Assessing the Probability of Bankruptcy

Hillegeist, Stephen A.; Keating, Elizabeth K.; Cram, Donald P.; Lundstedt, Kyle G.

doi:10.1023/b:rast.0000013627.90884.b7

Cited by 1,059 publications

(636 citation statements)

References 35 publications

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“…Hillegeist et al (2004), e.g., incorporate BSM PDs in a hazard model and find that they contain significantly more information on credit risk than popular accounting-based measures. In related exercises, Bharath and Shumway (2004) and Duffie, Saita, and Wang (2007) reach the conclusion that a volatility-adjusted leverage-which is a key parameter in the BSM model and is also known as distance to default (DD)-plays an important role in, respectively, a sufficient statistic for the PD and forecasts of the PD term structure.…”

Section: Related Literaturementioning

confidence: 99%

An Empirical Evaluation of Structural Credit Risk Models

Tarashev

2005

SSRN Journal

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This paper evaluates the capacity of five structural creditrisk models to forecast default rates. In contrast to previous studies with similar objectives, the paper employs firm-level data and finds that model-based forecasts of default rates tend to be unbiased and to deliver point-in-time errors that are small in both statistical and economic terms. In addition, inand out-of-sample regression analysis reveals that the models account for a significant portion of the variability of credit risk over time but fail to fully reflect its dependence on macroeconomic cycles.JEL Codes: G33, E44, G28, C13.

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Section: Related Literaturementioning

confidence: 99%

An Empirical Evaluation of Structural Credit Risk Models

Tarashev

2005

SSRN Journal

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“…2 The purpose of this paper is to compare the two bankruptcy prediction models, DHM and Merton model, on their out-of-sample performance. Similar to Hillegeist et al (2004), we employ relative information content tests to compare the out-of-sample performance of their PB measures. 3 The variable Age denotes a firm's age which is defined as the number of calendar years it has been listed on the NYSE/AMEX/ NASDAQ (Shumway 2001).…”

Section: Methodsmentioning

confidence: 99%

“…Based on the above predictors, we calculated out-ofsample PB-DHM using an expanding rolling window approach (Hillegeist et al 2004;Chava et al 2008). For the first window, we estimated updated coefficients of DHM using the firm-year data from 1983 to 1994 and bankruptcy outcomes from 1984 to 1995.…”

Section: Stochastic Frontier Modelmentioning

confidence: 99%

“…1 The performance of bankruptcy prediction models was mainly assessed in the literature by performing prediction-oriented tests; see for example the above references. Recently, Hillegeist et al (2004) proposed a different approach to do it. They compared the information content about credit risk provided by out-of-sample values of probability of bankruptcy (PB) based on Merton model, Z-Score based on discriminant analysis model, and O-Score based on logit model.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, Agarwal and Taffler (2008) pointed out that there is little difference between PB based on Merton model and Z-Score, in terms of predictive ability and information content. The results in Hillegeist et al (2004) and Agarwal and Taffler (2008) were developed by comparing ''static'' bankruptcy prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Assessing bankruptcy prediction models via information content of technical inefficiency

et al. 2011

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We use a stochastic frontier model with firmspecific technical inefficiency effects in a panel framework (Battese and Coelli in Empir Econ 20:325-332, 1995) to assess two popular probability of bankruptcy (PB) measures based on Merton model (Merton in J Financ 29:449-470, 1974) and discrete-time hazard model (DHM; Shumway in J Bus 74: 2001). Three important results based on our empirical studies are obtained. First, a firm with a higher PB generally has less technical efficiency. Second, for an ex-post bankrupt firm, its PB tends to increase and its technical efficiency of production tends to decrease, as the time to its bankruptcy draws near. Finally, the information content about firm's technical inefficiency provided by PB based on DHM is significantly more than that based on Merton model. By the last result and the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagán in Q J Bus Econ 41:27-41, 2002), we conclude that PB based on DHM is a better credit risk proxy of firms.

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Financial Distress and Bankruptcy

Cornaggia

2011

Capital Structure and Corporate Financing Decisions

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Assessing the Probability of Bankruptcy

Cited by 1,059 publications

References 35 publications

An Empirical Evaluation of Structural Credit Risk Models

An Empirical Evaluation of Structural Credit Risk Models

Assessing bankruptcy prediction models via information content of technical inefficiency

Financial Distress and Bankruptcy

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