{"id":"W2112261373","doi":"10.1017/s0022109000004129","title":"Optimal Portfolio Choice with Parameter Uncertainty","year":2007,"lang":"en","type":"article","venue":"Journal of Financial and Quantitative Analysis","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":723,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Portfolio; Sample (material); Tangent; Econometrics; Asset (computer security); Portfolio optimization; Covariance matrix; Modern portfolio theory; Economics; Population; Separation property; Replicating portfolio; Mathematics; Computer science; Statistics; Financial economics; Physics","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":[0.001138391,0.0001649512,0.0007141302,0.0007266433,0.0001186402,0.00007006649,0.0001261374,0.000077812,0.000125488],"category_scores_gemma":[0.0004014732,0.0001307839,0.0002706233,0.00102122,0.0001669772,0.0004265016,0.00001939865,0.0001945535,0.000008368126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004086431,"about_ca_system_score_gemma":0.00005038083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003420445,"about_ca_topic_score_gemma":0.0002940171,"domain_scores_codex":[0.9985771,0.00001600192,0.0008416756,0.0002157657,0.00008372574,0.0002657041],"domain_scores_gemma":[0.998396,0.0002174079,0.0009790936,0.0001128315,0.0001791634,0.000115497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0006498309,0.0002006043,0.4061336,0.00002602021,0.001202489,0.0001203537,0.0007327406,0.001632287,0.00003498287,0.5865253,0.0008101033,0.001931655],"study_design_scores_gemma":[0.000708428,0.001202555,0.9650066,0.00002610562,0.0003354347,0.00001424771,0.0002876667,0.0009077116,0.0000398129,0.0121069,0.01907721,0.0002872639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583137,0.00226755,0.03397217,0.0001863093,0.0001075051,0.00005660349,0.00002796119,0.000004828521,0.005063356],"genre_scores_gemma":[0.9893762,0.0003716273,0.009595303,0.0002808511,0.0001198772,0.000001253606,0.000003800284,0.000009158663,0.0002418823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5744184,"threshold_uncertainty_score":0.5333214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03279361064038069,"score_gpt":0.2636494015134089,"score_spread":0.2308557908730282,"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."}}