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Prediction of Business Bankruptcy with the Help of Extreme Gradient Increase

Author

Listed:
  • Catalin-Emanuel CIOBOTA

    (Valahia University of Targoviste, Romania)

  • Manuela-Violeta TUREATCA

    (Dunarea de Jos University of Galati, Romania)

Abstract

Financial institutions use business failure forecasting models to manage their investments. The accuracy of the forecast is an important factor in determining how much capital is needed to cover credit losses. The majority of studies use traditional statistical methods to model business failures (e.g., discriminant analysis and logistic regression). Models constructed are usually quite inaccurate, however. It is due to the imbalance between the classes of training samples (bankruptcies account for a small percentage of all firms) that are used to construct the models. Currently, various machine learning methods such as the random forest method and the gradient boosting method are widespread. In this study, the main focus is on using extreme gradient growth to predict bankruptcy. Extreme gradient boosting, using LASSO or Ridge regularization, penalizes complex models to avoid overfitting. Also, during training, extreme gradient boosting fills in the missing values in the data set depending on the amount of loss. In this article, in order to increase the efficiency of extreme degree growth, it is proposed to use SMOTE technology to improve class balance. The quality values of the solutions obtained by the improved extremal degree increase are compared with the solutions obtained by other methods.

Suggested Citation

  • Catalin-Emanuel CIOBOTA & Manuela-Violeta TUREATCA, 2022. "Prediction of Business Bankruptcy with the Help of Extreme Gradient Increase," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 178-185.
    • Handle: RePEc:ddj:fseeai:y:2022:i:3:p:178-185
    DOI: 10.35219/eai15840409301
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    References listed on IDEAS

    as
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