Using Data Envelopment Analysis in Credit Risk Evaluation of ICT Companies

DOI 10.7160/aol.2020.120404
No 4/2020, December
pp. 47-60

Kavčáková, M. and Kočišová, K. (2020) “Using Data Envelopment Analysis in Credit Risk Evaluation of ICT Companies", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 4, pp. 47-60. ISSN 1804-1930. DOI 10.7160/aol.2020.120404.


The aim of the paper is to explore possibilities of diagnosis corporate credit risk through DEA and design an appropriate model for diagnosis of credit risk, which can be used in different sectors of national economy (e.g. agricultural, service sector or industry and innovation sector). The model differs from the conventional application of DEA because of variables selection and construction of production-possibility frontier. We illustrate application of models on sample 110 randomly selected companies during the 2013-2017 period. The reason for choosing the ICT companies is the fact that this sector is considered to be driving force behind the growth of the economy. The data has been obtained from Finstat. The results are divided into identification of 3 zones of corporate financial health with a different stage of credit risk. They show that DEA achieves a satisfactory value of a correct classification into the relevant zone (financial health, grey, and financial distress zone), but also the relatively high error rate of the DEA in the identification of companies in financial distress.


Companies in the information and communication technology (ICT) services industry, credit risk, Data Envelopment Analysis, financial health.


