Drives What Acquisitions in the EU Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors Fotios Pasiouras1, Sailesh Tanna2*, Chrysovalantis Gaganis3
School of Management, University of Bath, Bath, BA2 7AY, UK Department of Economics, Finance and Accounting, Faculty of Business Environment and Society, Coventry University, Coventry, CV1 5FB, UK 3 Financial Engineering Laboratory, Technical University of Crete, University Campus, Chania 73100, Greece
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Abstract We study the determinants of commercial bank acquisitions that occurred over the period 1997-2002 in the European Union single market, by evaluating the impact of bankspecific measures, such as size, growth and efficiency of banks, and external influences reflecting industry level differences in the regulatory and supervision framework, market environment and economic conditions. Our empirical analysis involves multinomial logit estimation at various levels in order to identify the characteristics that most consistently predict targets and acquirers from a choice-based sample of over 1400 commercial banks. The overall results indicate, relative to banks that were not involved in the acquisitions during 1997-2002, that (i) targets and acquirers were significantly larger, less well capitalized and less efficient in terms of expenses management, (ii) targets were less profitable with lower growth prospects, and acquirers more profitable with higher growth prospects, (iii) external factors have affected targets and acquirers differently, and their effects have not been consistent or robust to sample size changes.
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1. INTRODUCTION
The EU banking industry has witnessed a large number of Mergers and Acquisitions (M&As) in recent years. The European Central Bank (2000), for example, records 2,153 M&As of credit institutions between 1995 and the first half of 2000, while Beitel and Schiereck (2001) point out that during the period 1998-2000 more M&As deals occurred in the EU banking industry than during the previous 14 years. In terms of volume, data from the Securities Data Company (SDC) M&A Database1 indicate that the total value of European financial M&As increased from $22,769.6 million in 1990 to $147,025.6 in 1999, while over the same period, the average target value in Europe ($467.7 millions) was higher than in the US ($334 millions) and the main industrial countries on an aggregate basis ($383.2 millions). Theory suggests that M&As between banks can occur for several reasons. In general, the underlying motives can be classified as value-maximization (i.e. increase market power, replace inefficient management, achieve economies of scale and scope, decrease risk through geographic and product diversification) and non-value maximization ones (i.e. managerial motives, hubris, etc.). In addition to these firm level motives, banks decision for M&As might be influenced by external factors such as regulations and laws, globalisation, technological progress, to name just a few (Berger et al., 1999; Group of Ten, 2001). While there are numerous empirical studies investigating the relationship between financial characteristics and acquisition likelihood of industrial (i.e. non-financial) firms (e.g. Levine and Aaronovitch, 1981; Harris et al., 1982; Hasbrouck, 1985; Ambrose and
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Megginson, 1992; Powell, 1997; Gonzalez et al., 1997; Ali-Yrkko et al., 2005), investigation of such characteristics for the banking industry has in general been neglected (Cyree et al., 2000; Wheelock and Wilson, 2000). Furthermore, previous studies on bank acquisitions have traditionally focused on examining the financial characteristics of US banks (Hannan and Rhoades, 1987; Meric et al., 1991; Moore, 1996; Wheelock and Wilson, 2000, 2004), while there have been relatively few recent studies for other countries. Vander Vannet (1998) investigates the causes and consequences of M&As for EU banks, and some recent studies have also investigated the determinants of bank acquisitions for specific EU countries, such as Italy (Focarelli et al, 1999) and Greece (Pasiouras and Zopounidis, 2006). Pasiouras and Gaganis (2006a) investigate the financial characteristics of bank acquisitions covering the 5 principal EU banking sectors (France, Germany, Italy, Spain and UK), while in a later study they also focus on the Asian banking sector (Pasiouras and Gaganis, 2006b). Most of these studies use logistic regression models (either binary or multinomial); Wheelock and Wilson (2000) and Wheelock and Wilson (2004), however, use a proportional hazard model and a two-part hurdle model respectively. The empirical results, discussed further below, are in general mixed. Evidence on the impact of external factors on M&As decisions also comes mostly from studies that examine industrial sectors, with the neoclassical and behavioural approaches being the most commonly cited explanations. From the previously mentioned studies in the banking sector, only a few have examined the impact of external factors, by focusing on market characteristics such as concentration (Hannan and Rhoades, 1987; Moore, 1996; Wheelock and Wilson, 2004; Pasiouras and Zopounidis, 2006), growth
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(Hannan and Rhoades, 1987; Pasiouras and Zopounidis, 2006), profitability (Pasiouras and Zopounidis, 2006), and the number of banks in the market (Wheelock and Wilson, 2004). However, the neoclassical theory, proposed by Gort (1969) and more recently supported by Mitchell and Mulherin (1996) among others, assumes that legal and regulatory factors might also have a role to play in the reallocation of corporate assets through M&As activity. Thus, for example, Rossi and Volpin (2004) examine the influence of differences in law and regulation in their study of the determinants of M&As across 49 major countries, and find that the volume of M&A activity is significantly larger in countries with better accounting standards and stronger shareholder protection. In the bank M&As literature, the study of the impact of bank regulations and supervision approaches has also been investigated as forces hindering cross-border deals (Focarelli and Pozzolo, 2001a,b; Buch and DeLong, 2004a,b). Focarelli and Pozzolo (2001a), using data on 2,449 banks from 29 OECD countries, point out that cross-border M&As among banks are less frequent than in other sectors of the economy, and find that the difference depends partly on the level of regulatory restrictions. In another study, Focarelli and Pozzolo (2001b) examine where banks expand their cross-border shareholdings and find that potential profit opportunities and regulatory environments are the most important determinants. Buch and DeLong (2004a) provide further evidence on why cross-border mergers are rare compared to domestic mergers using a large sample of over 3000 international bank M&As. Treating the number of cross-border bank mergers for each country pair as the dependent variable in Tobit regressions, they find that information costs and regulations significantly influence cross-border merger activity. In a latter study of cross-border bank mergers for the OECD countries, Buch and DeLong
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(2004b) reveal that a fairly priced deposit insurance scheme in the acquirers country tends not only to increase the number of cross-border deals but also reduce the risk in both the home and world markets. Studies for the US banking industry are by their very nature limited to domestic M&As, although Wheelock and Wilson (2004) examine the impact of state branching laws and regulator evaluations of banks safety and soundness, focusing principally on acquirers. Their results indicate that the regulatory approval process serves as a real constraint on bank merger activity, although changes in branching restriction are not statistically significant. This paper adds to the recent literature by investigating the acquisition likelihood characteristics for the EU banking industry. As noted above, unlike the US, the literature associated with the investigation of the determinants of bank acquisitions in the EU has so far been limited, and we attempt to provide further evidence by concentrating on a recent period, 1997-2002, when M&A activity in the EU banking industry was intense. Our dataset consists of industry level data on 15 EU countries (EU15), and financial data for over 1,400 commercial banks operating in EU15, these being distinguished as acquirers, targets and non-involved banks2. We use this dataset to estimate the impact of bank characteristics, bank regulations, supervision, market structure, market liquidity and profitability on the probability of bank acquisition. In doing so, we concentrate on evaluating the influence of bank level characteristics versus country specific differences in the banks operating environment and economic conditions, as well as in their regulatory and supervision frameworks.
The terms acquirer, target and non-involved could alternatively be interpreted as acquiring, acquired and non-acquired banks respectively, and will be used interchangeably in this paper.
