A Comparative Study with Quantile Regression and Back Propagation Neural Network for Credit Rating
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".