are the most economically significant segment of this industry. However, our focus on this industry segment does not permit us to analyze the long-term performance effects of the numerous mergers that occur between privately-held U.S. commercial banks. Analysis of this latter segment of the banking sector might be an interesting avenue for future research. 18 One example of this relatively long post-merger integration period is the recently announced merger of First Union and Wachovia banks. In a communication to both banks’ customers (see First Union’s publication, Merger Update), the new management team of the merged entity outlined a merger integration plan that extends over 1 ½ years (from Spring 2002 to the end of 2003). In general, for larger banking mergers, an integration period of more than 1 year is not uncommon.

20

for the 1986-1997 period. To compute long-term buy-and-hold abnormal returns (BHARs),

we collected daily stock return data for each acquiring bank, as well as return data for the

value- and equal-weighted stock indexes, from the Center for Research in Security Prices

(CRSP) database for the 3-year period following the effective date of the merger. We use not

only the value- and equal-weighted CRSP indexes for the total U.S. market but also two

custom CRSP indexes that focus solely on commercial banks. These latter two indexes were

created using the CRSP database and the historical listing of U.S. banks defined by the

Standard & Poor’s Global Industry Classification Standards. As recommended by Lyon et al.

(1999), we compute benchmark portfolios that are specific to the banking industry in order to

reduce potential biases in the BHARs caused by rebalancing the benchmark portfolios,

skewness in these portfolios’ returns, and the listing of new issues within the benchmark

portfolios.

Note that the effective date is used as the basis for computing the abnormal return

because we are studying the long-term stock performance of bank mergers rather than the

short-term “announcement effect”. Our goal is to identify which financial factors under the

control of management significantly affect long-term stock performance when banks merge.

Since the effective date of bank mergers can be several months (and, at times, greater than 1

year) after the announcement date, the appropriate “starting point” for measuring long-term

stock performance should be when the management team has legal power to begin making

changes to the bank’s operations, capital structure, dividend policy, etc. This date is clearly

the merger’s effective date rather than the merger’s announcement date.19

19 Overall, our sample’s average short-term cumulative return for the four weeks preceding the mergers’ announcement dates was a statistically significant gain of 46.3% for the target firms. This finding is consistent with numerous papers in the literature that estimate short-term announcement effects related to mergers (both in studies of banking firms and non-financial companies).

21

As an example of the sample selection process, we assign a merger that has an

effective date of, say, August 14, 1987 to have t-1 data from December 31, 1986, t-0 data

from December 31, 1987, and t+3 data from December 31, 1990. Therefore, technically, the

“prior year’s” financial data from the Compustat and OCC data sets can be for a period longer

than one year in the past (but less than two years). Likewise, forward-looking financial data

from the Compustat and OCC data sets (e.g., the “three-year ahead” data) can be more than

three years after the effective date (but less than four years). When calculating the abnormal

stock returns, we use cumulative daily returns computed from the effective date through the

subsequent 36 months. Thus, the 3-year cumulative abnormal return for an acquiring firm

based on the CRSP data set covers a period that can be shorter than the time period measured

by the calendar year-end data from the OCC and Compustat. However, the differences in the

time intervals between the CRSP and OCC / Compustat data sets are relatively small (i.e., less

than six months on average) and do not affect our main findings.20 For the target banks, we

obtained Compustat data for the year prior to the effective date of the merger.

The subsequent merging of data from the SDC, Compustat, OCC, and CRSP data sets

yields a final sample of 106 mergers that meet all of the above criteria. These deals resulted

in aggregate purchase prices for the target banks’ common equity of over $76 billion and thus

represent an economically significant portion of all bank merger activity during 1987-1997.

VI. RESULTS

A. Summary Statistics

Table 1 reports some summary statistics for the variables used in our analysis. In

Panel D of the table we observe that the 3-year BHARs are, on average, significantly negative

20 There are no significant changes in our results due to the different time intervals because the cumulative abnormal returns are not materially different when they are computed using calendar year-end dates as the end of the holding period rather than computing these returns over a fixed 36-month interval.

22

when either the equal-weighted total market CRSP index or the two banking-specific custom

CRSP indexes are used (e.g., -62.5%, -42.3%, and –37.8% for the equal-weighted total market

CRSP index and the value- and equal-weighted banking indexes, respectively).21 The

standard deviations of these three sets of BHARs are also very large, ranging from 44.0% to

70.9%. Interestingly, the average 3-year BHAR using the value-weighted total market CRSP

index is a statistically insignificant 3.6%. Despite these differences in BHAR due to the

choice of benchmarks, the cross-sectional results described below are quite similar. This

consistency across four benchmarks is reassuring given Lyon et al.’s (1999) cautionary

statement that “analysis of long-run abnormal returns is treacherous”.

The average changes in some of the key independent variables (e.g., CHDIV,

CHECAP, and CHSGR in Panels B-D) are close to zero. However, the standard deviation of

these variables indicates that there is considerable cross-sectional variability in these factors.

For example, in Panel D of Table 1 the average three-year change in the acquirer’s sustainable

growth rate is –0.8% yet its standard deviation is 9.0%.22 Given that the mean of acquiring

banks’ lagged sustainable growth rate (i.e., LSGR) is 10.1%, the 3-year CHSGR indicates that

the average acquiring bank’s sustainable growth rate fell from 10.1% to 9.3% during the 3

years following their mergers. However, this change is not statistically significant due to the

relatively high variance of the 3-year CHSGR.

21 An examination of the BHARs for each merger showed that the majority of mergers resulted in negative BHARs. For example, the BHARs based on the banking-specific indexes reported positive BHARs for only 14% and 17% of the mergers using the value-weighted and equal-weighted indexes, respectively. For the value- and equal-weighted total market CRSP indexes, 47% and 19% of the mergers reported positive BHARs. Thus, the average BHARs presented above are representative of the overall sample and are not being skewed by a few large outliers. 22 Note that the changes for the above three key variables are simple differences over 1-3 year periods rather than percent changes. As an example, the 3-year CHSGR is defined as the difference between SGR at t+3 and SGR at t-1. Since the above three variables are already in ratio form, we did not compute percentage changes because such a calculation would result in unusually large swings and might not be meaningful in several cases. Thus, an average of –0.8% for 3-year CHSGR means that the average firm in our sample exhibited a decrease of 0.8 percentage points in its sustainable growth rate over the 3 years following the merger’s effective date.

