外文文献翻译--欧盟国内外银行盈利能力影响因素分析(节选)

中文2474字

1外文资料翻译译文

欧盟国内外银行盈利能力影响因素分析

摘要：

本文使用银行级数据，通过1995 - 2001年期间国内和外国银行在15个欧盟国家的商业运营情况来了解银行的具体特点和整体银行业环境对影响盈利能力。结果表明, 国内和外国银行的盈利能力不仅受银行具体特点的影响，也受金融市场结构和宏观经济条件的影响。除了在集中情况下国内银行利润, 所有的变量都是有重大意义的，尽管它们的影响和关系对国内和国外银行并不总是相同。

1 介绍

在过去的几年许多的因素造成了欧盟银行业竞争日益激烈。最重要的因素之一是针对服务、建立、运行和监督信贷机构的第二个欧洲指令出台，在银行和金融领域放松管制。这个指令为所有欧洲银行机构在单一欧洲金融市场和提供了平等的竞争条件，因此银行正在先前无法预料的国内外竞争之中。另外, 最近一些的技术进步对规模经济和范围提供了更多的机会，而采用欧元也加速了行业的变化。此外,宏观经济政策后大多数国家通货膨胀率和利率逐步降低。最后,在越来越多的欧洲国家非金融公司被允许提供传统的银行服务,并且在竞争中进一步提高，银行被迫产生新的产品和寻找新客户。许多银行为了参加欧洲市场和银行业扩大被迫增加规模，通过合并和收购的方式进行了前所未有的整合。

在环境快速变化的情况下，这些变化给在欧盟的银行带来很大的挑战，因此影响了他们的效能。格林指出，充足的收益是必要的条件让银行保持偿付能力,在一个合适的环境生存、发展和繁荣。考虑到银行业的健康发展和经济知识增长，影响银行的盈利能力的潜在因素不仅和管理者有关，而且和众多利益相关者如中央银行,银行家协会、政府以及其他金融当局有关。

2 文献综述

参考文献与本文可分为三大类。第一部分是研究集中于银行的盈利能力的决定因素。第二部分包括研究欧洲银行的利润和成本效率。第三由研究比较国内外银行。在下面几个部分中,我们讨论这些类别中的每一个。

3 决定因素和变量选择

3．1 因变量

本研究使用平均资产回报率(ROAA)来评估银行的性能。ROAA是把净利润表示为一个百分比的平均总资产。它显示了每欧元资产获得的利润并指明如何有效的银行的资产去设法创造收益。平均资产是用来在会计年度中发现发现资产上的任何差异。Golin(2001)指出,平均资产回报率是衡量盈利能力的关键。

3．2 决定因素和独立变量

四个银行特征用作内部决定因素。这些都是银行的总资产、成本收入比、权益与资产比率和银行的贷款的比率除以客户和短期融资。此外,六个外部因素是用来检查环境的影响对银行的表现。

4 数据和方法

我们的示例是一个平衡面板数据集，由在15个欧盟国家的584家商业银行从1995 - 2001年期间的4088组数据组合而成。表2和表3分别展示了目前银行的数量所属的国家和所有权和样本特征。

5 实证结果

表4报告对银行的平均资本回报率（ROAA）的实证估计。第一列体现了把所有的银行(584)同时考虑的结果。第二、三列体现了我们通过银行经营这所属的国家把银行分离出来的结果。我们定义一个银行是国外还是国内是依靠外国人的股份资本是否超过50%，这个子样本包括332家国内银行和218家外资银行。在这个阶段，约34个银行被排除在分析,由于我们没有足够的信息来辨别其是国内还是国外。

除了在集中情况下国内银行平均资本回报率，所有的变量都是显著的,尽管它们对国内外银行的平均资本回报率的影响和关系并不总是相同的。这个模型的解释力对国内银行是更高的(调整R2国内银行等于0.6371，而外资银行等于0.3903),而f统计所有模型的重要性在1%的水平。这意味着额外的因素可能会影响外国银行的盈利能力。像威廉姆斯(2003)正确地指出,外资银行运行在一个受两个因素影响的主机市场，分别是他们是属于外国跨国银行和他们的参与银行系统的主机。

由比较高的显着系数的股权资产（EQAS）和成本收入比（COST）显示，在一般情况下，平均资产回报率的主要决定因素是资本实力及费用管理效率。股权资

产与平均资本成本是正相关关系，无论我们考察国内银行还是国外的银行，而且它是国内银行盈利能力是最重要的决定因素。这一调查结果与以往的调查研究是一致的，资本充足的银行在破产和规模缩减方面面临着较低的成本，因此，它们的融资成本比较低或具有较低的外部资金的需求，促成了较高的盈利能力。成本收入比（COST）被预期是外资银行盈利能力的最重要的决定因素，是正如预期的那样的，呈现出负相关关系，这些费用的增加会在很大程度上减少在欧盟银行的经营利润。很多学者也发现了费用管理不善是盈利能力差的主要因素。因此，在欧盟的商业银行应采取必要的行动，以实现更有效的成本控制，以进一步增加他们的利润。外国（-0.309）和国内银行（-0.144）之间的系数的差异可能是由于不当的管理操作以及与监测机构存在的距离。

关于流动性，结果是喜忧参半。客户的净贷款及短期资金比率（LOFUND）在统计上显着，并与国内银行的盈利能力呈正相关，表明银行流动性资产持有量水平和盈利能力水平呈负相关关系，与预料的一样。对于外资银行来说，变量也很显着，但有一个负号，说明流动性和银行利润的正相关关系，出乎我们的意料，虽然与伯克（1989）和Kosmidou（2006）的研究一致。

