In this paper, we investigate the long-run post-merger performance of Asian acquiring bank by using 293 deals in the 1997-2007 periods. We find the Asian acquiring banks experience negative long-term abnormal returns and are not efficiency improving, followed by mergers and acquisition. However, DeYoung, Evanoff and Molyneux [1] find European bank mergers appear to have resulted in both efficiency gains and stockholder value enhancement and North American bank mergers are efficiency improving, although the event-study literature presents a mixed picture regarding stockholder wealth creation. Therefore, our empirical results show that the long-run stock returns and operating performance of Asian commercial bank mergers are different from those of the US and EU markets. In general, the long-run stock performance and operating performance of Asian commercial bank merger and Acquisition are negative and Asian commercial bank merger and Acquisition cannot create synergy in the long run.
In the last two decades, the merger activities in banking industry had been a major progress. The reasons for the trend of the consolidation in most countries were financial deregulation, globalization of real and financial markets and improvements in information technology (Group of Ten [
Within the merger activities, bank mergers happened in the fourth wave from 1980 to 1989, which originated from the US. Since the long-standing geographical restrictions of banking were revoked in the US in 1994 (Riegle-Neal Interstate Banking and Branch Efficiency), bank merger activities were popular within the banking industry. Europe caught up this trend due to the globalization and borderless economy. The banking industry followed the industrial development in Europe after Deregulation was exuberant. During the 1990s, the volume and number of merger and acquisitions increased in the EU (Altunbas and Marqués [
DeYoung, Evanoff and Molyneux [
While the Asian Financial Crisis happened from 1997 to 1999, this calamity raised fears of a worldwide economic meltdown. It also had significant macro-level effects in several Asian countries, including sharp reductions in the values of currencies, stocks, and other assets. Local bank regulators encouraged or even forced banks to merger as a way to reduce bank failure risk and stress. After survived from the Asian Financial Crisis, Asia was considered to be the area with the highest growth overall.
According to the report of the bank holding company Goldman Sachs in 2003, there has been rapid economic growth in the developing economies of Brazil, Russia, India and China (collective represented by the acronym “BRICs”). The combined economies of the BRICs are expected to eclipse the combine’s economies of the currently richest countries in the world by 2050. The report indicates that economic growth in Asia has been accelerating. Furthermore, Goldman Sachs also reports that the top eleven emerging countries (The Next Eleven), which are the most development potentials in 2008, include Bangladesh, Egypt, India, Iran, Moscow, Nigeria,
Pakistan, Philippines, South Korea, Turkey and Vietnam, and six from Asia country. Therefore, Asian financial markets are viewed by potential acquirers as virgin markets, compared to the US and the EU (Shih [
Most of literature has focused on two main aspects: the effect of merger announcement and post-merger performance. The first line of research has focused on the stock market on the effect of merger announcement. Cybo-Ottone and Murgia [
The second line of research had focused on the acquirers’ performance post-merger, Weassess post-merger performance from the market performance and the operating performance. In the market performance, the handful studies found that bank mergers accrued significant stock market valuation gain after the merger (Diaz, Olalla and Azofra [
Date Announced | Target Name | Target Nation | Acquirer Name | Acquirer Nation | Value of Transaction (US $mil) |
---|---|---|---|---|---|
1998/4/13 | BankAmerica Corp | US | NationsBank Corp, Charlotte, NC | US | 61633.4 |
1998/6/8 | Wells Fargo, San Francisco, CA | US | Norwest Corp, Minneapolis, MN | US | 34352.64 |
1999/8/20 | Dai-Ichi Kangyo Bank Ltd | Japan | Fuji Bank Ltd | Japan | 40096.64 |
1999/10/13 | Sakura Bank Ltd | Japan | Sumitomo Bank Ltd | Japan | 45494.37 |
1999/11/29 | National Westminster Bank PLC | UK | Royal Bank of Scotland Group | UK | 38412.86 |
2003/10/27 | FleetBoston Financial Corp, MA | US | Bank of America Corp | US | 49260.63 |
2004/1/14 | Bank One Corp, Chicago, IL | US | JPMorgan Chase & Co | US | 58663.15 |
2005/2/18 | UFJ Holdings Inc | Japan | Mitsubishi Tokyo Financial Grp | Japan | 41431.03 |
2005/6/30 | MBNA Corp | US | Bank of America Corp | US | 35810.27 |
2006/8/26 | San Paolo IMI SpA | Italy | Banca Intesa SpA | Italy | 37624.24 |
Notes: The financial industry sector of the ten top merger deals all is commercial banks and bank holding companies. The data sources from SDC Platinum database from Thomson Financial.