23

Overall, the CHDIV, CHECAP, and CHSGR variables suggest that, on average during

the 3 years following the mergers, the acquiring banks’ dividend pay-out ratio rose, their

safety increased (as measured by an increase in the equity-to-assets capital ratio), and their

growth rates declined. These data portray post-merger acquiring banks that are slightly safer,

slower-growing, and more generous with dividends than in their pre-merger days.

It should be noted that there is a relatively smaller number of observations for the

equity ownership variables (MGMTOWNi, MGMTOWN2i, OPTGRANTi, and BLOCKi) due

to the difficulty in manually collecting these data for the relevant companies. In particular,

we found relatively few observations of these variables for the target companies and therefore

report summary statistics for these variables based solely on the acquiring companies. In

addition, the relative scarcity of these data even for the acquiring companies substantially

reduces the number of observations available for our cross-sectional tests (from 106 to 36).

Thus, our main cross-sectional tests reported in Tables 2-4 omit these ownership-related

variables. However, we report in Table A1 of the Appendix the results of our model when

these additional acquirer-specific ownership variables are included. As we discuss in the next

section, the inclusion of these ownership variables does not alter our key findings.

B. Cross-Sectional OLS and Robustness Tests

Table 2 reports the results of estimating Equation (6) via OLS using a 1-year post-

merger time horizon and the four benchmarks. Tables 3 and 4 report similar results using 2-

and 3-year post-merger horizons, respectively. In Tables 2-4, the first two columns of

empirical results are based on BHARs computed with the equal-weighted and value-weighted

banking-specific custom CRSP indexes, respectively. The last two columns in these tables

are based on BHARs computed with the equal-weighted and value-weighted total market

24

CRSP indexes, respectively. Since the results of the 1- and 2-year post-merger periods are

qualitatively similar to those reported for the 3-year post-merger horizon (albeit statistically

weaker), we focus on the results displayed in Table 4.23

Table 4 shows that the independent variables that are consistently significant across

the various 3-year BHAR calculations are CHDIV, CHSGR, and LSGR. As predicted by

Equation (6), all three of these variables are positively related to the four sets of BHARs. Of

these three variables, the most significant factor is the 3-year change in the sustainable growth

rate (CHSGR).

The SGR relation of Equation (4) shows that this growth estimate might be correlated

with two of the other financial change variables used to estimate Equation (6). Specifically,

Equation (4) indicates that SGR is computed based on a firm’s dividend pay-out ratio and

financial leverage yet we have also included the changes in these variables in our empirical

model described by Equation (6). Thus, we test for possible multi-collinearity between any

and all independent variables included in our model using variance inflation factors (VIF).

We find that the highest VIF is less than 4.4 and therefore multi-collinearity between SGR

and the model’s other independent variables does not appear to be a problem when the ROA

component of SGR is not included in the model.24 In addition, the parameter estimate of the

CHSGR variable might possess an endogeneity bias because this variable is computed based

23 In general, the results using 1- and 2-year BHARs are more volatile in terms of parameter estimates and typically have lower adjusted R2 statistics than the tests based on the 3-year BHARs. Given the relatively long post-merger integration period of most banking deals, it is not surprising that the 3-year BHARs exhibit more stable, consistent results. However, the 1- and 2-year BHAR results do confirm the importance of the CHSGR variable as this variable’s parameter estimates are statistically significant for at least one benchmark portfolio within each of our three time horizons. Our overall results suggest that, in most cases, it takes 3 (or more) years to obtain the most accurate picture of a banking merger’s effectiveness. 24 When Equation (6) is estimated with all three sub-components of the 3-year change in SGR (CHDIV, CHECAP, CHROA), as well as CHSGR itself, we find that multi-collinearity becomes a problem. In particular, the VIF is 17.1 for the CHSGR variable and 10.25 for the CHROA variable. In addition, the VIF rises to 3.4 and 2.5 for the CHDIV and CHECAP variables, respectively. Thus, we report in Tables 2-4 the results of our model with CHROA omitted from the specification. As noted earlier, this approach reduces multi-collinearity substantially and yields more consistent results.

25

on the bank’s dividend payout and capital ratio (both of which are present in the current

model via the CHDIV and CHECAP variables). A robustness check to examine this possible

endogeneity of CHSGR is described at the end of this sub-section and empirical results of

these tests are reported in Table A2 of the Appendix.

Except for the lagged SGR variable (LSGR), no other control variable reports

consistently significant parameter estimates across the four sets of 3-year BHARs. The

significantly positive parameter estimates for LSGR suggest that the acquiring bank’s prior

growth rate can be an indicator of post-merger stock performance (relative to an appropriate

benchmark). It appears that firms with high past growth rates are more likely to increase their

sustainable growth rates after the merger is consummated. Since higher growth in dividends

increases a stock’s value, ceteris paribus, it is not surprising that we find a positive relation

between LSGR and the BHARs.

The lack of consistently significant results for the other control variables

suggests that other factors cited in the previous literature (e.g., regulatory effects, a bank’s

market share within a state, the relative sizes of the target and acquirer, and managerial

incentives) may be important determinants of a bank’s merger premium and short-term stock

performance. However, these control variables are not as important as a bank’s changes in its

dividend payout policy, riskiness, and dividend growth rate in determining long-term, post-

merger stock performance.

In addition, as noted in the previous sub-section, the lack of numerous observations for

the ownership-related variables (MGMTOWNi, MGMTOWN2i, OPTGRANTi, and BLOCKi)

constrains our analysis of the effect of these variables on bank mergers’ BHARs. In order to

maintain a meaningful sample size, we have omitted these variables from the tests reported in

Tables 2-4. However, we have also estimated our model with the inclusion of the above four

26

ownership variables and report these results in Table A1 of the Appendix. As can be seen

from a review of Table A1, the key variables of our model (i.e., CHSGR and CHDIV)

maintain their statistical significance while LSGR possesses the correct sign but displays

weaker significance. As noted earlier, the number of observations for these tests is

substantially reduced due to the scarcity of the ownership data. Despite this reduction in

sample, it is encouraging that including these additional variables does not alter our findings.