无论是国内或国外银行，规模（SIZE）之间的关系和银行的业绩是负的。负系数表明，在这两种银行中，大银行的收入水平就会低于的利润，研究也发现要么经济规模和范围为较小的银行或金融机构的规模不够大。范德Vennet（1998）发现的证据表明，在欧盟规模经济效益只有在根据资产小于10亿欧元的最小银行才有效，此后报酬不变和规模不经济的最大银行超过1000欧亿。

6 结语

在最近几年中，许多因素加剧了欧盟银行业的竞争，尤其是银行的操作环境的急速变化给银行带来了巨大的挑战。这是合理的假设去认为所有这些变化都必然会对银行的表现有一定的影响。格林在2001年指出，银行需要足够的资金以维持偿债能力并在合适的环境中生存。银行的效率和经济增长之间的关系是有据可查的。与此同时，银行的破产会对经济产生不利的后果。因此，认识影响银行的盈利能力的潜在因素是必不可少的，不仅是为了银行经理，也是为了在15个欧盟国家众多的利益相关者，如的中央银行，银行协会，政府和其他金融机构。从这项研究中得出的结论对于那些经济和银行系统正经历根本性变化的新欧盟

国家都是很有意义的。

2外文原文

Factors influencing the profitability of domestic and foreign commercial banks in the European

Union

Abstract

Using bank level data this paper examines how bank's specific characteristics and the overall banking environment affect the profitability of commercial domestic and foreign banks operating in the 15 EU countries over the period 1995–2001. The results indicate that profitability of both domestic and foreign banks is affected not only by bank's specific characteristics but also by financial market structure and macroeconomic conditions. All the variables, with the exception of concentration in the case of domestic banks profits, are significant although their impact and relation with profits is not always the same for domestic and foreign banks.

Keywords Banks; European Union; Profitability

1. Introduction

Over the last years a number of factors have contributed to the growing competition in the European Union (EU) banking sector. One of the most important factors is deregulation, promoted by the Second European Directive on Banking and Financial services, concerning establishment, operation and supervision of credit institutions. This Directive sets out the principles of banking in the Single European financial market and provides equal competitive conditions for all European banking institutions. As a result banks now compete in previously inaccessible domestic and foreign markets. Furthermore, a number of recent technological advances offered more opportunities for economies of scale and scope while the adoption of euro accelerated the changes in the industry. For instance, income generation from foreign exchange transactions has been lost while the pricing of banking products and services has become more transparent, enhancing competition. Furthermore, the macroeconomic policies that were followed in most countries gradually reduced inflation and interest rates. Finally, in more and more European countries non-financial firms were allowed to offer traditional banking services, leading to further increase in competition. Therefore, banks were forced to generate new products and seek new customers. This is reflected in the continued diversification

across geographical areas and business lines. Many banks have been forced to increase in size in order to compete in the enlarged European market and the banking industry experienced an unprecedented level of consolidation through mergers and acquisitions.

It is reasonable to assume that all these changes posed great challenges to banks in the EU as the environment in which they operated changed rapidly, a fact that consequently had an impact on their performance. As Golin (2001) points out adequate earnings are required in order for banks to maintain solvency, to survive, grow and prosper in a suitable environment. Given the relation between the well-being of the banking sector and the growth of the economy (Rajan and Zingales, 1998, Levine, 1997 and Levine, 1998), knowledge of the underlying factors that influence banks’ profitability is essential not only for the managers of the banks but for numerous stakeholders such as the Central Banks, Bankers Associations, Governments, and other Financial Authorities. Knowledge of these factors would also be of particular interest to the new EU countries whose economies and banking systems are experiencing fundamental changes during this period.

The aim of this paper is to extent earlier work on the determinants of profitability of banks in the EU and examine to what extent the performance of commercial banks operating in EU markets is influenced by internal factors (i.e. banks’ specific characteristics) and to what extent by external factors (i.e. macroeconomic and financial market structure) in view of the ongoing process of integration and concentration. Although a growing literature uses efficient frontier approaches to examine the profit and cost efficiency of EU banks (e.g., Altunbas et al., 2001 and Schure et al., 2004), to our best knowledge, there are only few studies that focus on the determinants of profitability while focusing on the EU as a total1 (e.g., Molyneux and Thorton, 1992 and Staikouras and Wood, 2003).

Molyneux and Thorton (1992) were the first that examined the determinants of banks profitability operating in 18 European countries over the period 1986–1989. Most recently the European banking sector was examined by Staikouras and Wood (2003) that considered banks from 13 EU countries over the period 1994–1998. The present study attempts to provide additional and more recent evidence on the determinants of banks profitability in the EU. In order to accomplish this task, our paper differs from the earlier mentioned studies in several aspects. First of all, we include more recent years in the analysis by examining the period 1995–2001. Furthermore, we examine more factors by introducing the influence of additional financial market structure variables such as stock market capitalization to GDP, stock market capitalization to assets of deposits money banks and assets of deposits money banks to GDP, not considered in the above studies. Finally, we are the first that distinguish between foreign and domestic banks. During the last years both developed and developing countries around the world have relaxed restrictions on foreign banking and most of them now allow more foreign banks to undertake more banking-related activities in their domestic banking markets, mainly because of the increasingly importance of international trade in goods and financial services. As Goddard et al. (2001) point out since 1989 the number of foreign banks has increased in every banking market in

Europe, which now hold a large proportion of banking assets in the UK (53% of banking sector assets in 1999), Belgium (24% of assets in 1999), Portugal (12% of assets in 1999) and France (12% in 1999). Previous studies that distinguish between domestic and foreign banks focus mostly on differences on profit and cost efficiencies using frontier approaches (e.g., Berger et al., 2000 and Sathye, 2001) or financial characteristics that differentiate these two groups of banks (e.g., Kosmidou et al., 2006a) and not on whether the internal and external determinants of profitability among domestic and foreign banks are different.