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The distinguishing aspect of our study is the examination of a broad range of policy influences that proxy for bank regulations and supervision standards, such as the level of accounting and information disclosure requirements, the degree of official disciplinary power, deposit insurance schemes, capital adequacy requirements, restrictions on bank activities and diversification guidelines. We obtain this information from the World Bank database, developed by Barth et al. (2001), and presume that these policy variables have either a direct impact on M&As or an indirect impact, for example, by limiting the investment opportunities of banks or influencing their risk-taking behaviour.3 As noted above, Focarelli and Pozzolo (2001a,b) and Buch and De Long (2004a,b) consider the impact of the regulatory environment on cross border deals. In contrast, we consider the impact of country-specific differences in the regulatory environment on commercial bank M&As in the EU single market, where such deals have been largely domestic.4 Although this makes our study somewhat related to the studies of Rossi and Volpin (2004) and Wheelock and Wilson (2004), it should be noted that the former uses, in the main, the volume of merger activity as a dependent variable5 and does not focus on the banking industry, while the latter concentrates on investigating characteristics of US bank acquirers that originate from the CAMEL approach with a limited set of further attributes to represent market environment and regulations. Using multinomial logit estimation to determine the impact of the above factors on the probability of acquisition, we show, with a fair degree of consistency across
Many studies argue that regulations such as capital requirements, deposit insurance scheme, restrictions on bank activities, disciplinary power of the authorities can have an impact on the risk taking behavior of banks (e.g. Besanko and Kanatas, 1996; Demirguc-Kunt and Kane, 2002; Hovakimian et al, 2003; Fernandez and Gonzalez, 2005; Gonzalez, 2005; Pennacchi, 2006). Amihud et al. (2002) and Buch and DeLong (2004b) point out that one way to take advantage of such regulations is to acquire a risky bank. 4 See Table 1 and discussion of our sample below. 5 Other studies that examine cross-border mergers also focus mostly on the number of mergers (i.e. activity) rather than on the probability of individual banks to engage in M&As, as we do.
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various levels of estimation, that both targets and acquirers were significantly larger, less well capitalized and less efficient in terms of expenses management, relative to their nonacquired peers. Furthermore, targets were less profitable with lower liquidity and lower growth in total assets, whereas acquirers tended to be relatively more profitable banks with higher growth prospects. These bank-specific influences are invariant to robustness tests conducted by disaggregating the sample according to bank size, location of operation and different time periods. But the impact of regulatory and market
environments are not robust to these sample splits and therefore depends crucially on whether the banks involved in acquisitions were large or small, and specifically where they operated. Besides, some regulatory influences were not uniform on targets and acquirers. Nevertheless, we find supporting evidence to suggest that banks that operated in countries with higher disciplining power of the authorities were less likely to engage in acquisitions, as targets or acquirers, while greater degree of economic freedom has been associated with more acquisitions. Similarly, banks were more inclined to engage in acquisitions in market environments favouring higher profitability, higher liquidity, lower concentration and lower industry size, although these influences were not robustly significant. Furthermore, regulatory factors were found to have a greater influence on banks acquisitions in the principal banking sectors (i.e. the five largest countries of the EU) than in the rest of the EU where market influences were more prevalent. The rest of this paper is organized as follows. Section 2 presents a review of prior literature related to our study. Section 3 outlines the data and methodology, while Section 4 discusses the empirical results. Finally, Section 5 outlines the concluding remarks and some directions for future research.
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2. BACKGROUND DISCUSSION
In this section we review the relevant literature to provide justification for the importance and use of bank specific characteristics, bank regulation and supervision standards, and market-related economic conditions associated with M&As decisions in the banking industry. The discussion is split into three sub-sections, referring to each of the three broad categories in turn.
(i) Bank M&As and bank specific characteristics
The causes of M&As have long been debated in the literature. Following the neoclassical perspective, all firm decisions including acquisitions are made with the objective of maximizing shareholders wealth. M&As in this context serve as a means to increase market power, replace inefficient management, achieve economies of scale and scope, decrease risk through geographic and product diversification, among others. However, an influential view in the literature is that M&As are driven by agency conflicts of interest between managers and shareholders. According to this view, many acquisitions are undertaken by managers in order to enhance their salary and prestige, diversify personal risk or secure their job through empire-building, at the expense of shareholders. Another interesting hypothesis, proposed by Roll (1986), suggests that managers commit errors of over-optimism (hubris) in evaluating M&As opportunities due to excessive prediction or faith on their own abilities, and engage in M&As even when there is no synergy. The empirical evidence is inconclusive and indicates that various bank specific characteristics
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can influence M&As in the banking industry. Hence, while the discussion below is devoted to financial characteristics, non-financial attributes such as corporate governance characteristics and managerial incentives can be important as well (Hadlock et al., 1999; Brook et al., 2000; Bliss and Rosen, 2001; Hughes et al., 2003).
Capital Strength Harper (2000) argues that The key factor driving mergers and acquisitions in financial systems is the industrys need to rationalize its use of capital (p. 68). This argument is based on the belief that nowadays risks are traded on markets rather than absorbed through capital held on a balance sheet. Hence, in order to remain competitive banks face the need either to release surplus capital or to raise the rate of return to the capital they retain. This can be achieved through M&As. Most of the prior studies report a negative relationship between capital ratios and acquisition probability although not statistically significant in all cases (e.g. Hannan and Rhoades, 1987; Moore, 1996; Wheelock and Wilson, 2000). There are two possible explanations for this. First, a lack of financial strength tends to attract buyers who can infuse capital into the acquired banks (Moore, 1996; Wheelock and Wilson, 2000). Second, buyers are attracted by banks with skillful managers who are able to operate successfully with high leverage (Wheelock and Wilson, 2000). Banks may also undertake M&As to meet capital regulatory requirements. In a recent study, Valkanov and Kleimeir (2006) examine a sample of 105 US and European bank mergers from 1997 to 2003 and find that US target banks are better capitalized than their acquirer and non-acquired peers and that US banks maintain higher capital levels
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than European banks. They suggest that US banks strategically raise their capital levels to avoid regulatory scrutiny.
Performance
According to the inefficient management hypothesis, acquisitions serve to drive out bad management that is not working in shareholder interests. Thus, as discussed by Hannan and Rhoades (1987), poorly managed banks are likely targets for acquirers who think that they can manage more efficient the assets of the acquired bank and increase profits and value. However, the empirical results are mixed. Moore (1996), Focarelli et al. (1999), Wheelock and Wilson (2000) and Pasiouras and Gaganis (2006a) find evidence of an association between poor performance (measured either in terms of profitability, expenses management or both) and an increase of the acquisition likelihood, in contrast to Hannan and Rhoades (1987) and Pasiouras and Zopounidis (2006).
Size Size may influence M&As in several ways. First, large banks are more expensive to be acquired. Second, larger banks have greater recourse to fight hostile acquisitions, as well as resources to acquire other banks. Third, a larger acquired bank is likely to be more difficult to be absorbed in the existing organization of the acquiring bank. Hannan and Rhoades (1987) and Moore (1996) find the effect of size to be insignificant. Wheelock and Wilson (2000), however, report that smaller banks are more likely to be acquired than larger ones, while Wheelock and Wilson (2004) find that the probability of engaging in mergers as acquirer increases with bank size. Focarelli et al. 11
(1999) reports a significant negative effect of size (measured by total assets) on acquisitions in Italy, while Pasiouras and Zopounidis (2006) find a negative, although not robustly significant, effect of size in Greece (depending on the measure of size).
Growth Growth can affect bank acquisition in two opposing ways. On the one hand, as Kocagil et al. (2002) point out, empirical evidence suggests that some banks with relatively high growth rates experience problems because their management and/or structure is not able to deal with and sustain exceptional growth. Hence, acquirers may purchase a bank with good growth prospects, but with limited financial or managerial capacity may fail to capitalize on potential growth. On the other hand, Moore (1996) argues that a slow growing bank may attract a buyer seeking to accelerate its growth rate and thereby increase its market value. Hannan and Rhoades (1987) find growth to be positively related to inside market acquisitions and negatively related to outside market characteristics, albeit insignificant in both cases. Pasiouras and Gaganis (2006b) also do not find a statistically significant relationship between growth and the probability of being involved in an acquisition, either as target or acquirer. However, Moore (1996) and Pasiouras and Zopounidis (2006) find asset growth to be negatively related to the acquisition likelihood. Pasiouras and Gaganis (2006a) also find growth to be negatively related to the acquisition likelihood but statistically significant only in the case of Germany and Spain. Finally, Wheelock and Wilson (2004) report that acquirers tended to have a recent history of rapid growth.