Lastly, we perform an additional form of robustness testing by re-formulating our

model as a two-stage least squares specification (2SLS) in the Appendix. We perform these

additional tests because one might argue that the inclusion of the CHSGR in a model that

already contains CHDIV and CHECAP could create an endogeneity problem. That is, the

parameter estimates for CHSGR might be biased because the definition of SGR relies on the

levels of the bank’s dividend payout and capital ratios that, in turn, directly affect the CHDIV

and CHECAP variables. To account for this potential endogeneity, we specify a 2SLS model

where CHSGR is a function of CHDIV, CHECAP, and CHROA in a first-stage regression.

The details of this 2SLS model are discussed in the Appendix and the empirical results of

these tests are presented in Table A2. A review of that table shows that our main findings are

not affected by the possible endogeneity of our CHSGR variable. Thus, CHSGR and LSGR

continue to be the most statistically significant factors affecting a bank merger’s BHAR with

the CHDIV variable providing a weaker positive contribution.

C. Estimates of the Economic Significance of Selected Variables

We can examine the results presented in Tables 2-4 in another way by examining the

economic significance (as opposed to the statistical significance) of our model’s variables.

Table 5 displays our estimates of the economic impact for each of our 4 BHAR estimates

27

related to moving from 1 standard deviation below the mean of each independent variable to 1

standard deviation above the mean. The economic impact is measured by the change in the

BHAR caused by this 2-standard deviation shift in the relevant variable.

For example, the first row shows how the average firm’s BHAR would be affected

when the change in the dividend payout ratio (CHDIV3) was increased from 1 standard

deviation below the sample average of 6.7% to 1 standard deviation above this average (e.g.,

if the 3-year CHDIV rose from –12.5% to +25.9%). As can be seen in Table 5, the change in

BHAR can be between 44 and 66 percentage points (depending on the benchmark chosen) for

a bank that achieves such an improvement in the dividend pay-out ratio.25 This analysis

shows that the CHDIV variable is not only statistically significant but also can have an

economically meaningful effect on an acquiring bank’s post-merger stock performance.

Table 5 shows that the most economically significant variable is also the one that is

the most statistically significant variable in Table 4 (i.e., the 3-year CHSGR). Increasing a

bank’s 3-year CHSGR from 1 standard deviation below the sample mean of –0.8% to 1

standard deviation above this mean can yield extremely large increases in BHAR (ranging

from +110% to +204%, depending on the chosen benchmark). Given that the average BHAR

in our sample is around -38% to –63%, the large potential increase in BHAR caused by an

improvement in CHSGR alone is big enough to turn an under-performing stock into a

superior stock performer. As another example of the economic impact of the model’s key

variables, we note that the lagged SGR variable (LSGR) also exerts a strong positive impact

on an acquirer’s BHAR with an average improvement of +42.8%. In this regard, Table 5

helps us identify which factors are the most important in terms of determining those bank

mergers that could be “winners” for long-term investors.

25 These estimates of the economic significance are computed by multiplying the parameter estimate for the relevant variable found in Table 4 by two times the variable’s standard deviation reported in Table 1.

28

It should also be noted that changes in the key independent variables are not mutually

exclusive. For example, a 2-standard deviation increase in CHSGR might also be related to a

decrease in the firm’s dividend payout ratio because this latter variable is inversely related to

SGR, according to Equation (4). Thus, the estimated changes reported in Table 5 may be an

over-statement of the actual merged entities’ BHARs because Table 5’s figures assume that

all other variables are held constant (which may not be true in practice). Despite this caveat,

CHSGR can still have a large positive effect on long-term BHARs because even when a 2-

standard deviation increase in CHSGR coincides with a 2-standard deviation decrease in

CHDIV, the net effect on the average BHAR is +81.4% (i.e., net effect = average CHSGR

effect – average CHDIV effect = +138.1% - 56.7% = +81.4%). Thus, the sustainable growth

rate factor can still have a meaningful impact on long-term post-merger stock performance

even when we account for the negative effect of a declining dividend payout ratio.

VII. CONCLUSIONS

An opportunity to increase the growth of the firm has frequently been cited as a

motivation of mergers and acquisitions. Proponents contend that the higher growth in the size

of the firm will result in increased cash flows and shareholder return. Opponents, however,

argue that the higher growth is motivated by a desire by managers to increase their

compensation. Sustainable growth refers to the revenue growth the firm can achieve given its

operating and financial constraints. Further, sustainable growth can be viewed as a summary

measure that integrates capital structure and dividend decisions with operating performance.

An improvement in sustainable growth and/or its components over time as a result of the

merger should also lead to an increase in shareholder return. If, however, the merger

29

produces only an increase in the size of the firm without a commensurate increase in

sustainable growth, the merger should lead to a decrease in shareholder return.

In this paper, we develop a model of sustainable growth that incorporates the bank’s

return on assets, dividend payout, and equity capital ratio. These factors can be used to

determine whether the stated motivations of bank mergers are realized. We identify the

impact of bank mergers on the long run performance of the acquiring bank during 1987-1997

by analyzing one-year, two-year, and three-year cumulative buy-and-hold returns for each

deal on a cross-sectional basis.

We find that the acquiring bank’s estimated sustainable growth rate prior to the

acquisition, as well as post-acquisition changes in this growth rate and the bank’s dividend

payout ratio are statistically and economically significant determinants of the merged bank’s

abnormal stock return performance over the three years following the merger. Improving a

bank’s sustainable growth rate from one standard deviation below the sample mean to one

standard deviation above the mean, an increase of 18 percentage points, can increase a

merged bank’s cumulative 3-year buy-and-hold abnormal return by an average of 138.1%.

This result is sufficiently large to enable the average bank merger to significantly out-perform

relevant stock market benchmarks over a 3-year post-merger period. This finding is robust

even after controlling for differences in bank regulations over the sample period, differences

in the relative size and market share of the acquirer and target banks, managerial and

blockholder ownership, and possible endogeneity of the bank’s sustainable growth rate. Thus,

we conclude that the above control variables found in the prior merger literature may be

important determinants of a bank’s merger premium and short-term stock performance but

they are not as important as a bank’s changes in its dividend payout policy, riskiness, and

dividend growth rate in determining long-term, post-merger stock performance.

