{"id":"W4293232070","doi":"10.48550/arxiv.2203.02599","title":"A reverse ES (CVaR) optimization formula","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CVAR; Mathematics; Simple (philosophy); Function (biology); Applied mathematics; Mathematical optimization; Convex function; Optimization problem; Regular polygon; Expected shortfall; Finance; Risk management; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.004047472,0.001193298,0.001278985,0.001086791,0.0004811456,0.00264605,0.00143512,0.001763833,0.007856729],"category_scores_gemma":[0.0179817,0.0005068509,0.0009634083,0.0009929659,0.001792371,0.00422776,0.001927258,0.00316117,0.001011175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001476413,"about_ca_system_score_gemma":0.001321542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001050111,"about_ca_topic_score_gemma":0.0007121157,"domain_scores_codex":[0.9979368,0.0006889993,0.0001277747,0.0004540936,0.0006346639,0.000157688],"domain_scores_gemma":[0.9957396,0.002548111,0.0004644131,0.0003903742,0.0007197782,0.0001377707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005446539,0.00003601694,0.0007628127,0.0001340877,0.00004554817,0.0001494199,0.00008037189,0.2918565,0.002291101,0.6501693,0.006407034,0.04801336],"study_design_scores_gemma":[0.00001121155,0.00004631703,0.0002362232,0.00004081165,0.00001745285,0.0001202735,0.00001829411,0.688464,0.0009643273,0.3067325,0.003322063,0.0000265775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01431754,0.0009186724,0.9683052,0.001533954,0.0001587804,0.0000486853,0.0002366774,0.0002919366,0.01418868],"genre_scores_gemma":[0.7055342,0.001851132,0.2691159,0.001448939,0.0004925945,0.0003732851,0.0006486544,0.0007097985,0.01982556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007856729,"threshold_uncertainty_score":0.02628338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1882988114867621,"score_gpt":0.266905202768457,"score_spread":0.07860639128169483,"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."}}