1

1

Integration and Information Technology Effects on Merger Value in the U.S. Commercial Banking Industry

Ali Tafti College of Business, University of Illinois at Urbana-Champaign, 350 Wohlers Hall, Champaign, IL 61820

I study the effect of integration and information technology (IT) investment on the value derived from 118

mergers in the U.S. banking industry. I quantify integration scale on a per-merger basis and examine its

effect on long-term changes to acquiring firms’ performance. I also examine the effect of ex ante

integration risk on stock market reactions to merger announcements. While, in theory, merger integration

processes are supposed to lead to greater cost efficiency, some researchers have argued that integration

processes can be disruptive to firm performance. The empirical results suggest that neither profitability nor

cost efficiency improves with the scale of integration processes and that cost efficiency becomes worse as

the scale of integration increases. However, the results also show that integration processes are more

beneficial to acquiring firms with greater IT investment. Moreover, the results show that acquirers’

investments in IT mitigate the potentially negative effects of integration risk on stock market reactions to

merger announcements. These results highlight the role of IT in facilitating large-scale merger integration

processes.

Key words: Information Technology, Integration, Mergers and Acquisitions (M&A), Banks, Firm Performance, Cumulative Abnormal Returns

1. Introduction

The merger of two firms is a complex event, involving the integration of two distinct entities with

their own intricate organizational structures, cultures, business processes, and information technology (IT)

systems (Focarelli and Panetta 2003). Merger integration challenges are especially prominent in the

commercial banking industry, in which the integration between bank business processes and systems is

critical to the success of merging banks. In the words of a banking analyst at Merrill Lynch: “Most

mergers fall down on IT. Few boards have got the message that integrating IT systems is critical” (Piggott

2000, p. 6). For example, the merger between PNC and Riggs National Bank left customers unable to

access their account balance information for several days while these firms were converting systems

(O'Hara 2005). The merger between Wells Fargo and First Interstate involved glitches in systems

integration that caused many customer deposits to be posted into incorrect accounts (Authers 1998). In

this study, I examine the relationship between the scale of merger integration and the value derived from

mergers, and how this relationship is influenced by IT investment. First, I examine the effect of the scale of merger integration processes on the value derived

from bank mergers. I measure the scale of integration processes as the total cost of integration

incurred in a merger as a proportion of the acquiring firm’s total assets. Typically, integration costs

2

2

are incurred in the first three years following the effective date of the merger, which is considered the

gestation period of the merger (Berger et al. 1998). Integration processes may involve the

reengineering of business processes, restructuring of the organizational hierarchies of merging firms,

or relocation of corporate branches, among other integration activities. Corporate acquisitions

sometimes involve a nominal purchase of one firm by another, without actually involving a

substantial degree of integration between the operations of merging firms. Such mergers would have a

comparatively small scale of merger integration relative to the size of the acquisition. While

integration should in theory lead to improved cost efficiency, the evidence from some prior case

studies suggests that a large scale of merger integration can be disruptive and negatively affect the

value derived from mergers (Shaver 2006). To the best of my knowledge, no prior study has

quantified investments made specifically for the integration of distinct corporate entities. The current

study quantifies merger integration investments on a per-merger basis and examines their impact on

merger value.

Because integration scale may be endogenous to the anticipated performance outcomes of a

merger, I use a measure of ex ante integration risk as an instrumental variable. Integration risk refers to

the potential for complications in the integration process that are likely to cause an increase in subsequent

integration costs. Integration risk is heightened, for example, by potential incompatibilities in

organizational culture or by legacy IT systems that are known to be costly to integrate. The measure of

integration risk used in this study is based on a content analysis and coding of analyst reports and articles

published in the banking industry press shortly after the merger announcement but before the effective

date after which integration costs are incurred. In addition, I examine the effect of integration risk on

short-term cumulative abnormal returns (CAR), which measure stock market reactions to merger

announcements.

Next, I examine how investments in IT moderate the performance impacts of merger integration.

Prior anecdotal evidence from industry trade journals suggests that IT capabilities can enhance the value

that banks derive from the process of merger integration (Aberg and Sias 2005; Kendler 2005). For

example, prior to its 1997 merger with Mellon, Bank of New York had invested in information systems

with “real-time, global” capabilities which were considered to be highly complementary with Mellon’s

client information front end, allowing for greater post-merger cost efficiency (Feig 2007). Scholars of the

banking industry suggest that advances in IT have enabled widespread consolidation over the past two

decades (Berger, Demsetz and Strahan 1999; Holmstrom and Kaplan 2001). As automation and

digitization have reduced constraints on the scale and geographic scope of firm operations, the creation of

larger banks has become more feasible. Despite the widely acknowledged role of IT in mergers and

acquisitions (M&As), there remains surprisingly little empirical evidence regarding how IT investment

3

3

moderates the relationship between merger integration and merger value. This is because IT investment

has not been quantified in the context of merger performance. Empirically assessing the effect of IT

investment on the value derived from integration will provide a better understanding of the factors that

lead to greater merger success, thus enabling managers to better assess the potential value of a prospective

merger. I examine how IT investment moderates the effect of integration scale on long-term performance

of acquiring banks, and also how it moderates the effect of integration risk on stock market reactions to

merger announcements.

I choose the U.S. banking industry as the setting for this study for several reasons. First, this is an

economically significant setting, as the M&A activity in this sector represents a substantial portion of

total U.S. economic activity, often involving firms with assets of hundreds of billions of dollars. Second,

bank operations are IT intensive. Almost every complex business process in banking involves substantial

IT capabilities. The reliability of those processes is critical, and any disruption in operations may affect

bank profits (Davamanirajan et al. 2006). Third, the U.S. banking industry provides unique advantages in

the data available for empirical study, including the existence of federal regulations that standardize how

banks report key firm performance metrics on a quarterly basis and their general practice of reporting IT

investment and merger-related integration costs in public financial statements. Finally, by considering

only the largest U.S. commercial banks, this study conducts analyses in a homogenous industry setting in

which firms perform similar business functions and are subject to similar temporal industry conditions. I

draw from a data set of mergers from 1994 to 2006 among large public U.S. commercial banks and

consider stock market reactions to merger announcements as well as long-term changes in financial

performance.

2. Background Literature

Prior research has yielded few definitive answers regarding whether bank merger integration

processes lead to greater cost efficiency or profitability. Theory would suggest that the integration process

creates economies of scale and thus should improve operating efficiency (Berger et al. 1999; Delong and

Deyoung 2007). However, in the period from 1991 to 1997, profit productivity of banks engaging in

mergers increased substantially while cost productivity of banks declined (Berger and Mester 2003).

Some research has shown that banking M&As have led to greater profit-related efficiencies and that

neither the resulting cost reduction nor consolidation-driven price increases were driving these

efficiencies (Akhavein, Berger and Humphrey 1997). Conversely, prior evidence suggests that the stock

market reacts more favorably to managerial projections of cost reduction than to projections of revenue

increases and that managers either overstate the expected revenue gains or the stock market undervalues

them (Houston, James and Ryngaert 2001). Altunbas and Marques (2008, p. 207) argue that long-term

gains in postmerger performance may be reduced by the difficulties in integrating large institutions: “As a

4

4

financial institution becomes more complex, it is more difficult for managers to control the [merged]

entity, possibly leading to less efficient internal control procedures and duplicated or overlapping

expenses.” These prior studies suggest that challenges related to integration processes should have

tangible impacts on postmerger performance, although the actual effects of integration are not well known

because integration has not been quantified.

While studies demonstrate that IT investments have substantial and measurable economic value,

there is an emerging recognition among scholars of the need to understand the role of IT investment in the

merger integration process. Tanriverdi and Uysal (2010) study the role of cross-business integration in the

merger context, defining cross-business integration as “the extent to which a multibusiness firm uses

common IT resources and common IT management processes across its business units.” Although they do

not quantify IT investments or the integration investments made specifically for mergers, they find that

capital markets value the cross-business IT integration capabilities of acquiring firms. Several qualitative

and case studies have focused on the role of IT in the merger integration process (Giacomazzi et al. 1997;

Johnston and Yetton 1996; Robbins and Stylianou 1999; Stylianou, Jeffries and Robbins 1996). These

case studies have been valuable in highlighting the challenges and benefits of IT in the merger integration

process and have provided insights into how IT can generate value in the context of mergers. The current

paper contributes to this literature through an empirical study in a homogeneous industry setting, using

quantitative measures of IT spending and integration scale and examining outcomes in long-term

accounting performance in addition to short-term abnormal stock returns.

3. Hypotheses

3.1 Integration and Merger Performance 3.1.a Scale of Merger Integration and Its Effect on Long-Term Performance

The decision to integrate involves a trade-off between the potential benefits from leveraging

synergies against the potential costs and risks of integration (Zollo and Singh 2004). The scale of

integration depends on the extent to which the acquiring firm decides to integrate a newly acquired firm

or to keep it running as an independent entity. While acquisitions can be done as a nominal purchase of

one firm by other without actually integrating, it is generally believed that integration processes enable

merging firms to become more cost efficient (Altunbas and Marques 2008; Johnston and Yetton 1996;

Tanriverdi and Uysal 2010). The improved cost efficiency could be a result of the streamlining of

operations, the consolidation and merging of administrators and other employees, the consolidation of

office branches, and other integration activities. In theory, when these activities are well executed, the

resulting merged organization should become more cost efficient. However, the actual performance

impacts of integration are not well known.

