participants at Boston College and Southern Illinois University at Carbondale, and .... (1998) examine the affect of bank mergers on small business financing and ...
PERFORMANCE CHANGES AROUND BANK MERGERS: REVENUE ENHANCEMENTS VERSUS COST REDUCTIONS
Marcia Millon Cornett* Jamie John McNutt* Hassan Tehranian**
August 2003 Revised: April 2004
*Southern Illinois University at Carbondale **Boston College The authors are grateful to ADS Financial Services Solutions of Quincy Massachusetts for funding this project. ADS is an IT strategy consulting and systems integration firm dedicated to serving the needs of the financial services industry. The authors are also grateful for comments from Jim Booth, Ed Kane, Evren Ors, Phil Strahan, seminar participants at Boston College and Southern Illinois University at Carbondale, and to an anonymous reviewer and the Editor of the Journal.
Performance Changes Around Bank Mergers: Revenue Enhancements Versus Cost Reductions
ABSTRACT
This paper examines operating performance around commercial bank mergers. We find that industry adjusted operating performance of merged banks increases significantly after the merger, large bank mergers produce greater performance gains than small bank mergers, activity focusing mergers produce greater performance gains than activity diversifying mergers, geographically focusing mergers produce greater performance gains than geographically diversifying mergers, and performance gains are larger after the implementation of nationwide banking in 1997. Further, we find improved performance is the result of both revenue enhancements and cost reduction activities. However, revenue enhancements are most significant in those mergers that also experience reduced costs.
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Performance Changes Around Bank Mergers: Revenue Enhancements Versus Cost Reductions Historically in the United States, the ability of commercial banks to expand domestically has been constrained by regulation. For example, at the beginning of the 20th century most U.S. banks were unit banks consisting of a single office. Throughout the century regulators altered the ability of banks to expand both intrastate and interstate. Specifically, over the years and in a very piecemeal fashion, states liberalized restrictions on within-state branching such that by 1994 only one state (Iowa) had not deregulated intrastate banking. However, these laws were often more restrictive in that they allowed banks from only a certain geographic region to enter their banking markets by acquisition. In particular, acquisitions by out-of-state banks from New York and California were generally prohibited. Further, it has long been recognized that nationwide banking expansion through MBHCs is potentially far more expensive than through branching. Separate corporations and boards of directors must be established for each bank in the MBHC, and it is hard to achieve the same level of economic and financial integration with MBHCs as with branches. Moreover, most of the major banking competitor countries, such as Japan, Germany, France, and the United Kingdom, had nationwide branching. In contrast to earlier regulations which restricted geographic expansion by banks, in the fall of 1994, the U.S. Congress passed the Riegle-Neal Interstate Banking and Branching Efficiency Act that allowed U.S. banks to branch interstate by consolidating out-of-state bank subsidiaries into a branch network and/or by acquiring banks or individual branches of banks through merger or acquisition.1 The effective date for these new branching powers was June 1, 1997. The implication of the Riegle-Neal Act is that full interstate banking became a reality in the United States in 1997. The relaxation of branching restrictions, along with recognition of the potential cost and revenue benefits from a merger, set off a wave of consolidation in the U.S. banking system that is reshaping the U.S. banking industry into a nationwide banking system along European and Canadian lines.2 In this paper, we test directly for changes in operating performance resulting from bank mergers by using pre- and post-merger accounting and stock return data for commercial bank mergers completed between 1990 and 2000, a period coinciding with the passage of the Riegle-Neal Act. We test for changes in overall industry-adjusted operating performance and long-run stock returns of banks around a merger. In addition, we identify sources (revenue enhancement versus cost reduction) of any changes in operating performance. Changes in revenue enhancement measures include an analysis of loan portfolio performance
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as well as off-balance-sheet activities. Changes in cost cutting measures include an analysis of interest as well as non-interest expenses. While previous research has examined the performance of banks around a merger (we discuss these in the next section of the paper), changes in long-term operating performance (including an extensive examination of the revenue enhancement versus cost reduction sources for any performance changes) for both large and small bank mergers have yet to be examined. This paper fills this void. Further, the paper examines performance changes before and after the passage of the Riegle-Neal Act. The empirical results lead to the conclusion that industry adjusted operating performance of merged banks increases significantly after a merger. We also find that large bank mergers produce greater performance gains than small bank mergers, activity focusing mergers produce greater performance gains than activity diversifying mergers, geographically focusing mergers produce greater performance gains than geographically diversifying mergers, and performance gains are larger after the implementation of full nationwide banking in 1997 via the Riegle-Neal Act. Further, we find the improved performance is the result of both revenue enhancement and cost reduction activities. However, the revenue enhancement opportunities appear to be most profitable in those mergers that offer the greatest opportunity for cost cutting activities, i.e., activity focusing and geographically focusing mergers. Finally, we find that along with these increases in accounting-based operating performance, the merged banks also experience abnormal long-run stock returns. The remainder of the paper is organized as follows. Section I summarizes the literature on bank merger performance. Section II discusses the sources of revenue enhancements and cost reductions associated with banks mergers. Section III describes the data and methodology used in the study. Section IV summarizes the empirical results and Section V concludes the paper. I. Literature Review Research on the consolidation in the banking industry is abundant. Berger, et al. (1999) provide an extensive look at the literature to date. In this section, we review just a portion of the literature most relevant to our work. A variety of regulatory and economic factors impact the attractiveness of commercial bank mergers. The empirical evidence on the success of these mergers has followed at least two levels. First, how do investors react when a bank merger is announced? Second, once bank mergers have taken place, do they produce, in aggregate, gains in efficiency and profitability? Studies that evaluate the reaction of investors in the stock market to news of a merger measure abnormal returns (ARs) for the bidding and/or target banks. For example, in one of the early studies of bank
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mergers, Cornett and De (1991) examine interstate merger proposals during the period 1982 through 1986. They find that on the day of the merger announcement, both bidding and target bank stockholders enjoy significant positive ARs. They also find that bidding bank returns are higher for those banks seeking to acquire a target in states with more restrictive banking pact laws, which prohibit nationwide entry, and where the target bank is not a failed bank. Benston, et al. (1995) examine prices bid to acquire target banks in the early to mid1980s and find that acquirers bid more for merger partners that offer earnings diversification opportunities. More recently, Brook, et al. (1998) look at the impact of corporate governance variables on ARs associated with the passage of the Riegle-Neal Act. They find a strong positive relation between banks’ ARs and their performance prior to the passage of Riegle-Neal. Further, consistent with management entrenchment, banks with higher insider ownership, lower outside block ownership and/or less independent boards have lower ARs. Becher (2000) finds that bank mergers from 1990 through 1997 produce positive ARs for targets and bidders. Kane (2000) examines bank megamergers in the mid-1990s. He finds that large bank bidders gain value when a target is large and when the large target is headquartered in the same state as the bidder. The gain is likely due to the fact that megamergers are more likely to create a bank that regulators would find too big to fail. DeLong (2001 and 2002) and Cornett, et al. (2003) find that mergers of banks that are activity or geographically focusing earn significantly higher ARs than those that are activity or geographically diversifying. The conclusion (although not explicitly tested) from these results is that focusing mergers involve banks engaged in similar types of business (either activity or geographically). Thus, they increase opportunities for greater cost efficiency or for concentrating the bidder’s existing market power and brand recognition. In contrast, diversifying mergers are those in which the bidder bank has lines of business that are not common to those of the target bank. These mergers required the bidder bank to extend operations into new areas and devote additional resources beyond the current operations. Even though the expectation, on announcement, might be favorable for enhanced profitability and performance as a result of a bank merger, the second group of studies examine whether mergers actually prove successful in the post-merger period. In early studies, Cornett and Tehranian (1992) study the postmerger performance of 30 large bank mergers between 1982 and 1987. They find that superior performance results from improvements in these banks’ ability to (1) attract loans and deposits, (2) increase employee productivity, and (3) enhance asset growth. Spong and Shoenhair (1992) study the post-merger performance of banks that merged interstate in 1985 through 1987. They find that acquired banks either maintain or increase earnings and demonstrate some success in controlling and reducing overhead and personnel costs.