the performance of Italian banks found a positive market prices changed effects after the acquisition. Houston et al. [
In the operating performance, the previous studies of the US and the EU bank mergers assumed that the merger was driven by efficiency and profit ability issues. DeLong and DeYoung [
Altunbas and Marqués [
The previous studies usually analyzed the banks’ performance by two main measures. One measure is via accounting ratios of performance (such as ROA) or productive efficiency indicators (such as Efficiency) (e.g., Hagendorff and Keasey [
The majority measure of studies is by using event study. It based on changes in stock market prices around the merger periods. These studies typically try to ascertain whether the announcement of bank mergers creates shareholder value (normally in the form of cumulative abnormal t returns, CAR) for the shareholders of the target, the acquirer and/or the combined entity (e.g., Campa and Hernando [
As reviewed above, most of literature analyzed the effect of the merger on bank’s performance in the short-term. To contrast with the above literature, we investigate the acquirers’ post-merger performance in the long-term. We employ the Buy-and-Hold Abnormal Returns (BHAR) to calculate the long-term performance during post-merger periods. If BHAR is larger than zero or equal to zero, there is significant persistence in post-merger period. There are some studies used BHAR to calculate the long-term performance. Liu et al. [
In this paper, we examine the long-term performance of Asian acquiring banks after the merger by using 293 deals from 1997 to 2007 particularly commercial bank and bank holding company. This paper contributes to the literature in several aspects. First, we examine whether the acquirer can improve the long-term performance after the merger and persist for 36 months by using the Buy-and-Hold Abnormal Returns (BHAR). Second, we verify whether the deal attribute has a significant correlation with the long-term performance. Third, we would like to find if the operating indicator can affect the long-run performance and have a significant correlation with long-term stock returns. Fourth, we further investigate the effects of the deal attribute on the operating indicator, and find if the effect of the deal attribute can be decreased in the long-term. Finally and more importantly, we compare our results with the long-run performance and the previous studies of the US and the EU.
The remainder of this paper is organized as follows. In Section 2, we describe our empirical methodology including data, sample, BHAR, as well as the regression models used to estimate the long-run banks performance. In Section 3, we present and discuss our empirical results. In Section 4 we conclude.
The data of merged banks were from the SDC Platinum database from Thomson Financial. The accounting data for each of the merged banks were taken from Data Stream. To be included in the sample, a merger must have been announced between 1997 and 2007 in Asia, because the focus of this study is in the bank merger occurring after the Asian Financial Crisis. This was an excellent time for aggressive merger acquisitions because of the vast number of banks with many branches that were undervalued or in distress. However, we investigate the long-termperformancein3 years (36 months); the accounting data must until to 2010. We begin with an original sample of 17804 deals in
1) The target in Asia.
2) Both the target and acquirer are commercial bank and bank holding company.
3) The target was located in one of the 12 Asian nations ranked highest in M&A frequency by preliminary observation: China, Hong Kong, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam.
Year | Number of deals | Domestic Mergers | The same Industry Sector | Number of CB and BHC | Average Value of Transaction (US$ mil) | Max Value of Transaction (US$ mil) |
---|---|---|---|---|---|---|
1997 | 743(4.17%) | 526(70.79%) | 347 | 99 | 76.67 | 3017.63 |
1998 | 1065(5.98%) | 700(65.73%) | 437 | 159 | 53.867 | 1300 |
1999 | 1504(8.45%) | 1090(72.47%) | 650 | 248 | 242.37 | 45494.36 |
2000 | 1200(6.74%) | 869(72.42%) | 573 | 196 | 148.39 | 14983.74 |
2001 | 1195(6.71%) | 857(71.72%) | 565 | 177 | 97.87 | 5679.7 |
2002 | 1428(8.02%) | 1105(77.38%) | 706 | 167 | 55.22 | 2932.53 |
2003 | 1931(10.85%) | 1504(77.89%) | 913 | 183 | 64.52 | 16650.2 |
2004 | 2154(12.1%) | 1673(77.67%) | 1002 | 175 | 69.56 | 29261.48 |
2005 | 2084(11.71%) | 1546(74.18%) | 980 | 194 | 113.23 | 41431.03 |
2006 | 2034(11.42%) | 1503(73.89%) | 952 | 188 | 89.39 | 10731.6 |
2007 | 2466(13.85%) | 1821(73.84%) | 1120 | 202 | 112.64 | 8976.57 |
Total | 17804(100%) | 13194(74.11%) | 8245 | 1988 |
Notes: The data sources rom SDC Platinum database from Thomson Financial. The financial industry sector is categorized by definition of SDC Platinum database from Thomson Financial includes (1) Commercial Banks and Bank Holding Companies (2) Savings and Loans, Mutual Savings Banks (3) Credit Institutions (4) Real Estate, Mortgage Bankers and Brokers (5) Investment and Commodity Firms/Dealers/Exchanges (6) Insurance (7) Other Finance. The same industry sector: the number of deals which both targets and acquirers are from same industry sector. The parentheses show the ratio of the years to total number of deals.
4) All the financial statements are available from DataStream.
5) The acquirer still operating in 2010.
Our final sample consists of 293 commercial bank and bank holding company in Asia, of which 183 were domestic and 110 were cross-border. We concentrate on the merger involving Asia commercial bank and bank holding company because there been a great number of mergers at this level, and we assess the effects of a unique financial product market on performance.
