Financial reporting standards' change and the efficiency measures of EU banks

Accepted Manuscript Financial reporting standards' change and the efficiency measures of EU banks

Augustinos Dimitras, Chrysovalantis Gaganis, Fotios Pasiouras PII: DOI: Reference:

S1057-5219(18)30266-7 doi:10.1016/j.irfa.2018.08.008 FINANA 1244

To appear in:

International Review of Financial Analysis

Received date: Revised date: Accepted date:

14 November 2014 16 July 2018 8 August 2018

Please cite this article as: Augustinos Dimitras, Chrysovalantis Gaganis, Fotios Pasiouras , Financial reporting standards' change and the efficiency measures of EU banks. Finana (2018), doi:10.1016/j.irfa.2018.08.008

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ACCEPTED MANUSCRIPT Financial reporting standards' change and the efficiency measures of EU banks Augustinos Dimitras1, Chrysovalantis Gaganis2, Fotios Pasiouras3* 1

School of Social Sciences, Hellenic Open University, Greece Department of Economics, University of Crete, Greece

Financial Engineering Laboratory, Technical University of Crete, Greece

Abstract

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This study aims to examine whether and how bank efficiency estimates are influenced by the shift from local GAAP to IFRS in EU. We use financial statements prepared during the transition period, and stochastic frontier analysis to obtain cost and profit

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efficiency estimates for a sample of 141 banks from 15 countries. Our main finding is that the bank efficiency estimates have been significantly influenced by the transition

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to IFRS. However, different consequences are reported for profit and cost efficiency.

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The paper offers some interesting conclusions on bank efficiency estimates’ variation due to an important change in the accounting rules.

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Keywords: Bank efficiency, GAAP, IFRS, Stochastic frontier analysis

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JEL Codes: D61, G21, M41

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Author for correspondence. E-mail: [email protected] Acknowledgments: We would like to thank an anonymous reviewer for various comments. An earlier version of the manuscript was prepared while Fotios Pasiouras was a Reader in Banking & Finance at the University of Surrey.

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ACCEPTED MANUSCRIPT Financial reporting standards' change and the efficiency measures of EU banks

1. Introduction In June 2002, the European Union (EU) approved a regulation requiring listed EU

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companies, including banks and insurance firms, to prepare their consolidated

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accounts from 2005 onward in accordance with the International Financial Reporting Standards (IFRS) / International Accounting Standards (IAS). For ease of expression

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‘IFRS’ will be used to denote both International Accounting Standards and

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International Financial Reporting Standards. As mentioned in Jermakowicz and Gornik-Tomaszewski (2006), this regulation introduced the biggest changes to

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financial reporting in Europe in 30 years, having a direct impact on approximately 7,000 listed EU companies, and an indirect impact on many consolidated subsidiaries.

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The purpose of the present study is to examine whether and how the adoption

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of the IFRS influences the cost and profit efficiency estimates of EU banks. Our exercise is motivated by the potential problems of comparing financial performance

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of firms under different accounting practices (see e.g. Whittington, 2000). Despite these problems, the vast majority of the bank efficiency studies pool their sample

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from the pre- and post-IRFS period without taking into account the potential differences. The requirement for EU firms to publish their statements under both GAAP and IFRS at the time of the IFRS adoption offers an opportunity to conduct a natural experiment and compare the results obtained under the two different accounting standards for a given sample of firms, in our case European banks. While looking at the past, our question remains timely, because of current and future IFRS changes that range from minor modifications to significant amendments

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ACCEPTED MANUSCRIPT of fundamental principles. Some of them like the new standard “IFRS 13 Fair Value Measurement”, amendments to “IFRS 7 Financial Instruments: Disclosures” and “IAS 19 Employee Benefits”, were effective for periods beginning on or after 1 January 2013, whereas others like the three new standards on Consolidated Financial Statements (IFRS 10), Joint Arrangements (IFRS 11) and Disclosure of Interests in

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Other Entities (IFRS 12) were effective for periods beginning on or after 1 January

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2014 (e.g. see Ernst & Young, 2010; 2012). This procedure is continuous and, for

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example, for annual periods beginning on or after 1 January 2019 the IFRS 16 'Leases' is effective and the same holds for the amendments to seven standards,

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namely the IFRS 3, IFRS 11, IAS 12, IAS 23, IFRS 9, IAS 28 and IAS 19 as well as one IFRIC (IFRIC 23). At the same time the use of IFRS is expanding throughout the

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world and the problem of comparisons arises in more and more countries. The literature has explored other issues related to the adoption of IFRS in

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Europe. Some of them, prepared prior to the IFRS adoption, provide general

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discussions of the possible consequences of the IFRS regulation (e.g. Delvaille et al., 2005), while other papers, published after the IFRS implementation, are of a more

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empirical nature examining the effects of the IFRS adoption in various aspects among others the impact on taxation (Haverals, 2007), earnings and capital management

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(Leventis et al., 2011), market value relevance of accounting data (Devalle et al., 2010), perceptions of disclosure quality (e.g. Daske and Gebhardt, 2006 and Daske et al., 2013), accounting systems’ classification (Nobes, 2011) and convergence of National Accounting Standards with IFRS (e.g. Fontes et al., 2005 and Street and Larson, 2005). While there are a few studies that examine the impact of the IFRS adoption on the financial results (e.g. see Callao et al., 2007; Gray et al., 2009; Iatridis et al., 2010) they tend to use univariate tests and/or multivariate regression techniques

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ACCEPTED MANUSCRIPT (e.g. logistic regression) to compare selected accounting items and financial ratios (e.g. return on equity, etc.) under national General Accepted Accounting Principles (GAAP) and the IFRS. The banking industry has been traditionally excluded from the aforementioned studies, due to the particular aspects of their financial statements. The frontier technique that takes into account simultaneously the various inputs and

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outputs of a bank and provides an overall objective numerical score and ranking, an

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efficiency proxy that complies with an economic optimization mechanism (see) has

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been employed to analyse the behaviour of the banking sector (see Thanassoulis et al., 1996; Berger and Humphrey, 1997; Berger, 2007; as well as Fethi and Pasiouras,

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2010 for details of the bank efficiency techniques). The differences in bank efficiency estimates obtained under local GAAP and IFRS have been studied by Dimitras et al.

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(2010) for Greek banks using Data Envelopment Analysis (DEA) to obtain estimates of technical efficiency.

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In the present study we rely on a Stochastic Frontier Analysis (SFA) to obtain

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estimates of cost and profit efficiency. Cost efficiency is a wider concept than technical efficiency, since it refers to both technical and allocative efficiency. Profit

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efficiency is an even wider concept as it combines both costs and revenues in the measurement of efficiency. Also, we use a cross-country sample using data from the

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EU-15 countries, while Dimitras et al. (2010) use a small sample of Greek banks, allowing us to generalize from our results. The switch from local GAAP to IFRS can potentially influence the efficiency estimates in a number of ways. More specifically, as mentioned above, to estimate bank efficiency one has to take simultaneously into account inputs like deposits, fixed assets and personnel expenses, along with bank outputs like loans and other earning assets. Depending on whether one estimates cost efficiency or profit efficiency, one

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ACCEPTED MANUSCRIPT must also consider total costs or bank profits. As discussed in a report published by Ernst & Young (2005) the European bank’s equity, gross assets, and income have been significantly affected by the adoption of IFRS. For example, the adoption of IAS 39 has resulted in a slight increase in provisions recorded by the banks that was accompanied by a decrease in equity. Furthermore, changes in the loans write off

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policy could influence the reported value of loans, which constitute one of the main

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bank outputs. Additionally, loans, held to maturity, assets and most liabilities are

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required to be recorded at amortized costs. Thus, banks have to report a constant effective interest rate over the life of each instrument with the main consequence

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being that most fees received by the banks when entering into loans are required to be deferred and recognized only over the life of the loans. As a result of such changes,

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most banks had to report a reduction or an increase in equity at transition, depending upon the level of up-front fees, incremental costs and previous accounting practices.

