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The impact of mean reversion of bank profitability on post-merger performance in the banking industry
Journal of Banking & Finance 30 (2006) 3503–3517 www.elsevier.com/locate/jbf
The impact of mean reversion of bank profitability on post-merger performance in the banking industry Morris Knapp a, Alan Gart
b,*
, Mukesh Chaudhry
b
a
b
Miami-Dade College, 300 N.E. Second Ave., Miami, FL 33132, United States Indiana University of Pennsylvania, Eberly College of Business, Indiana, PA 15705, United States Received 15 July 2004; accepted 25 January 2006 Available online 7 August 2006
Abstract This research study examines the tendency for serial correlation in bank holding company profitability, finding significant evidence of reversion to the industry mean in profitability. The paper then considers the impact of mean reversion on the evaluation of post-merger performance of bank holding companies. The research concludes that when an adjustment is made for the mean reversion, post-merger results significantly exceed those of the industry in the first 5 years after the merger. Ó 2006 Elsevier B.V. All rights reserved. JEL classification: G2/G21/G34 Keywords: Banking; Mergers; Acquisitions; Mean reversion
1. Introduction The banking industry has been undergoing an extensive period of restructuring as a result of technological innovations and regulatory changes. The number of banking mergers has accelerated in the last decade. A whole literature that analyzes the results of these
* Corresponding author. Address: 978 Warfield Lane, Huntingdon Valley, PA 19006, United States. Tel.: +1 215 947 6860. E-mail address: [email protected] (A. Gart).
0378-4266/$ - see front matter Ó 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2006.01.005
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mergers has developed; the general conclusion is that while mergers are good for the owners of the bank being acquired, the results for the acquirer are, at best mixed (Walter, 2004). Market reactions at the time of announcement tend to be either neutral or slightly negative. This result is counterintuitive. It seems unlikely that managers would continually make major mergers that are not clearly in the best interests of the shareholders. The shareholders would rebel and remove them from office, as has happened repeatedly in other industries when management fails to perform. Several studies have evaluated post-merger performance in the banking industry. These studies have generally indexed performance to some industry standard and then compared this indexed performance before and after the merger. The change in performance against the standard is ascribed as the effect of the merger. This approach does a good job of eliminating the effects of macroeconomic forces, regulatory changes, and other industry-wide phenomena. It assumes that absent a major event such as a merger, performance against the index is a random variable with a constant mean. Many, but not all academic research studies of banking mergers have found the average merger to be unsuccessful. This seems contrary to conventional wisdom as numerous mergers are continually being consummated. The implication is that something may have been omitted in the prior studies. The research paper of Fama and French (FF, 2000) suggests a solution to this conundrum. They find that profitability in most firms tends to revert to the mean in their industry. In most previous research on banking mergers no adjustment was made for mean reversion of post-merger bank performance as many of these studies were only event studies and the long term effects such as mean reversion were ignored. The omission of an adjustment for the mean reversion trend is a major part of the negative findings of prior post-merger studies. When we adjust for the mean reversion trend, we find significant improvements in merger related BHC ROE in 4 of the 5 post-merger years studied. The cash flow results are even stronger, with significant improvement versus the industry in all 5 years. One explanation of mean reversion is the basic economic argument that competitive forces, in the long-run, would bring about equality in the rates of return across the firms. The contribution of this paper lies in several areas. First, it extends the work of FF (2000) on mean reversion to cover BHCs that were expressly omitted because FF analyzed only non-regulated industrial firms. Second, it looks at post-merger performance results and compares them with the results of the acquirer in the year before the merger. Finally, and more importantly, this research demonstrates the importance of adjusting post-merger results for mean reversion trends when analyzing the effects of mergers. 2. Literature review The literature on post-merger performance is extensive. Rhoades (1994) summarizes 19 studies from the period 1980–1993 that examined the post-merger performance of banking institutions. Most of the studies compared operating performance of merging institutions with that of a group of banks that did not merge. A few studies used an econometrically derived production or profit function as the yardstick, and then measured efficiency against this measure. In general, most studies found no improvement in profitability or efficiency after a merger. These results were consistent across the methodologies.
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Berger and Humphrey (1992) find that average X-efficiencies are about 20–25% of the costs in the banking industry. This suggests that the potential exists for achieving cost efficiency through mergers. However, in actuality, bank mergers on average in the 1980s did not seem to generate great cost efficiencies. The overall conclusion of both cost ratio studies and X-efficiency studies is that changes in efficiency on costs were on the order of 5% or less on average from US banking mergers in the 1980s (see DeYoung (1998) and Peristiani (1997)). Akhavein et al. (1997) found cost X-inefficiencies to be slightly lower. In addition, Berger and Humphrey (1992) found no apparent correlation between mergers that reduce costs and those that could be predicted to reduce costs. In Peristiani’s investigation of the post-merger performance of the acquiring banks that participated in a merger during the period 1980–1990, the evidence suggests that acquirers failed to improve X-efficiency after the merger. Craig and dos Santos (1997) find that merged banks out-performed the industry in the post-merger period. Their findings show that when you compare the post-acquisition risk measures of the post-merger banking organizations with the pre-acquisition risk of the associated acquirers there is a pronounced reduction in risk. Pilloff (1996) finds no significant change in post-merger ROE. However, when he utilizes operating income before provisions instead of net income to calculate ROE, there is a significant increase in post-merger returns. Akhavein et al. (1997) find that while there is no significant change in ROE after mergers between 1981 and 1989, there is a significant improvement in profit efficiency. Peristiani (1997) finds no improvement after merger, although he finds that the merged bank out-performed the combined results of the acquirer and the target before the merger. DeLong (2001) finds that bank mergers that enhance value upon announcement can be distinguished from those that do not create value. Mergers that have both similar activity and geography (focus mergers) enhance stockholder value, while those mergers that diversify either geographically and by activities, or both, do not create value. Houston et al. (2001) suggest that most of the estimated value gains from bank mergers stem from the opportunity to cut costs by eliminating overlapping operations and consolidating backroom operations. They also find that mergers occurring in the 1990s generate higher abnormal returns than mergers prior to 1990. Berger and DeYoung (2002) find that banking organizations exercise significant control over their affiliates and that this control has been increasing over time and that agency costs associated with distance have decreased somewhat over time. These findings are consistent with an increasing ability of banks to expand geographically. They also conclude that ‘‘distance has a negative effect on costs, profits, and managerial control in banking’’. However, between 1985 and 1998 the inefficiencies associated with distance appear to have ‘‘mitigated in banking’’ because of advances in telecommunications, computers, software, etc. DeLong and DeYoung (2004) find that the market seems to be enhancing its ability to predict successful bank mergers over time and that the type of bank merger that enhances long-term value changes over time. They also conclude that the ability of the market to distinguish mergers that will improve performance increased during the 1990s. The mergers that increase long-term ROAs or improve the efficiency ratio during the first years of their study tend to focus activities (similarity in activities), while those factors that influence performance in the later years tend to have similar geographic locations and/or payment in stock.
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3. Methodology Mean reversion and post-merger performance both require an index or standard against which to measure the performance of an individual BHC. The frontier methodology such as that of Akhavein et al. (1997) measures the distance of an acquirer from the efficient profit frontier before and after a merger. Cornett and Tehranian (1992) use an industry average cash flow return. DeLong (2003) uses profit-based industry average returns. This paper uses both profit based and cash flow based industry average returns. The difference between the return of an individual BHC and that of the industry is deemed the industry-adjusted return, the economists’ abnormal return. Mean reversion, and postmerger performance are studied by examining the behavior of these industry-adjusted returns. 3.1. Mean reversion FF find that over time the return on assets of firms moves to the industry mean ROA. Since the industry mean changes from year to year because of economic and market conditions and technological changes, the mean reversion tendency is best seen by examining the deviation from the mean. If the mean deviation tends to move toward zero, there is mean reversion. The tendency for mean reversion in earnings is logical. If one firm develops a way to out-perform its peers, others will try to copy it. Thus, the edge developed by one firm is quickly dissipated as their techniques become known to other firms. Similarly, if a firm underperforms the industry, it will quickly look to copy its more successful peers and so move back to the average. The mean reversion analysis follows FF (2000), which is, in turn based on an earlier model developed by Fama and McBeth (1973). A typical mean reversion equation can be written in the following manner: y 1 y 0 ¼ by 0 þ e;
ð1Þ
where y1 is the continuously compounded rate of return for period 1 and y0 is the continuously compounded rate of return for period 0 and the intercept term is ignored. Eq. (1) can be rewritten as: y 1 ¼ ð1 þ bÞy 0 þ e or y 1 ¼ py 0 þ e;
ð2Þ
where p = (1 + b). The simultaneous hypothesis that 1 6 b 6 0 and 0 6 p 6 1 are assumed in Eq. (2). However, if the estimated values are b < 0 or equivalently p < 1, it is referred to as the ‘‘mean reversion hypothesis’’. Some authors have attempted to relate the mean reversion hypothesis to the convergence hypothesis, stating that when no convergence is found or if b = 0, it implies concurrent rejection of the convergence hypothesis. But, Lichtenberg (1994) points out that mean reversion may be a necessary but not a sufficient condition for convergence. As suggested in FF and Lichtenberg we develop cross-sectional regression models for the change in IAROE based on the IAROE in the base year. Models are calculated for changes of 1, 2, 3, 4, 5, and 6 year’s duration for each of the years 1987–2002.