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The acquired banks also tend to become more active lenders. More recently, Boyd and Graham (1998) study small bank mergers from 1989 through 1991. Comparing industry adjusted return on assets (ROA) before versus after a merger, they find that 1989 mergers saw large ROA increases, 1991 mergers resulted in decreases, and 1990 mergers had results somewhere in the middle. However, for all years the merged banks outperformed the banking industry. Using 1994 data, Hughes, et al. (1999) find that banks that increase geographic diversification outperform banks that do not. Berger, et al. (1998) examine the affect of bank mergers on small business financing and find that merged banks reduce the percentage of small business lending in their loan portfolios. However, this drop is picked up by other banks in the same local market. In a review of the literature on bank mergers through 1999, Berger, et al. (1999) conclude that overall findings are consistent with increases in market power for some types of consolidation, improvements in profit efficiency and diversification of risks, but little or no cost improvement on average, and relatively little effect on the availability of services to small customers. Finally, Berger and DeYoung (2001) examine the effects of geographic expansion on bank efficiency using cost and profit efficiencies estimated for over 7,000 U.S. banks from 1993 to 1998. They find both positive and negative links between geographic scope and bank efficiency. Parent organizations exercise some control over the efficiency of their affiliates, although the control dissipates with physical distance to the affiliate. On average, they find that the distance related effect is small and thus their results suggest that more efficient banks could export efficient practices to their affiliates and overwhelm any effects of distance. The results suggest that some banks may operate efficiently only within a single region, while others may operate efficiently on a nationwide or even international basis. More closely related to our study, Houston, et al. (2001) look at analysts' estimates of projected cost savings and revenue enhancements associated with bank mergers. They find that analysts’ estimates of increases in combined bank value associated with a merger are due mainly to estimated costs savings rather than projected revenue enhancements. Rhoades (1998) looks at nine large bank mergers with substantial market overlap in the early 1990s. He finds that all produced significant cost cutting in line with the pre-merger projections. Piloff (1996) looks at 48 bank mergers in the 1980s, relating announcement period ARs to accounting based performance measures. He finds higher ARs for mergers that offer the greatest potential for cost reductions (measured by geographic overlap and pre-merger cost measures). Piloff also finds that industry-adjusted profitability of the merged banks does not change, that total expenses to assets increases, and that revenues rise in the five year period around the merger. Finally, Avery, et al. (1999) look at mergers
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during the period 1975 through 1998 involving banks with significant geographic overlap (measured by the number of branches in a ZIP code per capita). They find that these mergers result in a significant decrease in branches per capita. The previous studies on bank mergers recognize that revenue enhancements and cost cutting are reasons for a merger. However, other than Cornett and Tehranian (1992) (which looked at 30 mergers between 1982 and 1987) there has yet to be a study that directly compares the pre-merger to actual postmerger performance of merged banks (involving a specific examination of the resulting revenue enhancement and cost cutting opportunities available). Further, there has yet to be a study that includes a period after the implementation of the Riegle-Neal Act in 1997. This paper examines both of these issues. It is the job of bank regulators to review and evaluate the acceptability of bank mergers. Knowledge of any sources of possible revenue enhancement and/or cost cutting activities in past mergers should assist regulators in making decisions about future mergers that will maximize the profitability and efficiency of the overall banking industry. This is particularly important now that the Riegle-Neal Act has allowed for complete nationwide banking. II. Cost Cutting Versus Revenue Enhancement Motivations for Bank Mergers A major reason that banks decide to expand geographically via a merger relates to the exploitation of potential cost and revenue synergies from merging. Indeed, in recent years, a merger wave among banks has occurred, including some megamergers among large banks, driven by the desire to achieve greater cost and revenue synergies. II. 1. Cost Synergies A reason frequently given for bank mergers is the potential cost synergies that may exist. For example, in 1996, Chase Manhattan and Chemical Bank merged, creating the (then) largest banking organization in the United States with assets of $300 billion. Annual cost savings from the merger were estimated at $1.5 billion, to be achieved by consolidating certain operations and eliminating redundant costs, including the elimination of some 12,000 positions from a combined staff of 75,000 located in 39 states and 51 countries. The $30 billion merger of BancOne and First Chicago in 1998 was estimated to produce $930 million in cost savings and $275 million in additional revenue resulting from synergies in their credit card and retail and commercial banking business. Similarly, Milwaukee-based Firstar’s $18.7 billion acquisition of Minneapolis-based U.S. Bancorp in late 2000 was expected to reduce combined expenses by $206 million per year (an amount equivalent to 5 percent of the combined bank’s expenses prior to the acquisition) and Boston-based FleetFinancial’s purchase of New Jersey’s Summit Bancorp for $7 billion in 2001 was estimated to save as
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much as $275 million in annual expenses (or about 30 percent of the combined banks expenses). In both cases cost savings were estimated to come through the closing of overlapping branches and laying off personnel. Finally, in 2001 First Union acquired Wachovia for $14.6 billion. The merger of these two North Carolina banks was expected to reduce annual expenses by $890 million through the consolidation of 250 to 300 branches and cutting of some 7,000 jobs. II. 2. Revenue Synergies The revenue synergies argument has three dimensions. First, acquiring a bank in a growing market may enhance revenues. For example, while the 2000 merger of J.P. Morgan and Chase Manhattan to form J.P. Morgan Chase was estimated to produce a cost savings of $1.5 billion, the CEOs of both companies stated that the success of the merger was pinned on revenue growth. The merger combined J.P. Morgan’s greater array of products with Chase’s broad client base. The merger added substantially to many businesses (such as equity underwriting, equity derivatives, and asset management) that Chase had been trying to build on its own through smaller deals and gave it a bigger presence in Europe where investment and corporate banking were fast growing businesses. Second, the acquiring bank’s revenue stream may become more stable if the asset and liability portfolio of the target institution exhibits different credit, interest rate, and liquidity risk characteristics from the acquirer. Specifically, in the 1980s, U.S. real estate declined in value in the Southeast, then in the Northeast, and then in California with a long and variable lag. Thus, a geographically diversified real estate portfolio may be far less risky than one in which both bidder and target banks specialize in a single region. However, it should also be noted that better diversification improves the bank’s risk-return trade-off. As a result of better diversification, the acquirer may make investment decisions that increase the risk of its cash flows. Third, expanding into markets that are less than fully competitive offers an opportunity for revenue enhancement. That is, banks may be able to identify and expand geographically into those markets in which economic rents potentially exist, but in which regulators will not view such entry as potentially anticompetitive. In this paper, we look explicitly at various revenue enhancement and cost reduction activities that result in overall performance changes of merged banks. III. Data and Methodology III.1. Sample Selection This study examines long-term operating performance of bank mergers involving publicly and nonpublicly traded banks over the period 1990 through 2000. The initial list of completed bank mergers was
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obtained from the Federal Reserve Board of Chicago website. Initial announcement dates were obtained from Mergerstat. To be included in the sample, we require the following. 1. The merger may not involve a federal government assisted purchase of a failing bank. 2. Only mergers involving different bank holding companies and/or unit banks are included, i.e., mergers of banks that are subsidiaries of a common bank holding company are excluded. 3. Merging banks must have at least $250 million in book value of total assets at year-end prior to the merger. 4. The book value of the target bank’s assets to bidder bank’s assets must be at least 10 percent. 5. The bidder or target bank can be involved in no other merger (that passes the above filters) in the year before or after the merger in question.3,4,5 6. The bidder bank must have at least one industry match bank (discussed below). The final sample contains 134 mergers made during the period 1990 through 2000. Table I lists the distribution of the sample of mergers by year of merger completion. The first three years in the sample period (1990 - 1992) were years in which the economy was recessionary and bank failures were numerous.6 Those banks healthy enough to expand could do so through federal government assisted purchases of failed banks (often at prices below fair market value) rather than through a merger with another healthy bank. As a result, the number of mergers of non-failing banks was relatively small (16 of the 134 mergers in our sample occurred in 1990 through 1992). In 1993, the economy recovered, interest rates fell sharply, and bank profits began to soar. Additionally, interstate banking restrictions continued to fall as regional pacts were enacted. Along with increased profits, the search for cost cutting, and access to new markets came an increase in the number of bank mergers (48 of the 134 mergers in the sample occurred in 1993 through 1996). As can be seen in Table I, the majority of the sample (70 of the 134 mergers) comes from the last four years (1997- 2000) of the eleven-year period examined, after the implementation of the Riegle-Neal Interstate Banking and Branching Efficiency Act. Thus, the implementation of nationwide banking appears to have had a definite impact on merger activity. Table II presents descriptive statistics (mean, median, standard deviation, minimum, and maximum) for the sample of 134 bank mergers. Financial statement data are collected for the bidder and target banks involved in all 134 transactions using data from the FDIC Call Report files, available at the Federal Reserve Bank of Chicago’s website.7 Data on mergers involving a subsidiary of a bank holding company are collected at the holding company level.8 As shown in Panel A of Table II, the mean book value of the bidder banks at
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year-end prior to the merger is $19,360.0 million and of the targets is $10,268.1 million. The relative size of the target bank to bidder bank book values (Asset Ratio reported in Panel B of Table II) averages 48.41 percent. Thus, the target banks are about half the size of the bidder banks prior to the merger. The mean capital ratio (total core capital (equity) to book value of total assets, or leverage ratio) at yearend prior to the merger for the bidder banks is 7.98 percent and for target banks 8.17 percent. Regulators consider a bank to be adequately capitalized when its leverage ratio is greater than or equal to 4 percent and well capitalized when this ratio is greater than or equal to 5 percent. Thus, on average both the bidder and target banks in the sample are more than adequately capitalized prior to the merger.9 One measure we use to evaluate bank performance is operating pretax cash flow return on assets (OPCFROA) (equal to earnings before income taxes and extraordinary items plus interest on subordinated notes and debentures to total assets). At year-end prior to the merger OPCFROA is 2.21 percent for bidder banks and 1.87 percent for target banks. MSA Overlap, listed in Panel B of Table II, is a measure of the degree to which the bidder and target banks’ geographic area of operations overlap and thus, the degree to which the merged banks may be able to cut duplicate cost functions. MSA Overlap is defined as the number of bidder metropolitan statistical areas (MSAs) that match target MSAs divided by target MSAs at year-end prior to the merger. If MSA Overlap is equal to 1, the bidder and target areas of operations overlap completely (i.e., the bidder operates in all of the target banks’ MSAs prior to the merger). If MSA Overlap is equal to 0, the bidder and target banks’ area of operations do not overlap at all (i.e., the bidder operates in none of the MSAs in which the target operates prior to the merger). Thus, the higher the MSA Overlap, the greater the degree of overlapping operations for the bidder and target banks and the greater the opportunity for cost reductions after the merger.10 As reported in Panel B of Table II, the mean MSA Overlap for the sample banks is 36.07 percent. As mentioned above, Kane (2000) finds that bank megamergers in the mid-1990s created value for the bidder bank. Further, Cornett and Tehranian (1992) find that large bank mergers between 1982 and 1987 produced post-merger operating performance that was significantly greater than the industry. In the event that performance changes may be unique to large banks, we examine changes in operating performance around bank mergers for the full sample, as well as for large bank versus small bank mergers. Following Federal Deposit Insurance Corporation (FDIC) and Federal Reserve guidelines, we define a large bank merger as one in which the book value of the bidder bank assets at year-end before the merger is greater than or equal to $1
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billion. One-hundred fourteen of the sample mergers involve a bidder bank with book value of total assets greater than or equal to $1billion at year-end prior to the merger and 20 involve a bidder bank with a book value of total assets less than $1 billion. Also as mentioned earlier, previous research finds that activity diversification affects announcement period abnormal returns around mergers. One measure of activity diversification (used by Morck, Shleifer, and Vishny (1990), Benston, et al. (1995), DeLong (2001), and Cornett, et al. (2003)) utilizes the correlation coefficient of stock returns for the merger partners. Our sample of 134 bank mergers includes 99 cases in which both the bidder and target banks have publicly traded common stock. Following Cornett, et al., we calculate the correlation coefficient of daily stock returns for the bidder and target banks involved in these 99 bank mergers in the 120-day period from t = -136 to t = -16 days prior to the merger announcement. The sample’s median correlation coefficient in preannouncement returns of the bidder and target banks is 0.105. We classify mergers with a preannouncement correlation coefficient of bidder and target returns less than this median value (n = 49) as activity diversifying acquisitions. Those with a correlation coefficient of returns greater than the median value (n = 49) are classified as activity focusing.11 Similar to high MSA Overlap, focusing mergers are those in which the bidder and target banks are involved in related activities or lines of business prior to the merger. These mergers tend to concentrate market power or brand recognition allowing for greater revenue enhancement and greater cost cutting after the merger. Similar to low MSA Overlap, diversifying mergers are those in which the bidder bank has lines of business that are not common to those of the target bank. These mergers require the bidder bank to extend operations into new areas and devote additional resources beyond the current operations. Finally from Table II, we search the LEXIS/NEXIS data base to identify the method of financing used in each merger. As reported in Panel B, 79 of the mergers are completely stock financed, 8 are completely cash financed, 11 involve a combination of stock and cash, and we could not identify the method of financing used in 36 of the mergers. III.2. Performance Measures The measures we use to test for changes in operating performance around bank mergers are those in Cornett and Tehranian (1992) and Cornett, Mehran, and Tehranian (1998). Cornett and Tehranian look at mergerrelated operating performance in commercial banks. Cornett, et al. look at performance around voluntary versus involuntary equity issuances by commercial banks. We collect cash flow data for the merged banks both before
