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Measuring the effects of M&As on Eurozone bank efficiency: an innovative approach on concentration and credibility impacts

  • S.I.: Regression Methods based on OR techniques
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Abstract

The present paper examines the Mergers and Acquisitions’ (M&As’) effects on the European banking efficiency levels based on a sample of 43 listed commercial banks in eight countries (Portugal, Italy, Ireland, Greece, Spain, Germany, France and Finland). We applied a Data Envelopment Analysis (DEA) in two groups of countries and conducted a second-stage analysis under two different models adjusted for credit-risk factors. This is the first time that DEA, an effective nonparametric method for evaluating efficiency, has been applied in the exploration of the European banking sector M&As that occurred during a crisis period characterized by uncertainty and inconvenient circumstances that influenced performance. The results imply significant effects of the capital adequacy ratio and non-performing loans on DEA scores, and demonstrate the past effects on their performance. Moreover, the results reveal that M&As negatively affect the efficiency levels in both “strong” and “weak” banking systems, whereas technical efficiency levels are positively affected by the growth perspective of the origin countries, regardless of whether banks are involved in one or more M&As. The M&As’ inability to improve the financial stability of the banking system during the examined crisis period of 2007–2015 led to an examination of the merging banks’ concentration levels, before and after the M&A strategy. The results revealed a positive merger effect, only when a M&A takes place under specific levels of market concentrations (i.e., when competition increases).

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Notes

  1. Banker et al. (1984) suggest the use of VRS in order to break down overall efficiency into pure technical efficiency, which refers to the ability of managers to use their resources efficiently, and scale efficiency, which refers to the exploitation of economies of scale by working at a production level where the profitability boundary is experiencing CRS. Pure technical efficiency scores resulting from variable economies of scale are higher or equal to those achieved under constant economies of scale.

  2. Simar and Wilson (2007) recommended two algorithms (a 1st algorithm and a 2nd algorithm) that are differentiated by either the use of uncorrected (1st algorithm) or bias-corrected (2nd algorithm) efficiency scores. In contrast to naive two-step approaches, the Simar and Wilson (2007) algorithms take into consideration the fact that as DEA efficiency scores are bounded, they depend on how inefficiency is designated and the ability of DEA to derive a multiplicated and generally unidentified correlation pattern among estimated efficiency scores.

  3. The main advantages of this approach include its easy application, its ability to use multiple macroeconomic variables simultaneously without influencing the number of profitable banks, the needlessness of determining the orientation of the effect of each factor in efficiency, its ability to handle parallel continuous and categorical variables, and its ability to be used when some (or all) of the environmental variables are common to a subset of units (Pastor 2002).

  4. Simar and Wilson (2007) argue that the conventional Tobit and truncated censored two-step regression analysis approaches for the determination of DEA estimated efficiency scores are inappropriate for two main reasons. First, as the two-step approaches lack a well-defined data generating mechanism, the censored regression models are unfit for producing unbiased estimators under high differentiations in their standards errors. The DEA efficiency score artificially takes the value of 1 (θ = 1) by making the assumption that the sample is finite and that a DMU is fully efficient in this specific determinate sample; this in turn creates differences between the true production possibility frontier and the estimated DEA frontier. Second, DEA efficiency scores are correlated under unknown forms.

  5. According to the OECD database, a BL indicator covers the banking sector (covering central banks and monetary financial institutions, S121 and S122_3, respectively, in the System of National Accounts terminology), as well as other financial intermediaries, except insurance corporations and pension funds (S123). Data are compiled under the 2008 System of National Accounts (SNA 2008).

  6. It is important to include the variable of non-performing loans in this analysis since numerous studies found that banks that face potential failure have high ratios of non-performing loans and also have low measured efficiency (Fiordelisi 2009; Berger and Humphrey 1997; Wheelock and Wilson 1995). In other words, non-performing loans and efficiency are closely related.

  7. The only listed banks removed from the dataset include Banca Sistema (Italy), Abanca Corporacion Bancaria (Spain), Fidor Bank AG (Germany), and Fonciere de Paris (France), as their data are not available for more than two-thirds of the examined period.

  8. Luxembourg and Estonia are not included in our final dataset, as no listed banks can be found according to the Orbis database report.

  9. Statistically significant at the 15% level.

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Authors and Affiliations

  1. Audencia Business School, Nantes, France

    Emilios Galariotis

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Kyriaki Kosmidou, Dimitrios Kousenidis, Eirini Lazaridou & Trifon Papapanagiotou

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  1. Emilios Galariotis
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Appendix

Appendix

Tables 7 and 8.

Table 7 Estimations of Tobit (Naïve) and truncated (Conventional) censored regression models on three different datasets
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Table 8 Statistical differences tests of M&As dummy variables against DEA technical efficiency scores, on estimations of algorithms 1 and 2, under basic and credit risk models
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Galariotis, E., Kosmidou, K., Kousenidis, D. et al. Measuring the effects of M&As on Eurozone bank efficiency: an innovative approach on concentration and credibility impacts. Ann Oper Res 306, 343–368 (2021). https://doi.org/10.1007/s10479-020-03586-9

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