The rest of the paper is structured as follows: Section 2 provides a literature review of related studies. Section 3 describes the dependent and independent variables while Section 4 presents the data and methodology used in the study. The empirical results are presented in Section 5. Finally, in Section 6, the concluding remarks are discussed.

2. Literature review

Prior literature related to the present paper can be classified in three broad categories. The first consists of studies that focus on the determinants of banks’ profitability. T he second consists of studies that examine the profit and cost efficiency of European banks. The third consists of studies that compare domestic and foreign banks. In the following sections we discuss each one of these categories.

2.1. Studies on the determinants of profitability

Following the early studies of Short (1979) and Bourke (1989) a number of more recent studies have attempted to identify some of the major determinants of banks’ profitability. They consider internal and external factors and examine a single country (e.g., Berger, 1995, Angbazo, 1997, Guru et al., 1999, Ben Naceur, 2003, Mamatzakis and Remoundos, 2003, Kosmidou et al., 2005 and Kosmidou, 2006) or a panel of countries (Molyneux and Thorton, 1992, Demirguc-Kunt and Huizinga, 1999, Abreu and Mendes, 2001, Staikouras and Wood, 2003, Hassan and Bashir, 2003 and Goddard et al., 2004). In the discussion that follows we focus on the studies that examine the EU banking market.2

The study of Molyneux and Thorton (1992) is one of the first that examines the determinants of banks profitability in several countries. The results indicate a positive association between the return on equity and the level of interest rates, bank concentration and the government ownership. In a more recent study, Abreu and Mendes (2001) examine Portugal, Spain, France and Germany and find that loan to assets and equity to assets ratios have a positive impact on interest margins and profitability. They also find that operating costs have a positive impact on net interest margins measures but not on profits measures, while the opposite holds for bank's market share. From the macroeconomic variables, inflation is relevant in all cases, while the nominal effective exchange rate does not have an impact on performance. The unemployment rate has a negative sign in all regressions and is significant in the case of profits although not on net interest margins measures. Staikouras and Wood (2003) examine the performance of a sample of banks operating in thirteen EU banking markets. The results indicate that loans to assets ratio and the proportion of

loan loss provisions are inversely related to banks’ return on assets, as well as that banks with greater levels of equity are relatively more profitable. The funds gap ratio is also significant and positively related to performance. Furthermore, the authors found no evidence to support either the structure–conduct–performance or the efficient hypothesis. Two of the three macroeconomic indicators, the variability of interest rates and the growth of GDP had a negative impact, while the level of interest rates had a positive effect. Goddard et al. (2004) investigate the determinants of profitability in Denmark, France, Germany, Italy, Spain and the UK, for the period 1992–98. They find only weak evidence for any consistent or systematic size–profitability relationship and a positive relationship between capital-assets ratio and profitability. The relationship between the importance of off-balance-sheet business in a bank's portfolio and profitability is positive for the UK, but either neutral or negative elsewhere.

2.2. Studies on the profit and cost efficiency of EU banks

In recent years, there has also been an increase of academic studies that focus on the efficiency of financial institutions using frontier analysis. Berger and Humphrey (1997) outline 130 studies, covering 21 countries, multiple time periods and various types of institutions that applied three parametric (i.e. stochastic frontier approach (SFA), distribution free approach (DFA), thick frontier approach (TFA)) and two non-parametric (i.e. data envelopment analysis (DEA), free disposal hull (FDH)) frontier approaches for determining the best-practice frontier against which relative efficiencies are measured.3 The efficiency of a bank is measured relatively to that of the best-practice banks of similar size, with most studies focusing on cost efficiency rather than profit efficiency.4 Some recent studies also consider both cost and profit efficiency (e.g., Berger and Humphrey, 1997 and Berger and Mester, 1997), as well as risk variables (e.g., Berger and DeYoung, 1997, Berg et al., 1992, McAllister and McManus, 1993, Mester, 1996 and Rao, 2005).

Although the EU is considered relatively under researched (given its size and importance) there is now a growing strand of literature that examines the efficiency of EU banking institutions. Examples of such studies are Altunbas et al. (2001), Bikker (2002), Maudos et al. (2002), Schure et al. (2004) and Staikouras et al. (2005). The studies of Dietsch and Weil (1998), Cavallo and Rossi (2002), Casu and Molyneux (2003), are also interesting as they focus on most of the main EU banking sectors. Earlier studies, as the ones of Berg et al. (1993), Pastor et al. (1995), Lang and Welzel (1996), Lozano-Vivas (1997), Dietsch and Lozano-Vivas (2000), focus mostly on sub-sets of selected markets or individual countries such as the nordic countries, France, Germany and Spain among others.

2.3. Studies on foreign versus domestic banks

Other studies have employed similar techniques to compare the efficiency of foreign and domestic banks. Hasan and Hunter (1996), Mahajan et al. (1996), and Chang et al. (1998) conclude that foreign banks in the US are less cost efficient than domestic banks, while Seth (1992) and Nolle (1995), find that foreign-owned banks are not as profitable as domestically owned banks. Similar results were obtained in studies

which examined the Australian market. Using DEA Sathye (2001) found foreign banks to be less efficient than domestic, while comparable results were obtained by Avkiran (1997).