Panel A of
Year | Number of deals | Domestic Mergers | Average Value of Transaction (US$ mil) | Max Value of Transaction (US$ mil) | |||
---|---|---|---|---|---|---|---|
Panel A: Sorted by Year | |||||||
1997 | 10(3.41%) | 4(40%) | 44.46 | 124.74 | |||
1998 | 14(4.78%) | 10(71.43%) | 301.94 | 933.28 | |||
1999 | 32(10.92%) | 23(71.88%) | 149.85 | 847.08 | |||
2000 | 42(14.33%) | 30(71.43%) | 159.16 | 1319.94 | |||
2001 | 20(6.83%) | 12(60%) | 566.98 | 3753.9 | |||
2002 | 24(8.19%) | 19(9.17%) | 36.73 | 95.67 | |||
2003 | 25(8.53%) | 18(72%) | 132.46 | 615.62 | |||
2004 | 25(8.35%) | 12(48%) | 712.22 | 2125.66 | |||
2005 | 29(9.9%) | 11(37.93%) | 2822.21 | 41431.03 | |||
2006 | 37(12.63%) | 23(62.16%) | 1067.42 | 10731.6 | |||
2007 | 35(11.95%) | 21(60%) | 277.73 | 1874.93 | |||
Total | 293(100%) | 183(62.46%) | |||||
Area | Number of deals | Target Nation | Number of deals | Domestic Mergers | Average Value of Transaction (US$ mil) | Max Value of Transaction (US$ mil) | |
Panel B: Sorted by Country | |||||||
North Asia | 80 | China | 30(10.24%) | 2(6.67%) | 425.42 | 2,500 | |
Hong Kong | 14(4.78%) | 10(71.43%) | 384.8 | 1319.95 | |||
South Korea | 18(6.14%) | 7(38.89%) | 610.78 | 3277.6 | |||
Taiwan | 18(6.14%) | 13(72.22%) | 583.93 | 1547.94 | |||
Asia- Pacific | 58 | India | 27(9.22%) | 25(92.59%) | 73.3 | 168.38 | |
Indonesia | 31(10.58%) | 7(22.58%) | 15.36 | 70.43 | |||
Japan | 87 | Japan | 87(29.69%) | 85(97.7%) | 1762.86 | 41431.03 | |
Southeast Asia | 68 | Malaysia | 11(3.75%) | 8(72.73%) | 224.69 | 474.52 | |
Philippines | 22(7.51%) | 12(54.55%) | 77.9 | 291.9 | |||
Singapore | 7(2.39%) | 6(85.71%) | 1284.82 | 3753.91 | |||
Thailand | 18(6.14%) | 8(44.44%) | 109.97 | 319.33 | |||
Vietnam | 10(3.41%) | 0(0%) | 21.38 | 27 | |||
Total | 293 | 293(100%) | 183(62.46%) | ||||
Notes: The data sources from SDC Platinum database from Thomson Financial. The parentheses show the percentage.
Panel B of
Panel A of
Panel B of
Frequency | Number of Deals | Domestic Mergers | Cross-Border Mergers | Average Transaction Value of Domestic Mergers (US$ mil) | Average Transaction Value of Cross-Border Mergers (US$ mil) | ||
---|---|---|---|---|---|---|---|
Panel A: Sorted by Frequency | |||||||
1 times | 55(18.77%) | 46(83.64%) | 9(16.36%) | 1630.05 | 566.42 | ||
2 - 4 times | 128(43.69%) | 106(82.81%) | 22(17.19%) | 825.65 | 701.47 | ||
more than 5 times | 110(37.54%) | 31(28.18%) | 79(71.82%) | 282.11 | 1060.27 | ||
Total | 293(100%) | 183(62.46%) | 110(37.54%) | ||||
Acquirer Area | Number of Deals | Average Value of Transaction (US$ mil) | Max Value of Transaction (US$ mil) | ||||
Panel B: Sorted by Acquirer Region | |||||||
Asia | 220(75.09%) | 294.69 | 41431.03 | ||||
US & Canada | 22(7.51%) | 464.73 | 2500 | ||||
UK | 14(4.78%) | 898.65 | 3277.6 | ||||
European | 19(6.48%) | 300.55 | 847.08 | ||||
other area | 18(6.14%) | 104.84 | 356.84 | ||||
Total | 293(100%) | ||||||
Notes: The data sources from SDC Platinum database from Thomson Financial. Panel A shows the division of times of launching mergers by specific acquirer from 1997 to 2007. Domestic Merger is defined as the transaction involves two commercial banks or bank holding companies of the same country. Cross-Border Merger is defined as the transaction involves two commercial banks or bank holding companies of the different countries. The parentheses show the percentage.
Area | Target Nation | Number of Transaction Value ≤ US$100 mil. | Number of Transaction Value > US$100 mil. |
---|---|---|---|
North Asia | China | 21(70%) | 9(30%) |
Hong Kong | 7(50%) | 7(50%) | |
South Korea | 11(61.11%) | 7(38.89%) | |
Taiwan | 11(61.11%) | 7(38.89%) | |
Asia-Pacific | India | 26(96.3%) | 1(3.7%) |
Indonesia | 31(100%) | 0(0%) | |
Japan | Japan | 66(75.86%) | 21(24.14%) |
Southeast Asia | Malaysia | 5(45.45%) | 6(54.55%) |
Philippines | 20(90.91%) | 2(9.09%) | |
Singapore | 2(28.57%) | 5(71.43%) | |
Thailand | 14(77.78%) | 4(22.22%) | |
Vietnam | 10(100%) | 0(0%) | |
Total | 224(76.45%) | 69(23.55%) |
Notes: The data sources from SDC Platinum database from Thomson Financial. The parentheses show the percentage.
