{"id":"W3150771006","doi":"10.1080/14697688.2021.1879392","title":"Robust portfolio rebalancing with cardinality and diversification constraints","year":2021,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"CVAR; Portfolio; Computer science; Diversification (marketing strategy); Risk measure; Cardinality (data modeling); Portfolio optimization; Mathematical optimization; Expected shortfall; Econometrics; Value at risk; Economics; Risk management; Mathematics; Financial economics; Data mining; Finance; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.0008924912,0.0001078058,0.0002289462,0.00007878745,0.0002003072,0.0001342105,0.0001275938,0.00004501678,0.00008284597],"category_scores_gemma":[0.0009547158,0.00008432492,0.00003628797,0.0007946689,0.0003108477,0.0004164124,0.00004461494,0.00008888028,0.00004711783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000211683,"about_ca_system_score_gemma":0.000135788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003146494,"about_ca_topic_score_gemma":0.00003320835,"domain_scores_codex":[0.9983901,0.000160357,0.0003036724,0.0004872373,0.0004996921,0.0001589577],"domain_scores_gemma":[0.9982291,0.0003838728,0.0002624436,0.0003209079,0.0007550185,0.00004860526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001757096,0.00009897728,0.6653268,0.00001002519,0.00005733695,0.0002595874,0.003268975,0.02105427,0.0004769209,0.2253526,0.007793103,0.07612573],"study_design_scores_gemma":[0.000689992,0.0001555556,0.9516639,0.00006494841,0.00003219441,0.00009327361,0.004440394,0.01098489,0.001278362,0.007628208,0.02263028,0.0003379893],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.786543,0.000812994,0.1962738,0.0005343182,0.0001544568,0.0001309494,0.00005269792,0.0000263856,0.01547143],"genre_scores_gemma":[0.9622586,0.0009762699,0.03521012,0.00007999142,0.00001629441,0.000004988764,0.00001862591,0.000006068031,0.001429085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2863371,"threshold_uncertainty_score":0.3438671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1469799693599546,"score_gpt":0.3534641680214585,"score_spread":0.2064841986615039,"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."}}