30

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Franks, J., Harris, R., and S. Titman, 1991, The postmerger share-price performance of acquiring firms, Journal of Financial Economics 29, 81-96. Frame, W. Scott, and William D. Lastrapes, 1998, Abnormal Returns in the Acquisition Market: The Case of Bank Holding Companies, 1990-1993, Journal of Financial Services Research, 14:2, 145-163. Fruhan, William E., 1979, Pyrrhic Victories in Fight for Market Share, Harvard Business Review, 100-107. Hakes, David R., Kenneth H. Brown, and Allen Rappaport, 1997, The Impact of State Deposit Caps on Bank Merger Premiums, Southern Economic Journal, Vol. 63, Issue 3, 652-662. Hamada, R.S., 1972, The effect of the firm’s capital structure on the systematic risk of common stocks, Journal of Finance, Vol. 50, 435-452. Healy, Paul M., Palepu, Krishna G., and Richard S. Ruback, 1992, Does corporate performance improve after mergers?, Journal of Financial Economics, Vol. 31, 135-175. Hempel, G.H., and D.G. Simonson, 1991, Bank Financial Management: Strategies and Techniques for a Changing Industry, John Wiley & Sons, Inc., New York. Higgins, Robert C, 1977, How Much Growth Can A Firm Afford?, Financial Management 6,7-16. Higgins, Robert C., 1981, Sustainable Growth Under Inflation, Financial Management 10, 36-40. Higgins, R. and R. Kerrin, 1983, Managing the Growth-Financial Policy Nexus in Retailing, Journal of Retailing, 19-48. Houston, Joel F. and Michael D. Ryngaert, 1994, “The Overall Gains of Large Bank Mergers”, Journal of Banking and Finance, Vol. 18, 6, 1155-1176. Houston, Joel F., James, Christoher M., and Michael D. Ryngaert, 2001, Where Do Merger Gains Come Form? Bank Mergers From Their Perspective Of Insiders and Outsiders, Journal of Financial Economics 60, 285-331. Hughes, J.P., Lang, W.L., Mester, L.J., and C-G. Moon, 1996, Efficient banking under interstate branching, Journal of Money, Credit, and Banking, Vol. 28, 1045-1071. Hunter, William C., and Larry D. Wall, 1989, Bank Merger Motivations: A Review of the Evidence and an Examination of Key Target Bank Characteristics, Economic Review, Vol. 74, No. 5, 2-19. Johnson, Dana J., 1981, The Behavior Of Financial Structure And Sustainable Growth In An Inflationary Environment, Financial Management 10, 30-35.

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Kyd, Charles W, 1981, Managing The Financial Demands Of Growth, Management Accounting 63, 33-43. Loughran, T. and A.M. Vijh, 1997, Do long-term shareholders benefit from capital acquisitions? Journal of Finance 52, 1765-1790. Lyon, J.D., Barber, B.M., and C-L. Tsai, 1999, Improved Methods for Tests of Long-Run Abnormal Stock Returns, Journal of Finance, Vol. 54, No. 1, 165-201. Madura, J., and K.J. Wiant, 1994, Long-term valuation effects of bank acquisitions, Journal of Banking and Finance 18, 1135-1154. Martin, David L., 1998, Merger Calculus: When Not to Sell the Bank, Bank Accounting & Finance, Vol. 11, No. 3, 19-24. Mergerstat Review 2000, Mergerstat Corp., Santa Monica, CA. Merger Update, General communication to customers of First Union and Wachovia Corporations, Spring 2002. Olson, Gerard T., John Clark, and Thomas Chiang, 1986, Sustainable Growth: A Dynamic Model, Journal of the Midwest Finance Association, Vol. 15, 1-13. _______, and ______, 1993, A Cash Flow Model of Attainable Growth", Journal of Financial and Strategic Decision Making, Volume 6, Number 2, Fall 1993, pp 73-88. Platt, Harlan D., Marjorie B. Platt and Guangli Chen, 1995, Sustainable Growth Rate Of Firms In Financial Distress, Journal of Economics and Finance 19, 147-151. Pagano, Michael S., 2001, How Theories of Financial Intermediation and Corporate Risk-Management Influence Bank Risk-Taking Behavior, Financial Markets, Institutions, and Instruments, Vol. 10, No. 5, 277-323. Palia, Darius, 1993, The Managerial, Regulatory, and Financial Determinants of Bank Merger Premiums, Journal of Industrial Economics, Vol. 41, Issue 1, 91-102. Peristiani, S., 1997, Do mergers improve the X-efficiency and scale efficiency of U.S. banks? Evidence from the 1980s, Journal of Money, Credit and Banking 29, 326-337. Piloff, S.J., 1996, Performance changes and shareholder wealth creation associated with mergers of publicly traded banking institutions, Journal of Money, Credit and Banking 28, 294-310. Rhoades, Stephen A., 1982, Bank Expansion and Merger Activity by State, 1960-1975, Journal of Bank Research, Vol. 12, 254-256.

33

______, 1987, The operating performance of acquired firms in banking, In Issues after a Century of Federal Competition Policy, edited by R.L. Willis, J.A. Caswell, and J.D. Culbertson, (Lexington, MA: Lexington Books), 277-292. ______, 2000, Bank Mergers and Banking Structure in the United States, 1980-98, Board of Governors of the Federal Reserve System, Staff Study 174, 1-32. Schranz, Mary S., 1993, Takeovers Improve Firm Performance: Evidence from the Banking Industry, Journal of Political Economy, Vol. 101, No.21, 299-326. Shawky, Hany A., Tobias Kilb, and Carsten F. W. Staas, 1996, Determinants of Bank Merger Premiums, Journal of Economics and Finance, Vol. 20, No. 1, 117-131. Solomon Elinor Harris, 1970, Bank Merger Policy and Problems: A Linkage Theory of Oliogopoly, Journal of Money, Vol.2, Issue 3, 323-336. Sudarsanam, Sudi, Ayo Salami, and George Aledandrou, 2002, Rational Expectations, Analyst’s Forecasts of Earnings and Sources of Value Gains for Takeover Targets, Journal of Banking and Finance, Vol. 26, 153-177. Varadarjan, Poondi, 1983, The Sustainable Growth Model: A Tool for Evaluating the Financial Feasibility of Market Share Strategies, Strategic Management Journal, 353-367.

34

Appendix

This appendix presents empirical results for two sets of robustness tests. First, Table

A1 reports the results for Equation (4) when the acquirer’s equity ownership control variables

are included in the model (i.e., MGMTOWNi, MGMTOWN2i, OPTGRANTi, and BLOCKi).

