{"id":"W3093094698","doi":"10.1016/j.ejor.2023.02.014","title":"Portfolio selection: A target-distribution approach","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS","keywords":"Efficient frontier; Portfolio; Portfolio optimization; Variance (accounting); Selection (genetic algorithm); Econometrics; Computer science; Modern portfolio theory; Asset (computer security); Gaussian; Rate of return on a portfolio; Post-modern portfolio theory; Mathematical optimization; Investment strategy; Mathematics; Economics; Replicating portfolio; Finance; Market liquidity; Artificial intelligence","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":[0.006778728,0.001462107,0.002357301,0.002596691,0.0006013543,0.003185416,0.002963181,0.002381745,0.003497958],"category_scores_gemma":[0.01444778,0.0006771552,0.001350175,0.002706494,0.001344316,0.003414072,0.002680227,0.002425281,0.0008263155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001503952,"about_ca_system_score_gemma":0.001288152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007052832,"about_ca_topic_score_gemma":0.0006186477,"domain_scores_codex":[0.9957283,0.00185199,0.0001615761,0.0006515007,0.001315906,0.0002906778],"domain_scores_gemma":[0.9942461,0.003586854,0.000531238,0.0005855205,0.0008284209,0.0002218476],"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":[0.0001377437,0.0001935635,0.002837065,0.0002208318,0.000313279,0.0003001122,0.0001166364,0.628826,0.003127583,0.2238232,0.003706224,0.1363978],"study_design_scores_gemma":[0.00002732617,0.00008143915,0.0004612733,0.00002342682,0.00003099917,0.0001293067,0.00001523594,0.9102654,0.0008755751,0.08563398,0.002436723,0.00001944632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003640232,0.0003363687,0.993763,0.000251813,0.0000231999,0.00004043343,0.00004081064,0.0001075853,0.001796611],"genre_scores_gemma":[0.6308563,0.001810763,0.3571843,0.0005574403,0.0004660281,0.0005010505,0.0005110962,0.0002660773,0.007846896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006778728,"threshold_uncertainty_score":0.03584981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2537355990549406,"score_gpt":0.4552823099094369,"score_spread":0.2015467108544963,"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."}}