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Loan activity The importance of loans for EU banks becomes apparent when reviewing data from the European Central Bank report (2004) on the stability of the EU banking sector, which indicates that the share of customers loans in total assets was 50.57% in 2003. Therefore loan activity may be another factor influencing the decision to acquire a bank. Hannan and Rhoades (1987) argue that, on the one hand, a high level of loans would seem to indicate aggressive behaviour by the target bank and a strong market penetration with important established customer relationships that would make it an attractive target; whereas, on the other hand, a low level of loan activity may indicate a bank with conservative or complacent management, which an aggressive acquiring bank could turn around to increase returns. Hannan and Rhoades (1987) find a negative effect of loan activity on the acquisition likelihood (although not significant). Moore (1996) also finds a negative (and significant) effect in both in-market and out-of-market acquisitions. The results of Wheelock and Wilson (2000, 2004) are somewhat mixed (using total loans to total assets ratio), with negative (but not significant) effect on the probability of acquisition in some cases, and positive but not always significant in other cases. Pasiouras and Zopounidis (2006) also find it to be negatively related to the probability of acquisition, although not statistically significant in all cases, while Pasiouras and Gaganis (2006a) report mixed results across the principal EU banking sectors.
Liquidity
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The liquidity position of a bank is another factor that may influence its attractiveness as an acquisition target. However, it is difficult to determine a priori what the effect of liquidity and the direction of its influence will be. Without the necessary liquidity and funding to meet obligations, a bank may fail unless external support is given (Golin, 2001). Hence, banks might be acquired because they have moved into liquidity difficulties, indicating that low liquidity increases acquisition likelihood. On the other hand, excess liquidity may signal a lack of investment opportunities or a poor allocation of assets, making banks attractive targets because of their good liquidity position (i.e. the size of liquid assets influences acquisition). The results of Wheelock and Wilson (2000) indicate that low liquidity makes banks less attractive targets, thus providing support to the first argument, while Pasiouras and Zopounidis (2006) report a negative relationship between liquidity and acquisition likelihood although not statistically significant.
(ii) Bank M&As and regulations and supervision
Capital requirements Hannan and Pilloff (2004) argue that the regulatory capital in M&As can affect acquisition activity either due to the excess regulatory capital hypothesis or the relative capital advantage hypothesis. Under the excess regulatory capital hypothesis, merger activity would increase as a result of the excess regulatory capital that would be created by the lower capital requirements stemming from the adoption of advanced internal
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ratings-based (A-IRB) approach to regulatory capital requirements6. The relative capital advantage hypothesis emerges due to differences in the capital standards applied to AIRB banks and other banking organizations not using the A-IRB approach7. They use data from US banks and examine the excess regulatory capital hypothesis but do not find convincing evidence suggesting that past changes in excess regulatory capital or past changes in capital standards had substantial effects on merger activity. However, Valkanov and Kleimeier (2006) find evidence to support the excess regulatory capital hypothesis. Following an event study methodology, they find that more value is created for targets with high excess capital and in M&As involving targets with considerably higher excess-capital ratios than their acquirers. Capital regulations can also have indirect effects on M&As through their impact on the risk-taking behaviour of banks. The main argument in support of capital requirements is that capital serves as the last line of defence against the risk of banks insolvency, as any losses a bank suffers could be potentially written off against capital. Even in the case where insolvency becomes unavoidable, capital protects to some degree depositors, creditors and investors (Le Bras and Andrews, 2004). However, another strand of the literature indicates that capital requirements may increase risk-taking behaviour (e.g. Koehn and Santomero, 1980; Besanko and Kanatas, 1996; Blum, 1999; Calem and Rob, 1999). Other studies provide mixed results. Kendall (1992) suggests that
This can occur for two reasons. First, while regulators may prevent banks with no excess regulatory capital to engage in M&As, as the combined entity might violated minimum capital adequacy standards, banks with levels of regulatory capital in excess of the required minimum are less likely to violate minimum standards, increasing the probability to acquire other banks. Second, with an increase in excess regulatory capital, banks should increase their return on equity either by increasing the amount of earning assets against which a given amount of capital is held or by reducing capital held against a given amount of earning assets. This could result in an increase of banks valuation, leading to an increase in acquisition activity. 7 Under this hypothesis, A-IRB banks would acquire banks not subject to A-IRB standards because acquired banks would worth more to A-IRB banks than to current owners.
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higher capital requirements may cause riskier bank behaviour at some points in time, but do not imply a trend towards a riskier banking system. Beatty and Gron (2001) indicate that capital regulatory variables have significant effects for low-capital banks but not necessarily for other banks.
Restrictions on bank activities Barth et al. (2004) outline several theoretical reasons for restricting bank activities as well as alternative reasons for allowing banks to participate in a broad range of activities. For example, they mention that to the extent that moral hazard encourages riskier behaviour, banks will have opportunities to increase risk if allowed to engage in a broader range of activities (Boyd et al., 1998). Furthermore, large financial conglomerates may reduce competition and efficiency. On the other hand, fewer regulatory restrictions permit the utilization of economies of scale and scope (Claessens and Klingebiel, 2000), while they might also increase the franchise value of banks and result in more prudent behaviour. Finally, broad activities may enable banks to diversify income streams. Hence, higher restrictions on bank activities that will affect banks opportunities to diversify risks, and limit the potential for economies of scope and scale, might influence their investment decision by motivating them to engage in M&As as an alternative way to achieve their desired outcomes.
Diversification and liquidity related regulations As Liang and Rhoades (1991) mention, a predicted benefit of mergers, particularly conglomerate mergers, is that diversification across different markets will reduce a firms
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risk. For example, Liang and Rhoades (1988) point out that geographic diversification potentially permits banks to reduce their insolvency risk primarily through reduction in credit and liquidity risk. However, banks might achieve diversification by following alternative strategies such as making loans abroad or investing in various liquid assets. Hence, regulations that encourage or restrict banks with respect to liquidity as well as asset geographical diversification might also have an impact on M&As.
Deposit insurance scheme The literature suggests that the deposit insurance scheme of a country can also have an impact on the risk behaviour of banks and their investment decisions. For example, Krugman (1998) suggests that banks that are over-guaranteed and under-regulated tend to over-invest. Other studies indicate that deposit insurance schemes may encourage excessive risk-taking behaviour (Merton, 1977; Bhattacharya and Thakor, 1993; Bhattacharya et al., 1998; Hendrickson and Nichols, 2001; Demirguc-Kunt and Kane, 2002). The deposit insurance scheme might also have an effect on the stability of the banking systems as a whole (Demirguc-Kunt and Detragiache, 2002; Barth et al., 2004). However, Kane (2000), Cull et al. (2001), Demirguc-Kunt and Detragiache (2002), Demirguc-Kunt and Kane (2002), and Laeven (2002) conclude that a sound legal system with proper enforcement of rules reduces the adverse effects of deposit insurance on bank risk-taking, while Gonzalez (2005) finds that deposit insurance has a positive influence on bank charter value, mitigating the risk-shifting incentives it creates. Finally, Buch and Delong (2004b) in their study of cross-border bank mergers find that fairly priced deposit
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insurance in the acquirers country tends to increase the number of mergers banks participate in.
Disclosure requirements Rossi and Volpin (2004) argue that accounting and information disclosure may affect M&As because good disclosure is a necessary condition for identifying potential targets. They also argue that accounting standards reveal corporate governance as they decrease the scope for expropriation by making corporate accounts more transparent. Their
empirical results indicate that the volume of M&A activity is significantly larger in countries with better accounting standards, hence providing support to their argument. Disclosure requirements may also have an impact on the risk-taking behaviour of banks, and consequently on their investment behaviour (e.g. M&As). Fernandez and Gonzalez (2005) find that accounting and auditing systems are complements for minimum capital requirements and substitutes for restrictions on bank activities and official discipline suggesting that accounting and auditing systems can be effective devices to counteract tendencies for firm risk-taking associated with bank safety nets. Qian and Strahan (2005) also find that loan concentration is higher, loan maturity is longer and financial covenants are more common when the accounting framework results in better information for investors.
