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Methods of forecasting insolvency: problems and prospects


Boyko I.P., Kazakov A.V., Kolyshkin A.V.
(about the authors)

Boyko Ivan Petrovich –

Kazakov Aleksandr Valerevich –

Kolyshkin Aleksandr Viktorovich –

Published in:
Russian Journal of Entrepreneurship
– Volume 18, Number 8 (April 2017)



Keywords: crisis management, financial inconsistency, forecasting bankruptcy, logit-regression


Citation:
Boyko I.P., Kazakov A.V., Kolyshkin A.V. (2017). Methods of forecasting insolvency: problems and prospects. Russian Journal of Entrepreneurship, 18(8), 1313-1326. doi: 10.18334/rp.18.8.37770


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Abstract:

National and foreign economists study the problem of forecasting bankruptcy. At the same time there is no method that predicts the onset of bankruptcy in the medium term with sufficient accuracy. In order to solve this problem foreign economists suggest using the indexes, characterizing external economic conditions, age of firm and others. On the other hand we should clearly distinguish the concepts of "bankruptcy" and "insolvency" and direct models towards prediction of the very insolvency because it has an economic nature. In addition, there are statistical methods that can overcome the shortcomings of the conventional logistic regression. The article reviews current prospective approaches to improving the accuracy of forecasting insolvency. The paper also gives recommendations to domestic researchers.








References:
Altman E., Iwanicz-Drozdowska M. (2016). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model Journal of International Financial Management & Accounting.
Altman E.I., Haldema, R., Narayanan P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations Banking Finance. (1). 29-51.
Balcaen S., Manigart S., Ooghe H. (2011). From distress to exit: determinants of the time to exit Journal of Evolutonary Economics. 407-445.
Balcaen S., Ooghe H. (2006). 5 years of studies on business failure: an overview of the classic statistical methodologies and their related problems British Accounting Review. 63-93.
Bardos M. (2007). What is at stake in the construction and use of credit scores? Computational Economics. (29). 159-172.
Barinova V., Radnabazarova S., Sorokina A. (2014). Bystrorastuschie kompanii v Rossii: analiz statisticheskikh dannyh i rezultaty keys-stadi [Rapid-growing companies in Russia: analysis of statistic data and case-study results ]. Drukerovskiy vestnik. (3). 112-129. (in Russian).
Betts J., Belhoul D. (1987). The effectiveness of incorporating stability measures in company failure models Journal of Business Finance and Accounting. (3(14)). 323-334.
Bogomolova I., Plekanova I., Yuyukin A. (2016). Covremennye podkhody k prognozirovaniyu bankrotstva predpriyatiy [Modern approaches to forecasting bankruptcy of enterprises]. Journal of Economy and Entrepreneurship. (5(16)). 1125-1131. (in Russian).
Cox D.R. (1972). Regression Models and Life-Tables Journal of the Royal Statistical Society. (34).
Dambolena I., Khoury S. (1980). Ratio stability and corporate failure Journal of Finance. (4(33)). 1017-1026.
Davis A., Huang X. (2004). The stock performance of firms emerging from Chapter 11 and accidental bankruptcy Paper presented at the FMA Meeting. 6-9.
Fyodorova E., Dovzhenko S., Fyodorov F. (2016). Modeli prognozirovaniya nesostoyatelnosti rossiyskikh predpriyatiy: otraslevye osobennosti [Bankruptcy-prediction models for Russian enterprises: specific sector-related characteristics]. Problemy prognozirovaniya. (3(156)). 32-40. (in Russian).
Hill N., Perry S., Andes S. (1996). Evaluating firms in financial distress: an event history analysis Journal of Applied Business Research,. (3(12)). 60-71.
Khaydarshina G. (2009). Sovershenstvovanie metodov otsenki riska bankrotstva rossiyskikh predpriyatiy v sovremennyh usloviyakh [Improving methods for assessing risk of bankruptcy of russian enterprises under modern conditions]. Imuschestvennye otnosheniya v rossiyskoy federatsii. (5). 86-95. (in Russian).
Kolyshki, A.V., Gilenko E.V., Dovzhenko S.E., Zhilkin S.A., Choe S.E. (2014). Prognzirovanie finansovoy nesostoyatelnosti predpriyatiy [Forecasting the financial insolvency of enterprises]. Vestnik SPbGU. Ser. 5. 122-142. (in Russian).
Lugovskaya L. (2010). Predicting default of Russian SMEs on the basis of financial and non-financial variables Journal of Financial Services Marketing. (4(14)). 301-313.
Luoma M., Laitinen E. (1991). Survival analysis as a tool for company failure prediction Omega International Journal of Management Science. (6(19)). 673-678.
Macas Nunes P., Goncalves M. (2013). The influence of age on SMEs' growth determinants: empirical evidence Small Business Economics. (2(40)). 249-272.
Makeeva E., Neretina E. (2013). The Prediction of Bankruptcy in a Construction Industry of Russian Federation Journal of Modern Accounting and Auditing. (2(9)). 256-271.
Mensah Y. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study Journal of Accounting Research,. (1(22)). 380-395.
Moreno A., Casillas J. (2007). High-growth SMEs versus non-high growth SMEs: a discriminant analysis Entrepreneurship and Regional Development. (19). 69-88.
Moses D., Liao S. S. (1987). On developing models for failure prediction Journal of Commercial Bank Lending. (69). 27*38.
Ooghe H., Spaenjers C.,Vandermoere P. (2009). Business Failure Prediction: Simple-Intuitive Models Versus Statistical Models IUP Journal of Business Strategy. 7-44.
Platt H., Platt M. (1990). Development of a class of stable predictive variables: the case of bankruptcy prediction Journal of Business Finance & Accounting. (1(17)). 31-51.
Platt H., Platt M. (1991). A note on the use of industry-relative ratios in bankruptcy prediction Journal of Banking and Finance. (15). 1183-1194.
Rickne A. (2006). Connectivity and performance of science-based firms Small Business Economics. (26). 393-407.
Shirokova G., Shatalov A. (2008). Faktory rosta rossiyskikh predprinimatelskikh firm: rezultaty empiricheskogo analiza [Factors determining growth of Russian entrepreneurial firms: results of empirical analysis] (in Russian).

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