{"id":"W2397767657","doi":"10.1016/j.najef.2016.04.004","title":"Can statistics-based early warning systems detect problem banks before markets?","year":2016,"lang":"en","type":"article","venue":"The North American Journal of Economics and Finance","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Sophistication; Warning system; Portfolio; Econometrics; Early warning system; Business; Function (biology); Actuarial science; Economics; Financial economics; Computer science","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":[0.00344875,0.0007794222,0.001017984,0.002933491,0.0003709538,0.002581416,0.001026695,0.002148328,0.002382277],"category_scores_gemma":[0.03925582,0.0004143584,0.0002807742,0.001404026,0.0006347646,0.004194797,0.000786448,0.001435579,0.001001136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005745342,"about_ca_system_score_gemma":0.001581295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003508815,"about_ca_topic_score_gemma":0.003948352,"domain_scores_codex":[0.9989761,0.0003897409,0.000115114,0.0001727676,0.0002336926,0.000112555],"domain_scores_gemma":[0.9799557,0.01046553,0.003904051,0.0009362468,0.004110878,0.000627687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0019141,0.0007628495,0.3177337,0.0004035306,0.0004335805,0.0003985398,0.0003248893,0.1603371,0.00642901,0.02581012,0.05590216,0.4295504],"study_design_scores_gemma":[0.0001843216,0.0003279707,0.05927452,0.00009517044,0.0001132532,0.000239566,0.0004098173,0.8750798,0.004827437,0.05392434,0.005416278,0.0001074824],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5932215,0.004093687,0.3522294,0.02770421,0.002493891,0.0002055702,0.002949759,0.004471177,0.01263079],"genre_scores_gemma":[0.9676046,0.0005890509,0.02831178,0.0008990528,0.000514094,0.00004556638,0.000646672,0.00005801516,0.001331195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003508815,"threshold_uncertainty_score":0.01823896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009820405280929635,"score_gpt":0.1737111088350229,"score_spread":0.1638907035540932,"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."}}