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How relevant is the choice of risk management control variable to non-parametric bank profit efficiency analysis? The case of South Korean banks

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Abstract

Adopting a profit-based approach to the estimation of the efficiency of South Korean banks over the 2007Q3 to 2011Q2 period, we systematically analyse, within a non-parametric DEA analysis, how the choice of risk management control variable impacts upon such estimates. This is in recognition of previous findings that such estimates are dependent on the choice of risk management control variable and the lack of guidance from such studies on the optimal choice of risk control variable. Using the model of Liu et al. (Ann Operat Res 173:177–194, 2010), we examine the dependency of the estimated efficiency scores on the chosen risk control variables embracing loan loss provisions and equity as good inputs and non-performing loans as a bad output. We duly find that, both for individual banks and banking groups, the mean estimates are indeed model dependent although, for the former, rank correlations do not change much at the extremes. Based on the application of the Simar and Zelenyuk (Econom Rev 25:497–522, 2006) adapted Li (Econom Rev 15: 261–274, 1996) test, we then find that, if only one of the three risk control variables is to be included in such an analysis, then it should be loan loss provisions. We also show, however, that the inclusion of all three risk control variable is to be preferred to just including one, but that the inclusion of two such variables is about as good as including all three. We therefore conclude that the optimal approach is to include (any) two of the three risk control variables identified. The wider implication for research into bank efficiency is that the optimal choice of risk management control variable is likely to be crucial to both the delivery of risk-adjusted estimates of bank efficiency and the specification of the model to be estimated.

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Notes

  1. One such study was undertaken by Hadad et al. (2012) who examined the sensitivity of bank efficiency scores to the choice of risk control variable (i.e., loan loss provisions or equity) within an Indonesian context.

  2. During the GFC, long-term overseas borrowing by Korean banks declined dramatically as international funding markets dried up, falling from US $11.3 billion in 2007 to US $6.23 billion in 2008 and then to US $4.25 billion in 2009.

  3. Korean banks’ ‘substandard’ loans rose from 0.72 % of total loans in December 2007 to 1.9 % by December 2010. This subsequently led to the establishment of a government bank recapitalisation fund with an endowment of 20 trillion Korean Won (KRW) (US $13.5 billion) and the provision of a government guarantee to over US $100 billion of banks’ overseas borrowings.

  4. “The amount of losses which have been specifically identified is recognized as an expense and deducted from the carrying amount of the appropriate category of loans and advances as a provision for losses on loans and advances. The amount of potential losses not specifically identified but which experience indicates are present in the portfolio of loans and advances is also recognized as an expense and deducted from the total carrying amount of loans and advances as a provision for losses on loans and advances” (International Accounting Standard IAS 30).

  5. An NPL under Basel II (Committee 2004) is any loan that is past due for more than 90 days, but it is subject to wide national variation. If we consider how many days a bank has to allow for a 100 % consumer loan write-down as a non-performing loan in South America, it is 366 days in Argentina, 180 in Chile, 90 in Columbia, 120 in Ecuador, 126 in Mexico and 120 in Peru (for more details see Galindo and Rojas-Suarez 2011).

  6. One of the first bank efficiency studies to include equity as a risk variable was that of Hughes and Mester (1993), who argued that, “recognizing that financial capital is an input but omitting it in the cost function is equivalent to assuming that the unit price of financial capital is perfectly correlated with one of the other input prices or is the same for all banks (and so its price need not be included separately in the cost function), and that the level of financial capital is determined endogenously as that level which minimizes cost. If we believed that the bank were operating with the cost-minimizing level of financial capital but that the price of financial capital and price of deposits differed, we would include the unit price of financial capital in the cost function. However, there is good reason to suspect that the level of financial capital a bank holds may not be explained entirely by cost minimization. First, regulators set a minimum capital-asset ratio for banks and this may constrain banks from operating at the cost-minimizing financial capital level. Second, if the bank exhibits some risk aversion, then, because lower capital implies higher probability of default (capital acts as a cushion for losses), banks may choose a noncost-minimizing level of financial capital” (pp. 295–296).

  7. One of the first bank efficiency studies to include non-performing loans was Mester (1996) who argued that “while the macroeconomy can affect nonperforming loans, it is felt equally across banks. It is the differences in nonperforming loans across banks that capture differences in quality across banks” (p. 1035). The inclusion of nonperforming loans was therefore included, along with equity, in a stochastic cost frontier model to account for bank risk.

