Corporate Bankruptcy Prediction Using the Principal Components Method

Alexander Grigoriev, Konstantin Tarasov*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


A huge number of articles and papers devoted to the study of bankruptcy prediction problems. Solving the problem of predictive ability many difficulties arise from the processing of data ending with the choice of models and algorithms. Efficiency is formed on the basis of three key aspects, such as tools, data quality and algorithms, formed based on the correct formulation of the problem. This research raises the problem of predicting the probability of bankruptcy using the method of neural network modeling. The paper proposes an effective prediction algorithm, in comparison with conventional parametric methods and is able to correctly classify on average more than 94% of observations in the sample of Russian small, medium and large businesses. Also during the research, the issue of data processing was touched upon.By the principal components method of neural networks, factors affecting the bankruptcy and key turning points that could lead to destabilization of the company’s normal operations were discovered. Increasing the accuracy of the forecast can be achieved by using more sophisticated algorithms, which are hybrid models.
Original languageEnglish
Pages (from-to)20-38
Number of pages19
JournalJournal of Corporate Finance Research
Issue number4
Publication statusPublished - Dec 2019


  • corporate bankruptcy
  • bankruptcy prediction
  • principal components method
  • neural simulation

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