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Finally, among other issues, the aforementioned report highlights that the level of use

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of the held-to-maturity category for financial assets is considerably lower than the use of cost-based accounting for financial instruments under previous GAAP.

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However, despite the above changes, it is also likely that the overall efficiency score for a bank will not be influenced as changes in one accounting item (i.e. in our

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case input/output) may offset changes in another. Predicting the final outcome becomes even more complicated, taken into account that the impact of a given change is not universal across banks. For example, the Ernst & Young report (2005) mentions that at the same time that one bank experienced a 22% net increase of equity, another experienced a decrease by 18%, as a result of the IFRS implementation. On top of that, while the level of efficiency estimates may change towards one direction (i.e. increase or decrease on average), what is probably more important is whether the

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ACCEPTED MANUSCRIPT banks are ranked on approximately the same way or not when looking at their efficiency under the two accounting settings (see also Bauer et al., 1998 for a discussion of the consistency criteria when comparing estimates of bank efficiency). Clearly, the only way to answer these questions is to conduct an empirical analysis as the one of the present study.

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The rest of the paper is structured as follows. Section 2 provides a background

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discussion on the adoption of IFRS, and the related empirical studies. Section 3

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presents the methodology and the data used in the study. Section 4 discusses the

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results. Section 5 concludes.

2.1. IFRS adoption in the EU

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2. Background discussion

IFRS were proposed and adopted to provide a realistic representation of the financial

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situation of firms and to enable the financial statements’ comparison of firms acting in

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different countries and economic environments. European Union countries adopted IFRS in line with the European Regulation (EC) 1606/2002 on the application of

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International Accountant Standards (IAS). The transition procedure from Local GAAP to IFRS was defined by European Committee for the European Union

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countries in IFRS 1 standard on “First Time Application of International Financial Reporting Standards”, that explains how a company should make the transition to IFRS from any Local GAAP for the fiscal years starting on or after January 1 st, 2005 for listed companies. For the banking sector in Europe that was characterized by a diversification in accounting standards among EU counties (World Bank, 2003), the application of IFRS was a great chance for harmonization of the applied accounting standards, in

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ACCEPTED MANUSCRIPT order to enhance comparability of financial statements and improve market discipline in benefit of the banks, although there is some skepticism on the advantages of IFRS due to the possibility to increase the volatility of the figures provided (Pagratis and Stringa, 2009). In the year of transition from local GAAP to IFRS 2005 all companies were

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required to present their 2005 financial statements under IFRS along with the 2004

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restated IFRS compliant financial statements for comparison purposes. In spite of this

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requirement, the banks of some EU countries did not have to apply IFRS for regulatory and supervising purposes in 2005 (Austria, Belgium, Germany, Hungary,

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Luxembourg, Slovenia, Sweden and the United Kingdom). At the same time, the banks of other countries (Denmark and Finland) could not fulfil the request to provide

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2005 IFRS compliant accounts and 2004 restated IFRS compliant accounts to the regulating and supervising authorities (European Central Bank, 2006).

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In this framework, a number of changes emerged between the banks’ 2004

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restated IFRS compliant financial statements and the 2004 local GAAP compliant financial statements reported earlier. The differences between the IFRS and the

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GAAP based financial statements of the banks in each country are analysed in a number of studies and reports (e.g. Bank of Greece, 2006). According to a survey

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conducted by the Committee of European Banking Supervisors - CEBS (2006) that covers a large sample of credit institutions from 18 EU countries, the implementation of the IFRS proved to have a significant impact on the key accounts reported by these institutions. The comparison of the 2004 GAAP and restated IFRS compliant financial statements brought to light the main changes. The net worth of the institutions included in the sample of the above study, declined by 5% while total assets and liabilities improved by 9% and 10% respectively. The increase in

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ACCEPTED MANUSCRIPT financial assets, accounted at fair value under IFRS, was associated with a 67% decrease in assets in the investment book, still accounted at cost value. Loans and other claims decrease slightly by 3%, due to the different pricing method employed (discounting of future flows), in spite of the increase provided by the addition of securitized loans to the credit institutions’ loan book. Real estate and other fixed

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assets increased by 19% as a result of the use of the fair value option under the

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IFRS. Post-retirement personnel benefits increased by 33% due to the recognition

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of the actuarial deficits of pension funds and defined benefit plans as liabilities. Deferred tax assets and liabilities increased by 75% and 51% respectively. From a

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supervisory point of view, the decrease in accounting net worth of the mentioned earlier, would have led to decline of 12% in the supervisory own funds of the credit

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institutions. This decline, due to adjustments made following a recommendation from the CEBS adopted by the domestic supervisory authorities was limited to 2%.

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It has to be mentioned that the changes differ in large among the various

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institutions. The deviation of the IFRS from the local financial reporting standards previously applied by country is one of the reasons and it can explain a large part

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of the differences in the key account numbers. Street (2002) provided a detailed listing of deviations of national GAAP from IFRS on approximately 80 accounting

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measures. Many of these deviations remained unchanged until the implementation of IFRS in 2005 resulting in a great change in the reported figures. But the practices and the needs of each specific bank can explain another part of this deviation, as different banks have different needs, organization and culture, thus make use of alternative options provided by the local financial reporting standards or the IFRS. Therefore, it is difficult to attribute the differences in the key accounts

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ACCEPTED MANUSCRIPT reported by each specific bank to certain changes of the standards, unless a large amount of information is available.

2.2. Empirical studies on the impact of IFRS adoption on financial results A number of papers presented the main differences between IFRS and local

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GAAP worldwide and the expected impact on the financial statements prior the

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application of IFRS. Jermakowicz and Gornik-Tomaszewski (2006) rely on the

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responses of 112 publicly traded EU companies as for the expected impact of firsttime adoption on their equity at the date of the opening IFRS balance sheet, earnings

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and equity at the end of the first year of reporting under IFRS, and earnings in the second and third years of reporting under IFRS. The authors mention that most of the

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respondents indicated that the impact of adopting IFRS would be positive rather than negative for all these financial variables.

