Determining Factors and their Impacts on the Ratings of Companies and Countries
Bibliographic record
Abstract
In the face of the latest world financial crises, the ratings released by the regulatory agencies have gained distinction in the financial market. This paper proposes models to predict the future ratings of companies and countries. The analysis was carried out using quarterly data from 2010 to 2018 from companies in Brazil, South Africa, Germany, Argentina, Australia, Canada, Chile, China, Colombia, South Korea, the United States, France, Italy, Japan, Mexico, Peru, the United Kingdom, Russia, and India. The sample's number of companies and countries is limited to the availability of rating information and the other model information. We use the panel-ordered logit model for classifying the rating and the other economic and financial variables as an independent. The results show that the financial and economic variables are essential to predict the rating of financial and non-financial companies in Brazil as well as the sovereign rating of the sample countries. The predictive capacity of the models reached values close to 80%, emphasizing the forecasts of large banks with 94% accuracy. For the country sample, the results are close to 80% accuracy. With the results of the research, improvement in the financial and economic indicators and the increase in the predictive capacity of the market agents for the prior determination of future ratings of financial companies are expected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".