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The equations are in the format IARt IARt3 ¼ a þ bIARt3 þ et :
ð3Þ
In each equation, IARt IARt3 is the change in industry-adjusted return from the third year prior to the current year, and IARt3 is the industry-adjusted return in the third year prior to the current year. Similar equations are developed for periods of 1–6 years prior for both IAROA and IAROE. Mean slopes are then calculated for each of the six change periods, 1, 2, 3, 4, 5, and 6 years long. Kraus and Litzenberger (1976) show that the variance of the mean regression coefficient can be calculated as follows: X Varðbk Þ ¼ ðbky bk Þ2 =N ðN 1Þ; ð4Þ y¼1
where k is the number of years back (1–6) the reversion is being tested, y is the calendar year for which the cross-sectional regression has been run and b is the mean of the crosssectional regression coefficients. Y is summed for years 1–n. The t statistic for b is then pffiffiffiffiffiffiffiffiffiffiffiffiffi tð bÞ ¼ b= varðbÞ; ð5Þ so the mean slopes are tested for significance. We use the mean R2 rather than the R2 of a pooled regression because, as Johnston and DiNardo (1997) point out, the R2’s of pooled data tend to be very high, and lead to erroneous conclusions. A negative slope would indicate mean reversion. It would indicate that BHCs that outperformed the industry in the base year would move closer to the industry in the following period. 3.2. Post-merger performance Most post-merger studies such as DeLong (2001) assume that the industry adjusted level of performance in the base year would remain unchanged except for random forces in the absence of some unusual force such as a merger. The impact of the merger in that instance is the difference between the pre-merger adjusted return and that observed in the post-merger period. However, if there is a significant mean reversion effect and adjusted returns move toward zero over time, the pre-merger adjusted return misstates what should be expected in the post-merger period, and therefore obscures the actual result of the merger. In this study, the expected post-merger return reflects the mean reversion trend. The post-merger performance analysis is conducted in two steps. First, we calculate the change in industry-adjusted returns from the year before the effective date of the merger (the base year) to years 1, 2, 3, 4, and 5 years after the merger. Because the industry returns capture all the industry general forces, the change in industry-adjusted returns captures only the firm specific activity, the merger. The second step is to include the impact of the trend for mean reversion. The mean regression model derived from the reversion analysis is used to calculate the expected change in return resulting from mean reversion. The effect of the merger, then, is the difference between the actual change from the base year and the change projected from the mean reversion model. The mean changes from this analysis are compared to the mean changes in step 1 to examine the extent of the influence of mean reversion on post-merger analysis. Both IAROA and IAROE are examined together with the cash flow returns to
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add robustness to the study. Separate regressions are used for lags of 1, 2, 3, 4, and 5 years, rather than a single multiple regression, to avoid problems of multicollinearity. 3.3. Pre-merger base for comparison Any analysis of changes depends on the base from which the change is measured. The banking literature is divided on how to measure post-merger change. Cornett and Tehranian (1992) examine changes from the acquirer’s performance in the base year while Craig and dos Santos (1997) and Akhavein et al. (1997) do a pro forma combination of the merger partners and examine the change from the performance of the combination. These two approaches really ask two different questions. The post-merger changes from the acquirer’s results in the base year measure the effects of the merger on the acquiring institution. They help to answer the question, ‘‘Did the management of the acquirer make a correct decision for its shareholder in making the acquisition?’’ An improvement in returns relative to the industry suggests a positive result; a decline suggests a suboptimal choice of a merger partner or the inability to integrate the target into the acquirer. This approach is appropriate when considering a merger from an investor’s point-of-view. The measurement of post-merger changes from the pro forma combination of the two partners in the year before the merger is concerned with whether there has been an overall improvement in performance of the two institutions. It asks the question ‘‘Is the combined institution more effective in delivering banking services than were the two separate organizations?’’ If the acquirer is able to improve the performance of the weaker target following a merger then the overall results have improved even if the results to the acquirer’s shareholders have not been affected. Details of a parallel analysis using the pro forma combined results of the acquirer and target in the year before the merger as the base year have been omitted in the interest of brevity. The results were similar, but the significance was not as strong. Interested readers may obtain the full study from the authors. 4. Data 4.1. Merger sample Our sample of 80 material mergers came from a Salomon Smith Barney Merger and Acquisition Database; each merger included in the study had a value in excess of $25 million and was consummated in the years 1987–1998. This data base consists of 651 mergers announced during the years 1986–1998. Of these mergers, 167 lacked data in the Y9C data base of the Federal Reserve Bank of Chicago. These were primarily acquisitions involving commercial banks directly, rather than BHCs. Commercial banks, S&Ls and foreign banks do not report on form Y9C. Most commercial banks are owned by a bank holding company, but some, mostly small banks, are not. Another 68 announced mergers were not completed by the end of 1998. Following the methodology utilized by Houston et al. (2001), the sample is limited to mergers where the acquired BHC is large enough to produce a detectable change in the results of the acquirer in the post-merger period. Only mergers where the target’s assets were at least 10% of those of the combined firms in the year preceding the merger, or where target net income was at least 10% of the combined total were included. This screen eliminated 319 mergers from the study. Mergers
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selected for this study were also limited to those where the acquirer did not make another material acquisition during the period under examination. When a second acquisition is made during this period, it is impossible to separate the effects of the first merger from those of the second. When an acquirer makes a later acquisition, the first merger is included for the post-merger period before the subsequent merger is executed, but no further. The subsequent merger is not included in the sample. Also, 11 mergers were only included for a portion of the 5 year post-merger period because they made a second acquisition. Time series studies such as this are subject to survivorship bias. To minimize the effects of this bias, we do not require that a BHC be present in all years to be included. The extent to which calculations of differently derived samples produce the same result adds robustness to the findings. Since, this study uses cross-sectional data, it minimizes the survivorship bias problems discussed in Brown et al. (1992). In addition, the power of Table 1 Merger sample information Acquirer base year statistics Mean IAROA Median IAROA Mean IAROE Median IAROE Mean IACFROA Median IACFROA Mean IACFROE Median IACFROE Mean total assets in $000’s Median total assets in $000’s Maximum total assets in $000’s Minimum total assets in $000’s
0.002025*** 0.00193*** 0.03325*** 0.03782*** 0.00313*** 0.00318*** 0.0507*** 0.0532*** 19,706,471 4,920,030 182,926,000 218,737
Target base year statistics Mean IAROA Median IAROA Mean IAROE Median IAROE Mean IACFROA Median IACFROA Mean IACFROE Median IACFROE Mean total assets in $000’s Median total assets in $000’s Maximum total assets in $000’s Minimum total assets in $000’s
0.00163 0.000217*** 0.01642 0.003531*** 0.00369*** 0.0012*** 0.0439*** 0.0136*** 9,109,737 1,098,577 121,173,000 137,170
Merger characteristics % Interstate Mean abnormal returns Median abnormal return
25% 1.01% 2.04%
This table shows the major characteristics of the merger sample including the mean and median returns for the acquirer and target, the mean size of the acquirer and target, the percent of mergers that were interstate in nature, and the abnormal returns in the stock market for the merger. The mean returns are tested for significance; the medians are tested using a signs test. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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cross-sectional tests is higher because we are able to employ a large sample of firms, unlike time-series analysis. Table 1 summarizes the main characteristics of the merger. Acquirer returns were, on average, significantly above the industry mean at the 1% level. Table 2 details the number of mergers by year and gives the mean total assets for each year’s acquirers and targets. For comparison, the mean total assets for all BHCs reporting on Form Y9C is also given. In all years, acquirers are well above the industry average in asset size. However, there is a wide variability among acquirers. The majority of the sample mergers involved BHCs in the same state, but 25% were interstate mergers. Clearly this is not just an activity of money center and super-regional banks. Merger activity pervades the entire industry. The targets are somewhat smaller and less profitable than the acquirers. The average merger involves a target with assets of 39% of those of the acquirer, but once again, there is a wide variability in the size of the targets. 4.2. Financial data The financial data for this study comes from the quarterly Y9C reports prepared by all BHCs with assets in excess of $150 million. Details on the number of BHCs in the industry and the average total assets for the industry each year are given in Table 2. For simplicity, we use all BHCs in the file when calculating the industry returns. Eliminating the 80 BHCs in the study from a population of over 1200 in each year will not materially alter the average returns. We calculate the returns in the standard manner using the averages of the four quarterly daily average balances for the asset or equity denominator. Accounting net income is used for the accounting ROA and ROE. Following Cornett and Tehranian (1992) cash flow is defined as income from operations less interest expense on mandatory subordinated debentures. All four returns, ROA, cash flow ROA, ROE, and cash flow ROE, are calcu-
Table 2 Annual data
86 87 88 89 90 91 92 93 94 95 96 97 98
Number of mergers
Mean assets of targets ($000’s)
Mean assets of acquirers ($000’s)
Mean assets of industry ($000’s)
Number of BHCs in industry
7 5 3 4 2 5 7 6 9 9 9 14
1,477,647 5,264,557 1,196,807 4,999,707 4,138,172 18,896,234 3,498,944 1,055,580 6,276,525 17,859,598 5,559,630 19,119,859
9,284,430 16,301,213 5,497,259 15,334,398 16,476,298 46,864,206 22,988,248 2,263,716 12,004,907 33,031,829 12,687,394 27,921,460
2,030,175 2,104,470 2,026,647 2,094,149 2,027,994 2,044,883 2,101,615 2,324,040 3,109,343 3,348,695 3,454,999 3,631,657 3,497,822
1236 1241 1286 1303 1357 1379 1380 1360 1093 1099 1151 1209 1277
This table details the number of mergers and the average total assets of the acquirers and targets by year. It compares these with the number of BHC’s in the industry reporting data on form Y9C and the mean total assets for the industry.
