Implicaciones de la existencia de múltiples definiciones de régimen

Given the presentation of the importance of banking institutions to economic development and the rationale for embarking on banking reforms, this section dwells on the review of measures adopted in the evaluation of bank performance in different periods and

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jurisdictions. Literature suggests that the measures fall predominately under three main approaches (Ratio analysis, Regression analysis, and frontier analysis).

3.8.1 Ratio Analysis

Considerable studies in an attempt to analyse and evaluate the performance of banking institutions have relied on conventional financial ratios (Ayadi, Adebayo, & Omolehinwa, 1998; Sharma, Sharma, & Barua, 2013). The return on assets (ROA) and return on equity (ROE) are the most popular and commonly utilised financial ratios for evaluating bank performance (Badreldin, 2009; Karr, 2005; Wilcox, 1984). Karr (2005) suggested that the ROA and ROE measures mostly correlate with each other and at the same time provide the same indication of performance related to the tendency and movement of financial performance. Although widely used, the shortcomings of adopting the use of financial ratios have been criticised on a large scale. Indicators such as the ROA and ROE only provide a narrow and incomplete picture of performance, while other financial ratio indicators may give contradictory results. Simply put, one principal disadvantage of financial ratios analysis is that each single ratio must be compared against a benchmark ratio one at a time while it is assumed that all other factors are fixed, and the chosen benchmarks are appropriate for comparison (Avkiran, 2011). Another main weakness of using financial ratios is that each ratio examines only part of a bank’s activities and in the process fails to capture the multidimensional nature of banks, and thus it fails to provide enough performance information (LaPlante & Paradi, 2015). More so, financial ratios offer a retrospective and not prospective examinations and are based on accounting data, while neglecting economic data (Karr, 2005).

Examples of studies that relied on financial ratios to measure bank performance include: Kumbirai & Webb (2010) who used financial ratios to analyse the performance of commercial banks in South Africa from 2005 -2009; Kirikal, Sorg, & Vensel (2004) used a variant of the traditional financial ratios called DuPont Financial Ratios Analysis to examine the performance of Estonian banks. While Chandani, Mehta, & Chandrasekaran (2014) made use of financial ratios as computed from financial statements and the CAMEL model (a combination of financial ratios used by bank regulators) to ascertain the performance of domestic banks in India. Similarly, Sargu & Roman (2013) used the CAMELS framework to

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analyse the financial soundness of commercial banks in Bulgaria, Czech Republic and Romania.

3.8.2 Regression Analysis

The regression analysis is a common methodology used in bank performance studies. The central advantage of regression analysis is that it allows for measurement errors and statistical inference. The advantage of regression analysis over traditional ratio analysis is that the effect of multiple independent variables on the dependent variable can be estimated simultaneously. More so, for instance, regression analysis can be used to provide information about the average performance of banks included in a given sample and this information can be utilised to estimate the expected performance of other banks (Paradi & Zhu, 2013).

Even though regression analysis is effective in a wide range of circumstances in measuring and ascertaining relationships between variables, it has some inherent limitations that make it unsuitable for utterly reflecting the increasing multifaceted nature of banking. One limitation of regression analysis is that it is a parametric method that requires a general production model to be specified. Another limitation of regression analysis is that it is a central tendency method in which predicted values results from a regression model provide the average or anticipated level of outcome given particular inputs, instead of the maximum realisable outcome. Also, regression analysis is only appropriate when modelling single input – multiple outputs or multiple inputs – single output systems (Paradi & Zhu, 2013; Tonidandel & LeBreton, 2011).

Examples of studies that utilized regression analysis in bank performance studies include: Alkhatib (2012) employed three indicators (ROA as a financial performance indicators, the Tobin’s Q model as a market financial performance indicator and economic value added as an economic, financial performance indicator) to design a multi-regression model in order to measure bank performance in Pakistan. Doucouliagos, Haman, & Askary (2007) used regression models to explore the relationship between directors’ remuneration and performance in Australian banks using panel data from 1992 to 2005. Using a three-stage least squares equation in addition to other regression models, Limpaphayom & Polwitoon (2004) examined the relationship between bank relations and market performance in

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Thailand. Castelli, Dwyer, & Hasan (2012) used regression models to examine the connection between the number of bank relationships and firm performance in small Italian firms that are financed by banks.

