IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v62y2019icp1-19.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S105905601830203X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2019.02.016?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.

    Citations

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


    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. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    10. 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.
    11. 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.
    12. 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.
    13. 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

    Statistics

    Access and download statistics

    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:reveco:v:62:y:2019:i:c:p:1-19. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/locate/inca/620165 .

    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.