Разработка моделей прогнозирования банкротства в современных российских условиях

  • Александр Валерьевич Казаков Санкт-Петербургский государственный университет, Российская Федерация, 199034, Санкт-Петербург, Университетская наб., 7–9 https://orcid.org/0000-0003-0279-9920
  • Александр Викторович Колышкин Российский государственный педагогический университет им. А. И. Герцена, Российская Федерация, 191186, Санкт-Петербург, наб. реки Мойки, 48 https://orcid.org/0000-0002-7551-3391

Аннотация

В статье рассматриваются проблемы прогнозирования банкротства в России. Данный вопрос приобретает все большую актуальность в последние годы в связи с падением доходов населения. Для прогнозирования банкротства предприятий широко применяются модели прогнозирования банкротства, однако в силу ряда ограничений они могут иметь низкую точность. Поэтому в зарубежной литературе идет активное обсуждение путей улучшения качества данного метода. В России наблюдается рост числа созданных моделей, но на данный момент не было обширных исследований, оценивающих их эффективность. Среди целей исследования были поставлены: детальный обзор отечественной литературы по прогнозированию банкротства с целью оценить точность и выявить недостатки существующих моделей; построение нового набора моделей с учетом проанализированных недостатков; выдвижение предложений по дальнейшему усовершенствованию моделей для будущих исследований. Проведенный анализ моделей выявил их неэффективность. В качестве причин этого были выделены: проблема стационарности данных, низкое качество бухгалтерской отчетности и недостаточный объем данных, используемых для построения моделей, а также негативное влияние практики манипуляций с бухгалтерской отчетностью и криминальных банкротств. Был создан набор моделей, который оказался эффективным (точность предсказаний около 70 %) и устойчивым во времени. Кроме того, был предложен способ повышения качества моделей прогнозирования путем учета всех возможных сценариев несостоятельности предприятия (включая ликвидацию, продажу и приостановку деятельности) методами статистической классификации. В завершение предлагается использовать анализ по закону Бенфорда для выявления групп предприятий, осуществляющих манипуляции с отчетностью. Проведенная оценка данного метода показала его пригодность для этих целей.

Ключевые слова:

прогнозирование банкротства, финансовая несостоятельность, антикризисное управление, логит-регрессия

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Биографии авторов

Александр Валерьевич Казаков, Санкт-Петербургский государственный университет, Российская Федерация, 199034, Санкт-Петербург, Университетская наб., 7–9

аспирант

Александр Викторович Колышкин, Российский государственный педагогический университет им. А. И. Герцена, Российская Федерация, 191186, Санкт-Петербург, наб. реки Мойки, 48

кандидат экономических наук

Литература

Литература на русском языке

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Колышкин А. В., Гиленко Е. В., Довженко С. Е., Жилкин С. А., Чое С. Е. Прогнозирование финансовой несостоятельности предприятий // Вестник Санкт-Петербургского университета. Серия 5. Экономика. 2014. Вып. 2. С. 122–142.

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References in Latin Alphabet

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Translation of references in Russian into English

Abbakumov V. L., Lezina T. A. Biznes-analiz informatsii. Statisticheskie metody [Analysis of Business Information. Statistical methods]. St. Petersburg, Economics Publ., 2009. 374 p. (In Russian)

Agarwal V., Taffler R. Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 2008, vol. 32, iss. 8, pp. 1541–1551.

Alaka H. A., Oyedele L. O., Owolabi H. A. Methodological approach of construction business. Construction Management and Economics, 2016, iss. 34, no 11, pp. 808–842.

Altman E. I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 1968, vol. 23, no. 4, pp. 589–609.

Balcaen S., Manigart S., Ooghe H. From distress to exit: determinants of the time to exit. Journal of Evolutionary Economics, 2011, pp. 407–446.

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Beaver W. H. Financial ratios as predictors of failure. Journal of Accounting Research, 1966, no. 4, pp. 71–111.

Bellovary J., Giacomino D., Akers M. A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 2007, vol. 33, pp. 34–56.

Benford F. The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 1938, vol. 78, pp. 551–572.

Betts J., Belhoul D. The effectiveness of incorporating stability measures in company failure models. Journal of Business Finance and Accounting, 1987, vol. 14, iss. 3, pp. 323–334.

Bharath S., Shumway T. Forecasting default with the KMV-Merton model (December 17, 2004). AFA 2006 Boston Meetings Paper. 36 p. Available at: https://ssrn.com/abstract=637342 (accessed: 22.03.2017).

Blum M. Failing company discriminant analysis. Journal of Accounting Research, 1974, vol. 12, no. 1, pp. 1–25.

Campbell J. Y., Hilscher J., Szilagyi J. In search of distress risk. Journal of Finance, 2008, vol. 63., iss. 6, pp. 2899–2939.

