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University of Groningen

Faculty of Economics & Business

ReMSc International Economics and Business

Master Thesis

The Effect of merger and acquisition on Bank Performance

Case of the EU(15) countries, 2002-2008

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The Effect of Merger and Acquisition on Bank Performance

Case of the EU(15) countries, 2002-2008

Yifei Li

ABSTRACT

This paper investigates the effect of merger and acquisition on bank performance. By examining the merger and acquisition deals occurred in EU(15) between the year 2002 and 2008, this paper analyzes whether M&A could lead to performance improving. For the long-term performance measurements, this paper chooses to use both accounting ratios and cost efficiency to test the changes of bank performance before and after M&A. Event study is used to examine short-term impact of M&A. Empirical results find that acquirer banks usually get higher average ROAA and ROAE ratios and they are slightly more efficient than target bank. Non-M&A banks generate lower ROAE and ROAA ratios but higher cost efficiency scores than M&A banks. The abnormal returns of targets and acquirers changed significantly around the M&A announcement date. Targets generate positive returns around M&A announcement. The results reflect that M&A deal may change bank performance in short period and have impact on the bank operating ratios but the effect is not significant in long time period.

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Table of content

1. Introduction ... 4

2. Theory and Literature review ... 7

2.1 Merger and acquisition theory ... 7

2.2 Performance effects of bank mergers and acquisitions ... 14

3. Methodology ... 18

3.1 Accounting ratios for performance comparison ... 18

3.2 Cost efficiency for performance comparison ... 18

3.2.1 Stochastic frontier analysis ... 19

3.2.2 Overview of bank cost function ... 20

3.2.3 Cost efficiency measure and model ... 21

3.3 Event Study ... 24

3.3.1 Procedures of Event Study ... 25

3.4 Data and sample ... 29

4. Empirical Result ... 30

4.1 Efficiency result ... 30

4.2 Results on M&A effects ... 31

4.2.1 Comparison of bank performance by different bank types and countries ... 31

4.2.2 Efficiency hypotheses results ... 31

4.2.3 Event study result ... 33

5. Conclusion ... 35

6. Acknowledgement ... 36

7. References ... 37

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1. Introduction

Financial institutions have been consolidating since the 1980s. After the recent financial crisis, many financial firms became bankrupt and were restructured within the financial markets. The number of financial firms, especially banks, has decreased dramatically in the last decade. Compared with failing firms, most of the surviving firms are larger, more diversified and have more foreign subsidiaries. Researchers (e.g. Berger et al., 1999; Group of Ten, 2001) have investigated the incentives for the firms consolidation in general. For the industry sector, technological and financial innovation is the major force that promotes the consolidation of firms. Meanwhile, the development of technology also changes the production function of financial service institutions. Moreover, financial innovation (e.g. creation of new risk management tools) has changed the external environment (strategic and competitive conditions) of financial firms.

The trend for financial deregulation also accelerates the consolidation of financial institutions through merger and acquisition (M&A). In the meantime, mergers and acquisitions among financial institutions increased visibly with an average growth rate of 76%. It is a common way to consolidate and re-structure firms through M&A. Last decade has witnessed the unprecedented wave of mergers and acquisitions in banking industry at global level especially for European regions (Rezitis, 2006). These two trends can also be seen from figure 1 and figure 2. Figure 1 shows the changes of number of credit institutions and M&A in EU(15) countries in the year of 1999 and 2009. According to this statistics of European Central Bank (ECB), except Ireland, the number of credit institutions declined dramatically with an average rate of 16% from 1999 to 2009. M&A deals in EU(15) countries rise significantly in figure 2.

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incentives are synergies that increase profit and promote firm value. The firm value is supposed to increase after M&A for both the acquirers and targets or the new combined firms. Based on the synergies, market reactions in short-term should illustrate the effect of these synergies through increasing the stock prices of both the acquirer and target. In the long run, acquirers or the new combined firms should also perform better than the performance in pre-M&A period. Nevertheless, evidences in the last decade demonstrated that ninety percent of the M&As failed to meet their objectives. Many empirical results also have shown that only the target firms increase the firm values, whereas the values of acquirers or combined firms are destroyed in short time period after M&A. In addition, analysis on long-term (more than 1 or 2 years) performance of M&A shows different results. Some researchers found acquirers perform better after M&A. Other scholars discovered no increase on firm value when measured by accounting ratios or X-efficiency indicators.

Considering the trends showed in figure 2 and the mix empirical results on the effects of M&A, it is worth to study whether M&A can lead to performance improving for banks in most recent years. Moreover it is also worth to compare the post-M&A performance of bank with that of non-M&A banks. Only in this way could we get a complete overview of M&A effect in the whole banking industry. Given the above explanation this paper intends to get insight into the effects of mergers and acquisitions involving European banks during 2002 to 2008.

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view of the whole sample and explain the impact of bank type and country on bank performance.

This paper will use parametric approach of stochastic frontier analysis (Aigner et al., 1977; Battese and Corra, 1977; Meeusen and Broeck, 1977; Battesse and Coelli, 1995) to calculate the cost efficiency of the sample banks and compare with the banks not involved in merger and acquisitions. This technique not only provides results of efficiency rankings but also is in accordance with the result of standard non-parametric measures of performance (Weill, 2004). Event study (McKinlay, 1997) which is based on daily data provides information on market reactions immediate after M&A announcement. It is used to examine the effect of M&A on banks returns in short time period.