Fewer studies have examined European markets. After estimating separate frontiers for foreign and domestic banks in Spain, Hasan and Lozano-Vivas (1998) find that foreign banks are about equal as profit efficiency as domestic banks. Berger et al. (2000) estimate cost and profit frontiers to compare the efficiency of banks in France, Germany, Spain, UK and US. For the US case, the results showed that domestic banks are on average less cost efficient than foreign banks. For the EU countries, cost efficiency and profit efficiency were found higher for domestic banks than foreign banks in three cases (i.e. France, Germany, UK), but the difference was not found to be statistically significant. Using a multicriteria decision aid methodology, Kosmidou et al. (2004) find that domestic banks exhibit higher overall performance compared to foreign banks operating in the UK. Kosmidou et al. (2006a) examine how foreign banks differ from domestic banks in the UK and find that the later are characterized by higher return on equity, net interest revenue to total earning assets, and loans to customer and short term funding.

Studies that compare the performance of foreign and domestic banks in developing countries yield in general different results. Demirguc-Kunt and Huizinga (1999) as well as Claessens et al. (2001) find foreign banks to be disadvantaged compared to domestic banks in developed countries although not in less developed countries. Finally, in a more recent study, Fries and Taci (2005) examine the cost efficiency in 15 post-communist countries. The results indicate that privatized banks with majority foreign ownership are the most cost efficient ones and those with domestic ownership are the least, though both being more efficient than state-owned banks.

3. Determinants and variable selection

3.1. Dependent variable

This study uses return on average assets (ROAA) to evaluate bank's performance. ROAA is the net profits expressed as a percentage of average total assets. It shows the profits earned per euro of assets and indicates how effectively the bank's assets are being managed to generate revenues. Average assets are being used in order to capture any differences that occurred in assets during the fiscal year. As Golin (2001) points out, return on average assets is the key measure of profitability.

3.2. Determinants and independent variables

Four bank characteristics are used as internal determinants of performance. These are the bank's total assets, the cost to income ratio, the ratio of equity to assets and the ratio of bank's loans divided by customers and short term funding. In addition, six external determinants are used to examine the impact of environment on bank's performance (Table 1).

Table 1. Variables description

Variables Description

Variables Description

Dependent

ROAA The return on average total assets of the bank

Independent

Banks characteristics (internals factors)

EQAS This is a measure of capital adequacy, calculated as equity to total assets. High capital-asset ratios are assumed to be indicators of low leverage and therefore lower risk

COST This is the cost to income ratio. It provides information on the efficiency of the management regarding expenses relative to the revenues it generates. Higher ratios imply a less efficient management

LOFUND This is a measure of liquidity calculated as loans to customers and short term funding. Higher figures denote lower liquidity

SIZE The accounting value of the bank's total assets (in €)

Macroeconomic and financial structure (external factors)

INF The annual inflation rate

GDPGGR The real gross domestic product (GDP) growth

CONC The C5 concentration measure calculated by dividing the assets of the five largest banks with the assets of all banks operating in the country

ASSGDP The ratio total assets of the deposit money banks divided by the GDP (ASSGDP). It reflects the overall level of development of the banking sector and measures the importance of bank financing in the economy (in constant US$ 1995)

MACPASS The ratio stock market capitalization to total assets of the deposit money banks.a This variable serves as a proxy of financial development as well as a measure of the size of financial market and the relationship between bank and market financing (in constant US$ 1995)

MACGDP The ratio stock market capitalization to GDP. It measures the overall level of development of the market and its importance in financing the economy (in constant US$ 1995)

Notes: the data for the calculation of internal factors and CONC were obtained from Bankscope Database. The data for the external factors were obtained from Euromonitor International Database which uses sources such as International Monetary Fund's (IMF) International Financial Statistics (IFS), World Economic Outlook/UN/National Statistics and World Bank.

The ratio of equity to assets (EQAS) is used as a measure of capital strength. Generally speaking, banks with high capital-asset ratios are considered relatively safer in the event of loss or liquidation. Therefore, the conventional risk–return hypothesis would imply a negative relationship between equity to assets ratio and bank performance. However, the lower risk increases banks creditworthiness and

consequently reduces the cost of funding. At the same time, banks with higher equity to assets ratio will normally have lower needs of external funding and therefore higher profitability.

Another basic policy of commercial banks refers to their liquidity management and specifically the process of managing assets and cash flow to maintain the ability to meet current liabilities as they come due. Without the required liquidity and funding to meet obligations, a bank may quickly fail, or at least be technically insolvent. The ratio of net loans to customers and short term funding (LOFUND) is used to measure the relationship between liquidity management and performance. This ratio shows the relationship between comparatively illiquid assets (i.e. loans) and comparatively stable funding sources (i.e. deposits and other short term funding). Therefore, the lower the value of this ratio, the more liquid the bank is. Since liquid assets are associated with lower rates of return a positive relationship is expected between this variable and performance.

The cost to income ratio (COST) is used to measure the impact of efficiency in expenses management on banks performance. This ratio shows the costs of running the bank, the major element of which is staff salaries and benefits, and is expected to have a negative relationship with bank's performance.

Bank's size (SIZE) is considered an important determinant of its performance. The reason is that large size may result in economies of scale that will reduce the cost of gathering and processing information (Boyd and Runkle, 1993). As in most studies in banking, we use total assets of the bank as a proxy for its size to account for size related economies or diseconomies of scale.

