Examining What Best Explains Corporate Credit Risk: Accounting-Based Versus Market-Based Models

Trujillo‐Ponce, Antonio; Samaniego‐Medina, Reyes; Cardone‐Riportella, Clara

doi:10.3846/16111699.2012.720598

Cited by 38 publications

(11 citation statements)

References 49 publications

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“…Later on, researchers including Chava and Jarrow (2004) and Agarwal and Taffler (2008) articulated that market-based variables reflecting both internal and external information increase the overall predictability of distress prediction models. Further, Trujillo-Ponce et al (2014) suggested that a combined model with both accounting and market-based variables is the best option, as both types of information are important for distress prediction.…”

Section: Traditional Distress Prediction Modelsmentioning

confidence: 99%

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Ashraf

Fėlix

Serrasqueiro

2019

JRFM

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Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.

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Section: Traditional Distress Prediction Modelsmentioning

confidence: 99%

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Ashraf

Fėlix

Serrasqueiro

2019

JRFM

View full text Add to dashboard Cite

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“…Trujillo -Ponce et al (2014) showed that the comprehensive model, including market-based and accounting-based factors, was the most reliable model for predicting financial distress, compared with the Z-core and KMV-Merton model. Using 2186 credit default swaps (CDS) for the European market in both the pre-GFC (2002)(2003)(2004)(2005)(2006) and GFC (2007GFC ( -2009 periods, the comprehensive model was used to forecast the default probability in the volatile periods.…”

Section: Literature Reviewmentioning

confidence: 99%

Corporate Financial Distress of Industry Level Listings in Vietnam

Pham

Hồ

et al. 2019

JRFM

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Any critical analysis of the corporate financial distress of listed firms in international exchange would be incomplete without a serious dissection at the industry level, because of the different levels of risks concerned. This paper considers the financial distress of listed firms at the industry level in Vietnam over the last decade. Two periods are considered, namely during the Global Financial Crisis (GFC) (2007–2009) and post-GFC (2010–2017). The logit regression technique is used to estimate alternative models based on accounting and market factors. The paper also extends the analysis to include selected macroeconomic factors that are expected to affect the corporate financial distress of listed firms at the industry level in Vietnam. The empirical findings confirm that the corporate financial distress prediction model, which includes accounting factors with macroeconomic indicators, performs much better than alternative models. In addition, the evidence confirms that the GFC had a damaging impact on each sector, with the Health & Education sector demonstrating the most impressive recovery post-GFC, and the Utilities sector recording a dramatic increase in bankruptcies post-GFC.

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“…Several variations and extensions have been made to this basic model. Some of these are: Black and Cox (1976) [8]; Longstaff and Schwartz (1995) [9]; Brockman and Turtle (2003) [10]; Bandopadhyay (2007) [11]; Baninoe (2010) [12]; Laitinen (2010) [13]; Antonio et al (2012) [14]; and Kumar and Kumar (2012) [15]. Jensen and Meckling (1976) [16] and Vishny (1986, 1997) [17] conducted research on the impact of ownership patterns on default.…”

Section: Review Of Literaturementioning

confidence: 99%

A Survival Approach to Prediction of Default Drivers for Indian Listed Companies

Gupta¹

2017

TEL

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The objective of the research study is to identify the key predictors that can explain default risk for Indian listed companies using survival analysis. The author has applied the semi-parametric Cox proportional hazard model to test the impact of financial ratios, capital market ratios, macro-economic variables, size and age of companies, and the ownership structure of promoters to a dataset of 859 companies panning across 10 sectors. Unlike traditional models on default prediction, survival models focus on "time to default" as the dependent variable. The empirical findings reveal that return on capital employed (ROCE), return on net worth (ROE), interest coverage ratio, exchange rate volatility, GDP growth rate, stock index, promoters holdings % and the percent of shares pledged are all significant predictors of default. Among the market variables, it is seen that beta and the ratio of market value of equity/book value of debt are statistically significant variables in explaining default risk. The empirical findings also generate the hazard ratio for each covariate which examines the predicted change in the hazard for a unit increase in the predictor. The author extends the research by applying the marketbased KMV structural model to obtain continuous observations of default probability and regressing the same against all the 1 covariates (Gupta et al.,. It is observed that the set of significant covariates are almost common to those generated by our survival approach. The study concludes in emphasizing the significance of survival models in default prediction as unlike traditional accounting-based and market-based models, these models assess relationship between survival time and covariates. The application of survival models is strongly recommended for credit risk evaluation and modeling as structuring of loans can be done by lenders by assessing the survival times of different firms across the entire observation period being considered.

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Examining What Best Explains Corporate Credit Risk: Accounting-Based Versus Market-Based Models

Cited by 38 publications

References 49 publications

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Corporate Financial Distress of Industry Level Listings in Vietnam

A Survival Approach to Prediction of Default Drivers for Indian Listed Companies

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