Default Prediction Model for Emerging Capital Market Service Companies

Vladislav  Afanasev

doi:10.17323/j.jcfr.2073-0438.17.1.2023.64-77

Vladislav Afanasev HSE University, St.Petersburg, Russia https://orcid.org/0000-0002-4041-4465

DOI: https://doi.org/10.17323/j.jcfr.2073-0438.17.1.2023.64-77

Keywords: services sector, default prediction, credit risk, machine learning algorithms

Abstract

The author tested the hypothesis that default prediction based on financial data may be inapplicable to Russian service sector organizations by analyzing the differences in the accuracy of models based solely on financial data for service providers from Russia and developed European countries.

Logistic regression, Random Forest and K-nearest neighbors machine learning methods were used as modeling tools on a sample of 404 Russian firms and 304 firms from developed European countries.
The results suggest that the prediction error is significantly higher in the case of Russian firms than in the case of firms from the control group (European service firms). Thus, the use of financial ratios for default prediction for service firms in Russia seems insufficient.

These findings can be used by organizations that provide credit scoring, and by any other market participants interested in the financial stability assessment of their counterparties.

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References

Altman E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance. 1968;23(4):589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x

Matenda F.R., Sibanda M., Chikodza E., Gumbo V. Corporate default risk modeling under distressed economic and financial conditions in a developing economy. Journal of Credit Risk. 2021;17(1):89-115. https://doi.org/10.21314/JCR.2020.267 DOI: https://doi.org/10.21314/JCR.2020.267

Fedorova E., Musienko S., Fedorov F. Analysis of the external factors influence on the forecasting of bankruptcy of Russian companies. Vestnik Sankt-Peterburgskogo universiteta. Ekonomika = St Petersburg University Journal of Economic Studies. 2020;36(1):117-133. (In Russ.). https://doi.org/10.21638/spbu05.2020.106 DOI: https://doi.org/10.21638/spbu05.2020.106

Grigoriev A., Tarasov K. Corporate bankruptcy prediction using the principal components method. Journal of Corporate Finance Research. 2019;13(4):20-38. https://doi.org/10.17323/j.jcfr.2073-0438.13.4.2019.20-38 DOI: https://doi.org/10.17323/j.jcfr.2073-0438.13.4.2019.20-38

Ohlson J.A. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research. 1980;18(1):109-131. https://doi.org/10.2307/2490395 DOI: https://doi.org/10.2307/2490395

Hunter J., Isachenkova N. Failure risk: A comparative study of UK and Russian firms. Journal of Policy Modeling. 2001;23(5):511-521. https://doi.org/10.1016/S0161-8938(01)00064-3 DOI: https://doi.org/10.1016/S0161-8938(01)00064-3

Gruszczyński M. Financial distress of companies in Poland. International Advances in Economic Research. 2004;10(4):249-256. https://doi.org/10.1007/BF02295137 DOI: https://doi.org/10.1007/BF02295137

Sirirattanaphonkun W., Pattarathammas S. Default prediction for small-medium enterprises in emerging market: Evidence from Thailand. Seoul Journal of Business. 2012;18(2):25-54. https://doi.org/10.35152/snusjb.2012.18.2.002 DOI: https://doi.org/10.35152/snusjb.2012.18.2.002

Ahmadpour Kasgari A., Divsalar M., Javid M.R., Ebrahimian S.J. Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses. Neural Computing and Applications. 2013;23(3-4):927-936. https://doi.org/10.1007/s00521-012-1017-z DOI: https://doi.org/10.1007/s00521-012-1017-z

Kovacova M., Kliestik T. Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. 2017;12(4):775-791. https://doi.org/10.24136/eq.v12i4.40 DOI: https://doi.org/10.24136/eq.v12i4.40

Odom M., Sharda R. A neural network model for bankruptcy prediction. In: 1990 IJCNN International joint conference on neural networks (San Diego, CA, 17-21 June 1990). Piscataway, NJ: IEEE; 163-168. https://doi.org/10.1109/IJCNN.1990.137710 DOI: https://doi.org/10.1109/IJCNN.1990.137710