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and after the bank merger announcement. A comparison of the post-merger values with the pre-merger benchmark allows us to measure the impact of the bank merger on the performance of the merged banks. Like Cornett and Tehranian and Cornett, et al., we use operating pretax cash flows (defined as income before taxes and extraordinary items plus interest on subordinate notes and debentures) divided by the book value of assets at year-end prior to the merger (OPCFROA) to evaluate performance.12,13 To measure pre-merger performance, we combine OPCFROA data for the bidder and target banks to obtain pro forma performance for the merged banks. We obtain a pre-merger performance benchmark for years -1 to -2 before the merger by aggregating OPCFROA for the bidder and target banks in each of the two pre-merger years. We divide the measure by the book value of assets to provide a return metric that is comparable across firms. The performance measure for the combined banks is the weighted average of values for the bidder and target banks separately, where the weights are the relative sizes (measured by book value of assets) of the two firms at year-end prior to the merger.14 The OPCFROAs of the merged banks are computed for years 1 to 2 after the merger. Again, the cash flow measure is deflated by the book value of assets to yield a normalized measure of performance. We examine cash flow performance changes for the full sample, i.e., merged bank performance around the bank merger announcement. We also compare performance based on the bidder bank’s asset size at year-end prior to the merger, activity focusing versus activity diversifying mergers, and high MSA versus low MSA overlap. Changes in the pre- and post-merger operating performance are examined on both an unadjusted basis and industry match adjusted basis. Industry match adjusted comparisons allow us to examine performance changes in merged banks irrespective of any industry-wide factors that may be affecting OPCFROA. Thus, a change in unadjusted operating performance may be due to factors other than the bank merger. We classify industry banks as all banks not involved in a merger in the year before or after the merger in question,15,16 in the same Federal Reserve district as the bidder bank, and in the same asset class as the bidder bank.17,18 Matching the merged banks to these characteristics allows us to compare their performance to that of their most similar competitors. To identify the sources of the changes in operating pretax cash flow performance, we evaluate eight common bank performance indicators (similar to Cornett and Tehranian (1992)): 1) Profitability Indicators
Measure overall performance
2) Capital Adequacy Indicators
Measure the bank’s ability to meet regulated capital standards and still attract loans and deposits
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3) Asset Quality Indicators
Measure changes in the bank’s loan quality
4) Operating Efficiency Indicators
Measure the bank’s ability to generate revenue, pay expenses, and measure employee productivity
5) Loan Composition Indicators
Measure changes in the composition of and return on the bank’s loan portfolio
6) Non-interest Income Indicators
Measure changes in income generated from other than lending activities at the bank
7) Off-Balance-Sheet Indicators
Measure changes in the bank’s off-balance-sheet activities
8) Liquidity Risk Indicators
Measure changes in the bank’s cash position
The specific measures used to represent these factors are defined in Table III. The values used to calculate the performance measures are year-end figures as reported in the FDIC Report of Income and Report of Condition data files available at the Federal Reserve Bank of Chicago website. We examine these eight indictors of bank performance in an attempt to identify specific areas within the merged banks that might drive any differences in operating pretax cash flow performance. We examine these ratios for the full sample of merged banks and for subsamples based on bank size, activity focusing versus activity diversifying mergers, and high versus low MSA overlap. There is collinearity between some of the specific ratios representing the different factors (e.g., loans to equity and loans to assets). Therefore, changes in the various areas of performance, reported in the next section, may be a result of common elements. Differences in pre- and post-merger performance of the merged banks are tested using the t-statistic,
N t = ∑ (d post − d pre ) / N i =1
/
(σ /
N ),
(1)
where d post reflects the post-merger performance of the merged banks, d pre measures the pro forma premerger performance of the combined banks, σ is the standard deviation of the distribution of the change in performance of the merged banks, and N is the numbers of merged banks in the sample. Both the actual bank performance and the industry match adjusted bank performance are tested using equation 1. In addition to long-term cash flow operating performance, we examine stock price performance around the 99 mergers in which both the bidder and target were publicly traded commercial banks. Specifically, we compute buy-and-hold returns for the merging banks during the year before and the two years after the merger. The buy and hold return is estimated in a manner used in Spiess and Affleck-Graves (1995), and Titman
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(1997), and Cornett et al. (1998) and is similar to variations explored in Barber and Lyon (1997) and Kothari and Warner (1997). Specifically, for those mergers in which both the bidder and target are publicly traded, the buyand-hold return is calculated over the twelve calendar months prior to the month of the merger and over the two years after the merger (starting the month after the merger date and ending the month of the two-year anniversary). The percentage buy-and-hold return for bank i in the year prior to the merger is calculated as
−1 Ri , pre = ∏ (1 + rit ) − 1 × 100% t = −12
(2)
where t= -1 is the month prior to the merger, t = -12 is the month of the one-year anniversary prior to this date, and rit is the is the weighted average of the returns for the target and the bidder banks involved in merger i (where the weights are the relative book values of assets of the two firms at year-end before the merger announcement) in month t. The percentage buy-and-hold return for bank i in the two years after the merger, Ri,post, is calculated as
Ri , post
24 = ∏ (1 + rit ) − 1 × 100% t =1
(3)
where t = 1 is the month after the merger date. Barber and Lyon (1997) document that long-run buy-and-hold abnormal returns are positively skewed, which leads to negatively biased t-statistics. They show that calculating abnormal returns using buy-and-hold reference portfolios eliminates the skewness bias. Accordingly, in order to evaluate the long-run stock return performance, for each bank we compare the buy-and-hold return with that of a benchmark portfolio consisting of CRSP-listed stocks in the same size quintile, the same book-to-market quintile, and the same momentum quintile (stock return performance over the previous year). These are also the factors used in Safieddine and Titman (1999) to evaluate post-takeover performance and the Daniel, et al. (1997) three-factor model for evaluating mutual fund performance. For pre-merger values, we again use the weighted average of the values for the factors for the target and the bidder banks involved in the merger (where the weights are the relative book values of assets of the two firms at year-end before the merger announcement). We follow the procedure of Daniel, et al. (1997) in constructing 125 reference portfolios based on size, book-to-market ratio, and momentum characteristics. Our reference portfolios include all firms listed on the NYSE, AMEX, and Nasdaq exchanges from 1990 through 2000 provided that the following three requirements are met: (1) COMPUSTAT data are available for the firm at least two years prior to the inclusion of the firm into the portfolio; (2) the firm has market value data available on the CRSP data tapes at the end of December and
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the end of June preceding the inclusion; and (3) in the twelve months prior to the inclusion, at least six monthly returns are available on the CRSP data tapes. The exact process works as follows. First, all NYSE firms are sorted into quintiles according to their market equity value, calculated for the last day of June from 1990 to 2000, AMEX and Nasdaq firms are then put into quintiles according to their size.19 Within each quintile we further sort firms into five portfolios according to their book-to-market ratios. Finally, for each size and book-tomarket sorted portfolio, we sort the firms into quintiles according to their preceding twelve-month return. This process gives us a total of 125 portfolios. Once we form the 125 benchmark portfolios, we match each of our sample bank stocks to a benchmark portfolio according to its size, book-to-market ratio, and momentum rank. We are able to identify a benchmark portfolio for each of our sample banks. We calculate the buy-and-hold return of the reference portfolios by first compounding the returns on the securities and then summing across the securities as follows
n
Rj = ∑ i =1
s +τ ∏ (1 + Rit ) − 1 t =s n
(4)
where n is the number of securities in the reference portfolio in month s, the beginning period for the portfolio return calculation. (This method is also used in Barber, Lyon, and Tsai (1999)). We then calculate the abnormal returns of a particular bank stock by subtracting the return on the matched benchmark portfolio from the bank stock return. In the event of a firm delisting in the buy-and-hold reference benchmark portfolio, like Barber et al. (1997), we replace the missing return data with the corresponding return on the reference portfolio to which the delisted security belongs. The composition of the benchmark portfolios is updated each year to reflect changes in the sample bank’s characteristics. However, because we are simply rolling over the returns without adding or subtracting money from the benchmark, over time the values of the benchmark and the bank will diverge as their compound returns will diverge. The calculation of these benchmarks is equivalent to a strategy of investing in an equally weighted size, book-to-market, and momentum portfolio with annual rebalancing. We compare the buy-and-hold returns for the sample bank portfolio to those for the benchmark portfolio in the year prior to and the two years after the merger. IV. Empirical Results IV. 1. Cash Flow Measures Table IV presents bank median annual OPCFROA for the merged banks in the years surrounding the
13
bank mergers. The median OPCFROA for the 134 banks is 1.79% and 1.92% in years –2 and -1, respectively, before the bank merger and is 2.59% and 2.27% in years 1 and 2, respectively, after the merger. The resulting median annual performance over the two years before versus the two years after the merger increases from 1.90% to 2.49%. The difference (0.59%), however, is not significantly different from zero. It is difficult to draw conclusions from median bank results because these data do not adjust for industry factors that may be affecting the cash flow returns of the banks. Any trend would affect values for the merged bank medians, so a change may be due to factors other than the bank merger. To account for the impacts of contemporaneous events, we also report industry match adjusted median performance measures in Table IV. The median industry matched adjusted OPCFROA is -0.29% and 0.12% in years -2 and -1 before the merger. Neither of these is statistically significant. In the two years after the merger, the median industry matched adjusted OPCFROA is 1.12% and 0.96%, respectively, and both are significant at the one percent level. The resulting median industry adjusted performance over the two years before versus two years after the merger increases from -0.10% (not significantly different from zero) to 1.02% (significant at the one percent level). The increase in the industry adjusted performance (1.12%) is also significant at the one percent level. Thus, the merged banks performed similarly to the industry before the merger, but outperformed the industry significantly after the merger. To ensure that the results are not driven by outliers, we calculate the proportion of the sample banks whose industry adjusted OPCFROA is greater than zero and perform a Wilcoxon signed rank test to determine significance. During the two pre-merger years the portion of positive industry adjusted cash flow returns in the full sample of 134 banks is 52 percent. During the post-merger period, however, the positive portion is 78 percent, significant at better than the one percent level. Thus, the results are not driven by outliers. Rather, it appears that the mergers produce a significant increase in the cash flow returns for the merged banks in comparison with the industry. As mentioned above, the distribution of the sample is not uniform across the sample period. For example, 70 of the 134 mergers occur between 1997 and 2000, just after the Riegle-Neal Act was implemented. To address this clustering of the data, we analyze operating pretax cash flow return on assets in an alternative manner. Specifically, for each year we rank order the operating pretax cash flow returns. We then select the median operating cash flow return as the representative bank for that year. For example, we rank order the 16 mergers in 2000 by operating pretax cash flow returns and select the eighth (median) observation to represent 2000. This process is repeated for each year leaving us with a sample of 11 firms. The conclusions from data on these 11 firms are identical to those from Table IV. That is, matched adjusted
14