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Callao et al. (2007) use a sample of 26 large listed Spanish firms to conduct

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univariate test (t-test and Wilcoxon) of the differences between accounting figures (e.g. fixed assets, equity) and traditional financial ratios (e.g. current ratio, return on

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equity, etc.) under the Spanish GAAP and the IFRS. They first use half-year data from 2004, and they find statistically significant differences in five out of twelve

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balance sheet items, one out of four income statement items, and six out of nine financial ratios. The results do not vary much when they consider year-end data. Stenka and Ormrod (2008) examine the interim statements of 50 non-financial FTSE 100 firms, by focusing on the reconciliation disclosures of equity and profits (losses) of first time adopters of IFRS, as reported under previous year GAAP and the corresponding IFRS figures. They conclude that the largest single effect on profit was the change in the treatment of goodwill accounting for 24% of the 39% in profit from

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ACCEPTED MANUSCRIPT the adoption of IFRS. In the case of equity, the adoption of the new pension accounting recognition rules had the largest impact (-13%) on the decrease of equity under IFRS. In another UK study, Iatridis (2010) uses logistic regression to investigate the impact of the implementation of the IFRS on key financial measures (e.g. profitability, leverage, liquidity, etc.). The results from a sample of 241 listed

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firms show that the IFRS implementation has a positive impact on the overall

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financial performance and position of the firms. Papadamou and Tzivinikos (2013)

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use a sample of the 10 Greek banks listed on the Athens Stock Exchange to examine whether IFRS introduction has enhanced the risk-relevance of accounting data. The

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authors conclude that the transition to IFRS in Greece lead to higher risk-relevance of accounting variables. More recently, Manganaris at al (2015) examined the

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relationship between the value relevance of accounting information and the conditional conservatism of the European banking sector using a bank data set from

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15 European countries before and after mandatory IFRS adoption. They concluded

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that the IFRS adoption led to higher value relevance and lower conditional conservatism.

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Studies outside Europe use similar approaches. For example, Bao et al. (2010) use both univariate tests (t-tests) and multivariate tests (ANOVA, probit and logit

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analyses) to compare the ratios between IFRS and US GAAP. They conclude that IFRS firms have a significantly higher current ratio, a significantly lower asset turnover ratio, and a significantly lower debt-to-asset ratio. Blanchette et al. (2011) also follow two approaches to compare financial ratios of 9 companies calculated under Canadian GAAP and IFRS. First, they compare the means, medians and variances of the selected financial ratios. Second, they regress each one of the IFRS values as the dependent variable, on its corresponding GAAP value. Their results can

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ACCEPTED MANUSCRIPT be summarized as follows: (i) there are no statistically significant differences in the means and medians of financial ratios, calculated under IFRS and GAAP, with the exception of cash-flow-coverage at the 10% level, (ii) there is a significant difference in the distribution of values around medians for several ratios such as current and quick ratios, debt, interest coverage, etc. (iii) the results of regression analysis confirm

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the increased volatility of IFRS leverage and profitability ratios.

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Beuren et al (2008) investigated the impact of differences between the IFRS

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and US GAAP on the financial ratios of 37 English companies traded in the New York Stock Exchange (NYSE). The research by means of regression and correlation

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analysis shows that there is a significant correlation and that the ratios are not affected in a significant way by the divergences in the accounting standards under

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consideration but the researchers mention that individual differences in the ratios were found that need to be considered by the analysts since the variations, either positive or

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negative, in all the ratios can distort the analysis.

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In another interesting work, Gray et al. (2009) follow a setting that deviates from the above studies by bringing together IFRS, European GAAP, and US GAAP.

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More detailed, they focus on 134 European companies listed on the NYSE or NASDAQ to examine whether “European” and US GAAP measures of income and

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equity converged after the IFRS adoption. The comparison of the figures among different accounting frameworks is contacted by calculating an index of “comparability”. Their main results can be summarized as follows: (i) between 20002004, European and US GAAP measures are generally comparable in respect of income and equity, (ii) UK GAAP yields significantly lower measures of equity than US GAAP, (iii) IFRS income measures for 2004-2006 for European companies, filling IFRS-based financial statements with the SEC in 2005, are in general

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ACCEPTED MANUSCRIPT significantly higher than the corresponding US GAAP income measures, (iv) the significant differences in equity for companies reporting under US GAAP and UK GAAP, persisted in the post-IFRS adoption period. Chalmers et al. (2011) in a longitudinal study that covers pre-IFRS and postIFRS periods during 1990–2008 investigate whether the adoption of IFRS increases

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the value relevance of accounting information for Australian firms and they found that

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earnings become more value-relevant whereas the book value of equity does not.

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As mentioned earlier, in a work that is closely related to ours, Dimitras et al. (2010) deviate from the above studies using a frontier technique rather than financial

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ratios. Their sample consists of ten Greek banks, and they use DEA to calculate scale efficiency, technical efficiency under variable returns to scale (i.e. pure technical

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efficiency) and constant returns to scale (i.e. overall technical efficiency). The authors use three approaches for the selection of inputs and outputs, namely the

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intermediation approach, the value added approach, and the operating approach. They

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conclude that the change in the accounting rules results in important differences in estimated efficiency of the banks in their sample.

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Finally, Rodriguez-Perez et al. (2011) also rely on DEA to compare the efficiency scores obtained from financial statements prepared under the historical cost

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approach, and the fair value approach that it is in accordance with IFRS. Using a sample of Spanish insurance firms and restated data from 2003, they conclude that there are only a few cases where a change in the valuation basis leads to a relevant change in DEA scores.

2.3. Cross-country studies on bank efficiency

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ACCEPTED MANUSCRIPT Our work also relates to numerous cross-country studies on bank efficiency. These studies investigate a variety of issues like the association between efficiency and macroeconomic conditions (Dietsch and Lozano-Vivas, 2000), financial structure (Girardone et al., 2009), market power (Williams, 2012), accessibility of banking services (Dietsch and Lozano-Vivas, 2000), institutional development (Lensink et al.,

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2008), prudential regulations (Pasiouras et al., 2009), supervisory structure (Gaganis

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and Pasiouras, 2013), financial freedom (Chortareas et al., 2013), consumer protection

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(Pasiouras et al., 2018). Some of the studies use global samples (e.g. Pasiouras et al., 2009) while others focus on specific regions like Europe (e.g. Chortareas et al., 2013),

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Africa (Triki et al., 2017), Asia (Lin et al., 2016), Latin America (Williams, 2012), etc. However, to the best of our knowledge, with the exception of the two country-

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specific studies by Dimitras et al. (2010) and Rodriguez-Perez et al. (2011), no other

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3. Data & Methodology

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study has explored the impact of IFRS adoption on bank efficiency measures.

3.1. Data

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As discussed earlier, listed EU companies, have to prepare their consolidated accounts in accordance with the IFRS as of January 1, 2005. However, when reporting the

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2005 IFRS results, banks also had to provide the 2004 figures according to IFRS for comparison purposes. These disclosure requirements allow us to consider financial statements for the year 2004 prepared under both IFRS (i.e. the restated comparative figures) and local GAAP (i.e. the initial information disclosed prior to the adoption of IFRS). All bank-specific data are obtained from Bankscope database of Bureau van Dijk. When both consolidated and unconsolidated financial statements are available

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ACCEPTED MANUSCRIPT we use the former. However, for banks that publish only unconsolidated financial statements (mostly Italian ones), we include those in our analysis. After excluding banks with missing data, our final sample consists of 141 commercial banks, operating in 15 European countries. The geographical distribution of the banks in the case of the consolidated statements is as follows: Austria (3), Belgium (1), Denmark

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(7), Finland (3), France (12), Greece (5), Ireland (3), Italy (19), Luxembourg (1),

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Netherlands (5), Portugal (6), Spain (10), Sweden (3), UK (15). The corresponding

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figures in the case of the unconsolidated statements are: Denmark (2), Germany (1), Ireland (1), Italy (35), Luxembourg (1), Netherlands (1), Portugal (1), Spain (2), UK

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(4). Our approach to focus on commercial banks, is consistent with the many studies on cross-country bank efficiency (e.g. Lozano-Vivas and Pasiouras, 2010; Gaganis

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and Pasiouras, 2013; Gaganis et al., 2013), and allows us to examine a homogenous sample (to the extent that it is possible) in terms of the services offered, the employed

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production technology, etc. It should be mentioned that many cross-country European

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studies examine various types of banks like commercial, cooperative, savings, etc. (e.g. Maudos et al., 2002; Altunbas et al., 2007; Mamatzakis et al., 2008). However,

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we believe that it is more appropriate to estimate a type-specific frontier, while

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focusing on commercial banks, as one can be more confident about homogeneity, etc.

