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Journal of Quantitative Economics

23.04.2024 | Original Article

Dynamics of Operational Efficiency in Credit Lending and Recovery of Stressed Assets: An Alternative Approach with Undesirable By-Products

verfasst von: Gargi Sanati, Anup Kumar Bhandari

Erschienen in: Journal of Quantitative Economics

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Abstract

Our study estimates the operational efficiency of Indian banks during 2009–2010 to 2017–2018, considering advances, and recovery of stressed assets as desirable outputs, while NPAs and slippages are undesirable by-products. Our first stage DEA analyses conclude that public sector banks have significant scope to improve their lending and recovery efficiency, while also reducing their stressed assets. Our analyses also reveal that banks are more efficient in managing credit risks for shorter-term loan portfolios and secured loans, while positive economic externalities are more prominent in determining the efficiency of banks in priority sector lending. We also observe that a competitive scenario within the banking sector, overall macroeconomic growth, and low cost of funds play vital roles in improving banks' operational efficiency and reducing credit risk. Our findings suggest that liquid assets might be more beneficial than illiquid collaterals in improving the recovery of stressed assets.

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Fußnoten
1
To add further, Das et al. (2005) also study profit and revenue efficiencies for Indian banking sector.
 
2
As per bank’s annual report, reduction of NPA can have three segments: reduction from upgradation, reduction from write off, and reduction from cash recovery.
 
3
Due to large scale merger drives of the public sector banks, we keep aside the later periods from our analyses.
 
4
Another strand of literature is to use bootstrap-based analyses to (a) obtain biased-corrected TES at the first stage; and (b) truncated regression for drawing consistence inference at the second stage, a la Simar and Wilson (2007). However, the question of bias at the conventional DEA estimation arises in the absence of true data generating process while using sample data. Nonetheless, we restrain ourselves from doing so, since we use almost the entire set of the banks under the two chosen groups (for which consistent data is available) and only a handful of them is left out. Hence, extent of bias, if any, is expected to be negligible.
 
5
The basic assumptions about the production technology that are made in this method are as follows: (a) All observed input–output bundles are feasible; (b) the production possibility set is convex implying that given a set of N feasible input–output bundles, any weighted average of these N input bundles can produce the same weighted average of the corresponding N output bundles and (c) any input or output is freely disposable. However, free disposability assumption needs to be adjusted if bad output(s) or some input(s) use of which beyond a threshold level may have detrimental effects on production process. Nonetheless, these assumptions enable one to construct a production possibility frontier on the basis of the observed inputs-output bundles of a given set of banks, following the DEA method.
 
6
One may also define an input-oriented technical efficiency. This is not done in the present study. See Coelli et al. (1998) and Ray (2004) for further details.
 
7
For extensive mergers and acquisitions of public sector banks in India in the year 2018, 2019 and 2020, we exclude these years from our analyses. This helps us to keep the standard of the data without losing much of information.
 
8
To clarify further, TES = 92.2% implies that there exists a further [{(1/0.922) – 1} × 100]% of output expansion opportunities.
 
