{"id":"W2339592748","doi":"10.1007/s42488-019-00007-w","title":"Multiportfolio optimization with CVaR risk measure","year":2019,"lang":"en","type":"article","venue":"Journal of Data Information and Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Risk measure; Expected shortfall; Coherent risk measure; Measure (data warehouse); Sensitivity (control systems); Convexity; Mathematical optimization; Computer science; Econometrics; Risk analysis (engineering); Economics; Risk management; Mathematics; Data mining; Business; Financial economics; Engineering; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002807505,0.00008711776,0.0001687756,0.0004722156,0.00007421238,0.0003745462,0.0005231368,0.00003315974,0.0001678182],"category_scores_gemma":[0.0001431972,0.0000533023,0.00002541109,0.0004085535,0.00001855555,0.007103585,0.0001598483,0.00009443051,0.00008415862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001525454,"about_ca_system_score_gemma":0.00003215255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006944282,"about_ca_topic_score_gemma":0.000003421772,"domain_scores_codex":[0.9978455,0.00005436203,0.0008124813,0.0001073028,0.001085996,0.00009433107],"domain_scores_gemma":[0.9975472,0.00005459689,0.001342991,0.0005170553,0.0004594292,0.00007873679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000213968,0.00003538782,0.01969364,0.00001669969,0.0001027996,0.000004426204,0.0004608172,0.4664057,3.911751e-7,0.001077918,0.02749838,0.4844898],"study_design_scores_gemma":[0.002229042,0.0002086518,0.01818458,0.00005253318,0.0001032556,0.00005540116,0.002168929,0.3559377,0.000004840082,0.0001880472,0.6207059,0.0001610761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0101315,0.00009049681,0.9723918,0.0003624756,0.0003187111,0.0004163196,0.00004492412,0.00001280832,0.01623098],"genre_scores_gemma":[0.5270388,0.02470548,0.4447623,0.001299655,0.0001637625,0.000006033141,0.0004010594,0.00002041874,0.001602485],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5932075,"threshold_uncertainty_score":0.5149928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04343219132625476,"score_gpt":0.3129802019149304,"score_spread":0.2695480105886756,"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."}}