3.2. Methodology

This study employs the stochastic frontier approach (SFA), introduced by Aigner et al. (1977) to generate efficiency estimates for each one of the 141 banks in our sample. In its general form the cost model can be written in our cross-country setting as follows:

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ACCEPTED MANUSCRIPT ln C i ,c  C (qi ,c , pi ,c ;  )  u i ,c  vi ,c , i  1,2,...,N ; c = 1, 2, …K

(1)

where: Ci ,c is the total cost (i.e. interest and non-interest expenses) of bank i in country c; q i ,c is a vector of outputs; pi ,c denotes a vector of values of input prices associated with a suitable functional form;  is a vector of unknown scalar

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parameters to be estimated; the v i ,c are random errors, assumed to be independently

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and identically distributed as N (0,  v2 ) ; The error component u i,c s are the non-

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negative inefficiency effects in the model which are assumed to be distributed independently of v i ,c , derived from a N (0,  u2 ) distribution truncated above the zero.

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There is a debate in the literature regarding the proper definition of inputs and

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outputs. For example, while referring to DEA applications, Bergendahl (1998) highlights that ‘‘There have been almost as many assumptions of inputs and outputs

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as there have been applications of DEA” (p. 235). Apparently, one could easily argue

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along the same lines in the case of SFA. Berger and Humphrey (1997) identify two main approaches for the selection of inputs and outputs. These are: (i) the

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‘‘production approach”, which assumes that banks produce loans and deposits account services, using labour and capital as inputs, and that the number and type of

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transactions or documents processed measure outputs, and (ii) the ‘‘intermediation approach”, which perceives banks as financial intermediaries between savers and investors. After reviewing over 150 studies, Fethi and Pasiouras (2010) conclude that the intermediation approach is the one favoured in the literature in recent years. However, there appears to be a controversy even within this approach concerning the role of deposits (Berger and Humphrey, 1997). Consequently, some studies use only earning assets as outputs, a selection that is in line with the asset approach of Sealey

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ACCEPTED MANUSCRIPT and Lindley (1977), while others consider deposits as an additional output, a selection that is more closely related to the so-called value-added approach. Additionally, many have criticized the above framework, because it ignores non-traditional services and it may penalize banks that are heavily involved in such services (e.g. Siems and Clark, 1997; Rogers, 1998). The underlying reason for this is

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that resources used to produce the non-traditional services are included in the input

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vector without accommodating the relevant variables in the output vector. Therefore,

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many studies include off-balance-sheet items or non-interest income in the output vector (e.g. Rogers, 1998; Stiroh, 2000; Altunbas et al., 2001; Lozano-Vivas and

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Pasiouras, 2010; Gaganis et al., 2013).

Considering that there is no general agreement in the literature with regards to

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the definition of the relevant output vector, we estimate various specifications. This allows us to examine whether the results of models commonly used in the literature

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would differ, depending on the use of GAAP (i.e. GAAP-specific model) versus IFRS

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(i.e. IFRS-specific model). In all the cases, we estimate two models, one for cost (Model C) and one for profit (Model P) efficiency.

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Model C1 is a “traditional” cost model under the intermediation approach where we assume that banks have two outputs, namely loans (Q1) and other earning

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assets (Q2). Then, we estimate Model C2 which is identical to Model C1 but with non-interest income (Q3) used as an additional output. Finally, in section 4.2 we provide additional results, while considering deposits as a further output. Models P1 and P2 are identical to Models C1 and C2, but they correspond to profit rather than cost functions. The specification of the profit frontier model is the same as that of the cost frontier (equation (1)) with profit before tax (PBT) replacing total costs as the dependent variable. Our approach is consistent with other cross-

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ACCEPTED MANUSCRIPT country studies (e.g. Lozano-Vivas and Pasiouras, 2010). Since tax rates differ across countries, using profit after tax would possibly make banks in countries with higher rates to appear as less efficient while in fact they are not. However, the sign of the inefficiency term now becomes negative (-uijt). Thus, as in most previous studies, we estimate an alternative profit frontier,

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which ignores output prices. This approach appears to be more appropriate, than the

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estimation of a standard profit function, in cross-country samples with a diverse group

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of countries and competition levels (see Berger and Mester, 1997; Kasman and Yildirim, 2006). Furthermore, since a number of banks in the sample exhibit negative

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ln PBT  PBT 

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 1 , where (PBT ) min is the absolute value of the minimum

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min

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profits (i.e. losses), the dependent variable in the profit model is transformed to

PBT over all banks in the sample.

In all the specifications, we use three input prices. Consistent with many

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previous studies (see e.g. Pasiouras et al., 2009; Mamatzakis et al., 2013; Gaganis et al., 2013), these are: cost of borrowed funds (W1), calculated as the ratio of interest expenses to customer deposits and short term funding; cost of labour (W2), calculated

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by dividing the personnel expenses by total assets, and cost of physical capital (W3),

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calculated by dividing overhead expenses other than personnel expenses by the book value of fixed assets. In calculating W3, we use total assets rather than the number of employees due to data unavailability. Our approach is consistent with several other studies (e.g. Altubas et al., 2001; Lozano-Vivas and Pasiouras, 2010). To impose linear homogeneity restrictions we normalize the dependent variable and all input prices by W3. The linear homogeneity, imposed by the normalization by W3 may not be necessary for the alternative profit function; however, it is commonly used (e.g. Stiroh, 2000; Berger and Bonaccorsi di Patti, 2006). Berger et al. (2000) justify its use 17

ACCEPTED MANUSCRIPT by mentioning that output prices generally move with input prices, so it is reasonable to assume that if all input prices double, output prices would approximately double, as would profits and revenues. Furthermore, as in Berger et al. (2000) and Stiroh (2000), among others, we normalize the dependent variable of our functions, and the outputs by the quantity of

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equity (EQ). Berger et al. (2000) point out that the normalization by equity capital

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controls for heteroskedasticity, reduces scale biases in estimation, it provides the

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grounds for a more economic interpretation, and it controls for financial leverage. Using the multi-product translog specification, the cost function in the case of Model C2 (we arbitrarily select this model for presentation, the remaining models

Q  Q  Q  TC  W1  W 2    0   1 ln  1    2 ln  2    3 ln  3    4 ln     5 ln   W 3 * EQ EQ EQ EQ W 3   W3      

MA

ln

NU

being subject to similar transformations) is given as:1

2

 6

Q  Q  Q  Q  1   Q1   1   Q   ln     7 ln  1  ln  2    8 ln  1  ln  3    9  ln  2     2   EQ   2   EQ    EQ   EQ   EQ   EQ  2

2

Q  Q  1   Q  1   W1    W1   W 2    10  2  ln  3    11  ln  3     12  ln      13 ln   ln   2   EQ   2  W 3  W3  W3   EQ   EQ 

(2)

2

PT E

D

2

 Q   W1   Q  W 2   Q   W1  1  W 2    14  ln      15 ln  1  ln     16 ln  1  ln     17 ln  2  ln   2   W 3   EQ   W 3   EQ   W 3   EQ   W 3 

AC

CE

 Q  W 2   Q3   W 1   Q3   W 2   ln   ln    18 ln  2  ln     19 ln     20 ln    u ic  vic EQ W 3 EQ W 3          EQ   W 3 

The individual bank (in)efficiency scores are calculated from the estimated frontiers as CEic= exp(uic) and PEFic = exp(-uic), the former taking a value between one and infinity and the latter between zero and one. In both cases, scores closer to 1 indicate higher efficiency.

