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Bankruptcy prediction using Partial Least Squares Logistic Regression

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  • Ben Jabeur, Sami

Abstract

In the current conditions of economy there is an increasing number of companies that are facing economic and financial difficulties which may, in some cases, lead to bankruptcy. This research is motivated by the inadequacies of traditional forecasting models. The Partial Least Squares Logistic Regression (PLS-LR) allows integrating a large number of ratios in the model; in addition, it solves the problem of correlation, and taking into account the missing data in the matrix. Indeed, the results obtained are very satisfactory and confirm the superiority of this method compared to conventional methods. The proposed model gives the opportunity to consider all the indicators in predicting financial distress, the reduction of the environment's uncertainty, the control's improvement and the coordination between the different company stakeholders.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:joreco:v:36:y:2017:i:c:p:197-202
    DOI: 10.1016/j.jretconser.2017.02.005
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    3. 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.
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    5. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    6. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    7. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
    9. 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.
    10. 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).
    11. Rémi Stellian & Jenny P. Danna‐Buitrago, 2020. "Financial distress, free cash flow, and interfirm payment network: Evidence from an agent‐based model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 598-616, October.

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