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lated separately for each BHC in the database. The post-merger changes are the changes in the industry-adjusted returns from the base year to the industry-adjusted returns in the post-merger years. Changes are calculated for all four returns for each of the first 5 calendar years after the effective date of the merger. The year in which the merger took place is not included. 5. Empirical results 5.1. Mean reversion analysis results The results of the mean reversion analysis are presented in Table 3. Here we analyze the changes in each of the four return variables studied, IAROA, IACFROA, IAROE, and IACFROE from the prior years and the relationship of the change to the value of the return in the base year using regression analysis. If there were no mean reversion effect, the regression coefficient for the base year return would be insignificant; a negative sign would indicate mean reversion. Previous merger studies have indicated that mergers can take some time to produce results, so longer term trends will be needed for that part of the paper. Thus, the analysis is performed for each year for base years 1, 2, 3, 4, 5, and 6 years previous. The overall summary regression equations for each of the four return measures studied resulting from this analysis are given in Table 3. It is clear from the results that there is a strong mean reversion trend. All the regression coefficients for the regressions for the individual years have a negative coefficient, which indicates mean reversion, and the mean regression coefficients are all significant at the 1% level. The mean R2 values range from 19.4% to 38.4% for the normal IAROA equations, and all but one of them exceed 20%. The cash flow ROA equations have slightly lower R2 values than do the accounting equations. The R2 values for the accounting ROE equations range from 32.3% to 56.8%; those for the cash flow ROE, from 33% to 56%. These R2s indicate that mean reversion explains a substantial portion of the variability in the changes in industry adjusted returns. The increasing strength of the explanatory power and significance of the regressions as the length of time from the base period increases are also worth noting. As the time period increases, the R2 and t statistics both become noticeably stronger, and the regression coefficients become more negative. By the sixth year, the R2 for IAROE has risen to 56.8%, and the regression coefficient has become 0.85. This means that the mean reversion equation explains slightly over half of the variability in changes in ROE versus the industry. Similar, albeit slightly weaker patterns can be observed for IAROA and the cash flow returns, lending robustness to the findings. It is clear from the preceding discussion that there is significant mean reversion in industry-adjusted returns. Any analysis of changes in BHC returns over time must adjust for the impact of mean reversion if it is to accurately measure the impact of the merger. The strength and duration of the mean reversion effect is clear evidence of the highly competitive nature of the banking industry. There are more than 1200 bank holding companies with assets in excess of $150 million, and there are over 8000 individual commercial banks. In addition, banks must increasingly deal with competition from other sectors of the financial services industry where improved technology is making it easier for them to compete with banks. There is a strong incentive for bankers to stay abreast of new developments in the industry. There are several effective organizations that disseminate
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Table 3 Summary of lagged regression analysis Panel A IAROA DIAROA(t,t1) t DIAROA(t,t2) t DIAROA(t,t3) t DIAROA(t,t4) t DIAROA(t,t5) t DIAROA(t,t6) t Panel B IACFROA DIACFROA(t,t1) t DIACFROA(t,t2) t DIACFROA(t,t3) t DIACFROA(t,t4) t DIACFROA(t,t5) t DIACFROA(t,t6) t Panel C IAROE DIAROE(t,t1) t DIAROE(t,t2) t DIAROE(t,t3) t DIAROE(t,t4) t DIAROE(t,t5) t DIAROE(t,t6) t Panel D IACFROE DIACFROE(t,t1) t DIACFROE(t,t2) t DIACFROE(t,t3) t DIACFROE(t,t4) t DIACFROE(t,t5) t DIACFROE(t,t6) t
a
b
R2
0.000375
0.2768 4.996*** 0.4039 4.940*** 0.5269 5.723*** 0.6468 6.081*** 0.693 7.913*** 0.750 9.524***
0.194
0.0003 0.00014 0.0000 0.00025 0.000
0.0002 0.0007 0.0010 0.0013 0.0014 0.0015
0.0054 0.00273 0.00086 0.0001 0.00108 0.000
0.001196 0.012111 0.014133 0.016574 0.019703 0.016435
0.2343 7.4522*** 0.3956 7.0925*** 0.5217 8.0192*** 0.6315 7.9796*** 0.6902 9.9280*** 0.7481 11.8708*** 0.5303 5.359*** 0.5626 6.545*** 0.661 6.890*** 0.7466 7.587*** 0.7600 8.046*** 0.850 15.367*** 0.50221 4.38977*** 0.61983 9.46699*** 0.70057 9.90407*** 0.8531 9.45592*** 0.81972 14.1441*** 0.86502 15.8349***
0.249 0.247 0.305 0.326 0.384
0.112 0.174 0.242 0.287 0.324 0.383
0.363 0.323 0.361 0.419 0.409 0.568
0.3725 0.3309 0.3894 0.4598 0.4727 0.5596
This table presents the specific regression equations for each of the six return variable studied for lags of 1–6 years. In each equation, DIA(return)ti is the change in the industry-adjusted return from the ith year prior to the current year, and IA(return)ti is the industry-adjusted return in the ith year prior to the current year. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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best practices and new innovations for the banking industry. Intra-industry mobility of executives also facilitates the movement of information. In this type of environment, anyone who develops a better path-breaking business model will quickly find it duplicated by others, and those who lag behind have clear examples among their peers on how to improve their performance. 5.2. Base year performance Table 4 shows the mean industry adjusted return ratios for acquirers for a sample of 80 mergers where the target was large enough to materially affect the results of the merged company in the last year before the merger was executed. For all measures of return, the average return for the acquirers in the year before the merger was significantly above the industry mean. In addition, between 70% and almost 90% of the acquirers, depending on the return measure used, had returns above the industry average. This analysis allows us to accept the second of our hypothesized conditions for the explanation of the absence of positive post-merger returns observed in many studies: acquirers tend to out-perform the industry in the year before a merger. 5.3. Post-merger performance versus the acquirer The preceding analysis shows that there is significant mean reversion in BHC returns, and that acquirers outperform the industry in the year before the merger. Thus, absent any benefits from the merger, one would expect the performance of the merged company to decline from the performance of the acquirer in the year before the merger. Table 5 summarizes the impacts of mergers for a sample of 80 material mergers from the period 1987 to 1998, ignoring the effects of mean reversion for the four different measures of performance discussed above. When the effects of mean reversion are ignored, the post-merger changes in industry-adjusted returns are consistently negative for both IAROA and IAROE. The change is only significant at the 5% level for IAROA in the second year, and it is never significant at the 5% level for IAROE. The cash flow returns are less strongly negative. This is consistent with the findings of Cornett and Tehranian (1992) who find stronger results for cash flow returns. Table 4 Base year returns for acquirer
Mean Standard error of mean Median Standard deviation n t % Of acquirers above industry mean
IAROA
IACFROA
IAROE
IACFROE
0.00203 0.00042 0.00193 0.00376 79 5.212*** 70.89%
0.00313 0.00060 0.00318 0.00534 79 5.763*** 89.87%
0.03325 0.00685 0.03782 0.06084 79 4.858*** 76.92%
0.05069 0.01034 0.05325 0.09189 79 4.903*** 75.64%
This table shows descriptive statistics for the industry adjusted returns for the acquirer in the last calendar year before the merger was implemented. The t statistic shows the results of testing the hypothesis that the mean industry adjusted return equals 0. The last line shows the percentage of acquirers whose return exceeded that of the industry in the base year. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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Table 5 Post-merger industry-adjusted returns Year 3
Year 4
Year 5
Panel A: Change in IAROA resulting from merger Mean 0.000664 0.002166 SD 0.003336 0.005666 Standard error of mean 0.000375 0.000682 n 79 70 3.175*** t 1.769*
Year 1
Year 2
0.000490 0.004557 0.000598 60 0.819
0.000254 0.004608 0.000639 49 0.398
0.001450 0.004640 0.000734 37 1.977*
Panel B: Change in IACFROA resulting from merger Mean 0.000323 0.002510 SD 0.004747 0.00984 Standard error of mean 0.000534 0.00118 n 79 70 t 0.606 2.133**
0.000521 0.00615 0.00079 60 0.656
0.000166 0.00635 0.00090 49 0.184
0.001402 0.00649 0.00107 37 1.314
Panel C: Change in IAROE resulting from merger Mean 0.001026 0.020140 SD 0.058129 0.096530 Standard error of mean 0.006540 0.011621 n 79 70 t 0.157 1.733**
0.000517 0.081550 0.010708 60 0.048
0.002189 0.076898 0.010664 49 0.205
0.017468 0.083308 0.013172 37 1.326
Panel D: Change in IACFROE resulting from merger Mean 0.006222 0.021269 SD 0.083526 0.17195 Standard error of mean 0.009397 0.02055 n 79 70 t 0.662 1.035***
0.010612 0.11178 0.01443 60 0.735
0.005982 0.10960 0.01550 49 0.386
0.024014 0.11119 0.01828 37 1.314
This table shows the mean change in industry-adjusted returns from the return of the acquirer in the year before the merger for each of the first 5 calendar years after the merger was effective. These changes have not been adjusted for the effects of mean reversion noted in Table 3. Panel A shows the changes in mean IAROA, where ROA is calculated in the normal manner using net income as the numerator; Panel B shows the changes in IACFROA, where cash flow is the numerator. Panels C and D show the data for parallel calculations for ROE. * Significant at the 0.10 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
