Machine Learning Methods of Bankruptcy Prediction Using Accounting Ratios

  • Li Y
  • Wang Y
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Abstract

The aim of bankruptcy prediction is to help the enterprise stakeholders to get the comprehensive information of the enterprise. Much bankruptcy prediction has relied on statistical models and got low prediction accuracy. However, with the advent of the AI (Artificial Intelligence), machine learning methods have been extensively used in many industries (e.g., medical, archaeological and so on). In this paper we compare the statistical method and machine learning method to predict bankruptcy with utilizing China listed companies. Firstly, we use statistical method to choose the most appropriate indicators. Different indicators may have different characteristics and not all indicators can be analyzed. After the data filtering, the indicators are more persuasive. Secondly, unlike previous research methods, we use the same sample set to conduct our experiment. The final result can prove the effectiveness of the machine learning method. Thirdly, the accuracy of our experiment is higher than existing studies with 95.9%.

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APA

Li, Y., & Wang, Y. (2018). Machine Learning Methods of Bankruptcy Prediction Using Accounting Ratios. Open Journal of Business and Management, 06(01), 1–20. https://doi.org/10.4236/ojbm.2018.61001

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