M&A Strategic effects of Asia-Pacific Banks

Yoko Shirasu1

Abstract

This paper empirically examines the effects of the Asian bank’s M&A focusing on
management strategies for banks’ acquisition actions, from long-term aspects.
Investors value high capitalized sound acquire banks with high liquidity that promote
the purchase of new loan business from target banks with large non-performing loans. It
looks like as the target bank bailout. For acquirer banks, strategy changes after
acquisitions, whereas acquire banks obtain more loans and enhance adequacy capital.
However as the years go by, acquirer banks suffer from non- performing loans. Even more,these banks also end up incurring more costs and losing their profitability in the long run.
Additionally, we consider the country’s characteristics, the legal system, and financial
regulation. Among the different countries system, especially target banks countries havestronger/ more stringent legal and regulation rules, such that acquire banks can enjoy
higher equity adequacy at a less cost. In Asia, the legal system with strong investor
protection and more stringent financial regulations play an important role in resolving the
problems facing target banks in Asia.
It is no doubt that Asian banks M&A are meaningful in growing new loans andenhancing capital adequacy. However, banks don’t become profitable. And the mostimportant point is that the strong legal system and stringent regulations can enable Asianbanks to operate effectively by using M&A between different economic system countries.

1 Professor, Department of Economics, Aoyama Gakuin University. E-mail: shirasu@cc.aoyama.ac.jp.

The author is grateful to Katsushi Suzuki, Masaharu Hanazaki, Kotaro Inoue, Vadym Volosovych, He Fan, Barbara Casu, Hubert de
La Bruslerie, Sascha Kolaric, Serif Aziz Simsir, and Jeffrey Callen, as well as the seminar participants of the 2016 Paris Financial
Management Conference, the Finance Seminar of Hitotsubashi University, 2016 Nippon Finance Association, 2016 Japan Finance
Association, 2016 Southernwest Finance Association, 2015 Midwest Finance Association, 2015 European Financial Management
Association, and 2014 European Financial Management Association. This work was supported by the Public Interest Foundation
KAMPO 2016. And the JSPS KAKEN Grant Number is [15K03547]. All remaining errors are mine.

11. INTRODUCTION
Since the 1990s, large Asian and European financial institutions have aggressively
promoted alliances and M&A within the Asian financial markets. Asian financialorganizations followed their global client firms where client firms expand their business
operation. These firms do not promote strategic businesses for clients but for themselvesin association to mergers and acquisition as well as financial alliances. .
This paper empirically examines the effects of the Asian stock market’s performanceand management strategies for banks’ acquisition, from long-term aspects based on
research done from the year 2000. We examine the strategic management factor as
performed in Altunbas and Marques (2008) and explain the country’s characteristics that
relate to bank financial outcomes.
Investors value efficient and effective banks with low loans that promote the purchaseof new loan business through mutual complementary. For acquirer banks, strategy
changes after acquisitions whereas acquire banks obtain more loans and enhance equity.
However as the years go by, acquirer banks suffer from non- performing loans. Even more,these banks also end up incurring more costs and losing their profitability in the long run.
Additionally, considering the country’s characteristics, the English legal system, theregulation scope, and the Regulation entry are the one of the important issues regarding
creating effective Asian banks. The legal and regulatory system can facilitate the Asianbanks effectiveness and efficiency through M&A, however, Asian banks will lose theirprofitability. The most fundamental fact is that the strong legal system and stringent
regulations can enforce the Asian banks to operate according to the regulations throughM&A between different economic system countries.
The structure of this paper is as follows. Section 1 discusses the research motivationand section 2 the relevant literature. Section 3 outlines three key discussion issues. Section2

4 describes the study’s data and empirical methods. Section 5 presents the Asian banks’
data description. Section 6 provides the study’s empirical results, and Section 7 concludes
the paper.

2. LITERATURE
We present below a survey of studies on market evaluation in M&A.
Many research studies have been done on financial conglomerates. Laeven andLevine (2007) find the diversification discount in the financial conglomerate. Baele et al.
(2007) postulate that the connection between diversification and bank returns is thecontrary of that in Europe compared to other developed financial markets, such as the
U.S. They recognize a significant relationship between the degree of functional
diversification and franchise value. According to Artikis et al. (2008), there is a soundexplanation of the market dynamics and incentives for bank-insurance collaboration. The
authors argue that the market dynamics and incentives give banking firms the opportunity
to utilize their network of branches. According to recent studies, it is evident that the focusof research is not only on diversifications but also concentrates on the cross-border bank
M&A activities. As Caiazza et al (2012) expounds comprehensive empirical literature
research of cross-border bank M&A.. The authors are for “acquire to restructure”
hypothesis which posits that targets banks are typically less powerful banks that are
acquired for the purpose of restructuring with the intention of boosting sales.
A wide variety of empirical studies has scrutinized the organization value offinancial companies. These can be categorized into three main groups: Research on
creating firm value, (Field et al., 2007), and (Staikouras, 2009); Second, research on
destroying company value by (Laeven & Levine, 2007), (Schmid & Walter, 2009),(Lelyveld & Knot, 2009); Third, studies on neutral firm value (Allen & Jagtiani, 2000).

3Now, we consider Asia’s bad loan problems. Studies on Japanese financial institutions
have examined their changing business strategies by targeting only the banking sector,
which has suffered because of nonperforming loans for a long time (Yamori et al.2003),(Sakai et al. 2009). Most studies are nothing more than defensive M&A analyses ofdefensive nonperforming loans problems, business restructuring, and efficiency. In thisstudy, we comprehensively consider the aggressive business strategies of financial
institutions, especially those of large insurance companies, and analyze not only M&A
but also aggressive strategic alliances.
Rossi and Volpin (2004), Moeller and Schllingmann (2005), and Fauver et al. (2003)
empirically show that differences in nationality, legal and market systems, regulatory
systems, and bidder/target maturity vary according to firm value. Steigner and Sutton(2011) show greater cultural distance has a positive influence on the long term
performance. By contrast, we comprehensively examine financial institutions’ aggressive
business strategies, analyzing not only M&A but also aggressive strategic alliances in
Asia. My study thus expands the scope of the previous research. Stingner and Sutton(2011) show that greater culture distance has a positive influence on long term
performance. Barth et al. (2001, 2004, 2008) empirically show the difference between
broad array of bank regulations and supervisory practice and bank development,
performance and stability. And some literature shows the evidence that regulatory andcultural barriers limit the international expansion of banks (De Haas and Van leyeveldt
2010), more profitable and larger banks find it easier to overcome such barriers (Calzolariand Liranth 2011), proposed policy measures to increase supervision of banks’international activities (Ongena et al.2013).
Finally many studies on changing business strategies focus on M&A. Recent studies
on changing business strategies and the difference between M&A and alliances have been
conducted by Makimoto (2007) and Chiou and White (2005). Makimoto (2007) defines4the difference between M&A and alliances. While the purpose of M&A is improved
financial statements, the purpose of alliances is improved research and development(R&D). Chiou and White (2005) examine the wealth effects of Japanese financial
institutions’ strategic alliances and find that, first, strategic alliances increase the value ofpartner firms, second, the smaller partner experiences a larger percentage of gain, and,third, inter-group alliances result in increased market value.

3. DISCUSSION ISSUESThis paper presents three main discussion issues pertaining to the strategic
management change of acquired banks and Asian stock market’s response from long term
aspects. We define “alliance” as cases involving less than 50% cumulative share/asset
holdings and “M&A” as cases involving more than 50% cumulative share holdings.

[Discussion]
Discussion 1: What the strategic management factors have impacts acquisitions? Do
the similarities or differences of strategic management factors between
acquirer and target affect the market evaluation? We examine the five
strategic management factors: earning diversification strategy, risk
strategy, cost controlling strategy, capital adequacy level strategy, andliquidity risk strategy. And we check the relationship market response
and the similarity or difference of strategic management factors between
acquirer and target.
To assure the economic benefits, we test the effects of not only loanbusiness growth, cost efficiency and holding rich liquidity but also ROA
and Market-to-book.

5

In short, we test Asian stock market response to which type of strategic
management factors of acquire and target banks when effective M&A deals
are announced, and whether the markets evaluate either the similarity ofstrategic management factors or difference of management factors.

Discussion 2: After the acquisition, one year after and three years after, which
management strategic factor’s changes affect the acquired banks? We
examine the five strategic management factors: earning diversification
strategy, risk strategy, cost controlling strategy, capital adequacy levelstrategy, and liquidity risk strategy.

Discussion 3: The available evidence on the differences according to target’s country
characteristics could help us understand some of the factors in acquiring
banks. The difference of legal system (English law origin, French law
origin, and the other law origin), the degree of economic freedom, andfinancial regulation system (scope regulation, entry regulation and selfmonitoring regulation) are considering.

As Asian countries have survived some financial crisis since late the 1990s’, ourresearch mainly, focuses on credit risk strategy and capital adequacy strategy.