5

5

The integration process following a merger can be disruptive and detrimental to financial

performance (Shaver 2006). Large-scale integration processes can exacerbate the operational disruptions

and general sense of uncertainty experienced by employees. Because integration processes deepen the

interdependence between firms, they can lead to contagion effects in which shocks in the organizational

environment reverberate across the boundaries of the merging firm (Shaver 2006). Such contagion effects

result when unplanned events such as operational glitches or disruptions in routines become more

detrimental to a highly integrated combined entity than if the same two firms were operating

independently (Shaver 2006). In addition, the integrated firm has fewer slack resources that would be

needed to take advantage of new opportunities arising in the business environment (Shaver 2006). When

firms allocate substantial resources to “fire fighting,” or to resolving crises and operational disruptions as

they arise in the process of integration, these resources are diverted from other activities, such as process

rationalization, that help create long-term gains in operational efficiency. Large-scale systems integration

projects can consume organizational resources such as staff labor and training. Complications in merger

integration can hinder the governance of business processes. A merger integration project can be a

turbulent event within the organization in terms of its effect on employee morale, its upheaval of

organizational routines and structures, and its role in the disruption of business processes. These effects

may last even after integration investments have been made.

Due to the complex and mission critical nature of business processes within banks, glitches and

disruptions in the course of integration can be disastrous to a bank’s reputation. Several mergers in the

1990s were known to have particularly cumbersome integration projects in which systems conversion

glitches became publicly known, resulting in the loss of customers and compromising the banks’

reputation. Among such cumbersome integration projects were the Wells Fargo–First Interstate Merger in

1996 (Authers 1998; Wahl 1998), the Nations Bank–Barnett merger in 1997 (Breitkopf 2001), and the

Fleet–BankBoston merger in 1999 (Marlin 2003; Moyer 2001). Although the integration process usually

nears completion two or three years after the merger date (Focarelli and Panetta 2003), the impact of

glitches in the integration process can be palpable in ongoing profitability and cost efficiency well beyond

this period. As the scale of an integration project increases relative to the size of the merging banks,

governance and rationalization of processes may become more difficult, leading to the possibility of

customer defection. Disruptions stemming from the integration can hamper the merging firms’ ability to

generate synergies, such as when potential synergies in cross-selling fail to be realized or joint

optimization of processes fail to occur. A large integration project can create a disruptive effect in firm

operations, increase the occurrence of errors, and make it more difficult for the firm to realize gains from

the merger (Alaranta 2006).

6

6

HYPOTHESIS 1A. The scale of merger integration has a negative effect on the long-term firm performance of acquirers. 3.1.b Integration Risk and Stock Market Reactions to Merger Announcements

Soon after a merger is announced, but before the actual expenditures in the integration process,

analysts and managers often comment on the prospect of integration risk in the impending merger.

Integration risk is due to potential incompatibilities in organizational culture or structure,

incompatibilities in IT systems, or the existence of legacy IT systems that are costly to convert. For at

least the past several decades, news periodicals with wide circulation have discussed the role of

integration risk in bank mergers, suggesting a level of public awareness and possible stock market

sensitivity to this issue (Arend 1991; Gahagan 2009; Hansell 1998; McKenzie 2008). Table 1 highlights

examples of analysts’ and managers’ public statements about impending integration risks of some of the

bank mergers in the data sample. Prior research suggests that improved cost efficiency through integration

is one of the primary justifications that bank managers give when acquiring other banks, and stock

markets react more favorably to this rationale than to other reasons managers give for acquisitions, such

as the prospect of increased revenue (Houston et al. 2001). However, if integration risk is high, this could

lead to large and costly integration efforts that are disruptive to the long-term value of merging firms.

Stock market reactions to the merger would therefore reflect analysts’ awareness of such implications.

Integration risk can drive up integration costs and thus drive up the scale of the integration project.

Therefore, in line with Hypothesis 1a, I hypothesize that stock markets react negatively to mergers in

which integration risk is high.

HYPOTHESIS 1B. Integration risk has a negative effect on the stock market reaction to an acquisition. 3.2 IT Investment, Integration, and Merger Performance 3.2.a The Moderating Influence of IT Investment in the Effect of Integration Scale on Long-Term Performance

While the integration process can be disruptive and detrimental to merger value, investments in

IT can mitigate this effect and enable firms to derive greater value from mergers. Prior literature reveals

three primary mechanisms by which IT investments can enhance the value that is created from the

integration process: (1) extensibility of IT across larger scales of operations, or IT economies of scale, (2)

the rationalization or streamlining of business processes to facilitate integration, and (3) the flexibility of

IT to reconfigure firm resources and enable interfirm synergies to be created.

First, merging firms can leverage IT-related economies of scale, which is the ability to extend IT

capabilities to a greater scale of business operations to improve cost efficiency. Given the role of IT in

both coordination and control, IT investment can play a substantial role in generating economies of scale

and scope (Dewan, Michael and Min 1998). This has implications for the role of IT in the context of

7

7

corporate mergers. Through automation and digitization, business processes can be made to extend across

a greater scale of operations, coordination capabilities are enhanced, and transactions become much more

efficient (Melville, Gurbaxani and Kraemer 2007). Greater automation reduces firms’ reliance on manual

processes, which in turn reduces human labor costs and enables firms to operate more efficiently on a

larger scale.

Second, firms can leverage value from a merger through rationalization of business processes—

the practice in which firms identify suboptimal or inefficient business processes and then reconfigure and

streamline them (Dietz 2006; Hammer and Champy 1993). This creates the conditions for better

management, oversight, and understanding of business processes (Dietz 2006), leading to more efficient

use of non-IT resources, such as reallocations of personnel and physical premises in organizational

restructuring, training, and reconfiguration of business processes. Without a high level of transparency

into firm processes enabled by IT, the merger integration process can contribute to a chaotic environment

for bank personnel and make it difficult to systematically improve the efficiency of bank processes.

Rationalization of business processes through IT enables a firm to have more control of streamlined

business processes, allowing the firm to better manage business processes and eliminating redundancy of

business processes.

Third, investments in IT enable core business processes to be rendered in digital form, increasing

the flexibility of firms to reconfigure business processes (Byrd and Turner 2000; Duncan 1995;

Sambamurthy, Bharadwaj and Grover 2003). As banks invest in IT, they are more likely to have

established flexible technology architectures that enable greater strategic agility for organizational

restructuring related to M&As (Broadbent, Weill and St. Clair 1999; Byrd and Turner 2000). IT

capabilities can enable banks to generate new sources of customer value and maintain greater customer

retention during the merger, leading to fewer service disruptions and an enhanced capability to create new

products. As business processes become more digitized and IT infrastructure capabilities increase, a

bank’s flexibility for organizational transformation may also increase, reducing the potential detrimental

impact of integration. IT investments can also enhance the dynamic capabilities of process

reconfiguration or resource recombination, which become increasingly valuable as integration processes

increase in scale (Malhotra, Gosain and El Sawy 2005).

To the extent that a large scale of integration has latent potential for creating synergies in a

merger, I argue that IT investment enables firms to better realize such synergies. Thus, the effect of

integration scale on merger performance should improve with IT investment. Investments in IT systems

such as business process management, customer relationship management, and enterprise resource

planning, can enable firms to cope with larger integration processes (Tanriverdi and Uysal 2010). As

firms become more capable of coping with large-scale organizational change, they can better evaluate and

8

8

optimize those processes in both greater scale and greater precision. Enhanced capabilities in realizing

merger synergies lead to improved profitability and operating efficiency. IT capabilities can enable banks

to increase the diversity of new IT-enabled products and services that reach a broader base of customers

(Berger 2003; Sambamurthy et al. 2003). Therefore, investments in IT can increase banks’ ability to

generate greater value in the process of merger integration, leading to improvements in long-term

performance.

HYPOTHESIS 2A. In the long run, merger integration scale is more beneficial to acquiring firms with greater IT investment. 3.2.b The Moderating Influence of IT Investment in the Effect of Integration Risk on Stock Market Reactions to Merger Announcements

As Tanriverdi and Uysal (p. 2) point out, bank merger executives are not known for paying much

attention to the role of IT when making merger decisions; instead, the perception is that they focus

primarily on “statutory and regulatory issues, financial and tax structuring, and business synergies.”

However, recent evidence suggests that analysts have become increasingly sophisticated in their

understanding of merger capabilities, and over time stock markets have improved at predicting successful

mergers (Delong and Deyoung 2007). As is evident in numerous analyst reports and articles in the

business press, some of which are quoted in Table 1, IT capabilities are among the factors that markets

analysts consider when reacting to a merger. High ex ante integration risk is likely to drive up the

subsequent scale of the merger integration project (Shaver 2006). With this in mind, and building on the

arguments that support Hypothesis 2a, that IT investment enhances the value derived from large-scale

integration projects, it follows that stock markets should react more favorably to integration risk when

acquiring firms make greater IT investments.

HYPOTHESIS 2B. The stock market reacts more positively to mergers with high integration risk when the acquiring firm has greater IT investment.

The influence of IT investment on merger performance depends on the way merging firms

consolidate their IT systems, such as whether acquiring firms decide to leverage and integrate systems

(“absorption” model) or to phase out and replace the IT systems of the target firms (the “best of breeds”

model) (Johnston and Yetton 1996). For this reason, it might be expected that the effect of IT investment

made by acquiring and target firms will differ. It is plausible that the acquiring firm’s IT investment will

have greater influence than the target firm’s IT investment, particularly if the acquiring firm becomes

dominant in establishing organizational policies, making decisions about bank operations, and

implementing business process routines. Therefore, I consider the extent to which the IT investment of

both the target and acquiring firms help generate value as the scale of merger integration increases.