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2. Theory and Literature review

2.1 Merger and acquisition theory

According to the early research on the underlying rationale of M&A (table 1), there are two major motives (internal and external) for banks to engage in M&A deals in general. The internal incentives include both the performance motives and managerial motives. The external motives refer to the development of external environment of firms, such as technical innovation, globalization, and deregulation. The following content will first explain each of specific motives and then illustrate the theories that related to these motives of M&A. Seven hypotheses regarding the effects of M&A will be raised based on both the theories and motives.

The internal motives refer to the firm-specific consideration during M&A decision-making. Based on the effect of M&A on acquirer and target, there are three types of theories that explain the internal motives of M&A. The first one is value-increasing approach (Hitt et al., 2001). Value-increase theory considers that firms engage in M&As in order to pursue synergies (increase of the firm value). According to different sources of potential merger gain, the synergies include the efficiency synergies due to the increase of efficiency, operating synergies due to the increase of economies of scale, and the collusive synergies due to the increase of market power.

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The target is considered to have valuable asset but is less efficient in management. Therefore, it would create value for acquirer after the M&A.

In the second place, according to the corporate control theory, inefficient managers will be replaced by management team that can offer higher value to the firm through M&As. The management team will generate positive returns after remove the underperforming managers and improve firm performance (Weston et al., 2004). Consequently, in an efficient merger market, managers who cannot generate profit maximization will not survive. Inefficient mangers thus provide the market for corporate control (Manne, 1965).

Merger theory referring to the operating synergies assumes that the scale of operations of both acquirer and target is insufficient to generate economies of scale before the merger (Lensink and Maslennikova, 2008). In addition, it also assumes the existence of economies of scale and scope in the industry. Economies of scope could be achieved when both the acquirer and target are able to gain from each other and the combined firm could utilize the knowledge of specialized function effectively. Operating synergies has become one of the main motives for banks to engage in M&As (e.g. Hughes et al., 1999). Based on the analyzing of US bank merger wave in 1990s, Hughes et al. (1999) and Berger et al. (1999) suggested that cutting costs and achieving economies of scale are the primary goals of bank M&As.

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Leibenstein (1966) frontier-based efficiency is the effectiveness of the firm to produce an output under a given set of inputs. It measures the distance of the position of equilibrium of each bank form the optimal operative frontier (Beccalli and Frantz, 2008).

Market power theory proposes that collusive synergy that results from the increase of market power is a valid motive for M&As. All other things being equal, firms with more market power can charge higher prices and gain from the consumer surplus (Feinberg, 1985). Furthermore, according to basic economic competition theory, there will be less competition forces if the market has fewer players, and small number of players also increases collusive behavior among them. Horizontal mergers (mergers in the same industry) can lead to more collusive behaviors between the remaining firms. In addition, researchers (e.g. Calomiris and Pornrojnangkool, 2005; Garmaise and Moskowitz, 2006) have showed that credit become more expensive after bank M&As. Increased market concentration result to higher prices for borrowers and higher benefits for the banks. Therefore, striving for market power is an important motive of banks M&A because takeover could reduce the number of players and decrease the competition. Consequently, it is plausible to predict positive abnormal returns for both the acquirer and target firms after the announcement and deal close date of M&A.

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creation. However Cornett and Tehranian (1992) show positive relationship between merger and abnormal returns.

Besides efficiency and operating synergies, banks also tend to achieve financial synergies through M&As. Financial synergies come from three sources. Firstly, acquirers could avoid expensive capacity building by acquiring new assets of target firms. Secondly, banks could cut cost through internal financing. Acquirer usually has strong cash flows that could help target to avoid expensive external financing. Thirdly, the new combined firm would increase debt capability and increase tax savings on investment income.

Based on the synergies-related theories, we will get the following hypothesis (table1):

H1: The average cost efficiency and accounting ratios (ROAE and ROAA) of acquirer bank is higher than that of target banks before 2 years of merger and acquisition in EU(15).

H2: The average cost efficiency and accounting ratios (ROAE and ROAA) of the acquirer bank after 2 years of the merger and acquisition is improved compared with that before 2 years of merger and acquisition in EU(15).

H3: The average cost efficiency and accounting ratios (ROAE and ROAA) of acquirer banks involved in merger and acquisition after 2 years of merger and acquisition is higher than that of the banks not involved in merger and acquisition in EU(15)..

H4: The abnormal return of both the acquirer and target banks countries changed significantly after the announcement of M&As in EU(15).

H5: The abnormal return increased significantly for both acquirer and target banks countries in EU(15).

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terms of executive compensation. Because one of the direct consequences of M&A is the increase of firm size, managers are likely to engage in M&A deals if the executive compensation is connected to the firm size. Secondly, bank managers attempt to increase firm size in order to attain the status of “to-big-to-fail” by M&A. Banks provide financial services to both financial and non-financial institutions. So the development of banks has crucial impact on the growth of other industry sectors. Government tends to give subsidies to large banks in order to avoid investor and creditor risk or the systemic risk caused by bank failures. Therefore, bank managers may have the incentives to seek growth-by-acquisition in order to increase firm size.

Acquisition probability theory is based on the management motives. It assumes that firms pursue M&A for good market timing instead of economic benefit. Managers make M&A decisions when they are under outside pressure or considering personal interest maximization. According to this theory, the abnormal target returns of target are expected to be positive due to the takeover premium. However, acquirers are expected negative abnormal returns because the transaction is not benefit to the firm.

Another theory considering abnormal returns is pre-emptive merger theory. It suggests that acquirer tends to takeover a specific target in order to prevent it from being taken over by its competitors. Therefore, managers pay most of their attention on limiting possible losses due to the competitive disadvantages during the decision-making of pre-emptive M&A. Compared to the potential target being taken over by the competitor, it is better to maintain the firm’s competitive advantage by M&A even though it will decrease the firm value. Consequently, at the M&A announcement, target firms would get positive gains because of the takeover premium but acquirers are expected negative gains.

