{"id":"W4235592999","doi":"10.1017/s0022109017000709","title":"CoMargin","year":2017,"lang":"en","type":"article","venue":"Journal of Financial and Quantitative Analysis","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Clearing; Collateral; Margin (machine learning); Derivatives market; Business; Systemic risk; Stability (learning theory); Probability of default; Externality; Actuarial science; Computer science; Econometrics; Credit risk; Economics; Finance; Microeconomics; Futures contract; Financial crisis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557374,0.0005618854,0.0005232714,0.002436014,0.0005240061,0.001548442,0.0008173201,0.0004741966,0.007201696],"category_scores_gemma":[0.009558271,0.0001815856,0.0003010666,0.001753941,0.0003875223,0.001155453,0.0007591159,0.0004703626,0.001269762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004004,"about_ca_system_score_gemma":0.00141486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01314795,"about_ca_topic_score_gemma":0.01812109,"domain_scores_codex":[0.998938,0.000246308,0.0000560193,0.0002651607,0.000413039,0.00008142905],"domain_scores_gemma":[0.9963407,0.001039273,0.0009067247,0.0007777351,0.000791106,0.000144387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004828185,0.0001500773,0.1604481,0.0003299807,0.0002223359,0.0003572893,0.0003712058,0.1739387,0.005427222,0.05850177,0.03531583,0.5644546],"study_design_scores_gemma":[0.00006936023,0.0002539335,0.09270897,0.00009473188,0.00007708408,0.0005106124,0.0001591007,0.7858742,0.01038327,0.03263894,0.07712866,0.0001010801],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3036174,0.001253784,0.620616,0.0006447979,0.0001684295,0.0005546311,0.01205511,0.009179406,0.05191048],"genre_scores_gemma":[0.8528674,0.0002411187,0.1342493,0.0001129407,0.00007388402,0.0001363629,0.004595789,0.0003117626,0.007411398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01314795,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04292225289527968,"score_gpt":0.2872063537349795,"score_spread":0.2442841008396999,"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."}}