Turning to the external determinants, two sets of variables have been considered in this study, indicating macroeconomic conditions and financial structure characteristics. The two macroeconomic variables used are gross domestic product growth (GDPGR) and inflation (INF).

GDP is among the most commonly used macroeconomic indicators and it is a measure of total economic activity within an economy. The real GDP growth, used in this study, is expected to have a positive impact on bank's performance according to the well-documented literature on the association between economic growth and financial sector performance.

The relationship between inflation and banks performance depends on whether the inflation is anticipated or unanticipated (Perry, 1992). In the first case (i.e. anticipated inflation) banks can timely adjust interest rates, which consequently results in revenues that increase faster than costs, with a positive impact on profitability. In the second case (i.e. unanticipated inflation) banks may be slow in adjusting their interest rates resulting in a faster increase of bank costs than banks revenues. This will consequently have a negative impact on bank profitability.

We finally examine how the performance of banks is related to the relative development of the banking industry and the stock market using the ratios stock market capitalization to GDP (MACGDP), stock market capitalization to total assets of deposit money banks (MACPASS), total assets of deposit money banks to GDP (ASSGDP) and banking industry concentration (CONC). MACPASS reflects the

complementarity or substitutability between bank and stock market financing, while ASSGDP and MACGDP measure the overall level of development of the banking sector and the stock market, respectively as well as their importance in financing the economy. Concentration is the proportion of an industry's total assets controlled by its largest firms. According to the structure-conduct performance (SCP) hypothesis, banks in highly concentrated markets tend to collude and therefore earn monopoly profits (Short, 1979, Gilbert, 1984 and Molyneux et al., 1996). Collusion may result in higher rates being charged on loans, less interest rates being paid on deposits and so on (Goddard et al., 2001). CONC is calculated as the total assets held by the five largest commercial banks in the country divided by the total assets of all commercial banks in the country.

4. Data and methodology

4.1. Data

Our sample is a balanced panel dataset of 584 commercial banks operating in the 15 EU countries over the period 1995–2001 consisting of 4088 observations.5Table 2 and Table 3 present the number of banks by country and ownership and the sample characteristics, respectively.

Table 2. Banks in sample by country and ownership

Country Domestic

banks

Foreign

banksa

Not available or complete

information for ownership in

Bankscope

Total

number of

banks

Austria 13 7 0 20 Belgium 5 15 0 20 Denmark 31 2 9 42 Finland 2 0 1 3

France 90 40 14 144 Germany 57 26 5 88 Greece 6 0 0 6

Ireland 3 8 0 11

Italy 49 4 1 54 Luxembourg 3 54 0 57

The Netherlands 10 13 1 24 Portugal 7 4 0 11

Spain 28 11 3 42 Sweden 5 0 0 5

UK 23 34 0 57

Total 15 EU 332 218 34 584

a We define a bank to be foreign when foreigners own more than 50% of its share capital.

Table 3. Sample characteristics: independent variables means (and S.D.)

S ND GDP ASS GDP GR C

Austria 8.564

9

(6.15

01)

63.96

75

(16.0

473)

56.240

2

(27.67

08)

2174.029

3

(4697.87

90)

0.369

(0.03

00)

0.4173

(0.060

7)

0.1524

(0.013

0)

1.69

86

(0.73

12)

2.28

57

(1.05

48)

88.7

943

(1.65

49)

Belgium 8.490

1

(12.4

237)

65.97

92

(31.2

047)

40.444

6

(28.81

99)

7102.865

(23011.8

401)

0.756

3

(0.24

60)

0.1040

(0.053

0)

0.0661

(0.019

5)

1.75

00

(0.58

36)

2.40

00

(1.18

32)

91.5

886

(3.81

20)

Denmark 10.98

35

(4.05

47)

69.01

87

(16.4

308)

68.606

4

(21.91

84)

3647.028

2

(18890.2

384)

0.387

4

(0.04

58)

1.3548

(0.203

2)

0.5309

(0.120

7)

2.28

71

(0.31

77)

2.52

86

(0.49

49)

86.8

100

(2.18

05)

Finland 5.243

8

(1.12

79)

65.28

71

(11.96

74)

63.196

7

(13.03

29)

11079.57

62

(8659.08

61)

0.188

9

(0.04

85)

8.1501

(7.036

7)

1.3410

(0.887

1)

1.48

86

(0.82

62)

4.25

71

(1.69

10)

99.1

886

(0.20

73)

France 9.567

4

(11.05

28)

70.05

57

(45.3

486)

70.780

2

(69.80

05)

12622.71

31

(59381.6

366)

0.396

9

(0.07

43)

1.9497

(1.091

9)

0.6991

(0.289

9)

1.36

00

(0.53

16)

2.55

71

(1.03

77)

60.5

285

(2.84

59)

Germany 9.088

6

(10.8

851)

65.72

06

(23.7

526)

64.273

(41.01

26)

2734.817

2

(5428.54

43)

0.297

4

(0.05

22)

1.6206

(0.603

9)

0.4788

(0.168

0)

1.56

86

(0.59

95)

1.65

71

(0.68

84)

77.2

729

(5.45

76)

Greece 8.764

(6.80

35)

66.02

69

(16.1

258)

48.500

(13.12

78)

15401.60

24

(13779.4

125)

0.114

6

(0.02

01)

5.6998

(4.363

3)

0.6713

(0.510

4)

5.22

57

(2.31

41)

3.34

29

(0.74

42)

87.3

471

(5.48

60)

Ireland 8.309

4

(5.26

82)

33.10

19

(23.6

464)

58.491

7

(27.43

79)

