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Analysis of bankruptcy factors of the Russian developers
 
PIIS042473880018089-6-1
DOI10.31857/S042473880018089-6
Publication type Article
Status Published
Authors
Anastasia Kosyakina 
Occupation: Junior researcher at Laboratory of Socio-Economic Problems of Regulation
Affiliation: Institute for control and supervision, Russian Presidential Academy of National Economy and Public Administration
Address: 82, Vernadsky Ave., Moscow, 119571, Russian Federation
Journal nameEkonomika i matematicheskie metody
EditionVolume 58 Issue 4
Pages92-101
Abstract

In 2019, there was a transition to project financing of housing construction in Russia. As a result, by the end of 2021, more than 73% of the area of apartments under construction are sold using escrow accounts. However, about 21% of the apartments being sold are being built by developers who are in the process of bankruptcy. Regional primary housing markets are, firstly, segmented, and secondly, in some regions – mainly in the regions of the Far North, Eastern Siberia and the Far East – there is a high concentration of developers, which can lead to additional negative consequences in terms of the development of housing markets of these regions in case of bankruptcy of regional companies. In this situation, it is important to form additional tools for assessing the risks of bankruptcy of companies in order to be able to prevent negative consequences in a timely manner in the future. Despite the fact that a large number of scientific publications of both foreign and domestic researchers have been devoted to the issues of modeling the risks of bankruptcy of firms, only a small number of authors have addressed the issues of assessing the financial stability of Russian developers. The novelty of this study consists in constructing a model for predicting the probability of bankruptcy of developers in Russia (that is, a risk management system) based on an original database, which, in addition to financial coefficients from companies' reports, also includes industry-specific indicators. The aim of the work is to build a quantitative model of bankruptcy risks of developers (in the short and medium term) based on easily observable characteristics of their activities. The results obtained show that the probability of bankruptcy is high for companies with low values of return on assets, asset turnover ratio and current liquidity ratio. Additional factors that reduce the likelihood of bankruptcy are the fact of the developer's group membership, experience in housing construction. Also, the financial stability of companies in the industry as a whole is affected by macroeconomic shocks.

Keywordshousing construction, probability of bankruptcy, financial stability, construction companies, developers' operations, logit models, project financing, risk-oriented regulation
AcknowledgmentThe article was prepared as part of the research work of the state assignment of the RANEPA
Received24.12.2021
Publication date07.12.2022
Number of characters42636
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1. REFERENCES (with English translation or transliteration)

