{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006533484,0.0007674456,0.001607056,0.0009541534,0.0002908388,0.002370541,0.0009834226,0.001589043,0.002794979],"category_scores_gemma":[0.0361029,0.0008344504,0.0005253577,0.0006175728,0.001227461,0.004327465,0.001210366,0.001229152,0.0002756161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575529,"about_ca_system_score_gemma":0.0008853568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001438014,"about_ca_topic_score_gemma":0.0006042923,"domain_scores_codex":[0.9976575,0.001434148,0.0001026119,0.0002881844,0.0003427791,0.0001747122],"domain_scores_gemma":[0.9874417,0.01036687,0.000775038,0.0006005722,0.0005348137,0.0002811878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002405986,0.0000810593,0.002848978,0.00006145197,0.000105552,0.0001758265,0.00008084006,0.8948675,0.0007658167,0.07529979,0.000706696,0.02476591],"study_design_scores_gemma":[0.000028508,0.00003797981,0.0005894091,0.00001661768,0.00001441375,0.00003394425,0.00001999274,0.9478321,0.0004553778,0.05073604,0.0002204288,0.00001524108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3159451,0.0006495888,0.6750647,0.001428211,0.00004173789,0.0001041565,0.0001314168,0.0002659764,0.006369004],"genre_scores_gemma":[0.9607856,0.0001265483,0.03724998,0.00009716724,0.00002216901,0.0000596349,0.00007632703,0.00003917896,0.001543341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006533484,"threshold_uncertainty_score":0.03455281,"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."}}