12959.75

45

(21601.9

533)

1.216

(0.36

63)

0.5604

(0.140

7)

0.6529

(0.156

7)

2.88

00

(1.53

54)

9.32

86

(1.68

07)

77.9

629

(3.59

21)

Italy 8.732

8

(6.40

68)

76.51

04

(30.1

354)

78.508

4

(53.09

46)

10373.10

82

(23514.5

038)

0.118

4

(0.03

84)

4.5216

(3.091

0)

0.4266

(0.187

1)

2.88

57

(1.19

05)

2.05

71

(0.65

22)

55.5

066

(3.98

18)

Luxembourg 5.410

7

(5.81

41)

49.62

99

(24.3

863)

24.653

6

(19.65

74)

5077.403

5

(7990.22

27)

18.34

69

(8.34

14)

0.1161

(0.054

9)

1.7163

(0.223

3)

1.78

00

(0.78

28)

5.47

14

(2.62

23)

34.0

235

(8.36

10)

The 7.77157.1187.66235287.940.5352.6122 1.3146 2.47 3.2590.6

S ND GDP ASS GDP GR C

Netherlands 1

(4.74

30) 74

(19.9

921)

5

(88.11

55)

46

(103203.

8604)

7

(0.09

11)

(1.089

7)

(0.339

7)

71

(0.85

81)

71

(0.95

30)

986

(2.69

30)

Portugal 8.635

6

(5.17

57)

61.84

27

(22.0

134)

62.300

6

(21.70

88)

6979.411

7

(9089.01

45)

0.342

2

(0.10

15)

1.3624

(0.734

0)

0.4191

(0.159

3)

3.09

43

(0.79

60)

3.44

29

(0.88

94)

71.11

57

(8.08

93)

Spain 12.46

20

(11.90

28)

70.37

21

(57.6

361)

67.269

6

(39.25

86)

15220.04

59

(52251.6

256)

0.182

4

(0.04

43)

3.8529

(1.945

8)

0.6206

(0.195

8)

2.93

86

(0.94

16)

3.51

43

(0.77

54)

80.6

065

(2.68

02)

Sweden 6.334

3

(5.23

22)

63.87

43

(20.9

667)

151.97

56

(187.0

575)

13032.10

29

(21321.0

600)

0.202

1

(0.04

11)

5.5151

(0.759

3)

1.1095

(0.240

1)

1.02

43

(0.95

16)

3.05

71

(1.34

79)

97.2

900

(1.86

91)

UK 11.68

85

(9.93

06)

59.50

15

(25.9

507)

56.334

7

(46.38

84)

26666.03

63

(73111.3

215)

1.312

1

(0.11

09)

1.2270

(0.185

7)

1.6039

(0.229

4)

2.67

43

(0.69

61)

2.77

14

(0.40

61)

57.0

943

(4.36

75)

All financial and ownership data of individual banks as well as information concerning market concentration were drawn from Bankscope Database of Bureau van Dijk's company. Using BANSCOPE has two advantages. Firstly, it has information for 11,000 banks, accounting for about 90% of total assets in each country. Secondly, the accounting information at the bank level is presented in standardized formats, after adjustments for differences in accounting and reporting standards. Country and market specific data such as inflation, real GDP growth, market capitalization, etc., were obtained from Euromonitor International Database which collects data from sources such as International Monetary Fund's (IMF), International Financial Statistics (IFS), World Economic Outlook/UN/National Statistics and World Bank.

4.2. Methodology

In order to examine the internal and external factors that affect the profitability of banks in European Union, the following model has been developed:

(1)

zit=boit+bmitYm it+bdjtYdit+ε

where i refers to an individual bank; t refers to year; j refers to the country in which bank i operates; zit the dependent variable that refers to the return on average assets (ROAA) and is the observation of a bank i in a particular year t; Ym represents the internal factors/determinants of a bank; Yd represents the external factors/determinants of a bank; ?is an error term.

Model (1) is estimated through a fixed effects regression taking each bank's ROAA as

the dependent variable. The opportunity to use a fixed effects rather than a random effects model has been tested with the Hausman test. If the value obtained by the

Hausman test is larger than the critical chi-square or

, then the fixed effects estimator is the appropriate choice. Therefore, a fixed effects model is a natural choice since our estimating sample is identical to the population of interest (Judge et al., 1988). Moreover, the fixed effects approach is further supported by the absence of significant heteroscedasticity in the residuals from our estimated model.

Based on the Breusch-Pagan test6 (Baltagi, 2001) and in order to investigate whether there is evidence of heteroscedasticity in the residual variance we calculate the Lagrange multiplier (LM) and compare the relevant statistic of each model with the

critical chi-square value . Values below this would reject the null hypothesis of heteroscedastic residual variance. The model (1) is finally estimated using White's transformation to control the cross-section heteroscedasticity of the variables.

Extending Eq. (1) to reflect the variables, as described in Table 1, the model is formulated as follows:

(2)

zit=bot+bit(EQASit+COSTit+LOFUNDit+SIZEit)+bjt(INFjt+GDPGRjt+CONCjt+A SSGDPjt+MACPASSjt+MACGDPjt)

5. Empirical results

Table 4 reports the empirical estimations of Eq. (2) for banks’ ROAA. The first column presents the results when all the banks (584) are simultaneously considered. Columns two and three present the results when we split the banks according to the country of origin of their owner. We define a bank to be foreign (domestic) when foreigners own more (less) than 50% of its share capital. The sub-samples include 332 domestic banks and 218 foreign banks. About 34 banks were excluded from the analysis at this stage, as we had not enough information to classify them either as domestic or foreign.