4. Research Design and Methodology

9

9

4.1 Data

The unit of analysis in this study is the merger event. The aim in sample selection is to cover as

large an amount of economic activity as possible in the U.S. banking industry while maintaining

homogeneity in the sample of banks. From the Thomson SDC Platinum database of M&As, I retrieved

285 merger announcements from 1994 through 2006 in which the acquiring and target firms are

depository institutions (Standard Industrial Classification codes 6000, 6011, 6021, 6022, 6029, 6035,

6036) or office of a bank holding company (6712) and which meet the following sampling criteria: (1)

The merger was successfully completed, (2) the merger resulted in the acquiring firm having a majority

stake in the target firm, (3) the transaction involved more than 10% of the target firm’s shares, (4) both

the acquirer and target firm are based in the United States, (5) transaction (or deal) values are over

$100,000,000 (a cutoff chosen in order to focus on economically significant acquisitions), and (6) the

identifying information given in the SDC database for both banks (name, home state, symbol, and

CUSIP) could be matched unambiguously to a U.S. bank holding company in the databases of the Federal

Reserve Board (FRB). These sampling criteria ensure that the final sample of bank mergers is relatively

homogenous with respect to the business activities of acquiring and target banks.

For long-term firm performance and other bank metrics (cost efficiency and profitability), I used

the quarterly Y-9C statements that U.S. bank holding companies submit to the Federal Reserve Board

(FRB), which allows for consistency in the reporting of bank metrics. Because the Federal Reserve tracks

all accredited U.S. commercial banks, the FRB data set can also be used to assess how well any particular

data sample represents and compares with the population of banks in the U.S. banking system. The

sample size was reduced further because of missing data on IT investments or integration costs. The final

sample size of 118 mergers, involving 55 different acquiring bank holding companies, represents on

average approximately 43% of the total M&A activity among U.S. banks in each year from 1994 through

2006 (before the onset of the banking financial crisis at the end of 2007). The sample size is reduced to

102 mergers for analyses involving long-term firm performance change because some acquiring banks

were themselves acquired within three years following an acquisition.

Figure 1 shows the year-by-year proportion of M&A deal activity in the sample in comparison to

all completed U.S. commercial banking mergers. This figure shows representation in terms of both annual

number of mergers and the sum of bank merger deal values. As Figure 1 illustrates, the relatively small

number of bank M&As in the sample constitutes a large portion of the total domestic M&A activity in the

U.S. banking system. For example, in 1998, the total number of completed domestic bank mergers was

306, only 14 of which are in the sample. However, these 14 mergers represent nearly half of the total bank

merger deal values in 1998, with $142 billion in total deal values compared with $153 billion in deal

values for mergers outside the sample. The dip in 2002 suggests a year of little M&A activity for U.S.

10

10

banks in terms of transaction volume. Kolmogorov–Smirnov two-sample tests showed no significant

difference among performance outcomes and available independent variables (normalized by firm assets)

between mergers in the sample and mergers that were not in the final sample.

For guidance in collecting and interpreting IT investment and merger integration data from

annual 10K reports of banks, I first conducted interviews with three senior-level IT executives at major

banks, each of whom had experience with at least one major merger integration project and also had

knowledge of overall IT investment levels. After the guidelines for data collection had been established, a

small staff of supervised research interns proceeded to collect IT investment and merger integration data

from annual 10K reports of both target and acquiring firms in the years preceding each merger. IT

investments are reported as a combination of equipment, hardware, software, telecommunications, data

processing, compensation of IT staff, and payments to outside IT service providers. Merger integration

investments include the following expenditures that were associated with each merger in the sample:

severance and personnel changes, the closing or opening of building space, branch sales, operations

restructuring, changes to organizational hierarchy, IT systems conversion projects, and computer

hardware and equipment replacement. Integration investments are listed on a per-merger basis and are

typically incurred in full within the three years following the merger effective date. General IT

investments did not overlap with IT systems conversion expenses that pertained specifically to the

mergers. For 29 of the mergers, IT-systems (merger-related) conversion and IT-integration costs were

reported separately rather than lumped in with other integration expenses. In these mergers, IT-related

systems conversion accounted for an average of 36% of all merger integration expenses. For other

mergers, the overall merger integration costs were reported by the banks without breaking down the

portion that was due specifically to IT-related systems conversion; however, the financial reports stated

that IT-related systems conversions were part of the overall integration expenses and not included in

general IT investments. This enabled a consistent separation of measurement between overall merger

integration investments, which included merger-related systems integration, and general IT investments.

The current study also measures integration risk associated with each merger. Integration risk was

sometimes anticipated to be high as a result of incompatible IT systems, organizational structures, or firm

cultures. This measure is based on the content of analyst reports and managerial statements in the days

following a merger announcement. To construct this measure, the supervised research assistants

conducted a comprehensive search in Factiva for analyst and managerial statements about each merger

published from one year before up to the effective date of the merger. With the help of content-analysis

software, a set of keywords related to the integration process was identified.1 Manual reviews of merger-

1 Keywords were conjugates of the words: integration, consolidation, systems, conversion, migration, transition, compatibility, data, technology, information technology (IT), software, hardware, and platform.

11

11

related press reports were then conducted to verify that the keywords were comprehensive. Passages with

these keywords were then extracted from the texts. The passages were coded for whether integration risk

was deemed to be high by the statements of analysts and managers quoted in the articles (a binary

variable). This was done independently by three graduate students pursuing PhDs in the field of business

administration, each of whom had more than eight years of prior experience in management or

development of IT systems. I provided further review and validation. Coding results were subsequently

verified by two senior-level IT managers, each of whom have more than ten years of experience in the

banking sector. Table 1 shows coding examples.

The Appendix lists the control variable definitions and their data sources. All monetary values in

this study are adjusted for annual inflation (using 2011 U.S. dollars). I discuss the construction of the

dependent variables in sections 4.2 and 4.3. Table 2 presents pairwise correlations and summary statistics

of main variables of interest.

It is worthwhile to mention some summary statistics that convey the scale of the firms in the

sample. On average, acquiring firms have approximately $80 billion in assets, compared with $23.6

billion for target firms. Acquirers invested an average of approximately $432 million annually on IT in

the year before the acquisition, while target firms invested $94 million annually on IT. Total integration

costs amount to approximately $248 million per merger. Table 2 shows that 22% of the mergers are

anticipated to have high integration risk based on analyst reports or managerial statements before the

merger effective date. For mergers deemed to have high integration risk at the outset (IntegrRisk = 1),

integration costs are greater by an average of 0.8% of the acquiring firm’s assets, amounting to

approximately $640 million more than in mergers deemed to have low integration risk. Long-term cost

efficiency of the acquiring banks improves substantially, relative to the industry, from one year before to

three years after the merger (Mean ΔCosteff = –0.78), while profitability improves much less. This is

consistent with Houston et al.’s (2001) arguments. Stock markets tend to punish acquiring firms when

mergers are announced, but they preserve the value of the combined firms, as is evident in negative

averages of cumulative abnormal returns (CAR1) for acquiring firms and near-zero averages for

combined CAR1 values. Mean values for cumulative abnormal returns using alternative time periods

(denoted as CAR2–CAR4 in this paper) are about the same. This suggests that announcement period

merger gains tend to accrue toward the target firm shareholders, consistent with Jensen and Ruback

(1983) and Jarrell, Brickley, and Netter (1988). Table 2 shows a correlation of 0.75 between anticipated

integration risk (IntegrRisk) and the subsequent scale of integration normalized by acquiring firm assets

(IntegrScale/Assets). Considering that these two measures are constructed very differently, this high

correlation provides some insight into the nature of merger integration because it suggests that integration

scale is driven by incompatibilities and other preexisting sources of integration difficulty.

12

12

Table 2 also shows a positive correlation between industry-relative changes to cost efficiency

(ΔCosteff) and profitability (ΔROA). Such a correlation is possible if a firm’s asset base grows more

quickly than its net income, which should happen as a result of the merger. As I elaborate in the next

subsection, measures of long-term performance change are relative to a matched cohort of banks in the

same asset size range that did not engage in acquisitions over the same period.

4.2 Estimation Model 1: Long-Term Change in Financial Performance I present two long-term financial performance outcomes of merging banks: (1) change in cost

efficiency (CostEff), which is the ratio of noninterest operating expenses over operating income (a

declining ratio means that efficiency has improved) and (2) change in profitability, which is measured

using return on assets (ROA), a ratio of net interest income over total firm assets. These metrics are used

in the banking and merger literature to measure long-term financial performance (Delong and Deyoung

2007). I calculated the change in performance ratio as a difference between performance ratio three years

after the end-of-quarter reporting date closest to the merger announcement date and the performance ratio

at the end-of-quarter date closest to one year before the merger announcement date:

∆Cost Efficiency = Cost Efficiencyt + 3 – Cost Efficiencyt – 1,

where subscript t is the closest end-of-quarter date to the date of merger announcement. I use this time

window because bank mergers have a gestation period of about three years (Berger et al. 1998; Focarelli

and Panetta 2003; Rhoades 1998). Integration investments are made during the first two or three years

immediately following the merger, and so the benefits of the integration process during this period would

have required some time to take hold.

Next, I adjusted the performance ratio changes for time-specific industrywide trends by

subtracting the change in the performance ratio, over the same period, for the mean of an annual set of

banks that, hereinafter, I refer to as the “cohort out-sample” or “cohort matched sample.” The annual

cohort out-sample includes the population of all U.S. bank holding companies in the same asset size range

as banks in the sample, excluding any bank holding companies that were also involved in mergers in that

year or that appeared in the sample. A different cohort out-sample is determined for each year. I

calculated the industry-adjusted long-term change in performance ratio as follows:

,

where n is the number of commercial banks in the cohort out-sample and f is the acquiring bank.

