Intelligent Bankruptcy Prediction Models Involving Corporate Governance Indicators, Financial Ratios and SMOTE

Azzeddine Idhmad, Mohammed Kaicer, Chaymae Nejjar, Abdelghani Benjouad

Abstract


This study enhances bankruptcy prediction models by investigating synergies between predictors, utilizing a diverse dataset of financial statements and corporate governance data. Rigorous feature selection identifies key financial ratios (FRs) and corporate governance indicators (CGIs) to enhance model interpretability. Multiple machine learning algorithms construct and assess the models, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks. Integration of CGIs with FRs aims to identify effective combinations that improve model performance with an accuracy respectively 90%, 95%, 97%, and 98%. Researchers explore feature weighting techniques and ensemble methods, examining their impact on accuracy, sensitivity, and specificity. The study also explores how regulatory frameworks and governance practices affect bankruptcy prediction, analyzing data across periods to uncover changes in predictive power under varying conditions. The findings have implications for investors, institutions, and policymakers, offering more accurate risk assessments and emphasizing the interplay between financial performance and governance quality for corporate well-being.




Keywords


Machine learning algorithms; Bankruptcy prediction; Corporate Governance; Indicator Financial ratios

References


Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

Bebchuk, L. A., & Fried, J. M. (2003). Executive compensation as an agency problem. Journal of economic perspectives, 17(3), 71-92. Brown,

L. D., & Caylor, M. L. (2005). Corporate governance and firm valuation. Journal of Accounting and public policy, 24(4), 391-418.

Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537-569.

Core, J. E., Holthausen, R. W., & Larcker, D. F. (1999). Corporate governance, chief executive officer compensation, and firm performance. Journal of financial economics, 51(3), 371-406.

Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and con- trol. The Journal of Law and Economics, 26(2), 301-325.

Hermalin, B. E., & Weisbach, M. S. (2003). Boards of directors as an endogenously determined institution: A survey of the economic literature. Economic Policy Review, 9(1), 7-26.

Kao, H. L., & Wu, H. Y. (2015). The relationship between cor- porate governance and financial distress: Evidence from Taiwanese firms. Emerging Markets Finance and Trade, 51(6), 1084-1101.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (2000). Investor protection and corporate governance. Journal of financial economics, 58(1-2), 3-27.

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection.Journal of Machine Learning Research, 3, 1157–1182.

Yermack,D. (1996). Higher market valuation of companies with a small board of directors. Journal of financial economics, 40(2), 185-211.

Powers, D. M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437.

Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10(3), e0118432.

Powers, D. M. (2011). The problem with kappa. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR), 2011 (pp. 421-425). IEEE.

Provost, F., & Fawcett, T. (2001). Robust classification for imprecise en- vironments. Machine Learning, 42(3), 203-231.

Huang, C.-L., Tsaih, R.-C., & Tzeng, G.-H. (2016). A hybrid approach for credit scoring analysis. Expert Systems with Applications, 42(11), 5178-5186.

Kim, J.-H., & Kang, K.-K. (2018). Credit scoring using ensemble learning and feature selection methods.Applied Sciences, 8(9), 1651.

Lee, H., Lee, J., & Kim, C. (2020). Bankruptcy prediction for SMEs using data mining techniques and financial ratios. Sustainability, 12(12), 5020.

Zhang, W., Li, C., & Chen, S. (2017). Feature selection using random forest for bankruptcy prediction. Systems Engineering Procedia, 7, 221-228.

Zhang, J., & Zhou, C. (2019). Bankruptcy prediction using gradient boosting decision trees and financial ratios. International Journal of Financial Studies, 7(4), 62.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.

He, H., & Ma, Y. (2013). Imbalanced learning: Foun- dations, algorithms, and applications. Hoboken, NJ: John Wiley & Sons. Altman, E. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

Zhang, H., & Wu, J. (2001). Bankruptcy prediction using support vector machine. Proceedings of the 2001 International Joint Conference on Neural Networks, 2, 1470-1473.

Lee, D. Y., Jung, K., & Kim, S. Y. (2018). Predicting corporate bankruptcy: A comparative study of machine learning tech- niques. Expert Systems with Applications, 95, 255-263.

Alves, C., Ferreira, J., & Figueira, J. R. (2017). A comparison of statistical, machine learning, and hybrid methods in bankruptcy prediction. Expert Systems with Applications, 83, 405-417.

Ahn, J. M., Bae, H. J., & Hahn, M. S. (2019). Bankruptcy pre- diction model using mutual information-based feature selection. Sustainability, 11(15), 4128.

Krawczyk, B., Wo´zniak, M., & Schaefer, G. (2016). Cost-sensitive learning with SMOTE for credit card fraud detection. Expert Systems with Applications, 59, 172-18

Beaver, W. H. (1966). Financial ratios predictors of failure. Journal of Accounting Research, 4, 71–111.

Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38, 63–93.

Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques –A review. European Journal of Operational Research, 180, 1–28.

Lee, T.-S., & Yeh, Y. H. (2004). Corporate governance and financial distress: Evidence from Taiwan. Corporate Governance: An International Review, 12(3), 378–388.

Lin, W.-Y., Hu, Y.-H., & Tsai, C.-F. (2012). Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems, Man and Cybernetics –Part C: Applications and Reviews, 42(4), 421–436.

Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and ensemblebased soft computing techniques in bankruptcy prediction: A survey. Soft Computing, 14, 995–1010.


Full Text: PDF

Refbacks

  • There are currently no refbacks.


 

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats

Error. Page cannot be displayed. Please contact your service provider for more details. (3)