Literatur
Acharya, V.V., S.T. Bharath, and A. Srinivasan. 2007. Does Industry-wide Distress affect Defaulted firms? Evidence from Creditor Recoveries. Journal of Financial Economics 85 (3): 787–821.CrossRef
Altman, E., A. Resti, and A. Sironi. 2004. Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence. Economic Notes 33 (2): 183–208.CrossRef
Asarnow, E., and D. Edwards. 1995. Measuring Loss on Defaulted Bank Loans: A 24-year Study. Journal of Commercial Lending 10 (2): 11–23.
Banker, R.D., and R. Natarajan. 2008. Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis. Operations Research 56 (1): 48–58.CrossRef
Banker, R.D., A. Charnes, and W.W. Cooper. 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30 (9): 1078–1092.CrossRef
Bhandari, A.K. 2012. Total Factor Productivity Growth and Its Decomposition: The Indian Banking Sector during Liberalisation. Economic and Political Weekly 47 (12): 68–76.
Bhandari, A.K. 2014. Banks Ownership and Efficiency in India: Some Fresh Evidence. Keio Economic Studies 50: 1–28.
Carty, L. and D. Lieberman (1996), Defaulted Bank Loan Recoveries, Moody’s Special Comment, November.
Caselli, S., S. Gatti, and F. Queri. 2008. The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans. Journal of Financial Services Research 34: 1–34.CrossRef
Chambers, R.G., Y. Chung, and R. Färe. 1996. Benefit and Distance Functions. Journal of Economic Theory 70 (2): 407–419.CrossRef
Charnes, A., W.W. Cooper, and E. Rhodes. 1978. Measuring the Efficiency of Decision Making Units. European Journal of Operational Research 2 (6): 429–444.CrossRef
Chen, Z.-H., P.H.-B. Tao, M. Cai, H.-F. Lin, X.-J. Lin, Q. Shu, and R.-N. Zhang. 2015. Using a Two-Stage Data Envelopment Analysis to Estimate the Efficiency of County Hospitals in China: A Panel Data Study. The Lancet 386 (Supplement 1): S64.CrossRef
Coelli, T.J., D.S.P. Rao, and G.E. Battese. 1998. An Introduction to Efficiency and Productivity Analysis. Boston: Kluwer Academic Publishers.CrossRef
Das, A., and S. Ghosh. 2006. Financial deregulation and efficiency: An empirical Analysis of Indian Banks During the Post Reform Period. Review of Financial Economics 15: 193–221.CrossRef
Das, A., A. Nag, and S.C. Ray. 2005. Liberalisation, Ownership and Efficiency in Indian Banking: A Nonparametric Analysis. Economic and Political Weekly 40 (12): 1190–1197. 
Dermine, J., and C. Neto de Carvalho. 2006. Bank loan losses-given-default: A case study. Journal of Banking & Finance 30: 1219–1243.
Färe, R., and S. Grosskopf. 2000. Theory and Application of Directional Distance Function. Journal of Productivity Analysis 13 (1): 93–103.CrossRef
Färe, R., S. Grosskopf, D.W. Noh, and W. Weber. 2005. Characteristics of a Polluting Technology: Theory and Practice. Journal of Econometrics 126 (2): 469–492.CrossRef
Franks J., A. Servigny, and S. Davydenko (2004), A Comparative Analysis of the Recovery Process and Recovery Rates for Private Companies in the U.K., France, and Germany. Standard & Poor’s Risk Solutions Report, New York.
Frye, J., (2000), Depressing Recoveries, Policy Studies, Federal Reserve Bank of Chicago, October.
Grunert, J., and M. Weber. 2009. Recovery Rates of Commercial Lending: Empirical Evidence for German Companies. Journal of Banking and Finance 33: 505–513.CrossRef
Hoff, A. 2007. Second stage DEA: Comparison of Approaches for Modelling the DEA Score. European Journal of Operational Research 181 (1): 425–435.CrossRef
Hurt, L., and A. Felsovalyi. 1998. Measuring Loss on Latin American Defaulted Bank Loans, a 27-Year Study of 27 Countries. The Journal of Lending and Credit Risk Management 80: 41–46.
Jayaraman, A.R., and M.R. Srinivasan. 2016. Analyzing Profit Efficiency of Banks in India with Undesirable Output—Nerlovian Profit Indicator Approach. IIMB Management Review 26: 222–233.CrossRef
Khieu, H.D., D.J. Mullineaux, and H.C. Yi. 2012. The Determinants of Bank Loan Recovery Rates. Journal of Banking and Finance 36 (4): 923–933.CrossRef
Kosak, M., and J. Poljšak. 2010. Loss Given Default Determinants in a Commercial Bank Lending: An Emerging Market Case Study. Journal of Economics and Business 28 (1): 61–88.
Luenberger, D.G. 1992. Benefit Functions and Duality. Journal of Mathematical Economics 21 (5): 461–481.CrossRef
Misra, R., Rajmal, and R. Verma. 2016. Determinants of Recovery of Stressed Assets in India: An Empirical Study. Economic and Political Weekly 51 (43): 62–71.
Ray, S.C. 2004. Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research. Cambridge: Cambridge University Press.CrossRef
Samut, P.K., and R. Cafri. 2016. Analysis of the Efficiency Determinants of Health Systems in OECD Countries by DEA and Panel Tobit. Social Indicator Research 129 (1): 113–132.CrossRef
Simar, L., and P.W. Wilson. 2007. Estimation and Inference in Two-Stage, Semi-Parametric Models of Production Processes. Journal of Econometrics 136 (1): 31–64.CrossRef
Zaman, M.S., and A.K. Bhandari. 2020. Financial Deregulation, Competition and Cost Efficiency of Indian Commercial Banks: Is there any Convergence? Indian Economic Review 55 (2): 283–312.CrossRef
Metadaten
Titel
Dynamics of Operational Efficiency in Credit Lending and Recovery of Stressed Assets: An Alternative Approach with Undesirable By-Products
verfasst von
Gargi Sanati
Anup Kumar Bhandari
Publikationsdatum
23.04.2024
Verlag
Springer India
Erschienen in
Journal of Quantitative Economics
Print ISSN: 0971-1554
Elektronische ISSN: 2364-1045
DOI
https://doi.org/10.1007/s40953-024-00389-8

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