Based on the acquisition probability and pre-emptive mergers theory, we can get the following hypotheses (table 1):

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the announcement of M&As.

H7: The abnormal return of target banks in EU(15) countries increased significantly after the announcement of M&As.

Besides the internal motives, Bae and Aldrich (1990) pointed out that the consolidation wave is an important strategy for banks to response to the external development such as globalization, financial deregulation, and technological innovation, especially for banks in developed countries. The external motives mainly include three aspects. The first one is the technological innovation. Most of the firms choose M&A to acquire other firms with new technical products or services in order to achieve technological innovation. The second one is globalization. The trend for globalization promotes banks to expand market by cross-border M&As. Compared with green-field investment, acquiring banks can take advantage of target banks’ resources through cross-border M&A. Thirdly, the deregulation provides a more competitive environment that encourages banks to enter new market and threaten peer banks through M&A.

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banks take. Financial stability is largely affected by the presence of negative externalities (i.e. economic shocks) that have system-wide impacts. Banks are much more interconnected than other non-financial firms. Moreover, they face more risks than other firms and are more easily affected by economic shocks. Excessive risk taking by shareholders will lead to banking failures that have systemic externalities. One bankruptcy may affect other institutions and even endanger the whole economy. Banks are considered to have high potential for economies of scale especially for small banks (Molyneux et al., 2001).

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2.2 Performance effects of bank mergers and acquisitions

The debate that whether mergers can have a beneficial impact on banking and the public as a whole has become more and more important following the rapid pace of consolidation (Peristiani, 1996). A question arising from this debate is whether mergers are an effective restructuring tool. There are mainly three approaches to evaluate bank performance before and after mergers and acquisitions. One uses measurement of accounting profitability like return on equity and cash flow return. According to the literature by Delong and DeYoung (2007), accounting ratios measure actual financial performance during certain time period and provide researchers a way to study crucial elements of financial performance and also get overview of the overall financial performance. Therefore Delong and DeYoung (2007) evaluate the long-run change in financial performance after mergers by seven factors which are ROA, ROE, interest margin, cost efficiency, loans-to-assets, core deposits-to-assets and noninterest income ratio. They found from these measurements that banks financial performance improved after merger activities. Altunbas and Marques (2007) use the change between the merged banks’2 years average ROE after the acquisition and weighted average of ROE of the merged banks’2 years before acquisition as dependent variable of the model. Pilloff (1996) measured financial effect of acquisitions on bank performance by comparing profitability and efficiency in pre-merger period to that of post-merger period. Comett and Tehranian (1992) examine the pre and post-merger performance of 20 large holding-firm mergers during 1982 to 1987 and show the cash flow returns are better improved than a national group of publicly traded non-merger banks. However these accounting ratios are historical figures and often neglect current market values (Pilloff, 1996).

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become one of the most effective tools used in financial economics (Fama, 1991). Regarding the early literatures, the basic statistical format of event study has not changed over time. It focuses on measuring the sample securities’ mean and cumulative mean abnormal return around the time of an event. The magnitude of abnormal return at the event time represents the impact of this event on the shareholder values. Consequently, event studies emphasizing on the announcement effects for a short-term around an event provide useful information for explaining the corporate policy decisions (Khotari and Warner, 2006). There are several models (including constant returns model, market model, and economic model) for estimating normal performance. The market model relates the return of any security to the return of the market portfolio and can lead to better detection of event effects (MacKinlay, 1997). Therefore, this paper applies event study to test the short-term effect of M&A on bank performance through market model.

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Although event study provides useful information on short term effect of M&A, it this kind of market reaction evaluation only rely on market expectations and do not capture the actual gains after mergers and acquisitions (Pilloff , 1996). It is essential to apply an alternative approach in order to overcome this disadvantage. The third approach tests the changes of X-efficiency of banks before and after mergers and acquisitions. Given the explanation of Pilloff (1996) that the main motive for bank mergers and acquisitions is cost reduction thus most of the empirical studies examine X-efficiency to demonstrate whether bank performance improved after mergers and acquisitions. According to Leibenstein (1966) technical-efficiency is the effectiveness of the firm to produce an output under a given set of inputs. X-inefficiency happens under the situation that technical-efficiency can not achieve because of lack of competitive pressure. It measures the distance of the position of equilibrium of each bank form the optimal operative frontier (Beccalli and Frantz, 2008). X-efficiency includes cost and profit efficiency. Cost-efficiency is most commonly specified efficiency criterion to evaluate how close a bank’s cost is to a best-practice bank’s cost would produce with the same bundle of outputs. Similarly profit efficiency measures how close a bank is to the realization of the maximum possible profit under certain level of input and output.

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behavior is driven by cost efficiency rather than market power. Moreover banks will be forced to achieve maximum X-efficiency considering the competition pressure and technological advances (Vennet, 2002).

X-efficiency estimation follows two main approaches which are parametric and non-parametric approaches. Bauer et al. (1988) provide three parametric approaches (SFA, DFA and TFA) and one non-parametric approach (DEA) to estimate X-efficiency. For comparison of these different approaches, Bauer et al. (1988) found that the result following parametric approach is more consistent with standard measure of performance than DEA. However Weill (2004) argues that this finding is relevant for USA banking data. Resti (1997) found similar results using both SFA and DEA after examine 270 Italian banks. Drake and Weyman-Jones (1996) investigate British building societies and get high correlation between the efficiency scores which derived by SFA and DEA.