4. DATA AND METHODOLOGY
4.1 Data
Data on alliance and M&A announcements were drawn from Thomson ONE
Investment Banking and cover the period between 2000 and 2011. We collect all thetransactions of Asian listed banks that have at least acquired or targeted either the equity

6

or assets of domestic or foreign firms. We require at least one of the firms to be a bank,while the target could be a company in another industry. The investigation uses Asian datafrom all the Asia-Pacific countries (see Appendix 1). All sample transactions have a dollar
value and announcement data. Although the number of all announced data is 1907, theeffective data are 1137
All equity return data are from the Thomson One Stock Priced Daily Data.
Accounting data are from Thomson One Investment Banking. The data necessary to
calculate the geographical and industrial diversification measures come from the Standard
Industrial Classifications (SIC) codes and its geographic segment.
The sample comprises 1137 bank transactions. Either the acquirer or the target has aregular common stock listing on Asian-Pacific stock markets (see Appendix 1) and haveaccounting data based on dollar values. In this long analysis, we employ completed transactions of bank acquisitions.
We use countries’ credit ratings obtained from S&P long-term foreign currency
sovereign rating and legal systems obtained from La Porta et al. (1997), Fauver et al.
(2003) and Beck et al. (2003). Additionally, we employ country’s EFW index2, obtained
from Moeller et al. (2005) 3 . Barth et al. (2008) deriver the available dataset of bankregulatory environment by the World Bank Website4, we use it. The level of economic
activities is included as potential determinant of individual bank acquisition. The
macroeconomic environment is likely to affect bank activities and investment decisions(Pana et al. 2010). It is measures as annual growth date of gross domestic product.

4.2 Data selection
We select our sample data from Thomson ONE Investment Banking and cover the
2 The Economic Freedom of the World (EFW) index, maintained by the World Bank, measures the overall level of acountry’s restrictiveness in terms of its economic, institutional, and developmental environments.
3 Moeller et al. (2005) has obtained EFW index from the World Bank.
4http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037%7EpageP
K:64214825%7EpiPK:64214943%7EtheSitePK:469382,00.html

7period between 2000 and 2011 which are formally announced as the acquisition period,at least one of the firms has to be an Asia-Pacific countries’ bank. All sample transactions
have a dollar value. Although the number of announced data is 1907, the effective dataare 1137, for transactions in the initial sample.
For example, first, in cases of 12month acquirer’s long-term return analyses, weconstructed our sample from the 1137 transactions by using the following procedures: (1)selected observations that acquires industry is the Asian banks or financial holding
companies (800 observations); (2) selected observations of having 12month acquiring
stock return (662 observations); (3) deleted observations with ABHR greater / lower than
99th/ 1st percentile (650 observations); (4) selected observations that target industry isbanks or financial holding companies (233 observations). As the results, 233 observations
are the maximum for our 12month acquirer long-term analysis. And not all observations
have all kinds of financial data, there are many missing about Tier 1 ratio, non-performing
loan and loan-loss provision data etc. Then the number of observations is less than 233.
Second, to determine long-term regression analyses by strategy variables, weconstructed our sample by following procedures: (1) selected observations that acquire
industry is banks or financial holding companies (800 observations); (2) selected
observations of having total asset data (563 observations). And not all observations have
all kinds of financial data, there are many missing data. Then the number of observations
is less than 563.

4.3 ABHR: adjusted buy- and –hold returns
In discussion 1 for long-term analysis, our econometric study’s methods are basedon adjusted buy- and –hold (ABHR) returns. While the stock market reacts to new
information and does so fairly quickly, there is some evidence of poor stock prices.
Capital market players may need the time to revise their judgments based on new

8

information about the acquisition integration and response of rivals. This implies that thewealth effects from acquisitions may need to be assessed over long-run event windows.
The windows we used are 12 months and 36months after the announcement, effective
data only, and used methodologies implied are ABHR.
We adopt buy- and –hold (BHR) returns for one year and three years after the actual
acquisitions. To accurately measure the long-term stock performance, we compute the
ABHR, which subtracts the matched bank’s BHR from event firm’s BHR. We pick up amatched bank for each of the event firms from the same country that do not adopt M&A
during the same year of bank M&A event as below,




t 1

t 1

ABHRit  BHRi  BHRm   [1  Rit ]   [1  Rmt ](1)

where Rit is event bank’s t month return, Rmt is matched bank’s month return, τis thewindow terms, 12 month or 36month.
To control the Fama and French’s (1992) three factors, we required the matching bankto have the book to market ratio, book value of equity over the market value of equity, inthe year before the announcement, ranging between 50 percent and 200 percent of theevent bank’s book to market ratio. And then, we choose as a matching bank the nonacquisition bank that is closest to the event firm in the market value the year before theannouncement. In the following analyses, we delete observation with ABHR greater /lower than 99th/ 1st percentile to eliminate abnormal values.
For discussion 1 for long-term analysis, we carried out a regression analysis using
The ABHR regression, the 12 month or 36 month cross-section of acquirers withconsidered heteroscedasticity. We set the dependent variables the ABHR, presented inprevious paragraphs. The independent variables are five strategic factors as shown in
Altunbas and Marques (2008), control variables (Market-to-book and size, in (asset) andsome dummy variables (cross border dummy, effective year dummy, acquire country

9dummy and target country dummy). As Asian countries use accounting systems different
from those in the U.S. and Europe, we cannot use the same strategic accounting variables
used in Altunbas and Marques (2008). We present five strategic variables along with their
proxy variables in the bank industry case, as seen in Appendix 2. We employ thedifference between acquirers and the targets about every strategic variable as independent
variable. These are both acquirer and targets being just bank cases. If the sign is positive,
it means that the acquirer’s ratio is bigger than target’s ratio. And inversely, if the sign isnegative, the acquirer’s ratio is smaller than target’s ratio.

4.4 Difference in Different Methods
For discussion 2 and 3 for long term analysis, we regression analyze using difference
estimation (DID) methods, dependent variables in strategic variables. In DID methods, it
is better to employ group data similar to treatment group’s outcome distributions5. We set
all M&A transactions as the treatment group, and all non-M&A Asian listed bank’s dataas the control group. We adapt strategic variables to this research. The econometric model
is below.

StrategicVariable[ SV ]it   0  1 Time it   2 Trend it  3 Trend  Time it   it

(2)

Where Strategic Variableit is the strategic variables used in Altunbas and Marques(2008). Timeit is year dummy, if pre-acquisition are zero and post one year or three year
acquisitions are one, Trendit is dummy variable if acquisitions data are one, nonacquisitions data are zero and (Trend ×Time)it is cross term. The dependent variables are
strategic variables and independent variables are intercept term, trend dummy variables
and cross term variables. It is general to assess the significance of coefficient of crossterm variables. In general, we hope to assess whether good effects of acquisitions or not,5 See Meyer(1995)10

then we test the sign and significant of coefficients of cross terms.
In this paper, in practice, as following to Inui et al. (2013) econometric methods, we
set another model of DID methods, as below.,

SVit 1  SVit 1  0  1 Trend it  2Z it   it (3)SVit 3  SVit 1  0  1 Trend it  2Z it   it (4)where, Z it is the vector of control variables. We employ control variables, ln (asset),
GDP growth rate of target and acquirer countries, cross border dummy, diversification
dummy, effective year dummy, acquire country dummy and target country dummy.
Equation (6) estimates the change M&A effects of the SV from t-1 to 1+1, Equation (7)estimate the change M&A effects from t-1 to 1+3. Trend is dummy variable if acquisitions
data are one, non-acquisitions data are zero. We assess the significance of coefficient ofTrend variables.
Now, we explain the country characteristics. In order to investigate the acquisitions
affects strategic variables across affects target’s country characteristics differently, theaffected acquirer’s countries are divided into (1) the difference of legal law system,
English law origin, French law origin and the other law origin, (2) the difference of EFW,
(3) the difference of the strength of financial regulation, (3-1) bank activities scope
regulation, (3-2) foreign bank entry regulation, (3-3) bank self-monitoring regulation (socalled disclosure regulation) .
To investigate the difference of the country’s characteristics, between acquirers andtargets country, following Nguyen and Wilson's (2015) methods, we set anothereconometric model of DID methods, as below, for example legal system case.

SVit 1  SVit 1   0  1  SameLawTrend i  2  Different[ English]LawTrend i  3  Different[ French]LawTrend i (5)  4  Different[Other ]LawTrend i   4 Z it   it11

SVit 3  SVit 1   0  1  SameLawTrend i  2  Different[ English]LawTrend i  3  Different[ French]LawTrend i (6)  4  Different[Other ]LawTrend i   4 Z it   it

Where, the dependent variable is the change of the strategic variables. And we split“Law Trend” variable into four law trend dummy variables. If acquirers and targets aresame the legal system, “Same Law Tread” is one, non-acquisitions data including nonacquisitions data are zero. If acquirers are different legal system and the target is English
legal system, “Different [English] Law Tread” is one, the others data are zero. If acquirers
are different legal system and the target is French legal system, “Different [French] Law
Tread” is one, the others data are zero. If acquirers are different legal system and the target
is the other legal system, “Different [Other] Law Tread” is one, the others data are zero.
We assess the significance of coefficient of some kinds of Trend variables.
For sample of EFW and financial regulation, we split “Trend” variable into three trend
dummy variable. For example EFW for one year case are as following.