4. Empirical Results 1

Appendix I presents the corresponding profit model.

18

ACCEPTED MANUSCRIPT 4.1. Base Results Table 1 presents descriptive statistics for the bank-level variables used in the cost and profit functions, while distinguishing between the GAAP and the IFRS data. Although the inputs and outputs are used in natural logarithms we present descriptive statistics

PT

of the levels to be more informative.

RI

[Insert Table 1 Around Here]

SC

The largest differences are observed in the case of “other earning assets”

NU

(+30.23%), the “cost of borrowed funds” (+10.34%), and the “cost of labour” (+8.33%). The change in the value of “other earning assets” can be mostly likely

MA

attributed to the valuation of derivatives and other assets under the fair value principle of IAS 39. A closer look at the data reveals that around 67% of the banks in the

D

sample experience an increase in the value of their other earning assets, which clearly

PT E

outweighs the smaller negative change or no change recorded in the case of the remaining banks in the sample. The change in the case of cost of borrowed funds

CE

(W1) is mostly due to the change in interest expenses (+20.58%) and to a lesser extend due to value of deposits and short term funding (+5.21%). The first reason for

AC

this is that under IRFS the deposits can be valued at either the face value or the amortised cost depending on their type.2 Second, under IFRS interest income and

2

For example, Lloyds TSB Group (included in our sample) mentions in its 2005 annual report that “The fair value of deposits repayable on demand is considered to be equal to their carrying value. The fair value for all other deposits and customer accounts is estimated using discounted cash flows applying either market rates, where applicable, or current rates for deposits of similar remaining maturities” (p. 110).

19

ACCEPTED MANUSCRIPT expense for all interest-bearing financial instruments are recognized in the consolidate income statement using the effective interest method.3 Turning to the cost of labor (W2), we observe that the difference is mostly due to the valuation of total assets (+9.67%) rather than the change in the personnel expenses (+3.86%). In contrast to the increase in W1 and W2, we observe a decrease

PT

in the cost of physical capital (W3) by -8.28%. This is due to the decrease in the value

RI

of fixed assets (-3.04%) which counterbalances the small increase in the non-

SC

personnel overhead expenses (0.59%).4

Equity has also decreased slightly. There exist a number of potential reasons

NU

for changes in the bank equity for each specific entity. These reasons include changes in insurance (i.e. many activities which were previously treated as insurance are being

MA

accounted under IAS 39), pensions, dividends, but most importantly the implementation of IAS 32 and 39.5 We also observe a small decrease in profits that

PT E

D

could be attributed to IAS 12 (income tax) and IAS 39 (financial instruments).6

3

AC

CE

The effective interest method is a method of calculating the amortised cost of a financial asset or a financial liability and of allocating the interest income or interest expense over the relevant period. The effective interest rate is the rate that exactly discounts the estimated future cash payments and receipts through the expected life of the financial asset or liability (or, where appropriate, a shorter period) to the net carrying amount of the financial asset or liability. When calculating the effective interest rate, the bank estimates future cash flows considering all contractual terms of the financial instruments but not future credit losses. 4 IFRS required the revaluation of property at fair value. For example, Emporiki bank (included in our sample) reports in its 2005 annual report that “On transition date to IFRS (1 January 2004) the Group valued land and buildings at fair value based on professional valuations. This fair value was considered as deemed cost” (p. 84). As a result, the Balance Sheet Reconciliation on 31st December 2004 shows that the value of Property, plant and equipment under the Greek GAAP equals 745,968 thousand euros while the corresponding figure under IFRS is 375,710 thousand euros. 5 For example, as mentioned in the Ernst & Young report (2005) the impact of the adoption of IAS 19 (pension) had a negative impact on most European banks, which recorded the full pension deficit in equity on transition to IFRS with a negative consequence on capital. The IAS 32 requires any holding in the entity’s own shares to be deducted from equity. The adoption of the IAS 39 requirement has resulted in an increase in provisions recorded by the banks and therefore a decrease in equity. 6 For example, reconciliation between IFRS and Greek GAAP that is available at the 2005 annual report of Emporiki Bank highlights various differences between the two frameworks. More detailed, according to IFRS, dividends and profit appropriation are recognized when they are approved by the shareholders while in the case of the Greek GAAP dividends and profit approximation are recognized when they are proposed. Furthermore, under Greek GAAP, expenses from finance leases are recognized to the Income Statement on a straight-line method, whereas under IFRS expenses from finance leases are recognized using the effective interest rate method. Directly attributable fees and

20

ACCEPTED MANUSCRIPT Apparently, the aforementioned increase in interest, personnel, and other overhead expenses, contribute towards an increase in total cost by 8.26%. Table 2 presents the mean cost and profit efficiency scores of the GAAPspecific and the IFRS-specific models.7 To compare the efficiency estimates obtained under these two accounting standards, we use two criteria. First, we examine the

PT

difference in the means, assessed with a Kruskal-Wallis test. Second, we examine the

RI

Spearman rank-order correlation coefficients as in Bauer et al. (1998), and Lozano-

SC

Vivas and Pasiouras (2010), among others. As mentioned in Bauer et al. (1998), different frontier methods may generate similar rankings of banks using the efficiency

NU

scores even when the estimates of the efficiency levels are different across banks. This allows us to detect how close the rankings of banks are among the IFRS-specific

MA

and GAAP-specific models.

The results in Panel A show that the average cost efficiency obtained from

D

Model C1 under GAAP equals 1.4091. Thus, the average bank in our sample incurs

PT E

costs that are 40.91% above the frontier cost or optimum cost. When we include noninterest income in the output vector (i.e. Model C2) the average cost efficiency equals

CE

1.2268. Thus, banks now appear to be 22.68% inefficient, on average. This improvement in cost efficiency is in accordance with the results of Lozano-Vivas and

AC

Pasiouras (2010), providing support to the argument that ignoring non-traditional activities may result in misleading efficiency estimates. The corresponding figures for the IFRS models are relatively better, being equal to 1.3524 and 1.0062, respectively. Thus, banks appear to be more cost inefficient (on average) when using GAAP instead of IFRS. Turning to Panel B, we observe that the average profit efficiency in commissions relating to loans are also recognized to the income statement using the effective rate method in the case of IFRS, whereas under Greek GAAP these fees and commissions are recognized to the Income Statement when they are received or incurred. 7

The coefficients of the estimated frontier functions are shown in Appendix II.