For bank profit and cost efficiency analysis, Bos and Kolari (2005) compares them between US and EU banks and finds that cost and profit functions for banks in both regions are strikingly similar and European banks have lower cost and profit efficiencies than U.S. banks. Among these literatures little has found positive effects of mergers and acquisitions on bank performance in US (Beccalli and Frantz, 2008). Gugler et al. (2002) explore the effects of mergers all over the world during 15-year period and find that there are a large percentage of mergers that decrease profits and efficiency. Huizinga et al (2001) analyze 52 EU bank mergers from 1994 to 1998 and found that cost efficiency of post-merger banks is higher than per-merger and profit efficiency improves only marginally.

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3. Methodology

3.1 Accounting ratios for performance comparison

This paper uses two important accounting ratios to capture the performance changes before and after M&A: ROAA (return on average asset) and ROAE (return on average equity). ROAA mainly reflects a bank’s profit relative to size and it is equal to net profit divided by average total asset. This ratio measures how efficient a bank can exploit its assets and it is commonly used for performance comparison among banks. ROAE refers to net income over the average shareholders equity. This reflects a bank’s profitability and presents how much profit a bank could make using the investment of shareholders. In this paper the accounting ratios and CE in the year of M&A will not be compared because of the strong impact of the deal on bank accounting operation. Instead I will capture ROAA and ROAE in both one year and two years before and after M&A.

In the field of performance study, accounting data is usually used as indicator, but it also has limitations. It is subject to noise problem and cannot reflect the value effects over long horizons after M|&As. It also has potential difficulty in capturing cross country data because of the different accounting rules. In addition, it only measures financial gains that reflect a fraction of total economic gains of M&As. Given these limitations, this paper use cost efficiency and event study to examine the effect of M&A on bank performance in long and short time period respectively.

3.2 Cost efficiency for performance comparison

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most of bank markets exist. Amel et al (2004) consider that the main reason for banks involving in M&A deals is to gain efficiency and get larger market in order to attract more consumers. From this point of view banks tend to maximize profit and minimize cost by M&A and achieve efficient operation goal. Therefore efficiency is an important factor to reflect the impact of M&A on bank performance.

3.2.1 Stochastic frontier analysis

This paper follows the parametric approach to investigate the cost efficiency of M&A and non-M&A banks. As mentioned in section 2, cost efficiency is wildly used concept for measuring the bank performance and it not only gives information on the production wastes and also on whether the firm gets optimal outputs under certain inputs (Weill, 2004). The method used to generate cost efficiency is stochastic frontier approach. The main stochastic frontier model specifications contain an error components specification which both allowed time-varying efficiencies and directly affected by certain variables (Battese and Coeill, 1992, 1995).

The stochastic frontier model can be referred to as a “composed error” model which means that a firm’s cost can be explained by an error term that includes two components, one is a random noise Vs’ and the other is technical inefficiency Us’. The standard model for estimation cost efficiency is

ci=c(yi,wi,;β)exp(vi+ui) (3.1)

Equation (3.1) is the cost function of an industry where ci is the total costs of the firm

yi is the vector of the firm output

wi is the price of the inputs

βis the unknown parameter vi is random variable

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In this equation vi is assumed to be iid N(0,σv2) and ui is assumed to calculate for cost

inefficiency of production. In order to make the noise vector captures noise specifically, it is better assume vi and ui are independent from each other.

If the equation transforms to log-linear Cobb-Douglas function, then it can be written as

lnci=β0+βylny+

βnlnwi,n+vi+ui (3.2)

The model can be estimated by maximum likelihood method under these assumptions for ui and vi. Following the maximum likelihood method in the study of Kumbhakar

and Lovell (2000) the parameter β and ui can be estimated by two steps. First step use

OLS to evaluate the slop parameters and the second step use the maximum likelihood to calculate the intercept and the variances of the two random variables. Then the β and ui can be got if the likelihood function is maximized.

Given the parameters can be estimated using the maximum likelihood method then the average error term which is equal to average of the efficiency term ui can be

computed. Then the cost efficiency can be obtain with the equation

CEi=exp(ui) (3.3)

3.2.2 Overview of bank cost function

Most of the literatures focus on cost and profit efficiency study of banking sector. The comparison of these two efficiency items rely on the assumption that whether the market is perfect (Cavallo and Rossi, 2002). In order to analyze the research question , this paper choose to use cost function approach and make the assumption that European Union banking industry markets is perfectly competition markets. Because the cost function approach can estimate multiple outputs model while the production function approach only make assumption of single output using stochastic frontier analysis.

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of the inputs and outputs and how to measure them. There are mainly two approaches in empirical study. One is production approach and the other one is intermediation approach. This paper follows the latter approach. This method views banks as collectors of funds and intermediated into loans and other assets (Humphrey, 1985). Thus the number of deposit accounts and loans can measure the banks output. Cavallo and Rossi (2002) consider the intermediation approach is better in analyzing financial institutions as a whole. In addition, frontier efficiency analysis can be better used under intermediation approach given it excludes interest payments.

3.2.3 Cost efficiency measure and model

For the inputs and outputs indicators I follow the intermediation approach and refer to Ferrier and Lovell (1990), Allen and Rai (1996), Eisenbeis et al. (1999) and Koetter (2005) to compute the result. The three outputs are total securities (y1), off-balance

sheet items (y2) and total customer loans (y3) and the first input is fixed assets (x1)

followed by total assets (x2) and total borrowed funds (x3). The input prices are w1,

price of fixed assets (=Non-interest expenses /Fixed assets); w2, price of labor

(=Personnel expenses/Total assets); w3, price of loanable funds (=Interest

expenses/Total borrowed funds). The indicators are presented below.