cash flow returns are not significantly different from zero in the two years before the merger and are positive and significant in the two years after the merger. Thus, the clustering of mergers across the 11 years does not appear to be affecting the results.20 Because earlier literature (e.g., Cornett and Tehranian (1992) and Kane (2000)) often focused on the performance of large banks around a merger, we split the sample into two groups based on the asset size of the bidder bank before the merger. Following Federal Deposit Insurance Corporation (FDIC) and Federal Reserve guidelines, we define a large bank merger as one in which the book value of the bidder bank assets at year-end before the merger is greater than or equal to $1 billion and small bank mergers are those in which the bidder bank has a book value of assets less than $1 billion. Table V reports the results. Panel A lists bank median and industry match adjusted OPCFROA for large bank mergers, while Panel B lists the results for small bank mergers. For both groups, OPCFROAs are not significantly different from industry matched banks before the merger. The median industry match adjusted OPCFROA is 0.06% and 0.02% (not significantly different from zero) in the two years before the merger for large bank and small bank mergers, respectively. However, both groups’ OPCFROAs are significantly larger than the industry after the merger. The median industry match adjusted OPCFROA is 1.13% (significant at the one percent level) in the two years after the bank merger for large banks and 0.54% (significant at the 5 percent level) for small bank mergers. Differences in industry match adjusted OPCFROA for large versus small bank mergers are reported in Panel C. Notice that while both groups outperform the industry after the merger, we see significant differences across the two subsamples. Specifically, large banks’ post-merger performance improvements are significantly larger than those for small banks. The difference (0.59%) is significant at the five percent level. Thus, large banks appear to be better able to capitalize on revenue enhancement and/or cost cutting opportunities resulting from a merger than small banks. We look at the specific areas in which these opportunities may arise below. Table VI reports results based on whether the bank merger is activity focusing or diversifying. As described above, for those 99 mergers in which both the bidder and target have publicly traded stock, we classify mergers with a preannouncement correlation coefficient of bidder and target stock returns greater than the median value, 0.105 (n = 49) as activity focusing acquisitions. Those with a correlation coefficient of returns less than the median value (n = 49) are classified as activity diversifying. Panel A of Table VI lists bank median and industry match adjusted OPCFROA for the activity focusing bank mergers, while Panel B lists the results for diversifying mergers. For both groups, OPCFROAs are not significantly different from industry matched banks before the merger. The median industry match adjusted OPCFROAs are 0.06% and 0.04% (not significantly different from zero) in the two
15
years before the bank merger for focusing and diversifying mergers, respectively. However, the median industry match adjusted OPCFROA is 1.41% (significant at the one percent level) in the two years after the bank merger for focusing mergers and 0.20% (not significantly different from zero) for diversifying mergers. Further, the difference in post-merger industry match adjusted OPCFROA for focusing versus diversifying mergers (reported in Panel C), 1.21%, is significant at the one percent level. Focusing mergers are those in which the bidder and target banks are involved in related activities or lines of business prior to the merger. These mergers tend to concentrate market power or brand recognition allowing for greater revenue enhancement and greater cost cutting after the merger. Diversifying mergers are those in which the bidder bank has lines of business that are not common to those of the target bank. These mergers required the bidder bank to extend operations into new areas and devote additional resources beyond the current operations. We analyze these opportunities in detail below. Table VII reports results based on MSA overlap. As described above, MSA overlap is defined as the number of bidder metropolitan statistical areas (MSAs) that match target MSAs divided by the number of target MSAs at year-end prior to the merger. Panel A of Table VII lists bank median and industry match adjusted OPCFROA for the bank mergers with an MSA overlap greater than 0.50 (high MSA overlap), while Panel B lists the results for mergers with MSA overlap less than 0.50 (low MSA overlap). For both groups, OPCFROAs are not significantly different from industry matched banks before the merger. The median industry match adjusted OPCFROAs are -0.09% and 0.05% (not significantly different from zero) in the two years before the bank merger for high and low MSA overlap mergers, respectively. However, the median industry match adjusted OPCFROA is 1.21% (significant at the one percent level) in the two years after the bank merger for high MSA overlap mergers and 0.33% (not significantly different from zero) for low MSA overlap mergers. Further, the difference in post-merger industry match adjusted OPCFROA for high versus low MSA overlap mergers (reported in Panel C), 0.88%, is significant at the one percent level. MSA overlap is a measure of the degree to which the bidder and target banks’ geographic area of operations overlap and thus, the degree to which the merged banks may be able to cut duplicate cost functions. The higher the MSA overlap, the greater the degree of overlapping operations for the bidder and target banks, and the greater the opportunity for cost reductions after the merger. We analyze these opportunities in detail below. IV. 2. Accounting Measures Having found changes in banks’ industry adjusted OPCFROAs after a merger, we next attempt to identify the specific revenue enhancement and cost cutting sources of these changes by examining accounting ratios commonly used to evaluate various areas of bank performance. These ratios are listed in Table III. As
16
we did for OPCFROA, we measure all accounting ratios for two years before and two years after the bank merger. The results for the full sample are reported in Table VIII and for the sample grouped by bank size, activity focusing versus diversifying (in this panel, we examine results for only those 99 mergers in which the bidder and target bank are publicly traded), and high versus low MSA overlap are reported in Table IX. We examine changes in pre- and post-merger values on both an industry match adjusted (as defined above) and an unadjusted basis. However, because industry adjusted measures are more pertinent in evaluating bank performance, we report and discuss only industry match adjusted values. Profitability indicators: For the merged banks, all three profitability indicators increase significantly in the period surrounding the merger. For example, the median industry match adjusted return on assets (ROA, Ratio 1) increases from -0.10% in the two years before the merger to 0.52% in the two years after the merger. The difference, 0.62%, is significant at the 5 percent level. The results are not driven by outliers, as the Wilcoxon signed rank test is also significant at the five percent level.21 Similarly, the increase in the mean industry adjusted return on equity (ROE, Ratio 2) and net interest margin (NIM, Ratio 3) before versus after the bank merger, 4.04% and 0.83%, respectively, are both significant at better than the 5 percent level. Similar to the results for OPCFROA, traditional accounting measures of profitability show that bank mergers result in long-term performance improvements for the merged banks relative to the industry. From Table IX, we also see that regardless of how the sample is split (large banks versus small banks (Panel A), activity focusing versus diversifying (Panel B), and high versus low MSA overlap (Panel C)), industry adjusted performance measured by ROE, ROA, and NIM generally increases after a merger. Only ROA for diversifying mergers, 0.32% (Panel B), and low MSA overlap mergers, 0.29% (Panel C), do not see a significant increase over the industry after a merger. Further, Table IX reports that the increase in industry adjusted ROA around a merger is significantly larger for activity focusing mergers than diversifying mergers (0.83%) and for high MSA overlap than low MSA overlap mergers (1.02%). The increase in ROE is significantly larger for large bank mergers (2.78%), activity focusing mergers (1.63%), and high MSA overlap mergers (4.71%). Finally, the increase in industry adjusted NIM around a merger is significantly larger for high MSA overlap mergers (0.69%). Capital adequacy indicators: Table VIII reports a significant increase, 0.79%, in the mean industry adjusted capital ratio (Ratio 4) of banks around mergers. Similarly, both the mean industry adjusted loans to total capital (Ratio 5) and deposits to total capital (Ratio 6) increase significantly (by 2.43X and 4.41X, respectively). Thus, as the sample banks’ capital to assets increases compared to the industry, each dollar of capital supports a
17
greater number of both loans and deposits in banks after a merger. The increase in equity along with the ability of each dollar of equity to support more dollars of loans and deposits may explain the improvement in long-term performance. For example, an increase in loans can lead to enhanced revenues. From Table IX, we also see that regardless of how the sample is split industry adjusted capital adequacy indicators generally increase after a merger. Only total capital to assets for small bank mergers (0.55% in Panel A), activity diversifying mergers (0.58% in Panel B), and low MSA overlap mergers (0.48% in Panel C) do not see a significant increase over the industry after a merger. Further, Table IX reports that the increase in industry adjusted capital adequacy does not generally differ across splits of the sample. The increase in capital to assets is significantly larger in high than low MSA overlap mergers (0.93% in Panel C); and deposits to capital is significantly larger for large bank mergers than small bank mergers (2.67X in Panel A) and for high than low MSA overlap mergers (2.59X in Panel C). Asset quality indicators: Table VIII reports decreases in the mean industry adjusted allowance for loan losses to loans (Ratio 7) and loan loss provision to loans (Ratio 8) for banks around a merger. Thus, while loans increase relative to equity, they do so without an increase in nonperforming loans. In fact, industry adjusted allowance for loan losses to loans decreases significantly, -0.75% (significant at the five percent level). Table IX reports that, while loan portfolio non-performance decreases for all splits of the sample, there are no differences across the various splits (e.g., the decrease in the allowance for loan losses to loan in large versus small bank mergers is -0.04% in Panel A). Operating efficiency indicators: Operating efficiency indicators allow us to look specifically at cost reduction opportunities associated with a merger. As reported in Table VIII, almost all of the operating efficiency measures (Ratios 9 through 16) change significantly before versus after the bank merger in a manner that suggests the merger results in significant cost cutting. On an industry adjusted basis, after a merger noninterest expenses drop relative to non-interest revenue (-1.79%, Ratio 9) and net operating income (-0.93%, Ratio 10); personnel expenses to total assets fall (-0.49%, Ratio 12); branches to total assets decrease (-1.22%, Ratio 13); and total assets (37.64X, Ratio 15) and net income (7.75X, Ratio 16) to employees increase significantly. From Table IX, we also see that regardless of how the sample is split operating efficiency generally increases after a merger. Further, Table IX reports that the increase in operating efficiency (reduction in costs) around a merger is significantly larger for activity focusing mergers than diversifying mergers and for high MSA overlap than low MSA overlap mergers.
18
Loan composition indicators: Loan composition indicators (Ratios 17 through 26) allow us to look specifically at revenue enhancement opportunities associated with a merger. As reported in Table VIII, many of these ratios change significantly before versus after the bank merger in a manner that suggests the merger results in significant revenue enhancement. On an industry adjusted basis, after a merger return on loans increases significantly (0.52%, Ratio 17). Further, this significant increase occurs consistently across types of loans: industry adjusted commercial and industrial (C&I) loan returns increase 1.32% (Ratio 18), real estate loan returns increase 0.80% (Ratio 19), and consumer loan returns increase 0.90% (Ratio 20). As a percent of the overall loan portfolio and of total assets, only real estate loans (1.03% (Ratio 22) and 1.95% (Ratio 25), respectively) increase significantly relative to the industry. From Table IX, we also see that these results are generally consistent across various splits of the sample. Further, Table IX reports that the increases in loan returns (revenue enhancement) around a merger are significantly larger for activity focusing mergers than diversifying mergers and for high MSA overlap than low MSA overlap mergers. Significantly, this implies that the revenue enhancement gains appear to be more pronounced in those mergers that also offer the greatest opportunities for cost cutting, i.e., activity focusing and high MSA overlap mergers. Non-interest income and off-balance-sheet indicators: Similar to loan composition indicators, non-interest income and off-balance-sheet (OBS) indicators allow us to examine revenue enhancement (other than through loans) opportunities. As reported in Table VIII, few of these ratios change significantly before versus after a bank merger in a manner that suggests the merger results in significant revenue enhancement. On an industry adjusted basis, after a merger only the banks’ activity in industry adjusted derivatives to total assets increases significantly (31.05%, Ratio 33). In the 1990s, only 600 of the approximately 9,000 banks in the U.S. held derivative securities of their balance sheets. These holdings tended to be concentrated among the largest banks. As banks merge, they become larger and are more likely to hold derivatives. Thus, we see this significant growth in derivatives to total assets for the bank mergers in the sample. This increase in derivatives is also the reason that total OBS items to total assets increases (45.85%, Ratio 34). From Table IX, we see that these results are generally consistent across various splits of the sample. Liquidity ratios: Ratios 35 through 38 in Tables VIII and IX show changes in banks’ industry adjusted liquidity ratios around a bank merger. As reported in Table VIII, none of these ratios change significantly before versus after a bank merger. Further from Table IX, we see that these results are generally consistent across various splits of the sample. Thus, the changes reported above occur without affecting the liquidity situation of the
19
merged banks. Summarizing the results in Tables VIII and IX, we find that bank mergers are associated with a significant increase in industry adjusted performance around the merger. The merged banks experience a significant increase in their industry adjusted accounting-based measures of profit (ROA, ROE, and NIM) as well as OPCFROA. The performance increases are attributed to increases in both cost cutting as well as revenue enhancement activities. However, the revenue enhancement opportunities appear to be most profitable in those mergers that offer the greatest opportunity for cost cutting activities, i.e., activity focusing and high MSA overlap mergers.22 IV. 3. Regression Results The results so far show that long-term operating performance and other accounting measures of profitability increase around a bank merger. Another test of the change in performance around bank mergers examines the relation between the change in OPCFROA and factors found significant in impacting this performance measure around a bank merger. Specifically, we examine the following regression: ∆OPCFROAi
=
a0 + b1 ln(SIZEi) + b2 RELSIZi + b3 MSAi + b4 CORRi + b5 CARi+ b6 YEARi
where ∆OPCFROAi
=
change in the mean industry adjusted OPCFROA for merged banks in years –1 to –2 before to +1 to +2 after the ith bank merger,
ln(SIZEi)
=
natural log of the book value of the bidder bank’s assets at year-end before the ith bank merger,
RELSIZi
=
ratio of target bank total assets to bidder bank total assets at year-end before the ith bank merger,
MSAi
=
number of bidder bank MSAs that match target bank MSAs divided by target bank MSAs at year-end prior to the ith bank merger,
CORRi
=
preannouncement correlation coefficient of bidder and target returns for the ith bank merger,23
CARi
=
combined bidder and target bank merger announcement abnormal stock returns in the two days around the first public announcement of the ith bank merger, and
YEARi
=
a dummy variable equal to 0 if year is 1990-1996 and 1 if year is 19972000.