21

ACCEPTED MANUSCRIPT the case of the traditional GAAP with two outputs (i.e. Model P1) equals 0.9764, while the inclusion of non-interest income in the output vector (i.e. Model P2) results in a lower efficiency that equals 0.5976. In contrast to the cost efficiency estimates, we now observe that banks appear to be more profit inefficient (on average) when using IFRS instead of GAAP, with the average efficiency being 0.5976 (Model P1)

PT

and 0.4798 (Model P2).

RI

As efficiency is a relative and sample-specific metric of performance, a direct

SC

comparison with the results of other studies could be criticized as not meaningful due to differences in the exact banks used in the sample, but also due to differences in the

NU

function of the frontier, the inputs and outputs, etc. Yet, a tentative comparison may be possible with the efficiency estimates of some other European bank efficiency

MA

studies like those of Casu and Girardone (2004), Bos and Kolari (2005), Cavallo and Rossi (2002), Guevara and Maudos (2002), Pastor and Serrano (2005), Maudos et al.

D

(2002) as well as Liadaki and Gaganis (2010).

PT E

This is not the first study to obtain contradictory results between cost and profit efficiency. As discussed in earlier studies, cost efficient banks are not

CE

necessarily the most profit efficient and vice versa (see e.g. Berger and Mester, 1997; Rogers, 1998; Pasiouras et al., 2009). One explanation for the differences in the

AC

results of cost and profit efficiency is that profit efficiency is more likely driven by revenues rather than costs (Rogers, 1998). In this specific case the difference can be mainly related to the large increase in the mean of Total cost (8.26%) in relation to the very small reduction of mean of Profits before Taxes (-0.44%) and the severe increase of the standard deviation of Total cost (see Table 1). The results of the Kruskal-Wallis test show that these differences are statistically significant at the 1% level. Yet, we observe that the Spearman’s rank

22

ACCEPTED MANUSCRIPT correlation coefficients range between 0.574 (model C2) and 0.893 (model P1). Thus, despite the differences at the mean level of efficiency, the GAAP and IFRS data may provide consistent rankings of the banks, at least in the case of profit efficiency.

PT

[Insert Table 2 Around Here]

The key finding from the above results is that the bank efficiency estimates can vary

RI

significantly when different accounting standards are employed, even for the same set

SC

of banks. This is consistent with the findings of Dimitras et al. (2010) for Greece. It

NU

also provides partial support to the findings of past accounting studies discussed in Section 2.2. that compare specific accounting figures. A direct comparison with these

MA

studies is not feasible as in our case we use an aggregate indicator of efficiency, whereas they mostly consider accounting items on isolation. However, a tentative

D

comparison of the findings implies that the results are towards the same direction –

PT E

i.e. there are important differences in the firm outcome under investigation when we

CE

switch from the use of GAAP to the use of IFRS.

4.2. Further analysis

AC

In the previous section, we discussed the results obtained using two specifications under the assumption of an intermediation approach for a sample of 141 banks. In this section, we present additional analysis using (i) alternative specifications, and (ii) two sub-samples of the original dataset, (iii) an extended sample as for the period that we cover. First, we re-estimate the base models using the dual approach, instead of the intermediation approach. In this case we assume that deposits have both input and output characteristics. As discussed in Berger and Humphrey (1997) the underlying 23

ACCEPTED MANUSCRIPT idea is that on the one hand deposits have input characteristics because they are a source of funding for banking institutions that is paid by interest expenses; however, on the other hand deposits have also output characteristics because they are associated with liquidity, safekeeping and payment services provided to customers. Therefore, consistent with earlier studies, we include the monetary value of deposits as an

PT

additional output in Models C1 and C2 (P1 and P2), while retaining W1 (i.e. interest

RI

expense / deposits) as one of our input prices (e.g. Cavallo and Rossi, 2002; Bonin et

SC

al., 2005; Berger et al., 2009). We label these models C3 and C4 (P3 and P4), accordingly. The results in Panel A of Table 3 show that the inclusion of deposits in

NU

the output vector does not alter significantly our main findings. More precisely: (i) banks appear to be more cost efficient but less profit efficient under IFRS, (ii) the

MA

Spearman's rank correlation coefficients between the GAAP models and the corresponding IFRS ones continue to be moderate in the case of cost efficiency, albeit

D

relatively higher in the case of profit efficiency, (iii) the differences in the mean

PT E

efficiency estimates are statistically significant in all four cases. Next, we re-estimate our base models while using (i) only consolidated

CE

statements (N =93), and (ii) only unconsolidated statements (N = 48). We present these results in Panels B and C. The results in Panel B are consistent with the ones

AC

obtained earlier. However, when we look at Panel C, the differences in the mean efficiency are no longer significant, with the exception of model P2. One potential reason is that the unconsolidated statements express only the commercial banking business. The consolidated statements can furthermore incorporate different business with a large variety of financial (e.g. leasing, investment banking, insurance, etc.) or even non-financial activities that influence the results. Nonetheless, these results should be interpreted with caution, considering (i) the relatively small sample when

24

ACCEPTED MANUSCRIPT we split our dataset, and especially when using unconsolidated statements only (i.e. N=48), and (ii) that the sample that includes unconsolidated statements only is dominated by Italian banks (i.e. 72.92%). We have to mention that the parametric frontier techniques require, in general, large samples in order to obtain reliable results. Behr and Tente (2008) show that the mean average error between estimated and true

PT

efficiency scores decreases with increasing sample sizes as well as with increasing λ

RI

(i.e. the relation of inefficiency variance to variance of the normal noise). Their simulations reveal that in the case of maximum likelihood estimations, the mean

SC

average deviations for small n, λ-combinations are about four times the value

NU

obtained for large n, λ.

Then, we re-estimate our base model while using an extended sample that

MA

includes an additional dataset of 41 commercial banks, for which their restated data were first made available in year-end 2005 rather than in year-end 2004. The sample

D

includes both consolidated and unconsolidated statements, and as such its size

PT E

increases from 141 to 182 observations. The results in Panel D are consistent with the ones of the base model.

CE

[Insert Table 3 Around Here]

AC

As a final note we present in Table 4, the average cost and profit efficiency estimates for those countries for which we have more than five observations - the rest being grouped in the category “other” countries. While the small number of banks by country does not allow us to conduct any meaningful tests on the mean differences and the rank correlations, a simple comparison of the means shows that there are important differences at the level of efficiency in almost all the cases.

25

ACCEPTED MANUSCRIPT [Insert Table 4 Around Here]

5. Conclusions This study brings together the bank efficiency literature and the studies that examine the comparability of financial ratios and accounting items under GAAP and IFRS. To

PT

this end, we estimate both cost and profit efficiency using data for 141 banks

RI

operating in 15 EU countries. We start our analysis by developing a standard

SC

stochastic frontier analysis model under the intermediation approach, assuming that banks produce two outputs namely loans and other earnings assets. We subsequently

NU

expand the output vector to account for non-traditional services and the output characteristics of deposits.

