8 resultados para Rating credit agencies

em Brock University, Canada


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This thesis examines the quality of credit ratings issued by the three major credit rating agencies - Moody’s, Standard and Poor’s and Fitch. If credit ratings are informative, then prices of underlying credit instruments such as fixed-income securities and credit default insurance should change to reflect the new credit risk information. Using data on 246 different major fixed income securities issuers and spanning January 2000 to December 2011, we find that credit default swaps (CDS) spreads do not react to changes in credit ratings. Hence credit ratings for all three agencies are not price informative. CDS prices are mostly determined by historical CDS prices while ratings are mostly determined by historical ratings. We find that credit ratings are marginally more sensitive to CDS than CDS are sensitive to ratings.

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This thesis investigates whether there are changes in risk-taking behavior following an upgrade or downgrade in credit ratings. Research on effects of rating changes on capital markets is well-documented but the literature on how rating changes may affect firm behavior is sparse. Following, a downgrade in credit rating, managers may increase risk-taking to improve their overall performance or reduce risk-taking following upgrades to ensure that their performance is assessed more on the basis of what they may deem success in the form of an upgrade. Using a sample of firms trading in the U.S from 1994-2013, we find evidence of change in risk-taking behavior. We use cross-sectional regressions and matching using propensity scores and Barber and Lyon (1997) methodology to measure changes in risk-taking and we do find evidence of changes in managerial risk-taking behavior. Furthermore, we find that the direction of change (increase or decrease) in some cases is dependent on the type of measure rather than the type of rating change.

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This study sought to compare the results of the Motivation Assessment Scale (MAS; Durand & Crimmins, 1988), Questions About Behavior Function Scale (QABF; Matson & Vollmer, 1996) and Functional Analysis Screening Tool (FAST; Iwata & Deleon, 1996), when completed by parent informants in a sample of children and youth with autism spectrum disorders (ASD) who display challenging behaviour. Results indicated that there was low agreement between the functional hypotheses derived from each of three measures. In addition, correlations between functionally analogous scales were substantially lower than expected, while correlations between non-analogous subscales were stronger than anticipated. As indicated by this study, clinicians choosing to use FBA questionnaires to assess behavioural function, may not obtain accurate functional hypotheses, potentially resulting in ineffective intervention plans. The current study underscores the caution that must be taken when asking parents to complete these questionnaires to determine the function(s) of challenging behaviour for children/youth with ASD.

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The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.

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October 18, 1814. Read, and committed to the Committee of the whole House on the report of the Committee of Ways and Means on so much of the President's message as relates to the finances of the United States.

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Credit to S.D. Woodruff from Pratt and Company for $150.00, Sept. 7, 1876.

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Credit to S.D. Woodruff from Pratt and Company for $62.00, Nov. 6, 1876.