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Improving the Accuracy of Financial Bankruptcy Prediction Using Ensemble Learning Techniques

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Pan-African Conference on Artificial Intelligence (PanAfriConAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2069))

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Abstract

Financial institutions have been seeking ways to improve their bankruptcy prediction capabilities to mitigate the disruptive effects of future bankruptcies. One such way is using machine learning models. However, financial datasets are often imbalanced, posing a significant challenge for building effective predictive models. In this work, three resampling techniques are used to produce the datasets that were used for model building: oversampling, undersampling, and hybrid sampling. We evaluate the effectiveness of these sampling techniques on five machine learning models (Logistic Regression, Bagging, Random Forest, Support Vector Machine, Neural Networks) in predicting financial bankruptcies. We also investigate the impact of ensembling on model performance by stacking the high-performing individual models using a logistic regression meta-classifier. Our results show that hybrid sampling provides a better balance of accuracy and accountability for the minority (bankrupt) class, which makes it a suitable balancing technique for imbalanced financial datasets. Additionally, ensembling the models using stacking improved the performance of the models, resulting in a better performance for predicting bankruptcies. Remarkably, our proposed model demonstrated an outstanding accuracy of 99.75% while models from existing literature, and previous studies reported accuracies ranging from 83% to 98% for similar ensemble stacking tasks. Results from this study will be useful for practitioners in the finance sphere in making informed decisions, managing risks and choosing the right models for bankruptcy prediction.

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Data Availability Statement

The data and R codes used in this work can be found in: https://drive.google.com/drive/folders/1LdK_fdKicEf8qC_iYTcffF0UoRoBBwU5?usp=share_link

References

  1. Adamus, R.: Bankruptcy proceedings in relation to bond issuers in Poland. Soc. Polit. Sci. 1, 146–149 (2013)

    Google Scholar 

  2. Alam, T.M., et al.: Corporate bankruptcy prediction: an approach towards better corporate world. Comput. J. 64(11), 1731–1746 (2021). https://doi.org/10.1093/comjnl/bxab095

    Article  Google Scholar 

  3. Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968)

    Article  Google Scholar 

  4. Altman, E.I.: Predicting financial distress of companies: revisiting the z-score and zeta® models. In: Handbook of Research Methods and Applications in Empirical Finance, pp. 428–456. Edward Elgar Publishing (2013)

    Google Scholar 

  5. Anjum, S.: Business bankruptcy prediction models: a significant study of the Altman’s z-score model. Available at SSRN 2128475 (2012)

    Google Scholar 

  6. Balcaen, S., Ooghe, H.: 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. Br. Account. Rev. 38(1), 63–93 (2006). https://doi.org/10.1016/j.bar.2005.09.001

    Article  Google Scholar 

  7. Barboza, F., Kimura, H., Altman, E.: Machine learning models and bankruptcy prediction. Expert Syst. Appl. 83, 405–417 (2017). https://doi.org/10.1016/j.eswa.2017.04.006

    Article  Google Scholar 

  8. Bertsimas, D., King, A.: Logistic regression: from art to science. Stat. Sci. 32(3), 367–384 (2017)

    Article  MathSciNet  Google Scholar 

  9. Bondarenko, P.: Enron scandal. Encyclopedia Britannica (2019)

    Google Scholar 

  10. Butcher, B., Smith, B.J.: Feature Engineering and Selection: A Practical Approach for Predictive Models. Chapman & Hall/CRC Press, Boca Raton (2020)

    Google Scholar 

  11. Buyrukoglu, S., Savaş, S.: Stacked-based ensemble machine learning model for positioning footballer. Arab. J. Sci. Eng. 48(3), 1371–1383 (2023)

    Article  Google Scholar 

  12. Charan, R., Useem, J., Harrington, A.: Why companies fail. Fortune 27, 36–44 (2002)

    Google Scholar 

  13. Domingos, P.: Bayesian averaging of classifiers and the overfitting problem. In: ICML, vol. 747, pp. 223–230 (2000)

    Google Scholar 

  14. Fauzi, M.A., Yuniarti, A.: Ensemble method for Indonesian twitter hate speech detection. Indones. J. Electr. Eng. Comput. Sci. 11(1), 294–299 (2018)

