Estimation of Credit Capacity Using Data Envelopment Analysis

Bogusław Guzik

doi:https://doi.org/10.59139/ps.2009.02.1

Bogusław Guzik Akademia Ekonomiczna w Poznaniu Przegląd Statystyczny. Statistical Review, vol. 56, 2009, 2, pages: 3-24 Published online: 30 June 2009 DOI https://doi.org/10.59139/ps.2009.02.1

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ARTICLE

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ABSTRACT

The article points out some disadvantages of traditional (based on DEA methodology) procedure of estimating credit capacity which consists in solving CCR and BCC models and estimating a discriminant function where efficiency indicator is a dependent variable and inputs and outputs used in DEA models are independent variables. Since the main problems with this procedure are connected with discriminant function, the author suggests a procedure of credit capacity estimation which uses no discriminant function. The new method is based on DEA methodology, particularly on super-efficiency DEA models (SE-DEA models) with permitted benchmarks. Comparing the credit capacity indicator (here: ranking indicator) with cut-off points enables objects classification.

KEYWORDS

DEA, SE-CCR, credit capacity, DEA with permitted benchmarks

REFERENCES

[1] Altman E.I., [1968], Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, „The Journal of Finance”, 23, 4.

[2] Andersen P., Petersen N.C., [1993], A procedure for ranking efficient units in Data Envelopment Analysis, „Management Science”, 39, 10.

[3] Banker R.D., Charnes A., Cooper W.W., [1984], Some models for estimating technical and scale inefficiencies in data envelopment analysis, „Management Science”, 30.

[4] Banker R.D., Gilford J.L., [1988], A relative efficiency model for the evaluation of public health nurse productivity, Mellon University Mimeo, Cornegie.

[5] Charnes A., Cooper W.W., Rhodes E., [1978], Measuring the efficiency of decision making units, „European Journal of Operational Research”, 2.

[6] Cook W.D., Seiford L.M., [2009], Data envelopment analysis (DEA) – Thirty years on, „European Journal of Operational Research”, 192, 1.

[7] Chang E.W.I., Chiang Y.H., Tang B.S., [2007], Alternative approach to credit scoring by DEA: Evaluating borrowers with respect to PFI projects, „Building and Environment”, 42, 4.

[8] Emel A.B., Oral M., Reisman A., Yolalan R., [2003], A credit scoring approach for the commercial banking sector, „Socio-Economic Planning Sciences”, 37, 2.

[9] Feruś A., [2006], Zastosowanie metody DEA do określania poziomu ryzyka kredytowego przedsiębiorstw, „Bank i Kredyt”, 7.

[10] Gospodarowicz A., [2004], Możliwości wykorzystania metody DEA do oceny ryzyka kredytowego w kontekście Nowej Umowy Kapitałowej, w: Przestrzenno-czasowe modelowanie i prognozowanie zjawisk gospodarczych, (red. A. Zeliaś), Wyd. Akademii Ekonomicznej w Krakowie, Kraków.

[11] Hamrol M., Chodakowski J., [2008], Prognozowanie zagrożenia finansowego przedsiębiorstwa. Wartość predykcyjna polskich modeli analizy dyskryminacyjnej, Badania Operacyjne i Decyzje, 3. [12] Min J.H., Lee Q-C., [2008], A practical approach to credit scoring, „Expert System with Applications”, 35, 4.

[13] Paradi J.C., Asmild M., Simak P.C., [2004], Using DEA and worst practice DEA in credit risk evaluation, „Journal of Productivity Analysis”, 21.

[14] Truott M.D., Rai A., Zhang A., [1996], The pontential use of DEA for credit applicant acceptance system, „Computers and Operations Research”, 23, 4.

[15] Yeh Q.J., [1996], The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation, „Journal of Operational Research Society”, 47, 8.