{"id":"W4386925133","doi":"10.2139/ssrn.4578801","title":"A Machine Learning Approach in Stress Testing Us Bank Holding Companies","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Stress testing (software); Business; Stress (linguistics); Computer science; Linguistics; Operating system","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":[0.001770408,0.0005332804,0.0005159321,0.001958709,0.0004616928,0.0008258023,0.0009838537,0.001159259,0.001922525],"category_scores_gemma":[0.005526968,0.0002034745,0.000409401,0.001248002,0.0003272044,0.0007653536,0.0005362997,0.0008768875,0.0003395626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005146778,"about_ca_system_score_gemma":0.0006425804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008818278,"about_ca_topic_score_gemma":0.009530215,"domain_scores_codex":[0.9991373,0.0004643914,0.00007018968,0.000133268,0.0001070116,0.00008787217],"domain_scores_gemma":[0.9958775,0.003286516,0.0001397561,0.0001421933,0.0004293179,0.0001247541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007312403,0.00170139,0.1146895,0.0001077053,0.0002456967,0.0003627124,0.0001713583,0.2575301,0.006578787,0.003888624,0.004027742,0.6099653],"study_design_scores_gemma":[0.00001999307,0.00009590206,0.008486078,0.000005346717,0.00001951817,0.00002833903,0.00005282655,0.9884448,0.0007388681,0.001907808,0.0001941042,0.000006479328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7753324,0.000837451,0.2160707,0.001474229,0.0001020758,0.0001678469,0.0005300653,0.0007672891,0.004718112],"genre_scores_gemma":[0.9752718,0.00006241476,0.02313618,0.00006996951,0.00005697471,0.00002955246,0.0002292858,0.00001119014,0.001132721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008818278,"threshold_uncertainty_score":0.0175339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0297755632748335,"score_gpt":0.2315375124485417,"score_spread":0.2017619491737082,"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."}}