5.4. Adjusting for the impact of mean reversion Table 6 presents the post-merger performance after adjusting for mean reversion. The table is in the same format as Table 5, with the addition of a t-test for the significance of the difference between the adjusted and unadjusted results. The mean regression equations found in the mean reversion analysis are used to project expected IAROA and IAROE in the post-merger period. The combination of above average acquirer performance in the year before the merger found in Table 4 and a significant pattern of mean reversion in industry adjusted returns in Table 3 suggests that the negative post-merger performance noted in Table 5 may simply be the result of the mean reversion pattern, and may be totally unrelated to the merger. When mean reversion is considered in calculating the impact of the merger in Table 6, the picture is quite different. The post-merger results exceed the industry for 4 of the 5 years for IAROA, although the difference is only significant at the 10% level and then only
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Table 6 Changes in industry-adjusted returns corrected for mean reversion Year 1
Year 3
Year 4
Year 5
0.000828 0.004507 0.000592 60 1.399 3.878***
0.000946 0.003467 0.000481 49 1.968* 3.096***
0.000418 0.003652 0.000577 37 0.724 3.634***
Panel B: Corrected change in IACFROA resulting from merger Mean 0.002809 0.001864 SD 0.00604 0.00993 Standard error of mean 0.00068 0.00119 n 79 70 t 4.1368*** 1.571
0.003518 0.01048 0.00135 60 2.560**
0.004723 0.00802 0.00113 49 4.165***
0.004743 0.00983 0.00162 37 2.934***
Panel C: Corrected change in IAROE resulting from merger Mean 0.020412 0.004420 SD 0.051373 0.079687 Standard error of mean 0.005780 0.009593 n 79 70 t 3.532*** 0.461 t Diff. 5.567*** 4.853***
0.025673 0.070446 0.009250 60 2.775*** 3.841***
0.023001 0.047983 0.006654 49 3.457*** 3.534***
0.018369 0.047349 0.007487 37 2.454** 3.636***
Panel D: Corrected change in IACFROE resulting from merger Mean 0.056914 0.049462 SD 0.08970 0.17150 Standard error of mean 0.01009 0.02050 n 79 70 t 5.639*** 2.413**
0.085219 0.16558 0.02138 60 3.987***
0.088310 0.12465 0.01763 49 5.009***
0.088245 0.14031 0.02307 37 3.826***
Panel A: Corrected change in IAROA resulting Mean 0.000454 SD 0.003148 Standard error of mean 0.000354 n 79 t 1.282 t Diff. 6.535***
Year 2 from merger 0.000871 0.005353 0.000644 70 1.352 5.226***
This table shows the corrected mean change in industry-adjusted returns from the return of the acquirer in the year before the merger for each of the first 5 calendar years after the merger was effective. These changes have been corrected for the effects of mean reversion noted in Table 3. Panel A shows the corrected changes in mean IAROA, where ROA is calculated in the normal manner using net income as the numerator; Panel B shows the corrected changes in IACFROA, where cash flow is the numerator. Panels C and D show the data for parallel calculations of ROE. The last line of each panel shows the results of a test of the significance of the difference in means between the corrected and uncorrected returns. * Significant at the 0.10 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
in year 4. The post-merger results for IAROE exceed those of the industry in all 5 years, and the difference is significant at the 1% level in 4 of the 5 years. The cash flow returns support these conclusions. IACFROA is significantly positive in all but the second year, and IACFROE is significantly positive in all years. When adjusted for the effects of mean reversion, the impact of the 80 mergers studied was to improve performance, and these impacts are long-lasting. They were still observable even in the fifth year after the merger. The last line of Table 6 gives the t-statistic for the differences in means for the paired sample of adjusted and post-merger changes in IAROA and IAROE. The differences are always significant at the 1% level. The negative sign of the t-statistic indicates the return after adjustment for mean reversion is higher than the unadjusted return. Thus,
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we can say that the failure to include the effects of mean reversion significantly misstates the post-merger changes in both IAROA and IAROE in all five post merger years. This could lead to the erroneous conclusions that the mergers had caused a significant decline in IAROA in 1 year when it had not, and that IAROE had not improved in 5 years when it had shown improvement. More important than the procedural implications of these findings are the economic results. The answer to the basic question, ‘‘Does the merger make sense for the acquirers?’’ is, ‘‘Yes.’’ The data show that bank holding company mergers do, on average benefit the shareholders of the acquiring bank. The post-merger change in industry adjusted returns corrected for mean reversion is significantly positive in most years for both cash flow measures of return. It is also significantly positive in most years for the accounting IAROE. That is, the average acquirer is more profitable after a merger than it would have been had it not made the merger. This is an important finding, as it supports the strong trend in the industry toward mergers. 6. Conclusions This paper reaches three notable conclusions. First, the paper finds that similar to the results of FF (2000) there is mean reversion in corporate earnings over time for the BHCs. BHCs that outperform the industry in 1 year tend to move back toward the industry mean over time, as do those BHCs that under-perform the industry. The findings of significant mean reversion should not be a surprise, as the competitive nature of the industry makes it imperative for bankers to stay current with new developments. The second finding is that the trend toward mean reversion must be considered when examining changes over time to identify the impact of an unusual shock such as a merger. It is not sufficient to control for changes in industry performance over time and to say that everything else must be the result of the significant event. When there is a mean reversion trend in the data, ignoring the trend will misstate the impact of the merger. The post-merger impact appears stronger when measured against the acquirer’s results alone. In the banking industry, acquirers tend to be over-achievers, companies that have outperformed their peers on average. The mean reversion trend means that one would expect the acquirers’ industry-adjusted returns to decline in subsequent years and move toward the industry norms for a period of time. If this trend is ignored, all of its impact is ascribed to the result of the merger. In this study, correction of this error is the reason why significant improvements in IAROE related to the merger in 4 of the 5 years was found. It appears likely that this omission is a major part of the negative findings of prior post-merger studies. Third, and perhaps most important, this paper finds that when post-merger performance is measured correctly, BHC mergers add to profitability. The positive post-merger results found in this paper are consistent with the industry practice. There is extensive merger activity throughout the banking industry; merger activity has been taking place for a long time. It seems unlikely that banks would continue to acquire other banks if it were clearly not in the interests of the acquirer to do so. Furthermore, the tools for analysis of potential acquisitions are widely known as there are several textbooks on this topic. It would seem unlikely that all of these efforts would consistently produce bad decisions. Rather, it appears that the effects of mean reversion have masked the true positive impacts of the mergers.
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References Akhavein, J.D., Berger, A.N., Humphrey, D.B., 1997. The effects of megamergers on efficiency and prices: Evidence from a bank profit function. Review of Industrial Organization 12, 95–134. Berger, A.N., DeYoung, R., 2002. Technological progress and the geographic expansion of the banking industry. In: Paper Presented at the Eastern Finance Association Meeting, Baltimore, MD. Berger, A.N., Humphrey, D.B., 1992. Megamergers in banking and the use of cost efficiency as an antitrust defense. The Antitrust Bulletin 37, 541–600. Brown, S.J., Goetzmann, W.N., Ibbotson, R.G., Ross, S.A., 1992. Survivorship bias in performance studies. Review of Financial Studies 5, 553–580. Cornett, M.M., Tehranian, H., 1992. Changes in corporate performance associated with bank acquisitions. Journal of Financial Economics 31, 211–234. Craig, B., dos Santos, J.C., 1997. The risk effects of bank acquisitions. The Federal Reserve Bank of Cleveland, Economic Review, 25–35. DeLong, G.L., 2001. Stockholder gains from focusing versus diversifying bank mergers. Journal of Financial Economics 59, 221–252. DeLong, G.L., 2003. Does long-term performance of mergers match market expectations? Evidence from the U.S. banking industry. Financial Management 35, 5–25. DeLong, G.L., DeYoung, R. 2004. Learning by Observing: Information Spillovers in the Execution and Valuation of Commercial Bank M&As, Federal Reserve Bank of Chicago, Working Paper 2004-17. DeYoung, R., 1998. Management quality and X-inefficiency in national banks. Journal of Financial Services Research 13, 5–22. Fama, E.F., French, K.R., 2000. Forecasting profitability and earnings. Journal of Business 73, 161–175. Fama, E.F., McBeth, J.D., 1973. Risk, return and equilibrium: Empirical tests. Journal of Political Economy 81, 607–636. Houston, J.F., James, C.M., Ryngaert, M.D., 2001. Where do merger gains come from? Bank mergers from the perspective of insiders and outsiders. Journal of Financial Economics 60, 285–331. Johnston, J., DiNardo, J., 1997. Econometric Methods, fourth ed. McGraw-Hill, New York. Kraus, A., Litzenberger, R., 1976. Skewness preference and the valuation of risk assets. The Journal of Finance 31, 1085–1100. Lichtenberg, F.R., 1994. Testing the convergence hypothesis. The Review of Economics and Statistics 76 (3), 576– 579. Peristiani, S., 1997. Do mergers improve the X-efficiency and scale efficiency of U.S. banks? Evidence from the 1980’s. Journal of Money, Credit and Banking 29, 326–337. Pilloff, S.J., 1996. Performance changes and stockholder wealth creation associated with mergers of publicly traded banking institutions. Journal of Money, Credit and Banking 28, 294–310. Rhoades, S.A., 1994. A Summary of Merger Performance Studies in Banking, 1980–93, and an Assessment of ‘‘Operating Performance’’ and ‘‘Event Study’’ Methodologies, Federal Reserve Board of Governors, Staff Study 167. Walter, I., 2004. Mergers and Acquisitions in Banking and Finance: What Works, What Fails, and Why. Oxford University Press, New York.
