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Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios

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  • Giordani, Paolo
  • Jacobson, Tor
  • Schedvin, Erik von
  • Villani, Mattias

Abstract

We demonstrate improvements in predictive power when introducing spline functions to take account of highly nonlinear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive nonlinearities yields substantially improved bankruptcy predictions, on the order of 70%–90%, compared with a standard logistic model. The spline model provides several important and surprising insights into nonmonotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than those given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.

Suggested Citation

  • Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    • Handle: RePEc:cup:jfinqa:v:49:y:2014:i:04:p:1071-1099_00
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    Cited by:

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    3. Matias Quiroz & Robert Kohn & Mattias Villani & Minh-Ngoc Tran, 2019. "Speeding Up MCMC by Efficient Data Subsampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 831-843, April.
    4. Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019. "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series 372, Sveriges Riksbank (Central Bank of Sweden).
    5. Niklas Amberg & Tor Jacobson & Erik von Schedvin & Robert Townsend, 2021. "Curbing Shocks to Corporate Liquidity: The Role of Trade Credit," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 182-242.
    6. 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).
    7. Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019. "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series 371, Sveriges Riksbank (Central Bank of Sweden).
    8. Xueyan Dong & Kam C. Chan & Yujia Cui & Jenny Xinjiao Guan, 2021. "Strategic deviance and cash holdings," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(3-4), pages 742-782, March.
    9. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.
    10. Muhammad Zubair Mumtaz & Zachary Alexander Smith, 2018. "IPOs in the U.S. from 2005 to 2015: Using the Spline Regression Technique to Estimate Aggregate Issuance and Performance," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 68(2), pages 165-199, April.
    11. 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.
    12. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
    13. Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
    14. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    15. Ida Nervik Hjelseth & Arvid Raknerud & Bjørn H. Vatne, 2022. "A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection," Working Paper 2022/7, Norges Bank.
    16. Quiroz, Matias & Villani, Mattias, 2013. "Dynamic mixture-of-experts models for longitudinal and discrete-time survival data," Working Paper Series 268, Sveriges Riksbank (Central Bank of Sweden).
    17. 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.

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    More about this item

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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