{"id":"W4413119066","doi":"10.1016/j.ejor.2025.07.057","title":"Worst-case values of target semi-variances with applications to robust portfolio selection","year":2025,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Portfolio; Computer science; Econometrics; Mathematical optimization; Mathematics; Statistics; Economics; Artificial intelligence; Financial economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01162389,0.002097138,0.001440853,0.001603964,0.0005060812,0.003461308,0.0019293,0.00261142,0.002768996],"category_scores_gemma":[0.05516845,0.0009742648,0.001394702,0.001705907,0.002083983,0.003520107,0.002317518,0.002621184,0.000564262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090937,"about_ca_system_score_gemma":0.0009555357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007589991,"about_ca_topic_score_gemma":0.0006591672,"domain_scores_codex":[0.9952394,0.002701507,0.0002186039,0.0004928328,0.001099155,0.0002484914],"domain_scores_gemma":[0.9663326,0.02868833,0.001478928,0.001352747,0.001840109,0.0003072961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001385957,0.00004168333,0.0003540746,0.0001317292,0.00006816746,0.0001509104,0.00007485433,0.8937557,0.001561734,0.07971866,0.000954444,0.02304937],"study_design_scores_gemma":[0.000008922791,0.00002749554,0.0001157938,0.00003205496,0.0000139347,0.00007259823,0.00001368291,0.9453654,0.0008395035,0.05307567,0.000415813,0.00001913886],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006797565,0.0005259034,0.9894622,0.000255186,0.00004476456,0.00001741644,0.00004478822,0.0001317871,0.00272033],"genre_scores_gemma":[0.5913108,0.001457633,0.4005826,0.0002040712,0.0003260162,0.0002004388,0.0003158565,0.0004974937,0.005105082],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01162389,"threshold_uncertainty_score":0.06147373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.144665977723288,"score_gpt":0.4422429272366033,"score_spread":0.2975769495133153,"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."}}