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Doumpos M., Zopoudinis C. A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal, 1999, vol. 3, no. 2, pp. 71–101.

Du Jardin P. Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems with Applications, 2017, vol. 75, pp. 25–43.

Durtschi C., Hillison W. The Effective Use of Benford’s Law to Assist in Detecting Fraud in Accounting Data. Journal of Forensic Accounting, 2004, vol. 5, pp. 17–34.

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Hoffmann P. S., Bertin M. J. Firm Size as Determinant of the Nonlinear Relationship Between Bank Debt and Growth Opportunities: The Case of Chilean Public Firms. Emerging Markets Finance & Trade, 2014, vol. 50, iss. 1, pp. 265–293.

Jones F. L. Current techniques in bankruptcy prediction. Journal of Accounting Literature, 1987, vol. 6, pp. 131–164.

Kahya E., Theodossiou P. Predicting corporate financial distress: a time-series CUSUM methodology. Review of Quantitative Finance and Accounting, 1999, vol. 13, iss. 4, pp. 323–345.

Kealhofer S. Quantifying credit risk I: Default prediction. Financial Analysts Journal, 2003, vol. 59, iss. 1, pp. 30–44.

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Mensah Y. M. An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study. Journal of Accounting Research, 1984, vol. 22, iss. 1, pp. 380–395.

Oderda G., Dacorogna M. M., Jung T. Credit risk models — Do they deliver their promises? A quantitative assessment. Economic Notes, 2003, vol. 32, iss. 2, pp. 177–195.

Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 1980, vol. 18, iss. 1, pp. 109–131.

Ooghe H., Balcaen S. Are Failure Prediction Models Transferable From One Country To Another? An Empirical Study Using Belgian Financial Statements. Working Paper Series. Faculty of Economics and Business Administration. Ghent, Ghent University, 2002. 60 p. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.482.7221&rep=rep1&type=pdf (accessed: 28.02.2017).

Platt H. D., Platt M. B. A note on the use of industry-relative ratios in bankruptcy prediction. Journal of Banking and Finance, 1991, vol. 15, iss. 6, pp. 1183–1194.

Platt H. D., Platt M. B. Predicting corporate financial distress: reflections on choice-based sample bias. Journal of Economics and Finance, 2002, vol. 26, iss. 2, pp. 184–199.

Reisz A. S., Perlich C. A market-based framework for bankruptcy prediction. Journal of Financial Stability, 2007, vol. 3, iss. 2, pp. 85–131.

Richardson F. M., Davidson L. F. On Linear Discrimination With Accounting Ratios. Journal of Business Finance and Accounting, 1984, vol. 11, iss. 4, pp. 511–525.

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Serrasqueiro Z., Nunes P. M., Leitao J., Armada M. Are there non-linearities between SME growth and its determinants? A quantile approach. Industrial and Corporate Change, 2010, vol. 19, iss. 4, pp. 1071–1108.

Taffler R. J. Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of Royal Statistical Society. Series A, 1982, vol. 145, iss. 3, pp. 342–358.

Tam Cho W. K., Gaines B. J. Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance. The American Statistician, 2007, vol. 61, iss. 3, pp. 218–223.

Tamari M. Financial ratios as a means of forecasting bankruptcy. Management International Review, 1966, vol. 6, iss. 4, pp. 15–21.

Tsenzharik M. Benford’s Law As a Tool For Detecting of Financial Statements Falsification. Oil, Gas & Energy Quarterly, 2013, vol. 61, iss. 3, pp. 487–496.

Ward T. J., Foster B. P. A note on selecting a response measure for financial distress. Journal of Business Finance and Accounting, 1997, vol. 24, iss. 6, pp. 869–879.

Wiklund J., Shepherd D. Where to from here: EO as experimentation, failure, and distribution of outcomes. Entrepreneurship Theory and Practice, 2011, vol. 35, pp. 925–946.

Zavgren C. The prediction of corporate failure: the state of the art. Journal of Accounting Literature, 1983, vol. 2, pp. 1–37.

Zavgren C. V., Friedman G. E. Are Bankruptcy Prediction Models Worthwhile? An Application in Securities Analysis. Management International Review, 1988, vol. 28, no. 1, pp. 34–44.

Zmijewski M. E. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 1984, vol. 22, pp. 59–82.

Опубликован
2018-06-29
Как цитировать
Казаков, А. В., & Колышкин, А. В. (2018). Разработка моделей прогнозирования банкротства в современных российских условиях. Вестник Санкт-Петербургского университета. Экономика, 34(2), 241-266. https://doi.org/10.21638/11701/spbu05.2018.203
Раздел
Корпоративные финансы и учет