The dependent variable, ∆OPCFROA, is the mean change in industry adjusted operating pretax cash flow return on assets in the two years after the bank merger relative to that in the two years before the merger. The independent variables ln(SIZE), MSA, and CORR are those which were explored in the previous sections. By including these variables in cross-sectional regressions we can identify their impact on the change in OPCFROA in a continuous manner rather than through portfolio analysis of two subsamples each. Further,
20
cross-sectional analysis allows us to identify any interdependencies that may exist. The variable RELSIZ measures the size of the target bank relative to the bidder bank. Our selection process requires that the book value of the target bank’s assets be at least 10 percent of the bidder bank’s assets at year-end before the merger. However, any merger gains are most likely to be detected when the target bank is large in relation to the bidder. CAR is the announcement period abnormal stock return for the combined bidder and target bank in the two days around the first public announcement of the bank merger. The inclusion of this variable allows us to examine the degree to which the stock market anticipates improvements in the operations of the merged bank when the merger is announced. Finally, YEAR is a dummy variable equal to zero if the merger occurs in the years 1990 through 1996 and one if the merger occurs in the years 1997 through 2000. The inclusion of this variable allows us to examine whether the implementation of full nationwide banking via the Riegle-Neal Act has an impact on the change in operating performance of the merged banks around the merger. To test for a stock price reaction to the merger we apply a standard event-study methodology described in detail in Dodd and Warner (1983). The estimation period is from t = -136 to t = -16 relative to the initial date of the merger announcement, day t = 0. Abnormal returns, ARi, are calculated for each bank over the interval t = -1 to 0.24 To examine the relation between the merged banks’ operating performance around the merger and the stock market’s announcement period reaction to the merger, we compute an aggregate marketadjusted return for the two banks involved in each merger. This return is the weighted average of the marketadjusted returns for the target and the bidder banks, where the weights are the relative book values of assets of the two firms at year-end before the merger. Table X reports the merger announcement abnormal returns for the target, bidder, and combined banks over the two-day announcement period. Similar to past studies (i.e., Cornett and Tehranian (1992)) the target bank’s abnormal return, 16.87%, is positive and significant (Zstatistic = 22.09), the bidder bank’s abnormal return, -0.65%, is negative and significant (Z-statistic = -2.34), and the combined banks’ abnormal return, 2.83%, is positive and significant (Z-statistic = 4.12). The results of our regression analysis are reported in Table XI. Consistent with the results from the previous section, we find that the larger the bidder bank (the coefficient on ln(SIZE) is 0.063, t-statistic = 2.25), the higher the MSA overlap (the coefficient on MSA is 0.621, t-statistic = 4.71), and the more related the banks are in terms of activity (the coefficient on CORR is 0.274, t-statistic = 2.63), the greater the improvement in OPCFROA around a bank merger. Further, the bigger the target bank relative to the bidder the greater the improvement in performance around the merger (the coefficient on RELSIZ is 0.149, t-statistic = 3.07). We find a significant positive relation between the two-day abnormal stock market return at the merger announcement and
21
the change in operating performance (the coefficient on CAR is 0.501, t-statistic = 3.83). Thus, stock market participants appear to accurately predict the size of the change in operating performance of the merged banks at the time the merger is announced. Finally, we find that the increase in OPCFROA around a merger is significantly higher for mergers occurring in 1997 through 2000 (after the implementation of full nationwide banking via the Riegle-Neal Act) than for those occurring in 1990 through 1996 (the coefficient on YEAR is 0.019, t-statistic = 2.28). Thus, while operating performance increases around bank mergers throughout the sample period, the implementation of full nationwide banking in 1997 appears to have created a period in which cost savings and revenue benefits from a merger resulted in even greater performance gains. In the previous section, we found that, while both cost cutting and revenue enhancement opportunities explain the improved operating performance around bank mergers, revenue enhancement was most significant in those cases where cost cutting opportunities were most prevalent (i.e., MSA overlap was high and the merger was focusing in activity). To test this further, we conduct regression analysis with a split of the sample into two groups; those banks with an MSA overlap greater than 0.5 and those with an MSA overlap less than 0.5. In Table XI, we see that while this split of the sample does not change the results that the independent variables are significant, high MSA overlap mergers exhibit higher significance levels on all of the regression variables. Thus, the relation between the change in operating performance around a bank merger and bank size, relative size of the target versus the bidder bank, the extent to which the merged banks are focusing in their activities, and the stock market’s reaction to the merger are stronger when the merger involves a greater opportunity for cost cutting due to overlapping geographic areas of operations. IV. 4. Long-Term Stock Returns Table XII presents data on stock returns for longer periods around mergers by the 99 banks in the sample in which the bidder and target were publicly traded. This table provides evidence on the degree to which the market is surprised by the post-merger operating performance. Panel A of Table XII provides data on holding period stock returns for the year prior to the merger, Panel B presents the average buy-and-hold stock returns for two years after the merger. For both periods we list the return on the sample banks, the return on the benchmark matched portfolio, and the difference in these two returns (matched adjusted return). To ensure that results are not driven by outliers, we report the percent of the matched adjusted returns that are positive. As reported in Table XII, during the year prior to the merger, the merged banks have holding period returns equal to the benchmark portfolio. This result is expected and, in fact, intentional because one of the matching variables is momentum—the company’s stock performance during the prior year. During the two
22
years after the merger, however, the banks experience an average matched adjusted return of 2.74% (significant at better than the 5 percent level). For this period 73.13% of the sample has positive matched adjusted returns. Thus, outliers are not driving the results. These data show that abnormal stock performance for banks that merge is positive after the merger. As market participants see and evaluate the actual postmerger performance of the bank (as seen in Table IV), they adjust the stock price, incorporating the exact appreciation in the bank’s performance. V. Conclusion This paper examines bank performance around mergers. While previous research has examined the performance of banks around a merger, changes in long-term operating performance (including an extensive examination of the revenue enhancement and cost reduction sources for any performance changes), for both large and small bank mergers have yet to be examined. This paper fills this void. Further, the paper examines performance changes before and after the passage of the Riegle-Neal Act. The empirical results lead to the conclusion that industry adjusted operating performance of merged banks increases significantly after a merger. We also find that large bank mergers produce greater performance gains than small bank mergers, activity focusing mergers produce greater performance gains than activity diversifying mergers, geographically focusing mergers produce greater performance gains than geographically diversifying mergers, and performance gains are larger after the implementation of full nationwide banking in 1997 via the Riegle-Neal Act. Further, we find the improved performance is the result of both revenue enhancement and cost reduction activities. Additionally, the revenue enhancement opportunities appear to be most profitable in those mergers that offer the greatest opportunity for cost cutting activities, i.e., activity focusing and geographically focusing mergers. Finally, we find that along with the increase in accounting based operating performance, the merged banks also experience abnormal long-run stock returns. Historically in the United States, the ability of commercial banks to expand domestically has been constrained by regulation. The results of this paper lead to the conclusion that the elimination of these constraints through the adoption of intrastate and interstate banking laws has improved the efficiency with which banks operate.
23
FOOTNOTES 1
To date, the most common approach to interstate branching has been through merger and acquisition.
2
This consolidation trend has been most evident among the largest U.S. banks in a wave of megamergers.
3
Bank holding companies involved in more than one merger are screened from both the sample and the
industry control groups discussed below. 4
We also examine operating performance around mergers in which neither the bidder or target bank was
involved in a merger in the two years before or after the merger in question. The results and conclusions when we allow only one merger in a five-year window are the same as those reported in the paper allowing for no merger in a narrower, three-year window. 5
While this filter results in the elimination of some frequent acquirers, it is imposed to reduce the problem of
confounding events. If mergers do affect bank performance, the inclusion of banks that are involved in multiple mergers during the period of analysis would decrease the ability to empirically document the impact of a given merger. Indeed, a finding of no change in bank performance for frequent acquirers may be the result of the inclusion of multiple mergers over a period of successive years, each of which may increase or decrease the combined bank’s performance. 6
For example, the number of bank failures during the three years 1990 through 1992 was 169, 127 and 122,
respectively. The number of bank failures in 1993 through 1995, on the other hand, was 41, 13 and 6, respectively (see FDIC, Failed Bank Cost Study, 1996). 7
Data on the number of branches come from the FDIC website.
8
Thus, we will mean bank holding company whenever we refer to bank or bank holding company.
9
In addition to a leverage ratio of greater than or equal to 4 percent, adequate capitalization requires that core
capital to risk-weighted assets (or Tier I capital ratio) be greater than or equal to 4 percent and total capital to risk weighted assets (or total capital ratio) be greater than or equal to 8 percent. A well capitalized bank must have a Tier I ratio of at least 6 percent and a total capital ratio of at least 10 percent. We look only at the leverage ratio in this paper as a measure of capitalization. 10
Examining bank mergers from 1980 through 1998, Rhoades (2000) finds that the period is characterized by
a substantial increase in the average local market concentration in the majority of MSAs, whereas the average concentration in non-MSA counties declined somewhat. Further, concentration of control over aggregate U.S. bank deposits among the largest banks increased substantially, with the share of the 100 largest banks rising from about 47% to 71% and the share of the 10 largest rising from around 19% to 37%.
