A Comparative Study with Quantile Regression and Back Propagation Neural Network for Credit Rating
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Bibliographic record
Abstract
In this study, we use the quantile regression and the back propagation neural network to construct a credit rating model for companies listed in Taiwan Stock Exchange and Over-The-Counter. The data we use is from 1997 to 2013 in Taiwan. The data in the period from 1997 to 2005 is in sample and the data in the period from 2006 to 2013 is out of sample. TCRI established by TEJ is used as a dependent variable to analyze the relationship between 12 financial ratios and credit rating. Our results show that the average forecasting correction rate based on the propagation neural network, which is about 70%, is higher than that based on the quantile regression, which is about 60%. However, investors and financial institution are mainly concerned about the companies facing bankruptcy so they are more interested in which companies bear higher risk. In this case, the quantile regression can provide higher forecasting correction rate for low-credit-ranking companies, which is about 80%, than that provided by the back propagation neural network, which is about 55%.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it