Coats P.K., Fant L.F. Recognizing financial distress patterns using a neural network tool. Financial Management. 1993;22(3):142-155. https://doi.org/10.2307/3665934 DOI: https://doi.org/10.2307/3665934

Altman E.I., Marco G., Varetto F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance. 1994;18(3):505-529. https://doi.org/10.1016/0378-4266(94)90007-8 DOI: https://doi.org/10.1016/0378-4266(94)90007-8

Zhang G., Hu M.Y., Patuwo B.E., Indro D.C. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research. 1999;116(1):16-32. https://doi.org/10.1016/S0377-2217(98)00051-4 DOI: https://doi.org/10.1016/S0377-2217(98)00051-4

Kumar P.R., Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review. European Journal of Operational Research. 2007;180(1):1-28. https://doi.org/10.1016/j.ejor.2006.08.043 DOI: https://doi.org/10.1016/j.ejor.2006.08.043

Cao Y., Liu X., Zhai .J, Hua S. A two-stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance & Economics. 2022;27(1):455-472. https://doi.org/10.1002/ijfe.2162 DOI: https://doi.org/10.1002/ijfe.2162

Mselmi N., Lahiani A., Hamza T. Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis. 2017;50:67-80. https://doi.org/10.1016/j.irfa.2017.02.004 DOI: https://doi.org/10.1016/j.irfa.2017.02.004

Xie C., Luo C., Yu X. Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality & Quantity. 2011;45(3):671-686. https://doi.org/10.1007/s11135-010-9376-y DOI: https://doi.org/10.1007/s11135-010-9376-y

Shumway T. Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business. 2001;74(1):101-124. https://doi.org/10.1086/209665 DOI: https://doi.org/10.1086/209665

Fernando J.M.R., Li L., Hou G. Financial versus non-financial information for default prediction: Evidence from Sri Lanka and the USA. Emerging Markets Finance and Trade. 2020;56(3):673-692. https://doi.org/10.1080/1540496X.2018.1545644 DOI: https://doi.org/10.1080/1540496X.2018.1545644

Blanco-Oliver A., Irimia-Diéguez A., Oliver-Alfonso M., Wilson N. Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables. Finance a úvěr – Czech Journal of Economics and Finance. 2015;65(2):144-166. URL: http://journal.fsv.cuni.cz/storage/1321_blanco_oliver.pdf

Boubaker S., Cellier A., Manita R., Saeed A. Does corporate social responsibility reduce financial distress risk? Economic Modelling. 2020;91:835-851. https://doi.org/10.1016/j.econmod.2020.05.012 DOI: https://doi.org/10.1016/j.econmod.2020.05.012

Altman E.I., Sabato G., Wilson N. The value of non-financial information in SME risk management. Journal of Credit Risk. 2010;6(2):95-127. https://doi.org/10.21314/JCR.2010.110 DOI: https://doi.org/10.21314/JCR.2010.110

Muñoz‐Izquierdo N., Laitinen E.K., Camacho‐Miñano M del‐Mar, Pascual‐Ezama D. Does audit report information improve financial distress prediction over Altman’s traditional Z-Score model? Journal of International Financial Management & Accounting. 2020;31(1):65-97. https://doi.org/10.1111/jifm.12110 DOI: https://doi.org/10.1111/jifm.12110

Makeeva E., Sinilshchikova M. News sentiment in bankruptcy prediction models: Evidence from Russian retail companies. Journal of Corporate Finance Research. 2020;14(4):7-18. https://doi.org/10.17323/j.jcfr.2073-0438.14.4.2020.7-18 DOI: https://doi.org/10.17323/j.jcfr.2073-0438.14.4.2020.7-18

Feng M., Shaonan T., Chihoon L., Ling M. Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research. 2019;274(2):743-758. https://doi.org/10.1016/j.ejor.2018.10.024 DOI: https://doi.org/10.1016/j.ejor.2018.10.024