24
11
The merger involving the bidder and target bank with a preannouncement correlation coefficient of 0.105 is
not examined here. 12
Unlike accounting return on assets, this performance measure excludes the effect of interest on debt used
as capital financing by the bank. Interest paid on deposits is an expense associated with the operating process of the bank and is included in the performance measure. However, interest on subordinated notes and debentures is a function of the choice of capital funding of the bank: debt versus equity. By excluding this interest from the performance measure it is unaffected by the method of financing (cash, debt, or equity) the merger. It is important to control for these financing differences in measuring post-merger performance. If an acquisition is financed by debt or cash, its post-merger profits will be lower than if the same transaction is financed by stock, because income is computed after interest on subordinated debt, but before allowing for any cost of equity. Since the differences in earnings reflect the financing choice and not differences in economic performance, it is misleading to compare reported accounting earnings, which are computed after interest expense on subordinated debt, for firms that use different methods of merger financing. We use operating cash flows before interest expense on subordinate debt deflated by the value of assets to measure performance. This cash flow return is unaffected by the choice of financing. 13
We also do not use a market value based measure of operating performance such as a q ratio because we
do not require the banks to be publicly traded to be in the sample. Market value based measures of performance would require us to limit the sample of banks to those that are publicly traded. 14
If banks were involved in more than one merger (that passed our filters or did not) in the two years before
the merger, the pro forma performance measure for the combined banks is the weighted average of values for all of the merged banks, where the weights are the relative sizes of the banks at year-end before the merger. 15
Banks involved in a merger, but eliminated for one of the reasons listed in Section III.1 were not included as
industry comparison banks. 16
We also look at an industry comparison group including only banks that are involved in a merger in the year
before or after (but not the year of) the merger in question, are headquartered in the same Federal Reserve district as the bidder bank, and in the same asset class as the bidder bank. In order to get a sufficient sample size, these mergers may or may not pass our size and relative size filters. The conclusions drawn from the results do not change with the filter we use. 17
We start with four asset size groupings commonly used by the Federal Reserve: less than $100 million
(because our size filter is $250 million, we never use this size group), between $100 million and $1 billion,
25
between $1 billion and $10 billion, and greater than $10 billion. The asset filter we use includes the same group as the bidder bank and the next closest group. For example, if the bidder bank’s asset size was $1.5 billion, industry banks include all banks in the same Federal Reserve district and with asset size between $100 million and $10 billion. If, however, the bidder bank is greater than $50 billion in assets, only the same group as that for the bidder bank is used. 18
We also look at two other filters to obtain our industry match adjusted performance measures. First, we
classify the industry as all banks in the same Federal Reserve district as the bidder banks, disregarding asset size. Alternatively, we calculate the average of the ratio of C&I loans to total assets for the bidder banks in the two years prior to the merger. Using this ratio, we divide the sample into quartiles. We then identify industry matches as those banks in the same Federal Reserve district as the bidder bank, in the same asset size class as the bidder bank, and in the same quartile of C&I loans to total assets as the bidder bank in the two years prior to the merger. All sample banks and other banks involved in more than one merger are eliminated from the universe of control banks as we identify our industry match banks. The conclusions drawn from the results do not change with the filter we use. 19
In the pre-merger year, if the bidder and target are listed on different exchanges, we use the exchange on
which the bidder is listed. 20
Copies of the results are available from the authors.
21
In all cases where the median values of the accounting ratios are significant across various splits of the
sample the Wilcoxon signed rank test is also significant. Thus, we will not report this result each time. 22
It should be noted that merging banks tend to write off many of their bad loans and other assets and take
extraordinary charges prior to completing a merger. This so-called “big bath” affect could be at least part of the reason for improved performance (particularly for revenue enhancements) of the merged banks relative to the industry. However, this would not explain the differences in results for both cost cutting and revenue enhancements when we split the sample by bank size, activity focus, and MSA overlap 23
As mentioned in Section III, the sample of 134 bank mergers includes 99 cases in which both the bidder and
target banks have publicly traded common stock, and therefore which have a value for CORR. For the 35 mergers in which CORR could not be calculated, we use the median value of CORR for the sample of 99 bank mergers, 0.105, in the regression. We also ran the regressions using only the 99 bank mergers in which both the bidder and target banks traded, and a value for CORR could be calculated. The results and conclusions from these regressions are the same as those using the full sample of 134 bank mergers.
26
24
Standard event-study methodology is used to obtain average standardized abnormal returns (AR),
cumulative abnormal returns (CAR), average standardized abnormal returns (ASAR), and the Z-statistics reported in this section.
27
Table I Year of the Completion of Bank Mergers Between 1990 and 2000 The completion dates of the mergers are compiled using the Web site of the Federal Reserve Bank of Chicago. Bidder and target bank holding companies must have at least $250 million in total assets and the target assets to bidder assets ratio must be at least 10 percent. To control for confounding events, we eliminate any merger from the sample if the bidder or target bank was involved in more than one acquisition over the year before or after the merger in question.
Year Merger was Completed
Number of Mergers
1990 6 1991 3 1992 7 1993 12 1994 14 1995 11 1996 11 1997 15 1998 24 1999 15 2000 16 _______________________________________________________________________________________
28
Table II Summary Statistics for Commercial Bank Mergers Between 1990 and 2000 Data were obtained from Bank Holding Company Report of Income and Report of Condition data tapes. All dollar amounts are in millions of dollars. Operating pretax cash flow return on assets (OPCFROA) is defined as earnings before income taxes and extraordinary items plus interest on subordinated notes and debentures to total assets at year-end prior to the merger. Capital ratio is defined as total capital to total assets at year-end prior to the merger. Asset ratio is defined as target total assets to bidder total assets at year-end prior to the merger. MSA Overlap is defined as the number of bidder metropolitan statistical areas (MSAs) that match target MSAs divided by target MSAs at year-end prior to the merger. Variable Panel A – Bidder and Target Bank Statistics Assets ($): Mean Median Standard deviation Minimum Maximum
Bidders
Targets
$ 19,360.0 4,212.4 45,536.5 260.0 394,587.3
$ 10,268.1 878.4 31,077.7 252.7 250,246.8
Capital Ratio (%): Mean Median Standard deviation Minimum Maximum
7.98 7.87 1.40 5.16 14.10
8.17 8.01 3.93 -15.41 31.98
OPCFROA (%): Mean Median Standard deviation Minimum Maximum
2.21 2.07 1.36 0.42 17.52
1.87 1.77 2.15 -2.51 24.02
Panel B – Combined Bank Statistics Asset Ratio (%): Mean Median Standard deviation Minimum Maximum
48.41 31.94 55.70 10.05 534.46
MSA Overlap (%): Mean Median Standard deviation Minimum Maximum
36.07 22.50 40.49 0.00 100.00
Method of Payment Stock Cash Stock and cash Unknown
79 8 11 36
29
Table III Ratios Used to Analyze Performance Around Bank Mergers Between 1990 and 2000 Ratio Profitability indicators (1) Return on assets (2)
Return on equity
(3)
Net interest margin
Capital adequacy indicators (4) Total capital to assets (5) (6)
Loans to total capital Deposits to total capital
Asset quality indicators (7) Allowance for loan losses to loans (8)
Loan loss provision to loans
Operating efficiency indicators (9) Non-interest exp. to non-interest rev. (10) Non-interest exp. to net operating income
Definition Net income after taxes as a percent of book value of total assets Net income after taxes as a percent of book value of total equity capital Interest income minus interest expense as a percent of book value of total assets Total equity and subordinate debt as a percent of book value of total assets Total loans as a percent of book value of total capital Total deposits as a percent of book value of total capital Allowance for loan losses as a percent of total loans and leases Loan loss provision as a percent of total loans and losses
(14) Fixed assets to total assets (15) Total assets to employees (16) Net income to employees
Non-interest expenses as a percent of non-interest revenue Non-interest expenses as a percent of net interest income plus non-interest income Non-interest expenses as a percent of book value of total assets Salaries and employees benefits as a percent of total assets Numbers of branch offices to book value (in millions) of total assets Fixed assets as a percent of book value of total assets Book value of total assets to number of employees Net income after taxes to number of employees
Loan composition indicators (17) Return on loans (18) Return on C&I loans (19) Return on real estate loans (20) Return on consumer loans (21) C&I loans to total loans (22) Real estate loans to total loans (23) Consumer loans to total loans (24) C&I loans to total assets (25) Real estate loans to total assets (26) Consumer loans to total assets
Interest and fees on loans to total loans and leases Interest on commercial and industrial (C&I) loans to C&I loans Interest on real estate loans to real estate loans Interest on consumer loans to consumer loans C&I loans as a percent of total loans Real estate loans as a percent of total loans Consumer loans as a percent of total loans C&I loans as a percent of book value of total assets Real estate loans as a percent of book value of total assets Consumer loans as a percent of book value of total assets
(11) Non-interest exp. to total assets (12) Personnel exp. to total assets (13) Branches to total assets
Non-interest income indicators (27) Income from fiduciary activities to total assets (28) Service charges to total assets (29) Trading gains to total assets (30) Other non-interest income to total assets
Income from fiduciary activities as a percent of book value of total assets Service charges as a percent of book value of total assets Trading gains as a percent of book value of total assets Other non-interest income as a percent of book value of total assets
30
Table III continued Off-balance-sheet indicators (31) Letters of credit to total assets (32) Loans sold to total assets (33) Notional amount of derivatives to total assets (34) Total OBS items to total assets Liquidity risk indicators (35) Loans to total assets (36) Core deposits to total assets (37) Total loans to total deposits (38) Liquidity ratio
Letters of credit as a percent of book value of total assets Loans sold as a percent of book value of total assets Notional amount of derivatives outstanding as a percent of book value of total assets Total off-balance-sheet activities as a percent of book value of total assets Total loans as a percent of book value of total assets Demand deposits plus savings deposits plus time deposits as a percent of book value of total assets Total loans as a percent of total deposits Cash and book value of total investment securities as a percent of book value of total assets
31
Table IV Bank and Industry Matched Adjusted Median Annual Operating Pretax Cash Flow Return on Assets in the Years Surrounding 134 Commercial Bank Mergers Between 1990 and 2000 Operating pretax cash flow return on assets is income before taxes and extraordinary items plus interest on subordinate notes and debentures as a percent of the book value of assets as of the end of the year. Industry matched adjusted values are computed as the difference between the bank value and all banks that do not merge, are in the same Federal Reserve district, and in the same asset size class in the year prior to the merger. The ρ-values are based on a Mann-Whitney-Wilcox test.
Full Sample Year Relative to Merger -2 -1
Number of Observations 134 134
Bank Median (%)
Industry Matched Adjusted Median Percent (%) Positive (%)
1.79 1.92
-0.29 0.12
33 61c
1.90
-0.10
52
2.59 2.27
1.12a 0.96a
78b 74b
Median annual performance for years 1 to 2
2.49
1.02a
78b
Median annual difference between performance in years (-2 to -1) and years (1 to 2)
0.59
1.12a
78c
Median annual performance for years -2 to -1 1 2
134 121
a
Significantly different from zero at the one percent level. Wilcoxon signed ranks test statistic is significant at the one percent level. c Wilcoxon signed ranks test statistic is significant at the five percent level. 32 b
Table V Bank and Industry Matched Adjusted Median Annual Operating Pretax Cash Flow Return on Assets Based on Bank Size for the Sample Banks Operating pretax cash flow return on assets is income before taxes and extraordinary items plus interest on subordinate notes and debentures as a percent of the book value of assets as of the end of the year. Industry matched adjusted values are computed as the difference between the bank value and all banks that do not merge, are in the same Federal Reserve district and in the same asset size class in the year prior to the merger. Large bank mergers are those where the bidder bank’s pre-merger assets are greater than $1 billion. Small bank mergers are those where the bidder bank’s pre-merger assets are less than $1 billion. The p-values are based on a Mann-Whitney-Wilcox test. Panel A: Large Bank Mergers Year Relative to Merger
Number Bank of Median Observations (%)
-2 -1
114 114
Panel B: Small Bank Mergers
Industry Number Matched Adjusted of Median (%) Observations
1.85 1.94
-0.13 0.20
1.90
0.06
2.69 2.44
1.16 a 1.08
Median annual performance for years 1 to 2
2.50
1.13
Median annual difference between performance in years (-2 to -1) and years (1 to 2)
0.60
1.07
Median annual performance for years -2 to –1 1 2
114 104
a b
Industry Matched Adjusted Median(%)
Percent Difference (%)
1.78 1.89
-0.07 0.09
-0.06 0.11
1.86
0.02
0.04
2.17 2.09
0.58 b 0.48
a
2.12
a
0.26
0.52
a
20 20
Bank Median (%)
Panel C: Differences in Match Adjusted Performance
20 17
Significantly different from zero at the one percent level. Significantly different from zero at the five percent level.
