{"id":"W4402118037","doi":"10.1016/j.ejor.2024.08.030","title":"End-to-end, decision-based, cardinality-constrained portfolio optimization","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cardinality (data modeling); Computer science; Portfolio; End-to-end principle; Mathematical optimization; Portfolio optimization; Operations research; Branch and bound; Business; Mathematics; Algorithm; Artificial intelligence; Data mining; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.03744718,0.0001851969,0.0003221146,0.002411415,0.0004505076,0.002243495,0.001129172,0.00004868127,0.005564064],"category_scores_gemma":[0.01045746,0.000131996,0.0002739761,0.003061358,0.0002046234,0.0009448124,0.0001573363,0.0007016083,0.001311183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548607,"about_ca_system_score_gemma":0.001719697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006975129,"about_ca_topic_score_gemma":0.000002950396,"domain_scores_codex":[0.988996,0.002456129,0.001655315,0.0005064807,0.005980371,0.0004056866],"domain_scores_gemma":[0.9906459,0.003396132,0.0001876257,0.0004585051,0.004795882,0.0005159994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001770662,0.00005262776,0.0006860828,0.000002763652,0.00003307453,0.0007569973,0.0002189389,0.8036945,0.0001868066,0.003858053,0.05245331,0.1378797],"study_design_scores_gemma":[0.001126182,0.001104671,0.008434458,0.0003384027,0.00003433182,0.0006520059,0.0005924074,0.4034101,0.0004502989,0.002828008,0.5805998,0.0004293481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02465327,0.0008149778,0.9030114,0.006672477,0.001304004,0.0003087538,0.00006116904,0.00003231808,0.06314163],"genre_scores_gemma":[0.9290251,0.0002335796,0.06552546,0.000354495,0.001083411,0.000003635742,0.00002582272,0.00004474615,0.003703709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9043719,"threshold_uncertainty_score":0.9994664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1495532177060752,"score_gpt":0.4483302913334653,"score_spread":0.2987770736273901,"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."}}