The impact of mean reversion of bank profitability on post-merger performance in the banking industry Morris Knapp a, Alan Gart
b,*
, Mukesh Chaudhry
b
a
b
Miami-Dade College, 300 N.E. Second Ave., Miami, FL 33132, United States Indiana University of Pennsylvania, Eberly College of Business, Indiana, PA 15705, United States Received 15 July 2004; accepted 25 January 2006 Available online 7 August 2006
Abstract This research study examines the tendency for serial correlation in bank holding company profitability, finding significant evidence of reversion to the industry mean in profitability. The paper then considers the impact of mean reversion on the evaluation of post-merger performance of bank holding companies. The research concludes that when an adjustment is made for the mean reversion, post-merger results significantly exceed those of the industry in the first 5 years after the merger. Ó 2006 Elsevier B.V. All rights reserved. JEL classification: G2/G21/G34 Keywords: Banking; Mergers; Acquisitions; Mean reversion
1. Introduction The banking industry has been undergoing an extensive period of restructuring as a result of technological innovations and regulatory changes. The number of banking mergers has accelerated in the last decade. A whole literature that analyzes the results of these
* Corresponding author. Address: 978 Warfield Lane, Huntingdon Valley, PA 19006, United States. Tel.: +1 215 947 6860. E-mail address: [email protected] (A. Gart).
0378-4266/$ - see front matter Ó 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2006.01.005
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mergers has developed; the general conclusion is that while mergers are good for the owners of the bank being acquired, the results for the acquirer are, at best mixed (Walter, 2004). Market reactions at the time of announcement tend to be either neutral or slightly negative. This result is counterintuitive. It seems unlikely that managers would continually make major mergers that are not clearly in the best interests of the shareholders. The shareholders would rebel and remove them from office, as has happened repeatedly in other industries when management fails to perform. Several studies have evaluated post-merger performance in the banking industry. These studies have generally indexed performance to some industry standard and then compared this indexed performance before and after the merger. The change in performance against the standard is ascribed as the effect of the merger. This approach does a good job of eliminating the effects of macroeconomic forces, regulatory changes, and other industry-wide phenomena. It assumes that absent a major event such as a merger, performance against the index is a random variable with a constant mean. Many, but not all academic research studies of banking mergers have found the average merger to be unsuccessful. This seems contrary to conventional wisdom as numerous mergers are continually being consummated. The implication is that something may have been omitted in the prior studies. The research paper of Fama and French (FF, 2000) suggests a solution to this conundrum. They find that profitability in most firms tends to revert to the mean in their industry. In most previous research on banking mergers no adjustment was made for mean reversion of post-merger bank performance as many of these studies were only event studies and the long term effects such as mean reversion were ignored. The omission of an adjustment for the mean reversion trend is a major part of the negative findings of prior post-merger studies. When we adjust for the mean reversion trend, we find significant improvements in merger related BHC ROE in 4 of the 5 post-merger years studied. The cash flow results are even stronger, with significant improvement versus the industry in all 5 years. One explanation of mean reversion is the basic economic argument that competitive forces, in the long-run, would bring about equality in the rates of return across the firms. The contribution of this paper lies in several areas. First, it extends the work of FF (2000) on mean reversion to cover BHCs that were expressly omitted because FF analyzed only non-regulated industrial firms. Second, it looks at post-merger performance results and compares them with the results of the acquirer in the year before the merger. Finally, and more importantly, this research demonstrates the importance of adjusting post-merger results for mean reversion trends when analyzing the effects of mergers. 2. Literature review The literature on post-merger performance is extensive. Rhoades (1994) summarizes 19 studies from the period 1980–1993 that examined the post-merger performance of banking institutions. Most of the studies compared operating performance of merging institutions with that of a group of banks that did not merge. A few studies used an econometrically derived production or profit function as the yardstick, and then measured efficiency against this measure. In general, most studies found no improvement in profitability or efficiency after a merger. These results were consistent across the methodologies.
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Berger and Humphrey (1992) find that average X-efficiencies are about 20–25% of the costs in the banking industry. This suggests that the potential exists for achieving cost efficiency through mergers. However, in actuality, bank mergers on average in the 1980s did not seem to generate great cost efficiencies. The overall conclusion of both cost ratio studies and X-efficiency studies is that changes in efficiency on costs were on the order of 5% or less on average from US banking mergers in the 1980s (see DeYoung (1998) and Peristiani (1997)). Akhavein et al. (1997) found cost X-inefficiencies to be slightly lower. In addition, Berger and Humphrey (1992) found no apparent correlation between mergers that reduce costs and those that could be predicted to reduce costs. In Peristiani’s investigation of the post-merger performance of the acquiring banks that participated in a merger during the period 1980–1990, the evidence suggests that acquirers failed to improve X-efficiency after the merger. Craig and dos Santos (1997) find that merged banks out-performed the industry in the post-merger period. Their findings show that when you compare the post-acquisition risk measures of the post-merger banking organizations with the pre-acquisition risk of the associated acquirers there is a pronounced reduction in risk. Pilloff (1996) finds no significant change in post-merger ROE. However, when he utilizes operating income before provisions instead of net income to calculate ROE, there is a significant increase in post-merger returns. Akhavein et al. (1997) find that while there is no significant change in ROE after mergers between 1981 and 1989, there is a significant improvement in profit efficiency. Peristiani (1997) finds no improvement after merger, although he finds that the merged bank out-performed the combined results of the acquirer and the target before the merger. DeLong (2001) finds that bank mergers that enhance value upon announcement can be distinguished from those that do not create value. Mergers that have both similar activity and geography (focus mergers) enhance stockholder value, while those mergers that diversify either geographically and by activities, or both, do not create value. Houston et al. (2001) suggest that most of the estimated value gains from bank mergers stem from the opportunity to cut costs by eliminating overlapping operations and consolidating backroom operations. They also find that mergers occurring in the 1990s generate higher abnormal returns than mergers prior to 1990. Berger and DeYoung (2002) find that banking organizations exercise significant control over their affiliates and that this control has been increasing over time and that agency costs associated with distance have decreased somewhat over time. These findings are consistent with an increasing ability of banks to expand geographically. They also conclude that ‘‘distance has a negative effect on costs, profits, and managerial control in banking’’. However, between 1985 and 1998 the inefficiencies associated with distance appear to have ‘‘mitigated in banking’’ because of advances in telecommunications, computers, software, etc. DeLong and DeYoung (2004) find that the market seems to be enhancing its ability to predict successful bank mergers over time and that the type of bank merger that enhances long-term value changes over time. They also conclude that the ability of the market to distinguish mergers that will improve performance increased during the 1990s. The mergers that increase long-term ROAs or improve the efficiency ratio during the first years of their study tend to focus activities (similarity in activities), while those factors that influence performance in the later years tend to have similar geographic locations and/or payment in stock.
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3. Methodology Mean reversion and post-merger performance both require an index or standard against which to measure the performance of an individual BHC. The frontier methodology such as that of Akhavein et al. (1997) measures the distance of an acquirer from the efficient profit frontier before and after a merger. Cornett and Tehranian (1992) use an industry average cash flow return. DeLong (2003) uses profit-based industry average returns. This paper uses both profit based and cash flow based industry average returns. The difference between the return of an individual BHC and that of the industry is deemed the industry-adjusted return, the economists’ abnormal return. Mean reversion, and postmerger performance are studied by examining the behavior of these industry-adjusted returns. 3.1. Mean reversion FF find that over time the return on assets of firms moves to the industry mean ROA. Since the industry mean changes from year to year because of economic and market conditions and technological changes, the mean reversion tendency is best seen by examining the deviation from the mean. If the mean deviation tends to move toward zero, there is mean reversion. The tendency for mean reversion in earnings is logical. If one firm develops a way to out-perform its peers, others will try to copy it. Thus, the edge developed by one firm is quickly dissipated as their techniques become known to other firms. Similarly, if a firm underperforms the industry, it will quickly look to copy its more successful peers and so move back to the average. The mean reversion analysis follows FF (2000), which is, in turn based on an earlier model developed by Fama and McBeth (1973). A typical mean reversion equation can be written in the following manner: y 1 y 0 ¼ by 0 þ e;
ð1Þ
where y1 is the continuously compounded rate of return for period 1 and y0 is the continuously compounded rate of return for period 0 and the intercept term is ignored. Eq. (1) can be rewritten as: y 1 ¼ ð1 þ bÞy 0 þ e or y 1 ¼ py 0 þ e;
ð2Þ
where p = (1 + b). The simultaneous hypothesis that 1 6 b 6 0 and 0 6 p 6 1 are assumed in Eq. (2). However, if the estimated values are b < 0 or equivalently p < 1, it is referred to as the ‘‘mean reversion hypothesis’’. Some authors have attempted to relate the mean reversion hypothesis to the convergence hypothesis, stating that when no convergence is found or if b = 0, it implies concurrent rejection of the convergence hypothesis. But, Lichtenberg (1994) points out that mean reversion may be a necessary but not a sufficient condition for convergence. As suggested in FF and Lichtenberg we develop cross-sectional regression models for the change in IAROE based on the IAROE in the base year. Models are calculated for changes of 1, 2, 3, 4, 5, and 6 year’s duration for each of the years 1987–2002.