I estimated the empirical models using ordinary least squares (OLS) and instrumental variables

regression models, with heteroskedasticity-robust standard errors clustered on the identifier of the

acquiring bank. The models provide a test for the effect of IT investment, integration scale, and the

13

13

interaction between IT and integration on long-term changes in financial performance relative to the

cohort out-sample:

ΔAdj. Performance Ratiof = Constant + β1 IntegrScale + β2 IntegrScale × AcqIT + β3 IntegrScale × TgtIT

+ β4 AcqIT + β5TgtIT + Controls + ε, (1)

where Controls represents all control variables (see the Appendix). The variable IntegrScale represents

merger-related restructuring and integration costs, given as a ratio over total acquiring firm assets. This

figure includes merger integration costs stemming from IT systems conversion, computer hardware and

equipment replacement, severance and personnel changes, the closing or opening of building space,

branch sales, and operations restructuring. Annual and quarterly financial statements were examined up to

four years after the merger effective date to verify total integration investments for each merger. The

variables AcqIT and TgtIT represent information technology investment, as ratios over total assets of

acquiring and target firms, respectively, measured for the year before the merger. These figures include

amounts invested annually in data processing, IT equipment, IT personnel, software, hardware, outside IT

services, and other IT-related expenses. These amounts are listed in annual 10K and quarterly financial

statements filed with the U.S. Securities and Exchange Commission (SEC).

A potential source of endogeneity in the model stems from the simultaneous determination of

integration scale and postmerger performance because integration scale reflects an acquiring firm’s

decision to invest in a merger integration project to generate synergies, and this may be influenced by

many unobservable firm capabilities. To correct for this, a suitable instrumental variable would directly

affect integration scale and would have the indirect path through integration scale as its only considerable

influence on long-term performance. I use the merger’s anticipated integration risk (IntegrRisk) as such

an instrumental variable for integration scale. Integration risk arises out of preexisting incompatibilities

between disparate IT systems, business processes, or organizational cultures, based on an analysis of

industry press reports and managerial statements found in Factiva. To the extent that the commitment to

integrate the target firm has already been made by the time of the merger announcement, integration risk

drives up the subsequent costs of integration, causing an expansion in the scale of the integration project.

Instrument validity requires the integration risk to be uncorrelated with the error term, εi, of equation (1).

The relevance condition of the instrument requires that θ1 ≠ 0 in the reduced form equation:

IntegrScale = δ0 + θ1IntegrRisk + δ1AcqIT + δ2TgtIT + Controls + r. (2)

To test the validity of the instrument, integration scale was first regressed only on the relevant instrument;

this confirmed that the relationship is positive and statistically significant at p < 0.001. To instrument for

the interaction terms of interest, a set of linear predictions for IntegrScale is generated by conducting an

OLS regression of IntegrScale on IntegrRisk and all included instruments (i.e. AcqIT, TgtIT, and

Controls). This predicted value is then multiplied by the acquiring and target firms’ asset-normalized IT

14

14

investment; such a procedure is detailed in Wooldridge (2002). A test of the full first-stage equation (2)

with all included instruments confirms that θ1 > 0 (p < 0.001), and the F-statistic for the excluded

instrument (8.49) is highly significant at p < 0.001. These test statistics are cluster robust. Corresponding

to the rationale behind this instrument, the result suggests that ex ante integration risk drives up

subsequent integration scale. The Cragg–Donald statistic (F = 15.24) lends further confidence in the

strength of this instrument, when compared with the Stock–Yogo critical values (Stock and Yogo 2005).

The Anderson–Rubin statistic (χ2 = 4.6, p < 0.05) also confirms the joint significance of equation (2)

predictors.

Together, these tests suggest that the instrumental variable is both relevant and valid. The two-

stage least squares (2SLS) models are exactly identified, which means that tests of overidentification are

not applicable. To show robustness of estimates, OLS results are shown alongside the main 2SLS results.

4.3 Short-Term Stock Market Reactions to Merger Announcements: CARs

I use an event study methodology to examine how stock markets react to integration risk together

with the IT investments of merging firms:

CARi = Constant + β1 IntegrRisk + β2IntegrRisk × AcqIT + β3 IntegrRisk × TgtIT + β4AcqIT + β5TgtIT +

Controls + ε. (3)

To calculate CAR, I used OLS to estimate a daily market model for the test period t = (–300, –

46), where t is the number of days before the announcement of the merger, Ri,t is the daily return of the

bank’s stock, and Rm,t is the daily return of the value-weighted index of U.S. stocks listed by the Center

for Research on Security Pricing (CRSP):

.

I use this regression to estimate and for each firm in the test period. The CAR for firm i around the

event date is calculated as the sum of the daily estimated abnormal return in a short window of time that

begins ten days before the merger announcement and ends one day after the merger announcement.

.

This choice of time window allows for the possibility of information leakage regarding the bank merger

before the actual announcement but is also short enough to reduce the likelihood that other confounding

events influence this measure. In addition to the primary event window, t = (–10 days, +1 day), I also

consider longer time windows of t = (–10 days, +5 days), t = (–10 days, +30 days), and t = (–30 days, +10

days). The longer time windows incorporate information that leaks before or emerges after the merger

announcement and are important in this particular study, which focuses on the effects of integration risk

that may enter the realm of public discussion somewhat gradually after it is known that the merger is

15

15

going to take place. The longer time windows also allow for robustness checks of the primary event-

window results. Data on daily stock market values come from the CRSP.

5. Results

To test Hypotheses 1a and 1b, I report the results of the OLS and 2SLS models for both cost

efficiency and profitability in Table 3. While I primarily interpret the 2SLS models for hypothesis testing,

the OLS models provide additional insight as well as additional evidence of robustness. To test

Hypotheses 2a and 2b, I report results involving CARs in Table 4. To facilitate interpretation of direct

effects in the regression results, I mean-center the main independent variables of interest in both sets of

regression models. Thus, direct effects of each variable are to be interpreted at the mean values of the

other variables.

In general, the control variable effects align with the findings of prior research. Consistent with

Delong and Deyoung (2007), Hotmkt has a significant, negative effect on the shortest window of

cumulative abnormal returns (CAR1) (Columns 1 and 2 in Table 4), while most of the control variable

coefficients are insignificant. Investors discount mergers involving larger deal values; this reflects the

relatively greater risk of large mergers and perhaps also awareness that, on average, mergers destroy

value (Andrade, Mitchell and Stafford 2001; Ravenscraft and Scherer 1987). Consistent with (Schwert

2000), evidence that markets pay less for hostile takeovers is weak. As target firms approach acquiring

firms in size (mergers of equals), mergers create value for the combined firms but, in general, not for the

acquiring firm itself, consistent with prior research suggesting that target firms tend to be the winners in

mergers of equals (Andrade et al. 2001; Jarrell et al. 1988; Jensen and Ruback 1983).

Hypothesis 1a predicts that a large scale of merger integration has a negative effect on acquirers’

firm performance after the gestation period of the merger. A positive coefficient estimate of β1a in the

cost efficiency result (Column 2, Table 3) and a negative coefficient estimate β1a in the profitability result

(Column 4, Table 3) would provide support for this hypothesis. Although the cost efficiency model shows

some support for this hypothesis (in the instrumental variables regression model), the effect of integration

scale on profitability is not statistically significant. The results imply that efforts to derive greater cost

efficiency may be hampered in the long run by disruptive effects associated with the scale of integration.

Regarding cost efficiency, it is important to consider the nonsignificant coefficient estimate of β1a in the

OLS model (Column 1). In the OLS model, IntegrScale is not just the integration scale predicted by ex

ante integration risk; it also incorporates investments made in absence of such risk, in particular when the

acquiring firm perceives benefits in integrating. The role of integration as an investment that generates

benefits to cost efficiency may offset the effect of integration as an unwanted cost driven by

incompatibilities between the merging firms. Thus, integration costs have a significant detrimental effect

on cost efficiency only when such integration costs are driven by ex ante integration risk.

16

16

Hypothesis 1b posits that integration risk has a negative effect on stock market reactions to the

merger. This hypothesis is not supported, as is evident in the nonsignificant coefficient estimates for β1b

in Table 4. Because stock markets have a favorable view of mergers that are motivated by cost efficiency

gains (Houston et al. 2001), this may be offsetting the potentially detrimental effects of merger integration

risk that would accompany large-scale integration projects motivated by cost efficiency.

Hypothesis 2a pertains to the interaction effect between the scale of merger integration and IT

investment of the acquiring firm. This hypothesis is supported, as is evident in the statistically significant

coefficient estimates for β2a in Table 3, which are negative in the model for cost efficiency and positive in

the model for profitability. The results suggest that the challenges involving large-scale merger

integration can be mitigated by IT investment, or that IT capabilities can enable merging firms to derive

greater value from their merger integration processes. An interpretation of the economic significance of

the coefficient estimates is that integration processes begin to have a positive effect on profitability for

acquiring firms that invest at least 0.86% of assets annually in IT. For the acquiring firm of average asset

size, this amounts to approximately $689 million a year invested in IT in 2011 dollars, or the 85th

percentile in IT investment among acquirers in the sample. While the industry-relative decline in cost

efficiency is mitigated by IT investment, for integration scale to begin having a beneficial effect on long-

term cost efficiency would require levels of IT investment that are well beyond the range of the sample.

