{"id":"W4323058863","doi":"10.2139/ssrn.4376779","title":"Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Portfolio; Selection (genetic algorithm); Scale (ratio); Portfolio optimization; Econometrics; Computer science; Economics; Mathematical optimization; Financial economics; Mathematics; Machine learning; Geography","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":[0.006317255,0.0001992966,0.0002959959,0.0005944823,0.0007771023,0.0001989858,0.0002192293,0.0001291932,0.00003514432],"category_scores_gemma":[0.0005511426,0.0001435809,0.0001222641,0.00141657,0.00005297878,0.0006889306,0.00003145354,0.0006656777,0.00001877863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004795993,"about_ca_system_score_gemma":0.001564901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004580912,"about_ca_topic_score_gemma":0.001308268,"domain_scores_codex":[0.9965391,0.0002208446,0.0005391443,0.0004612464,0.0007772156,0.001462444],"domain_scores_gemma":[0.9982895,0.0004089515,0.0004375398,0.0001710078,0.0005652221,0.000127838],"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":[0.01232519,0.0008256744,0.3019826,0.00002594936,0.001237907,0.00003331117,0.01165273,0.430049,0.001221988,0.06118543,0.03306518,0.146395],"study_design_scores_gemma":[0.01253194,0.001658338,0.03908271,0.00003800668,0.0001627371,0.00125047,0.01965434,0.668313,0.0001376025,0.2456748,0.01085764,0.0006383999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2175647,0.000918679,0.779106,0.0008290752,0.0002899444,0.000751096,0.0000264378,0.0001247064,0.0003893013],"genre_scores_gemma":[0.9729124,0.007611216,0.008480801,0.00007536649,0.0004054715,0.00006475473,0.00006265148,0.00004313431,0.01034424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7706252,"threshold_uncertainty_score":0.5976921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803588261629432,"score_gpt":0.3237959155933546,"score_spread":0.2957600329770603,"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."}}