Table 4. Regressions results (dependent variable: ROAA)

All banks in samplea Domestic banks in

sampleb

Foreign banks in

samplec

EQAS 0.228045 (51.44043)*** 0.238877

(30.70840)***

0.240276

(41.46373)***

COST ?0.198939

(?48.87565)***

?0.143611

(?30.10036)***

?0.309044

(?36.14324)***

LOFUND 0.040155 (14.42604)*** 0.046915

(9.738826)***

?0.020300

(?9.405973)***

SIZE ?0.005713

(?21.15411)***

?0.004235

(?8.549996)***

?0.009339

(?5.378429)***

INF 0.001506 (6.617562)*** 0.002199 ?0.004812

All banks in samplea Domestic banks in

sampleb

Foreign banks in

samplec

(7.716279)*** (?9.258363)***

GDPGR 0.000821 (2.667922)*** 0.010174

(23.25582)***

?0.001819

(?2.772711)***

CONC 0.037344 (32.73005)*** ?0.001712

(?1.618062)

0.116854

(27.25397)***

ASSGDP ?0.021273

(?45.93489)***

?0.025387

(?15.65656)***

?0.035854

(?39.40473)***

MACPASS 0.015120 (25.77822)*** 0.011691

(16.33099)***

0.014071

(11.98591)***

MACGDP 0.066306 (80.14221)*** 0.051569

(54.31495)***

0.074522

(28.16418)***

Adjusted R2 0.5609 0.6371 0.3903

F-value 61.3783 78.0738 35.6322

LM 340.667 1.519 × 1052 3.835 × 1050 Hausman test 27.825 34.285 9.649

t-Values in parentheses. ***significant at the 1% level.

a 584 banks, period 1995–2001, no. of observations = 4088.

b 332 banks, period 1995–2001, no. of observations = 2324.

c 218 banks, perio

d 1995–2001, no. of observations = 1526.

All the variables are significant, with the exception of concentration in the case of domestic banks ROAA, although their impact and relation with ROAA is not always the same for domestic and foreign banks. The explanatory power of the model is much higher for domestic banks (adjusted R2 equal to 0.6371 for domestic banks compared with 0.3903 for foreign banks) while F-statistic for all models is significant at the 1% level. This implies that additional factors may influence the profitability of foreign banks. As Williams (2003) correctly points out, foreign banks operating in a host market are being affected by both the fact that they are owned by a foreign multinational bank and their participation in the host banking system.

Capital strength and efficiency in expenses management are the main determinants of ROAA in all cases as the relatively high significant coefficients of the equity to assets (EQAS) and cost to income (COST) ratios show. Equity to assets is positive related to ROAA whether we examine domestic or foreign banks and appears to be the most significant determinant of profitability for domestic banks. This finding is consistent with previous studies (e.g., Berger, 1995, Demirguc-Kunt and Huizinga, 1999, Staikouras and Wood, 2003, Goddard et al., 2004, Kosmidou et al., 2005 and Kosmidou, 2006) providing support to the argument that well capitalized banks face lower costs of going bankrupt and reduce, thus, their cost of funding or that they have lower needs for external funding resulting in higher profitability. As expected the coefficient of the cost to income ratio (COST), which appears to be the most significant determinant of profitability for foreign banks, is negative showing that an increase (decrease) in these expenses reduces (increases) the profits of banks

operating in the EU to a large extent. Guru et al. (1999), Kosmidou et al. (2005) and Kosmidou (2006) among others also found poor expenses management to be among the main contributors to poor profitability. Thus, commercial banks in the EU should take the necessary actions to achieve a more efficient cost control in order to further increase their profits. The difference in the coefficient between foreign (?0.309) and domestic banks (?0.144) could be the result of diseconomies of operating or monitoring an institution from a distance as Berger et al. (2000) suggested for the foreign banks.

Referring to liquidity, the results are mixed. The ratio net loans to customer and short term funding (LOFUND) is statistically significant and positively related to the profitability of domestic banks, indicating a negative relationship between bank profitability and the level of liquid assets held by the bank, and consistent with our expectations and some earlier studies (e.g., Molyneux and Thorton, 1992 and Guru et al., 1999). In the case of foreign banks, the variable is also significant but has a negative sign, indicating a positive relationship between liquidity and banks profits, contrary to our expectations although consistent with the studies of Bourke (1989) and Kosmidou (2006).

The relation between size (SIZE) and bank's performance is negative whether we examine domestic or foreign banks. The negative coefficient indicates that in both cases, larger (smaller) banks tend to earn lower (higher) profits and provides support to the studies that found either economies of scale and scope for smaller banks or diseconomies for larger financial institutions. Vander Vennet (1998) found evidence of economies of scale only for the smallest banks with assets under ECU 10 billion in the EU, with constant returns thereafter and diseconomies of scale for the largest banks exceeding ECU 100 billions, while similar results were obtained in other studies that examined single countries in the EU (e.g., Pallage, 1991, Rodriguez et al., 1993 and Kosmidou et al., 2006b).

