Subject. It is very important for corporate governance and a choice of partners to evaluate the company’s position. Therefore, bankruptcy forecast methods have been actively studied in theoretical and practical proceedings for a long time. Recurring crises and high market dynamics make the subject especially relevant. Objectives. I develop the instrumental method based on machine learning to predict corporate bankruptcy. The study also reviews data sources, the potential of forecasting models, and chooses inputs for company analysis. Methods. I applied methods of analysis and synthesis, and the systematization, formalization, comparative analysis. I referred to theoretical and methodological principles set forth in national and foreign proceedings on the company analysis and bankruptcy prediction. I investigate issues of data compilation, and building the artificial neural network for teaching the model. Results. I proposed and tested the instrumental method to predict bankruptcy. I suggest using my own sets of indicators for forecasting, which I selected by analyzing key indicators of financial sustainability, efficacy, and key external factors influencing market actors. The article presents a data sample for teaching purposes, which includes both the Russian and foreign companies, thus expanding its size. I devised machine learning models generating high-precision forecasts. Conclusions and Relevance. The findings contribute to bankruptcy prediction methods and can be used for administrative decision-making to automate their own analysis or analyze other entities, which the company cooperates with.
Keywords: bankruptcy forecast, machine learning, company analysis, artificial neural networks
Altman E.I. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 1968, vol. 23, iss. 4, pp. 589–609. URL: Link
Almaskati N., Bird R., Yeung D., Lu Y. A Horse Race of Models and Estimation Methods for Predicting Bankruptcy. Advances in Accounting, 2021, vol. 52, 100513. URL: Link
Gorbachev A.S., Drogovoz P.A. [Forecasting as a tool for advanced development of technological competencies in industry]. Kreativnaya ekonomika = Creative Economy, 2020, vol. 14, no. 12, pp. 3427–3438. (In Russ.) URL: Link
Drogovoz P.A., Rassomgin A.S. [Review of modern methods of data analisys and their usage for management problem solving]. Ekonomika i predprinimatel'stvo = Journal of Economy and Entrepreneurship, 2017, no. 3-1, pp. 689–693. (In Russ.)
Hebb D.O. The Organization of Behavior: A Neuropsychological Theory. Wiley, 1949.
Callan R. Osnovnye kontseptsii neironnykh setei [The Essence of Neural Networks]. Moscow, Vil'yams Publ., 2017, 288 p.
Gorbachevskaya E.N., Krasnov S.S. [The history of the development of neural networks]. Vestnik volzhskogo universiteta im. V.N. Tatishcheva = Vestnik of Volzhsky University after V.N. Tatishchev, 2015, no. 1, pp. 52–56. (In Russ.)
Haykin S. Neironnye seti: polnyi kurs [Neural Networks: A Comprehensive Foundation]. 2nd ed. Moscow, Vil'yams Publ., 2006, 1104 p.
Luger D.F. Iskusstvennyi intellekt: strategii i metody resheniya slozhnykh problem [Artificial Intelligence: Structures and Strategies for Complex Problem Solving]. Moscow, Vil'yams Publ., 2005, 864 p.
Debok G., Kohonen T. Analiz finansovykh dannykh s pomoshch'yu samoorganizuyushchikhsya kart [Russian edition. The analysis of financial data through self-organizing maps]. Moscow, Al'pina Pablisher Publ., 2001, 317 p.
Krasnov M.A. [The method for predicting the dynamics of financial time series in investing]. Terra Economicus, 2009, vol. 7, no. 1-2, pp. 93–98. URL: Link (In Russ.)
Kohonen T. Self-Organizing Maps. NY, Springer-Verlag, 2001, 502 p.
Silva B., Marques N. Ubiquitous Self-Organizing Map: Learning Concept-Drifting Data Streams. In: Rocha A., Correia A., Costanzo S., Reis L. (eds) New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, Springer, Cham, 2015, vol. 353. URL: Link
Zagoruiko N.G., Kutnenko O.A. [Training dataset censoring]. Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika = Tomsk State University Journal of Control and Computer Science, 2013, no. 1, pp. 66–73. URL: Link (In Russ.)
Bishop C.M., Svensen M., Williams C.K.I. Developments of the Generative Topographic Mapping. Neurocomputing, 1998, vol. 21, iss. 1-3, pp. 203–224. URL: Link00043-5
Hochreiter S., Schmidhuber J. Long Short-term Memory. Neural Computation, 1997, vol. 9, iss. 8, pp. 1735–1780. URL: Link
Gorbatkov S.A. et al. Metodologicheskie osnovy razrabotki neirosetevykh modelei ekonomicheskikh ob"ektov v usloviyakh neopredelennosti [Methodological foundations for the development of neural network models of economic objects in conditions of uncertainty]. Moscow, Ekonomicheskaya gazeta Publ., 2012, 494 p.
Koroteev M.V. [Review of some contemporary trends in machine learning technology]. E-Management, 2018, vol. 1, no. 1, pp. 26–35. (In Russ.) URL: Link 10.26425/2658-3445-2018-1-26-35
Bughin J., Hazan E., Ramaswamy S. et al. Artificial Intelligence: The Next Digital Frontier? McKinsey Global Institute, 2017, 80 p.
Drogovoz P.A., Koren'kova D.A. [Modern tools for agile management of IT projects and prospects for its improvement using artificial intelligence technologies]. Ekonomika i predprinimatel'stvo = Journal of Economy and Entrepreneurship, 2019, no. 10, pp. 829–833. (In Russ.)
Bikonov D.V., Brazhkin A.A., Puzikov A.D. et al. [High-level parallel programming system for multicore hybrid processor]. Nanoindustriya = Nanoindustry, 2020, vol. 13, no. S4, pp. 94–96. (In Russ.) URL: Link
Makarov V.L., Bakhtizin A.R., Beklaryan G.L. [Developing digital twins for production enterprises]. Biznes-informatika = Business Informatics, 2019, vol. 13, no. 4, pp. 7–16. (In Russ.) URL: Link
Kaftannikov I.L., Parasich A.V. [Problems of training sample formation in machine learning problems]. Vestnik Yuzhno-ural'skogo gosudarstvennogo universiteta. Seriya: Komp'yuternye tekhnologii, upravlenie, radioelektronika = Bulletin of South Ural State University. Series: Computer Technologies, Automatic Control, Radioelectronics, 2016, vol. 16, no. 3, pp. 15–24. (In Russ.)
Santoro E., Gaffeo E. Business Failures, Macroeconomic Risk and the Effect of Recessions on Long-Run Growth: A Panel Cointegration Approach. Journal of Economics and Business, 2009, vol. 61, iss. 6, pp. 435–452. URL: Link
Platt H.D., Platt M.B., Pedersen J.G. Bankruptcy Discrimination with Real Variables. Journal of Business Finance & Accounting, 1994, vol. 21, iss. 4, pp. 491–510. URL: Link
Veganzones D., Séverin E. An Investigation of Bankruptcy Prediction in Imbalanced Datasets. Decision Support Systems, 2018, vol. 112, pp. 111–124. URL: Link
Bol'shakov M.A. [Preparation of data monitoring system of IT infrastructure for critical state detection model based on neural networks]. Naukoemkie tekhnologii v kosmicheskikh issledovaniyakh Zemli = H&ES Research, 2019, vol. 11, no. 4, pp. 65–71. URL: Link (In Russ.)
Goodfellow L., Bengio Y., Courville A. Deep Learning (Adaptive Computation and Machine Learning Series). The MIT Press, 2016, 800 p.