In this section, we illustrate the empirical model. We introduce BHAR methodology to examine the long-term performance of the acquiring banks. We identify three biases in test statistics based on abnormal returns calculated (Barber and Lyon [
· new listing bias, which arises because in event studies of long-run abnormal returns, sampled firms generally have a long post-event history of returns, while firms that constitute the index (or reference portfolio) typically include new firms that begin trading subsequent to the event month.
· rebalancing bias, which arises because the compound returns of a reference portfolio, such as an equally weighted market index, are typically calculated assuming periodic (generally monthly) rebalancing, while the returns of sample firms are compounded without rebalancing.
· skewness bias, which arises because long-run abnormal returns are skewed.
We define BHAR as the value of holding a long position in market index return of the acquirer bank after the merger is completed. The equation is stated below, and the MSCI AC ASIA index is used to measure the benchmark in this paper.
where
BHAR is considered in the regression models by rolling every 36-month from 1997 to 2007. Because it can eliminate the problem of cross-sectional dependence among samples banks (Gregory [
Variables are separated the deal attribute from the acquirer bank’s operating indicators. The deal attribute includes domestic merger, transaction value, frequency of mergers and the relative assets of the target and acquirer. The operating indicator includes (1) ROA, (2) Efficiency, (3) Loan ratio, (4) Risk, (5) Capital ratio, (6) Deposit ratio and (7) Assets growth. The detail explanation of variables is discussed in 2.4. We calculate the operating indicator by the average between the rolling periods from 1997 to 2007. The equation is stated below.
where
To be robustness our study, we setup there regression models. Model 1 investigates the correlation between the deal attribute and long-term abnormal returns of the acquirer. We add the operating indicator into model 2, such that we are able to find the interaction between the deal attribute and the operating indicator and whether the interaction can create the higher long-term synergy. In model 3, we add the dummy variable for the effect of the Asian Financial Crisis, which was happened in 1997 to 1998. The regression models are as follows:
To further investigate the effects of the deal attribute on the operating indicator, the regression analysis are performed on the variables which are significant in the 36-month period. We introduce the frequency of mergers, domestic merger, downturn and dummy, four dummy variables into the regression analysis. The dummy variable is added to represent the transaction happened in 1997. Additionally, transaction value and relative size are also added to this regression analysis. We use the following model for this regression analysis.
The deal attribute includes frequency of mergers, domestic merger, transaction value and the relative assets of the target and acquirer. The operating indicator include (1) ROA, (2) Efficiency, (3) Loan ratio, (4) Risk, (5) Capital ratio, (6) Deposit ratio and (7) Assets growth. The explanation of variables stated in
We adopt the operating variable from the previous studies. First, ROA is measured as net income to average total asset. The bank is a high leverage financial institution, therefore ROA have more explanatory than ROE comparatively. Second, the most common way to calculate efficiency is the cost to net income. It’s net income to be generated from each dollar of cost. Third, loan ratio is measured as the ratio of net loan to total assets, which takes into account the prominence of loan in banks’ total assets. Risk is measured as the level of loan loss provisions divided by net interest revenues (Altunbas and Marqués [
Variables | Definition |
---|---|
Frequency of merger | =1, if frequency of merger is 1 - 4 =0, if frequency of merger >5 |
Domestic merger | =1, if the merging banks are from the same country =0, otherwise |
Transaction value | Log(transaction value) |
Relative size | Target total assets/Acquirer total assets |
ROA | Net income/Average total assets |
Efficiency | Total costs/Net income |
Loan ratio | Net loans/Total assets |
Risk | Loan loss provisions/Net interest revenues |
Capital ratio | Total capital/Total assets |
Deposit ratio | Total deposit/Total capital |
Assets growth | (Total assets at t − total assets at t − 1)/Total assets at t − 1 |
Downturn | =1, if the merger happened in 1997 and 1998 =0, otherwise |
Notes: The signal “*” indicated that the expected sign is uncertainty. The signal “+” (“−”) indicated that there is positive (negative) post-merger effect on the variables.
Fourth, capital ratio is measured as the ratio of equity to total assets. This variable becomes more important in recent years, considering a focal point of bank regulation. Altunbas and Marqués [
Finally, assets growth was entered in Cheng et al.’s study [
Several studies show that the abnormal returns of the acquirer is significant positive in the long run after the merger in the US and the EU banks market. In this study, we assume that the long-run performance by using BHAR reveals a significant positive effect after the merger and can persist for 36-month. We also forecast that the effect of the deal attribute is decreases gradually as time goes on. Numerous studies find the positive reactions to the efficiency and ROA on post-merger performance. Then, we expect that both efficiency and ROA of the acquirer would indicate significant improvement in long-term performance. The rest operating indicator like loan ratio, risk, and capital ratio are expected to have significant positive result, too. Except for deposit ratio, it is expected to have significant negative effect on the long-run performance and the assets growth is expected to have no signification with long-run performance.
Finally, we compare our results with the long-run performance and the previous studies of the US and the EU. Additionally, we hypothesize that the effect were in influenced by the Asian Financial Crisis is significant.