Therefore, integration investments can lead to tangible benefits in profitability at high enough levels of IT

investment, although IT investment can, at best, only mitigate the detrimental effects of integration on

cost efficiency. This lends insight into some prior research findings showing a greater profitability impact

of mergers than cost efficiency impact (Akhavein et al. 1997).

Hypothesis 2b pertains to the interaction between integration risk and acquirers’ IT investment,

predicting that stock markets reward mergers involving a high integration risk when acquiring firms have

a higher level of IT investment. This hypothesis is supported in all of the time-window variations of the

CAR measure, as is evident in estimates of β2b in Table 4. This suggests that stock market reactions to

mergers with the prospect of high integration risk improve with acquirers’ IT investment. This result

implies that stock markets recognize IT capabilities of the acquiring firm as important to overcoming

challenges of merger integration, which would make sense considering the prevalence of discussion

among analysts about IT capabilities and integration prospects of merging banks.

The results do not show that target firms’ IT investment has a significant effect on merger value.

It could be that acquiring firms are not leveraging or integrating target firms’ IT capabilities to the extent

that might be expected, nor would they necessarily be following the “best-of-breeds” model, as defined in

Johnston and Yetton (1996), in which the superior IT infrastructure takes hold and the inferior one is

discarded or phased out. Rather, it appears that the target firm’s IT infrastructure and capabilities are

17

17

generally phased out in favor of the acquiring firms’ IT infrastructure, perhaps due to the cultural or

political dominance of the acquiring firms’ IT organizations.

Another finding is that the acquiring firm’s premerger IT investments do not directly lead to long-

term gains in cost efficiency and profitability, although IT investment becomes more valuable with a

greater scale of merger integration. Although prior research shows that IT investments generally create

value in the single-firm context (Bharadwaj, Bharadwaj and Konsynski 1999; Brynjolfsson and Hitt 1996;

Kohli and Devaraj 2003), in the merger context, it may be more difficult for firms to derive value from IT

unless IT assets are accompanied by sufficient investments in merger integration. One plausible

explanation is that the ability to scale IT resources to a larger organizational context requires

complementary investments in integration. The acquiring firm may find that its own IT capabilities,

which are valuable in the single-firm context, do not generate value in the merger context if not

accompanied by sufficient merger integration. Another possible explanation is that large IT systems,

without systematic efforts of integration, can be sources of disruption in an organization that is

undergoing a large-scale change such as a merger; but this disruption is mitigated by deliberate

integration efforts that streamline large IT systems. Thus, integration investments can enable acquiring

firms to better leverage value from their IT investments in the merger context, just as IT investments can

help firms leverage value that is latent as potential synergies in merger integration projects.

5.1 Additional Robustness Analyses

I conducted additional analyses to verify robustness of the results with respect to several possible

sources of endogeneity and selection bias. I also used alternative measures to verify robustness.

One plausible form of endogeneity stems from banks’ selection of target firms and the possible

simultaneity between this selection process and the constructs of interest in this study. For various

reasons, some mergers are announced but are never completed. Because such mergers do not actually

enter the integration phase, their failure could be a result of a failure of the acquiring bank to select a

“good” target bank—one that is more likely to be successfully integrated. However, additional empirical

tests showed no relationship between acquirers’ premerger IT investment and the likelihood that an

announced merger reaches completion. A second plausible form of endogeneity may stem from

integration investment choices; specifically, the acquiring firm may choose to invest more resources in

integration projects when observing that the merger appears to be generating value. The instrumental

variable model addresses this concern by filtering out the firms’ endogenous response to subsequent signs

of merger performance that appear after the merger effective date. Postmerger investment endogeneity is

not a serious concern with IT investments, because IT investment measures are based on the financial

reports of the closest quarter one year before the merger date.

I tested the robustness of empirical results using alternative measures of key variables. The main

18

18

model adjusted the long-term changes in performance by subtracting the performance change of a

matched out-of-sample cohort of commercial banks. I also considered an alternative form of industry

adjustment in which performance ratios are first adjusted by the mean performance of the matched cohort

at each end of the period, and then the long-term change of this adjusted performance ratio is calculated.

Second, I conducted tests by using financial performance ratios that were adjusted by the matched cohort

medians rather than matched cohort mean values. Third, for calculations of combined CAR, I considered

equity-weighted combinations of target and acquirer rather than asset-weighted combinations. Fourth, I

repeated all analyses using Winsorized variables. Winsoring is a process that identifies the highest and

lowest 1% of values for each variable and replaces those values by the next value inward from the

extremes, blunting the influence of any potential outliers (Cox 1998). Fifth, I examined the stability of the

estimates by reducing the number of control variables and conducting tests with many different

combinations of control variables. Eliminating the least significant of the control variables had little effect

on estimates, as verified by a Hausman test comparing partial and full models; this lends further

confidence to the stability of estimates with respect to the number of control variables. Even in full

estimation models, variance inflation factors did not show substantial multicollinearity. Sixth, for firms

with sufficient longitudinal data, I converted annual IT investment flows to IT capital stock using a

perpetual inventory model, as in Chun et al. (2008), and conducted similar empirical tests. Although

conversion to IT capital stock reduced the sample size because there was not sufficient longitudinal data

for all banks in the sample, estimates using IT capital stock measures were broadly similar.

Finally, I ruled out the possibility that integration costs were factoring into the calculations of

long-term changes to profitability or cost efficiency. Integration investments were typically made within

the first two years after the effective date and, in almost all cases, were made within the first three years

after the merger. I calculated long-term performance changes using the reporting quarter immediately

following the third year after the effective date. Therefore, netting out the integration costs from the

operating costs had negligible effect on the coefficient estimates. The results are also robust to subtraction

of IT expenses from the measure of overall operating expenses.

6. Conclusions

This is the first study to quantify the scale of integration for each merger and to examine

integration as conceptually distinct from the acquisition itself. It is also the first study to quantify IT

investments and measure their performance impacts in the merger context.

The first empirical result suggests that the process of merger integration can be disruptive to cost

efficiency, particularly as integration costs are driven by ex ante integration risks. The effect of such

disruptions can last beyond the period when integration investments have been made. Although improved

19

19

cost efficiency is a primary motivation for bank mergers, cost efficiency declines relative to the industry

as the scale of integration increases. This does not mean that integration investments are to be avoided.

The data suggest that integration costs are driven by incompatibilities and other integration difficulties;

that is, they are primarily crisis driven. This can be inferred from the strong positive correlation between

ex ante integration risk and post-merger integration scale. If integration investments were instead driven

by a coherent and deliberate strategy, which is more likely to be the case when acquirers engage in

mergers with low integration risk, firms might experience a more beneficial effect of integration

investments on cost efficiency.

The second main result suggests that IT investment can mitigate the potentially disruptive effects

of integration and, furthermore, that integration scale is not always detrimental. For acquirers above the

85th percentile in IT investment, integration scale has a positive effect on long-term profitability.

Although integration scale does not become beneficial to cost efficiency at any feasible level of IT

investment, IT investment reduces this detrimental effect. These findings suggest that the effect of

integration on long-term performance is contingent on the IT capabilities of the acquiring firm. If

acquiring firms anticipate a high degree of integration risk in an impending merger, they should expect a

high level of subsequent integration costs and should consider whether their current IT capabilities are

sufficient to prepare them for the ensuing challenges. If their IT capabilities are insufficient, potential

acquirers should either reconsider committing to the merger or should bolster their IT capabilities by

increasing their level of annual IT investment.

The third finding shows that short-term stock market reactions are in line with long-term financial

performance, in that acquirer’s IT investment positively moderates the effect of integration risk on CARs

from the merger announcement. This corresponds with the abundant evidence in banking industry trade

journals suggesting that market analysts show awareness of IT capabilities and integration challenges in

evaluating the potential merits of a merger (Hovanesian 2006; Kendler 2005; Marlin 2003; Piggott 2000).

While prior evidence reveals that stock markets have generally become better at predicting successful

mergers over time (Delong and Deyoung 2007), prior research did not pinpoint the specific kinds of

merger capabilities that stock markets have improved at evaluating. As might be expected from the

abundant business press reports on banks’ IT capabilities that exist in the merger context, the findings

suggest that stock markets are sensitive to banks’ IT capabilities in the presence of merger integration

risk.

There is no statistically significant impact of target firms’ IT investment on merger performance.

Perhaps this is because the acquiring firms’ IT dominates and holds sway over a true “best-of-breeds”

model or “absorption model” (Johnston and Yetton 1996). That is, the IT resources of the acquiring firm

20

20

are favored for preservation over the IT resources of the target firm because acquiring firms have greater

control over key decisions of the merged firm.

This study has important implications regarding how potential acquirers should anticipate and

deal with the risks of merger integration. If the risks are high, firms should expect a larger subsequent

scale of merger integration. However, a merger involving a large scale of integration has latent potential

for generating long-term profits, and so firms should not necessarily avoid acquisitions involving high

integration risk. Rather, they should consider their IT capabilities and whether their long-term objectives

are to improve profitability or cost efficiency. If an acquirer’s objective is to generate profits from a

merger involving high integration risk, it needs to make sure it has sufficient IT capabilities to handle the

ensuing integration challenges. If a potential acquirer’s objective is to improve cost efficiency, it should

focus on acquisitions with low integration risk. This does not mean that it should avoid making

integration investments in mergers but rather that such integration investments need to be deliberate and

strategy driven rather than crisis driven.

The complexities and risks of merger integration can be substantial, particularly in the

commercial banking industry. The literature in economics and finance has been largely silent on the role

of integration costs and IT capabilities in mergers, although industry practitioners have long considered

these key factors in the success of mergers. The empirical results of this study suggest that the emphasis

on integration and IT capabilities, prominent in the banking trade journals, has been appropriate.