As Palia (1993) and others have noted, these variables might have an impact on long-term

bank merger performance because the existing literature suggests that ownership variables

influence short-term measures such as bank merger premiums. As the results reported in

Table A1 demonstrate, these ownership variables do not alter our primary findings that

CHSGR and CHDIV are the most significant influences (both economically and statistically)

on 3-year abnormal stock performance. In addition, the empirical results using 1- and 2-year

changes are qualitatively the same as those presented here for the 3-year changes. Thus, to

conserve space, we do not report the results for the 1- and 2-year BHARs in Table A1.

The ownership variables are statistically insignificant for all benchmarks except the

Value-Weighted CRSP Stock Index (VWCRSP). In this latter case, only one variable,

OPTGRANT, is significant at the .10 level. Overall, the general weakness of these ownership

variables suggest that the level of the target’s managerial ownership of equity and options, as

well as the level of outside blockholder ownership, do not exhibit a strong long-term influence

on post-merger bank stock performance. This is somewhat surprising because Table 1 reports

summary statistics for these ownership variables that indicate that managerial equity

ownership is fairly large (e.g., managers own 5.9% of the target bank’s equity, on average).

Second, Table A2 reports the empirical results of a two-stage least squares regression

(2SLS) that explicitly accounts for possible endogeneity of the CHSGR variable. The model

is specified as follows:

35

CHSGR = ∆SGR = f3(∆D, ∆ECAP, ROAt-1, Acquirer’s Market-to-Book Equity Ratiot-1)

(A1)

∆V = BHAR = f4(∆d, ∆ECAP, ∆SGR, Controls) (A2)

where, Equation (A1) is the first-stage regression and Equation (A2) is the second-stage

regression that incorporates the first-stage effects related to the possible endogeneity of the

CHSGR variable. Equation (A1)’s independent variables are the three variables used as the

basis for forming SGR, as well as acquirer’s lagged market-to-book equity ratio. The latter

variable is included in order to capture any effects on the bank’s SGR due to bank-specific

market power and/or growth opportunities. Using the above model, any potential bias in the

parameter estimates caused by CHSGR’s determination by the right-hand-side variables of

(A1) can be ameliorated by the above two-stage procedure.

Table A2’s results show that the main findings of our paper are not affected by

explicitly modeling the endogeneity of CHSGR in our specification. As Table A2

demonstrates, the 3-year changes in CHSGR and LSGR remain statistically and economically

significant and, in fact, the explanatory power of the model (measured by adjusted R2)

actually rises when the 2SLS technique is used.26 Only the CHDIV variable reports

statistically weaker parameter estimates but even in this case the t-statistics are significant at

the .10 level for the custom bank stock portfolio benchmarks. As in Table A1, the empirical

results using 1- and 2-year changes are qualitatively the same as those presented here for the

3-year changes. Thus, to conserve space, we do not report the results for the 1- and 2-year

BHARs in Table A2. Overall, Tables A1-A2 confirm our results reported in Tables 2-4.

26 To conserve space, we have not included the results of the first-stage regression based on Equation A1. However, it should be noted that this first-stage regression yielded significant parameter estimates for all variables except the market-to-book ratio and that the adjusted R2 was quite high at .7567.

36

TABLE 1. SUMMARY STATISTICS

Description

Variable

N

Mean

Std. Dev.

Min.

Max.

Panel A: Cross-Sectional Variables Equity Value of Deal ($ mil.) valdeal 100 0.7661 1.4470 0.0096 11.2045Acquirer’s State Ranking acqrank 106 3.0283 1.1419 1.0000 4.0000Target’s State Ranking tarrank 106 3.6604 0.7793 1.0000 4.0000Same State Merger Dummy state 106 0.4151 0.4951 0 1.0000Target’s Prior Sustainable Growth tarsgrlg 69 0.1057 0.2493 -0.1251 2.8050Acquirer’s Prior Sustainable Growth lsgr 105 0.1005 0.0414 -0.0977 0.2258Relative Asset Size relsize 94 0.2949 0.5605 0 5.0570Acquirer’s Capital Ratio acqecap 106 0.0769 0.0175 0.0397 0.1255Acquirer’s Market-Book Equity Ratio acqmb 90 1.6800 0.5251 0.3555 3.0920Acquirer’s Dividend Pay-out acqdiv 105 0.3665 0.1962 0 1.8267Target’s Capital Ratio tarecap 93 0.0736 0.0220 0.0125 0.1363Target’s Market-Book Equity Ratio tarmb 78 1.3753 0.5151 0 2.8396Target’s Dividend Payout tardiv 69 0.3864 0.9276 0 7.63113-Year Cum. Bank’s Stock Return ret3 92 0.6858 0.7265 -0.5483 3.3831Management Equity Ownership mgmtown 59 0.0589 0.0752 0.0019 0.3854Management’s Stock Option Grants optgrant 65 0.0025 0.0075 0 0.0599Outside Equity Blockholders block 66 0.0594 0.1063 0 0.48463-year Cum. Return (VW CRSP) vwcrsp3 106 0.6325 0.2887 0.0725 1.19563-year Cum. Return (EW CRSP) ewcrsp3 106 1.2537 0.5778 0.1258 3.52403-year Cum Return (VW Bank Index) vwbank3 106 1.0798 0.6300 0.0513 2.62313-year Cum. Return (EW Bank Index) ewbank3 106 1.0359 0.6058 -0.0388 2.7504Deposit Cap Dummy for Acquirer acqcap 106 0.2925 0.4571 0 1.00Branching Limits Dummy - Acquirer acqbranch 106 0.9811 0.1367 0 1.00Deposit Cap Dummy for Target tarcap 106 0.2453 0.4323 0 1.00Branching Limits Dummy - Target tarbranch 106 0.9340 0.2495 0 1.00

37

TABLE 1. SUMMARY STATISTICS (cont.)

Description Variable N Mean Std. Dev. Min. Max.

