Journal of Applied Economic Research
ISSN 2712-7435
Default Prediction for Russian Food Service Firms: Contribution of Non-Financial Factors and Machine Learning
Egor O. Bukharin 1,
Sofia I. Mangileva 2,
Vladislav V. Afanasev 1
1 Higher School of Economics, Saint-Petersburg, Russia
2 Yakov & Partners, Moscow, Russia
Abstract
The food service industry's instability due to COVID-19 and sanctions has heightened the need for developing an efficient tool to assess default risks in this industry. Default prediction modelling relies heavily on how well a model fits the specific environment. Due to that, some adjustments have to take place in order to adapt the classical default prediction models to the Russian food service industry. We build hypotheses that adding non-financial factors and employing modern prediction methods can increase the accuracy of the models significantly. The aim of this study is to determine the effect of non-financial factors' inclusion and modern modelling methods on the accuracy of default prediction for the food service industry in Russia. Tests for a sample of 1241 firms for the period from 2017 to 2021 have shown that creating a prediction model with modern methods, such as Random Forest and XGBoost increases the accuracy of the prediction from 70% to about 80%, compared to standard Logit model. The addition of non-financial factors to the models also increases the accuracy slightly, which however, does not provide a significant effect. The most important metrics in predicting default turned out to be Current Liquidity Ratio and the ratio of Working Capital to Total Assets. The most important non-financial factors are Total Assets and Age. Our results correspond with existing research in this field and form a new knowledge layer due to being applied to a specific industry. The results can be used by banks or other counterparties that interact with food service industry firms in order to assess their credit risk.
Keywords
default prediction; food service; non-financial factors; Machine Learning.
JEL classification
G32, G33, G21, C58References
1. Beaver, W. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, Vol. 4, 71–111. https://doi.org/10.2307/2490171
2. Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, Vol. 23, No. 4, 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
3. Taffler, R. (1982). Forecasting Company Failure in the UK Using Discriminant Analysis and Financial Ratio Data. Journal of the Royal Statistical Society. Series A (General), Vol. 145, No. 3, 342–358. https://doi.org/10.2307/2981867
4. Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Vol. 18, No. 1, 109–131. https://doi.org/10.2307/2490395
5. Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, Vol. 22, 59–82. https://doi.org/10.2307/2490859
6. Kwon, T., Lee, Y. (2018). Industry specific defaults. Journal of Empirical Finance, Vol. 45, 45–58. https://doi.org/10.1016/j.jempfin.2017.10.002
7. Pan, W.-T. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge Based Systems – KBS, Vol. 26, 69–74. https://doi.org/10.1016/j.knosys.2011.07.001
8. Brown, I., Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, Vol. 39, Issue 3, 3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033
9. Jaki, A., Ćwięk, W. (2021). Bankruptcy Prediction Models Based on Value Measures. Journal of Risk and Financial Management, Vol. 14, Issue 1, 6. https://doi.org/10.3390/jrfm14010006
10. Xie, C., Luo, C., Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: The case of Chinese listed companies. Quality & Quantity, Vol. 45, No. 3, 671–686. https://doi.org/10.1007/s11135-010-9376-y
11. Boubaker, S., Cellier, A., Manita, R., Saeed, A. (2020). Does corporate social responsibility reduce financial distress risk? Economic Modelling, Vol. 91, 835–851. https://doi.org/10.1016/j.econmod.2020.05.012
