{"id":"W4225682213","doi":"10.3390/jrfm15050223","title":"Deep Partial Hedging","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Cooperation in Science and Technology","keywords":"Context (archaeology); Transaction cost; Replicate; Stochastic game; Computer science; Database transaction; Pairs trade; Econometrics; Artificial intelligence; Mathematical economics; Economics; Microeconomics; Mathematics; Financial economics; Algorithmic trading; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007307324,0.0006509278,0.0007093229,0.0002930891,0.0002695596,0.00122031,0.0009423963,0.0008320367,0.006932687],"category_scores_gemma":[0.002049553,0.0003757428,0.0005878906,0.0003615692,0.0006175028,0.001721583,0.001459306,0.001412992,0.0005960132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006012294,"about_ca_system_score_gemma":0.0005972773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00162295,"about_ca_topic_score_gemma":0.002539583,"domain_scores_codex":[0.9998016,0.00004418343,0.00001410665,0.00004509915,0.0000625973,0.00003247561],"domain_scores_gemma":[0.9995046,0.0002125987,0.0000458652,0.0001185625,0.0000830654,0.00003532899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008894251,0.0000672496,0.001064221,0.0001092298,0.0001005821,0.0001665071,0.00007619287,0.7322775,0.004947994,0.1074753,0.00322947,0.1503969],"study_design_scores_gemma":[0.000003913189,0.00001793579,0.0001021193,0.000007022055,0.000005888965,0.00002906026,0.000003247317,0.9680865,0.0006967257,0.03018221,0.0008603837,0.000004927693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03085039,0.0006968112,0.9569682,0.0003500293,0.0001281183,0.00003607599,0.0001434024,0.0005215855,0.01030541],"genre_scores_gemma":[0.8754405,0.0005197129,0.1079988,0.0002606888,0.00007128827,0.00007608329,0.0002627808,0.000100356,0.01526978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006932687,"threshold_uncertainty_score":0.02319211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340910780995689,"score_gpt":0.1907916558125911,"score_spread":0.1773825480026342,"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."}}