Input(X) X1 Fixed assets

X2 Total assets

X3 Deposit

Output(Y) Y1 Total securities

Y2 Off-balance sheet items

Y3 Total customer loans

Price(W) W1 Other operating expenses /Fixed assets

W2 Personnel expenses/Total assets

 W3 Interest expenses/Deposit

Total cost(TC)

TC Total operating expenses

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The model I use refers to the study by Weill. It is a translog cost function associated with input cost share equations

i i m i m m i n i i i m i mi i n ni i m n m n nm k n n k nk m j j mj n m n n m m v u t Z t y t w w t Z y Z w w Z y w w w w w w y y w w y W TC

t

i

m m + + + + + + + + + + + + + + + + =      

∑∑

∑∑

∑∑

ln ln ) ln( 2 1 ln ln ln ) ln( 2 1 ln ln ) ln( ) ln( ) ( ln 2 1 ln ln 2 1 ) ( ln ln ln 2 3 2 1 0 3 2 1 0 3 3 3 3 0 3

)

(

)

(lnZ

δ

ζ

κ

τ

τ

ι

η

δ

δ

γ

β

α

β

α

β

Where TC is total cost, ym is the mth bank output (m=1,2,3), wn is nth imput price

(n=1,2,3), w3 is price of borrowed funds. This equation can be estimated by maximum

likelihood method and I refer to the method of Greene(2005) and follow the bank stochastic panel frontier model with time-variant inefficiency. In the equation I use the Lang and Welzel (1996) to scale input prices by the price of borrowed funds. The error term εi concludes two components which are random noise vi and inefficiency

item ui. In year t the bank could not get optimal cost because of these two components.

In this equation the random noise is assumed to be i.i.d. with vi~N(o,σv2) and it is

independent of the explanatory variables. The inefficiency term is also i.i.d with ui~N(0.σu2) and follow the half-normal distribution. In addition ui is independent from

vi. Bank-specific efficiency scores are computed using the conditional expectation of

ui considering εi. The point estimation of cost efficiency are got from E(ui|εi).

Therefore cost efficiency is between 0 and 1 where a bank with cost efficiency of 1 means it is fully efficient. If a bank’s cost efficiency is 0.8, it represents 80% of observed cost could have been sufficient to produce an identical output.

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reinstate with time went by. From this point of view the cost efficiency is bank-specific given it is affected by fixed effect.

Considering the above analysis we can illustrate the first three hypotheses by the figure below,

This figure assumes that the locus of cost efficiency presents an increasing positive trend for all banks over observed time period. Bank A represents the banks not involved in merger and acquisition in the observed period and its cost efficiency is rising continuously. The less efficient Bank B acquired bank C in year t3 and become

a new bank D. Then the cost efficiency of Bank D improved fast due to the merger. Considering the M&A deals may have intense impact on the accounting operations for the acquirer and target banks. Consequently the year when the deal happened will not be analyzed. In order to study the cost efficiency changes in pre- and post-M&A period I compare the differences of cost efficiency surrounding two-year of the deal. Because two year before the deal could reflect relatively more exact efficiency conditions for both the acquirer and target banks in pre-M&A period. After one and two year of the deal, the bank may go through adaptive phase and the CE at that time could be more representative.

The first three hypotheses could be illustrated as

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H1: B1>C1

H2: B1<D1

H3:A1<D1

For the comparison test, I choose to use T-test first to estimate the whether the average bank performance indicators is significantly different before and after M&A or between different type of M&A banks. However T-test assumes the mean of the comparison variables are normally distributed. Considering the variables that need to be compared are not really normal distributed (see Figure 3-5). From figure 3 and 4, the distribution of ROAA and ROAE is clearly not normally distributed. In addition from figure 5 the cost efficiency is apparently half-normal distributed. Therefore beside T-test I choose sign test which is nonparametric analysis for testing the equality of matched pair of variablesSnedeco and Cochran, 1989. This method only requires the comparison pair have the same distribution and not limit to normal distribution. The hypotheses could be better test in that case.

3.3 Event Study

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deviation between the expected security return and observed return reflects the effect of M&As.

3.3.1 Procedures of Event Study

Based on MacKinlay (1997), the procedure of event study in this paper contains six steps.

Defining the time frame

Event day

The event day is the first trading day when new acquisition information reaches the market. According to Piloff (1998), the accuracy of defining the event day is crucial for event study because stock market reaction can only be observed to unexpected news. If the date is set after the effective market information release, the market will accommodate the event information and not provide the exact value of the difference between the normal return and observed return. In this paper, announcement date will be used as the event day.

Event window

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Estimation window

The estimation window is the control period prior to the event window. This is used to estimate the normal return model that will be used to calculate abnormal return after the event window. The event period is not included in the estimation window in order to prevent the event from affecting the estimation of normal return model. In event study, it is customary to set the end boundary of the estimation window right next to the start boundary of event window because the closer the boundary of the event window is set to event day, the more likely the parameters of the normal return model to be affected by the M&A announcement. Based on the method of Cybo-Ottone and Murgia (2000), this paper defines estimation window to be [-270, 21].

Selection criteria

The criteria consists restrictions imposed by the return data listing on specific stock market. This paper analyzes the EU banks from European stock market and collect data from the total return index in DataStream. In addition, this paper chooses to use EU stock market index as the benchmark for the estimation of the abnormal returns of both acquirers and targets.

Measuring abnormal return

The abnormal return refers to the difference of the actual ex post return of the security over the event window and the normal return of the firm over the event window. The normal return is the expected return without conditioning of the event happening. Therefore, for firm i and event date τ the abnormal return is

) ( τ τ τ τ R E R X ARi = ii (1) τ i

AR is the abnormal return

τ

i

R is the actual return )

(Rτ Xτ

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X is the conditioning information for the normal return model and this paper chooses to use market model for parameter estimation.