The impact of the indicators of macroeconomic conditions on ROAA is significant in all cases but with opposite signs for domestic and foreign banks. Inflation (INF) is positively related to domestic banks, implying that during the period of our study the levels of inflation were anticipated by domestic banks. This gave them the opportunity to adjust the interest rates accordingly and consequently to earn higher profits. On the other hand in the case of foreign banks inflation brought a higher increase in costs than revenues as the negative relationship between inflation and foreign banks profits indicates. These mixed results could be attributed to different levels of knowledge of country macroeconomic conditions and expectations concerning inflation rate between domestic and foreign banks. The results about the impact of GDPGR on domestic banks ROAA are consistent with the results of Kosmidou et al. (2005), Kosmidou (2006), and Hassan and Bashir (2003) among others, and provide further support to the argument of positive association between economic growth and financial sector performance. However, there is a negative association between GDPGR and foreign banks ROAA. There are two possible explanations for this difference. First, banks in countries with higher GDP normally operate in more mature environments resulting in more competitive interest and profit

margins (Staikouras and Wood, 2003) and a number of studies have shown that foreign banks are disadvantaged compared to domestic banks in developed countries (e.g., Peek et al., 1999, Berger et al., 2000, Claessens et al., 2001 and Sathye, 2001). Second, since GDP growth is assumed to have an impact on numerous factors related to the supply and demand of loans and deposits, the opposite signs of the coefficient of this variable between the two groups of banks could be related to the fact that (usually) domestic and foreign banks tend to serve different customers who may react different under the same macroeconomic conditions.

MACPASS and MACGDP are statistically significant and positively related to both domestic and foreign banks ROAA. ASSGDP is negatively related to ROAA in both cases, while the impact of concentration on ROAA differs between foreign and domestic banks. The positive and statistically significant relationship between either MACGDP or MACPASS and ROAA indicates that a larger stock market compared to either the economy or the banking sector increase bank profits. This finding confirms the empirical results of Ben Naceur (2003) who examined the Tunisian banking industry and suggested that as stock market enlarges, more information become available. This leads to an increase of potential number of customers to banks making easier the process of identification and monitoring of borrower that increases bank activity and profitability. The results about ASSGDP are consistent with the findings of Demirguc-Kunt and Huizinga (1999) who found that in countries where banking assets constitute a large portion of the GDP, banks have smaller margins and are less profitable. The results also indicate a negative but not statistically significant relationship between concentration and domestic banks profits consistent with previous studies that found no evidence to support the structure–conduct–performance (SCP) hypothesis. From the 45 studies reviewed by Gilbert (1984) only 27 provide evidence that the SCP paradigm hold, while Staikouras and Wood (2003) also found no evidence to support the SCP hypothesis in their study of the EU banking sector over the period 1994–1998. Surprisingly, the results indicate that concentration has a positive and statistically significant impact on foreign banks profits. This is inconsistent with the results of Williams (2003) that examined the Australian market and found that concentration reduces profits of the foreign entrants and acts as an effective barrier to entry. We also expected foreign banks profits to be negatively related to concentration since foreign banks are usually smaller than domestic banks.

A possible explanation in our case is that the majority of foreign banks in the sample come from few countries in which foreign banks dominate the market (e.g., Luxembourg) or hold a considerable proportion of banking sector's assets (e.g., UK).

6. Concluding remarks

In recent years numerous factors contributed to the increase of competition in the EU banking sector and posed great challenges to banks as the environment in which they operate changed rapidly. It is reasonable to assume that all these changes must have had some impact on banks’ performance. As Golin (2001) points out adequate earnings are required in order for banks to maintain solvency and to survive in a suitable environment. The relation between banks’ efficiency and the growth of the

economy is n ow well documented. At the same time banks’ insolvencies have adverse consequences on the economy. Hence, knowledge of the underlying factors that influence banks’ profitability is essential, not only for the managers of the banks but for numerous stakeholders such as the Central Banks, Bankers Associations, Governments, and other Financial Authorities in the 15 EU countries. The conclusions drawn from this study would also be of particular interest to the new EU countries whose economies and banking systems are experiencing fundamental changes during this period.

For the reasons mentioned above this paper analyzed how financial characteristics and the overall banking environment affected the profitability of banks operating in the 15 EU banking industries. A balanced pooled times series dataset of 584 commercial banks operating during the period 1995–2001 provided the basis for the econometric analysis. Banks were also split according to their ownership resulting in two sub-samples of 332 domestic and 218 foreign banks.

The results indicated that profitability of commercial banks in European Union regardless of their ownership is affected by both internal characteristics and changes in the overall banking environment. The explanatory power (in terms of adjusted R2) of the model for domestic banks was much higher than that for foreign banks while F-statistic was significant at the 1% level for all models.

Equity to assets was positively related to ROAA whether we examined domestic or foreign banks and appeared to be the most significant determinant of profitability for domestic banks. The cost to income ratio which was also significant although negatively in both cases appeared to be the most significant determinant of profitability for foreign banks. The difference in the impact of cost to income ratio on profits between foreign and domestic banks could be the result of diseconomies of operating or monitoring an institution from a distance. The relation between size and profitability was also negative for both domestic and foreign banks providing support to the studies that found either economies of scale and scope for smaller banks or diseconomies for larger financial institutions. The above results lead us to the conclusion that banks should direct their attention in a more efficient cost control rather than following policies such as consolidation that would further increase their size. The impact of GDP growth and inflation, on ROAA was significant in all cases but with opposite signs for domestic and foreign banks. These mixed results concerning macroeconomic conditions could be attributed to different levels of knowledge of country macroeconomic conditions and expectations concerning inflation rate between domestic and foreign banks. Additionally, domestic and foreign banks tend to serve different customer segments that may react differently under the same macroeconomic conditions. Finally, the results indicated that MACPASS and MACGDP are statistically significant and positively related to both domestic and foreign banks ROAA. ASSGDP is negatively related to ROAA in both cases, while the impact of concentration on ROAA was different between foreign and domestic banks.