{"id":"W2506490916","doi":"10.12735/jfe.v4n2p46","title":"A Comparative Study with Quantile Regression and Back Propagation Neural Network for Credit Rating","year":2016,"lang":"en","type":"article","venue":"Journal of Finance & Economics","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile regression; Econometrics; Artificial neural network; Ranking (information retrieval); Quantile; Sample (material); Bankruptcy; Stock exchange; Actuarial science; Business; Economics; Statistics; Computer science; Artificial intelligence; Finance; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659129,0.0001051756,0.0002590725,0.00005290094,0.0001467166,0.0001132776,0.00007891312,0.00002987354,0.000008182546],"category_scores_gemma":[0.00002729579,0.00006366663,0.00003961233,0.00006570157,0.00003479095,0.001304858,0.00002673468,0.0000613191,0.000004173884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002768921,"about_ca_system_score_gemma":0.00002554587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001287093,"about_ca_topic_score_gemma":0.00009311428,"domain_scores_codex":[0.9993245,0.000006593283,0.0003498944,0.0001288743,0.00005333583,0.0001368238],"domain_scores_gemma":[0.9988253,0.00004207113,0.0008806132,0.00006870865,0.0001760429,0.000007235768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009122057,0.0009110911,0.728145,0.0003569831,0.0002138404,0.00002965922,0.0008437163,0.02614491,0.0009443836,0.02543239,0.04040781,0.1674481],"study_design_scores_gemma":[0.009944348,0.002320426,0.8395548,0.00153456,0.0002291589,0.0000369386,0.001197668,0.09359255,0.0002246781,0.004467476,0.04624217,0.0006552623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962122,0.0000755917,0.002488066,0.0003368242,0.0003908735,0.0003103212,0.000004342114,0.000006364392,0.0001753969],"genre_scores_gemma":[0.9974035,0.00003714194,0.000732005,0.00005999361,0.001685351,0.00001005895,0.000002157889,0.00000978677,0.00006002034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1667929,"threshold_uncertainty_score":0.259625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0262784748678595,"score_gpt":0.2428204100422121,"score_spread":0.2165419351743526,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}