{"id":"W4404518627","doi":"10.2139/ssrn.5015915","title":"Bayesian Clustering for Portfolio Credit Risk","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Portfolio; Credit risk; Bayesian probability; Econometrics; Economics; Computer science; Actuarial science; Business; Financial economics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006638304,0.001279317,0.003937958,0.004133568,0.001657616,0.003085082,0.004066617,0.004001019,0.006412168],"category_scores_gemma":[0.03670096,0.001994366,0.002224849,0.003797137,0.002261625,0.004525507,0.002572944,0.003721346,0.001641402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002990872,"about_ca_system_score_gemma":0.001837728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385559,"about_ca_topic_score_gemma":0.008779913,"domain_scores_codex":[0.9975867,0.001231208,0.0001212007,0.0005264269,0.0003760065,0.0001584647],"domain_scores_gemma":[0.9807268,0.01453403,0.001257567,0.001816799,0.001100855,0.0005639318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000141039,0.0001074823,0.001982089,0.0001633166,0.0002132225,0.00006473786,0.0001555707,0.6834279,0.0006644525,0.2532687,0.007301084,0.05251041],"study_design_scores_gemma":[0.00001587395,0.000008079087,0.000450031,0.00001700068,0.00001282573,0.00001734871,0.000009930625,0.8188082,0.0000847537,0.1799328,0.0006251188,0.00001801292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02095262,0.00139594,0.974059,0.0007096909,0.00006794362,0.00007491138,0.000456407,0.0004340865,0.001849316],"genre_scores_gemma":[0.560069,0.004746702,0.4067652,0.0005253458,0.001148691,0.0006628942,0.004744362,0.0008690999,0.02046859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385559,"threshold_uncertainty_score":0.03510714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201573015915324,"score_gpt":0.2455702402914886,"score_spread":0.2235545101323353,"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."}}