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The equations are in the format IARt IARt3 ¼ a þ bIARt3 þ et :
ð3Þ
In each equation, IARt IARt3 is the change in industry-adjusted return from the third year prior to the current year, and IARt3 is the industry-adjusted return in the third year prior to the current year. Similar equations are developed for periods of 1–6 years prior for both IAROA and IAROE. Mean slopes are then calculated for each of the six change periods, 1, 2, 3, 4, 5, and 6 years long. Kraus and Litzenberger (1976) show that the variance of the mean regression coefficient can be calculated as follows: X Varðbk Þ ¼ ðbky bk Þ2 =N ðN 1Þ; ð4Þ y¼1
where k is the number of years back (1–6) the reversion is being tested, y is the calendar year for which the cross-sectional regression has been run and b is the mean of the crosssectional regression coefficients. Y is summed for years 1–n. The t statistic for b is then pffiffiffiffiffiffiffiffiffiffiffiffiffi tð bÞ ¼ b= varðbÞ; ð5Þ so the mean slopes are tested for significance. We use the mean R2 rather than the R2 of a pooled regression because, as Johnston and DiNardo (1997) point out, the R2’s of pooled data tend to be very high, and lead to erroneous conclusions. A negative slope would indicate mean reversion. It would indicate that BHCs that outperformed the industry in the base year would move closer to the industry in the following period. 3.2. Post-merger performance Most post-merger studies such as DeLong (2001) assume that the industry adjusted level of performance in the base year would remain unchanged except for random forces in the absence of some unusual force such as a merger. The impact of the merger in that instance is the difference between the pre-merger adjusted return and that observed in the post-merger period. However, if there is a significant mean reversion effect and adjusted returns move toward zero over time, the pre-merger adjusted return misstates what should be expected in the post-merger period, and therefore obscures the actual result of the merger. In this study, the expected post-merger return reflects the mean reversion trend. The post-merger performance analysis is conducted in two steps. First, we calculate the change in industry-adjusted returns from the year before the effective date of the merger (the base year) to years 1, 2, 3, 4, and 5 years after the merger. Because the industry returns capture all the industry general forces, the change in industry-adjusted returns captures only the firm specific activity, the merger. The second step is to include the impact of the trend for mean reversion. The mean regression model derived from the reversion analysis is used to calculate the expected change in return resulting from mean reversion. The effect of the merger, then, is the difference between the actual change from the base year and the change projected from the mean reversion model. The mean changes from this analysis are compared to the mean changes in step 1 to examine the extent of the influence of mean reversion on post-merger analysis. Both IAROA and IAROE are examined together with the cash flow returns to
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add robustness to the study. Separate regressions are used for lags of 1, 2, 3, 4, and 5 years, rather than a single multiple regression, to avoid problems of multicollinearity. 3.3. Pre-merger base for comparison Any analysis of changes depends on the base from which the change is measured. The banking literature is divided on how to measure post-merger change. Cornett and Tehranian (1992) examine changes from the acquirer’s performance in the base year while Craig and dos Santos (1997) and Akhavein et al. (1997) do a pro forma combination of the merger partners and examine the change from the performance of the combination. These two approaches really ask two different questions. The post-merger changes from the acquirer’s results in the base year measure the effects of the merger on the acquiring institution. They help to answer the question, ‘‘Did the management of the acquirer make a correct decision for its shareholder in making the acquisition?’’ An improvement in returns relative to the industry suggests a positive result; a decline suggests a suboptimal choice of a merger partner or the inability to integrate the target into the acquirer. This approach is appropriate when considering a merger from an investor’s point-of-view. The measurement of post-merger changes from the pro forma combination of the two partners in the year before the merger is concerned with whether there has been an overall improvement in performance of the two institutions. It asks the question ‘‘Is the combined institution more effective in delivering banking services than were the two separate organizations?’’ If the acquirer is able to improve the performance of the weaker target following a merger then the overall results have improved even if the results to the acquirer’s shareholders have not been affected. Details of a parallel analysis using the pro forma combined results of the acquirer and target in the year before the merger as the base year have been omitted in the interest of brevity. The results were similar, but the significance was not as strong. Interested readers may obtain the full study from the authors. 4. Data 4.1. Merger sample Our sample of 80 material mergers came from a Salomon Smith Barney Merger and Acquisition Database; each merger included in the study had a value in excess of $25 million and was consummated in the years 1987–1998. This data base consists of 651 mergers announced during the years 1986–1998. Of these mergers, 167 lacked data in the Y9C data base of the Federal Reserve Bank of Chicago. These were primarily acquisitions involving commercial banks directly, rather than BHCs. Commercial banks, S&Ls and foreign banks do not report on form Y9C. Most commercial banks are owned by a bank holding company, but some, mostly small banks, are not. Another 68 announced mergers were not completed by the end of 1998. Following the methodology utilized by Houston et al. (2001), the sample is limited to mergers where the acquired BHC is large enough to produce a detectable change in the results of the acquirer in the post-merger period. Only mergers where the target’s assets were at least 10% of those of the combined firms in the year preceding the merger, or where target net income was at least 10% of the combined total were included. This screen eliminated 319 mergers from the study. Mergers
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selected for this study were also limited to those where the acquirer did not make another material acquisition during the period under examination. When a second acquisition is made during this period, it is impossible to separate the effects of the first merger from those of the second. When an acquirer makes a later acquisition, the first merger is included for the post-merger period before the subsequent merger is executed, but no further. The subsequent merger is not included in the sample. Also, 11 mergers were only included for a portion of the 5 year post-merger period because they made a second acquisition. Time series studies such as this are subject to survivorship bias. To minimize the effects of this bias, we do not require that a BHC be present in all years to be included. The extent to which calculations of differently derived samples produce the same result adds robustness to the findings. Since, this study uses cross-sectional data, it minimizes the survivorship bias problems discussed in Brown et al. (1992). In addition, the power of Table 1 Merger sample information Acquirer base year statistics Mean IAROA Median IAROA Mean IAROE Median IAROE Mean IACFROA Median IACFROA Mean IACFROE Median IACFROE Mean total assets in $000’s Median total assets in $000’s Maximum total assets in $000’s Minimum total assets in $000’s
0.002025*** 0.00193*** 0.03325*** 0.03782*** 0.00313*** 0.00318*** 0.0507*** 0.0532*** 19,706,471 4,920,030 182,926,000 218,737
Target base year statistics Mean IAROA Median IAROA Mean IAROE Median IAROE Mean IACFROA Median IACFROA Mean IACFROE Median IACFROE Mean total assets in $000’s Median total assets in $000’s Maximum total assets in $000’s Minimum total assets in $000’s
0.00163 0.000217*** 0.01642 0.003531*** 0.00369*** 0.0012*** 0.0439*** 0.0136*** 9,109,737 1,098,577 121,173,000 137,170
Merger characteristics % Interstate Mean abnormal returns Median abnormal return
25% 1.01% 2.04%
This table shows the major characteristics of the merger sample including the mean and median returns for the acquirer and target, the mean size of the acquirer and target, the percent of mergers that were interstate in nature, and the abnormal returns in the stock market for the merger. The mean returns are tested for significance; the medians are tested using a signs test. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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cross-sectional tests is higher because we are able to employ a large sample of firms, unlike time-series analysis. Table 1 summarizes the main characteristics of the merger. Acquirer returns were, on average, significantly above the industry mean at the 1% level. Table 2 details the number of mergers by year and gives the mean total assets for each year’s acquirers and targets. For comparison, the mean total assets for all BHCs reporting on Form Y9C is also given. In all years, acquirers are well above the industry average in asset size. However, there is a wide variability among acquirers. The majority of the sample mergers involved BHCs in the same state, but 25% were interstate mergers. Clearly this is not just an activity of money center and super-regional banks. Merger activity pervades the entire industry. The targets are somewhat smaller and less profitable than the acquirers. The average merger involves a target with assets of 39% of those of the acquirer, but once again, there is a wide variability in the size of the targets. 4.2. Financial data The financial data for this study comes from the quarterly Y9C reports prepared by all BHCs with assets in excess of $150 million. Details on the number of BHCs in the industry and the average total assets for the industry each year are given in Table 2. For simplicity, we use all BHCs in the file when calculating the industry returns. Eliminating the 80 BHCs in the study from a population of over 1200 in each year will not materially alter the average returns. We calculate the returns in the standard manner using the averages of the four quarterly daily average balances for the asset or equity denominator. Accounting net income is used for the accounting ROA and ROE. Following Cornett and Tehranian (1992) cash flow is defined as income from operations less interest expense on mandatory subordinated debentures. All four returns, ROA, cash flow ROA, ROE, and cash flow ROE, are calcu-
Table 2 Annual data
86 87 88 89 90 91 92 93 94 95 96 97 98
Number of mergers
Mean assets of targets ($000’s)
Mean assets of acquirers ($000’s)
Mean assets of industry ($000’s)
Number of BHCs in industry
7 5 3 4 2 5 7 6 9 9 9 14
1,477,647 5,264,557 1,196,807 4,999,707 4,138,172 18,896,234 3,498,944 1,055,580 6,276,525 17,859,598 5,559,630 19,119,859
9,284,430 16,301,213 5,497,259 15,334,398 16,476,298 46,864,206 22,988,248 2,263,716 12,004,907 33,031,829 12,687,394 27,921,460
2,030,175 2,104,470 2,026,647 2,094,149 2,027,994 2,044,883 2,101,615 2,324,040 3,109,343 3,348,695 3,454,999 3,631,657 3,497,822
1236 1241 1286 1303 1357 1379 1380 1360 1093 1099 1151 1209 1277
This table details the number of mergers and the average total assets of the acquirers and targets by year. It compares these with the number of BHC’s in the industry reporting data on form Y9C and the mean total assets for the industry.