In this section, we document the result of our empirical models in long-term post-merger performance. First, we show the descriptive statistics of the explanatory variables of the operating indicator, and the result for BHAR over 36-, 48-, 60-month and rolling BHAR every 36-monthpost-merger between 1997 and 2007. Then, we discuss the result of the regression analysis. Subsequently, we document the regression analysis of the operating indicator on the deal attribute.
The specification of t-statistics by using the Buy-and-Hold Abnormal Returns is presented in
Variable | 36-month | 48-month | 60-month | Rolling 36-month | ||||
---|---|---|---|---|---|---|---|---|
Mean | Med | Mean | Med | Mean | Med | Mean | Med | |
ROA | 0.0039*** | 0.0037*** | 0.004*** | 0.0033*** | 0.0039*** | 0.003*** | 0.0042*** | 0.0031*** |
Efficiency | 22.47** | 6.13 | 11.05*** | 6.79 | 10.10*** | 7.15 | 56.63*** | 5.81 |
Loan ratio | 0.62*** | 0.64 | 0.62*** | 0.64 | 0.63*** | 0.64 | 0.63*** | 0.63 |
Risk | 0.31*** | 0.19 | 0.30*** | 0.18 | 0.30*** | 0.18 | 0.24*** | 0.16 |
Capital ratio | 0.12*** | 0.10 | 0.12*** | 0.10 | 0.11*** | 0.10 | 0.12*** | 0.10 |
Deposit ratio | 8.25*** | 6.70 | 8.03*** | 6.66 | 8.90*** | 8.18 | 8.43*** | 7.23 |
Assets growth | 0.08*** | 0.05 | 0.08*** | 0.05 | 0.07*** | 0.04 | 0.07*** | 0.03 |
Note: The operating indicator is based on
Mean | Std Dev. | Med | Min | Max | t-Value | |
---|---|---|---|---|---|---|
BHAR[t + 1, t + 36] | −10.1 | 0.558 | −15.9 | −109.1 | 131.8 | −1.58 |
BHAR[t + 1, t + 48] | −23.5*** | 0.653 | −29.4 | −125.0 | 158.9 | −2.95 |
BHAR[t + 1, t + 60] | −13.9 | 0.707 | −9.5 | −164.9 | 149.8 | −1.4 |
Rolling BHAR[t + 1, t + 36] | −13.7*** | 0.425 | −15.9 | −93.9 | 150.4 | −6.06 |
Notes: “BHAR” is the Buy-and-Hold Abnormal Returns by using market returns. The benchmark is MSCI AC ASIA index. The rolling BHAR is rolling every 36-month from 1997 to 2007. There are 76, 67, 51 and 355 samples for 36-, 48-, 60-month and the rolling BHAR every 36-month period, respectively. BHAR is expressed in %. *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics). Standard errors are reported in parentheses.
Because the result shows that BHAR is a significant negative, we speculate that it may because the acquirer cannot outperform the market. So, we only calculate the returns of the acquirer before minus the benchmark and call the Buy-and-Hold Returns (BHR). The equation is stated below.
where
The result is presented in
To rationalize our result, we make the correlation coefficient matrix of the explanatory variables. In general, the correlation coefficients are smaller than 0.4, which means there are significant low correlations within the explanatory variables, and VIFs are less than 10 which prove that there is no multi collinear problem.
The regression results of the rolling BHAR every 36-month period on a set of variables shows at
In model 2, the adjusted R-Squared is 0.16, which is increased conspicuously. It reveals a higher explanation to long-run performance. The deal attribute are similar with model 1. If the merger has a lower transaction value in cross-border merger, there is a positive effect with long-term performance. The result for ROA suggests a high size of asset disposition on the acquirer tends to positively affecting at the level of performance after post-merger. Despite most of the previous studies have found positive reactions to the efficiency on post-merger
Mean | Std Dev. | Med | Min | Max | t-Value | |
---|---|---|---|---|---|---|
BHR[t + 1, t + 36] | 100.1*** | 61.1 | 102.9 | 0.02 | 304.9 | 14.29 |
BHR[t + 1, t + 48] | 103.5*** | 62.7 | 93.1 | 9.7 | 254.7 | 13.52 |
BHR[t + 1, t + 60] | 112.4*** | 62.3 | 100.7 | 17.9 | 329.4 | 12.89 |
Rolling BHR[t + 1, t + 36] | 95.1*** | 52.1 | 85.1 | 0.02 | 304.9 | 34.37 |
Notes: “BHR” is the Buy-and-Hold Returns by using market returns. The rolling BHR is rolling every 36-month from 1997 to 2007. There are 76, 67, 51 and 355 samples for 36-, 48-, 60-month and the rolling BHR every 36-month period, respectively. BHR is expressed in %. *, **, and *** indicate statistical significance at 10%, 5%, and 1%levels, respectively (Based on t-statistics). Standard errors are reported in parentheses.
