Applying Bankruptcy Prediction Models to Distressed High Yield Bond Issues

Marchesini, Roberto; Perdue, Grady; Bryan, Vicki

doi:10.3905/jfi.2004.391027

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…In their study of failures of high-yield bond prediction, Marchesini et al (2004) observe ''balance sheets … can be and have been severely manipulated. ''…”

Section: Literature Surveymentioning

confidence: 99%

Probability of default models of Russian banks

Peresetsky

Karminsky

Golovan

2011

Econ Change Restruct

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“…In their study of failures of high-yield bond prediction, Marchesini et al (2004) observe ''balance sheets … can be and have been severely manipulated. ''…”

Section: Literature Surveymentioning

confidence: 99%

Probability of default models of Russian banks

Peresetsky

Karminsky

Golovan

2011

Econ Change Restruct

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show abstract

“…The importance of the quality of the accounting data for statistical models of bank risk is demonstrated on US banks data in Gunther and Moore (2003). In their study of failures of high-yield bond prediction, Marchesini et al (2004) observe "balance sheets … can be and have been severely manipulated. "…”

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confidence: 99%

Probability of Default Models of Russian Banks

2004

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This paper presents results from an econometric analysis of Russian bank defaults during the period 1997-2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incorporation of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. Russian banking system trends after the crisis 1998 are analyzed with rolling regressions.

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“…SIZE I Ln(Total assets) Grunert et al (2005); Chi and Tang (2006); and Situm (2014) EBITDA/S Earnings before interest, taxes, depreciation, and amortization/sales H. D. Platt and M. B. Platt (2002) SIZE II Ln(Sales) Chancharat et al (2010); Situm (2014) CA/S Cash/sales Brabazon and Keenan (2004) EBT/TE Earnings before taxes/total equity Bruse (1978); Pompe and Bilderbeek (2005); and Sarlija and Jeger (2011) S/TE Sales/total equity Bruse (1978) TD/TE Total debt/total equity Casey and Bartczak (1985); Jones and Hensher (2004); and Chi and Tang (2006) TE/TD Total equity/total debt Dietrich et al (2005); Bahiraie et al (2009) TE/TA Total equity/total assets E. K. Laitinen and T. Laitinen (2000); Grunert et al (2005); and Bartual et al (2012) TD/TA Total debt/total assets Shah and Murtaza (2000); Brabazon and Keenan (2004); and Molina and Preve (2009) EBITDA/TA Earnings before interest, taxes, depreciation, and amortization/total assets Altman et al (2010) EBIT/S Earnings before interest and taxes/sales Sudarsanam and Lai (2001); Marchesini, Perdue, and Bryan (2004) EBITDA/TD Earnings before interest, taxes, depreciation, and amortization/total debt Sudarsanam and Lai (2001) GP/TA Gross profit/total assets Doumpos and Zopounidis (1998) EBIT/TD Earnings before interest and taxes/total debt Sudarsanam and Lai (2001); Charitou et al (2004) Note. Values in bold indicate statistically significant correlations at the 5% level.…”

Section: Appendix Amentioning

confidence: 99%

Analysis of Correlational Behavior of Solvent and Insolvent Firms Based on Accounting Ratios

Situm¹

2015

JMAA

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The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situation of a firm changes within an unobservable life cycle continuum, comprising different economic states which are not in fact properly defined. The aim of this study was to contribute towards a better understanding of the differences between solvent and insolvent firms for the periods of one and two years prior to insolvency respectively. Through the application of correlation and factor analysis, an attempt was made to detect behavioral patterns in accounting ratios, which can in turn explain differences and similarities between the two groups of firms. The results of this study show that although accounting ratios from two consecutive years had low correlations for both groups of firms, they were much higher for insolvent firms. This provides evidence that the economic and financial situation of insolvent firms is much more dependent on its history when compared to solvent firms. Moreover, there is evidence to suggest that the change of the economic and financial situation of insolvent firms within the life cycle continuum tends to follow a predetermined path, in contrast to the more random nature of a solvent firm's behavior. Additionally, the results showed that the factor loadings for solvent and insolvent firms differ for both observation periods, indicating that there are different underlying factors affecting the final outcomes for the two groups of firms. This is mainly attributable to disturbances in the scaling factors of total assets for both observation periods, as well as the disappearing size factor for the pre-distress year for insolvent firms, based on factor analysis.

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Applying Bankruptcy Prediction Models to Distressed High Yield Bond Issues

Cited by 12 publications

References 19 publications

Probability of default models of Russian banks

Probability of default models of Russian banks

Probability of Default Models of Russian Banks

Analysis of Correlational Behavior of Solvent and Insolvent Firms Based on Accounting Ratios

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