MA

Our main contribution to the literature is that we show that the bank efficiency estimates have been significantly influenced by the transition to IFRS. However,

D

interestingly enough we find that in the case of profit efficiency only the levels of

PT E

efficiency change substantially whereas the ranking of the banks remain relatively close. In contrast, in the case of cost efficiency the rank correlation coefficients are

CE

small to moderate, indicating that not only the levels of efficiency but also the rankings of banks change. Overall, these findings are robust to the alternative

AC

definitions of the output vector, sub-samples of the original dataset, and an extended sample as for the period that we cover. Accounting standards regulators and bank supervisors could keep these results in mind when establishing financial reporting changes for the banks. Probably a more gradual convergence between accounting regimes could be more beneficial for all those that base their decisions on efficiency measures of the banks and as well as for the banking regulatory authorities. At the same time, our findings could be of interest

26

ACCEPTED MANUSCRIPT to central banks that supervise the commercial banks in order to protect the economy and ensure the monetary policy application. While central banks primarily focus on the stability of the banks, the efficiency of banking institutions should not be neglected due to their intermediary role in the economy. Yet, this means that comparable and reliable estimates of bank efficiency should be available.

PT

Furthermore, researchers should adopt a cautious approach when examining

RI

bank efficiency during the period of the transition to the IFRS in the EU as well as

SC

during the implementation of the newly proposed IRFS changes that will become effective over the period 2013-2015. Comparing efficiency obtained through IFRS

NU

figures with those based on country-specific GAAP is not fully appropriate, and it may provide misleading results especially as it concerns the trend of the efficiency

MA

levels over the years.

These conclusions may apply to any study employing data that include a

D

change in the accounting regime, independently from the industry, the time period and

PT E

the methodology used. In such cases an examination of the consequence of the change in the data and the results is required in order to avoid reaching false conclusions

CE

based on results mainly affected by the accounting rules. The application of various established techniques employed to analyse market data in order to make fully

AC

justified decisions and enhance the knowledge about the market and the behaviour of economic entities may has to be postponed until a sufficient amount of data based on the new rules is available. Our study is not without its limitations. More detailed the change in a bank’s key accounts between GAAP and IFRS can be related to two factors. First, the change is bank specific as policy and setup may vary across banks in the same country. The larger the operations of a bank in a given the area for which the

27

ACCEPTED MANUSCRIPT accounting rules change, the larger the variation in its financial statements. Second, the divergence between national GAAP and IFRS, results in different changes in the financial statements among countries. It is obvious that the larger the divergence between national GAAP and IFRS for a country, the larger it will be the variation in the financial statements of the banks operating in this given country.

PT

While the first factor is adequately controlled for in our study, through the

RI

incorporation of multiple bank-specific inputs and outputs that capture various

SC

bank operations, we fail to control for the second factor. However, quantifying the degree of divergence between national GAAP systems and IFRS and incorporating

NU

it in the analysis is a difficult task that falls outside the scope of the present study.

AC

CE

PT E

D

MA

We hope that future research will improve upon that.

28

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Table 1- Descriptive statistics

St. Dev.

Change from GAPP to IFRS

5,581,231 1,345,541 68,033,051 75,662,153 1,704,474 6,998,286 0.035 3,570,125 106,033,879

8.26% -0.44% -0.53% 30.23% -2.54% -0.63% 10.34% 20.58% 5.21%

0.013 553,963 62,353,012 3.469 450,184

0.006 1,242,410 150,005,248 7.128 1,022,372

8.33% 3.86% 9.67% -8.28% 0.59%

622,100

1,760,537

-3.04%

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Total cost (th. Euros) Profits before Taxes (th. Euros) Loans (th. Euros) Other Earning Assets (th. Euros) Non-interest income (th. Euros) Equity (th. Euros) Cost of borrowed funds Interest expense (th. Euros) Deposits & short term funding (th. Euros) Cost of labour Personnel expenses (th. Euros) Total assets (th. Euros) Cost of physical capital Non-personnel overhead expenses (th. Euros) Fixed assets (th. Euros)

TC PBT Q1 Q2 Q3 EQ W1

T P E

A

C C

W3

Mean

St. Dev.

Mean

2,274,563 698,688 29,426,385 21,220,860 736,339 3,043,151 0.029 1,104,681 40,527,147

4,830,216 1,322,723 68,894,385 50,205,984 1,787,799 7,097,955 0.025 2,361,084 94,133,103

2,462,508 695,587 29,269,890 27,636,324 717,621 3,023,927 0.032 1,332,016 42,638,376

0.012 533,384 56,855,063 3.782 447,533

0.006 1,211,182 129,269,698 8.408 1,042,113

641,572

2,031,777

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W2

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C S U

N A

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ACCEPTED MANUSCRIPT Table 2 – Efficiency estimates: Base Model

1.4091

Spearman’s Rank Correlation Coefficient GAAP – IFRS

7.367 (0.007) 210.753 (0.000)

0.637 (0.000) 0.574 (0.000)

1.2268

C2 1.3524 1.0062 Panel B: Profit efficiency (N=141) P1

0.9764

0.4761

P2

0.5976

0.4798

210.753 (0.000) 17.064 (0.000)

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Average Efficiency Estimates Model GAAP IFRS Panel A: Cost efficiency (N=141)

0.893 (0.000) 0.847 (0.000)

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Notes: p-values are shown in parenthesis; Model C1 is a cost efficiency model that has two outputs namely loans, and other earnings assets. Model C2 is a cost efficiency model that has three outputs namely loans, other earning assets, and non-interest income. Models P1 and P2 are similar to C1 and C2 respectively, but they are profit efficiency models. Cost efficiency takes values between one and infinity, whereas profit efficiency ranges between zero and one. In both cases, scores closer to 1 indicate higher efficiency.

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ACCEPTED MANUSCRIPT Table 3 – Efficiency estimates: Additional Models Average Efficiency Estimates

IFRS

GAAP

Kruskal-Wallis test

GAAP vs IFRS

Spearman’s Rank Correlation Coefficient GAAP – IFRS

Panel A: Dual Approach (N = 141) 6.876 (0.009) 15.245 C4 1.2300 1.1200 (0.000) 58.194 P3 0.6750 0.4917 (0.000) 15.646 P4 0.6030 0.4980 (0.000) Panel B: Intermediation approach- Consolidated statements only (N=93) 138.754 C1 1.3490 1.0067 (0.000) 138.754 C2 1.2594 1.0043 (0.000) 17.244 P1 0.6444 0.5085 (0.000) 2.466 P2 0.5826 0.5210 (0.116) Panel C: Intermediation approach- Unconsolidated statements only (N=48) 0.950 C1 1.2055 1.1635 (0.330) 0.894 C2 1.1263 1.1532 (0.345) 1.938 P1 0.4098 0.5055 (0.164) 4.361 P2 0.4187 0.5415 (0.037) Panel D: Intermediation Approach – 2004 & 2005 (N=182) 11.937 C1 1.3515 1.2091 (0.001) 272.252 C2 1.3030 1.0061 (0.000) 272.252 P1 0.9756 0.4864 (0.000) 89.935 P2 0.6644 0.4809 (0.000) 1.2887

1.1624

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0.559 (0.000) 0.471 (0.000) 0.830 (0.000) 0.815 (0.000)

0.497 (0.000) 0.489 (0.000) 0.754 (0.000) 0.722 (0.000)

0.586 (0.000) 0.323 (0.025) 0.791 (0.000) 0.681 (0.000)

0.658 (0.000) 0.640 (0.000) 0.904 (0.000) 0.868 (0.000)

Notes: p-values are shown in parenthesis; Model C1 is a cost efficiency model that has two outputs namely loans, and other earnings assets. Model C2 is a cost efficiency model that has three outputs namely loans, other earning assets, and non-interest income. Models P1 and P2 are similar to C1 and C2 respectively, but they are profit efficiency models. Models C3 and C4 (P3 and P4) are similar to models C1 and C2 (P1 and P2), respectively, but they include deposits as an additional output. Cost efficiency takes values between one and infinity, whereas profit efficiency ranges between zero and one. In both cases, scores closer to 1 indicate higher efficiency.

