{"id":"W1987434446","doi":"10.1007/s10898-012-9969-1","title":"Portfolio selection under model uncertainty: a penalized moment-based optimization approach","year":2012,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ambiguity; Portfolio; Portfolio optimization; Moment (physics); Mathematical optimization; Flexibility (engineering); Realization (probability); Mathematics; Econometrics; Range (aeronautics); Robust optimization; Selection (genetic algorithm); Computer science; Project portfolio management; Downside risk; Artificial intelligence; Economics; Finance; Statistics; Project management","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":[0.00448492,0.001415108,0.003152167,0.00160101,0.00050273,0.001950278,0.002439278,0.00256414,0.002535017],"category_scores_gemma":[0.01131405,0.001415157,0.001669647,0.00141058,0.00111819,0.002535227,0.00196689,0.002136999,0.0003882382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008589448,"about_ca_system_score_gemma":0.001373893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000576,"about_ca_topic_score_gemma":0.001663397,"domain_scores_codex":[0.9980882,0.00115961,0.00007431058,0.0001810763,0.0003875654,0.0001092357],"domain_scores_gemma":[0.9939633,0.004585059,0.0004710009,0.0002837728,0.0005452759,0.0001515951],"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":[0.00005106493,0.00003708876,0.0001815754,0.00006346303,0.000109045,0.00005710031,0.0000164989,0.9676922,0.0004902888,0.01967256,0.0006685434,0.0109606],"study_design_scores_gemma":[0.000004754707,0.000008043829,0.00003001522,0.00000336103,0.00000776313,0.00000651016,8.5779e-7,0.9962181,0.00005544873,0.003572423,0.00008820836,0.000004447765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004205407,0.000245075,0.9942047,0.0002706144,0.0000305071,0.0000169204,0.00002630674,0.00009507651,0.0009053829],"genre_scores_gemma":[0.5621095,0.001225714,0.4265353,0.0005518719,0.0006024401,0.0004015577,0.0003813107,0.0004782924,0.007714068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00448492,"threshold_uncertainty_score":0.02371883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06337797441665845,"score_gpt":0.3594306067613594,"score_spread":0.2960526323447009,"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."}}