Variables | Model 1 | Model 2 | Model 3 |
---|---|---|---|
Frequency of merger | −0.071(0.307) | −0.053(0.47) | −0.045(0.535) |
Domestic merger | −0.124*(0.089) | −0.158**(0.014) | −0.161**(0.012) |
Transaction value | −0.062***(0.0001) | −0.044***(0.001) | −0.042***(0.002) |
Relative size | 0.00(0.713) | 0.00(0.523) | 0.00(0.471) |
ROA[t + 1, t + 36] | 20.542***(0.004) | 21.056***(0.003) | |
Efficiency[t + 1, t + 36] | 0.639*(0.098) | 0.67*(0.083) | |
Loan ratio[t + 1, t + 36] | 0.316***(0.007) | 0.298***(0.001) | |
Risk[t + 1, t + 36] | 0.127(0.146) | 0.129(0.141) | |
Capital ratio[t + 1, t + 36] | 2.117**(0.014) | 2.092**(0.016) | |
Deposit ratio[t + 1, t + 36] | −0.034(0.195) | −0.031(0.222) | |
Assets growth[t + 1, t + 36] | 0.00(0.201) | 0.00(0.169) | |
Downturn | 0.067(0.239) | ||
Intercept | 1.242***(0.00001) | 0.15(0.545) | 0.116(0.644) |
Observations | 355 | 355 | 355 |
Adj R-Squared | 0.0507 | 0.16 | 0.1663 |
Notes: The dependent variable are the Rolling BHAR relation to the performance of the MSCI AC ASIA index, every the 36-month between 1997 and 2007. We calculate the average in operating indicator in the rolling periods. P-value is reported in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics).
performance, we find slightly improvement in efficiency at 10% level of significant based on t-statistics. Generally, most studies showed that different cost structures could experience a drop in performance after post-merger. This finding may be related to the US evidence showing that there is very little improvement in cost efficiencies after post-merger (DeYoung, 1997 [
Capital ratio reveals a significant positive effect on the long-run performance-enhancing. Since capital ratio is considered as signal favorable asset quality for banks, it seems to be more explanatory for the long-run performance. Since the bank is a high liability industry as a result, capital ratio could not be higher. Loan ratio also reveals a significant positive effect on the long-term performance. The more money the bank loans, the more performance the bank makes. Both risk and deposit ratio have no significant effects on the long-run performance. And, assets growth have no significant negative effect on the long-run performance as expected, it probably come from the size of total asset for the acquirer after the merger do not changes after the merger.
Finally, we introduce dummy variables into model 3. The dummy variable is added for indicating the effects of the Asian Financial Crisis, the number of mergers that are international, and the relative assets of the merging partners. The result is in line with model 2, and there are no interactive effects in model 3. The dummy variable is totally insignificant with the long-run performance. This means that even the Asian financial Crisis brings the amount of mergers, it does not result in the value for the acquirer after the merger.
The regression results of the rolling BHR every 36-month period on a set of variables shows in
In
Variables | Model 4 | Model 5 | Model 6 |
---|---|---|---|
Frequency of merger | −0.042(0.467) | 0.33(0.455) | 0.336(0.54) |
Domestic merger | −0.061**(0.025) | −0.064***(0.0001) | −0.052***(0.0001) |
Transaction Value | −0.025*(0.069) | −0.082***(0.0001) | −0.078***(0.0001) |
Relative ratio | 0.00(0.308) | 0.00(0.226) | 0.00(0.273) |
ROA[t + 1, t + 36] | 45.103***(0.0001) | 45.922***(0.0001) | |
Efficiency[t + 1, t + 36] | 0.00(0.232) | 0.00(0.273) | |
Loan ratio[t + 1, t + 36] | 0.34**(0.01) | 0.37**(0.01) | |
Risk[t + 1, t + 36] | 0.349***(0.0001) | 0.352***(0.0001) | |
Capital ratio[t + 1, t + 36] | 0.079(0.938) | 0.119(0.906) | |
Deposit ratio[t + 1, t + 36] | 0.00(0.994) | 0.002(0.893) | |
Assets growth[t + 1, t + 36] | 0.902**(0.045) | 0.952**(0.035) | |
Downturn | 0.107(0.51) | ||
Intercept | 0.026(0.688) | 1.016***(0.001) | 0.960***(0.001) |
Observations | 355 | 355 | 355 |
Adj R-Squared | 0.0289 | 0.2427 | 0.2462 |
Notes: The dependent variable are the Rolling BHR every the 36-month between 1997 and 2007. We calculate the average in operating indicator in the rolling periods. P-value is reported in parentheses.*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics).
from 1997 to 2007. But, we find there is an irrational correlation effect between BHAR and operating indicator in
where
The result shows in
As same as
Variables | Model 7 | Model 8 |
---|---|---|
Frequency of merger | −0.052(0.344) | −0.052(0.345) |
Domestic merger | −0.087**(0.038) | −0.086**(0.045) |
Transaction Value | −0.014**(0.2) | −0.014**(0.209) |
Relative ratio | 0.00(0.186) | 0.00(0.187) |
ΔROA[t + 1, t + 36] | 22.678***(0.0001) | 22.661***(0.0001) |
ΔEfficiency[t + 1, t + 36] | 0.00(0.959) | 0.00(0.959) |
ΔLoan ratio[t + 1, t + 36] | 0.629(0.169) | 0.631(0.139) |
ΔRisk[t + 1, t + 36] | 0.163**(0.01) | 0.163**(0.01) |
ΔCapital ratio[t + 1, t + 36] | 0.02(0.185) | 0.02(0.185) |
ΔDeposit ratio[t + 1, t + 36] | 0.028(0.176) | 0.028(0.177) |
ΔAssets growth[t + 1, t + 36] | −0.043(0.771) | −0.043(0.771) |
Downturn | 0.004(0.952) | |
Intercept | 0.032(0.608) | 0.031(0.647) |
Observations | 355 | 355 |
Adj R-Squared | 0.1259 | 0.1233 |
Notes: The dependent variable are the Rolling BHAR relation to the performance of the MSCI AC ASIA index, every the 36-month between 1997 and 2007. We calculate the change in operating indicator in the rolling periods. P-value is reported in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics).