In relation to the above and due to that extensive use of regression analysis in banking studies, this study uses panel data regression which is a variant of regression analysis to ascertain the determinants of bank efficiency, performance, and financial stability. This technique is further elaborated upon in the research methodology chapter of this study, while related empirical studies that adopted regression analysis to ascertain the determinants bank efficiency, performance, and financial stability in different jurisdictions and Nigeria are presented in subsequent sections of this chapter.

3.8.3 Frontier Efficiency Methodologies

In recent times, research employing the frontier approach has become popular (Paradi & Zhu, 2013). The frontier approach is perceived to be robust when compared to traditional financial ratios analysis as it offers further meaningful insight into the efficiency and performance of organisations (Berger & Humphery, 1997). Frontier efficiency methodologies are benchmarking technique based models that assess how well organisations (Decision Making Units – DMUs) are performing compared to the best performing organisation (DMU) that are doing business under the same operational conditions. The best organisations are identified from the data set, and they are used as the efficient frontier. Hence, organisations are not benched marked against some abstract assumptions but rather against performing organisations operating in the same business clime. A central advantage of this methodology over other indicators of performance is that it provides overall objective numerical efficiency scores including economic optimisation mechanisms in complex operational climes and sums up performance in a single statistic (Berger & Humphrey, 1997; Paradi & Zhu, 2013).

Correspondingly, frontier efficiency techniques can be utilised in numerous ways to assist management in assessing whether they are performing better or worse than their competitors (peers) regarding cost minimization, revenue, scale technology, and profit maximisation. Consequently, bank managements can utilise the resultant feedback from frontier efficiency analyses to identify operational areas that require improvement; ascertain attractive targets

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for mergers and acquisitions; and set future development strategies. Frontier analysis can help provide recommendations to non-efficient organisations or institutions to improve their performance to catch-up with the efficient ones. More so, frontier efficiency analysis techniques can assist in determining the effects of environmental variables and achievable targets for inefficient organisations to provide further understanding into the production systems of organisations (Banker & Cummins, 2010).

Literature in the past three decades has led to the conclusion that there are five main frontier efficiency analysis techniques that have been employed in the evaluating performance and efficiency. The non-parametric linear programming approaches are Data Envelopment Analysis (DEA) and the Free Disposal Hull. While the other three approaches: Stochastic Frontier Analysis (SFA), Distribution-Free Approach (DFA) and Thick Frontier Approach (TFA) are parametric econometric models. These various approaches differ based on the imposed assumptions on the specifications of the efficient frontier, the existence of random error, and the distribution of the inefficiencies. The non-parametric linear programming approaches make use of few assumptions while identifying the best-practice frontier and they do not account for random errors. Whereas the parametric econometric approaches require a priori specification of the form of the production function, and characteristically they include an error term that captures inefficiency and random error. However, only the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA) are commonly used in banking studies (Berger & Humphrey, 1997; Dong, Hamilton, Tippett, 2014; Thanassoulis, Boussofiane, & Dyson, 1996, Borger, Ferrier, & Kerstens, 1998).

In line with the above advantages ascribed to the frontier approach over ordinary financial ratios and regression analysis, this study employs the frontier approach in measuring bank efficiency and performance. To be specific, this study has adopted a variant of the DEA approach called the DEA Window analysis to obtain efficiency estimates for Nigerian DMBs for the period of 2000 – 2013. In that regard, the DEA approach is discussed in the research methodology chapter before narrowing down to the specific DEA window approach employed to estimate bank performance and efficiency in Nigerian DMBs. Nonetheless, studies that adopted the DEA approach are briefly reviewed below.

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