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lated separately for each BHC in the database. The post-merger changes are the changes in the industry-adjusted returns from the base year to the industry-adjusted returns in the post-merger years. Changes are calculated for all four returns for each of the first 5 calendar years after the effective date of the merger. The year in which the merger took place is not included. 5. Empirical results 5.1. Mean reversion analysis results The results of the mean reversion analysis are presented in Table 3. Here we analyze the changes in each of the four return variables studied, IAROA, IACFROA, IAROE, and IACFROE from the prior years and the relationship of the change to the value of the return in the base year using regression analysis. If there were no mean reversion effect, the regression coefficient for the base year return would be insignificant; a negative sign would indicate mean reversion. Previous merger studies have indicated that mergers can take some time to produce results, so longer term trends will be needed for that part of the paper. Thus, the analysis is performed for each year for base years 1, 2, 3, 4, 5, and 6 years previous. The overall summary regression equations for each of the four return measures studied resulting from this analysis are given in Table 3. It is clear from the results that there is a strong mean reversion trend. All the regression coefficients for the regressions for the individual years have a negative coefficient, which indicates mean reversion, and the mean regression coefficients are all significant at the 1% level. The mean R2 values range from 19.4% to 38.4% for the normal IAROA equations, and all but one of them exceed 20%. The cash flow ROA equations have slightly lower R2 values than do the accounting equations. The R2 values for the accounting ROE equations range from 32.3% to 56.8%; those for the cash flow ROE, from 33% to 56%. These R2s indicate that mean reversion explains a substantial portion of the variability in the changes in industry adjusted returns. The increasing strength of the explanatory power and significance of the regressions as the length of time from the base period increases are also worth noting. As the time period increases, the R2 and t statistics both become noticeably stronger, and the regression coefficients become more negative. By the sixth year, the R2 for IAROE has risen to 56.8%, and the regression coefficient has become 0.85. This means that the mean reversion equation explains slightly over half of the variability in changes in ROE versus the industry. Similar, albeit slightly weaker patterns can be observed for IAROA and the cash flow returns, lending robustness to the findings. It is clear from the preceding discussion that there is significant mean reversion in industry-adjusted returns. Any analysis of changes in BHC returns over time must adjust for the impact of mean reversion if it is to accurately measure the impact of the merger. The strength and duration of the mean reversion effect is clear evidence of the highly competitive nature of the banking industry. There are more than 1200 bank holding companies with assets in excess of $150 million, and there are over 8000 individual commercial banks. In addition, banks must increasingly deal with competition from other sectors of the financial services industry where improved technology is making it easier for them to compete with banks. There is a strong incentive for bankers to stay abreast of new developments in the industry. There are several effective organizations that disseminate
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Table 3 Summary of lagged regression analysis Panel A IAROA DIAROA(t,t1) t DIAROA(t,t2) t DIAROA(t,t3) t DIAROA(t,t4) t DIAROA(t,t5) t DIAROA(t,t6) t Panel B IACFROA DIACFROA(t,t1) t DIACFROA(t,t2) t DIACFROA(t,t3) t DIACFROA(t,t4) t DIACFROA(t,t5) t DIACFROA(t,t6) t Panel C IAROE DIAROE(t,t1) t DIAROE(t,t2) t DIAROE(t,t3) t DIAROE(t,t4) t DIAROE(t,t5) t DIAROE(t,t6) t Panel D IACFROE DIACFROE(t,t1) t DIACFROE(t,t2) t DIACFROE(t,t3) t DIACFROE(t,t4) t DIACFROE(t,t5) t DIACFROE(t,t6) t
a
b
R2
0.000375
0.2768 4.996*** 0.4039 4.940*** 0.5269 5.723*** 0.6468 6.081*** 0.693 7.913*** 0.750 9.524***
0.194
0.0003 0.00014 0.0000 0.00025 0.000
0.0002 0.0007 0.0010 0.0013 0.0014 0.0015
0.0054 0.00273 0.00086 0.0001 0.00108 0.000
0.001196 0.012111 0.014133 0.016574 0.019703 0.016435
0.2343 7.4522*** 0.3956 7.0925*** 0.5217 8.0192*** 0.6315 7.9796*** 0.6902 9.9280*** 0.7481 11.8708*** 0.5303 5.359*** 0.5626 6.545*** 0.661 6.890*** 0.7466 7.587*** 0.7600 8.046*** 0.850 15.367*** 0.50221 4.38977*** 0.61983 9.46699*** 0.70057 9.90407*** 0.8531 9.45592*** 0.81972 14.1441*** 0.86502 15.8349***
0.249 0.247 0.305 0.326 0.384
0.112 0.174 0.242 0.287 0.324 0.383
0.363 0.323 0.361 0.419 0.409 0.568
0.3725 0.3309 0.3894 0.4598 0.4727 0.5596
This table presents the specific regression equations for each of the six return variable studied for lags of 1–6 years. In each equation, DIA(return)ti is the change in the industry-adjusted return from the ith year prior to the current year, and IA(return)ti is the industry-adjusted return in the ith year prior to the current year. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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best practices and new innovations for the banking industry. Intra-industry mobility of executives also facilitates the movement of information. In this type of environment, anyone who develops a better path-breaking business model will quickly find it duplicated by others, and those who lag behind have clear examples among their peers on how to improve their performance. 5.2. Base year performance Table 4 shows the mean industry adjusted return ratios for acquirers for a sample of 80 mergers where the target was large enough to materially affect the results of the merged company in the last year before the merger was executed. For all measures of return, the average return for the acquirers in the year before the merger was significantly above the industry mean. In addition, between 70% and almost 90% of the acquirers, depending on the return measure used, had returns above the industry average. This analysis allows us to accept the second of our hypothesized conditions for the explanation of the absence of positive post-merger returns observed in many studies: acquirers tend to out-perform the industry in the year before a merger. 5.3. Post-merger performance versus the acquirer The preceding analysis shows that there is significant mean reversion in BHC returns, and that acquirers outperform the industry in the year before the merger. Thus, absent any benefits from the merger, one would expect the performance of the merged company to decline from the performance of the acquirer in the year before the merger. Table 5 summarizes the impacts of mergers for a sample of 80 material mergers from the period 1987 to 1998, ignoring the effects of mean reversion for the four different measures of performance discussed above. When the effects of mean reversion are ignored, the post-merger changes in industry-adjusted returns are consistently negative for both IAROA and IAROE. The change is only significant at the 5% level for IAROA in the second year, and it is never significant at the 5% level for IAROE. The cash flow returns are less strongly negative. This is consistent with the findings of Cornett and Tehranian (1992) who find stronger results for cash flow returns. Table 4 Base year returns for acquirer
Mean Standard error of mean Median Standard deviation n t % Of acquirers above industry mean
IAROA
IACFROA
IAROE
IACFROE
0.00203 0.00042 0.00193 0.00376 79 5.212*** 70.89%
0.00313 0.00060 0.00318 0.00534 79 5.763*** 89.87%
0.03325 0.00685 0.03782 0.06084 79 4.858*** 76.92%
0.05069 0.01034 0.05325 0.09189 79 4.903*** 75.64%
This table shows descriptive statistics for the industry adjusted returns for the acquirer in the last calendar year before the merger was implemented. The t statistic shows the results of testing the hypothesis that the mean industry adjusted return equals 0. The last line shows the percentage of acquirers whose return exceeded that of the industry in the base year. ***Significant at the 0.01 level; **Significant at the 0.05 level; *Significant at the 0.10 level.