12. Iwanicz-Drozdowska, M., Laitinen, E.K., Suvas, A., Altman, E. (2016). Financial and nonfinancial variables as long-horizon predictors of bankruptcy. The Journal of Credit Risk, Vol. 12, No. 4, 49–78. https://doi.org/10.21314/JCR.2016.216
13. Lugovskaya, L. (2010). Predicting default of Russian SMEs on the basis of financial and non-financial variables. Journal of Financial Services Marketing, Vol. 14, Issue 4, 301–313. https://doi.org/10.1057/fsm.2009.28
14. Bhimani, A., Gulamhussen, M., Lopes, S. da R. (2013). The Role of Financial, Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction. European Accounting Review, Vol. 22, Issue 4, 739–763. https://doi.org/10.1080/09638180.2013.770967
15. Blanco-Oliver, A., Irimia-Dieguez, A., Oliver-Alfonso, M.D., Vázquez-Cueto, M.J. (2016). Hybrid model using Logit and nonparametric methods for predicting micro-entity failure. Investment Management and Financial Innovations, Vol. 13, 35–46. http://dx.doi.org/10.21511/imfi.13(3).2016.03
16. Altman, E., Sabato, G., Wilson, N. (2010). The value of non-financial information in SME risk management. Journal of Credit Risk, Vol. 6, 95–127. http://doi.org/10.21314/JCR.2010.110
17. Frank, R., Massy, W., Morrison, D. (1965). Bias in Multiple Discriminant Analysis. Journal of Marketing Research, Vol. 2, No. 3, 250–258. https://doi.org/10.2307/3150183
18. Wilson, R., Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, Vol. 11, No. 5, 545–557. https://doi.org/10.1016/0167-9236(94)90024-8
19. Altman, E., Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, Vol. 43, Issue 3, 332–357. https://doi.org/10.1111/j.1467-6281.2007.00234.x
20. Gruszczyński, M. (2004). Financial Distress of Companies in Poland. International Advances in Economic Research, Vol. 10, No. 4, 249–256. https://doi.org/10.1007/BF02295137
21. Hunter, J., Isachenkova, N. (2001). Failure risk: A comparative study of UK and Russian firms. Journal of Policy Modeling, Vol. 23, Issue 5, 511–521. https://doi.org/10.1016/S0161-8938(01)00064-3
22. Lin, L., Piesse, J. (2004). The identification of corporate distress in UK industrials: a conditional probability analysis approach. Applied Financial Economics, Vol. 14, Issue 2, 73–82. https://doi.org/10.1080/0960310042000176344
23. Sirirattanaphonkun, W., Pattarathammas, S. (2012). Default Prediction for Small-Medium Enterprises in Emerging Market: Evidence from Thailand. Seoul Journal of Business, Vol. 18, No. 2, 25–54. http://dx.doi.org/10.35152/snusjb.2012.18.2.002
24. Zhao, Y., Lin, D. (2023). Prediction of Micro- and Small-Sized Enterprise Default Risk Based on a Logistic Model: Evidence from a Bank of China. Sustainability, Vol. 15, Issue 5, 4097. https://doi.org/10.3390/su15054097
25. Mselmi, N. Lahiani, A., Hamza, T. (2017). Financial distress prediction: The case of French small and medium-sized firms. International Review of Financial Analysis, Vol. 50, 67–80. https://doi.org/10.1016/j.irfa.2017.02.004
26. Barboza, F., Kimura, H., Altman, E. (2017). Machine Learning models and bankruptcy prediction. Expert Systems with Applications, Vol. 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006
27. Wu, Y., Gaunt, C., Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, Vol. 6, Issue 1, 34–45. https://doi.org/10.1016/j.jcae.2010.04.002
28. Fedorova, E., Ledyaeva, S., Drogovoz, P., Nevredinov, A. (2022). Economic policy uncertainty and bankruptcy filings. International Review of Financial Analysis, Vol. 82, 102174. https://doi.org/10.1016/j.irfa.2022.102174
29. Situm, M. (2023). Financial distress in the Austrian tourism industry: hotels and restaurants analysis. European Journal of Tourism Research, Vol. 34, 3411. https://doi.org/10.54055/ejtr.v34i.2992
30. Kim, S., Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, Vol. 36, 354–362. https://doi.org/10.1016/j.econmod.2013.10.005
