IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v18y2018i3p437-466.html
   My bibliography  Save this article

Empirical comparison of hazard models in predicting SMEs failure

Author

Listed:
  • Jairaj Gupta
  • Andros Gregoriou
  • Tahera Ebrahimi

Abstract

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.

Suggested Citation

  • Jairaj Gupta & Andros Gregoriou & Tahera Ebrahimi, 2018. "Empirical comparison of hazard models in predicting SMEs failure," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 437-466, March.
    • Handle: RePEc:taf:quantf:v:18:y:2018:i:3:p:437-466
    DOI: 10.1080/14697688.2017.1307514
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697688.2017.1307514
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697688.2017.1307514?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. Basim Alzugaiby & Jairaj Gupta & Andrew Mullineux & Rizwan Ahmed, 2021. "Relevance of size in predicting bank failures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3504-3543, July.
    2. Barbaglia, Luca & Fatica, Serena & Rho, Caterina, 2023. "Flooded credit markets: physical climate risk and small business lending," Working Papers 2023-14, Joint Research Centre, European Commission.
    3. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    4. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    5. Ugur, Mehment & Vivarelli, Marco, 2020. "The role of innovation in industrial dynamics and productivity growth: a survey of the literature," GLO Discussion Paper Series 648, Global Labor Organization (GLO).
    6. Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021. "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers 2021-14, Banco de México.
    7. Li, Xia & Gupta, Jairaj & Bu, Ziwen & Kannothra, Chacko George, 2023. "Effect of cash flow risk on corporate failures, and the moderating role of earnings management and abnormal compensation," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Mehmet Ugur & Marco Vivarelli, 2021. "Innovation, firm survival and productivity: the state of the art," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 30(5), pages 433-467, July.
    9. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    10. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    11. 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.
    12. Gupta, Jairaj & Chaudhry, Sajid, 2019. "Mind the tail, or risk to fail," Journal of Business Research, Elsevier, vol. 99(C), pages 167-185.
    13. Ugur, Mehmet & Solomon, Edna & Zeynalov, Ayaz, 2022. "Leverage, competition and financial distress hazard: Implications for capital structure in the presence of agency costs," Economic Modelling, Elsevier, vol. 108(C).
    14. Akarsh Kainth & Ranik Raaen Wahlstrøm, 2021. "Do IFRS Promote Transparency? Evidence from the Bankruptcy Prediction of Privately Held Swedish and Norwegian Companies," JRFM, MDPI, vol. 14(3), pages 1-15, March.
    15. Adam P. Balcerzak & Ilona Pietryka (ed.), 2021. "Contemporary Issues in Economy. Proceedings of the International Conference on Applied Economics: Entrepreneurship and Management," Books, Institute of Economic Research, edition 1, volume 11, number 27.
    16. Mehmet Ugur & Marco Vivarelli, 2020. "Technology, industrial dynamics and productivity: a critical survey," DISCE - Quaderni del Dipartimento di Politica Economica dipe0011, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    17. Chris Charalambous & Spiros H. Martzoukos & Zenon Taoushianis, 2022. "Estimating corporate bankruptcy forecasting models by maximizing discriminatory power," Review of Quantitative Finance and Accounting, Springer, vol. 58(1), pages 297-328, January.
    18. Mingqian Zhang & Pierre Mohnen, 2022. "R&D, innovation and firm survival in Chinese manufacturing, 2000–2006," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(1), pages 59-95, March.

    More about this item

    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:taf:quantf:v:18:y:2018:i:3:p:437-466. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.