SVit 1  SVit 1   0  1  SameTrend i  2  Different[UnderMean]Trend i  3  Different[UpperMean]Trend i (7)  4 Z it   it
SVit 3  SVit 1   0  1  SameTrend i  2  Different[UnderMean]Trend i  3  Different[UpperMean]Trend i (8)  4 Z it   it

If acquirers and targets are the same system, “Same Tread” is one, the others data
including non-acquisitions data are zero. If acquirers are different system and the target
score is under than the mean, “Under median” is one, the others data are zero. If acquirers
are different system and the target score is upper than the mean, “Upper median” is one,12

the others data are zero.

4.5 Average Treatment Effect from Propensity Score Matching
For discussion 2 and 3 for long term analysis, we compute the averaged treatment
effects (ATE) using Propensity Score Matching (PSM) method. In our knowledge,
propensity score matching in relatively new to the econometric papers and one paper has
been used in M&A studies (Behr and Heid, 2011).
In this paper, we focus on the acquirer bank’s outcomes (Y) as some strategic variables.
Let Z denote the indication variable, that it is 1 if it is acquisitions data, and 0 if otherwise.
We observe Y1|z=1 but not Y0|z=0, which is a counterfactual outcome. The prima facieacquisition effects to observable variables by comparing the outcomes of authenticallyacquired data and factually non-acquisition data are

i ( ATEi )  E Y1i | z  1, x   E Y0i | z  0, x  .

(9)However,  i is generally a biased estimator of Δ unless the assignment to the actuationgroup (z=1) or the non- actuation group (z=0) is independent of the outcome variable. A
possible solution is to derive an unbiased estimator through conditioning on covariates.
Rosenbaum and Rubin (1983) have shown that it is a sufficient to condition on thePropensity Score. The propensity score is given by the probability to acquire by logit
regression with set of covariates x . The basic matching approach is that, for each factual
treatment acquirer data, a pair of non-acquisitions control data are selected from the poolof factually non-acquisitions data. For all Asian banks in the sample, we estimate thepropensity with year dummy variables, acquirer country dummy and target country
dummy. Our employed matching algorithm method is Greedy Matching6.

6 “Perhaps the most common matching algorithm is the so-called greedy matching. It includes Mahalanobis metricmatching, Mahalanobis metric matching with propensity scores, nearest neighbor matching, caliper matching, nearest
neighbor matching within a caliper, and nearest available Mahalanobis metric matching within a caliper defined by
the propensity score. All methods are called greedy matching.” (Guo 2015)13

After PSM, we checked the balanced box charts between treatment group and control
group and tested balance test comparing with raw data and matched data using
standardized difference and variance ratio.

5. SAMPLE DESCRIPTION
Graph 1 shows the number of acquisitions for Asian banks. Although the number of
all announce data from 2000 to end of 2011 is 1907, and the effective level data are 1137.
The reason for not including downloads in years 2012 and 2013 is because of theannouncement that was made towards the end of 2011, which is explained in the previous
sections. This graph shows the historical acquisition numbers. In 2001, the number
reached around 100 and the level of every year is same. After 2009 the number isdecreased; there have been fewer than ten recent acquisitions.

(Insert Graph 1 about here.)

Graph 2 shows the share of acquirer and target countries. Panel A shows the acquirer
share. The four largest countries are Japan (17%), Thailand (16%), Australia (15%), andIndia (14%). The top five counterparty industries are banks (35.35%), consumer credit
business (9.33%), securities (7.28%), investment advisory services (6.93%) and life
insurance (6.04%). Asian banks are almost tied with trade banks, at about 45%. Panel B
shows the target share. The five largest countries are Japan (17%), Indonesia (13%), India(12%), Taiwan (9%), and Korea (8%). The top five counterparty industries are banks(54.29%), other investments (21.36%), investment advisory services (4.29%), securities(3.45%), and life insurance (2.89%). Asian banks are tied with trade banks, at over 50%.

14

(Insert Graph 2 about here.)

Table 1 presents the basic statistics using our regression about ABHR, DID and PSM.
Panel A of Tables1 shows the one year financial and economic change after effectiveness,for both acquisition data called “treatment” data and non-acquisition data, called “control”
data. Although the number of all effective level data is 1137, because there are many
unlisted banks data and other industrial data, we can use only 500-600 deal data for our
empirical analysis. All Asian banks data without acquisition are control data. Panel B of
Tables1 shows the three-year change after effectiveness. The number of treatment data
is a little smaller than panel A.

(Insert Table 1 about here.)

Table 2 presents the number of max deals using analysis. Is means that the number ofdata having total asset data. Panel A of Tables2 shows about the treatment banks, andPanel B shows the control banks, all Asian banks without acquisitions. From 2001 to 2009,
the number of acquisition is high level. And in our available sample, many acquisition
deals occurred in Japan (118/563), Australia (95/563) and Thailand (94/563). And we cansee the target country in Panel B, singed “target” part. The highest share target country isJapan, 108/563 banks. Second highest target country is Thailand (95/563) and third
highest is Australia (73/563). Panel C of Tables2 shows about the control banks, all Asianbanks without acquisitions. The number of control banks grow about twice with eachpassing year, 207 banks in 2000 to 395 banks in 2013. The largest country is Japan, second
largest is India. In contrast, the smallest country is New Zealand and second smallest isVietnam. And we don’t have Vietnam data before 2005.

15

(Insert Table 2 about here.)

6. EMPIRICAL RESULTS
6.1 Discussion 1: Long-term Stock performances
We empirically extract the difference of strategic management factors between
acquirer and target from the ABHR. The matched-adjusted return for the ABHR from
12month and 36month surrounding the effective day is the dependent variable in eachcross-sectional regression model. As presented in the previous section 4.2, when wecompute the ABHR, we pick up a matched bank for each of event firms from the same
country and same year of bank M&A event. And we check the relationship between
ABHR and the similarity or the difference between the strategic factors of acquirer andtarget banks.
Consistent with Altunbas and Marques (2008), the independent variables are thedifference of strategic management factors between the acquirers and the targets that
include strategies such as earning diversification, risk, cost control, capital adequacylevel strategies and liquidity, including some control variables, Market-to-book size,
adding the cross-border dummy, year dummy, the country dummy of acquirer, and thecountry dummy of target.
Table 3 shows the results of the difference between the acquirers and the targets on
every strategic variable after 12 months and 36 months form effectiveness. These results
are both from the acquire and the target banks’ cases hence, the number of observation issmall. If the sign is positive, it means that the acquirer’s ratio is higher than the target’s
ratio. And inversely, if the sign is negative, the acquirer’s ratio is smaller than target’s
ratio.

16

(Insert Table 3 about here.)

Panel A of Table 3 shows the results after 12 month cases. The Graph 3 present the
distribution of ABHR for acquirer and target 7. For both of them, there is a right side
distortion in the shape of the ABHR distribution.

(Insert Graph 3 about here.)

From the empirical results of Panel A of Table 3, the two significant variables arenegative non-performing loan ratio and deposit-loan ratio, and positive liquidity ratio.
The results of Panel B of Table 3, the results after 36 months cases, and the two significant
variables are negative non-performing loan ratio and deposit-loan ratio, and three positive
total capital ratio, Tier1 capital ratio and liquidity ratio.
At the time one A year after effectiveness, market value acquire-banks with richliquidity. And Market value when acquire-banks with small-sized loan business, thetarget- banks with big-sized loan in spite of being much non-performing loan business,and passing three years, market evaluate becoming more adequate capitated acquirebanks. Investors value high capitalized sound acquire banks with high liquidity that
promotes the purchase of new loan business from target banks with large non-performing
loans. It seems that investors evaluate the acquirer’s loan purchase business from targets,however note that it looks like as the target bank bailout.

6.2 Discussion 2: Change of strategies
We empirically extract the change of strategies of acquirer after acquisitions. We

7 Bank acquirers and targets have negative median and mean ABHRs significantly. The target bank’s ABHR is

substantially higher than the acquirer’s ABHR. And we examine the alpha effects using CTPR econometric methods,
KENNETH FRENCH index results for 36month shows positively significant. Players price Asian M&A banks stockas overpriced.

17check the relationship between the acquirer’s change of outcomes (treatment data) andthe change of strategic factors compared to non-acquisitions deals (control data).
Consistent with Altunbas and Marques (2008), the outcome variables are the change ofstrategic management factors, such as earning diversification, risk, cost control, capital
adequacy-level strategies and liquidity, and economic profitability measures of acquirers
after one year and three years. For instance, a change of ROA, Market-to-book ratio,
including some control variables, the DGP growth of acquirer country, the DGP growthof target country, year dummy, the country dummy of acquirer and the country dummyof target, and adding the cross border dummy, the alliance dummy. We report justtreatment effect coefficients, omitting the coefficients of the DGP growth, year dummy,
the country dummy, the cross-border dummy and the alliance dummy.