Panel B: 1-Year Change Variables Chg. In Div. Pay-out chdiv1 93 0.0368** 0.1575 -0.5633 0.5590 Chg. In Cap. Ratio checap1 99 0.0018** 0.0087 -0.0305 0.0305 Chg. In SGR chsgr1 98 -0.0063 0.0804 -0.4039 0.2372 Chg. In ROA chroa1 99 0.0006 0.0047 -0.0191 0.0199 Chg. In ROE chroe1 99 -0.0009 0.0794 -0.4280 0.2095 VW CRSP BHAR vwcrspbhar1 106 0.0271 0.1924 -0.6304 0.6239 EW CRSP BHAR ewcrspbhar1 106 -0.1458* 0.2079 -0.6419 0.3033 VW Bank Index BHAR vwbankbhar1 106 -0.1102* 0.1665 -0.8144 0.3346 EW Bank Index BHAR ewbankbhar1 106 -0.1129* 0.1544 -0.6086 0.3425 Panel C: 2-Year Change Variables Chg. In Div. Pay-out chdiv2 86 0.0490** 0.2018 -0.8274 0.9628 Chg. In Cap. Ratio checap2 92 0.0019 0.0112 -0.0330 0.0262 Chg. In SGR chsgr2 92 -0.0123 0.0777 -0.3703 0.1928 Chg. In ROA chroa2 92 0.0007 0.0047 -0.0185 0.0193 Chg. In ROE chroe2 92 0.0006 0.0762 -0.4301 0.1784 VW CRSP BHAR vwcrspbhar2 101 0.0681** 0.3386 -1.0381 1.1422 EW CRSP BHAR ewcrspbhar2 101 -0.3045* 0.4687 -1.4254 0.8184 VW Bank Index BHAR vwbankbhar2 101 -0.2475* 0.2864 -1.0435 0.4965 EW Bank Index BHAR ewbankbhar2 101 -0.2270* 0.3015 -0.9594 0.6220

Panel D: 3-Year Change Variables Chg. In Div. Pay-out chdiv3 70 0.0673* 0.1918 -0.6095 0.8078 Chg. In Cap. Ratio checap3 79 0.0014 0.0135 -0.0401 0.0345 Chg. In SGR chsgr3 78 -0.0080 0.0902 -0.2630 0.2966 Chg. In ROA chroa3 79 0.0011*** 0.0055 -0.0158 0.0232 Chg. In ROE chroe3 79 0.0073 0.0789 -0.2304 0.2621 VW CRSP BHAR vwcrspbhar3 92 0.0358 0.5858 -1.0827 2.1875 EW CRSP BHAR ewcrspbhar3 92 -0.6246* 0.7094 -2.2692 1.5353 VW Bank Index BHAR vwbankbhar3 92 -0.4234* 0.4403 -1.5953 1.0744 EW Bank Index BHAR ewbankbhar3 92 -0.3780* 0.4636 -1.4956 1.2772

Note: The above four panels report summary statistics for various cross sectional variables (Panel A), as well as the 1-, 2-, and 3-year changes in selected variables (Panels B, C, and D, respectively). BHAR refers to buy-and-hold abnormal returns to the sample banks’ stock returns relative to a specific benchmark portfolio. SGR refers to the sample banks’ sustainable growth, as defined by Equation (4). For the 1-, 2-, and 3-year change variables (Panels B, C, and D), we report the results of t-tests for differences in the mean values from zero. * = significant at the 0.01 level, ** = significant at the 0.05 level, and *** = significant at the 0.10 level.

38

TABLE 2. IMPACT OF BANK MERGERS ON BUY-AND-HOLD ABNORMAL RETURNS OVER A 1-YEAR POST-MERGER TIME HORIZON

Description Variable EWBANK VWBANK EWCRSP VWCRSP Constant term intercept -0.12512 -0.13897 0.21853 0.10157 (-0.73) (-0.72) (0.93) (0.49) Chg. In Div. Payout chdiv1 0.03167 0.03694 0.25319 0.23066 (0.28) (0.29) (0.1094) (1.67) Chg. In Capital Ratio checap1 2.09742 2.39535 0.05832 3.26154 (1.04) (1.06) (0.02) (1.33) Chg. In SGR chsgr1 0.54841 0.49376 0.47845 1.15754 (1.62) (1.29) (1.02) (2.81) Relative Size relsize -0.01428 -0.01797 -0.02492 -0.03317 (-0.47) (-0.53) (-0.60) (-0.90) Acquirer’s Prior SGR lsgr 1.30949 1.09068 0.35350 0.88740 (2.21) (1.63) (0.43) (1.23) Acq.’s State Ranking acqrank -0.02735 -0.01789 -0.02467 -0.01256 (-1.64) (-0.95) (-1.08) (-0.62) Target’s State Ranking tarrank 0.02116 0.02133 0.00370 0.01255 (0.81) (0.72) (0.10) (0.39) Same State Merger state -0.02165 0.01090 -0.03518 0.00471 (-0.54) (0.24) (-0.63) (0.10) Deposit Cap - Acquirer acqcap -0.02020 -0.00343 0.02897 0.06547 (-0.35) (-0.05) (0.37) (0.94) Branching Limits- Acq. acqbranch -0.17014 -0.11385 -0.33233 -0.15047 (-1.25) (-0.74) (-1.77) (-0.91) Deposit Cap – Target tarcap -0.06725 -0.10502 -0.14259 -0.12975 (-1.11) (-1.53) (-1.70) (-1.76) Branching Limits- Targ. tarbranch 0.07511 0.01418 0.02900 -0.01667 (0.91) (0.15) (0.26) (-0.17)

Adj. R2 0.0573 -0.0163 0.0426 0.0633 F-statistic 1.41 0.89 1.30 1.46

Note: The above results are from an OLS regression based on Equation (6) using four sets of 1-year buy-and-hold abnormal returns computed for four different benchmark portfolios (an Equally Weighted Bank Index, Value Weighted Bank Index, Equally Weighted CRSP Index, and Value Weighted CRSP Index). T-statistics are reported in parentheses below each parameter estimate. Values in boldface denote parameter estimates that are statistically significant at the .10 level.