Market model for measuring normal performance

The market model is a statistical model that connects the return of any given security and the return of the market portfolio. The market model assumes a stable linear relation between the market return and the security return. For security i the market model is it m i i it R R =α +β +ε Eit=0) var(

ε

it)=

σ

ε2i (2) Where Rit is the period-t return on security i

Rm is the market portfolio

it

ε

is the zero mean disturbance term 





  are the parameters of the market model

The estimation of the market model is based on ordinary least squares. Based on … the abnormal return can be analyzed and measured.

τ τ τ i

α

i

β

i m i R R AR ^ ^ ^ − − = 1 ,..., 1 2 ,..., 2 5 ,..., 5 10 ,..., 10 20 ,..., 20 + − + − + − + − + − =

τ

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If the estimation window is large enough, the sample error will decrease to zero, then we can assume the variance of the abnormal return in the following and the abnormal return observations will become independent through time:

Var(εit)=

σ

ε2i (4)

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)) ( , 0 ( ~ ^ 2 ^ τ τ

σ

i i N AR AR (5)

Aggregation of abnormal returns

The next step is aggregation of the abnormal returns in order to draw inferences for the event. Specifically, the abnormal returns are first aggregated through time and then across securities:

= = = T i N i T N AR N CAR 1 , 1 , 1 ( ) τ τ (6)

T is the total number of days in the event period N is the number of banks

T-test

The T-test is used to examine whether the abnormal returns are significantly different from zero. The null hypothesis refers to the cumulative mean abnormal return in a given event window is zero.

H0: 0 ) ( ) ( , , = T N T N CAR CAR

σ

µ

H1: 0 ) ( ) ( , , ≠ T N T N CAR CAR

σ

µ

When the null hypothesis is rejected, it illustrates the M&As did have significant influence on return value of banks after announcement.

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3.4 Data and sample

The data set for accounting ratios and efficiency estimation is obtained from two sources: Zephyr database for data on the M&A operations; Bank scope for balance sheet data and profit and loss data of M&A and non-M&A banks.

The sample comprises European Union (15) banks (commercial, saving and cooperative banks) involved in M&A deals between 1/01/2002 and 31/12/2008 and the comparison sample contains non-M&A EU banks from 2002 to 2008. The sample includes three types of banks which are commercial, saving and cooperative banks. Commercial banks offer both commercial and retail services and they are usually profit oriented. Therefore this type of bank always focuses on the investment and wholesale services. While saving and cooperative banks emphasize on retail business. They mainly serve for the customers and small and medium sized companies ( Agarwal and Elston, 2001) and most of their funds is consumer deposits.

Table 3.1.1, 3.1.2 and 3.1.3 give an overview of the sample and data for long term effect estimation. It can be seen from the table that most of the merger and acquisitions deals occurred mainly in Germany, Italy, Spain and France during the year 2002 to 2008 for European Union (15) banks. Germany banks which not involved in M&As account for more than 50% of the total non-M&A banks. The M&A banks in Germany take about 25% of the entire M&A banks. Followed by Italy banks which occupy nearly one-quarter of the total sample.

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Because event study requires using stock market price to calculate the abnormal returns of the banks, the sample only consists of European Union (15) listing banks (commercial, saving and cooperative banks) involved in M&A operations during 01/0102002 to 31/12/2007. Table 4.1 provides an overview of the sample. Based on the table, banks in France and Germany take the largest portions of the entire sample. The number of France banks takes around 20 percents of the total acquirers and 36 percent of total targets. Banks in Germany account for about 16 percents and 18 percents of the total number of acquirers and targets respectively. There is similar number of acquirer banks in each year. In contrast, the number of targets in 2005 takes around one third of the total targets.

4. Empirical Result

4.1 Efficiency result

Table 3.2 summarizes the key variables for estimating cost efficiency. It is clear that the standard deviation of the three output terms is really large. This indicates that banks data in the sample is spread out over a large range of values. There are very large banks with over million Euros total asset while there are still small banks with little assets. Because of the large difference in outputs and inputs among the sample banks, input prices also have great deviations.

Table 3.3 shows the parameters and descriptive statistics for cost frontier. We can see all the dependent variables are statistic significant. According to the section 3, the error term εi in cost function concludes two components which are random noise vi

and inefficiency item ui. The inefficiency item Insig2u in table 3.3 shows statistic

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4.2 Results on M&A effects

4.2.1 Comparison of bank performance by different bank

types and countries

Table 3.4.1 shows M&A and non-M&A banks performance by different countries. For non-M&A banks Netherlands and Luxembourg generate highest ROAA and ROAE respectively. Denmark gets higher cost efficiency than other banks. Netherlands also have higher ROAA and ROAE among acquirer banks. UK acquirer banks are more efficient than other banks. Target banks from Denmark and Portugal have largest ROAA and ROAE ratios. Netherlands banks operate more efficient than other country’s banks.

Table 3.4.2 illustrates that the three types of banks which perform different role in M&A deal show different ROAA ROAE and cost efficiency scores. Generally in the sample the commercial banks have higher ROAA and ROAE while generate lowest cost efficiency scores compared with saving and cooperative banks. This may because of the commercial banks’ main objective is maximizing profit while cooperative and saving banks focus on retail business. For saving and cooperative banks, the latter one demonstrate better bank performance which can be seen from the higher values of ROAA ,ROAE and also cost efficiency.