(Insert Table 4 about here.)

The equation from (1) to (11) in Panel A of Table 4 shows the results of the DID
regression on the change of strategies for one year of acquirers by each variable regressed.
There are a few significant results on strategy change. It suggests that one-year duration
significantly makes notable impact on the bank acquisition deals, becoming large size,
growing more total loans and spending more total costs, in spite of no significant resultsof ROA and Market-to-book. And the equation from (1) to (11) in Panel B of Table 4shows the results of the change of strategies for three years. It suggests that a three-year
duration makes clear impacts on deals, growing more and more total loans (thecoefficients are bigger than the results for one year), becoming large total capital, keeping
richer liquidity, whereas having non-performing loans, in spite of no significant results of
ROA Market-to-book.
In summary for the change of acquirer’s strategy change analysis, there are no18

economic favorable results. In the initial stage after the acquisition, acquire banks becomelarger sized and grow more loans. However, after three years, may have been renewing
many loan agreements and acquiring many deposits, acquire banks become being higher
loan ratio, richer liquidity and finally qualifying as an adequate capital banks with a highcapital ratio, whereas the growth of non- performing loans. We suggest the volume of thegrowth of non- performing loans is so much, because the non- performing loans ratio issignificant, too. Even more, these banks also end up incurring more costs and losing theirprofitability in the long run. We can get significant strategic results about loan risk strategy.

6.3 Discussion 3: Characteristics of Asian countries
The goal of this section is to examine the acquirer’s effects, adding the difference ofa country’s characteristics between the acquirers and the targets country. We empiricallyexamine the country’s characteristics effects using DID econometric methods and ATEfrom PSM as robustness.

Discussion3-1: DID
First, we check the relationship between the acquirer bank’s outcomes, and thedifference of the acquirer’ and targets’ legal systems. The English origin legal system,with its common law origin and providing investors with strongest legal protection,
adversely, French origin legal system, civilian law origin and providing the least
protection. Rossi and Volpin (2004), Moeller et al. (2005) and Fauver et al. (2003)
empirically show that M&A returns differ according to differences in nationality and legal
systems. Although Fauver et al. (2003) empirically show that French origin legal system(civilian law system) has the greater magnitude than England origin legal system(common law system), Suzuki (2012) proposes that M&A premiums in common law
countries such as Australia, India, Malaysia, and Singapore are higher than in countries

19that do not use the common law. Second, we check the difference between the countries’
degree of economic freedom based on the EFW index8 of targets. Third, we check the
impacts of regulatory barriers on targets. Barth et al. (2001, 2004, and 2008) empiricallyshow the difference between broad array of bank regulations and supervisory practice(see Appendix2) and bank development, performance and stability. We focus on three
regulation systems; restrictions on bank scope restrictions on bank regulation, entry intobanking requirements regulations for foreign banks and private monitoring regulation,
generally called information disclosure.
The outcome variables are the change of strategic management factors,
diversification, risk, cost control, capital adequacy-level strategies and liquidity, andeconomic profitability measures of acquirers after one year and three years, such aschange of ROA, Market-to-book, including some control variables, the DGP growth of
acquirer country, the DGP growth of target country, year dummy, the country dummy ofacquirer and the country dummy of target, and adding the cross border dummy, thealliance dummy as same as 6.2.
In this paper, Trend is the dummy variable if acquisitions data are one, nonacquisitions data are zero, and we focus on the difference of systems between acquirer
and target for treatment deals not the target system itself. As mentioned before, forexample, we sprite “Trend” variable into four law trend dummy variables, “Same Law
Tread”, “Different [English] Law Tread”, “Different [French] Law Tread” and “Different[Other] Law Tread” and set them to be one, the others data including control data are zero.
For example EFW and regulations, we sprite “Trend” variable into three trend dummy
variables, “Same Tread”, “Under median” and “Upper median”. We assess thesignificance of coefficient of some kinds of Trend variables,
The equation from (12) to (41) in Panel A of Table 4 shows the results of the DID
8 At first, we plane to investigate the effects of country’s credit rating, and the correlation between EFW and rating is

so high (0.93), and then omitting the rating from results parts. The empirical results are similar the EFWs’.

20regression on the change of strategies with country characteristics one year of acquirers
by each variable regressed.
First, we consider that after one year of changing outcomes results of the differenceof some systems from equations from (12) to (16) in Panel A Table 4. Surprisingly, allMarket-to-book, categorized in “same” are positively significant. The same system
between acquisition deals and target deals promote banks quality higher before
acquisitions. However, all the total costs, categorized in “different” are positively
significant. In the different social system between acquisition deals and target deals,
acquire banks incur much more costs related to acquisitions comparing with preacquisitions.
Second, in contrast, we consider that after three years of changing outcome’s results(Panel B Table 4). Regrettably, Market-to-book is not significant, but rather all nonperforming loans categorized in “same” are positively significant.
Here, on the other hand, we argue the effects among the “different” system countries.
Comparing with the results of legal systems, in the English legal system, the strong
investor protection, acquirer-banks are positively highest coefficient of total capital. The
strong investor protections (English Common Law) promote banks to be more adequate
capitalized before acquisitions.
Next, we compare the results between restrictions on bank scope activities regulation(Regulation scope), and entry into banking requirements regulations (Regulation entry),and private monitoring regulation (Regulation monitoring). In “different; Upper Mean”
category, the strong scope activities banking requirements regulations are useful to be
costless and to become more adequate capitalized banks, whereas weak regulations are to
be high cost and unsound banks. And more surprisingly, in “different; Upper Mean”
category, the strong entry into banking requirements regulations are useful, too toeliminate of non-performing loans and to become the adequate capitalized banks, whereas

21

weak regulation is to be high cost and unsound banks. Adversely, the strong power ofprivate monitoring regulations is not significant results. We suggest self-disclosure
regulations are not effective in Asian financial market.
In short, English legal system with strong investor protection, strong regulation scope,and regulation entry are one of the important issues regarding having less non-performing
loans, minimized costs, and creating more adequate capital Asian banks.

Discussion3-2: ATE from PSM
In this paper, we compute the ATE using PSM method focusing on the acquirer bank’s
outcomes as some strategic variables for robustness. The outcome variables are thechanges of strategic management factors and the economic profitable measure ofacquirers after one year and three years, such as change of ROA. And treatment
propensity score is given by the probability to acquire by logit regression with set ofcovariates; acquiring bank size, credit risk of acquire banks, loan-deposit ratio of acquirebanks, cost ratio of acquirer banks, the DGP growth, the legal index, the EFW index, theregulatory index of acquirer / target country, and the year dummy (see detailed covariates
in Tables6).
The Table 5 shows the results of the ATE from PSM about changes of strategies witha country’s characteristics one or three years of acquirers by each variable computed. The
some ATE for one year change from acquisition, are increasing about total loans, total
capital and total cost significantly. After time passing, the ATE for three changes isincreasing about total loans, total capital, total cost and additionally non-performing loans,liquidity, significantly. And surprisingly, the ATE for three changes is significant
decreasing about ROA adversely (even though the significant level is 10%). The
increasing of change of acquirer’s strategic factors for the total loans, total capital, totalliquidity and non-performing loans are consistent with previous DID results. Whereas, in22

the DID regression, we cannot acquire the significant results about economic profitability;
ROA and Market-to-book, in the ATE from PSM, we can revile the non-profitability,
negative ROA, of acquirer banks in Asian bank M&A.
After PSM, we checked the balanced box charts between the treatment group and thecontrol group. We can see the overlap conditions after matching (Graph 4). And testedbalance test comparing with raw data and matched data using standardized difference andthe variance ratio (Table 6). If the variance ratios are near to one, it is good matchings.

We now summarize the empirical results. Investors value adequate capital banks withsmall loans and much liquidity that promote the purchase of new loan business throughmutually complementary. And in the initial stage after an acquisition, acquire banks
become large sized with high total loans. However, as the years go by, acquire banks have
more, richer liquidity and finally being adequate capital banks, whereas the growth ofnon- performing loans. And finally acquire banks spend much cost and finally lost the
profitability, become being lower ROA. Additionally, considering the country
characteristics, English legal system, regulation scope and Regulation entry are one of theimportant issues regarding creating sound Asian banks. Among the difference systemcountries, especially target banks countries have stronger/ more stringent legal andregulation rules, acquire banks are able to enjoy higher equity adequacy and less cost. In
Asia, the legal system with strong investor protection and more stringent financial
regulations play an important role in resolving the problems facing.