  1. AlAli, M., AlShamali, M., AlAwadhi, K. and AlSabah, A. (2018) "The use of Zmijewski model in examining the financial soundness of oil and gas companies listed at Kuwait Stock Exchange“, International Journal of Economics, Commerce and Management Research Studies, Vol. 1, No. 2, pp. 15-21. ISSN 2456-2165.
  2. Alin, A. (2010) "Multicollinearity“, Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2010, No. 2-3., pp. 370-374. ISSN 1939-5108. DOI 10.1002/wics.84.
  3. Altman, E. I. (1968) "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy“, Journal of Finance, Vol. 23, No. 4, pp. 589-609. ISSN 00221082. DOI 10.2307/2978933.
  4. Aminian, A., Mousazade, H. and Khoshkho, O. I. (2016) "Investigate the ability of bankruptcy prediction models of Altman and Springate and Zmijewski and Grover in Tehran Stock Exchange“, Mediterranean Journal of Social Sciences, Vol. 7, No. 4 S1, pp. 208. ISSN 2039-2117. DOI 10.5901/mjss.2016.v7n4S1p208.
  5. Bandyopadhyay, A. (2007) "Credit risk models for managing bank’s agricultural loan portfolio“, Munich Personal RePEc Archive.
  6. Banker, R. D., Charnes, A. and Cooper, W.W. (1984) "Some Models for Estimating Technical Scale Inefficiencies in Data Envelopment Analysis“, Management Science, Vol. 30, No. 9, pp. 1078-1092. ISSN 1526-5501. DOI 10.1287/mnsc.30.9.1078.
  7. Bányiová, T., Bieliková, T. and Piterková, A. (2014) "Prediction of agricultural enterprises distress using data envelopment analysis“, Proceedings of the 11th International Scientific Conference European Financial Systems, Masaryk University, Brno, pp. 18-25. ISBN: 978-80-210-7153-7.
  8. Beaver, W. H. (1966) "Financial ratios as predictors of failure“, Journal of Accounting Research, Vol. 30, No. 9., pp. 71-111. E-ISSN 1526-5501, ISSN 0025-1909. DOI 10.2307/2490171.
  9. Boďa, M. and Úradníček, V. (2019) "Predicting Financial Distress of Slovak Agricultural Enterprises“, Ekonomický časopis, Vol. 67, No. 4, pp. 426-452. ISSN 0013-3035.
  10. Bogetoft, P. and Otto, L. (2011) "Additional Topics in DEA", In: Benchmarking with DEA, SFA, and R. International Series in Operations Research & Management Science, Vol. 157, Springer, New York, NY. ISBN 978-1-4419-7960-5, E-ISBN 978-1-4419-7961-2. DOI 10.1007/978-1-4419-7961-2_5.
  11. Brewer, B. E., Wilson, C. A., Featherstone, A. M., Harris, J. M., Erickson, K. and Hallahan, C. (2012) "Measuring the financial health of US production agriculture“, Journal of ASFMRA, pp. 178-193. ISSN 0003116X.
  12. Delina, R. and Packová, M. (2013) "Validácia predikčnýchch bankrotových modelov v podmienkach SR“, E+ M Ekonomie a management, Vol. 2013, No. 3, p. 101-112. ISSN 2336-5604. (in Slovak)
  13. Ékes, K. S. and Koloszár, L. (2014) "The efficiency of bankruptcy forecast models in the Hungarian SME sector“, Journal of Competitiveness, Vol. 6, No. 2, pp. 56-73. ISSN 1804-1728. DOI 10.7441/joc.2014.02.05.
  14. Fakhri Husein, M. and Tri Pambekti, G. (2014) "Precision of the models of Altman, Springate, Zmijewski, and Grover for predicting the financial distress“, Journal of Economics, Business, and Accountancy Ventura, Vol. 17, No. 3, pp. 405-416. ISSN 2087-3735. DOI 10.14414/jebav.14.1703010.
  15. Feruś, A. (2008) "The DEA method in managing the credit risk of companies“, Ekonomika,Vilnjus University Press, E-ISSN 1392-1258, ISSN 2424-6166 Vol. 2008, No. 84. [Online]. Available: ISSN 1392-1258. [Accessed: 4 Jan. 2020].
  16. Grover, J. and Lavin, A. (2001) "Financial Ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy: a Service Industry Extension of Altman’s Z-Score Model of Bankruptcy Prediction“, Working Paper. Southern Finance Assosiation Annual Meeting.
  17. Hart, A. (2001) “Mann-Whitney test is not just a test of medians: differences in spread can be important”, BMJ, Vol. 323, No. 7309, pp. 391-393. ISSN 0959-8138, 1756-1833, 0959-8138, 1756-1833, 0007-1447, 0267-0623. DOI 10.1136/bmj.323.7309.391.
  18. Horváthová, J., Mokrišová, M. and Vrábliková, M. (2019) "Integration of balanced scorecard and data envelopment analysis to measure and improve business performance“, Management Science Letters, Vol. 9, No. 9. ISSN 1321-1340. DOI 10.5267/j.msl.2019.5.017.
  19. Charnes, A., Cooper, W. W. and Rhodes, E. (1978) "Measuring the Efficiency of Decision Making Units“, European Journal of Operational Research, Vol. 2, No. 6, pp. 429-444. ISSN 0377-2217. DOI 10.1016/0377-2217(78)90138-8.
  20. Charnes, A., Cooper, W. W., Golany, B., Seiford, L. M. and Stutz, J. (1985) "Foundations of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions“, Journal of Econometrics, Vol. 30, No. 12, pp. 91-127. ISSN 0304-4076. DOI 10.1016/0304-4076(85)90133-2.
  21. Chaudhuri, A. and Ghosh, S. K. (2017) "Bankruptcy prediction through soft computing based deep learning technique“, Springer. ISBN 978-981-10-6683-2.