    Google Scholar 

  15. Fitzpatrick, P.J.: A comparison of the ratios of successful industrial enterprises with those of failed companies. The Accountants’ Magazine (1932)

    Google Scholar 

  16. Garcia, J.: Bankruptcy prediction using synthetic sampling. Mach. Learn. Appl. 9, 100343 (2022). https://doi.org/10.1016/j.mlwa.2022.100343

    Article  Google Scholar 

  17. Horak, J., Vrbka, J., Suler, P.: Support vector machine methods and artificial neural networks used for the development of bankruptcy prediction models and their comparison. J. Risk Financ. Manag. 13(3), 60 (2020). https://doi.org/10.3390/jrfm13030060

    Article  Google Scholar 

  18. Hosseini, S., Pourmirzaee, R., Armaghani, D.J., et al.: Prediction of ground vibration due to mine blasting in a surface lead-zinc mine using machine learning ensemble techniques. Sci. Rep. 13, 6591 (2023)

    Article  Google Scholar 

  19. Hołda, A.: Zasada kontynuacji działalności i prognozowanie upadłości w polskich realiach gospodarczych. Zeszyty Naukowe/Akademia Ekonomiczna w Krakowie. Seria Specjalna, Monografie (174) (2006)

    Google Scholar 

  20. Jones, S.: Corporate bankruptcy prediction: a high dimensional analysis. Rev. Acc. Stud. 22, 1366–1422 (2017). https://doi.org/10.1007/s11142-017-9407-1

    Article  Google Scholar 

  21. Khan, S.I., Hoque, A.S.M.L.: SICE: an improved missing data imputation technique. J. Big Data 7, 37 (2020). https://doi.org/10.1186/s40537-020-00313-w

    Article  Google Scholar 

  22. Kiaupaite-Grushniene, V.: Altman z-score model for bankruptcy forecasting of the listed Lithuanian agricultural companies. In: 5th International Conference on Accounting, Auditing, and Taxation (ICAAT 2016), pp. 222–234. Atlantis Press (2016)

    Google Scholar 

  23. Kim, M.J., Kang, D.K.: Ensemble with neural networks for bankruptcy prediction. Expert Syst. Appl. 37(4), 3373–3379 (2010). https://doi.org/10.1016/j.eswa.2009.10.012

    Article  Google Scholar 

  24. Kitowski, J., Kowal-Pawul, A., Lichota, W.: Identifying symptoms of bankruptcy risk based on bankruptcy prediction models-a case study of Poland. Sustainability 14(3), 1416 (2022)

    Article  Google Scholar 

  25. Kou, G., et al.: Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decis. Support Syst. 140, 113429 (2021). https://doi.org/10.1016/j.dss.2020.113429

    Article  Google Scholar 

  26. Kufeoglu, S.: SDG-9: industry, innovation and infrastructure. In: Emerging Technologies (Sustainable Development Goals Series). Springer (2022). https://doi.org/10.1007/978-3-031-07127-0-11

  27. Learning, S.M.: Hybrid model for twitter data sentiment analysis based on ensemble of dictionary-based classifier and stacked machine learning classifiers-SVM, KNN and c5.0. J. Theoret. Appl. Inf. Technol. 98(04), 624–635 (2020)

    Google Scholar 

  28. Lian, W., Nie, G., Jia, B., et al.: An intrusion detection method based on decision tree-recursive feature elimination in ensemble learning. Math. Probl. Eng. 2020, 1–15 (2020)

    Google Scholar 

  29. Lombardo, G., Pellegrino, M., Adosoglou, G., Cagnoni, S., Pardalos, P.M., Poggi, A.: Machine learning for bankruptcy prediction in the American stock market: dataset and benchmarks. Future Internet 14(8), 244 (2022). https://doi.org/10.3390/fi14080244

    Article  Google Scholar 

  30. Maddikonda, S.S.T., Matta, S.K.: Bankruptcy prediction: mining the Polish bankruptcy data (2018)

    Google Scholar 

  31. Malek, N.H.A., Yaacob, W.F.W., Wah, Y.B., et al.: Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data. Indones. J. Electr. Eng. Comput. Sci. 29(1), 598–608 (2023)