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ACCEPTED MANUSCRIPT Table 4 – Average efficiency estimates by country N

GAAP

IFRS

Denmark France Greece Italy Netherlands Portugal Spain UK

9 12 5 54 6 7 12 19

1.1509 1.2481 1.5612 1.4420 1.1918 2.8989 1.2078 1.2849

1.1090 1.1907 1.1720 1.2703 1.2075 1.1839 1.2339 1.2370

% Change in efficiency 3.64 4.60 24.93 11.91 -1.31 59.16 -2.16 3.73

Others Denmark France Greece Italy Netherlands Portugal Spain UK Others Denmark France Greece Italy Netherlands Portugal Spain UK Others Denmark France Greece Italy Netherlands Portugal Spain UK Others

17 9 12 5 54 6 7 12 19 17 9 12 5 54 6 7 12 19 17 9 12 5 54 6 7 12 19 17

1.2547 1.1714 1.1868 1.3956 1.3413 1.2231 2.7073 1.2494 1.2452 1.2682 0.9767 0.9763 0.9762 0.9763 0.9762 0.9765 0.9765 0.9764 0.9765 0.7045 0.5927 0.5425 0.5741 0.5446 0.6642 0.6458 0.5592 0.6357

1.2006 1.0061 1.0062 1.0062 1.0062 1.0061 1.0062 1.0063 1.0062 1.0061 0.5925 0.4555 0.4518 0.4132 0.4646 0.4972 0.5265 0.5319 0.5334 0.6099 0.4805 0.4681 0.4186 0.5219 0.4954 0.5211 0.5193 0.5137

4.31 14.11 15.22 27.90 24.99 17.74 62.83 19.46 19.19 20.66 -39.33 -53.35 -53.72 -57.68 -52.41 -49.08 -46.08 -45.53 -45.37 -13.43 -18.92 -13.70 -27.07 -4.16 -25.41 -19.32 -7.13 -19.19

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Model

Model C1 is a cost efficiency model that has two outputs namely loans, and other earnings assets. Model C2 is a cost efficiency model that has three outputs namely loans, other earning assets, and noninterest income. Models P1 and P2 are similar to C1 and C2 respectively, but they are profit efficiency models. Cost efficiency takes values between one and infinity, whereas profit efficiency ranges between zero and one. In both cases, scores closer to 1 indicate higher efficiency.

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Appendix I - Model P2

ln

Q  Q  Q  TC  W1  W 2    0   1 ln  1    2 ln  2    3 ln  3    4 ln     5 ln   W 3 * EQ EQ EQ EQ W 3   W3       2

 6

Q  Q  Q  Q  1   Q1   1   Q   ln     7 ln  1  ln  2    8 ln  1  ln  3    9  ln  2     2   EQ   2   EQ    EQ   EQ   EQ   EQ  2

2

Q  Q  1   Q  1   W1    W1   W 2    10  2  ln  3    11  ln  3     12  ln      13 ln   ln   2   EQ   2  W 3  W3  W3   EQ   EQ 

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 Q   W1   Q  W 2   Q   W1  1  W 2    14  ln      15 ln  1  ln     16 ln  1  ln     17 ln  2  ln   2   W 3   EQ   W 3   EQ   W 3   EQ   W 3 

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 Q  W 2   Q3   W 1   Q3   W 2   ln   ln    18 ln  2  ln     19 ln     20 ln    u ic  vic  EQ   W 3   EQ   W 3   EQ   W 3 

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ACCEPTED MANUSCRIPT Appendix II – Coefficients of the Frontier Functions C2 and P2

4.3486 1.8968 4.3221 1.0125 -0.1334 7.2035 4.7535 -4.8514 -0.1081 5.9543 -0.0010 1.4947 2.1067 -2.3433 2.0548 -0.4534 -0.8641 3.4907 -2.8422 -1.9255 1.3674

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1.8865 0.2832 0.5817 0.1890 -0.0202 0.8953 0.1850 -0.1884 -0.0036 0.1594 -0.0000 0.0836 0.0971 -0.1045 0.1048 -0.0110 -0.0244 0.1007 -0.0763 -0.0804 0.0510

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0.1010 0.1659 0.9195 0.1876 -0.2259 0.0702 0.1591 -0.0165 0.0124 0.0648 -0.1229 0.1788 0.0021 -0.0390 0.0518 -0.0206 0.0599 -0.0421

4.6951 2.0066 3.5690 0.3843 0.7686 4.5162 4.5476 -4.4950 1.4363 4.9946 -0.3763 0.1515 1.0034 -2.0427 2.6944 0.0604 -1.0417 1.1855 -0.5665 1.1054 -0.8817

GAAP P2 Coefficient t-ratio 3.4484 3.5229 1.0091 1.9602 -0.4311 -0.6303 1.0932 1.6808 -0.4191 -0.7338 1.1073 1.7632 0.0008 0.0048 -0.3844 -1.9577 0.2671 1.9599 -0.0416 -0.2986 -0.4425 -2.6476 0.8065 2.7968 0.2950 0.8491 -0.2976 -0.8653 0.2271 0.6179 0.1133 0.8983 -0.0806 -0.5297 0.1400 0.6828 -0.2489 -1.3474 0.3595 1.7926 -0.3218 -1.6109

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2.1795 0.3686 0.7027

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Constant LN(Q1/EQ) LN(Q2/EQ) LN(Q3/EQ) LN(W1/W3) LN(W2/W3) (LN(Q1/EQ)^2)/2 LN(Q1/EQ)LN(Q2/EQ) LN(Q1/EQ)LN(Q3/EQ) (LN(Q2/EQ)^2)/2 LN(Q2/EQ)LN(Q3/EQ) (LN(Q3/EQ)^2)/2 ((LN(W1/W3))^2)/2 LN(W1/W3)LN(W2/W3) ((LN(W2/W3))^2)/2 LN(Q1/EQ)LN(W1/W3) LN(Q1/EQ)LN(W2/W3) LN(Q2/EQ)LN(W1/W3) LN(Q2/EQ)LN(W2/W3) LN(Q3/EQ)LN(W1/W3) LN(Q3/EQ)LN(W2/W3)

IFRS C2 Coefficient t-ratio

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IFRS P2 Coefficient 2.5594 1.5140 1.0027 -0.6489 -0.7833 1.9233 -0.3365 -0.7561 0.4939 -0.0986 0.0203 0.1977 0.3106 -0.3115 0.2739 0.2341 -0.3405 0.0639 -0.1754 0.1002 -0.0501

Notes: Model C2 is a cost efficiency model that has three outputs namely loans, other earning assets, and non-interest

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t-ratio 2.6974 2.5631 1.2742 -0.8464 -1.1378 2.9352 -1.8159 -3.4694 3.5592 -0.7322 0.1370 0.6937 1.1193 -1.1797 0.9134 2.1062 -2.5840 0.3452 -1.0036 0.3896 -0.2265

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Highlights

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We examine how bank efficiency estimates are influenced by the shift from local GAAP to IFRS in EU. We find that the efficiency estimates are influenced by the use of different accounting standards We find some differences between profit and cost efficiency

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