This study develops and evaluates a model that generates synthetic credit ratings using accounting...

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This study develops and evaluates a model that generates synthetic credit ratings using accounting and market based information. The model performs very well in explaining agency ratings, suggesting that fitted values for unrated companies are likely to be reasonably precise. In addition, the synthetic credit ratings help explain cross sectional differences in CDS spreads, even after controlling for contemporaneous agency ratings. The incremental information provided by agency ratings relative to the synthetic ratings has declined substantially in recent years, possibly due to new SEC regulation that limits rating agencies’ ability to obtain confidential information from rated companies. Consistent with the finding that agency ratings do not fully impound the information in the synthetic credit ratings, differences between the synthetic and agency ratings predict changes in agency ratings in subsequent months, especially for small companies. This relationship is very significant, both statistically and economically, and while it monotonically declines over the forecasting horizon, the difference between the synthetic and agency ratings predicts changes in agency ratings as far as 24 months later. There is no evidence of substantial improvement over the last thirty years in the timeliness of agency ratings with respect to the information in synthetic ratings. Investors, in contrast, appear to process the synthetic rating information in a timely fashion, as the difference between the synthetic and agency ratings does not predict changes in CDS spreads or in stock prices. JEL Classification: G12, G14, G24, G28, G32, M41 Keywords: Credit ratings, rating agencies, credit risk, CDS,
	
Document Preview: Synthetic Credit Ratings and the Inefficiency of Agency Ratings

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Doron Nissim
 
Columbia Business School

July 2017

Abstract 
 
This study develops and evaluates a model that generates synthetic credit ratings using accounting 
and market based information. The model performs very well in explaining agency ratings, 
suggesting that fitted values for unrated companies are likely to be reasonably precise. In addition, 
the synthetic credit ratings help explain cross sectional differences in CDS spreads, even after 
controlling for contemporaneous agency ratings. The incremental information provided by agency 
ratings relative to the synthetic ratings has declined substantially in recent years, possibly due to 
new SEC regulation that limits rating agencies’ ability to obtain confidential information from 
rated companies. Consistent with the finding that agency ratings do not fully impound the 
information in the synthetic credit ratings, differences between the synthetic and agency ratings 
predict changes in agency ratings in subsequent months, especially for small companies. This 
relationship is very significant, both statistically and economically, and while it monotonically 
declines over the forecasting horizon, the difference between the synthetic and agency ratings 
predicts changes in agency ratings as far as 24 months later. There is no evidence of substantial 
improvement over the last thirty years in the timeliness of agency ratings with respect to the 
information in synthetic ratings. Investors, in contrast, appear to process the synthetic rating 
information in a timely fashion, as the difference between the synthetic and agency ratings does 
not predict changes in CDS spreads or in stock prices.   
 
JEL Classification: G12, G14, G24, G28, G32, M41  
Keywords: Credit ratings, rating agencies, credit risk, CDS, financial statement analysis, market 
efficiency

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 604 Uris Hall, Columbia Business School, New York, NY...

Robert · Accepted Answer

‘Synthetic credit rating generators efficient and credit quality information’s than 
Agency Ratings ’, Do you agree? Explain. 
Different market participants use information of credit ratings differently, as they 
need. Not only participants are using the information for business analysis but also for 
investing. Presently, credit rating agencies are not paying much attention to all firms, more 
rating emphasis on large companies than small companies. The agencies are reviewing 
data and adjusting the relevant data for ratings, reviewing is on periodic basis in place of 
continuous basis, hence which have adverse impact on credit quality. Most of the Credit 
rating agencies(CRA) bothered on the stability of credit rating  rather than informative, 
could affect ratings timeliness altogether deliberately bias (that too selective ones). Though 
overall Credit rating agency explanatory power for CDS spreads is larger than that of the 
synthetic ratings, as Credit rating agencies considers both financial ratios and qualitative 
factors like regulatory issues, narrative disclosure, corporate governance, above and 
beyond some confidential information used in estimating the synthetic ratings. But with the 
introduction of SEC regulations, incremental information difference between agency 
ratings and synthetic ratings has consequently declined, in recent years. 
While adjusting ratings based on available information CRAs considers the companies 
relative credit quality which is not as systematically as synthetic rating. The synthetic 
ratings while calculating involves all related determinants of  across all rated companies 
and  evaluate using  mathematical and statistical tools like log, regressions, correlations 
and many, to determine transformed variables. (Based on competitors companies having 
same rating).Hence, the predictive- ability of synthetic rating is have weightage than 
CRAs. 
Evaluation of Synthetic rating accuracy in terms of their ability with reference to 
agency ratings and CDS spreads. 
EBITDA is considered to be the most important metric while evaluating debt 
capacity. It is the primary attribute to measure credit ratings. While Credit rating, analysts 
focused on EBITDA than EBIT which effect differently on evaluation of credit quality. 
Depreciation is measured on some assumptions like useful lives, residual value, can be 
easily figure out.

This study develops and evaluates a model that generates synthetic credit ratings using accounting and market based information. The model performs very well in explaining agency ratings, suggesting...

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