31. Gu, Z. (2002). Analyzing bankruptcy in the restaurant industry: A multiple discriminant model. International Journal of Hospitality Management, Vol. 21, Issue 1, 25–42. https://doi.org/10.1016/S0278-4319(01)00013-5
32. Breiman, L. (2001). Random Forests. Machine Learning, Vol. 45, No. 1, 5–32. https://doi.org/10.1023/A:1010933404324
33. Chen, T., Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 785–794. https://doi.org/10.1145/2939672.2939785
34. Kazakov, A., Kolyshkin, A. (2018). Bankruptcy prediction models development in modern Russian conditions. St Petersburg University Journal of Economic Studies, Vol. 34, No. 2, 241–266. (In Russ.). https://doi.org/10.21638/11701/spbu05.2018.203
35. Karminsky, A., Burekhin, R. (2019). Comparative analysis of methods for forecasting bankruptcies of Russian construction companies. Business Informatics, Vol. 13, No. 3, 52–66. https://doi.org/10.17323/1998-0663.2019.3.52.66
36. Afanasev, V., Tarasova, Y. (2022). Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data. Financial Journal, Vol. 14, No. 6, 91–110. https://doi.org/10.31107/2075-1990-2022-6-91-110
37. Menardi, G., Torelli, N. (2012). Training and assessing classification rules with unbalanced data. Data Mining and Knowledge Discovery, Vol. 28, 92–122. https://doi.org/10.1007/s10618-012-0295-5
38. Becerra-Vicario, R., Alaminos, D., Aranda, E., Fernández-Gámez, M.A. (2020). Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry. Sustainability, Vol. 12, Issue 12, 5180. https://doi.org/10.3390/su12125180
39. Fernando, J., Li, L., Hou, G. (2020). Financial versus Non-Financial Information for Default Prediction: Evidence from Sri Lanka and the USA. Emerging Markets Finance and Trade, Vol. 56, Issue 3, 673–692. https://doi.org/10.1080/1540496X.2018.1545644
40. Makeeva, E., Sinilshchikova, M. (2020). News Sentiment in Bankruptcy Prediction Models: Evidence from Russian Retail Companies. Journal of Corporate Finance Research, Vol. 14, No. 4, 7–18. https://doi.org/10.17323/j.jcfr.2073-0438.14.4.2020.7-18
About Authors
Egor Olegovich Bukharin
Master Student, Saint-Petersburg School of Economics and Management, National Research University Higher School of Economics, Saint-Petersburg, Russia (190121, Saint-Petersburg, Soyuza Pechatnikov street, 16); ORCID https://orcid.org/0009-0004-3135-5469 e-mail: egorbukharin01@gmail.com
Sofia Igorevna Mangileva
Senior Research Analyst, «Yakov & Partners», Moscow, Russia (125047, Moscow, Lesnaya street, 5, Building С); ORCID https://orcid.org/0009-0001-9574-1834 e-mail: sofiamangileva@gmail.com
Vladislav Viktorovich Afanasev
Lecturer, Department of Finance, Saint-Petersburg School of Economics and Management, National Research University Higher School of Economics, Saint-Petersburg, Russia (190121, Saint-Petersburg, Soyuza Pechatnikov street, 16); ORCID https://orcid.org/0000-0002-4041-4465 e-mail: vvafanasev@hse.ru
For citation
Bukharin, E.O., Mangileva, S.I., Afanasev, V.V. (2024). Default Prediction for Russian Food Service Firms: Contribution of Non-Financial Factors and Machine Learning. Journal of Applied Economic Research, Vol. 23, No. 1, 206-226. https://doi.org/10.15826/vestnik.2024.23.1.009
Article info
Received June 30, 2023; Revised December 5, 2023; Accepted January 12, 2024.
DOI: https://doi.org/10.15826/vestnik.2024.23.1.009
Download full text article:
~578 KB, *.pdf
(Uploaded
11.03.2024)
Created / Updated: 2 September 2015 / 20 September 2021
© Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»
Remarks?
select the text and press:
Ctrl + Enter
Portal design: Artsofte
Contact us
Rector's Office
Rector, Dr. Victor Koksharov
Tel. +7 (343) 375-45-03, e-mail: rector@urfu.ru
Vice-Rector for International Relations, Dr. Maxim Khomyakov
Tel. +7 (343) 375-46-27, e-mail: Maksim.Khomyakov@urfu.ru