39

TABLE 3. IMPACT OF BANK MERGERS ON BUY-AND-HOLD ABNORMAL RETURNS OVER A 2-YEAR POST-MERGER TIME HORIZON

Description Variable EWBANK VWBANK EWCRSP VWCRSP

Constant term intercept -0.43494 -0.35071 -0.20235 0.00387 (-1.15) (-1.01) (-0.37) (0.01) Chg. In Div. Payout chdiv2 -0.11633 -0.01758 -0.06194 0.11455 (-0.56) (-0.09) (-0.21) (0.51) Chg. In Capital Ratio checap2 -4.43630 -2.58181 -13.56522 -0.99613 (-1.19) (-0.75) (-2.48) (-0.24) Chg. In SGR chsgr2 1.52369 1.75391 -0.91645 1.98673 (2.23) (2.80) (0.92) (2.65) Relative Size relsize -0.00075176 -0.04579 0.00749 0.01838 (-0.00) (-0.32) (0.03) (0.11) Acquirer’s Prior SGR lsgr 2.44891 1.82298 2.28920 0.95039 (1.92) (1.56) (1.22) (0.68) Acq.’s State Ranking acqrank -0.00522 0.00854 -0.00793 0.02085 (-0.15) (0.26) (-0.15) (0.53) Target’s State Ranking tarrank 0.05785 0.05691 0.08475 0.04830 (1.03) (1.11) (1.04) (0.79) Same State Merger state -0.18488 -0.15215 -0.28252 -0.16147 (-2.04) (-1.83) (-2.13) (-1.62) Deposit Cap - Acquirer acqcap -0.10639 -0.13146 0.05863 -0.07175 (-0.92) (-1.25) (0.35) (-0.57) Branching Limits- Acq. acqbranch -0.19333 -0.14540 -0.76629 -0.22596 (-0.69) (-0.56) (-1.86) (-0.73) Deposit Cap – Target tarcap 0.03760 0.01393 -0.08598 0.01777 (0.29) (0.12) (-0.45) (0.12) Branching Limits- Targ. tarbranch 0.07761 -0.05845 0.30021 0.05722 (0.44) (-0.36) (1.16) (0.29)

Adj. R2 0.0549 0.0949 0.1285 0.0589 F-statistic 1.35 1.64 1.90 1.38

Note: The above results are from an OLS regression based on Equation (6) using four sets of 2-year buy-and-hold abnormal returns computed for four different benchmark portfolios (an Equally Weighted Bank Index, Value Weighted Bank Index, Equally Weighted CRSP Index, and Value Weighted CRSP Index). T-statistics are reported in parentheses below each parameter estimate. Values in bold face denote parameter estimates that are statistically significant at the .10 level.

40

TABLE 4. IMPACT OF BANK MERGERS ON BUY-AND-HOLD ABNORMAL RETURNS OVER A 3-YEAR POST-MERGER TIME HORIZON

Description Variable EWBANK VWBANK EWCRSP VWCRSP

Constant term intercept -0.77326 -0.80504 -0.72441 -0.87398 (-1.27) (-1.47) (-0.76) (-1.32) Chg. In Div. Payout chdiv3 1.32982 1.14579 1.70982 1.71524 (2.92) (2.82) (2.40) (3.47) Chg. In Capital Ratio checap3 4.46048 7.60984 -5.74457 3.75789 (0.92) (1.76) (-0.76) (0.72) Chg. In SGR chsgr3 6.11087 6.44014 6.79424 11.35037 (3.26) (3.84) (2.31) (5.57) Relative Size relsize -0.20201 -0.08539 -0.18424 0.04726 (-0.69) (-0.33) (-0.40) (0.15) Acquirer’s Prior SGR lsgr 3.54488 3.86332 5.69657 7.75541 (1.85) (2.25) (1.90) (3.73) Acq.’s State Ranking acqrank -0.07316 -0.04490 -0.10250 -0.02723 (-1.31) (-0.90) (-1.17) (-0.45) Target’s State Ranking tarrank 0.03737 0.03870 -0.02637 -0.11895 (0.41) (0.47) (-0.18) (-1.19) Same State Merger state -0.13057 -0.12419 -0.28059 -0.16189 (-0.94) (-0.99) (-1.28) (-1.07) Deposit Cap - Acquirer acqcap -0.10139 -0.08286 -0.12754 -0.38942 (-0.56) (-0.51) (-0.45) (-1.99) Branching Limits- Acq. acqbranch 0.13583 0.15673 -0.47819 0.54801 (0.33) (0.42) (-0.73) (1.21) Deposit Cap – Target tarcap -0.07998 -0.12859 -0.14306 0.35837 (-0.39) (-0.71) (-0.45) (1.63) Branching Limits- Targ. tarbranch 0.03859 -0.15228 0.59435 0.14342 (0.15) (-0.65) (1.45) (0.51)

Adj. R2 0.1246 0.1720 0.1288 0.3190 F-statistic 1.74 2.07 1.76 3.42

Note: The above results are from an OLS regression based on Equation (6) using four sets of 3-year buy-and-hold abnormal returns computed for four different benchmark portfolios (an Equally Weighted Bank Index, Value Weighted Bank Index, Equally Weighted CRSP Index, and Value Weighted CRSP Index). T-statistics are reported in parentheses below each parameter estimate. Values in bold face denote parameter estimates that are statistically significant at the .10 level.

41

TABLE 5. ESTIMATED IMPACT OF CHANGES IN SELECTED FINANCIAL VARIABLES ON AN ACQUIRING BANK’S BUY-AND-HOLD ABNORMAL RETURNS OVER A 3-YEAR POST-MERGER TIME HORIZON

Estimated Changes in BHARs based on a 2-Standard

Deviation Change in the Independent Variable

Variable EW Bank VW Bank EW CRSP VW CRSP Average ∆ Dividend Payout (CHDIV)

51.1%

44.2%

65.7%

65.9%

56.7%

∆ Equity Capital Ratio (CHECAP)

12.5%

21.3%

-16.1%

10.5%

7.1%

∆ Sustainable Growth (CHSGR)

110.0%

115.9%

122.2%

204.3%

138.1%

Relative Asset Size (RELSIZE)

-22.6%

-9.6%

-20.7%

5.3%

-11.9%

Lagged SGR (LSGR)

29.1%

31.7%

46.7%

63.6%

42.8%

Acquirer’s Asset Rank (RANKAcq.)

-16.7%

-10.2%

-23.4%

-6.2%

-14.1%

Target’s Asset Rank (RANKTarget)

5.8%

6.0%

-4.1%

-18.5%

-2.7%

Same-State Merger Dummy (STATE)

-12.9%

-12.3%

-27.8%

-16.0%

-17.3%

Acquirer’s State’s Cap (CAPAcq.)

-9.2%

-7.6%

-11.5%

-35.6%

-16.0%

Acquirer’s State’s Branching Limits (BRANCHAcq.)

