scholarly journals Evidence Of Nonstationarity In the Bond Rating Process For Newly Issued Bonds: A Note

2011 ◽  
Vol 8 (1) ◽  
pp. 124 ◽  
Author(s):  
Mai E. Iskandar
Keyword(s):  
Empirical Evidence ◽  
Prediction Models ◽  
Parameter Estimates ◽  
Lower Grade ◽  
Investment Strategies ◽  
Bond Rating ◽  
Rating Agencies ◽  
Rating Process ◽  
Rating Prediction ◽  
Grade One

This study documents nonstationarity of the bond rating process. The empirical evidence suggests that not only the parameter estimates exhibit nonstationarity but also the bond rating process itself. The source of nonstationarity is found to be externally caused and non agency-specific. Further examination leads us to stipulate that rating agencies apply stricter standards to lower grade issues than to higher grade one when the economy is in a recession. The results have implications for bond investment strategies as well as for the utilization of bond rating prediction models.

scholarly journals Novel SDDM Rating Prediction Models for Recommendation Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 101197-101206
Author(s):  
Diao Zhou ◽  
Shengnan Hao ◽  
Haiyang Zhang ◽  
Chenxu Dai ◽  
Yongli An ◽  
...  
Keyword(s):  
Prediction Models ◽  
Recommendation Systems ◽  
Rating Prediction

scholarly journals Do corporate bond rating revisions convey information about earnings and dividend changes?

2012 ◽  
Vol 9 (3) ◽  
pp. 373-393 ◽  
Author(s):  
Steven T. Anderson ◽  
Gurmeet Singh Bhabra ◽  
Harjeet S. Bhabra ◽  
Asjeet S. Lamba
Keyword(s):  
Stock Returns ◽  
Cash Flows ◽  
Corporate Bond ◽  
Bond Rating ◽  
Future Earnings ◽  
Rating Agencies ◽  
Abnormal Stock Returns ◽  
Rating Changes ◽  
Future Cash Flows ◽  
Rating Downgrades

We study the information content of corporate bond rating changes regarding future earnings and dividends. Consistent with previous findings, rating downgrades are associated with negative abnormal stock returns, while rating upgrades appear to be nonevents. For downgrades, earnings decline in the two years prior to and the year of the rating change announcement but increase in the year after the rating review. We also find that rating downgrades are followed by a subsequent downward adjustment in dividends. While rating upgrades follow a period of rising earnings, they do not signal any increase in future earnings and no subsequent dividend adjustments are observed. Overall, our results indicate that rating agencies respond more to permanent changes in cash flows and provide little information, if any, about future cash flows.

scholarly journals Bond Ratings And Their Determinants In A Changing Environment

2011 ◽  
Vol 9 (1) ◽  
pp. 132 ◽  
Author(s):  
Christina Ho ◽  
Ramesh P. Rao
Keyword(s):  
Changing Environment ◽  
Bond Ratings ◽  
Bond Rating ◽  
Rating Agencies ◽  
Stable Period ◽  
Over Time ◽  
Bond Rating Agencies

This study finds that bond rating agencies, to the extent that their behavior is captured in statistical rating models, tend to emphasize different variables over time and that this appears to be systematically related to the economic macro-environment. Specifically, the study finds that bond ratings are more sensitive to various measures of cashflow stability and solvency in an economically unstable period relative to a more stable period.

Debt Securities

2019 ◽  
pp. 139-156
Author(s):  
Alan N. Rechtschaffen
Keyword(s):  
Market Price ◽  
Cash Flows ◽  
Bond Rating ◽  
Treasury Securities ◽  
Rating Agencies ◽  
Interest Payments ◽  
Bond Rating Agencies

Debt instruments obligate an issuer to make interest payments and repay principal to the buyer according to the terms of an agreement between the lender and the borrower. The yield, or market price of these debt securities is related to the yield on U.S. Treasury securities. Treasuries remain the benchmark for risk-free credit investing, and other yields are related to the risk-free return Treasuries offer. Also known as bonds, debt instruments are attractive to investors because they can provide a reliable stream of cash flows in the form of interest payments and also might provide for the repayment of principal upon maturity. This chapter discusses the features of bonds, types of bonds, bond-rating agencies, special types of debt instruments, and the Securities Act.

scholarly journals Credit Rating Changes and Subordinated Bond Spread: Evidence from China

2015 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Robin Hang Luo ◽  
Jiaji Hao
Keyword(s):  
Credit Rating ◽  
Informational Content ◽  
Credit Rating Agencies ◽  
Sample Period ◽  
Bond Rating ◽  
Rating Agencies ◽  
Rating Changes ◽  
Statistical Results

We examine the bond spread reaction to subordinated bond rating changes during the sample period of 2006 to 2011 and find that bond spread reacted positively to downgrades, big in magnitude, but not statistically significant. The bond spread reaction to upgrades, however, was mixed and statistically insignificant, and small in magnitude. We conjecture that the insignificant statistical results regarding the effect of rating changes may be due to the lack of informational content of the ratings assigned to the subordinated bonds by Chinese credit rating agencies (CRAs). 

Incident Occurrence Models for Freeway Incident Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sravanthi Konduri ◽  
Samuel Labi ◽  
Kumares C. Sinha
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Traffic Volume ◽  
Incident Management ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Support Tool

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).

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