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Table 5 Post-merger industry-adjusted returns Year 3
Year 4
Year 5
Panel A: Change in IAROA resulting from merger Mean 0.000664 0.002166 SD 0.003336 0.005666 Standard error of mean 0.000375 0.000682 n 79 70 3.175*** t 1.769*
Year 1
Year 2
0.000490 0.004557 0.000598 60 0.819
0.000254 0.004608 0.000639 49 0.398
0.001450 0.004640 0.000734 37 1.977*
Panel B: Change in IACFROA resulting from merger Mean 0.000323 0.002510 SD 0.004747 0.00984 Standard error of mean 0.000534 0.00118 n 79 70 t 0.606 2.133**
0.000521 0.00615 0.00079 60 0.656
0.000166 0.00635 0.00090 49 0.184
0.001402 0.00649 0.00107 37 1.314
Panel C: Change in IAROE resulting from merger Mean 0.001026 0.020140 SD 0.058129 0.096530 Standard error of mean 0.006540 0.011621 n 79 70 t 0.157 1.733**
0.000517 0.081550 0.010708 60 0.048
0.002189 0.076898 0.010664 49 0.205
0.017468 0.083308 0.013172 37 1.326
Panel D: Change in IACFROE resulting from merger Mean 0.006222 0.021269 SD 0.083526 0.17195 Standard error of mean 0.009397 0.02055 n 79 70 t 0.662 1.035***
0.010612 0.11178 0.01443 60 0.735
0.005982 0.10960 0.01550 49 0.386
0.024014 0.11119 0.01828 37 1.314
This table shows the mean change in industry-adjusted returns from the return of the acquirer in the year before the merger for each of the first 5 calendar years after the merger was effective. These changes have not been adjusted for the effects of mean reversion noted in Table 3. Panel A shows the changes in mean IAROA, where ROA is calculated in the normal manner using net income as the numerator; Panel B shows the changes in IACFROA, where cash flow is the numerator. Panels C and D show the data for parallel calculations for ROE. * Significant at the 0.10 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
5.4. Adjusting for the impact of mean reversion Table 6 presents the post-merger performance after adjusting for mean reversion. The table is in the same format as Table 5, with the addition of a t-test for the significance of the difference between the adjusted and unadjusted results. The mean regression equations found in the mean reversion analysis are used to project expected IAROA and IAROE in the post-merger period. The combination of above average acquirer performance in the year before the merger found in Table 4 and a significant pattern of mean reversion in industry adjusted returns in Table 3 suggests that the negative post-merger performance noted in Table 5 may simply be the result of the mean reversion pattern, and may be totally unrelated to the merger. When mean reversion is considered in calculating the impact of the merger in Table 6, the picture is quite different. The post-merger results exceed the industry for 4 of the 5 years for IAROA, although the difference is only significant at the 10% level and then only
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Table 6 Changes in industry-adjusted returns corrected for mean reversion Year 1
Year 3
Year 4
Year 5
0.000828 0.004507 0.000592 60 1.399 3.878***
0.000946 0.003467 0.000481 49 1.968* 3.096***
0.000418 0.003652 0.000577 37 0.724 3.634***
Panel B: Corrected change in IACFROA resulting from merger Mean 0.002809 0.001864 SD 0.00604 0.00993 Standard error of mean 0.00068 0.00119 n 79 70 t 4.1368*** 1.571
0.003518 0.01048 0.00135 60 2.560**
0.004723 0.00802 0.00113 49 4.165***
0.004743 0.00983 0.00162 37 2.934***
Panel C: Corrected change in IAROE resulting from merger Mean 0.020412 0.004420 SD 0.051373 0.079687 Standard error of mean 0.005780 0.009593 n 79 70 t 3.532*** 0.461 t Diff. 5.567*** 4.853***
0.025673 0.070446 0.009250 60 2.775*** 3.841***
0.023001 0.047983 0.006654 49 3.457*** 3.534***
0.018369 0.047349 0.007487 37 2.454** 3.636***
Panel D: Corrected change in IACFROE resulting from merger Mean 0.056914 0.049462 SD 0.08970 0.17150 Standard error of mean 0.01009 0.02050 n 79 70 t 5.639*** 2.413**
0.085219 0.16558 0.02138 60 3.987***
0.088310 0.12465 0.01763 49 5.009***
0.088245 0.14031 0.02307 37 3.826***
Panel A: Corrected change in IAROA resulting Mean 0.000454 SD 0.003148 Standard error of mean 0.000354 n 79 t 1.282 t Diff. 6.535***
Year 2 from merger 0.000871 0.005353 0.000644 70 1.352 5.226***
This table shows the corrected mean change in industry-adjusted returns from the return of the acquirer in the year before the merger for each of the first 5 calendar years after the merger was effective. These changes have been corrected for the effects of mean reversion noted in Table 3. Panel A shows the corrected changes in mean IAROA, where ROA is calculated in the normal manner using net income as the numerator; Panel B shows the corrected changes in IACFROA, where cash flow is the numerator. Panels C and D show the data for parallel calculations of ROE. The last line of each panel shows the results of a test of the significance of the difference in means between the corrected and uncorrected returns. * Significant at the 0.10 level. ** Significant at the 0.05 level. *** Significant at the 0.01 level.
in year 4. The post-merger results for IAROE exceed those of the industry in all 5 years, and the difference is significant at the 1% level in 4 of the 5 years. The cash flow returns support these conclusions. IACFROA is significantly positive in all but the second year, and IACFROE is significantly positive in all years. When adjusted for the effects of mean reversion, the impact of the 80 mergers studied was to improve performance, and these impacts are long-lasting. They were still observable even in the fifth year after the merger. The last line of Table 6 gives the t-statistic for the differences in means for the paired sample of adjusted and post-merger changes in IAROA and IAROE. The differences are always significant at the 1% level. The negative sign of the t-statistic indicates the return after adjustment for mean reversion is higher than the unadjusted return. Thus,
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we can say that the failure to include the effects of mean reversion significantly misstates the post-merger changes in both IAROA and IAROE in all five post merger years. This could lead to the erroneous conclusions that the mergers had caused a significant decline in IAROA in 1 year when it had not, and that IAROE had not improved in 5 years when it had shown improvement. More important than the procedural implications of these findings are the economic results. The answer to the basic question, ‘‘Does the merger make sense for the acquirers?’’ is, ‘‘Yes.’’ The data show that bank holding company mergers do, on average benefit the shareholders of the acquiring bank. The post-merger change in industry adjusted returns corrected for mean reversion is significantly positive in most years for both cash flow measures of return. It is also significantly positive in most years for the accounting IAROE. That is, the average acquirer is more profitable after a merger than it would have been had it not made the merger. This is an important finding, as it supports the strong trend in the industry toward mergers. 6. Conclusions This paper reaches three notable conclusions. First, the paper finds that similar to the results of FF (2000) there is mean reversion in corporate earnings over time for the BHCs. BHCs that outperform the industry in 1 year tend to move back toward the industry mean over time, as do those BHCs that under-perform the industry. The findings of significant mean reversion should not be a surprise, as the competitive nature of the industry makes it imperative for bankers to stay current with new developments. The second finding is that the trend toward mean reversion must be considered when examining changes over time to identify the impact of an unusual shock such as a merger. It is not sufficient to control for changes in industry performance over time and to say that everything else must be the result of the significant event. When there is a mean reversion trend in the data, ignoring the trend will misstate the impact of the merger. The post-merger impact appears stronger when measured against the acquirer’s results alone. In the banking industry, acquirers tend to be over-achievers, companies that have outperformed their peers on average. The mean reversion trend means that one would expect the acquirers’ industry-adjusted returns to decline in subsequent years and move toward the industry norms for a period of time. If this trend is ignored, all of its impact is ascribed to the result of the merger. In this study, correction of this error is the reason why significant improvements in IAROE related to the merger in 4 of the 5 years was found. It appears likely that this omission is a major part of the negative findings of prior post-merger studies. Third, and perhaps most important, this paper finds that when post-merger performance is measured correctly, BHC mergers add to profitability. The positive post-merger results found in this paper are consistent with the industry practice. There is extensive merger activity throughout the banking industry; merger activity has been taking place for a long time. It seems unlikely that banks would continue to acquire other banks if it were clearly not in the interests of the acquirer to do so. Furthermore, the tools for analysis of potential acquisitions are widely known as there are several textbooks on this topic. It would seem unlikely that all of these efforts would consistently produce bad decisions. Rather, it appears that the effects of mean reversion have masked the true positive impacts of the mergers.
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References Akhavein, J.D., Berger, A.N., Humphrey, D.B., 1997. The effects of megamergers on efficiency and prices: Evidence from a bank profit function. Review of Industrial Organization 12, 95–134. Berger, A.N., DeYoung, R., 2002. Technological progress and the geographic expansion of the banking industry. In: Paper Presented at the Eastern Finance Association Meeting, Baltimore, MD. Berger, A.N., Humphrey, D.B., 1992. Megamergers in banking and the use of cost efficiency as an antitrust defense. The Antitrust Bulletin 37, 541–600. Brown, S.J., Goetzmann, W.N., Ibbotson, R.G., Ross, S.A., 1992. Survivorship bias in performance studies. Review of Financial Studies 5, 553–580. Cornett, M.M., Tehranian, H., 1992. Changes in corporate performance associated with bank acquisitions. Journal of Financial Economics 31, 211–234. Craig, B., dos Santos, J.C., 1997. The risk effects of bank acquisitions. The Federal Reserve Bank of Cleveland, Economic Review, 25–35. DeLong, G.L., 2001. Stockholder gains from focusing versus diversifying bank mergers. Journal of Financial Economics 59, 221–252. DeLong, G.L., 2003. Does long-term performance of mergers match market expectations? Evidence from the U.S. banking industry. Financial Management 35, 5–25. DeLong, G.L., DeYoung, R. 2004. Learning by Observing: Information Spillovers in the Execution and Valuation of Commercial Bank M&As, Federal Reserve Bank of Chicago, Working Paper 2004-17. DeYoung, R., 1998. Management quality and X-inefficiency in national banks. Journal of Financial Services Research 13, 5–22. Fama, E.F., French, K.R., 2000. Forecasting profitability and earnings. Journal of Business 73, 161–175. Fama, E.F., McBeth, J.D., 1973. Risk, return and equilibrium: Empirical tests. Journal of Political Economy 81, 607–636. Houston, J.F., James, C.M., Ryngaert, M.D., 2001. Where do merger gains come from? Bank mergers from the perspective of insiders and outsiders. Journal of Financial Economics 60, 285–331. Johnston, J., DiNardo, J., 1997. Econometric Methods, fourth ed. McGraw-Hill, New York. Kraus, A., Litzenberger, R., 1976. Skewness preference and the valuation of risk assets. The Journal of Finance 31, 1085–1100. Lichtenberg, F.R., 1994. Testing the convergence hypothesis. The Review of Economics and Statistics 76 (3), 576– 579. Peristiani, S., 1997. Do mergers improve the X-efficiency and scale efficiency of U.S. banks? Evidence from the 1980’s. Journal of Money, Credit and Banking 29, 326–337. Pilloff, S.J., 1996. Performance changes and stockholder wealth creation associated with mergers of publicly traded banking institutions. Journal of Money, Credit and Banking 28, 294–310. Rhoades, S.A., 1994. A Summary of Merger Performance Studies in Banking, 1980–93, and an Assessment of ‘‘Operating Performance’’ and ‘‘Event Study’’ Methodologies, Federal Reserve Board of Governors, Staff Study 167. Walter, I., 2004. Mergers and Acquisitions in Banking and Finance: What Works, What Fails, and Why. Oxford University Press, New York.