  8. In another study, “the bad output of non-performing loans is defined as the sum of problem loans, which are part of the total loans. Problem loans are computed by adding the balance of loans to bankrupt borrowers and the balance of non-accrual delinquent loans” (Fukuyama and Weber 2008, p. 1860).

  9. Indeed, when undertaking a single country efficiency analysis utilising risk control variables, NPL could be a better risk indicator then either LLP or equity—see also Barros et al. (2012). However, in future, this condition could change as, under Basel III, banks are able to decrease NPL as borrowers begin to repay loans, even though the bank had previously classified the loan as bad. These new rules have not, as yet, been implemented by Korea and, as such, and as such have no effect on banks’ calculations of NPL across our sample period.

  10. See Liu and Sharp (1999) for further discussions.

  11. We also assume the regularity conditions from Li (1996, 1999) and Simar and Zelenyuk (2006) are satisfied.

  12. Denotes capital adequacy (C), asset quality (A), management skill (M), earnings (E) and liquidity (L).

  13. The need to include ‘fee and trading income’ was also noted by Doh (2012). He observes that South Korean capital flows were the most volatile in Asian countries pre and post-GFC, equalling +US $78 billion between Jan 1995 and Oct 1997, \(-\)US $21 billion between Nov 1997 and April 1998, \(-\)US $70 billion between Sept 2008 and Dec 2008 and \(-\)US $ 82 billion between Jan 2009 and Mar 2010.

  14. Indeed, those banks that sold hybrid and subordinated debt to the bank recapitalisation fund included the commercial banks Woori (KRW1,000 bn), Kookmin (KRW1,000 bn), Hana (KRW 400 bn) and the specialist National Federation of Fisheries (or Suhyup) (KRW100 bn), and the regional banks Kyoungnam (KRW116 bn) and Kwangju (KRW 87 bn). As at end of March 2011, only the commercial banks Woori (KRW300 bn), Kookmin (KRW400 bn) and Hana (KRW100 bn) redeemed the debt from the government as their balance sheets improved post-GFC.

  15. The results (not shown but available from the author) show that there is a close similarity between the CCR and BCC—efficiency scores of banks across all models except for Jeju Bank and Jeonbuk Bank, who suffer a near 50 % collapse in efficiency from one program to the other. This is due to scale inefficiencies—estimated at 0.4761 for Jeju Bank. In general, for all remaining banks and models the scale inefficiencies experienced are less than 0.10—hence the reason why the CCR results are excluded from the current discussion.

  16. This type of model-dependency result was also found by Altunbas et al. (2000) where they note for Japanese banks “that financial capital has the most noticeable influence on the scale economy and scale efficiency results. If one excludes it from the estimation the scale economy and scale efficiency estimates are similar (across) years as the cost function which has no risk and quality variables. Non-performing loans and the liquidity ratio appear to have little effect on the results. The result, however, should be treated with caution given that the influence of the financial capital variable (E) may be overstated because this variable is fully interactive with the output and input price variables in the cost function but the non-performing loan ratios and the liquidity ratio are not (see footnote 3). It could be the case that the inclusion of financial capital impacts the results most because Japanese banks experienced a decline in their capital strength over the period of study whereas changes in provisioning levels were more modest” (p. 1617).

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Acknowledgments

We thank the Editor and anonymous referees, and our colleagues for valuable comments that helped improving this paper. Valentin Zelenyuk acknowledges partial support from grant of Australian Institute for Business and Economics. Zhongbao Zhou and WenBin Liu would like to thank the support of National Natural Science Foundation of China (No. 71371067).

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Authors and Affiliations

  1. University of Nottingham, Nottingham, England, UK

    Richard Simper

  2. Loughborough University, Loughborough, England, UK

    Maximilian J. B. Hall

  3. University of Kent, Kent, England, UK

    WenBin Liu

  4. School of Economics, Australian Institute for Business and Economics (AIBE) and Centre for Efficiency and Productivity Analysis (CEPA), The University of Queensland, 530, Colin Clark Building (39), St Lucia, Brisbane, QLD, 4072, Australia

    Valentin Zelenyuk

  5. Hunan University, Changsha, People’s Republic of China

    Zhongbao Zhou

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  1. Richard Simper
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Correspondence to Valentin Zelenyuk.

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Simper, R., Hall, M.J.B., Liu, W. et al. How relevant is the choice of risk management control variable to non-parametric bank profit efficiency analysis? The case of South Korean banks. Ann Oper Res 250, 105–127 (2017). https://doi.org/10.1007/s10479-015-1946-x

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