Disciplinary power of supervisory agency Buch and DeLong (2004b) point out that weak supervision could alter banks decision making by fascinating them to engage in risky activities while ignoring activities that
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make good business sense. Obviously, one way to take advantage of a weak supervision system is to acquire a risky bank. Looking at cross-border M&As, Buch and Delong (2004a) point out that a tough supervisory system in the target country increases the number of bank mergers, while greater toughness of the acquiring countrys authorities discourage mergers. The disciplinary power of supervisor agencies might also have an indirect impact on M&As, through its influence on banks performance and development. While the results of Barth et al. (2004) indicate that there is not a strong association between bank development and performance and official supervisory power, Fernandez and Gonzalez (2005) report that in countries with low accounting and auditing requirements a more stringent disciplinary capacity of supervisors over management action appears to be useful in risk reduction.
Overall countrys legal environment and openness Banks will obviously be affected by the overall environment of the country in which they operate, with a number of aspects relating to the environment having an impact on their investment decisions. For example, La Porta et al. (1998) and Levine (1998) among others mention the effects of differences in the legal environments on the financial system. Rossi and Volpin (2004) show evidence of more M&A activity in countries with better investor protection. Other studies report an association between the legal system and countrys openness and the risk behavior of banks or the development of the banking sector. Hovakimian et al. (2003) find that the introduction of explicit deposit insurance has adverse effects in environments that are low in political and economic freedom and
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high in corruption. Barth et al. (2004) show that better-developed private property rights and greater political openness mitigate the negative association of moral hazard and bank fragility. Finally, Fernandez and Gonzalez (2005) report that banks in a poor legal system with improper enforcement of rules carry a higher risk.
(iii) Bank M&As and market characteristics The neoclassical theory proposed by Gort (1969) and more recently supported by Mitchell and Mulherin (1996), Andrade and Stafford (2004), and Jovanovic and Rousseau (2002) among others argues that apart from regulations there are several additional industry characteristics such as technological changes and capacity utilization that are strongly associated with M&As. Another influential view in the literature, known as the behavioural approach, argues that M&As are being driven by stock market conditions (Nelson, 1959; Stein, 1988, 1989, 1996; Baker and Wurgler, 2000; 2004; Jenter, 2005). Henceforth, various market related factors have been examined or conditioned in past studies, including liquidity (Schlingemann et al., 2002; Harford, 2005), profitability (Buch and DeLong, 2004a; Harford, 2005; Pasiouras and Zopounidis, 2006), growth (Hannan and Rhoades, 1987; Harford, 2005; Pasiouras and Zopounidis, 2006), concentration (Moore, 1996), the level of economic development (Buch and Delong, 2004a; Rossi and Volpin, 2004), the size of the financial system (Demirguc-Kunt and Huizinga, 1999, 2000), and financial deepening (Giovanni, 2005). We consider each of these in turn.
Market Liquidity
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Shleifer and Vishny (1992) show that in order for transactions to occur, buyers who intend to employ the assets in their first-best use must be relatively unconstrained. Schlingemann et al. (2002) reveal that industry-specific asset liquidity is important in determining which assets will be divested. Harford (2005) supports the neoclassical explanation that mergers occur in response to economic, regulatory and technological industry shocks that require large-scale reallocation of assets, but suggests that shocks are not enough on their own, as capital liquidity is also required.
Market Profitability The level or change in the profitability of the banking industry may also lead to higher acquisition activity as a result of attempts by banks to restructure or take advantage of investment opportunities that arise. Christensen and Montgomery (1981) show that firms in profitable industries tend to make more related acquisitions, while those from less profitable industries tend to be involved in unrelated acquisitions in order to improve their profit potential. Harford (2005) documents the existence of abnormally high changes in profitability prior to merger waves. However, Buch and Delong (2004a) find that the relative profitability of banking systems has little explanatory power for crossborder merger activity, while Pasiouras and Zopounidis (2006) report a negative but insignificant relation of market profitability with banks M&As in Greece.
Market growth The growth of the market might also affect acquisition activity in two opposing ways. Firms might be attracted to be involved in acquisitions within industries that have high
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growth rates, while in contrast low growth may indicate the need for restructuring in the industry, hence also leading to increased acquisition activity. Harford (2005) reports abnormally high growth measures (e.g. employees, sales) prior to waves providing recent support to earlier studies that indicate that firms are attracted to make acquisitions within industries with high growth rates (Christensen and Montgomery, 1981; Schoenberg and Reeves, 1999). However, other evidence from the US and Greek banking sectors suggest that market growth is not a significant determinant of acquisition likelihood (Hannan and Rhoades, 1987; Pasiouras and Zopounidis, 2006).
Concentration Finally, related to the discussion on both regulations and market characteristics is market concentration, as authorities try to prevent M&As if increases in concentration are expected to result in excessive increases in market power. Hannan and Rhoades (1987) find concentration negative and significant in explaining the likelihood of in-market acquisitions but positive and insignificant in explaining out-of-market acquisitions. Moore (1996) finds a positive and significant relationship between the probability of acquisition and market concentration for out-of market acquisitions, but not for in market acquisitions. Wheelock and Wilson (2004) and Pasiouras and Zopounidis (2006) find a negative relationship, while the results of Pasiouras and Gaganis (2006a) are mixed.
Economic Growth The investment decision of banks can also be influenced by the overall economic conditions of the country in which they operate. At one hand, banks could be involved in M&As during periods of boom to enhance their power and take advantage of the profit
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opportunities that arise. On the other hand, banks could be involved in M&As during periods of recession to be restructured and to avoid financial distress. Rossi and Volpin (2004) find the level of per capita GNP to be significant and positively related to the volume of M&As, but GDP growth to be negatively related and significant in four of their six specifications. Buch and DeLong (2004a) find the effect of GDP per capita of the acquirer country significantly positive, while that of the target country insignificant. In a latter study (Buch and DeLong, 2004b) they report that GDP has a positive and significant impact on the number of international bank mergers in either target or acquirers country.
Size of the banking industry The size of the banking industry might have an impact on banks interest margins and profits (Demirguc-Kunt and Huizinga, 1999, 2000; Pasiouras and Kosmidou, 2006), their opportunities to achieve economies of scale (Buch and DeLong, 2004a) and consequently their M&As decisions. Diaz et al. (2004) examine the change in profitability of EU banks that were involved in acquisitions and report that the size of the banking sector has a negative and significant impact on profitability. Buch and DeLong (2004a) find that the size of the target countrys banking systems has a negative impact on the probability of the merger, suggesting that banks do not invest in markets that have established a relatively large banking sector. Furthermore, De Nicolo (2000) argues that insolvency risk is lower in more developed financial markets, because these markets provide more financial instruments that are more liquid than in developing markets. This leads us to the hypothesis that banks could use these financial instruments for diversification purposes rather than being involved in M&As. On the other hand, Demirguc-Kunt and Huizinga
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(1999, 2000) suggest that the lower interest margins in larger banking sectors might be related to increased competition. Hence, banks in these sectors might see M&As as a way to increase their power and competitiveness.