33
b
0.58 b 0.60
a
0.54
b
0.59
b
0.55
b
b
Table VI Bank and Industry Matched Adjusted Median Annual Operating Pretax Cash Flow Return on Assets Based on Whether the Merger is Activity Focusing or Diversifying Operating pretax cash flow return on assets is income before taxes and extraordinary items plus interest on subordinate notes and debentures as a percent of the book value of assets as of the end of the year. Industry matched adjusted values are computed as the difference between the bank value and all banks that do not merger, are in the same Federal Reserve district, and in the same asset size class in the year prior to the merger. Focusing mergers are those with a preannouncement correlation coefficient of bidder and target returns greater than the median (0.105). Diversifying mergers are those with a correlation coefficient less than the median. This breakdown of the sample requires that we examine only those 99 mergers in which both the bidder and target bank are publicly traded. The p-values are based on a Mann-Whitney-Wilcox test. Panel A: Focusing Mergers Year Relative to Merger -2 -1
Number of Observations
Bank Median (%)
49 49
1.85 1.92
0.03 0.11
1.89
0.06
2.79 2.58
1.53 a 1.24
2.66
1.41
0.77
1.35
Median annual performance for years -2 to -1 1 2 Median annual performance for years 1 to 2
Median annual difference between performance in years (-2 to-1) and years (1 to 2) a
Panel B: Diversifying Mergers
49 44
Industry Matched Adjusted Median (%)
Panel C: Differences in Match Adjusted Performance
Number of Observations
Bank Median (%)
49 49
1.73 1.90
- 0.04 0.10
0.07 0.01
1.82
0.04
0.02
1.86 1.83
0.25 0.16
1.28 a 1.08
a
1.83
0.20
1.21
a
0.01
0.16
1.19
a
49 44
Significantly different from zero at the one percent level.
34
Industry Matched Adjusted Median(%)
Percent Difference (%)
a
a
a
Table VII Bank and Industry Matched Adjusted Median Annual Operating Pretax Cash Flow Return on Assets Based on MSA Overlap Operating pretax cash flow return on assets is income before taxes and extraordinary items plus interest on subordinate notes and debentures as a percent of the book value of assets as of the end of the year. Industry matched adjusted values are computed as the difference between the bank value and all banks that do not merger, are in the same Federal Reserve district, and in the same asset size class in the year prior to the merger. High MSA overlap banks are those with an MSA overlap ratio in year one before the merger of greater than 0.50. Low MSA overlap banks are those with an MSA overlap ratio less than 0.50. The p-values are based on a Mann-Whitney-Wilcox test. Panel A: High MSA Overlap Mergers Year Relative to Merger -2 -1
Number of Observations 52 52
Median annual performance for years -2 to –1 1 2 Median annual performance for years 1 to 2 Median annual difference between performance in years (-2 to-1) and years (1 to 2)
a
52 47
Panel B: Low MSA Overlap Mergers
Bank Industry Number Bank Median Matched Adjusted of Median (%) Median (%) Observations (%) 1.75 1.86
- 0.34 0.09
1.83
- 0.09
Percent Difference (%)
1.80 1.96
- 0.18 0.20
-0.16 -0.11
1.89
0.05
- 0.14
1.86 1.79
0.37 0.31
0.88 a 0.84
a
1.82
0.33
0.88
a
-0.07
0.28
1.02
a
2.60 2.37
1.25 a 1.15
2.49
1.21
0.66
1.30
82 82
Industry Matched Adjusted Median (%)
Panel C: Differences in Match Adjusted Performance
82 74
Significantly different from zero at the one percent level.
35
a
a
a
Table VIII Comparison of Industry Matched Adjusted Performance for 134 Bidder and Target Banks in the Two Years before and after a Merger between 1990 through 2000 Industry matched adjusted values are computed as the difference between the weighted averages of the bidder and target values (with the weights being the relative asset values of the two banks) and all banks that do not merger, are in the same Federal Reserve district, and in the same asset size class as the bidder bank in the year prior to the merger. Industry Matched Adjusted Values Ratio
Pre-Merger
Post-Merger
Profitability indicators (1) Return on assets (2) Return on equity (3) Net interest margin
-0.10% -1.02% 0.07%
0.52% 3.02% 0.90%
0.62%b,c 4.04%a,c 0.83%b,c
Capital adequacy indicators (4) Total capital to assets (5) Loans to total capital (6) Deposits to total capital
-0.47% 0.73X -1.24X
0.32% 3.16X 3.17X
0.79%b,c 2.43Xa,c 4.41Xa,c
Asset quality indicators (7) Allowance for loan losses to loans (8) Loan loss provision to loans
0.17% -0.06%
-0.58% -0.37%
-0.75%b,c -0.43%
Operating efficiency indicators (9) Non-interest exp. to non-interest rev. (10) Non-interest exp. to net operating income (11) Non-interest exp. to total assets (12) Personnel exp. total assets (13) Branches to total assets (14) Fixed assets to total assets (15) Total assets to employees (16) Net income to employees
0.85% 0.42% 0.01% 0.08% 0.45% 0.03% 44.19X 1.16X
-0.94% -0.51% -0.37% -0.41% -0.77% -0.45% 81.83X 8.91X
-1.79%a,c -0.93%a,c -0.38% -0.49%b,c -1.22%a,c -0.48% 37.64Xa,c 7.75Xa,c
Loan composition indicators (17) Return on loans (18) Return on C & I loans (19) Return on real estate loans (20) Return on consumer loans (21) C & I loans to total loans (22) Real estate loans to total loans (23) Consumer loans to total loans (24) C & I loans to total assets (25) Real estate loans to total assets (26) Consumer loans to total assets
-0.16% -0.10% 0.08% 0.14% -0.01% 0.18% 0.09% -0.09% 0.22% 0.02%
0.36% 1.22% 0.88% 1.04% 0.51% 1.21% -0.29% 0.46% 2.17% -0.05%
0.52%b,c 1.32%a,c 0.80%b,c 0.90%a,c 0.52% 1.03%a,c -0.38% 0.55% 1.95%a,c -0.07%
Non-interest income indicators (27) Income from fiduciary activities to total assets (28) Service charges to total assets (29) Trading gains to total assets (30) Other non-interest income to total assets
-0.09% 0.06% -0.05% 0.09%
0.10% 0.11% 0.09% 0.13%
0.19% 0.05% 0.14% 0.04%
Off-balance-sheet indicators (31) Letters of credit to total assets (32) Loans sold to total assets (33) Notional amount of derivatives to total assets (34) Total OBS items to total assets
0.11% -0.07% 0.26% 2.12%
0.69% 0.19% 31.31% 47.97%
0.68% 0.26% 31.05%a,c 45.85%a,c
Liquidity risk indicators (35) Loans to total assets (36) Core deposits to total assets (37) Total loans to total deposits (38) Liquidity ratio
0.31% 0.09% -0.08% 1.21%
0.47% 0.29% 0.22% 1.45%
0.16% 0.20% 0.30% 0.24%
a
Significantly different from zero at the one percent level. Significantly different from zero at the five percent level. c Wilcoxon signed test statistic is significant at the five percent level. b
36
Difference
Table IX Comparison of Industry Matched Adjusted Performance for 134 Bidder and Target Banks in the Two Years before versus after a Merger, Based on Bidder Bank Asset Size, Focusing versus Diversifying Merger, and MSA Overlap Industry matched adjusted values are computed as the difference between the weighted averages of the bidder and target values (with the weights being the relative asset values of the two banks) and all banks that do not merger, are in the same Federal Reserve district, and in the same asset size class as the bidder bank in the year prior to the merger. Large versus small merger is the difference in pre- versus post-merger change in each financial ratio for large bank (bidder bank’s pre-merger assets are greater than $1 billion) versus small bank mergers. Focusing mergers are those with a preannouncement correlation coefficient of bidder and target returns greater than the median (0.105). Diversifying mergers are those with a correlation coefficient less than the median. High versus low MSA overlap merger is the difference in pre-versus post-merger change in each financial ratio for high MSA (MSA overlap ratio in the year prior to the merger is greater than 0.50) versus low MSA mergers. Panel A: Difference in Pre- versus Post-Merger Performance for Large versus Small Bank Mergers Industry Matched Adjusted Values Large Bank Small Bank Mergers Mergers
Ratio
Difference
Profitability indicators (1) Return on assets (2) Return on equity (3) Net interest margin
0.72%b,c 4.89%a,c 0.95%b,c
0.61%b,c 2.11%b,c 0.77%b,c
0.11% 2.78%b,c 0.18%
Capital adequacy indicators (4) Total capital to assets (5) Loans to total capital (6) Deposits to total capital
0.86%b,c 2.63Xa,c 4.69Xa,c
0.55% 1.48Xb,c 2.02Xb,c
0.31% 1.15X 2.67Xb,c
Asset quality indicators (7) Allowance for loan losses to loans (8) Loan loss provision to loans
-0.75%b,c -0.55%
-0.71%b,c -0.52%
-0.04% -0.03%
Operating efficiency indicators (9) Non-interest exp. to non-interest rev. (10) Non-interest exp. to net operating income (11) Non-interest exp. to total assets (12) Personnel exp. total assets (13) Branches to total assets (14) Fixed assets to total assets (15) Total assets to employees (16) Net income to employees
-1.72%a,c -0.83%a,c -0.41% -0.59%b,c -1.18%a,c -0.47% 50.62Xa,c 7.99Xa,c
-1.59%a,c -0.80%b,c -0.29% -0.55%b,c -0.18% -0.27% 20.09Xb,c 2.57Xb,c
-0.13% -0.03% -0.12% -0.04% -1.00%a,c -0.20% 30.53Xa,c 5.42Xa,c
Loan composition indicators (17) Return on loans (18) Return on C & I loans (19) Return on real estate loans (20) Return on consumer loans (21) C & I loans to total loans (22) Real estate loans to total loans (23) Consumer loans to total loans (24) C & I loans to total assets (25) Real estate loans to total assets (26) Consumer loans to total assets
0.40%b,c 1.22%a,c 0.85%b,c 0.96%a,c 0.39% 1.09%a,c -0.42% 0.49% 1.92%a,c -0.31%
0.32% 0.94%a,c 0.82%b,c 0.86%b,c 0.43%b,c 0.91%b,c 0.07% 0.51%b,c 1.28%a,c 0.10%
0.08% 0.28% 0.03% 0.10% -0.04% 0.18% -0.49%b,c -0.02% 0.64% -0.41%
Non-interest income indicators (27) Income from fiduciary activities to total assets (28) Service charges to total assets (29) Trading gains to total assets (30) Other non-interest income to total assets
0.09% 0.10% 0.08% 0.10%
0.05% 0.11% 0.07% 0.04%
0.04% -0.01% 0.01% 0.06%
Off-balance-sheet indicators (31) Letters of credit to total assets (32) Loans sold to total assets (33) Notional amount of derivatives to total assets (34) Total OBS items to total assets