7. CONCLUSION
This paper, representing research that began in 2000, empirically examines the effectsof the Asian bank’s M&A focusing on management strategies for banks’

23

acquisition

actions from long-term aspects.
First, investors value high capitalized sound acquires banks with high liquidity that
promote the purchase of new loan business from target banks with large non-performing
loans. It seems that investors evaluate the acquirer’s loan purchase business from thetargets, however, note that it resembles the target bank bailout.
Next, focusing on acquirer banks’ strategy changes after acquisitions, in the initialstage; acquire banks become larger in size, growing more gross loans. And as the years
go by, acquirer banks establish more and more loans, and obtain higher liquidity andenhance adequate equity. That is like as the success business story. But simultaneously
the shocking fact is that acquisitions accrue to acquire banks more non-performing loans.
Even more, these banks also end up incurring more costs and losing their profitability inthe long run.
Additionally, we consider the country characteristics, legal system, and financial
regulation. Whereas acquirer’s profitability among the same system countries are
increasing in the initial stage, however after three years go by, acquirer banks are notbeing profitable but rather they are suffering from non-performing loan problems. On theother hand, among the difference system countries, especially target banks countries havestronger/ more stringent legal and regulation rules, acquire banks can enjoy higher capital
adequacy and less cost. In Asia, the legal system with strong investor protection and more
stringent financial regulations play an important role in resolving the problems.
The Asian banks M&A are meaningful to the issue of growing new loans andenhancing capital adequacy. Regrettably, Asian banks don’t become profitable by M&A
because of non-performing loans. And the most important issue is that the legal systemwith investor protection and more stringent regulatory systems are able to enforce the
Asian banks being efficient through M&A between different economic system countries.
This study has considered some issues that have remained unexamined. We compute24the ATE from regression adjustment methods not only PSM. Although, the propensity
score is given by the probability to acquire by logit regression and compute the ATE withthe prima facie acquisition effects by factually acquired data and factually non-acquisition
data, there are no considerations about the change of effecting terms to the outcome,directly. We plan to compute ATE from regression adjustment methods. And we have toconsider the effects of a global financial crisis, comparing before the crisis and after thecrisis.
From long term aspects, the promotion or demotion of every strategy widely differsfrom legal systems and regulation system and each combination. To say it another way, if
we know the legal and regulation system for acquisition banks countries, we would
understand which strategies are advantageous and which strategies are disadvantageous.

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28

(Graph 1) The number of acquisitions for Asian banks by effective years(Announcement from 2000 to 2011)29

(Graph 2) The share of acquirer and target countries
Panel A) Acquirers

Chaina
3%

Japan
17%

India
14%

Taiwan6%

Indonesia
4%

Top five industries of counterparty ％
Bank
35.35
Consumer credit
9.33
Securities
7.28
Investment advisory services
6.93Life insurance
6.04

Australia
15%

HongKong
4%

Korea
6%
malaysia
6%
philippines
5%

Thailand16%

Panel B) Targets

Japan
17%
China
7%

India
12%
Indonesia
13%

Taiwan9%

HongKong
4%

Australia
4%
Thailand
5%

Korea
8%
Philippines
7%

Pakistan
4%30

Top five industries of counterparty ％Bank
54.29%
Other investment
21.36%
Investment advisory services
4.29%
Securities
3.45%
Life insurance
2.89%

(Graph 3) The distribution of ABHR for 12month
Acquire020Frequency

40

60

Target

-1

-.5

0

.5
ABHR_t12

31

1

1.5

(Graph4) The balanced box chart comparing with raw data and matched datadelta1Y total loans.20treatedcontroldelta1Y liquidity

delta1Y total capital
Raw

Matchedcontrol

treateddelta3Y ROAMatchedtreateddelta3Y total loans

Raw

MatchedRaw

Matched

1
.8
.6
.4
.2

delta3Y liquidity
Matched

.6

.8

.2

.2

.4

.6

Propensity Score

Raw

Matched

.8

1
.8
.6
.4

control

1

Raw

Matched

0

0

0

0

.2

.2

.4

.6

Propensity Score

.8

1

Raw

treateddelta3Y total capitaldelta3Y total costs
Matchedtreated

Propensity Score

delta3Y NPL
Rawcontrol

treated.4control0.20treated1

control

Propensity Score

.8
.4

.6

Propensity Score

.6
.4
0

0

.2

.2

.4

.6

Propensity Score

.8

.8

1

1

1

Raw

Propensity Score

.2
0

0

control

Matched

.8

1
.8
.4

.6

Propensity Score

.6
.2

.4

Propensity Score

treatedRaw

Matched
1

Raw

.8

.8
.6
.4
.2
0

controldelta1Y total costdelta1Y NPLMatched

.6

Raw
1

1

Matched.4

delta1Y ROA
Raw

treatedcontrol

control

control

treated32

treatedcontrol

treated(Table 1) basic statistics
Panel A) 1 year
Variable
Δ 1Y_loanloss provision ratio
Δ 1Y_loans ratio
Δ 1Y_total capital ratio
Δ 1Y_deposit-loans ratio
Δ 1Y_total cost ratio
Δ 1Y_Total capital ratio
Δ 1Y_Tier 1 capital ratio
Δ 1Y_Liquidity ratio
Δ 1Y_Total loans
Δ 1Y_Non-performing loan
Δ 1Y_Loan loss provisions
Δ 1Y_Total cost
Δ 1Y_Total capital
Δ 1Y_ROA
Δ 1Y_Size
Δ 1Y_Qratio
Δ 1Y_GDP grwoth(a)Δ 1Y_GDP grwoth(t)ABHR12month
ABHR36month

Obs Mean Std. Dev. Obs Mean Std. Dev.
Treatmen Banks
Control Banks
494 -0.038
1.158 2,910 -0.146
6.785
473 -0.010
0.077 2,846 -0.064
2.704
529 -0.004
0.049 3,377 -0.001
0.126
522 -0.047
0.390 3,351 -0.260
11.724545 0.776
29.3753264 -0.612
52.741563 -0.002
0.061 4,255 -0.001
0.074
402 -0.005
0.082 2,145 -0.001
0.103
548 0.004
0.055 3,973 0.002
0.069
527 0.140
0.2443374 0.090
0.199
472 0.075
0.543 2,839 0.012
0.625
494 0.086
0.428 2,875 0.028
0.432
543 0.133
0.315 3,264 0.066
0.291
560 0.158
0.336 4,217 0.109
0.322
563 0.000
0.030 4,258 0.000
0.189
563 0.146
0.234 4,262 0.103
0.228
548 -0.017
0.260 3,781 -0.020
0.534
806 3.781
4.171 6,632 3.790
4.239
793 3.989
4.315
6632 3.790
4.239
650 -0.086
0.417
594 -0.031
0.927-

Panel B) 3 year
Variable
Δ 3Y_loanloss provision ratio
Δ 3Y_loans ratio
Δ 3Y_total capital ratio
Δ 3Y_deposit-loans ratio
Δ 3Y_total cost ratio
Δ 3Y_Total capital ratio
Δ 3Y_Tier 1 capital ratio
Δ 3Y_Liquidity ratio
Δ 3Y_Total loans
Δ 3Y_Non-performing loan
Δ 3Y_Loan loss provisions
Δ 3Y_Total cost
Δ 3Y_Total capital
Δ 3Y_ROA
Δ 3Y_Size
Δ 3Y_Qratio
Δ 3Y_GDP grwoth(a)Δ 3Y_GDP grwoth(t)ABHR12month
ABHR36month

Obs Mean Std. Dev. Obs Mean Std. Dev.
Treatmen Banks
Control Banks
455 -0.249
1.188 2,740 -0.237 5.701635
430 -0.015
0.082 2,592 -0.161 3.732269
481 -0.006
0.068 3,100 -0.002 0.219027
475 -0.083
0.490 3,077 -0.514 14.61434
503 0.2832
34.455 2,998 -1.841 50.02508
521 -0.002
0.078 4,046 -7E-04 0.106191
361 0.0027
0.089 1,803 -0.071 2.792076
506 0.0135
0.081 3,777 0.0049 0.100088
479 0.387
0.355 3,099 0.2889
0.4285430 0.2996
0.928 2,592 0.0223 0.983636
455 0.2404
0.636 2,717 0.102 0.713012
501 0.3387
0.606 2,998 0.2114 0.50672
520 0.4293
0.458 4,005 0.3458 0.509835
521 -0.002
0.027 4,050 0.0012 0.205907
521 0.3906
0.338 4,054 0.3123 0.43795
509 -0.022
0.282 3,605 -0.02 0.946645
806 3.7806
4.171 6,632
3.79 4.239192
793 3.9891
4.315 6,632
3.79 4.239192
113 -0.014
0.411 103 0.2294
1.135 -

33

(Table 2) The number of max deals using analysis (the data having financial data, total assets)

Panel A) Treatment Banks (Entities)
Acquire; banks

Japan
India
Indonesia
Singapore
Sri Lanka
Thailand
Pakistan
Philippines
Malaysia
Korea; Republic (S. Korea)
Hong Kong
TaiwanChina
Vietnam
Australia
Total