Therefore, integration and IT capabilities should be considered more centrally in understanding the

determinants of merger value. Managers should pay greater attention to the potential disruptive effects of

the integration process and should consider IT investments a way to mitigate such potentially disruptive

effects.

REFERENCES

Aberg, L., D. Sias. 2005. Taming Postmerger IT Integration. The McKinsey Quarterly. January 18, 2005. Adams, J. 2004. E-banking: South Financial's single-vendor play. Bank Technology News 17(3) 1–4. Akhavein, J. D., A. N. Berger, D. B. Humphrey. 1997. The effects of megamergers on efficiency and

prices: Evidence from a bank profit function. Review of Industrial Organization 12 95–139. Alaranta, M. 2006. Managing the change process of the post-merger enterprise systems integration: A

case study. International Journal of Information Systems and Change Management 1(1) 62–81. Altunbas, Y., D. Marques. 2008. Mergers and acquisitions and bank performance in Europe: The role of

strategic similarities. Journal of Economics and Business 60(3) 204–222. Andrade, G., M. Mitchell, E. Stafford. 2001. New evidence and perspectives on mergers. Journal of

Economic Perspectives 15(2) 103–120. Arend, M. 1991. What it takes to merge bank DP groups. ABA Banking Journal\ 83(39) 1–5. Authers, J. 1998. Wells Fargo learns lesson. The Financial Times (June 9) 14. Berger, A. N. 2003. The economic effects of technological progress: Evidence from the banking industry.

Journal of Money, Credit, and Banking 35(2) 141–176.

21

21

Berger, A. N., R. S. Demsetz, P. E. Strahan. 1999. The consolidation of the financial services industry: Causes, consequences, and implications for the future. Journal of Banking and Finance 23(46) 135–194.

Berger, A. N., L. J. Mester. 2003. Explaining the dramatic changes in performance of US Banks: Technological change, deregulation, and dynamic changes in competition. Journal of Financial Intermediation 12 57–95.

Berger, A.N., A. Saunders, J.M. Scalise, G.F. Udell. 1998. The effects of bank mergers and acquisitions on small business lending. Journal of Financial Economics. 50(2) 187-229.

Bharadwaj, A., S. Bharadwaj, B. R. Konsynski. 1999. Information technology effects on firm performance as measured by Tobin's Q. Management Science 45(7) 1008–1024.

Breitkopf, D. 2001. Program revives names of banks B of A swallowed. American Banker 167(243). Broadbent, M., P. Weill, D. St. Clair. 1999. The implications of information technology infrastructure for

business process redesign. MIS Quarterly 23(2) 159–182. Brynjolfsson, E., L. Hitt. 1996. Paradox lost? Firm-level evidence on the returns to information systems

spending. Management Science 42(4) 541–558. Byrd, T. A., D. E. Turner. 2000. Measuring the flexibility of information technology infrastructure:

Exploratory analysis of a construct. Journal of Management Information Systems 17(1) 167–208. Chun, H., J.-W. Kim, R. Morck, B. Yeung. 2008. Creative destruction and firm-specific performance

heterogeneity. Journal of Financial Economics 89(1) 109–135. Cox, N.J. 1998. WINSOR: Stata module to Winsorize a variable. Statistical Software Components

s361402. Boston College Department of Economics (rev Aug. 09 2006). Davamanirajan, P., R.J. Kauffman, C.H. Kriebel, T. Mukhopadhyay. 2006. Systems Design, Process

Performance, and Economic Outcomes in International Banking. Journal of Management Information Systems. 23(2) 65-90.

Delong, G., R. Deyoung. 2007. Learning by observing: Information spillovers in the execution and evaluation of commercial bank M&As. Journal of Finance 62(1) 181–216.

Dewan, S., S.S. Michael, C.-K. Min. 1998. Firm characteristics and investments in information technology: Scale and scope effects. Information Systems Research 9(3) 219–232.

Dietz, J.L.G. 2006. The Deep structure of business processes. Communications of the ACM 49(5) 59–64. Duncan, N.B. 1995. Capturing Flexibility of information technology infrastructure: A study of resource

characteristics and their measure. Journal of Management Information Systems. 12(2) 37–58. Feig, N. 2007. Bank Systems & Technology. Bank Systems & Technology. 44(1) 18. Focarelli, D., F. Panetta. 2003. Are mergers beneficial to consumers? Evidence from the market for bank

deposits. American Economic Review 93(4) 1152–1172. Gahagan, J. 2009. Bank mergers and data integration: Survival of the boldest. E-Commerce Times

http://www.ecommercetimes.com/story/Bank-Mergers-and-Data-Integration-Survival-of-the-Boldest-65875.html, Jan. 19, 2009.

Giacomazzi, F., C. Panella, B. Pernici, M. Sansoni. 1997. Information systems integration in mergers and acquisitions: A normative model. Information & Management 32 289–302.

Hammer, M., J. Champy. 1993. Reengineering the Corporation: A Manifesto for Business Revolution. Nicholas Brealey, London, UK.

Hansell, S. 1998. Clash of technologies in merger. New York Times April 13 http://query.nytimes.com/gst/fullpage.html?res=9503E6D7103DF930A25757C0A96E958260.

Holmstrom, B., S. N. Kaplan. 2001. Corporate governance and merger activity in the United States: Making sense of the 1980's and the 1990's. Journal of Economic Perspectives. 15(2) 121-144.

Houston, J.F., C.M. James, M.D. Ryngaert. 2001. Where do Merger Gains Come From? Bank Mergers from the Perspective of Insiders and Outsiders. Journal of Financial Economics. 60(May-June) 285–331.

Hovanesian, M.D. 2006. JP Morgan: The bank of technology. BusinessWeek. June 19 39. Jarrell, G. A., J. A. Brickley, J.N . Netter. 1988. The market for corporate control: The empirical evidence

since 1980. Journal of Economic Perspectives 2(1) 49–68.

22

22

Jensen, M. C., R. S. Ruback. 1983. The market for corporate control: The scientific evidence. Journal of Financial Economics 11(1) 5–50.

Johnston, K. D., P. W. Yetton. 1996. Integrating information technology divisions in a bank merger fit, compatibility and models of change. Journal of Strategic Information Systems 5(3) 189–211.

Kendler, P. B. 2005. Mergers & acquisitions. Bank Systems & Technology September 29 14. Kohli, R., S. Devaraj. 2003. Measuring information technology payoff: A meta-analysis of structural

variables in firm-level empirical research. Information Systems Research 14(2) 127–145. Lamb, R. 2003. Mercantile bankshares's bid for F&M Bancorp raises doubt. The Baltimore Daily Record

March 14 Malhotra, A., S. Gosain, O. A. El Sawy. 2005. Absorptive capacity configurations in supply chains:

gearing for partner-enabled market knowledge creation. MIS Quarterly 29(1) 145–187. Marlin, S. 2003. Bank merger faces IT obstacles on way to savings. InformationWeek October 30

http://http://www.informationweek.com/news/global-cio/showArticle.jhtml?articleID=15800180. McKenzie, H. 2008. Joined-up thinking: Technology is increasingly becoming a big part of mergers and

acquisitions. The Banker February 1. Melville, N., V. Gurbaxani, K. Kraemer. 2007. The productivity impact of information technology across

competitive regimes: The role of industry concentration and dynamism. Decision Support Systems 43(1) 229–242.

Moyer, L. 2001. Fleet gives N.J. foes little ammo American Banker 166(163). O'Hara, T. 2005. For Riggs customers, an uneven transaction. The Washington Post. May 14. Osuri, L. T. 2005. A pinnacle milestone, a decade early. American Banker 170(191) 1–2. Padgett, T. 1999. Amsouth raises eyebrows with plan to buy first American of Tennessee. American

Banker 164(104) 1–4. Piggott, C. 2000. Will they be happy? The Banker 150(898) 2–7. Ravenscraft, D. J., F. M. Scherer. 1987. Life after takeover. Journal of Industrial Economics 36(2) 147–

156. Rhoades, S. A. 1998. The efficiency effects of bank mergers: An overview of case studies of nine

mergers. Journal of Banking and Finance 22 273–291. Robbins, S. S., A. C. Stylianou. 1999. Post-merger systems integration: The impact of IS capabilities.

Information & Management 36(4) 205–212. Sambamurthy, V., A. Bharadwaj, V. Grover. 2003. Shaping agility through digital options:

reconceptualizing the role of information technology in contemporary firms. MIS Quarterly 27(2) 237–263.

Schwert, G.W. 2000. Hostility in Takeovers: In the Eyes of the Beholder? Journal of Finance. 55(6) 2599-2640.

Serres, C. 2001. BB&T plans to buy two Kentucky banks, starts talks with other institutions. The News & Observer November 9.

Shaver, M. 2006. A paradox of synergy: Contagion and capacity effects in mergers and acquisitions. Academy of Management Review 31(4) 962–976.

Stock, J. H., M. Yogo. 2005. Testing for weak instruments in IV regression. D.W.K. Andrews, J.H. Stock, eds. Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. Cambridge University Press, Cambridge, UK, 80–108.

Stylianou, A. C., C. J. Jeffries, S. S. Robbins. 1996. Corporate mergers and the problems of IS integration. Information & Management 31(4) 203–213.

Tanriverdi, H., V. B. Uysal. 2010. Cross-business Information technology integration and acquirer value creation in corporate mergers and acquisitions. Information Systems Research, Articles in Advance DOI: 10.1287(isre.1090.0250).