Afik, Z., Arad, O., Galil, K. (2016). Using Merton model for default prediction: An empirical assessment of selected alternatives // Journal of Empirical Finance. Vol. 35, P. 43-67. DOI: https://doi.org/10.1016/j.jempfin.2015.09.004 
2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy // The Journal of Finance. Vol. 23. No. 4. P. 589-609. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x 
3. Balcaen, S., Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems // The British Accounting Reviev. Vol. 38. No. 1. P. 63-93. DOI: https://doi.org/10.1016/j.bar.2005.09.001 
4. Beaver, W. (1966). Financial ratios as predictors of failure // Journal of accounting research. Vol. 71-111. DOI: https://doi.org/10.2307/2490171 
5. Chava, S., Jarrow, R. A. (2004). Bankruptcy prediction with industry effects // Review of Finance. Vol. 8. No. 4. P. 537-569. DOI: https://doi.org/10.1093/rof/8.4.537 
6. Danenas, P., Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain // Expert Systems with Applications. Vol. 42. No. 6. P. 3194-3204. DOI: https://doi.org/10.1016/j.eswa.2014.12.001 
7. Demeshev, B., Tikhonova, A. (2014). Default Prediction for Russian Companies: Intersectoral Comparison // HSE Economic Journal. Vol. 18. No. 3. P. 359-386. (in Russian).
8. Ding, A. A., Tian, S., Yu, Y., Guo, H. (2012). A class of discrete transformation survival models with application to default probability prediction // Journal of the American Statistical Association. Vol. 107. No. 499. P. 990-1003. DOI: https://doi.org/10.1080/01621459.2012.682806 
9. Drobyshevsky, S., Zubarev, A. (2011). Sustainability of Russian Banks in 2007-2009 (p. 108). Moskva: Gaidar Inst.
10. Du, Jardine. P. (2010). Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy // Neurocomputing. Vol. 73. No. 10-12. P. 2047-2060. DOI: https://doi.org/10.1016/j.neucom.2009.11.034 
11. Fedorova, E., Timofeev, Ya. (2015). Developing the bankruptcy prediction models for russian businesses of the construction and agriculture industries // Finance and Credit. Vol. 32. No. 656. P. 2-10. (in Russian).
12. Gepp, A., Kumar, K. (2012). Business failure prediction using statistical techniques: A review // Some recent developments in statistical theory and applications. P. 1-25.
13. Hillegeist, S. A., Keating, E. K., Cram, D. P., Lundstedt, K. G. (2004). Assessing the probability of bankruptcy // Review of accounting studies. Vol. 9. No. 1. P. 5-34. DOI: https://doi.org/10.1023/B:RAST.0000013627.90884.b7 
14. Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression // Journal of Retailing and Consumer Services. Vol. 36. P. 197-202. DOI: https://doi.org/10.1016/j.jretconser.2017.02.005 
15. Karas, M., Srbov?, P. (2019). Predicting bankruptcy in construction business: Traditional model validation and formulation of a new model // Journal of International studies. Vol. 12. No. 1. P. 283-296. DOI: https://doi.org/10.14254/2071-8330.2019/12-1/19 
16. Lee, S., Choi, W. S. (2013). A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis // Expert Systems with Applications. Vol. 40. No. 8. P. 2941-2946. DOI: https://doi.org/10.1016/j.eswa.2012.12.009 
17. Lensberg, T., Eilifsen, A., McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming // European Journal of operational research. Vol. 169. No. 2. P. 677-697. DOI: https://doi.org/10.1016/j.ejor.2004.06.013 
18. Lin, T. H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models // Neurocomputing. Vol. 72. No. 16-18. P. 3507-3516. DOI: https://doi.org/10.1016/j.neucom.2009.02.018 
19. Makeeva, E., Bakurova, A. (2012). Forecasting Bankruptcy Oil and Gas Companies Using Neural Networks // Journal of Corporate Finance Research. Vol. 6. No. 3. P. 22-29. (in Russian).  DOI: https://doi.org/10.17323/j.jcfr.2073-0438.6.3.2012.22-30 
20. Makeeva, E. U., Neretina, E. A. (2013a). A binary model versus discriminant analysis relating to corporate bankruptcies: the case of Russian construction industry // Journal of Accounting, Finance and Economics. Vol. 3. No. 1. P. 65-76.
21. Makeeva, E., Neretina, E. (2013b). The prediction of bankruptcy in a construction industry of Russian Federation // Journal of Modern Accounting and Auditing. Vol. 9. No. 2. P. 256-271.
22. Martin, D. (1977). Early warning of bank failure: A logit regression approach // Journal of banking & finance. Vol. 1. No. 3. P. 249-276. DOI: https://doi.org/10.1016/0378-4266 (77)90022-X
23. Min, S. H., Lee, J., Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction // Expert systems with applications. Vol. 31. No. 3. P. 652-660. DOI: https://doi.org/10.1016/j.eswa.2005.09.070 
24. Mogilat, A. (2019). Modelling financial distress of Russian industrial companies, or What bankruptcy analysis can tell // Voprosy Ekonomiki. No. 3. P. 101-118. (in Russian). DOI: https://doi.org/10.32609/0042-8736-2019-3-101-118 
25. Odom, M. D., Sharda, R. (1990). A neural network model for bankruptcy prediction // IJCNN International Joint Conference on neural networks. P. 163-168. DOI: https://doi.org/10.1109/IJCNN.1990.137710 
26. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy // Journal of accounting research. Vol. 18. No. 1. P. 109-131. DOI: https://doi.org/10.2307/2490395 
27. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model // The journal of business. Vol. 74. No. 1. P. 101-124. DOI: https://doi.org/10.2139/ssrn.171436 
28. Slowinski, R., Zopounidis, C. (1995). Application of the rough set approach to evaluation of bankruptcy risk // Intelligent Systems in Accounting, Finance and Management. Vol. 4. No. 1. P. 27-41. DOI: https://doi.org/10.1002/j.1099-1174.1995.tb00078.x 
29. Sternik, S., Sternik, G. (2018). Methods of forecasting the input volume in the local market of housing construction and sales // Russian Journal of Housing Research. Vol. 5. No. 2. P. 131-151. (in Russian).
30. Zhdanov, V., Afanasyeva, O. (2011). Bankruptcy Risk Diagnostics Model for Aviation Enterprises // Journal of Corporate Finance Research. Vol. 20. No. 4. P. 77Ð89. (in Russian). DOI: https://doi.org/10.17323/j.jcfr.2073-0438.5.4.2011.77-89 
31. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models // Journal of Accounting research. Vol. 22. P. 59-82. DOI: https://doi.org/10.2307/2490859

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