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Predicting corporate bankruptcy: What matters?

Author

Listed:
  • Li, Leon
  • Faff, Robert

Abstract

or market-based information should be employed to predict corporate default is a long-standing debate in finance research. Incorporating a regime-switching mechanism, we establish a hybrid bankruptcy prediction model with non-uniform loadings in both accounting- and market-based approaches to reexamine the issue. We find the following. Creditors should increase the loading on market-based information when large and liquid corporations are considered. Conversely, for companies with incremental information involved in accounting reporting proxied by discretionary accruals, banks could emphasize accounting ratio-based variables more than they are already emphasized. Since managerial discretion in accounting numbers could serve as a tool to bring undisclosed information about the firm to the public, the weight on accounting-based information could be increased for firms with high information asymmetry. In addition, the loading on market-based (accounting-based) information should be increased (decreased) during periods of financial crisis, defined by negative gross domestic product growth.

Suggested Citation

  • Li, Leon & Faff, Robert, 2019. "Predicting corporate bankruptcy: What matters?," International Review of Economics & Finance, Elsevier, vol. 62(C), pages 1-19.
    • Handle: RePEc:eee:reveco:v:62:y:2019:i:c:p:1-19
    DOI: 10.1016/j.iref.2019.02.016
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    Citations

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    Cited by:

    1. 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.
    2. Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
    3. Serhiy Zabolotnyy & Mirosław Wasilewski, 2019. "The Concept of Financial Sustainability Measurement: A Case of Food Companies from Northern Europe," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    4. Carlos Pérez Montes & Alejandro Ferrer & Gabriel Jiménez & Laura Álvarez Román & Henrique Basso & Beatriz González López & Sergio Mayordomo & Álvaro Menéndez Pujadas & Myroslav Pidkuyko & Lola Morales, 2023. "Individual and sectoral analysis framework for the impact of economic and financial risks," Occasional Papers 2313, Banco de España.
    5. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    6. Lidiya Guryanova & Olena Bolotova & Vitalii Gvozdytskyi & Sergienko Olena, 2020. "Long-term financial sustainability: An evaluation methodology with threats considerations," RIVISTA DI STUDI SULLA SOSTENIBILITA', FrancoAngeli Editore, vol. 0(1), pages 47-69.
    7. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    8. Ruwani Fernando, Jayasuriya Mahapatabendige & Li, Leon & Hou, Greg, 2021. "Heterogeneity in capital structure adjustment revisited: Default versus non-default firms and short versus long time horizon," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 185-204.
    9. Dariusz Sala & Kostiantyn Pavlov & Olena Pavlova & Anton Demchuk & Liubomur Matiichuk & Dariusz Cichoń, 2023. "Determining of the Bankrupt Contingency as the Level Estimation Method of Western Ukraine Gas Distribution Enterprises’ Competence Capacity," Energies, MDPI, vol. 16(4), pages 1-13, February.
    10. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    11. Kabir, Md Nurul & Rahman, Sohanur & Rahman, Md Arifur & Anwar, Mumtaheena, 2021. "Carbon emissions and default risk: International evidence from firm-level data," Economic Modelling, Elsevier, vol. 103(C).
    12. Ning Wu & Jingyi Zhao & Mohammed Musah & Zhiqiang Ma & Lijuan Zhang & Yutong Zhou & Yongzheng Su & Joseph Kwasi Agyemang & Juliana Anyei Asiamah & Siqi Cao & Linnan Yao & Kaodui Li, 2023. "Do Liquidity and Capital Structure Predict Firms’ Financial Sustainability? A Panel Data Analysis on Quoted Non-Financial Establishments in Ghana," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    13. Liu, Bai & Ju, Tao & Bai, Min & Yu, Chia-Feng (Jeffrey), 2021. "Imitative innovation and financial distress risk: The moderating role of executive foreign experience," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 526-548.
    14. Yinghua Song & Minzhe Jiang & Shixuan Li & Shengzhe Zhao, 2024. "Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 593-614, April.
    15. Dagmar Camska & Jiri Klecka, 2020. "Comparison of Prediction Models Applied in Economic Recession and Expansion," JRFM, MDPI, vol. 13(3), pages 1-16, March.

    More about this item

    Keywords

    Regime-switching system; Z-score; Distance to default; Bankruptcy;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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