4.2.2 Efficiency hypotheses results

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is better than target bank before two years of M&A deal. As illustrated in table 6.1 the acquirer bank generate higher ROAA and ROAE ratios. The ROAE of acquirer bank is significantly higher than that of target bank during the observed time period. Considering the comparison of cost efficiency between acquirer and target bank we can clearly see from the table that the acquirer bank is more cost efficient than target bank. However the difference between averages cost efficiency of acquirer and target is not statistically significant. It can be seen that there is little differences between the acquirer and target. This result do not support the first hypothesize and shows that though acquirer bank is more efficient and profitable than target bank before merger and acquisition from the value of ROAA and ROAE , the banks which play different roles during M&A deal show little difference in cost efficient .

Then from the table 5.2 we could test the second hypothesize which compare the accounting ratios and cost efficiency of acquirer bank before and after one- and two- year of M&A deal. From this examination we could study the effect of the M&A and test whether M&A could lead to better bank performance. As can be seen from the table, for the accounting ratios the acquirer bank generate higher ROAA and ROAE after two years of M&A deal compared with that two years before. But the result shows that both ROAA and ROAE of acquirer bank after one year of deal are lower than the ratios one year before the deal. This may due to the strong impact of merger and acquisition on bank accounting practices. In one year period after M&A deal the new bank may face a lot of conflict and integration problems therefore achieve lower ROAA and ROAE. Though the accounting ratios are not getting better after one year of M&A it is not significantly different from that before one year of deal. In contrast with accounting ratios the cost efficiency of acquirer bank is improving after both one- and two-year of the M&A, Nevertheless both of the cost efficiency differences are not statistically significant.

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banking industry therefore this comparison can test whether banks involved in M&A is more efficient than banks not engaged in the deal. Table5.3 demonstrates the result of the third hypothesize test and it is clear that the ROAA and ROAE of acquirer bank are not significantly higher than that of non-M&A banks after both one- and two-year of the deal. While for the test of cost efficiency, the average cost efficiency of acquirer bank are apparently lower than that of non-M&A group and the differences are very significant. Therefore the third hypothesize is not fully supported by the data analyze.

In conclusion the empirical result unfortunately do not support hypothesizes 1,2 and 3. From the three hypothesize test we can see that M&A may have impact on bank performance. However whether the impact is good or bad for the bank remains not clear. Given the comparison of M&A bank, the acquirer are the banks with higher ROAE and ROAA ratios and they are relatively more cost efficient. For the acquirer bank, M&A could improve banking operation and increase cost efficiency in post-M&A period. Although acquirer banks also generate higher ROAE and ROAA, they do not show higher cost efficiency ratios when comparing with peer non-M&A bank in the same industry.

The result may affected by the different bank type different country regulation and corporate governance of the banks. In the sample most of the banks are from Germany, Italy and Spain. Among those banks commercial banks take a large proportion and emphasize on the investment and wholesale instead of retail services. Therefore the commercial bank may strongly affect the estimating of cost efficiency scores.

4.2.3 Event study result

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fifth event window. However, these negative returns are small and not significant different from zero. According to signed rank test, acquirers all show significant negative abnormal returns in five event windows. In table 5.4.2, targets show positive abnormal returns for all event windows based on T-test.

Cumulative abnormal return is the sum of all the abnormal returns of targets and acquirers. According to table 5.4.3 and table 5.4.4, cumulative abnormal return of acquirers are all significant different from zero. Specifically, acquirers generate negative cumulative abnormal returns in event window 1 and 5 based both t-test and signed rank test. The cumulative abnormal returns increased significantly during all the event windows. Based on table 5.4.3 and 5.4.4, cumulative abnormal returns of acquirers decreased significantly during event window 1 and 5, targets show positive returns in all five event windows.

Test of CAR is shown in table 5.4.5 and 5.4.6. From both t-test and signed rank test, targets achieved positive CAR around the M&A announcement date and the CARs are significantly different from zero. Regarding acquirers, the CAR declines significantly before and after 1 day of announcement, and then increased sharply from event window 2 to 4. However, acquirers’ returns decrease in event window 5 after the sharp increase. The changes of CAR can be also seen from figure 7 and 8.

Figure 7 and 8 also illustrate the change trend of CAR. In figure 7, acquirers in the sample show a sharp decrease during (-20, -10) period of the announcement date. CAR also declined immediately after the announcement of M&A. Then acquirer banks’ CAR increased dramatically after 5 days of the announcement date. Regarding the targets banks, CAR increased extremely after the M&A announcement. Both the two graphs illustrate that the returns of acquirers would decline and targets would increase positively after the announcement of M&A.

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results. Although the cumulative abnormal returns and CAR of acquirers changed significantly, the changes of abnormal returns of acquirers do not follow a single trend. In event window 1, acquirers generate negative returns and then increase significantly during event window 2, 3, and 4. However, they decline dramatically during event window 5.

5. Conclusion

This paper tends to get insight into the impact of merger and acquisition on bank performance. Seven hypotheses are generated based on different M&A theories. According to the hypotheses, both long-term and short-term effects of M&A on bank performance are examined by different indicators (hypotheses testing result is concluded in table 6). Specifically, in order to analyze the long-term effect of merger and acquisition this paper first collects all the M&A deals happened during 2002 and 2008 in EU(15) as the sample to compare both accounting ratios and cost efficiency changes. There are three hypothesizes to be examined regarding the efficiency estimation. The first two hypothesizes mainly study the M&A banks and last one relate to the comparison of M&A bank with non-M&A banks. The empirical result shows that acquirer bank generate higher ROAE and ROAA ratios and also little more cost efficient than target bank. M&A may lead to little performance improving for acquirer bank. The non-M&A bank’s ROAE and ROAA are lower than acquirer banks while the cost efficiency of bank not involved in M&A are higher than M&A banks. All the three hypothesizes are not significant tested especially for cost efficiency of the banks. This may interpret that M&A do have impact on bank performance but the effect is not significant surrounding 2 years of the deal happens.