  22. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) "An introduction to statistical learning“, New York: Springer, Vol. 112, pp. 3-7. ISBN 978-1-4614-7137-0.
  23. Janová, J., Vavřina, J. and Hampel, D. (2012) "DEA as a tool for bankruptcy assessment: the agribusiness case study“, Proceedings of the 30th International Conference Mathematical Methods in Economics, Vol. 2012, pp. 379-383. ISBN 978-80-7248-779-0.
  24. Jedik, A. and Stalgienė, A. (2018) "The likelihood of farms bankrupcy: The case of Lithuanian family farms“, Management Theory and Studies for Rural Business and Infrastructure Development, Vol. 40, No. 2, pp. 198-205. E-ISSN 2345-0355. DOI 10.15544/mts.2018.19.
  25. Káčer, M., Ochotnický, P. and Alexy, M. (2019) "The Altman’s Revised Z’-Score Model, Non-financial Information and Macroeconomic Variables: Case of Slovak SMEs“, Ekonomický časopis, Vol. 67, No. 4, pp. 335-366. ISSN 0013-3035.
  26. Karas, M., Reznakova, M. and Pokorny, P. (2017) "Predicting bankruptcy of agriculture companies: Validating selected models“, Polish Journal of Management Studies, Vol. 15, No. 1. ISSN 2081-7452. DOI 10.17512/pjms.2017.15.1.11.
  27. Kassambara, A. (2018) "Machine Learning Essentials: Practical Guide in R“, STHDA, p. 209. ISBN 9781986406857.
  28. Kiaupaite-Grushniene, V. (2016, December) "Altman Z-score model for bankruptcy forecasting of the listed Lithuanian agricultural companies“, 5th International Conference on Accounting, Auditing, and Taxation (ICAAT 2016), Atlantis Press. ISBN 978-94-6252-261-9. ISSN 2352-5428. DOI 10.2991/icaat-16.2016.23.
  29. Kotulič, R., Király, P. and Rajčániová, M. (2007) "Finančná analýza podniku“, Bratislava, Iura Edition, p. 206. ISBN 978-80-8078-117-0. (in Slovak)
  30. Kralicek, P. (1993) "Základy finančního hospodaření“, Prague, Linde, p. 110, ISBN 80-85647. (in Czech)
  31. Krausová, A. (2018) "Abuse of market power in ICT sector“, The Lawyer Quarterly, Vol. 8, No. 1, pp. 78-81. ISSN 1805-840X.
  32. Mann, H. B. and Whitney, D. R. (1947) “On a test of whether one of two random variables is stochastically larger than the other”, The Annals of Mathematical Statistics, Vol. 18, No. 1, pp. 50–60. ISSN 00034851.
  33. McKnight, P. E. and Najab, J. (2010) "Mann-Whitney U Test“, In: The Corsini Encyclopedia of Psychology. E-ISBN 9780470479216, ISBN 9780470170243. DOI 10.1002/9780470479216.
  34. Mendelová, V. and Bieliková, T. (2017) "Diagnostikovanie finančného zdravia podnikov pomocou metódy DEA: Aplikácia na podniky v Slovenskej republike“, Politická ekonomie, Vol. 2017, No. 1, pp. 26-44. ISSN 23368225. DOI 10.18267/j.polek.1125.
  35. Paradi, J. C., Asmild, M. and Simak, P. C. (2004) "Using DEA and Worst Practice DEA in Credit Risk Evaluation“, Journal of Productivity Analysis, Vol. 21, No. 2, pp. 153-165. ISSN 0895562X. DOI 10.1023/B:PROD.0000016870.47060.0b.
  36. Sueyoshi, T. and Goto, M. (2009) "Methodological Comparison Between DEA (Data Envelopment Analysis) and DEA-DA (Discriminant Analysis) from the Perspective of Bankruptcy Assessment“, European Journal of Operational Research, Vol. 199, No. 2, pp. 561-575. ISSN 03772217. DOI 10.1016/j.ejor.2008.11.030.
  37. Tone, K. (2001) "A Slacks-Based Measure of Efficiency in Data Envelopment Analysis“, European Journal of Operational Research, Vol. 130, No. 3, pp. 498-509. ISSN 03772217. DOI 10.1016/s0377-2217(99)00407-5.
  38. Vaněk, J., Jarolímek, J. and Vogeltanzová, T. (2011) "Information and Communication Technologies for Regional Development in the Czech Republic – Broadband Connectivity in Rural Areas“, Agris on-line Papers in Economics and Informatics, Vol. 3, No. 3, pp. 67-76. ISSN 1804-1930.
  39. Vavřina, J., Hampel, D., and Janová, J. (2013) "New approaches for the financial distress classification in agribusiness“, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Vol. 61, No. 4, pp. 1177-1182. E-ISSN 2464-8310, ISSN 1211-8516. DOI 10.11118/actaun201361041177.
  40. Wilcoxon, F. (1945) "Individual comparisons by ranking methods”, In: Breakthroughs in Statistics, pp. 196-202. ISBN 978-1-4612-4380-9. DOI 10.1007/978-1-4612-4380-9_16.
  41. Xin, A. L. J., Hoe, L. W. and Siew, L. W. (2019) "An empirical evaluation on the credit risk efficiency of financial institutions in Malaysia with data envelopment analysis model“, AIP Conference Proceedings, Vol. 2138, No. 1. DOI 10.1063/1.5121102.
  42. Zhao, J., Barry, P. J. and Katchova, A. L. (2008) "Signaling credit risk in agriculture: implications for capital structure analysis“, Journal of Agricultural and Applied Economics, Vol. 40, No. 3. pp. 805-820. ISSN 1074-0708. DOI 10.1017/S1074070800002340.

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