IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v52y2017i03p1211-1245_00.html
   My bibliography  Save this article

Firm Default Prediction: A Bayesian Model-Averaging Approach

Author

Listed:
  • Traczynski, Jeffrey

Abstract

I develop a new predictive approach using Bayesian model averaging to account for incomplete knowledge of the true model behind corporate default and bankruptcy filing. I find that uncertainty over the correct model is empirically large, with far fewer variables being significant predictors of default compared with conventional approaches. Only the ratio of total liabilities to total assets and the volatility of market returns are robust default predictors in the overall sample and individual industry groups. Model-averaged forecasts that aggregate information across models or allow for industry-specific effects substantially outperform individual models.

Suggested Citation

  • Traczynski, Jeffrey, 2017. "Firm Default Prediction: A Bayesian Model-Averaging Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(3), pages 1211-1245, June.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:03:p:1211-1245_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S002210901700031X/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
    2. Giorgio Brunello & Áron Gereben & Désirée Rückert & Christoph Weiss & Patricia Wruuck, 2022. "Do investments in human and physical capital respond differently to financing constraints?," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-14, December.
    3. Jia, Zhijie, 2023. "The hidden benefit: Emission trading scheme and business performance of downstream enterprises," Energy Economics, Elsevier, vol. 117(C).
    4. Oliver Blümke, 2022. "Multiperiod default probability forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 677-696, July.
    5. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    6. Peter Grundke & Kamil Pliszka & Michael Tuchscherer, 2020. "Model and estimation risk in credit risk stress tests," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 163-199, July.
    7. Zanka Mikhail, 2020. "A Comparison of Variables Selection Methods and their Sequential Application: A Case Study of the Bankruptcy of Polish Companies," Folia Oeconomica Stetinensia, Sciendo, vol. 20(1), pages 531-543, June.
    8. Candida Bussoli & Mariateresa Cuoccio & Claudio Giannotti, 2021. "Discriminant Analysis and Firms’ Bankruptcy: Evidence from European SMEs," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(12), pages 164-164, July.
    9. Michal Karas & Mária Režòáková, 2023. "A novel approach to estimating the debt capacity of European SMEs," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(2), pages 551-581, June.
    10. Qizhi Tao & Zohaib Zahid & Azhar Mughal & Farrukh Shahzad, 2022. "Does operating leverage increase firm's profitability and bankruptcy risk? Evidence from China's entry into WTO," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4705-4721, October.
    11. 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.
    12. Rolando Gonzales Martinez, 2018. "The Wage Curve, Once More with Feeling: Bayesian Model Averaging of Heckit Models," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 3(2), pages 79-92, December.
    13. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    14. Kaya, Orcun, 2022. "Determinants and consequences of SME insolvency risk during the pandemic," Economic Modelling, Elsevier, vol. 115(C).
    15. Florian Maier & B. Burcin Yurtoglu, 2022. "Board Characteristics and the Insolvency Risk of Non-Financial Firms," JRFM, MDPI, vol. 15(7), pages 1-19, July.
    16. Gross, Marco, 2022. "Beautiful cycles: A theory and a model implying a curious role for interest," Economic Modelling, Elsevier, vol. 106(C).
    17. Michel Alexandre & Gilberto Tadeu Lima & Luca Riccetti & Alberto Russo, 2023. "The financial network channel of monetary policy transmission: an agent-based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 533-571, July.
    18. Georgios Sermpinis & Serafeim Tsoukas & Ping Zhang, 2019. "What influences a bank's decision to go public?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1464-1485, October.
    19. David Johnstone & Stewart Jones & Oliver Jones & Steve Tulig, 2021. "Scoring Probability Forecasts by a User’s Bets Against a Market Consensus," Decision Analysis, INFORMS, vol. 18(3), pages 169-184, September.
    20. Sondershaus, Talina, 2019. "Spillovers of asset purchases within the real sector: Win-win or joy and sorrow?," IWH Discussion Papers 22/2019, Halle Institute for Economic Research (IWH).
    21. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    22. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, 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:cup:jfinqa:v:52:y:2017:i:03:p:1211-1245_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    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.