Financial deepening As previously mentioned, the behavioural approach states that stock market conditions might affect M&As because of valuation waves. Central to this hypothesis is the argument that bull markets encourage managers of firms with overvalued stock to use their stock to acquire undervalued targets. Verter (2002) and Giovanni (2005) among others confirm that stock market valuations and the size of the stock market are correlated with merger activity. In a recent study, Rhodes-Kropf and Viswanathan (2004) argue that the nave explanation that overvalued bidders wish to use stock is incomplete because targets should not be eager to accept stock. They show that potential market value deviations from fundamental values on both sides of the transaction can rationally lead to a correlation between stock merger activity and market valuation. Shleifer and Vishny (2003) propose an alternative theory of acquisitions, which in a sense is the opposite of Rolls (1986) hubris hypothesis, by arguing that managers rationally respond to less than rational markets. Specifically they argue that since financial markets are inefficient, so some firms are valued incorrectly. Managers, on the other hand, who are completely rational, can understand stock market inefficiencies, and therefore take advantage through M&As. In another recent study, Rhodes-Kropf et al. (2005) also support the idea that misvaluation drives mergers. However, in contrast to these studies, Rossi and Volpin (2004) who examine domestic, foreign and hostile deals do not find any evidence to support that stock market return has an impact on M&As. 24
3. RESEARCH DESIGN
(i) Data The sample used in this study consists of annual observations on the universe of commercial8 banks operating in the EU with available financial data in Bankscope between 1996 and 2002. This gave us a total of 1,407 banks, with the following geographical coverage: Austria (63), Belgium (52), Denmark (70), Finland (9), France (278), Germany (226), Greece (24), Ireland (35), Italy (159), Luxembourg (132), Netherlands (51), Portugal (34), Spain (106), Sweden (16), and UK (152). In attempting to estimate the probability of acquisition, many previous studies have followed a matched paired technique (e.g. Powell, 1997; Pasiouras and Gaganis, 2006b; Ali-Yrkko et al., 2005), where a sample of non-acquired firms is usually drawn by matching against the sample of acquired firms on the basis of company size, industry sector and year of acquisition. While the advantage of this sampling procedure is that it helps to reduce the cost of data collection and provides more information, a matched sample is limited in permitting investigation of the effects of industry sector or year of acquisition. Therefore, in the present study we adopt an unmatched sample as in Hannan and Rhoades (1987), Lennox (1999), Jayaraman et al. (2002), Worthington (2004), Pasiouras and Zopounidis (2006) among others. For the purposes of the present study we assume that in any given year between 1997 and 2002 a bank has three possible outcomes (i) be acquired by another bank
Only commercial banks were included in the sample to avoid comparison problems among different type of banks (e.g. cooperative, savings, etc).
8
25
(target), (ii) acquire another bank (acquirer), or (iii) maintain its status quo (notinvolved). Targets and acquirers are identified in Bankscope, Bankersalmanac and Zephyr databases. All bank-specific financial data are collected from Bankscope, while data on market characteristics (e.g. annual growth, concentration, financial deepening) and regulations (e.g. accounting requirements, capital requirements) are obtained from a combination of sources (see Table 2 for explanation and data sources). The sample is unbalanced in the sense that a complete panel of data is not available for each bank in the sample. Hence, the total number of banks with observed data in each year is lower than 1,407. Table 1 shows the total number of observations in the sample according to status (i.e. target, acquirer, non-involved), country, and year. The majority of the transactions in the sample are domestic (i.e. involving commercial banks from the same country) and this seems to be a general feature of bank M&As rather than a sample bias (Valkanov and Kleimeier, 2006)
[Insert Table 1 Around Here]
(ii) Variables Table 2 summarises the set of 21 independent variables we use in the regressions below, and for the purpose of discussion below we classify them as bank specific, regulatory and market related variables.
Bank specific variables Seven bank specific ratios are chosen to represent the dimensions of capital strength, profitability, expenses management, loan activity, liquidity, size and growth. Capital
26
strength is measured by the equity to total assets (EQAS) ratio, while profitability is measured with return on average equity (ROE). Efficiency in expenses management is represented by the cost to income ratio (COST), with higher values indicating less efficient management. Loan activity is captured through the banks net loans to total assets ratio (LOANS). Liquidity is measured with the liquid assets to customer & short term funding ratio (LIQ), which indicates the percentage of customer and short term funding that could be met if they were withdrawn suddenly. The higher this ratio the more liquid the bank is. Size is measured by the logarithm of total assets (SIZE), while the annual change in banks total assets is used as a measure of growth (GROWTH).
Regulations and supervisions related variables To examine regulation and supervision framework we use seven measures obtained from the World Bank database (Barth et al., 2001) and the Heritage foundation. Briefly, we capture for the extent of capital requirements (CAPRQ) on the basis of seven yes/no questions from Barth et al. (2001). Theoretically, CAPRQ takes values between 0 and 7 with higher values indicating more capital requirements. Restrictions on the activities that banks can undertake are represented by ACTRS, depending on whether securities, insurance and real estate activities are unrestricted, permitted, restricted or prohibited. We use LIQDIV to capture the degree to which banks are encouraged or restricted with respect to liquidity as well as asset and geographical diversification. It is determined on the basis of three yes/no questions and can theoretically take values between 0 and 3, with a higher value indicating greater liquidity and diversification. DEPINS captures the type of the deposit insurance regime a country has chosen to adopt, determined on the basis of three yes/no questions, with higher values indicating more deposit insurer power.
27
DISCRQ measures the accounting and information disclosure requirements in the banking sector, and can take values between 0 and 8 on the basis of eight yes/no questions, with higher values indicating more information disclosure requirements. OFFDISPR measures the official disciplinary power of the supervisory agency, indicating whether the authorities can take specific actions to prevent and correct problems in the banking industry. OFFDISPR can range between 0 and 14, with higher values indicating more power of the authorities. ECFR is the Heritage Foundation Economic Index that indicates the extent of economic freedom in each country. ECFR can take values from 1 to 5, where a score of 1 signifies an economic environment or set of policies that are most conductive to economic freedom, while a score of 5 signifies a set of policies that are least conductive to economic freedom9.
Market specific variables A further set of seven variables is chosen to control for various aspects of market characteristics. We measure market profitability (MROE) with the average return on equity of the commercial banking industry within a country, and market liquidity MLIQ with the ratio of average liquid assets to customer & short term funding. C5 is the measure of concentration in the banking sector, 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. MACGDP is the stock market capitalization to gross domestic product (GDP) ratio that measures financial deepening. The size of the banking system is
9 The Index is determined on the basis of 50 variables that cover: trade policy, fiscal burden of Government, Government intervention in the economy, monetary policy, capital flows and foreign investment, banking and finance, wages and prices, property rights, regulation, and informal market activity.
28
captured by CLAIMS, calculated as bank claims on the private sector over GDP. GDPGR is the annual growth of GDP, and serves as a proxy of a countrys overall economic development. Finally, we use a dummy variable to capture location effects. As the European Central Bank (2000) points out in its report on M&As in the banking sector, the process of industry concentration and consolidation seems to have taken place at faster pace and earlier in some of the smaller member states than in the larger ones. To capture the importance of such differences we introduce a dummy variable that indicates whether banks operate in one of the 5 large EU banking sectors10 (5EU=1) or not (5EU=0).
[Insert Table 2 Around Here]
(iii) Methodology In the present study we estimate a multinomial logit model of the following form:
P (Yi = l) =
e
2 l =0
l' X l' X
for l = 0, 1, 2
e
where P (Y) is the probability of occurrence of the dependent variable in year t. X is a vector of variables representing the influence of bank specific, market specific and country specific characteristics in year t-111, and i are the coefficients to be estimated relating to each outcome. The categorical dependent variable takes the value zero if the
The 5 largest EU banking sectors are: France, Germany, Italy, Spain and UK. Considering that bank acquisition can take some time to complete, when estimating our models we assume, as in previous studies (e.g. Hannan and Rhoades, 1987; Wheelock and Wilson, 2004) that acquisitions completed during year t, reflect banks and market characteristics during year t-1. Thus for an acquisition that occurred in 1997 we use observations on the independent variables from 1996.
11
10
29
bank is non-involved (reference group), one if the bank is acquired, and two if the bank is an acquirer12. Hence, in the remainder of the paper coefficients are reported and discussed relative to the group that is omitted (reference group). For example, a positive (negative) coefficient for targets increases (decreases) the probability of being acquired relative to non-involved banks. Similarly, a positive (negative) coefficient for acquirers increases (decreases) the probability of being engaged in acquisition as an acquiring bank relative to non-involved banks. The estimation of a logit model can be problematic when there are a few observations from one outcome (i.e. targets and/or acquirers) relative to another (i.e. noninvolved banks). The reason is that the information content of such a sample for model estimation is quite small, leading to relatively imprecise parameter estimates (Palepu, 1986). One approach to tackle this problem is to use a choice-based sampling technique to increase the sampling rate of acquirers and targets. An alternative procedure being used in the present study is to weight the data and compensate for differences in the sample13. Our base specification can be stated as follows:
' X = 0 + 1' BANK + 2' REGULATION + 3' MARKET
Note that the equation could easily be extended to the case of more than three alternative outcomes for j = 0, 1, J. 13 The following formula is used: Weighting for Group 0 (Non-involved) = (1/N0) * [(N0 +N1+N2)/3], Weighting for Group 1 (Acquired) = (1/N1) * [(N0 +N1+N2)/3], Weighting for Group 2 (Acquirers) = (1/N2) * [(N0 +N1+N2)/3]. For a more detailed discussion on logistic regression and rare events data see King and Zeng (2001).
