{"id":"W3021970279","doi":"10.3905/jpm.2007.684751","title":"Robust Portfolio Optimization","year":2007,"lang":"en","type":"article","venue":"The Journal of Portfolio Management","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intertek (Canada)","funders":"","keywords":"Portfolio optimization; Portfolio; Robust optimization; Black–Litterman model; Computer science; Project portfolio management; Application portfolio management; Post-modern portfolio theory; Asset allocation; Modern portfolio theory; Estimation; Asset (computer security); Mathematical optimization; Economics; Replicating portfolio; Financial economics; Project management; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0181876,0.0002126883,0.0003666878,0.00110351,0.0002480888,0.0001987513,0.001357657,0.00007833214,0.001293223],"category_scores_gemma":[0.0003227862,0.0001234596,0.0002325815,0.001827145,0.0001036251,0.0006664022,0.0001877961,0.0002480973,0.0000949301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008237365,"about_ca_system_score_gemma":0.0000547547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001306039,"about_ca_topic_score_gemma":0.000007375521,"domain_scores_codex":[0.9946597,0.0001958153,0.002020695,0.000219109,0.002493911,0.0004108281],"domain_scores_gemma":[0.9958311,0.0003703995,0.002037431,0.0007376989,0.0008147414,0.0002086229],"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":[0.0002184325,0.0001081077,0.002902007,0.000002606214,0.0001097709,0.0002103591,0.0002599815,0.8421768,0.000009785646,0.003624106,0.08847447,0.06190358],"study_design_scores_gemma":[0.005025895,0.001104239,0.1310052,0.0001761325,0.001515016,0.002214047,0.01646873,0.07350627,0.001010926,0.03505212,0.7314693,0.001452134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01679054,0.0004577019,0.858605,0.0006280453,0.00128823,0.0002861209,0.000001554583,0.00002522244,0.1219176],"genre_scores_gemma":[0.9165739,0.007456096,0.05287538,0.001139864,0.0009027264,0.000002328045,0.000006674577,0.00005282466,0.02099024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8997833,"threshold_uncertainty_score":0.9996197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07816600764453831,"score_gpt":0.3396825362592654,"score_spread":0.2615165286147271,"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."}}