2000
4
2

2001
14
5

3

2
2
9

1
3
1
1
2
9
26

3
7
1
2

6
51

2002
8
2

12
1
3
3
1
1
1

7
39

2003
14
4

8
1
2
1
4
3

8
45

2004
7
16
1
2

2005
5
11
3

11

2

2007
12
6
1
1

2008
15
5
4
2

13

7

5

2
3
6
1
1
1

1
2
3
2
1

3
2
2
4
2

6
1
3
2
1
1

6
2
2
8
4
1

4
55

9
50

34

2006
15
6

5
48

12
51

7
1
12
69

2009
10
5
3
2
8

2010
7
6
1

2011
4
3
2

2012
3

7
1

3

2

2

1
1
3

1
3
3
3
2
12
52

3

2

3
3
6
37

3
5
24

3
2
15

2013 Total
118
71
15
9
4
94
5
24
30
39
15
15
1
23
6
95
1
563

Target; Entities

Japan
India
Indonesia
Singapore
Sri Lanka
Thailand
Pakistan
Philippines
Malaysia
Korea; Republic (S. Korea)
Hong Kong
TaiwanChina
Kazakhstan
Vietnam
Macao
Australia
Tonga
New Zealand
Fiji
Samoa
Rus.
U.S.
kenya
The others
Total

2000
4
2
1

3
3
2
1
2

2001
13
5
1
2
2
9

3
7
2

2002
8
2

12
1
3
3
1
1
1

2003
14
4

1
8
2
1
3
3
1

2004
7
16
3
1

2005
4
10
4

2006
15
4
1
2

2007
10
5
4

2008
14
5
7
1

11

12

7

5

2
3
5
1
1
2

1
2
2
1
2
3

3
2
2
2
2
3

6
3
2
1
1
1

7
2
2
1
3
5

1
7

3
1

1

6

8

3

8

3

8

4
1
4
1
9

1

2009
10
5
4
2
9

2010
5
6
1

2011
2
3
1

2012
2

8
1

3

1

2

1

2
1

2
1

3

1
2
4

2

3

1

3

4

5

1

1

1

1

37

1
23

15

1

3
4
2
1
1
8
2

1

1
1
1
1

4

2

51

1
68

1
26

1
50

39

45

55

50

35

48

52

2013 Total
108
67
27
6
5
95
4
26
20
33
18
16
1
23
3
14
2
73
1
5
1
1
1
10
1
3
1
563

Industry of target
Industry Code Industry of target
100
Agriculture etc.
1000
Mine, Construction
2000
Food, textile etc.
3000
Steel, Electric etc.
4000
Transportation etc.
5000
Sales etc.
6000 Bank
6100 Consumer credit
6200 security
6300 Insurance
6400 Insurance Agent
6500 Real Estate
6700 Holding company
7000
Hotel, Leasure etc.
8000
Service etc.

36

Freq.
1
12
22
34
10
8
178
83
91
29
2
16
33
34
10
563

Target; Only Banks and holding companies

Japan
India
Indonesia
Singapore
Sri Lanka
Thailand
Pakistan
Philippines
Malaysia
Korea; Republic (S. Korea)
Hong Kong
TaiwanChina
Kazakhstan
Vietnam
Macao
Australia
Tonga
New Zealand
Fiji
Samoa
Rus.
U.S.
kenya
Total

2000
2
1

2
2

2001
12
1
1
1

2002
5
1

1

2
1
2
1
1
1

2

2

2003
6

1
1
1

2

2005
2
1
2

2006
7
1
1

1

1

1

2
1

1
1
1

2
1
1
1
2
3

1

1

1

2004
4
2
2

1
1
2

2
2

1

1

1

2007
3
2
4

3
1
1

1

2

2008
2
1
5
1

2009
2
2

1

1
1

2

1

1
4
2
1
3
1
2

2010
5
2

2011
2
1
1

2012
1

6

2
2
1
1
1
1
2

1
1
2
1

1
1
1

2

1

1
2
1
1
1
1
10

21

16

12

16

15

37

1
23

2

2

19

28

1
17

18

8

6

2013 Total
53
12
19
2
2
15
1
16
9
8
13
6
14
3
8
2
13
1
2
1
1
1
6
1
0
209

Panel B) Control Banks
2000
Japan
86
India
11
Indonesia
11
Singapore2
Sri Lanka
4
Thailand9
Pakistan
8
Philippines
10
Malaysia
14
Korea; Republic (S. Korea)
19
Hong Kong
8
Taiwan18
China
4Bangladesh
Vietnam
Australia
3
New Zealand
Total
207
Source:Thomson Reuter Data Base

2001
83
26
24
3
4
10
9
17
16
18
9
15
7

2002
87
27
23
4
7
7
7
16
15
23
11
19
8

2003
88
31
24
4
7
9
10
18
16
25
11
21
8

2004
92
31
17
3
8
8
13
18
12
24
7
23
10

2005
96
33
19
4
9
6
15
15
14
25
10
20
14
1
8

2006
95
38
27
3
11
6
17
15
11
27
8
20
12
5
3
7

2007
91
37
24
3
12
6
20
15
9
28
7
16
12
7
5
7

2008
94
39
26
3
12
7
19
19
8
27
7
20
15
9
5
7

2009
82
38
24
3
11
4
23
16
9
28
6
18
19
11
5
7

2010
93
40
26
3
11
9
19
21
11
25
8
20
15
25
5
9

2011
95
42
33
4
15
9
20
17
11
21
9
19
20
27
7
8

2012
101
41
42
4
15
10
21
19
11
19
9
19
21
27
5
12

8
1
250

8
1
263

10
1
283

1
9
1
277

289

305

299

317

304

340

357

376

38

2013 Total
102 1285
43
477
43
363
4
47
16
142
12
112
21
222
19
235
11
168
20
329
9
119
21
269
25
190
27
138
8
45
14
117
4
395 4262

(Table 3) Regressions of the difference between acquires and targets
Panel A) 1 year
the other operational income ratio(1)-0.6699(0.423)(2)-0.0481(0.971)loanloss provision ratio

(3)

(4)

-0.0479(0.283)-0.1339(0.163)non performing loan ratio

(5)

(6)

-1.0739(0.231)-5.6527 ***(0.004)loans ratio

(7)

(8)

-0.1654(0.319)-0.2882(0.132)

deposit-loans ratio

(9)

(10)

-0.0049 ***(0.000)-0.0187 ***(0.010)Included

Included
Included
Included
Included
Included
Included
64
0.7115

total cost ratio
total capital ratio
Tier1capital ratio
liquidity ratio
D cross border
Included
D YearD acquie country
D target country
Control variables(Market-to-book, lnAsset) Included
Intercept
Includedn106
r2
0.1547

Included
Included
Included
Included
Included
Included101
0.5133

Included

Included

Included
Included
64
0.0424

Included
Included
Included
Included
Included
Included
61
0.5549(11)(12)

0.0017 **(0.015)0.0015 *(0.099)Included

Included
Included48
0.0332

Included
Included
Included
Included
Included
Included45
0.7921

Included
Included97
0.1858

Included
Included
Included
Included
Included
Included930.6005

(13)

(14)

(15)

(16)

(17)

(18)

-0.0342(0.602)0.0209(0.750)0.4704(0.348)0.4866(0.574)

Included
Included
68
0.0652the other operational income ratio
loanloss provision ratio
non performing loan ratioloans ratio
deposit-loans ratio
total cost ratio
total capital ratio
Tier1capital ratio
liquidity ratio
D cross border
Included
D YearD acquie country
D target country
Control variables(Market-to-book, lnAsset) Included
Intercept
Includedn199
r2
0.2039Included
Included
Included
Included
Included
Included193
0.5598

Included

Included
Included200
0.1912

Included
Included
Included
Included
Included
Included
194
0.5477

Included

Included
Included
430.0554

Included
Included
Included
Included
Included
Included
41
0.476

0.4352 ***0.3075 ***(0.000)(0.008)
Included
Included
Included
Included
Included
Included
Included
Included
Included200
1940.25150.5674

The results of the 12month ABHR of acquires in each GDP weighted cross-sectional regression model. Heteroskedasticity-corrected P value are in parenthesis. The symbols ***, **, and * denote statisticalsignificant at the 1%, 5% and 10% level, respectively. The independent variables are the difference between acquires and targets of strategic factors after one year of acquisitions. .