Beaver W.H. Financial ratios as predictors of failure. Journal of Accounting Research. 1966;4:71-111. https://doi.org/10.2307/2490171 DOI: https://doi.org/10.2307/2490171

Zhu Y., Xie C., Wang G.-J., Yan X.-G. Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance. Neural Computing and Applications. 2017;28(1):41-50. https://doi.org/10.1007/s00521-016-2304-x DOI: https://doi.org/10.1007/s00521-016-2304-x

Barboza F., Kimura H., Altman E. Machine learning models and bankruptcy prediction. Expert Systems with Applications. 2017;83:405-417. https://doi.org/10.1016/j.eswa.2017.04.006 DOI: https://doi.org/10.1016/j.eswa.2017.04.006

Brown I., Mues C. An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications. 2012;39(3):3446-3453. https://doi.org/10.1016/j.eswa.2011.09.033 DOI: https://doi.org/10.1016/j.eswa.2011.09.033

Karminsky A. Corporate rating models for emerging markets. Korporativnye finansy = Journal of Corporate Finance Research. 2011;5(3):19-29. (In Russ.). https://doi.org/10.17323/j.jcfr.2073-0438.5.3.2011.19-29 DOI: https://doi.org/10.17323/j.jcfr.2073-0438.5.3.2011.19-29

Grishunin S., Egorova A. Comparative analysis of the predictive power of machine learning models for forecasting the credit ratings of machine-building companies. Journal of Corporate Finance Research. 2022;16(1):99-112. https: //doi.org/10.17323/j.jcfr.2073-0438.16.1.2022.99-112 DOI: https://doi.org/10.17323/j.jcfr.2073-0438.16.1.2022.99-112

Kachalin D. Analysis of Russian models of splitting (reorganization) of business that ensure compliance of its scale with special taxation regime. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience. 2011;(5):52-63. (In Russ.).

Donich S.R. Novelties in the tax administration: The concept of splitting a business. Vestnik Sibirskogo gosudarstvennogo universiteta putei soobshcheniya: Gumanitarnye issledovaniya = The Siberian Transport University Bulletin: Humanitarian Research. 2021;(1):39-44. (In Russ.).

Williams C.C., Nadin S., Newton S., Rodgers P., Windebank J. Explaining off-the-books entrepreneurship: A critical evaluation of competing perspectives. International Entrepreneurship and Management Journal. 2013;9(3):447-463. https://doi.org/10.1007/s11365-011-0185-0 DOI: https://doi.org/10.1007/s11365-011-0185-0

Jaki A., Ćwięk W. Bankruptcy prediction models based on value measures. Journal of Risk and Financial Management. 2021;14(1):6. https://doi.org/10.3390/jrfm14010006 DOI: https://doi.org/10.3390/jrfm14010006

Jandaghi G., Saranj A., Rajaei R., Ghasemi A., Tehrani R. Identification of the most critical factors in bankruptcy prediction and credit classification of companies. Iranian Journal of Management Studies. 2021;14(4):817-934. https://doi.org/10.22059/IJMS.2021.285398.673712

Hosmer D.W. Jr., Lemeshow S., Sturdivant R.X. Introduction to the logistic regression model. In: Applied logistic regression. Hoboken, NJ: John Wiley & Sons, Inc.; 2013:1-33. (Wiley Series in Probability and Statistics). https://doi.org/10.1002/9781118548387.ch1 DOI: https://doi.org/10.1002/9781118548387.ch1

Hassanat A.B., Abbadi M.A., Altarawneh G.A., Alhasanat A.A. Solving the problem of the K parameter in the KNN classifier using an ensemble learning approach. International Journal of Computer Science and Information Security. 2014;12(8):33-39. https://doi.org/10.48550/arXiv.1409.0919

Breiman L. Random forests. Machine Learning. 2001;45(1):5-32. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324