12
30
where BANK represents the set of bank specific variables (EQAS, ROE, COST, LOANS,
LIQ, SIZE, GROWTH), REGULATION constitutes the set of country-specific regulatory
and supervision variables (CAPRQ, ACTRS, LIQDIV, DEPINS, DISCRQ, OFFDISCPR,
ECFR), and MARKET makes up the remaining set of market related country-specific
influences (MROE, MLIQ, C5, MAGGDP, CLAIMS, GDPGR, 5EU). In what follows, we estimate only two versions of the base model: Model 1 which allows BANK variables only, and Model 2 which adds the influence of REGULATION and MARKET characteristics. Thus, we separate the influence of bank-specific factors from country-specific regulatory and market-related factors. In this way, we estimate the impact of the external environment conditional on the internal, bank specific factors. The models are estimated using annual data, as available for all the bank specific and market variables, as well as economic freedom (ECFR). However, since the Barth et al. (2001) database provides information only for one point in time14, the rest of the regulatory variables (CAPRQ, ACTRS, LIQDIV, DEPINS, DISCRQ, OFFDISPR) are given the same value for each country over the time period of our estimation. While this may seem constraining, Barth et al. (2004) point out that such regulations change very little over time and control of these influences in their study did not alter their findings. Consequently, as in previous studies making use of this dataset (e.g. Focarelli and Pozzolo, 2001b, Demirguc-Kunt and Detragiache 2002, Buch and DeLong, 2004a,b, Fernandez and Gonzalez, 2005), we assume that regulatory influences remain constant over limited periods of time. Furthermore, following earlier studies that calculated industry-adjusted ratios to account for industry specific differences (Platt and Platt, 1990;
Barth et al. (2004) indicate that data are primarily from 1999. That is of the 107 responses reviewed 13 were received in November 1998, 65 were reviewed in 1999, and 29 in 2000 (19 of which in either January or February).
14
31
Barnes, 1990), we adjust15 all bank-specific variables by country to account for differences in average characteristics of banks across countries.
4. RESULTS
We first describe the data characteristics and the base results obtained for the total number of observations shown in Table 1. To test for the robustness of our base results, we then re-run our regressions on sub-samples of data, determined on the basis of size (large and small banks), location of operation (whether in the principal banking sectors or not), and over three successive two-year time periods (1997-98, 1999-00, 2001-02). Although the results reported below ignore time and country specific dummies, we performed all the regressions with appropriate dummies in order to test for data poolability, and these confirmed the applicability of our panel data approach.16
(i) Base results
Table 3 presents summary descriptive statistics, revealing apparently minor mean differences in the variables among the three categories of banks (acquired, acquiring and non-involved), with SIZE and GROWTH being the main exceptions. However, it is worth noting that despite recent efforts to harmonize bank regulations across the EU market, the
Standardizing by country averages deflates raw values and expresses all variables in relation to the average in the country. Also because values of the ratios were computed over different years, standardizing also controls for the mean shift in the ratios from year to year. Variables are adjusted as follows: Banks Country-Adjusted value of ratio X in year t = Banks raw value of ratio X in year t / Average value of ratio X in the commercial banking industry of the country where the bank operates in year t. Average values for each one of the 15 commercial banking industries were calculated from Bankscope, for each year of our analysis. We believe that the use of industry (country) relative data and the inclusion of country specific influences in model 2 is sufficient to make the joint effect additional country-specific dummies (other than 5EU) insignificant. The extended set of results is available from the corresponding author upon request.
16 15
32
data do in fact reveal significant cross-country differences in bank regulatory and supervisory practices. For example, CAPRQ ranges between 2 (Sweden) and 7 (Belgium, Denmark, Spain), and ACTRS takes values between 1 (Germany) and 2.333 (Belgium, Greece, Italy). DISCRQ ranges between 4 (Austria, Denmark, Germany, Ireland, Netherlands, Portugal) and 7 (Italy), while OFFDISPR takes values between 3 (Sweden) and 12 (Austria, UK). Finally, the average values of ECFR for each country over the entire period are between 1.60 (Ireland) and 2.95 (Greece). Table 4 reports the correlations among the variables, showing values lower than 0.6 (those between 0.5 and 0.6 indicated are in bold) implying that they can be included individually for estimation without particular concerns in terms of multicollinearity17.
[Insert Tables 3 and 4 Around Here]
Table 5 presents the results of the multinomial logit estimation for the full sample. Both models have significant chi-square values, allowing us to reject the hull hypothesis that all coefficients are zero. The McFaddens R2 increases from 0.11 (Model 1) to 0.15 (Model 2) with the significance of some market related and regulatory factors in the EU banking industry, especially for the acquiring banks.
[Insert Table 5 Around Here]
Among the bank-specific characteristics, the influence of COST and SIZE is significantly positive while that of EQAS is negative on both targets and acquirers. The
Judge et al. (1988) point out that correlations below 0.8 should not be too harmful as far as multicollinearity is concerned.
17
33
direction of these influences on both models uniformly indicates that both targets and acquirers were larger, less well-capitalized and less efficient in expenses management relative to non-involved banks. In addition, looking at the magnitude of these influences, it appears that both size and cost efficiency have a greater impact on acquirers than on targets. The influence of GROWTH and ROE is also significant, being negative on targets and positive on acquirers. Thus, acquired banks were less profitable with lower growth prospects than non-involved banks, while acquirers tended to be more profitable with higher growth (in total assets). Note also that coefficients of these bank-specific influences alter very little in magnitude with the addition of regulatory and market related factors. In addition, the effect of LIQ is negative and significant on the probability of being acquired, indicating that high (low) liquidity made targets less (more) attractive; in contrast to the results of Wheelock and Wilson (2000) for the US, who reported that low liquidity makes banks less attractive targets. insignificant in the full sample.18 Among the effects that proxy for the requirements and policies of regulatory and supervisory authorities, only CAPRQ, OFFDISPR and ECFR have a significant impact on bank acquisition likelihood for both targets and acquirers. The signs on these coefficients suggest that banks operating in countries with higher capital requirements19, lower disciplining power of the authorities, and authorities that are more conductive to economic freedom, are more likely to engage in acquisitions. In contrast, the significance of the coefficients on ACTRS, LIQDIV, DEPINS and DISCRQ (for acquirers) suggest that
LOANS is significant on acquirers in model 2 (only at 10% level) , the opposite being the case for LIQ. However, the correlation between these two variables is 0.56 (see Table 2), raising doubts about the significance of the estimates. 19 The positive influence of CAPRQ does not hold in the sub-samples; in fact, this effect is not significant for smaller banks and for banks operating in the non-principal banking sectors as noted below.
18
The influence of LOANS is broadly
34
regulatory environments which support lower restrictions on banks activities, lower liquidity requirements, more accounting and disclosure requirements, and higher deposit insurer power tend to increase the likelihood to engage as an acquirer, not as a target. Turning to the effects of market conditions we observe from the significance of coefficients on MROE, C5, MLIQ and CLAIMS that (from the point of view of both targets and acquirers) market environment supporting higher profitability, lower concentration, lower liquidity, and lower industry size tended to increase banks likelihood to engage in acquisition. Furthermore, the significantly negative coefficient on
5EU dummy suggests that operation in a principal banking sector reduced the acquisition
likelihood for both targets and acquirers. In contrast, acquisition activity has not been influenced by the proxy for financial deepening, the size of the stock market as measured by its capitalisation relative to GDP (MACGDP); whereas GDPGR has a significant impact on acquirers, suggesting that lower economic activity raised their probability to acquire other banks as a means to restructure.