Wahl, M. 1998. Analysts look for next big bank merger. The Charlotte (N.C.) Observer March 18. Wooldridge, J .M. 2002. Econometric Analysis of Cross Section and Panel Data. The MIT Press,

Cambridge, MA.

23

23

Zollo, M., H. Singh. 2004. Deliberate learning in corporate acquisitions: Post-acquisition strategies and integration capability in U.S. bank mergers. Strategic Management Journal 25(13) 1233–1256.

Figure 1: Sample Representation Over the Population of Completed Domestic U.S. Banking M&As The overall population consists of mergers in which: (1) the merger was successfully completed, (2) the merger resulted in the acquiring firm having a majority stake in the target firm, (3) the transaction involved more than 10% of the target firm’s shares, and (4) both the acquirer and target firm are based in the U.S..

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

M&A's outside of Sample $20 $53 $19 $45 $153 $48 $67 $23 $20 $28 $108 $28 Sample $2 $26 $13 $61 $142 $27 $47 $16 $1 $56 $26 $38

2 / 310

8/285

9/273

19/301

14/306

13/217

13/162

8/153 4/125

11/169 13/176 3/149

$-

$50

$100

$150

$200

$250

$300

$350

Ann

ual t

otal

Dea

l Val

ues (

$Bill

ions

)

bar height: by annual deal values label within bar: by # of mergers

table beneath graph: deal values of in-sample and out-sample set

24

24

Table 1: Classification of Integration Risk Based on Analyst and Managerial Commentary Examples from the final data sample. Extractions were done by automated content analysis using selected keywords. Keywords were conjugates of the words: integration, consolidation, systems, conversion, migration, transition, compatibility, data, technology, information technology (IT), software, hardware, and platform.

Extracted text (direct quotes) Integration Risk: (H)igh, (L)ow

Warren credits BB&T's success to the bank's "kinder, gentler integration approach." Unlike many competitors, which often start slashing jobs and closing branches after a merger is announced, BB&T leaves its acquired banks largely intact. Back-office functions such as accounting and mortgage processing are consolidated, but tellers, loan officers and executives usually remain in place (Serres 2001).

L

Because the two share the same operating system, conversion will be unnecessary and integration between the two companies should be smooth, Kelly added. Other reasons the two fit well, he said, is their solid mix of retail and commercial portfolios, as well as their similar community and customer-focused cultures (Lamb 2003).

L

"We'll have it all on a common back end," says ... an EVP at the South Financial Group, who says the product's ability to add on new pieces of technology without a lot of infrastructure hassle is also crucial. "It allows us to standardize all of these things in one environment and only have one code," he says. Financial Fusion operates in a J2EE environment, which the technology company says allows institutions to deploy new software across silos without having to manage different platforms, systems or code. "If an institution wants functionality that doesn't exist in a current tool, they can add that and do it in a manner that doesn't require converting from one platform to another, ... " (Adams 2004).

L

"This deal looks like a merger of equals, and it is much more difficult to achieve the kind of synergies and cost cuts that they are talking about," said Mr. Moore. "The deal may be much more difficult to integrate than they expect." ... "But now you have integration risk. Hopefully the earnings streams that they are buying is what they get given the customer attrition levels at First American." First American itself lost customers in banks it acquired, particularly in the Deposit Guaranty deal, they noted. (Padgett 1999)

H

Although a couple of panelists liked the deal, most saw more problems than solutions. While Chase scores high marks for increasing its market share, the deal falls down on fears over IT strategy, cultural problems and loss of staff....Worryingly for Chase is the fact that its merger with JP Morgan ranks top of the list of deals likely to be dogged by power struggles and top of the list of mergers that create organizations that are too large to manage. One panelist said the deal has the greatest potential to unravel: "Here you have two large institutions with little natural logic for merging."(Piggott 2000)

H

Jeff Davis, an analyst with First Horizon National Corp.'s FTN Midwest Research in Nashville, said integration may prove difficult because Pinnacle and Cavalry are such different companies. ... "There is going to be a negative with cultural differences," Mr. Davis said. "Pinnacle is entrepreneurial and high-growth, and Cavalry is an established institution that was once a thrift." (Osuri 2005)

H

25

25

Table 2: Correlations and Summary Statistics All monetary values are adjusted to 2011 U.S. dollars. Denominators in financial ratios are written here, but they are omitted for brevity elsewhere in this paper (e.g. IntegrScale/Assets is written as IntegrScale elsewhere in this paper).

M SD 1 2 3 4 5 6 7 8 9 10

1 ΔCosteff (vs. industry chg.) –0.78 7.52 1.00

2 ΔROA (vs. industry chg.) 8.63E-05 3.37E-03 0.28 1.00

3 Acq. CAR1 –0.03 0.06 0.03 –0.08 1.00

4 Comb. CAR1 0.00 0.06 0.02 –0.03 0.81 1.00

5 IntegrScale/Assets 3.44E-03 3.86E-03 0.04 –0.02 –0.24 0.06 1.00

6 IntegrRisk 0.22 0.42 0.09 –0.03 –0.11 0.08 0.75 1.00

7 Acq IT/Assets 3.48E-03 2.46E-03 –0.06 –0.18 0.00 0.09 0.30 0.25 1.00

8 Tgt IT /(Tgt. Assets)

2.37E-03 1.49E-03 –0.15 –0.03 0.10 0.01 –0.13 –0.07 –0.06 1.00

9 ln(DealVal) 13.02 1.66 –0.06 –0.04 –0.18 –0.09 0.36 0.31 0.17 0.03 1.00

10 Acq. Employees/Assets 3.51E-04 9.21E-05 –0.04 –0.06 –0.10 0.01 0.17 0.04 0.36 –0.01 0.01 1.00

11 Tgt. Employees/ (Tgt. Assets)

3.71E-04 1.13E-04 –0.05 0.08 –0.06 –0.06 –0.02 –0.03 0.09 0.26 –0.12 0.43

12 Tgt. Equity/ (Tgt. Assets)

0.09 0.02 0.04 0.16 –0.12 –0.19 –0.23 –0.20 –0.01 0.06 0.04 0.03

13 PctStock 88.20 27.09 –0.03 –0.01 –0.12 –0.03 0.23 0.19 0.15 –0.06 0.17 0.19

14 Megamerge 0.52 0.50 –0.08 –0.03 –0.14 –0.10 0.16 0.06 0.10 –0.03 0.74 0.06

15 Pooling 0.57 0.50 –0.13 –0.05 –0.09 0.06 0.42 0.34 0.27 0.03 0.20 0.39

16 Hostile 0.52 0.50 –0.14 0.11 –0.16 –0.02 0.33 0.27 0.29 0.12 0.18 0.35

17 GDP Growth 2.90 1.42 –0.05 0.11 0.01 –0.10 –0.02 0.04 0.12 0.14 0.04 0.13

18 ΔHerfindahl 0.00 0.00 0.15 0.09 0.11 0.12 0.16 0.17 0.15 –0.06 –0.16 0.27

19 Equal Size 0.25 0.25 0.06 0.03 –0.19 0.16 0.80 0.64 0.32 –0.13 0.46 0.26

20 Hot Market 0.01 0.03 –0.14 0.02 –0.13 –0.16 –0.02 –0.04 –0.03 0.05 –0.06 –0.09

M SD 11 12 13 14 15 16 17 18 19 10

11 Tgt. Employees/ (Tgt. Assets)

3.71E-04 1.13E-04 1.00

12 Tgt. Equity/ (Tgt. Assets)

0.09 0.02 0.25 1.00

13 PctStock 88.20 27.09 0.24 –0.04 1.00

14 Megamerge 0.52 0.50 –0.14 0.01 –0.02 1.00

15 Pooling 0.57 0.50 0.33 –0.07 0.44 0.05 1.00

16 Hostile 0.52 0.50 0.25 –0.02 0.30 0.05 0.56 1.00

17 GDP Growth 2.90 1.42 0.17 0.01 –0.14 0.04 –0.10 0.17 1.00

18 ΔHerfindahl 0.00 0.00 –0.01 –0.42 0.03 –0.10 0.19 0.05 0.11 1.00

19 Equal Size 0.25 0.25 –0.13 –0.17 0.20 0.21 0.28 0.23 –0.06 0.23 1.00

20 Hot Market 0.01 0.03 0.04 0.02 –0.06 0.03 –0.01 –0.05 0.00 –0.01 –0.10 1.00

26

26

Table 3: Effect of Integration Scale and IT on Long-term Change to Cost Efficiency and Profitability Relative to the Matched Out-sample Cohort of Banks N = 102; First-stage instrument is integration risk. IntegrScale, AcqIT, and TgtIT are mean-centered; thus, each of these three direct effects are interpreted at the mean values of the other two variables. Standard errors are robust to heteroskedasticity and are clustered on the ID of the acquiring firm (in parentheses). Significant at *10%, **5%, and ***1% level for two-tailed t-tests. (1) (2) (3) (4) VARIABLES ΔCosteff 3yr vs.