0.75% 0.24% 39.41%a,c 57.19%a,c
0.57% 0.21% 3.61%b,c 4.32%b,c
0.18% 0.03% 35.80%a,c 52.87%a,c
0.27% 0.19% 0.29% 0.37%
-0.06% 0.07% 0.09% -0.11%
Liquidity risk indicators (35) Loans to total assets (36) Core deposits to total assets (37) Total loans to total deposits (38) Liquidity ratio
0.21% 0.26% 0.38% 0.26%
37
Table IX (continued)
Panel B: Difference in Pre- versus Post-Merger Performance for Activity Focusing versus Diversifying Bank Mergers Focusing Mergers
Ratio
Industry Matched Adjusted Values Diversifying Mergers
Difference
Profitability indicators (1) Return on assets (2) Return on equity (3) Net interest margin
1.15%a,c 4.89%a,c 0.94%b,c
0.32% 3.26%b,c 0.57%b,c
0.83%b,c 1.63%b,c 0.37%
Capital adequacy indicators (4) Total capital to assets (5) Loans to total capital (6) Deposits to total capital
0.87%b,c 2.79Xa,c 4.43Xa,c
0.58% 1.57Xb,c 3.17Xb,c
0.29% 1.22X 1.26X
Asset quality indicators (7) Allowance for loan losses to loans (8) Loan loss provision to loans
-0.74%b,c -0.34%
-0.62%b,c -0.42%
-0.12% 0.08%
Operating efficiency indicators (9) Non-interest exp. to non-interest rev. (10) Non-interest exp. to net operating income (11) Non-interest exp. to total assets (12) Personnel exp. total assets (13) Branches to total assets (14) Fixed assets to total assets (15) Total assets to employees (16) Net income to employees
-2.26%a,c -1.07%a,c -0.63%b,c -0.77%a,c -1.52%a,c -0.80%b,c 55.63Xa,c 10.72Xa,c
-0.99%b,c -0.51% -0.11% -0.14% -0.74%b,c -0.17% 12.64Xb,c 2.68Xb,c
-1.27%a,c -0.56%b,c -0.52%b,c -0.63%b,c -0.78%b,c -0.63%b,c 42.99Xa,c 8.04Xa,c
Loan composition indicators (17) Return on loans (18) Return on C & I loans (19) Return on real estate loans (20) Return on consumer loans (21) C & I loans to total loans (22) Real estate loans to total loans (23) Consumer loans to total loans (24) C & I loans to total assets (25) Real estate loans to total assets (26) Consumer loans to total assets
1.42%a,c 1.09%a,c 1.10%a,c 0.64%b,c 1.29%a,c -0.28% 0.95%b,c 2.28%a,c -0.15%
0.69%b,c 0.53% 0.50%b,c 0.54% 0.52%b,c -0.12% 0.27% 1.18%b,c 0.08%
0.73%b,c 0.56%b,c 0.60%b,c 0.10% 0.77%b,c -0.16% 0.68%b,c 1.10%b,c -0.23%
Non-interest income indicators (27) Income from fiduciary activities to total assets (28) Service charges to total assets (29) Trading gains to total assets (30) Other non-interest income to total assets
0.19% 0.08% 0.15% 0.11%
0.09% 0.13% 0.08% 0.10%
0.10% -0.05% 0.07% 0.01%
Off-balance-sheet indicators (31) Letters of credit to total assets (32) Loans sold to total assets (33) Notional amount of derivatives to total assets (34) Total OBS items to total assets
1.11%a,c 0.16% 41.97%a,c 56.18%a,c
0.19% 0.15% 27.76%a,c 39.19%a,c
0.92%b,c 0.01% 14.21%a,c 16.99%a,c
0.44% 0.28% 0.31% 0.16%
0.32% 0.26% 0.27% 0.09%
0.10% 0.02% 0.04% 0.07%
Liquidity risk indicators (35) Loans to total assets (36) Core deposits to total assets (37) Total loans to total deposits (38) Liquidity ratio
38
Table IX (continued) Panel C: Difference in Pre- versus Post-Merger Performance for High MSA Overlap versus Low MSA Overlap Bank Mergers Industry Matched Adjusted Values High MSA Low MSA Overlap Mergers Overlap Mergers
Ratio
Difference
Profitability indicators (1) Return on assets (2) Return on equity (3) Net interest margin
1.31%a,c 6.83%a,c 1.29%a,c
0.29% 2.12%b,c 0.60%b,c
1.02%a,c 4.71%a,c 0.69%b,c
Capital adequacy indicators (4) Total capital to assets (5) Loans to total capital (6) Deposits to total capital
1.41%a,c 2.92Xa,c 5.74Xa,c
0.48% 1.84Xb,c 3.15Xb,c
0.93%b,c 1.08X 2.59Xb,c
Asset quality indicators (7) Allowance for loan losses to loans (8) Loan loss provision to loans
-0.68%b,c -0.57%
-0.62%b,c -0.48%
-0.06% -0.09%
Operating efficiency indicators (9) Non-interest exp. to non-interest rev. (10) Non-interest exp. to net operating income (11) Non-interest exp. to total assets (12) Personnel exp. total assets (13) Branches to total assets (14) Fixed assets to total assets (15) Total assets to employees (16) Net income to employees
-2.62%a,c -1.28%a,c -0.79%b,c -0.97%a,c -1.72%a,c -0.81%b,c 67.95Xa,c 11.38Xa,c
-1.08%b,c -0.59%b,c -0.18% -0.27% -0.64%b,c -0.12% 19.04Xb,c 4.45Xb,c
-1.54%a,c -0.69%b,c -0.61%b,c -0.70%b,c -1.08%a,c -0.69%b,c 48.91Xa,c 6.93Xa,c
Loan composition indicators (17) Return on loans (18) Return on C & I loans (19) Return on real estate loans (20) Return on consumer loans (21) C & I loans to total loans (22) Real estate loans to total loans (23) Consumer loans to total loans (24) C & I loans to total assets (25) Real estate loans to total assets (26) Consumer loans to total assets
0.75%b,c 1.69%a,c 1.19%a,c 1.23%a,c 0.41% 1.57%a,c -0.28% 1.07%b,c 2.73%a,c -0.12%
0.16% 0.77%b,c 0.59%b,c 0.64%b,c 0.39% 0.70%b,c -0.40% 0.31% 1.19%b,c -0.32%
0.59%b,c 0.92%b,c 0.60%b,c 0.59%b,c 0.02% 0.87%b,c 0.12% 0.76%b,c 1.54%b,c 0.20%
Non-interest income indicators (27) Income from fiduciary activities to total assets (28) Service charges to total assets (29) Trading gains to total assets (30) Other non-interest income to total assets
0.27% 0.12% 0.09% 0.06%
0.12% 0.09% 0.14% 0.01%
0.15% 0.03% -0.06% 0.05%
Off-balance-sheet indicators (31) Letters of credit to total assets (32) Loans sold to total assets (33) Notional amount of derivatives to total assets (34) Total OBS items to total assets
1.12%a,c 0.22% 43.19%a,c 67.46%a,c
0.27% 0.15% 18.84%a,c 30.89%a,c
0.85%b,c 0.07% 24.35%a,c 36.57%a,c
0.32% 0.27% 0.30% 0.32%
0.27% 0.18% 0.15% 0.18%
0.05% 0.09% 0.15% 0.14%
Liquidity risk indicators (35) Loans to total assets (36) Core deposits to total assets (37) Total loans to total deposits (38) Liquidity ratio a
Significantly different from zero at the one percent level. Significantly different from zero at the five percent level. c Wilcoxon signed test statistic is significant at the five percent level. b
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Table X Merger Announcement Abnormal Stock Returns in the two days Around the First Public Announcement of Bank Mergers Between 1990 and 2000 ________________________________________________________________________ Two day abnormal returns (%)a Z-statistic Target banks
16.87
22.09b
Bidder banks
-0.65
-2.34c
Combined banks
2.83
4.12b
a
Defined as the average cumulative abnormal return (CAR) from day t = -1 to t = 0, where CAR -1, 0 =
0
∑
t = −1
N ∑ ARit =1 i
/
N,
with N = number of banks in the sample and ARit = abnormal return for bank i on day t from the market model regression. b Significantly different from zero at the one percent level. c Significantly different from zero at the five percent level.
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Table XI Regression Results for the Change in Operating Pretax Cash Flow Return on Assets Around Bank Mergers Between 1990 and 2000 ∆OPCFROAi = a0 + b1 ln(SIZEi) + b2 RELSIZi + b3 MSAi + b4 CORRi + b5 CARi+ b6 YEARi where ∆OPCFROA = change in mean OPCFROA for the merged banks in years –1 to –2 before to years +1 to +2 after the ith bank merger, ln(SIZE) = natural log of the book value of bidder bank’s assets at year-end before the ith bank merger, RELSIZ = target total assets to bidder total assets at year-end prior to the ith bank merger, MSA = number of bidder metropolitan statistical areas (MSAs) that match target MSAs divided by target MSAs at year-end prior to the ith bank merger, CORR = the preannouncement correlation coefficient of bidder and target returns for the ith bank merger, CAR = combined bidder and target merger announcement abnormal stock returns in the two days around the first public announcement of the ith bank merger, and YEAR = dummy variable equal to 0 if year is 1990-1996 and 1 if year is 1997-2000. Full Sample (n = 134)
MSA Overlap < 0.5 (n = 82)
Coefficient
t-statistic
Coefficient
t-statistic
Coefficient
0.014 0.063 0.149 0.621 0.274 0.501 0.019
0.63 2.25b 3.07a 4.71a 2.63a 3.83a 2.28b
0.018 0.092 0.208 0.291 0.584 0.023
0.72 3.47a 3.22a 3.34a 4.11a 2.37b
0.012 0.043 0.133 0.227 0.443 0.015
Intercept ln(SIZE) RELSIZ MSA CORR CAR YEAR Adjusted R-squared P-value a
MSA Overlap ≥ 0.5 (n = 52)
0.37 0.0003a
0.41 0.0002a
Significantly different from zero at the one percent level. Significantly different from zero at the five percent level.
b
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t-statistic 0.69 2.18b 2.81a 2.27b 2.83b 2.07b 0.34 0.0004a
Table XII Holding Period Returns for the Years Surrounding Commercial Bank Mergers Between 1990 and 2000 Holding period returns are calculated during the year prior to the merger announcement and during the two years after the merger announcement. Mergers occurred during the period January 1990 to December 2000. Holding period returns are computed using the CRSP-listed closing price as the purchase price. The difference in holding period returns is calculated as ∏(1+Rit) - ∏(1+Rjt), where Rit is the buy-and-hold return starting twelve calendar moths prior to until the month prior to the merger announcement or starting the month after until the month of the two-year anniversary of the merger announcement, Rjt is the holding period return on a matching benchmark portfolio with annual rebalancing. Each issuing firm is matched with a characteristic benchmark portfolio of the same size quintile, the same book-to-market quintile, and the same momentum quintile. Panel A: One-Year Mean Pre-Merger Buy-and-Hold Returns Merged firms’ return
20.82%
Benchmark firms’ return
21.11%
Merged – Benchmark firms’ return
-0.29%
Percentage of the matched-adjusted returns that are positive
49.25%
Panel B: Two-Year Mean Post-Merger Buy-and-Hold Returns Merged firms’ return
14.76%
Benchmark firms’ return
12.02%
Merged – Benchmark firms’ return
2.74%a
Percentage of the matched-adjusted returns that are positive
73.13%b
a
Significantly different from zero at the five percent level. Wilcoxon signed rank test statistic is significant at the one percent level.
b
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