(ii) Robustness Large versus small banks
As Fields et al. (2004) point out large banks typically have much more complex financial profiles and more sources of liquidity than small banks, as well as considerably different risk profiles. Demsetz and Strahan (1997) also show that large bank holding companies are allowed to operate with lower capital ratios and typically engage in more risky activities. Hence, it is not surprising that size is frequently mentioned among the reasons for acquisitions related to both shareholder wealth maximization (e.g. economics of scale,
35
economies of scope) and managerial motives (e.g. empire building), which also suggests that other determinants of acquisitions may vary according to the size of the banking institutions. European The Central Bank (2000) also points out that with regard to the rationale for M&As there is a need to differentiate according to the size of the institutions involved and reports that small bank M&As are mostly being carried out for cost efficiency reasons and to achieve a size that allows survival. Larger bank M&As often have an element of strategic re-positioning and, like small bank M&As, are driven by scale economies. Furthermore, M&As between small institutions outnumbers those of large institutions by far. In view of these considerations, we sought to re-estimate the models on samples distinguished between large and small banks. Banks were classified accordingly by comparing their total assets with the average total assets of the banking industry in which they operate, in the corresponding year. Thus we constructed two sub-samples, one with 32 targets, 33 acquirers and 744 yearly observations of non-involved large banks, and another with 166 targets, 57 acquirers and 5,242 yearly observations of non-involved small banks20. This reveals, as one would expect, that our full sample contained a much larger proportion of smaller banks21. The results for the sub-samples are shown in Table 6.
[Insert Table 6 Around Here]
A bank is classified as large if its total assets are higher than the average total assets of the banking industry where it operates, and small otherwise. Obviously, such an approach does not consider medium sized banks. However, attempting to classify banks into more groups would result in only a few acquired and acquiring banks in each group. 21 Note that the criterion for splitting the sample on the basis of small-large banks is relative to the country in which the bank operates, and so this does not preclude investigation of the overall effect of bank size (total assets) on the probability of acquisition.
20
36
All the regressions have significant chi-squares values with McFaddens R2 being higher for model 2 given the significant influence of many of the country-specific factors. Among the bank specific factors, the positive influence of COST persists through both sub-samples, even after controlling for the environment in which banks operate (model 2), suggesting that cost efficiency has been a critical factor in influencing a bank, large or small, to engage in acquisition, as target or acquirer. In addition, its impact is greater on the acquirers among the small banks, resembling the full sample results. The coefficients of EQAS and ROE are also significant for the small banks. Moreover, in the large banks sample, their influence is significant on targets probabilities rather than on acquirers, which may seem plausible as large banks need not be profitable or less well capitalised to acquire others.22 Comparing the results of model 1 across the two sub-samples, we observe that, apart from EQAS, ROE and COST, the effect of bank SIZE is positive and significant to both targets and acquirers in the large banks sample, and to the acquirers in the small banks sample where its effect is higher in magnitude. By contrast, GROWTH is significant to both targets and acquirers among the smaller banks, but only to the targets among the larger banks. The effect of SIZE remains significant in model 2, confirming that irrespective of the environment bank size matters more for the large banks, as target or acquirer, and considerably more to the acquirers among the smaller banks, thus supporting the results of Wheelock and Wilson (2004) who found that the probability to engage in acquisition increased with bank size. Furthermore, size does not seem to
The influence of ROE is marginally significant in model 2 for the acquirers among the large banks, although not for model 1. While this suggests that large banks may or may not be more profitable to acquire less profitable banks, the insignificance of EQAS seems to suggest that large banks need not be less well capitalized to acquire others, or the insignificance may be attributed to the relative smallness of the sample size.
22
37
matter to the targets among the small banks. In contrast, growth matters more to the smaller banks, as target or acquirer, and also partly to the targets among the large banks (whose negative effect confirms that targets are those with lower growth opportunities). However, large banks tendency to acquire other banks does not seem to be influenced by higher growth opportunities. As with the full sample, the influence of LIQ is significant and negative in the small banks sample, confirming that higher liquidity makes small banks less inclined to engage in acquisition, as targets or acquirers. This effect does not necessarily hold for the large banks though. In contrast to the full sample results, the influence of LOANS is now significant to the acquirers, being negative for small banks but positive (with higher magnitude) for large banks. This result suggests that higher loan activity increases the tendency for large banks to acquire others, the opposite being the case for small banks, and presumably explains its insignificance in the full sample. Among the external factors, only CLAIMS is significant (and negative) for both targets and acquirers in both samples, confirming that the size of the banking industry influences negatively bank acquisition likelihood for both targets and acquirers, large or small. Although 5EU is also significant, it has a perversely positive impact on the large banks target group, implying that this groups targets and acquirers react differently depending on whether they operate in a non-principal banking sector or not. However,
ACTRS, DISCRQ and DEPINS continue to be significant to acquirers in both samples,
confirming that acquirers, large or small, tend to be influenced by lower restrictions on banks activities, more accounting and disclosure requirements, and higher deposit insurer power. But the negative influence of LIQDIV prevails only for the acquirers
38
among the large banks, albeit with higher magnitude. Of the remaining factors, there are more differences in their impact on targets and acquirers across the two sub-samples although greater consistency is observed between the results of the full and small banks samples. For example, CAPRQ, OFFDISCPR and MROE effects persist in both small and full samples with similar magnitude and signs, while ECFR is only significant for the targets. However, the small-large banks sample split also unravels the apparent
insignificance of some external effects in the full sample, since they appear with opposite signs in the sub-samples. For example, ACTRS, DISCRQ and DEPINS were found insignificant to the targets group in the full sample, although in the sub-samples their effects are significant but with opposite signs, revealing that these regulatory influences appear to affect the targets among the large and small banks differently. Similarly,
MACGDP is significant to the acquirers, large or small, although its opposite sign in the
two sub-samples may be the reason for its insignificance in the full sample. On the contrary, the significance of C5 in the full sample owes much to its significance to the large banks, rather than to the small banks. Likewise, the significance of GDPGR to the acquirers in the full sample is due much to its impact on the small banks. In summary, the multivariate logit results for the small and large banks appear to confirm the importance of bank specific factors, in particular COST, EQAS, ROE, SIZE and GROWTH, in influencing the acquisition probabilities of both targets and acquirers. These effects are more or less significant after conditioning upon the external factors, which tend to vary in their impact on targets and acquirers, large or small.
39
Large versus small banking sectors
The significance of the 5EU dummy in the large-small bank samples indicates that the motives for acquisitions might differ between the five principal banking sectors and the rest of the EU. Indeed, the Group of Ten (2001) report argues that the nature of
acquisition activity may differ between countries and the European Central Bank (2000) report goes far to suggest that there are specific developments in individual countries or regions affecting the motives for acquisition. For example, it argues that the acquisitions opportunities are likely to be different in a country were there have already occurred a number of acquisitions than in a country where there has been little or no acquisition activity in the recent past. Since the development of the banking sectors in the five larger EU countries differs to a large extent from that in the smaller countries, we can determine whether banks in these sectors have characteristics that indicate different motives for acquisitions. Table 7 presents the estimation results with sub-samples constructed on the basis of whether the banks operate in one of the five principal EU banking sectors (5EU) or not23. The first sub-sample accordingly consists of 129 targets, 49 acquirers and 3,998 yearly observations of non-involved banks operating in the five large markets (5EU). The second sub-sample consists of 69 targets, 41 acquirers and 1,988 yearly observations of non-involved banks operating in the rest of the EU-15 (non-5EU). Accordingly our full sample comprised nearly twice as many banks in 5EU compared to non-5EU (see Table 1). [Insert Table 7 Around Here]
Note that with this sample split we had to drop the dummy variable 5EU from the regressions to obtain estimable results.