Industry OLS

ΔCosteff 3yr vs. Industry

2SLS

ΔROA 3yr vs. Industry OLS

ΔROA 3yr vs. Industry 2SLS

β1a: IntegrScale 88.43 415.9** –0.127 –0.316 (131.0) (207.9) (0.115) (0.301) β2a: IntegrScale × Acq IT –11,774* –22,680** 59.40*** 61.63*** (6,155) (10,369) (18.88) (21.06) IntegrScale × Tgt IT –62,197 –9,342 76.21 72.84 (76,309) (70,664) (82.14) (104.7) Acq IT 161.9 215.7 –0.561** –0.557** (115.9) (155.6) (0.252) (0.219) Tgt IT –233.8 –112.8 –0.172 –0.209 (244.3) (203.4) (0.226) (0.253) Log(Deal Value) 0.561 0.701* –0.000320 –0.000416* (0.431) (0.424) (0.000309) (0.000242) Acq. Employees to

assets 290.5 4,402 –4.545 –6.874

(2,399) (2,825) (5.873) (5.385) Tgt. Employees to

assets 1,837 –244.8 2.450 3.604

(2,644) (2,321) (4.031) (3.469) Tgt. Equity-to-assets –8.718 –1.235 0.0335** 0.0288** (11.85) (12.46) (0.0134) (0.0130) Percent stock 0.00344 0.00395 7.46e–06 7.18e–06 (0.0114) (0.0110) (8.32e–06) (7.63e–06) Megamerger –0.537 –0.866 0.000984 0.00115 (0.623) (0.647) (0.000939) (0.000796) Pooling –0.833 –1.396* –0.000659 –0.000347 (0.710) (0.819) (0.00136) (0.00111) Hostile –0.489 –0.722 0.00163** 0.00177** (0.605) (0.566) (0.000715) (0.000754) GDP Growth –0.0579 –0.0799 0.000280 0.000303 (0.153) (0.169) (0.000220) (0.000196) ∆HHI –822.7 369.7 4.966 4.175 (2,365) (2,499) (3.187) (2.690) Equal size –3.265 –7.735** 0.00316 0.00580 (2.360) (3.846) (0.00245) (0.00387) Hotmkt –15.19* –18.08** 0.00219 0.00345 (7.692) (7.340) (0.00928) (0.00889) Same region –0.314 0.200 0.00151 0.00126 (0.855) (0.848) (0.00101) (0.000808) Constant –5.068 –6.663 –0.00260 –0.00146 (4.328) (4.301) (0.00381) (0.00306) Observations 102 102 102 102 R-squared 0.179 0.107 0.280 0.268 F stat 5.452*** 3.701*** 7.474*** 6.756***

27

27

Table 4: Effect of Integration Risk and IT Investment on Stock Market Reaction to Merger Announcement: Unabridged Results OLS Results, N = 118; Dummy variables were used for each year, although they are not shown here. Standard errors are robust to heteroskedasticity and are clustered on the ID of the acquiring firm; in parentheses. IntegrRisk, AcqIT, and TgtIT are mean-centered; thus, each of these three direct effects is interpreted at the mean values of the other two variables. Abbreviations: Acquirer (Acq.), and asset-weighted combination of target and acquirer (Comb.). CAR time windows: CAR 1: t = (–10 days, +1 day), CAR2: t = (–10 days, +5 days), CAR3: t = (–10 days, +30 days), CAR4: t = (–30 days, +10 days). Significant at *10%, **5%, and ***1% level for two-tailed t-tests.

(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Acq. CAR 1 Comb. CAR 1 Acq. CAR 2 Comb. CAR 2 Acq. CAR 3 Comb. CAR 3 Acq. CAR 4 Comb. CAR 4 β1b: IntegrRisk 0.00153 –0.0117 0.00343 –0.00705 0.0213 0.0116 0.0157 –0.00595 (0.0175) (0.0181) (0.0163) (0.0161) (0.0218) (0.0217) (0.0265) (0.0250)

β2b: IntegrRisk × Acq IT

9.947** 8.852** 11.33** 10.46*** 14.18*** 14.32*** 10.83** 7.016*

(4.114) (3.677) (4.855) (3.858) (4.423) (3.540) (4.773) (3.914)

IntegrRisk × Tgt IT

24.24* 14.71 20.59 11.49 41.46 27.76 18.36 –1.429

(14.09) (11.46) (13.45) (9.706) (26.31) (21.84) (17.84) (16.61)

Acq IT –1.086 –1.874 –1.946 –2.523 3.304 1.292 2.003 0.160

(2.851) (3.006) (3.043) (3.352) (3.543) (3.340) (2.723) (2.861)

Tgt IT 5.524 1.893 4.650 1.090 1.891 –1.559 5.181 –1.994

(4.717) (3.889) (5.152) (4.004) (7.624) (6.290) (6.020) (5.578) Log(Deal

Value) –0.00557 –0.00793** –0.00747** –0.00968*** –0.00663 –0.00789* –0.00618 –0.00931*

(0.00353) (0.00358) (0.00346) (0.00350) (0.00447) (0.00453) (0.00510) (0.00511) Tgt. Equity-to-

assets –0.197 –0.347 –0.274 –0.452 –0.111 –0.153 0.354 0.0537

(0.311) (0.281) (0.356) (0.304) (0.359) (0.326) (0.449) (0.348) Percent stock –5.58e-05 –0.000169 2.84e-05 –0.000105 –0.000100 –7.63e-05 –4.40e-05 –0.000207 (0.000170) (0.000164) (0.000148) (0.000142) (0.000230) (0.000208) (0.000263) (0.000215) Acq.

Employees to assets

–132.3 –131.3 –114.9 –108.5 –149.7 –123.4 –137.2 –137.0

(108.1) (104.3) (105.6) (96.42) (149.8) (136.4) (158.9) (151.2) Tgt. Employees

to assets –101.3 –27.11 –33.35 37.78 53.83 118.9 3.495 68.30

(85.85) (63.19) (83.28) (58.00) (113.4) (85.10) (127.5) (105.6) Pooling 0.00656 0.0126 0.00547 0.00965 –0.0175 –0.00417 0.0222 0.0284 (0.0107) (0.0116) (0.0116) (0.0131) (0.0223) (0.0209) (0.0341) (0.0353) Hostile –0.0382* –0.0112 –0.0392* –0.0131 –0.0355 –0.00249 –0.0283 –0.00461 (0.0206) (0.0199) (0.0211) (0.0199) (0.0262) (0.0248) (0.0207) (0.0206) GDP Growth 0.0251 0.00865 0.0184 0.00410 0.0142 –0.00336 –0.0352 –0.0258 (0.0292) (0.0266) (0.0309) (0.0299) (0.0268) (0.0280) (0.0462) (0.0508) ∆HHI 10.60 –21.94 4.414 –30.00 62.22* 32.02 29.64 –20.63 (26.26) (24.86) (25.57) (24.73) (35.32) (31.91) (38.52) (33.56) Equal size –0.0436 0.0696** –0.0517 0.0533* –0.0907** 0.0126 –0.0510 0.0970** (0.0373) (0.0332) (0.0353) (0.0306) (0.0446) (0.0382) (0.0530) (0.0454) Hotmkt –0.445** –0.420*** –0.313 –0.305 –0.188 –0.149 –0.330 –0.307 (0.172) (0.147) (0.214) (0.198) (0.244) (0.206) (0.264) (0.255) Same region 0.00649 0.00255 –0.00110 –0.00667 –0.00222 –0.00683 –0.0191 –0.0247 (0.0116) (0.0113) (0.0155) (0.0156) (0.0133) (0.0138) (0.0224) (0.0210) Constant 0.0783 0.143 0.0829 0.149 0.0591 0.0782 0.115 0.150 (0.102) (0.0923) (0.106) (0.0963) (0.111) (0.119) (0.142) (0.140) Observations 118 118 118 118 118 118 118 118 R-squared 0.346 0.312 0.355 0.320 0.485 0.505 0.344 0.297 F stat 15.34*** 14.01*** 24.08*** 12.82*** 14.93*** 16.76*** 7.820*** 8.136***

28

28

Appendix: Definitions and Data Sources of Control Variables Note: All monetary figures are inflation adjusted to 2011 dollars.

Variable Name Variable Construction/ Definition Data Source

TgtEMP, AcqEMP

Number of employees divided by total assets (acquirer and target firms). Federal Reserve Bank (FRB)

TgtEquity Target equity to assets ratio: Ratio of equity capital over total assets. Postmerger performance can be hampered when the target firm has depleted levels of capital (Delong and Deyoung 2007).

FRB

PctStock Percentage of merger transaction value that was paid using stock. SDC Platinum (SDC)

Pooling Indicates that “pooling” rather than purchase method is used to integrate target and acquiring firm accounting books; which was more common in mergers prior to 2001. This may affect postmerger performance ratios.

SDC

Deal Value The announced transaction value of the merger. SDC

Megamerger Megamerger: Boolean flag equal to one if both the acquirer and target firms have over $1 billion in assets.

FRB

Hostile Boolean flag indicates that the acquisition was hostile, or involuntary. FRB

Hot market Average CAR of prior fiver mergers in the data, as in (Delong and Deyoung 2007). Periods of time that investors respond especially positively to merger announcements may influence the proclivity of firms to enter a merger.

CRSP

GDP Growth Percentage change in the gross domestic product (GDP) in the merger announcement year. Phases of the economic cycle may affect postmerger performance (Delong and Deyoung 2007).

U.S. Bureau of Economic Advisors

HHI Chg. Change in Herfindahl index, a measure of market concentration, as a result of the merger. This controls for potential changes in market power and resulting price margins that may influence post-merger performance.

FRB

Equal size A measure that reflects similarity in size between acquirer and target; approaches unity as acquirer and target assets converge: Equal size = 1 – [ABS(acquirer assets – target assets)/MAX(acquirer assets, target assets)], consistent with Delong and Deyoung (2007).

FRB

SameRegion A binary variable indicating that the bank headquarters are in the same region, based on U.S. Census Bureau classification of states into the following regional categories: South, West, North East, and North Central

Annual 10K reports from SEC EDGAR (SEC)