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Event study results show that the cumulative abnormal returns changed significantly after the announcement of M&A during the year 2002 to 2007 for both acquirers and targets. Specifically, target banks generate positive abnormal returns during the five event window periods. However, the abnormal returns decreased sharply during the first and last event window in this paper and increased rapidly in other three event windows.

According to the empirical results, M&A do has significant impact on the short-term performance of both acquirers and targets. However, after short-term changes, bank performance does not improved significantly in long time period. Moreover, banks may gain more profits after M&A through increase of economies of scale or market power instead of improvement in cost efficiency. In addition, the managerial motives may play more important role during decision-making of banks’ M&As.

6. Acknowledgement

First, I would like to offer my sincerest gratitude to my supervisor, Dr. L.Dam, who always gave me valuable suggestions and advises during the thesis writing period. Secondly, I want to express my gratitude to my friends, who company me through the whole steps of thesis writing.

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8. Appendix

Table 1 Theory and Hypotheses Influencing

factor Approaches Motives Theory Hypotheses

Internal factors Value-increas ing Synergi es Efficien cy

Efficiency theory Acquirer Target Corporate control Positive CE Positive CE Operati ng Economies of scale Positive AB & CE Positive AB & CE Collusi

ve Market power theory Positive AB Positive AB Financi al Positive AB & CE Positive AB & CE Value-decrea sing Managerial Acquisition

probability Negative AB Positive AB Pre-emptive merger

theory Negative AB Positive AB

External factors

Technological innovation Globalization Financial deregulation Note: CE refers to cost efficiency

AB refers to abnormal return

Table 2 Banks number changes and M&A

Number of bank M&A

Country N1999 N2009 ∆N Growth rate N1999 N2009 ∆N Growth rate

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Spain 386 352 -34 -8.81 10 8 -2 -20.00

United Kingdom 502 389 -113 -22.51 40 62 22 55.00

Total 8824 6961 -1781 285 383 86

Average -16.40 75.66

Sources: ECB (2000, 2009) and Zephyr

Table 3: Long-term effect estimation result

Table 3.1 Number of M&A and non-M&A banks (by country and by M&A year)

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Sweden 4 5 7 8 4 11 6 45 Spain 8 15 15 15 11 27 14 105 UK 1 0 6 6 7 1 0 21 Total 173 226 254 236 217 197 220 1523 Table 3.1.3 Target Country 2002 2003 2004 2005 2006 2007 2008 Total Austria 1 2 4 6 0 0 16 29 Belgium 0 4 6 0 2 0 2 14 Denmark 0 0 0 0 2 0 4 6 Finland 1 0 0 0 2 2 0 5 France 13 21 16 20 15 25 8 118 Germany 47 51 21 31 7 11 28 196 Greece 0 0 0 3 3 1 2 9 Ireland 0 0 0 0 0 0 0 0 Italy 24 14 7 12 22 37 36 152 Luxembourg 0 2 4 3 0 2 2 13 Netherlands 0 0 0 0 0 1 2 3 Portugal 0 4 4 2 0 0 0 10 Sweden 2 0 2 2 0 0 0 6 Spain 2 4 2 4 5 7 14 38 UK 0 0 0 0 1 0 0 1 Total 90 102 66 83 59 86 114 600 Total observations: 22405

Table 3.2 Summarize of variables for cost efficiency

Variable Mean Std. Dev. Min Max

y11 2,320.69 27,650.83 0.03 1,637,583.00 y22 3,003.42 39,415.38 0.02 3,132,690.00 y33 6,433.61 60,895.40 0.02 3,172,226.00 w14 8.38 65.61 0.02 4,354.00 w25 0.02 0.02 3.0300E-07 0.62 w36 12.11 367.03 5.8600E-04 31,940.00 h57 504.88 4,408.91 0.01 204,870.60 Observations:22.405 1

Total security 2 Off-balance sheet items 3 Total customer loans 4 Fixed assets price 5

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Table 3.3 Parameters and descriptive statistics for cost efficiency Dependent variables lnTC/w3 Dependent variables lnTC/w3 ln(w1/w3) -0.0348*** lny3 0.910***  (-0.012)  (-0.0207) ln(w2/w3) 1.170*** lnz 0.0347  (-0.014)  (-0.0237) lny1lny1 0.0846*** lnzlnz 0.186***  (-0.00129)  (-0.00762) lny2lny2 0.0142*** ln(w1/w3)lnz 0.0351***  (-0.00182)  (-0.00307) lny3lny3 0.200*** ln(w2/w3)lnz -0.0300***  (-0.0036)  (-0.00312) lny1lny2 -0.00301** lny1lnz -0.00915***  (-0.0013)  (-0.00279) lny1lny3 -0.0509*** lny2lnz 0.0187***  (-0.00237)  (-0.00296) lny2lny3 -0.0273*** lny3lnz -0.168***  (-0.00242)  (-0.00481) ln(w1/w3)2 0.0190*** t2 0.0309***  (-0.00187)  (-0.00111) ln(w2/w3)2 0.0692*** Ln(w1/w3)t -0.00305***  (-0.00207)  (-0.00101) ln(w1/w3)ln(w2/w3) -0.0554*** Ln(w2/w3)t 0.00191*  (-0.00181)  (-0.00107) ln(w1/w3)lny1 0.0141*** lny1t 0.00763***  (-0.0011)  (-0.00103)

ln (w1/w3)lny2 4.09E-05 lny2t 0.00427***



(-0.00164)  (-0.00137)

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