Variables | Model 9 | Model 10 |
---|---|---|
Frequency of merger | 0.052(0.4) | 0.052(0.406) |
Domestic merger | −0.164**(0.013) | −0.17**(0.011) |
Transaction Value | −0.04***(0.001) | −0.041***(0.001) |
Relative ratio | 0.00(0.692) | 0.00(0.733) |
ΔROA[t + 1, t + 36] | 25.06***(0.0001) | 25.27***(0.0001) |
ΔEfficiency[t + 1, t + 36] | 0.00(0.831) | 0.00(0.831) |
ΔLoan ratio[t + 1, t + 36] | 1.31**(0.012) | 1.28**(0.015) |
ΔRisk[t + 1, t + 36] | 0.15***(0.004) | 0.15***(0.004) |
ΔCapital ratio[t + 1, t + 36] | 1.34*(0.053) | 1.33*(0.055) |
ΔDeposit ratio[t + 1, t + 36] | −0.066***(0.0001) | −0.067***(0.0001) |
ΔAssets growth[t + 1, t + 36] | 0.26(0.115) | 0.265(0.114) |
Downturn | −0.042(0.524) | |
Intercept | 1.23***(0.0001) | 1.24***(0.0001) |
Observations | 355 | 355 |
Adj R-Squared | 0.2626 | 0.2613 |
Notes: The dependent variable are the Rolling BHR every the 36-month between 1997 and 2007. We calculate the change in operating indicator in the rolling periods. P-value is reported in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics).
After we prove that the changed operating indicator is more suitable than the averaged operating indicator, we also show another regression analysis. In Shih’s study [
Because the Asian Financial Crisis was happened in 1997, to avoid that our result may be interfered by this crisis. We separate our sample form the transaction which happened in 1997-2000 and in 2001-2007. We want to find if there is a significant effect on the transaction which is passive or active in the long-run performance. This result shows in
In
Variables | Model 11 | Model 12 |
---|---|---|
Frequency of merger | 0.15(0.311) | −0.123*(0.066) |
Domestic merger | −0.249(0.154) | −0.104(0.229) |
Transaction value | −0.031(0.275) | −0.012(0.39) |
Relative size | −0.002(0.115) | 0.00(0.083) |
ΔROA[t + 1, t + 36] | 20.320***(0.0001) | 25.21***(0.0001) |
ΔEfficiency[t + 1, t + 36] | 0.00(0.898) | 0.00(0.782) |
ΔLoan ratio[t + 1, t + 36] | −0.184(0.792) | 1.062(0.125) |
ΔRisk[t + 1, t + 36] | 0.155***(0.008) | 0.007(0.96) |
ΔCapital ratio[t + 1, t + 36] | 2.103**(0.042) | −1.062(0.408) |
ΔDeposit ratio[t + 1, t + 36] | 0.054**(0.045) | 0.018(0.363) |
ΔAssets growth[t + 1, t + 36] | 0.156 (0.538) | −0.179(0.346) |
Intercept | 0.11(0.385) | 0.081(0.436) |
Observations | 165 | 190 |
Adj R-Squared | 0.0681 | 0.2032 |
Notes: We separate our sample form the merger happened in 1997-2000 (Model 11) from happened in 2001-2007 (Model 12). The dependent variable are the Rolling BHAR relation to the performance of the MSCI AC ASIA index, every the 36-month between 1997 and 2007. We calculate the change in operating indicator in the rolling periods. P-value is reported in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%levels, respectively (Based on t-statistics).
Variables | Model 13 | Model 14 |
---|---|---|
Frequency of merger | 0.081(0.516) | 0.013(0.924) |
Domestic merger | - | 0.154(0.239) |
Transaction value | −0.042***(0.003) | 0.03(0.301) |
Relative size | 0.076***(0.002) | 0.00(0.235) |
ΔROA[t + 1, t + 36] | 8.001(0.375) | 19.262***(0.004) |
ΔEfficiency[t + 1, t + 36] | 0.00(0.22) | −0.011**(0.012) |
ΔLoan ratio[t + 1, t + 36] | 0.134(0.857) | 0.794(0.436) |
ΔRisk[t + 1, t + 36] | −0.256*(0.069) | 0.154**(0.023) |
ΔCapital ratio[t + 1, t + 36] | 3.862*(0.089) | 1.847(0.628) |
ΔDeposit ratio[t + 1, t + 36] | 0.052***(0.004) | 0.02(0.755) |
ΔAssets growth[t + 1, t + 36] | −0.258(0.492) | 0.024(0.932) |
Intercept | −0.25*(0.075) | −0.136(0.382) |
Observations | 171 | 85 |
Adj R-Squared | 0.2027 | 0.1495 |
Notes: We classify our sample by the targets’ nation such as North Asia, Asia-Pacific, Japan and Southeast Asia. Because the result is not significant at all variables in North Asia and Asia-Pacific, we do not show their regression analysis in
the change every rolling 36-month from 1997 to 2007.