    Google Scholar 

  32. Misankova, M., Bartosova, V.: Comparison of selected statistical methods for the prediction of bankruptcy. In: Conference Proceedings of 10th International Days of Statistics and Economics, Melandrium, Prague, pp. 895–899 (2016)

    Google Scholar 

  33. Muller, D., Soto-Rey, I., Kramer, F.: An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks. IEEE Access 10, 66467–66480 (2022)

    Article  Google Scholar 

  34. Qu, Y., Quan, P., Lei, M., Shi, Y.: Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Comput. Sci. 162, 895–899 (2019). https://doi.org/10.1016/j.procs.2019.12.065

    Article  Google Scholar 

  35. Rodrigues, P.C., Awe, O.O., Pimentel, J.S., Mahmoudvand, R.: Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3(2), 137–157 (2020)

    Article  Google Scholar 

  36. Shekhar, S., Schrater, P.R., Vatsavai, R.R., Wu, W., Chawla, S.: Spatial contextual classification and prediction models for mining geospatial data. IEEE Trans. Multimedia 4(2), 174–188 (2002)

    Article  Google Scholar 

  37. Shetty, S., Musa, M., Brédart, X.: Bankruptcy prediction using machine learning techniques. J. Risk Financ. Manag. 15(1), 35 (2022). https://doi.org/10.3390/jrfm15010035

    Article  Google Scholar 

  38. Son, H., Hyun, C., Phan, D., Hwang, H.J.: Data analytic approach for bankruptcy prediction. Expert Syst. Appl. 138, 112816 (2019). https://doi.org/10.1016/j.eswa.2019.06.050

    Article  Google Scholar 

  39. Thilakarathna, C., Dawson, C., Edirisinghe, E.: Using financial ratios with artificial neural networks for bankruptcy prediction. In: 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, pp. 55–58. IEEE (2022). https://doi.org/10.1109/ICAICA54878.2022.9844640

  40. Xu, Y., Klein, B., Li, G., Gopaluni, B.: Evaluation of logistic regression and support vector machine approaches for XRF based particle sorting for a copper ore. Miner. Eng. 192, 108003 (2023)

    Article  Google Scholar 

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Authors and Affiliations

  1. African Institute for Mathematical Sciences, Limbe, Cameroon

    Anthonia Oluchukwu Njoku, Berthine Nyunga Mpinda & Olushina Olawale Awe

  2. State University of Campinas, Sao Paulo, Brazil

    Olushina Olawale Awe

Authors
  1. Anthonia Oluchukwu Njoku
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  2. Berthine Nyunga Mpinda
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  3. Olushina Olawale Awe
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Corresponding author

Correspondence to Anthonia Oluchukwu Njoku .

Editor information

Editors and Affiliations

  1. Ethiopian Artificial Intelligence Instit, Addis Adaba, Ethiopia

    Taye Girma Debelee

  2. HAWK University of Applied Sciences and Arts, Göttingen, Germany

    Achim Ibenthal

  3. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  4. Ethiopian Artificial Intelligence Instit, Addis Ababa, Ethiopia

    Yehualashet Megersa Ayano

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CRediT Authorship Contribution Statement

Anthonia Oluchukwu Njoku: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Software, Writing & editing, Visualization.

Berthine Nyunga Mpinda: Validation, Formal analysis, Review & editing.

Olushina Olawale Awe: Conceptualization, Methodology, Validation, Review & editing, Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Oluchukwu Njoku, A., Nyunga Mpinda, B., Olawale Awe, O. (2024). Improving the Accuracy of Financial Bankruptcy Prediction Using Ensemble Learning Techniques. In: Debelee, T.G., Ibenthal, A., Schwenker, F., Megersa Ayano, Y. (eds) Pan-African Conference on Artificial Intelligence. PanAfriConAI 2023. Communications in Computer and Information Science, vol 2069. Springer, Cham. https://doi.org/10.1007/978-3-031-57639-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-57639-3_1

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