39

Panel B) 3 year
the other operational income ratio(1)-0.1338(0.876)(2)
1.4107(0.228)loanloss provision ratio

(3)

(4)

0.055
(0.519)-0.1457(0.412)non performing loan ratio

(5)

(6)

-0.4012(0.694)-6.768 **(0.036)loans ratio

(7)

(8)

0.1484(0.579)-0.1054(0.732)

deposit-loans ratio

(9)

(10)

-0.0162 ***(0.000)-0.141(0.851)

Included

Included
Included
Included
Included
Included
Included59
0.7395

total cost ratio
total capital ratio
Tier1capital ratio
liquidity ratio
D cross border
Included
D YearD acquie country
D target country
Control variables(Market-to-book, lnAsset) Included
Intercept
Includedn96
r2
0.3178

Included
Included
Included
Included
Included
Included91
0.6804

Included

Included

Included
Included59
0.1151

Included
Included
Included
Included
Included
Included56
0.7334(11)(12)

0.0002(0.895)0.0007(0.700)Included

Included
Included45
0.1693

Included
Included
Included
Included
Included
Included
420.9202

Included
Included890.3011

Included
Included
Included
Included
Included
Included85
0.7029

(13)

(14)

(15)

(16)

(17)

(18)

0.1586(0.292)0.2558 **(0.034)1.6809 ***(0.003)3.0862 **(0.026)

Included
Included63
0.2444the other operational income ratio
loanloss provision ratio
non performing loan ratioloans ratio
deposit-loans ratio
total cost ratio
total capital ratio
Tier1capital ratio
liquidity ratio
D cross border
Included
D YearD acquie country
D target country
Control variables(Market-to-book, lnAsset) Included
Intercept
Includedn183
r20.1733

Included
Included
Included
Included
Included
Included178
0.5917

Included

Included
Included183
0.1769Included
Included
Included
Included
Included
Included178
0.5987

Included

Included
Included38
0.2118

Included
Included
Included
Included
Included
Included36
0.7946

0.9762 ***0.6055 **(0.001)(0.023)
Included
Included
Included
Included
Included
Included
Included
Included
Included183
178
0.23680.6064

The results of the 12month ABHR of acquires in each GDP weighted cross-sectional regression model. Heteroskedasticity-corrected P value are in parenthesis. The symbols ***, **, and * denote statisticalsignificant at the 1%, 5% and 10% level, respectively. The independent variables are the difference between acquires and targets of strategic factors after three year of acquisitions.

40

(Table 4) The DID results for acquirers
Panel A:Δ 1year
Dependent variable

Panel A) after 1(Delta
yearfor 1 years)
Panel B) after 3 year

regression

Δ ROA
(1)

Δ market-tobook(2)

Δ the otheroperational income
ratio

(3)

Δ non performing

Δ tier 1 capitalloan ratio

ratio

(4)

(5)

Δ liquidity
ratio
(6)

Δ size(7)

Δ total loans(8)

Δ nonperforming
loans

(9)

Δ total costs(10)

Δ total capital(11)

0.00060.0219-0.0004
0.03950.00080.00560.0297 *0.0346 *0.07060.0357 *0.0303(0.897)(0.117)(0.581)(0.407)(0.833)(0.244)(0.058)(0.065)(0.251)(0.100)(0.147)
4815
4323
4025
3315
2545
4516
4820
3897
3307
3802
4771
Simple regressionn0.00360.01660.01
0.00790.00790.02810.14250.2086
0.04370.18070.1047
r2
(12)
(13)
(14)
(15)
(16)
0.003
0.0392 **0.06010.00840.0089Legal lawsame
(0.539)(0.024)(0.330)(0.638)(0.607)0.00810.1419 **0.8162-0.0692
0.0677
English law origin(0.721)(0.038)(0.196)(0.505)(0.423)0.0056-0.0619-0.4450.06720.2339 ***French law origin(0.793)(0.614)(0.434)(0.651)(0.008)-0.02160.0997-0.47930.3341 ***-0.0713
Others law origin(0.223)(0.507)(0.444)(0.001)(0.378)
(17)
(18)
(19)
(20)
(21)
(22)
0.003
0.0403 **0.00250.068
0.0091
0.01
EFW
same
(0.542)(0.021)(0.751)(0.268)(0.610)(0.560)-0.0083-0.1161-0.0094-0.15170.4377 **0.2066 *Under Mean
(0.771)(0.390)(0.811)(0.699)(0.014)(0.058)0.015
0.1898-0.0053-0.1742-0.2158-0.1796
Upper Mean
(0.616)(0.165)(0.857)(0.587)(0.177)(0.158)
(23)
(24)
(25)
(26)
(27)
(28)
(29)
0.00290.0394 **0.00020.00290.06970.009
0.0111
Scope regulationsame(0.545)(0.023)(0.836)(0.710)(0.257)(0.613)(0.519)0.01920.2407 *-0.0038-0.0247-0.4388-0.1527-0.2154 *Under Mean
(0.628)(0.085)(0.262)(0.403)(0.257)(0.371)(0.066)-0.0023-0.0367
0.0061-0.03-0.3
0.4396 ***0.1487Upper Mean
(0.904)(0.780)(0.104)(0.389)(0.451)(0.004)(0.247)
(30)
(31)
(32)
(33)
(34)
(35)
0.003
0.0403 **0.00240.06650.011
0.0106
Entry regulationsame(0.539)(0.021)(0.755)(0.276)(0.535)(0.534)0.01840.1914-0.0197-0.4047-0.2052-0.2243 *Under Mean
(0.603)(0.116)(0.525)(0.306)(0.240)(0.059)-0.0026-0.1106-0.0251-0.2549
0.3744 **0.1254Upper Mean
(0.914)(0.506)(0.476)(0.466)(0.024)(0.349)
(36)
(37)
(38)
(39)
(40)
(41)
0.00310.0398 **0.00250.061
0.01090.0103Self-monitoring regulationsame(0.525)(0.022)(0.747)(0.319)(0.540)(0.546)0.0087-0.17620.0472-0.0682
0.15830.2449 **Under Mean
(0.727)(0.260)(0.182)(0.867)(0.376)(0.038)0.01830.17790.0094-0.1993-0.2835 *-0.1726
Upper Mean
41
(0.551)(0.173)(0.711)(0.450)(0.080)(0.155)
The results of the 1year DID of acquires with some control variables. Heteroskedasticity-corrected P value are in parenthesis. The symbols ***, **, and * denote statistical significant at the 1%, 5% and 10% level, respectively. The independent
variables are the difference between after one year (t=1) acquire's values and pre-effective year (t=0) values of strategic factors. The treatment banks are determined as acquired banks and the contronl banks are all asian banks withoutacquitions. In independent varibales, there are tratment dummy variables, treatment banks are 1, the others are 0.

Panel B:Δ 3year
Dependent variable

Δ ROA(Delta for 3 years)(1)
regression

Δ market-tobook(2)

Δ the otheroperational income
ratio

(3)

Δ non performing

Δ tier 1 capitalloan ratio

ratio

(4)

(5)

Δ liquidity
ratio
(6)

Δ size(7)

Δ total loans(8)

Δ nonperforming
loans

(9)

Δ total costs(10)

Δ total capital(11)-0.0054-0.0070.00060.1783 **0.09260.0156 *0.039
0.0755 **0.2861 ***0.05120.0809 *(0.443)(0.760)(0.645)(0.015)(0.313)(0.083)(0.239)(0.032)(0.003)(0.342)(0.074)
Simple regression
4566
4109
3703
3019
2162
4278
4570
3575
3019
3494
4520
n
0.00450.01010.013
0.014
0.01040.08340.17210.25480.10970.28620.1581
r2
(12)
(13)
(14)
(15)
(16)
-0.0046
0.02270.2509 ***0.0166
0.0542 *Legal lawsame
(0.439)(0.423)(0.008)(0.657)(0.099)0.0016
0.0925-0.5754-0.2277
0.2943 ***English law origin(0.919)(0.442)(0.455)(0.388)(0.001)0.0137-0.2815
0.23980.26310.1371
French law origin(0.480)(0.119)(0.792)(0.311)(0.310)-0.0184-0.0343
0.68130.4213-0.1918
Others law origin(0.461)(0.895)(0.452)(0.167)(0.165)
(17)
(18)
(19)
(20)
(21)
(22)
-0.0046
0.021-0.00490.2602 ***0.00050.055 *
EFW
same
(0.446)(0.457)(0.688)(0.006)(0.990)(0.091)0.00680.04
0.0974 *-1.0187 *-0.39090.2636 *Under Mean
(0.532)(0.820)(0.087)(0.081)(0.348)(0.074)-0.0087-0.2166 **-0.0781 *0.52410.3009-0.001
Upper Mean
(0.685)(0.013)(0.095)(0.365)(0.238)(0.994)
(23)
(24)
(25)
(26)
(27)
(28)
(29)
-0.00460.02010.0012-0.0046
0.256 ***
0.0007
0.0561 *Scope regulationsame(0.448)(0.474)(0.384)(0.704)(0.006)(0.985)(0.085)-0.0066-0.0833-0.0092
0.028
1.4834 *1.0477 *-0.3235 **Under Mean
(0.644)(0.707)(0.251)(0.721)(0.073)(0.055)(0.047)0.01280.16910.0198 **-0.0486-1.2744-1.2293 *0.4334 **Upper Mean
(0.347)(0.400)(0.041)(0.602)(0.143)(0.094)(0.020)
(30)
(31)
(32)
(33)
(34)
(35)
-0.00460.0204-0.00460.2557 ***0.00130.0556 *Entry regulationsame(0.450)(0.469)(0.702)(0.007)(0.973)(0.088)-0.0098-0.1701
0.02920.96770.9977 *-0.2782 *Under Mean
(0.516)(0.421)(0.719)(0.239)(0.066)(0.089)0.00870.06-0.045-2.1032 ***-1.2781 *0.4848 ***Upper Mean
(0.548)(0.777)(0.616)(0.010)(0.082)(0.010)
(36)
(37)
(38)
(39)
(40)
(41)
-0.00450.0201-0.00470.2542 ***-0.0005
0.0561 *Self-monitoring regulationsame(0.454)(0.474)(0.696)(0.007)(0.989)(0.085)0.0002
0.21840.01670.7852-0.04170.2541 *Under Mean
(0.995)(0.277)(0.764)(0.192)(0.810)(0.071)-0.0097-0.13140.012
0.22260.2509-0.1062
Upper Mean
(0.692)(0.418)(0.829)(0.725)(0.389)(0.396)
The results of the 3year DID of acquires with some control variables. Heteroskedasticity-corrected P value are in parenthesis. The symbols ***, **, and * denote statistical significant at the 1%, 5% and 10% level, respectively. The independent
42 factors. The treatment banks are determined as acquired banks and the contronl banks are all asian banks without
variables are the difference between three one year (t=3) acquire's values and pre-effective year (t=0) values of strategicacquitions. In independent varibales, there are tratment dummy variables, treatment banks are 1, the others are 0.

