Asset Quality/Risk

Risk is an essential element in the banking industry because banks produce their outputs by taking different kinds of risks. For instance banks deal with loans, investments and other financial services that correspond to credit, market and operation risk. Among all these risks, credit risk is vitally important for the profitable and sustainable growth of banking sector. Credit risk refers to the risk of loan default that originates initially in the form of accumulation of poor quality assets known as non-performing loans (NPLs) and results in the loan losses. It is well recognized in the banking literature that omitting the credit risk factor in performance appraisal model, would not only result in inaccurate conclusion regarding the inefficiency level (Hughes and Mester, 1993, Mester, 1996, Hughes et al., 2001) but might also lead to

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subsequent financial crisis in future. Thus, for a sound financial system it is necessary that financial institutions should be efficient and secure.

There is proliferation of banking efficiency studies. However, the banking literature that has considered the risk factor in efficiency evaluation is relatively limited. With respect to risk, banking efficiency literature can be distinguished into three distinct strands. The first strand has completely ignored risk factor and handled the issue of banks‟ performance evaluation by exploring the information embedded in physical inputs and outputs of banks. The aim of such studies is either refine/extend the existing estimation approach (Thompson et al., 1990, Berger et al., 1993a, Thompson et al., 1996, Seiford and Zhu, 2002, Sahoo and Tone, 2009a, Avkiran, 2011), studying the impact of regulations and reforms (Bauer et al., 1998, Ataullah et al., 2004, Chen et al., 2005, Das and Ghosh, 2006, Fethi et al., 2011) or discussing different other factors such as the impact of merger (Seiford and Zhu, 1999b, Al-Sharkas et al., 2008), ownership type (Sturm and Williams, 2004), and size (Das and Ghosh, 2006) etc. This strand of literature presents an interesting point of view in terms of methodological inductions, explanation of economic phenomenon and elaboration of banking operational mechanisms. Nonetheless, mere utilization of conventional inputs and outputs in the standard models of production may undermine their usefulness particularly in the banking context where risk has an important economic role (Hughes, 1995) and the conviction of conclusion derived from such research may be constrained and misleading (Mester, 1996).

This shortcoming has been addressed by the second strand of studies that has recognized the importance of credit risk. This strand of studies has explicitly accounted for credit risk/loan quality through a one-step approach by incorporating

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risk measures into the production model as either input or output vector while appraising banks‟ efficiency. These studies can be divided into three distinct groups.

The first group of studies has considered non-performing loans (NPLs) as an indicator of risk. For example Hughes and Mester (1993) and Mester (1996) used NPLs in stochastic cost function as a control for loan quality and considered them endogenous or bank specific factor. Being endogenous factor, NPLs reflect the negligence of management in the initial evaluation and monitoring of loans (Mester, 1996). Berger and Mester (1997) have also used NPLs in stochastic cost and profit function to control for external shocks while considering it as an exogenous/environmental variable. In addition to NPLs, all these studies have also treated financial equity capital as an input in the production models as a representative of insolvency risk.

In contrast, Berger and Deyoung (1997) considered NPLs both exogenous and endogenous factors. They used Granger Causality Model to empirically test the relationship between NPLs and cost efficiency of 600 U.S. commercial banks during 1985-1994 by developing four hypothesis that are; “bad luck” (exogenous), “bad management”, “skimping” and “moral hazard” (endogenous). They concluded that all four hypotheses have their own argumentation basis while having a negative relationship between NPLs and cost efficiency. Some studies have also considered the actual amount of loan losses (Berg et al., 1992, Paradi et al., 2011b) and NPLs to loans ratio in the production model to reflect risk (Altunbas et al., 2000).

There are few studies which have used NPLs as an input in the DEA and DEA like models for the efficiency evaluation of banks. However, these studies are silent regarding their inclusion of NPLs in the efficiency model implying that these studies have considered NPLs just as undesirable output without any explicit intention to account for risk factor (Lotfi et al., 2010, Asmild and Matthews, 2012).

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The second group of studies has also adopted a one-step approach. However, this group of studies, instead of using actual amount of loans at risk (NPLs), have considered the risk coverage or cost of risk-taking in lending termed as loan loss provision as input vector in deriving the production frontier in the DEA efficiency model using intermediation or profitability approach of banking behaviour model. The underlying assumption for using this variable is that despite being a cost for risk coverage, it signals a safer environment for the depositors (Brockett et al., 1997, Leightner and Lovell, 1998, Drake and Hall, 2003, Drake et al., 2006, Pasiouras, 2008a, Drake et al., 2009).

The third group of studies have considered multiple indicators for risk in the performance evaluation model. For example Charnes et al. (1990) have used loan loss provisions and loan losses as indicators of risk in their polyhedral cone ratio DEA model. Chang (1999) has used NPLs, loan loss provision and weighted risky assets as input in the DEA based production model. Paradi et al. (2011b) have used NPLs and loan loss provision in the input set of intermediation banking approach whereas included loan losses in the inputs of profitability approach in DEA based bank branches study of big five Canadian banks.

In contrast to the second strand, the third strand has incorporated risk in the efficiency studies as an exogenous variable by following the multistage evaluation method. Most of the studies have used frontier techniques (parametric or non-parametric) at the first stage to calculate the efficiency scores without risk. At the second stage these efficiency scores are regressed on or tested for correlation with a set of variables describing different characteristics being investigated including risk. However, there is huge diversity in the variables selected to represent risk. For example, risk has been incorporated in the form of NPLs (Staub et al., 2010), ratio of NPLs to total loans (Isik

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and Hassan, 2003), loan loss provision to total loans (Kwan, 2003, Havrylchyk, 2006, Sufian, 2009), and loans to total assets (Maudos et al., 2002, Ariff and Can, 2008). This literature discussion indicates that importance of credit risk in efficiency studies is well established. This literature on risk also clearly represents that there are only few studies that have addressed the issue of risk in DEA studies by incorporating risk measure in the efficiency model. Moreover, in spite of explicitly dealing with the credit risk through incorporation of risk measures, as done in the studies mentioned above, there is still a possibility that risk variable can be ignored in the final analysis due to the allocation of zero weight to that variable.

However, there is not a single study that has dealt with the risk element of poor quality assets and relevant additional information in the DEA based model. The current study has incorporated credit risk explicitly at the first stage and used the theoretical concept of production trade-offs11 to incorporate additional information regarding credit risk in the model. In this study, we are considering credit risk (the total amount of NPLs and loan loss provisions) in the model irrespective of their determinants. This is so, because in our opinion, macroeconomic factors are exogenous to the banking industry and similar for all banks. On the other hand, NPLs in all banks not only reflect poor quality asset but also capture the management‟s ability to control the exposure of risk. The purpose of incorporating credit risk factor in the study through production trade- offs is to investigate whether a banks technical efficiency is significantly different when risk is specified as compared to when risk is not specified.