23
40
As before, the significant and positive influence of both COST and SIZE holds in both sectors for both targets and acquirers, even after controlling for the regulatory and environmental factors.24 EQAS and ROE are also robustly significant and with expected signs for both groups in 5EU, and particularly for the targets in non-5EU. GROWTH is significantly negative on targets and positive on acquirers, particularly in non-5EU. The influence of LIQ remains negative in both banking sectors (but significant only on targets), while that of LOANS is significant in non-5EU but with opposite signs on targets and acquirers. While controlling for the environment in which banks operate (model 2) does not alter the significance of these bank-specific characteristics, there appears to be a great deal of variation in the impact of the external factors across the two banking sectors, specifically in relation to the earlier results. For example, the influence of CAPRQ is now negative (and significant for 5EU); as opposed to its positive impact found earlier (Tables 5 and 6). A possible explanation for this might be that capital requirements have adversely affected the risk-taking incentives of banks to engage in acquisition, partly as a result of other regulatory and supervision restrictions which seem to have affected targets and acquirers more differently in 5EU than in the rest of the EU. Most significant in 5EU is the positive impact of economic freedom (ECFR) on acquirers tendency to engage in acquisitions, and their behaviour is positively associated with the degree to which they
SIZE appears insignificant to the targets group in model 2, but not when we estimate this model by adding a large-small banks dummy, whose effect is also significant for the targets group but its inclusion does not result in higher overall explanatory power of the model. In general, we estimated all the models here (as well as the full sample model) with a large-small banks dummy and obtained broadly similar results (except for model 1 in non-5EU, where its effect as with model 2 is significant on targets but its inclusion, by making the effect of GROWTH insignificant, actually contributes to loss of fit).
24
41
are influenced with regard to liquidity and diversification guidelines (as shown by the positive impact of LIQDIV); this tendency is of course partly offset by the requirements on capital (CAPRQ). In contrast, acquirers in non-5EU have been more significantly influenced by restrictions on accounting transactions (ACTRS). Targets and acquirers in 5EU also seem to have reacted differently to market specific forces. In general,
regulatory influences appear to have been more significant in determining acquisition likelihood for banks operating in 5EU, whereas market related influences are more prevalent among the non-5EU banks. Nevertheless, both targets and acquirers have been influenced by market concentration (C5) and stock market capitalisation (MACGDP) in 5EU, and additionally by industry size (CLAIMS) and market profitability (MORE) in non-5EU25.
Regression over sub-periods
Pooling of sample across years in effect assumes that the determinants of acquisitions remain stable over time. However, previous studies that examine sub-samples find acquisition characteristics to change over different periods (Harris et al., 1982; Powell, 1997; Ali-Yrkko et al., 2005). In order to determine whether our estimates are consistent over time, we performed re-estimations by splitting the full sample according into three sub-periods of acquisition activity (1997-98, 1999-00, 2001-02). This partitioning was conveniently chosen to maintain a balanced sample size across the three sub-periods, and does not necessarily suggest that economic or industry factors changed significantly over the whole period. However, according to Table 1, acquisition activity in terms of the ratio
C5 also has a perversely positive impact on 5EU, in contrast to its negative impact in non-5EU, and this may be associated with the perverse effects of CAPRQ, ACTRS and LIQDIV in this sample.
25
42
of involved to non-involved banks was more intense in 1999-00 (0.06), followed by 2001-02 (0.05), and 1997-98 (0.03), and therefore it seems appropriate to investigate whether there are specific causal factors explaining these differences.
[Insert Table 8 Around Here]
Table 8 presents the results for the three sub-periods. Once again the influence of
COST, EQAS, SIZE and GROWTH is significant throughout in the absence of country-
specific factors (model 1), and in most cases remains robust after controlling for the latter (the only exceptions being SIZE and GROWTH in 2001-02). The influence of ROE is also significant to acquirers (albeit with negative sign in 1999-00) and targets in at least two of the three sub-periods. The effects of LIQ and LOANS are more significant on targets than on acquirers, their negative impact in the last two sub-periods being consistent with earlier results. Turning to external factors, the specific time period under study seems to play a significant role in determining which of these country-specific factors affect targets and acquirers. For example, among the regulatory factors, CAPRQ and ACTRS affect
acquirers in the first two sub-periods, and targets in the last sub-period, although their directional impact is consistent with the full sample results. Similarly, LIQDIV, DEPINS and DISCRQ have varying effects on targets and acquirers. However, OFFDISCPR negatively influences both targets and acquirers in all three sub-periods, and this result also prevails in the full sample, suggesting that higher disciplinary power of the supervisory authorities has adversely affected bank acquisition likelihood. Finally, ECFR stands out as highly significant in 1999-00, and its positive influence was also found in
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the full sample, implying that economic freedom, along with the official disciplinary power of the authorities, have been important factors in determining bank acquisition likelihood over this period of more intense acquisition activity. From the measures that proxy for the market environment, only CLAIMS and C5 have significant effects on targets and acquirers for at least two of the three sub-periods, although these are not uniformly negative as in the full sample. Nevertheless, their overwhelming significance across the other sub-samples suggests that industry concentration and size are additional market attributes that have influenced bank acquisition likelihood to a degree. The remaining variables have generally mixed affects, and their impact is not uniform on targets or acquirers across the sub-periods.
5. CONCLUSIONS We use financial and industry level data for a sample of over 1400 commercial banks drawn from EU-15 countries to identify the major determinants of acquisitions in the EU banking industry over the period 1997-2002. Our study extends previous empirical investigations that focused on banks financial and market characteristics by incorporating the influence of regulatory and supervision framework on bank acquisition likelihood, using a broad range of measures including capital adequacy requirements, the level of accounting and information disclosure requirements, the degree of official disciplinary power, an index of economic freedom, and a measure of liquidity diversification in the industry. Some of these industry level characteristics have not been investigated in the previous literature on EU banking.
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The main focus of our study has been to evaluate the influence of bank financial and country specific regulatory and market characteristics in attempting to estimate the probability of bank acquisition, as targets or acquirers relative to non-acquired banks. We employed a multinomial logit model to identify those characteristics that most consistently influenced targets and acquirers in estimations involving the full sample as well as sub-samples distinguished by large and small banks, principle and non-principle banking sectors, and over sub-periods of the analysis. The results indicated that both targets and acquirers were significantly larger in size, less well capitalized and less cost efficient, in comparison with non-involved banks. Furthermore, targets were less profitable banks with lower growth opportunities, whereas acquirers were more profitable with higher growth prospects. Whereas bank size as a motive of acquisition had a significant influence on larger banks, among smaller banks it mattered even more to the acquirers but less to the targets. By contrast, growth affected acquisitions mainly among the smaller banks. As for other bank specific influences, higher liquidity made smaller banks less likely to engage in acquisitions; whereas higher loan activity influenced bank acquisitions mainly in the non-principal banking sectors of the EU, with a negative effect on targets and positive on acquirers. Conditioning on bank specific factors, we also found that banks operating in countries with higher disciplining power of the authorities were less likely to engage in acquisitions. Other regulatory influences, in particular capital requirements and the
degree of economic freedom, were also found significant in the full sample, although their effects varied across the sub-samples. Restrictions on accounting transactions, deposit insurer power, disclosure requirements and diversification guidelines tended to
45
affect targets and acquirers differently. Among the market specific factors, profitability, liquidity, concentration and industry size were also significant in most cases. In general, regulatory factors had a greater influence on banks acquisitions in the principal banking sectors (i.e. the five largest countries of the EU), whereas in the rest of the EU market influences were more prevalent. Whilst investigating a comprehensive range of bank specific and country specific factors, we highlight at least two shortcomings in the present study. First, owing to data availability, we have noted that apart from the index of economic freedom, other regulatory variables have not changed over the time period of our analysis. Second, again owing to data availability, we have concentrated only on financial bank-specific characteristics. It is hoped that future research will extend the study to incorporate nonfinancial factors such as concentration of firm ownership, corporate governance, management experience and quality, as well as include other types of banks (e.g. savings, cooperatives) as the present one was limited to commercial banks.
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