3.7%

4.3%

-13.1%

15.0%

2.5%

Target’s State’s Cap (CAPTarget)

-6.9%

-11.1%

-12.4%

-31.0%

-15.4%

Target’s State’s Branching Limits (BRANCHTarget)

1.9%

-7.6%

29.7%

7.2%

7.8%

Note: Using the parameter estimates reported for the 3-year BHARs in Table 4, the above table shows how the average firm’s 3-year BHAR would be affected when a change in one of the independent variables displayed in the first column was increased from 1 standard deviation below the sample average for this variable to 1 standard deviation above its average. For example, if the 3-year CHDIV rose from –12.5% to +25.9%, then the average change in a merged bank’s 3-year BHAR would be +56.7% (as reported in the last column of the first row of results). Boldface items denote estimates based on statistically significant parameter estimates from Equation (6) using 3-year BHARs. The last column labeled Average reports the simple average of the four values reported in the second through fifth columns for each variable.

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TABLE A1. IMPACT OF BANK MERGERS ON BUY-AND-HOLD ABNORMAL RETURNS OVER A 3-YEAR POST-MERGER TIME HORIZON USING OWNERSHIP VARIABLES AS ADDITIONAL CONTROL VARIABLES

Description Variable EWBANK VWBANK EWCRSP VWCRSP Constant term intercept -1.3284 -1.46811 -1.99273 -1.29358 (-1.52) (-1.83) (-1.55) (-1.38) Chg. In Div. Payout chdiv3 1.98255 1.65197 3.10666 2.44602 (2.02) (1.83) (2.14) (2.32) Chg. In Cap. Ratio checap3 1.04060 3.99517 -14.3842 0.77482 (0.11) (0.46) (-1.03) (0.08) Chg. In SGR chsgr3 11.20869 10.96985 15.91825 14.74323 (3.04) (3.24) (2.93) (3.72) Relative Size relsize -0.38086 -0.05193 -0.52931 0.48278 (-0.74) (-0.11) (-0.70) (0.87) Acquirer’s Prior SGR lsgr 4.38484 5.94202 4.49522 5.80362 (1.24) (1.83) (0.86) (1.53) Mgmt Ownership mgmtown 0.02184 0.03525 0.06430 -0.03262 (0.54) (0.94) (1.07) (-0.75) Mgmt Ownership2 mgmtown2 -0.000550 -0.000752 -0.00203 0.0002515 (-0.52) (-0.78) (-1.31) (0.22) Management Options optgrant 0.38553 0.20942 0.03459 -0.89404 (0.79) (0.47) (0.05) (-1.70) Outside Blockholders block 0.00374 0.00401 -0.00802 -0.01106 (0.46) (0.54) (-0.67) (-1.26) Acq.’s State Ranking acqrank -0.08470 -0.05973 -0.12024 -0.04169 (-0.98) (-0.75) (-0.95) (-0.45) Target’s State Ranking tarrank -0.08818 -0.07290 -0.11686 0.15335 (-0.47) (-0.43) (-0.42) (0.77) Same State Merger state -0.20886 -0.24177 -0.47432 -0.26360 (-0.86) (-1.08) (-1.32) (-1.01) Deposit Cap – Acquirer acqcap 0.21073 0.21912 0.16506 0.08939 (0.59) (0.67) (0.31) (0.23) Deposit Cap – Target tarcap -0.18845 -0.24350 -0.29603 0.11143 (-0.52) (-0.73) (-0.55) (0.28) Branching Limits- Targ. tarbranch 0.89473 0.60523 1.74029 0.50318 (1.30) (0.96) (1.72) (0.68) Adj. R2 0.1526 0.1307 0.2716 0.3082

Note: The above results are from an OLS regression based on Equation (6) using four sets of 3-year buy-and-hold abnormal returns computed for four different benchmark portfolios (an Equally Weighted Bank Index, Value Weighted Bank Index, Equally Weighted CRSP Index, and Value Weighted CRSP Index). T-statistics are reported in parentheses below each parameter estimate. Values in boldface denote parameter estimates that are statistically significant at the .10 level.

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TABLE A2. IMPACT OF BANK MERGERS ON BUY-AND-HOLD ABNORMAL RETURNS OVER A 3-YEAR POST-MERGER TIME HORIZON USING

A TWO-STAGE LEAST SQUARES REGRESSION

Description Variable EWBANK VWBANK EWCRSP VWCRSP

Constant term intercept -1.21549 -1.01270 -1.45371 -0.54755 (-2.18) (-1.83) (-1.46) (-0.74) Chg. In Div. Payout chdiv3 1.053085 0.907801 1.476975 1.004261 (1.92) (1.66) (1.50) (1.37) Chg. In Capital Ratio checap3 2.017785 6.228253 -10.3052 3.118896 (0.47) (1.45) (-1.34) (0.54) Chg. In SGR chsgr3 7.833366 7.397655 9.595993 10.01552 (4.44) (4.22) (3.04) (4.25) Relative Size relsize -0.52288 -0.29017 -0.62808 0.003418 (-1.95) (-1.09) (-1.31) (0.01) Acquirer’s Prior SGR lsgr 7.683680 6.452533 11.75250 7.270193 (4.02) (3.39) (3.43) (2.84) Acq.’s State Ranking acqrank -0.04114 -0.03003 -0.05248 -0.01891 (-0.84) (-0.62) (-0.60) (-0.29) Target’s State Ranking tarrank 0.011814 0.022342 -0.06257 -0.20038 (0.14) (0.27) (-0.41) (-1.77) Same State Merger state -0.11772 -0.11621 -0.26143 -0.15711 (-0.96) (-0.96) (-1.20) (-0.96) Deposit Cap - Acquirer acqcap -0.13751 -0.13850 -0.14723 -0.48987 (-0.84) (-0.85) (-0.50) (-2.24) Branching Limits- Acq. acqbranch -0.22480 -0.11990 -0.86199 0.263256 (-0.62) (-0.33) (-1.33) (0.54) Deposit Cap – Target tarcap -0.03477 -0.09135 -0.07280 0.528241 (-0.19) (-0.50) (-0.22) (2.14) Branching Limits- Targ. tarbranch 0.416022 0.090962 1.067363 0.494381 (1.53) (0.34) (2.20) (1.36)

Adj. R2 0.3761 0.3033 0.2929 0.3209

Note: The above results are from an OLS regression based on Equation (6) using four sets of 3-year buy-and-hold abnormal returns computed for four different benchmark portfolios (an Equally Weighted Bank Index, Value Weighted Bank Index, Equally Weighted CRSP Index, and Value Weighted CRSP Index). T-statistics are reported in parentheses below each parameter estimate. Values in boldface denote parameter estimates that are statistically significant at the .10 level.