(Table 5) The ATE calculating from PSM for acquirers
Outcome variable

ATE from PSM: 1 yearn
ATE from PSM: 3 year
n

Δ ROA

Δ total loans

(1)

(2)

(3)

(4)

(5)

(6)

-0.0085(0.102)2963(7)-0.0132 *(0.073)2855

0.0568 ***(0.010)2888(8)0.0891 ***(0.009)2758

0.0673(0.263)2519(9)0.2015 **(0.043)2399

0.0533 **(0.033)2564(10)0.1680 ***(0.000)2468

0.0910 *(0.093)2960(11)0.1497 **(0.025)2817-0.0140 **(0.013)2564(12)0.0133 *(0.088)2474

Δ nonperformingloans

Δ total costs

Δ total capital

Δ liquidity

The results of the 1year and 3 year ATE from PSM for acquires with some control variables. P value are in parenthesis. The
symbols ***, **, and * denote statistical significant at the 1%, 5% and 10% level, respectively. The outcome variables are thedifference between after one/three year (t=1 or t=3) acquire's values and pre-effective year (t=0) values of strategic factors. The
treatment banks are determined as acquired banks and the contronl banks are all asian banks without acquitions.

43

(Table 6) The balanced check by variance ratio
Δ 1Y ROA
size
creditrisk
loandeporatio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
privatemoni_i~t
EFW_tlegal_e_t
Year Dummies

Raw
1.328
0.001
0.008
0.862
0.872
0.916
0.957
0.865
0.469

Δ 3Y ROA

Raw

size
creditrisk
loandeporatio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
privatemoni_i~t
EFW_tlegal_e_t
Year Dummies

1.32750.00080.00800.86250.87180.9156
0.95670.86531.0313

Matched
1.308
0.005
0.055
0.770
0.804
0.979
0.975
0.648
0.977

Matched1.30830.0046
0.05460.77030.80430.9786
0.97470.64780.9770Δ 1Y total loans

Raw

size
creditrisk
loandeporatio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_alegal_e_t
Year Dummies

1.43810.00061.03780.85380.85310.90500.4736
0.98560.77540.7096
1.09881.0770Δ 3Y total loans

Raw

size
costratio
capitalratio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_alegal_e_t
Year Dummies

1.28820.50501.43110.87440.87520.9292
0.4636
0.99850.81230.8846
1.06950.9085

Matched1.24410.00050.59120.76700.63850.80580.39640.91740.69440.70960.90490.9094

Matched1.05961.40420.86940.84610.85510.81570.58540.88400.72280.76430.89280.9144

Δ 1Y NPL

Raw

size
costratio
capitalratio
loandeporatio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_t
Year Dummies

1.47680.5022
1.19000.00740.85850.85440.90690.4666
0.99150.78130.84181.0836

Δ 3Y NPL

Raw

size
costratio
capitalratio
loandeporatio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_t
legal_o_t
Year Dummies

1.47600.53391.21410.00740.86090.86440.90770.48880.99180.76000.8293
1.07240.9095

Matched1.10040.53700.71160.01500.85780.70510.70830.29770.92200.65510.67280.8882

Matched1.27550.70410.82110.01590.86470.6436
0.71890.37440.84390.59040.67180.85310.8423

Δ 1Y total costs

Raw

size
creditrisk
capitalratio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_alegal_e_t
Year Dummies

1.43810.00061.03780.85380.85310.90500.4736
0.98560.77540.8376
1.09881.0770Δ 3Y total costs

Raw

size
creditrisk
capitalratio
gdpgwoth_a
gdpgwoth_tbkact_inx_t
compfor_inx_t
privatemoni_i~t
EFW_a
EFW_t
legal_e_alegal_e_t
Year Dummies

1.43470.00061.08630.8536
0.84900.94930.48300.9886
0.79210.85071.09041.0648The valance ratio is standardized different covariate's variance of treatments over controls.

44

Matched1.24410.00050.59120.76700.63850.80580.39640.91740.69440.70960.90490.9094

Matched1.30540.00060.81490.92150.66760.75580.27310.93680.60040.68760.92410.9452

Δ 1Y total capital

Raw

size
creditrisk
loandeporatio
gdpgwoth_a
gdpgwoth_t
privatemoni_i~t
EFW_tlegal_e_alegal_e_t
Year Dummies

1.07370.03530.00080.91950.93481.01950.87491.19291.1812

Δ 3Y total capital

Raw

size
creditrisk
loandeporatio
gdpgwoth_a
gdpgwoth_t
privatemoni_i~t
EFW_tlegal_e_alegal_e_t
Year Dummies

1.07780.03550.00080.92600.94011.02870.8945
1.1977
1.1838Matched1.08700.05640.00680.69080.7311
0.96810.5791
1.04111.0308Matched1.1971
0.05470.00530.78530.80020.99570.65201.02891.0260Δ 1Y liquidity Raw
size
costratio
creditrisk
capitalratio
loandeporatio
gdpgwoth_a
gdpgwoth_t
compfor_inx_t
privatemoni_i~t
EFW_tlegal_e_t
Year Dummies

1.43500.47300.0006
1.06370.00750.85280.85220.4716
0.98520.8344
1.0755

Δ 3Y liquidity Raw
size
costratio
creditrisk
capitalratio
loandeporatio
gdpgwoth_a
gdpgwoth_t
compfor_inx_t
privatemoni_i~t
EFW_tlegal_e_t
Year Dummies

1.42730.51890.0006
1.10140.00760.85700.8523
0.48110.98870.8505
1.0662

Matched1.3951
0.94400.00090.79600.01620.77580.80091.0396
0.89380.61500.8917

Matched1.2182
1.47380.00060.66050.01080.90650.80610.59070.96270.58710.9242

<Appendix 1>
Asia-Pacific Data

Asia-Pacific countries

Australia, Bangladesh, Bhutan, Brunei, Cambodia, China, Cook Islands, Federated States ofMicronesia, Fiji, French Polynesia, Guam, Hong Kong, India, Indonesia, Kiribati, Laos,
Macau, Malaysia, Maldives, Marshall Islands, Mongolia, Myanmar, N. Mariana Islands,
Japan, Nauru, Nepal, New Caledonia, New Zealand, Norfolk Islands, North Korea,
Pakistan, Palau, Papua New Guinea, Philippines, Singapore, Solomon Islands, Samoa (US),South Korea, Sri Lanka, Taiwan, Timor-Leste, Thailand, Tokelau, Tonga, Tuvalu, Vanuatu,
Vietnam, Wallis/Futuna Island, Western Samoa

45

<Appendix 2>
Strategy
1. Earning
diversification strategy

2. Risk strategy

3. Cost controlling
strategy
4. Capital adequacy
level strategy
5. Liquidity risk
strategy
The others

The strategy variables for Asian banks
Variables in Altunbas and Marques(2008)(1) Diversity of earnings
Other operational revenue／total assets(2) Off-balance sheet activity
off-balance sheet items／total assets(1) Credit risk
Loan loss provisions／net interest
revenue(2) Loan ratio
Loans／total assets(3) Deposit activity
Customer loans／customer deposits
Total costs／income

Proxy variables used in this paper

Total capital／total assets
Liquidity asset／total assets

Total capital ratio (Capital ratio 1) = total capital／total asset
Capital ratio 2 = tier 1 capital／risk asset
Liquidity ratio = Liquidity asset／total assets

ROA
Size
Market-to-book

ROA= net income/total asset
size= ln(asset)
Market-to-book=market value of capital/book value of capital46

The other operational income ratio = other operational revenue／total
assetsProvisions ratio (credit risk1) = loan loss provisions／net interest
revenue
Non-performing loan ratio (credit risk2) = non-performing loans／
total loans
Loan ratio = total loans／total assets
Deposit-loans ratio = total loans／total deposits
Total cost ratio = total costs／operating income