{"id":"W2971439546","doi":"10.1057/s41260-019-00132-6","title":"Sensitivity of optimal portfolio problems to time-varying parameters: simulation analysis","year":2019,"lang":"en","type":"article","venue":"Journal of Asset Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Expected shortfall; Portfolio optimization; Portfolio; Sensitivity (control systems); Diversification (marketing strategy); Mathematical optimization; Asset allocation; Computer science; Econometrics; Economics; Mathematics; Financial economics; Engineering; Business","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":[0.003247715,0.000662868,0.001135081,0.001212431,0.0004535377,0.001294722,0.0009720252,0.002812621,0.002393841],"category_scores_gemma":[0.02088884,0.0006175408,0.001265518,0.0009320679,0.001190087,0.001200781,0.0009105942,0.002013738,0.0001421854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001330502,"about_ca_system_score_gemma":0.0009219241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01260698,"about_ca_topic_score_gemma":0.004842736,"domain_scores_codex":[0.9991753,0.0004303273,0.0000400719,0.00008112041,0.0001112038,0.0001619564],"domain_scores_gemma":[0.9706315,0.02672298,0.0009242915,0.0006533539,0.0007598658,0.0003080477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006225744,0.00007942309,0.0008498257,0.00001800159,0.00002463606,0.00003198595,0.00001471485,0.996493,0.0002005662,0.001314615,0.0001166031,0.000794359],"study_design_scores_gemma":[0.0000197087,0.00003484748,0.0003396544,0.000005274027,0.00001030142,0.00001108469,0.00001269177,0.9985358,0.0001741646,0.0008024105,0.00004815156,0.000005910711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524338,0.0008359682,0.03806119,0.0007815222,0.00007078033,0.00008562004,0.0004514291,0.0001249809,0.007154802],"genre_scores_gemma":[0.9945858,0.000164638,0.004384473,0.0000454044,0.00000925211,0.00003898004,0.0001374612,0.0000141925,0.0006198675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01260698,"threshold_uncertainty_score":0.02506721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04155019774885427,"score_gpt":0.3471638455101629,"score_spread":0.3056136477613086,"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."}}