IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v166y2021ics0040162521000901.html
   My bibliography  Save this article

CatBoost model and artificial intelligence techniques for corporate failure prediction

Author

Listed:
  • Jabeur, Sami Ben
  • Gharib, Cheima
  • Mefteh-Wali, Salma
  • Arfi, Wissal Ben

Abstract

Financial distress prediction provides an effective warning system for banks and investors to correctly guide decisions on granting credit. Ensemble methods have demonstrated their performance in corporate failure prediction. Among the ensemble methods, gradient boosting has been successfully used in bankruptcy prediction. In this paper, we propose a novel approach to classify categorical data using gradient boosting decision trees, namely, CatBoost. First, we investigate the importance of the features identified by the CatBoost model. Second, we compare our approach with eight reference machine learning models at one, two and three years before failure. Our model demonstrates an effective improvement in the power of classification performance compared with other advanced approaches.

Suggested Citation

  • Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000901
    DOI: 10.1016/j.techfore.2021.120658
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521000901
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.120658?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    3. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    6. Huynh, Toan Luu Duc & Wu, Junjie & Duong, An Trong, 2020. "Information Asymmetry and firm value: Is Vietnam different?," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    7. du Jardin, Philippe, 2016. "A two-stage classification technique for bankruptcy prediction," European Journal of Operational Research, Elsevier, vol. 254(1), pages 236-252.
    8. Wruck, Karen Hopper, 1990. "Financial distress, reorganization, and organizational efficiency," Journal of Financial Economics, Elsevier, vol. 27(2), pages 419-444, October.
    9. Climent, Francisco & Momparler, Alexandre & Carmona, Pedro, 2019. "Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach," Journal of Business Research, Elsevier, vol. 101(C), pages 885-896.
    10. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    11. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    12. M. B. Rapanyane & F. R. Sethole, 2020. "The rise of artificial intelligence and robots in the 4th Industrial Revolution: implications for future South African job creation," Contemporary Social Science, Taylor & Francis Journals, vol. 15(4), pages 489-501, October.
    13. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    14. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
    15. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    16. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    17. Raffaella Calabrese & Giampiero Marra & Silvia Angela Osmetti, 2016. "Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 604-615, April.
    18. Stef, Nicolae, 2018. "Bankruptcy and the difficulty of firing," International Review of Law and Economics, Elsevier, vol. 54(C), pages 85-94.
    19. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    20. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    21. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    22. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    23. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    24. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    25. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    26. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    27. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    28. Sami Ben Jabeur & Amir Sadaaoui & Asma Sghaier & Riadh Aloui, 2020. "Machine learning models and cost-sensitive decision trees for bond rating prediction," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(8), pages 1161-1179, August.
    29. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    30. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    31. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    32. Ligang Zhou & Kin Lai & Jerome Yen, 2014. "Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 241-253.
    33. P. Du Jardin & E. Séverin, 2012. "Forecasting financial failure using a Kohonen map: a comparative study to improve bankruptcy model over time," Post-Print hal-00801853, HAL.
    34. Stephanie M. Bryant, 1997. "A case‐based reasoning approach to bankruptcy prediction modeling," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 6(3), pages 195-214, September.
    35. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    36. Carmona, Pedro & Climent, Francisco & Momparler, Alexandre, 2019. "Predicting failure in the U.S. banking sector: An extreme gradient boosting approach," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 304-323.
    37. Nicolae Stef, 2018. "Bankruptcy and the Difficulty of Firing," Post-Print hal-01664740, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    2. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    3. Abhinash Jenasamanta & Subrajeet Mohapatra, 2022. "An automated system for the assessment and grading of adolescent delinquency using a machine learning-based soft voting framework," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    4. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    5. Kocaarslan, Baris & Soytas, Ugur, 2023. "The role of major markets in predicting the U.S. municipal green bond market performance: New evidence from machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    6. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    7. Rashinda Wijethunga & Hooman Nouraei & Craig Zych & Jagath Samarabandu & Ayan Sadhu, 2024. "Precision Leak Detection in Supermarket Refrigeration Systems Integrating Categorical Gradient Boosting with Advanced Thresholding," Energies, MDPI, vol. 17(3), pages 1-23, February.
    8. Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
    9. Jamei, Mehdi & Karbasi, Masoud & Malik, Anurag & Jamei, Mozhdeh & Kisi, Ozgur & Yaseen, Zaher Mundher, 2022. "Long-term multi-step ahead forecasting of root zone soil moisture in different climates: Novel ensemble-based complementary data-intelligent paradigms," Agricultural Water Management, Elsevier, vol. 269(C).
    10. Hamza Bouguerra & Salah Eddine Tachi & Hamza Bouchehed & Gordon Gilja & Nadir Aloui & Yacine Hasnaoui & Abdelmalek Aliche & Saâdia Benmamar & Jose Navarro-Pedreño, 2023. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    11. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    12. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    13. Herrera, Rubén & Climent, Francisco & Carmona, Pedro & Momparler, Alexandre, 2022. "The manipulation of Euribor: An analysis with machine learning classification techniques," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    2. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    4. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    5. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    6. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    7. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    8. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    9. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    10. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    11. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    12. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    13. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    14. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    15. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    16. Sami Ben Jabeur & Rabi Belhaj Hassine & Salma Mefteh‐Wali, 2021. "Firm financial performance during the financial crisis: A French case study," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2800-2812, April.
    17. ben Jabeur, Sami & Mefteh-Wali, Salma & Carmona, Pedro, 2021. "The impact of institutional and macroeconomic conditions on aggregate business bankruptcy," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 108-119.
    18. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    19. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    20. du Jardin, Philippe, 2021. "Forecasting corporate failure using ensemble of self-organizing neural networks," European Journal of Operational Research, Elsevier, vol. 288(3), pages 869-885.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:166:y:2021:i:c:s0040162521000901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.