Because all samples in Japan are domestic merger, this variable is not available. We find that ΔROA is insignificant effect on the long-run performance in model 13, and cannot prove that Japan bank merger has profitability improvement after the merger. The improvement of efficiency also has insignificant effect on the long-run performance of Japan bank merger. Because that the Japanese has a high national consciousness, so we speculate that they may follow the rule of government announced to merge to help the bankruptcy bank. However, we prove that the long-run performance of Japan bank merger is not complete. Moreover due to the sample comes primarily from Japan, we suspect that it is maybe a reason for why the long-term performance in Asian bank merger cannot be similar with the US and the EU market. After comparing the two regression result between model 13 and model 14, we find a key point.
We mention before that the domestic bank merger which is passive and forcing transactions have a negative effect on the long-run performance, and the cross-border merger which is active transactions have a positive effect on the long-run performance. All samples in Japan are domestic merger (97.7%), and we can say it is a reason for why the performance of Japan bank merger is not complete. On the other hand, the transaction of Southeast Asia is major from cross-border merger (51.4%), and the cross-border acquirer has more aspiration to operating the combined bank to achieve a high performance. So, the performance of Southeast Asia is better that Japan.
Now, we further illustrate the effects of the deal attribute on the operating indicator in
Variables | ROA [t + 1, t + 36] | Loan ratio [t + 1, t + 36] | Capital ratio [t + 1, t + 36] | Deposit ratio [t + 1, t + 36] |
---|---|---|---|---|
Frequency of merger | −0.003***(0.001) | 0.042***(0.002) | −0.007(0.273) | 3.171***(0.0001) |
Domestic merger | −0.003***(0.004) | 0.033**(0.018) | −0.084***(0.0001) | 5.113***(0.0001) |
Transaction Value | 0.00(0.247) | −0.019***(0.0001) | 0.004***(0.0001) | −0.862***(0.0001) |
Relative ratio | 0.00(0.115) | 0.00(0.718) | 0.00(0.551) | −0.002**(0.021) |
Downturn | −0.002(0.114) | −0.038**(0.039) | 0.02**(0.021) | −0.174(0.769) |
Dummy | −0.001(0.6) | 0.089***(0.0001) | −0.02*(0.077) | −0.815(0.307) |
Intercept | 0.008***(0.0001) | 0.662***(0.0001) | 0.157***(0.0001) | 6.862***(0.0001) |
Observations | 355 | 355 | 355 | 355 |
Adj R-Square | 0.127 | 0.263 | 0.53 | 0.649 |
Notes: The dependent variable are the operating indicator over the 36-month between 1997 and 2007. The dummy variable is added to represent the transaction happened in 1997. P-value is reported in parentheses.*, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively (Based on t-statistics).
loan ratio and deposit ratio have great improvement. The relative size is found to have no impact on the operating indicator, and is consistent with previous studies. Judging from the above, the effect of the deal attribute on the operating indicator is significant but the level of effect is decreased gradually as time goes on.
In this paper, we investigate the long-run performance of Asian acquiring bank by using 293 bank mergers after the merger in the 1997-2007 periods. To analyze the long-run performance, we verify the acquirers’ long-run performance by the Buy-and-Hold Abnormal Returns (BHAR). BHAR is considered in the regression models by rolling every 36-month from 1997 to 2007 to eliminate the problem of cross-sectional dependence. Variables are separating the deal attribute from the acquirer bank’s operating indicators. Then, we also add a dummy variable for the effect of the Asian Financial Crisis, which happened in 1997 to 1998. We have the following conclusions:
First, we find significant negative long-run abnormal returns for Asian commercial banks and bank holding companies in the rolling period after post-merger and as well as the short-run performance. Our conclusion shows that the bank mergers cannot create the synergy in the long run. Second, consistent with the previous studies, our result also reveals that the deal attribute has significant negative related to the stock market returns.
Third, it shows that ROA has a significantly positive effect on the long-run performance, but unlike the previous studies, efficiency only shows a slightly significant improvement. The previous studies concluded that the early bank mergers can be efficiency improving, and stockholder value still remains inconclusive. In contrast, the recent bank mergers appear both efficiency and stockholder value enhanced. It could be because that the recent bank mergers learned best-practices (and worst-practices) from the early bank mergers (DeYoung, Evanoff and Molyneux [
Within the different calculations of the operating indicator in regression models, we find that the change in operating indicator is more suitable than the average in operating indicator for BHAR and BHR. Eventually, the effect of the Asian Financial Crisis is insignificant. This means that even the Asian financial Crisis brings the amount of mergers, it could not result in the value for the acquirer after the mergers. Therefore, our empirical results show that the long-run stock returns and operating performance of Asian commercial bank mergers are different from those of the US and EU markets.