Using Market Indicators to Refine Estimates of Corporate Bankruptcy Probabilities

Daria S. Leonteva ()
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Daria S. Leonteva: HSE University, Moscow 101000, Russian Federation

Finansovyj žhurnal — Financial Journal, 2022, issue 6, 74-90

Abstract: This study investigates an alternative approach to estimating the probability of default. The introduction of credit spreads as market measures of default into an accounting-based model attempts to enhance the predictive power of classical approach models which analyze only balance sheet data. This paper identifies which of the two market measures of credit spread — the Z-spread or the I-spread — has an advantage in the context of robustness of the bankruptcy prediction models. Using two techniques — logistic regression and a gradient boosting machine approach, as well as a sample of annual series of 80 financial ratios for 385 U.S. listed companies which issue corporate bonds — evidence is obtained that the I-spread has higher predictive power in both techniques. The better performance of the I-spread can be explained by the fact that the accuracy of the Z-spread calculation can be misleading because different methods of interpolation of the yield curve are used. In addition, the predictive power of the chosen techniques is also compared. The up-to-date gradient boosting machine framework performs better on the test sample. These findings may encourage managers to implement additional market characteristics in the analysis and apply modern techniques rather than the classic ones — logistic regressions and multiple discriminant analyses models — to predict inconsistency in corporate performance.

Keywords: bankruptcy prediction; credit spreads; logistic regression; gradient boosting machine (search for similar items in EconPapers)
JEL-codes: C53 G33 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:fru:finjrn:220605:p:74-90

DOI: 10.31107/2075-1990-2022-6-74-90

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