{"id":"W185892825","doi":"10.1023/a:1021806200854","title":"Portfolio Selection and Transactions Costs","year":2003,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Portfolio; Mathematics; Upper and lower bounds; Selection (genetic algorithm); Asset (computer security); Mathematical optimization; Covariance matrix; Econometrics; Finance; Statistics; Economics; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005177751,0.0001338152,0.0001548744,0.0003107745,0.0005549037,0.0002716608,0.00009078732,0.00007911523,0.0004658746],"category_scores_gemma":[0.0001237149,0.0001263123,0.00003828398,0.001065628,0.00008793553,0.0003846638,0.000009285953,0.00009000961,0.00003125069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003138891,"about_ca_system_score_gemma":0.00008530689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005996953,"about_ca_topic_score_gemma":0.000005222949,"domain_scores_codex":[0.9984401,0.0001127244,0.0004665946,0.0004255161,0.0004176443,0.0001373902],"domain_scores_gemma":[0.9986651,0.0003658479,0.0001857472,0.0001461724,0.0004752374,0.0001619029],"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":[0.000003125868,0.0000427044,0.001307695,0.000001005399,0.000007805085,1.291765e-7,0.00004497928,0.8722714,0.00000553307,0.1050076,0.0003470622,0.02096095],"study_design_scores_gemma":[0.000514026,0.00003027865,0.003011428,0.00000391176,0.00002913635,0.00008753952,0.0002651539,0.9035794,0.00003921272,0.03594692,0.05625942,0.0002335442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001289232,0.0001768134,0.9875532,0.000365177,0.00005152499,0.0003872238,0.00001651913,0.00007197254,0.01008834],"genre_scores_gemma":[0.8207384,0.0006500726,0.1766783,0.0003104399,0.00004752184,0.0001626762,0.00008353427,0.00001955851,0.001309464],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8194492,"threshold_uncertainty_score":0.5150864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03015802347876397,"score_gpt":0.3293895960633568,"score_spread":0.2992315725845928,"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."}}