Credit Scoring and Ratings Research Papers (original) (raw)

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Recent papers in Credit Scoring and Ratings

Purpose – This study aims to present an empirical model designed to forecast bank credit ratings
using only quantitative and publicly available information from their financial statements.
For this reason, the authors use the long-term ratings provided by Fitch in 2012. The sample consists
of 92 US banks and publicly available information in annual frequency from their financial statements
from 2008 to 2011.
Design/methodology/approach – First, in the effort to select the most informative regressors
from a long list of financial variables and ratios, the authors use stepwise least squares and select
several alternative sets of variables. Then, these sets of variables are used in an ordered probit
regression setting to forecast the long-term credit ratings.
Findings – Under this scheme, the forecasting accuracy of the best model reaches 83.70 percent
when nine explanatory variables are used.
Originality/value – The results indicate that bank credit ratings largely rely on historical data
making them respond sluggishly and after any financial problems are already known to the public

- by Theophilos Papadimitriou and +1
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- Econometrics, Credit Scoring and Ratings

Purpose – This study aims to present an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason, the authors use the long-term ratings provided by Fitch in 2012. The sample consists of 92 US banks and publicly available information in annual frequency from their financial statements from 2008 to 2011. Design/methodology/approach – First, in the effort to select the most informative regressors from a long list of financial variables and ratios, the authors use stepwise least squares and select several alternative sets of variables. Then, these sets of variables are used in an ordered probit regression setting to forecast the long-term credit ratings. Findings – Under this scheme, the forecasting accuracy of the best model reaches 83.70 percent when nine explanatory variables are used. Originality/value – The results indicate that bank credit ratings largely rely on historical ...

- by Periklis Gogas
- •
- Econometrics, Forecasting, Feature Selection, Credit Rating

Probability of Default (PD) is a financial term describing the likelihood of default over a particular time horizon. This concept has attracted a lot of interest ever since the late 1960 " s and has been extended to the banking sector to predict probability of failure as well as bank performance ratings. We derive the probability of bankruptcy and bank ratings in a Zimbabwean context based on data between 2009 and 2013, inclusive. We build a model to predict the probability of bank failure twelve months in advance for Zimbabwean banks based on twelve micro factors. Further, we build the corresponding rating model. The empirical analysis revealed that the warning signal so developed produced a robust result with a high prediction accuracy of 92.31% compared to 60% of the Altman " s Z Score model.

- by Victor Gumbo
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- Credit Scoring and Ratings

The paper aims to present duopoly based credit-rating business in Pakistan that comprises on two companies i.e., (a) Pakistan Credit Rating Agency Limited (PACRA) and (b) Japan Credit Rating – Vital Information Services Credit Rating Co. Ltd. (JCR-VIS). This research presents comparisons between two credit rating agencies on six operational issues; (a) Services Offered; (b) Pricing Issues; (c) Rating Processes; (d) Rating Scales and (e) Strategic Issues for both companies. It provides valuable insights in understanding the local credit rating industry and selection of a particular agency for credit rating.

- by Dr.Kamran Siddiqui
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- Credit Rating, Credit Rating Agencies, Credit Scoring and Ratings

Probability of Default (PD) is a financial term describing the likelihood of default over a particular time horizon. This concept has attracted a lot of interest ever since the late 1960"s and has been extended to the banking sector to predict probability of failure as well as bank performance ratings. We derive the probability of bankruptcy and bank ratings in a Zimbabwean context based on data between 2009 and 2013, inclusive. We build a model to predict the probability of bank failure twelve months in advance for Zimbabwean banks based on twelve micro factors. Further, we build the corresponding rating model. The empirical analysis revealed that the warning signal so developed produced a robust result with a high prediction accuracy of 92.31% compared to 60% of the Altman"s Z Score model.

There was an outcry from policymakers over sovereign credit rating downgrades of African countries during the unprecedented COVID-19 lockdown periods. This study investigates whether sovereign downgrades during the time African countries were hit by COVID-19 had an impact on sovereign bond yields. Applying an event study analysis on the Eurobonds yields of 4 African countries that were downgraded during this period shows that there is significant evidence of excess volatility around the downgrade event and a net increase in yields within the rating event window. The results align to the view that rating agencies negatively impact macroeconomic conditions through their procyclical ratings. Hence, ratings should be regulated and controlled during crises times to avoid the procyclical impact of ratings.

- by Misheck Mutize
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- Credit Rating, Credit Ratings, Sovereign Credit Rating, Credit Rating Agencies

Credit Scoring and Ratings Research Papers (original) (raw)

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