{"id":"W2922021540","doi":"10.1080/14697688.2021.1881598","title":"Active and Passive Portfolio Management with Latent Factors","year":2019,"lang":"en","type":"preprint","venue":"Quantitative Finance","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unobservable; Portfolio; Markov chain; Computer science; Semimartingale; Mathematical optimization; Uniqueness; Econometrics; Economics; Mathematics; Machine learning; Finance; Applied mathematics","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":[0.00496941,0.001175752,0.00122769,0.0007013962,0.0003584317,0.00368785,0.001612441,0.00164562,0.003577898],"category_scores_gemma":[0.01516473,0.0006643925,0.0008448114,0.000843955,0.001905738,0.005843602,0.001969335,0.001560672,0.0003733907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000664251,"about_ca_system_score_gemma":0.000743129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005372331,"about_ca_topic_score_gemma":0.0004698654,"domain_scores_codex":[0.9980286,0.000923888,0.00008462583,0.0004125335,0.0003891099,0.0001612829],"domain_scores_gemma":[0.9934103,0.004289895,0.0008921348,0.0006414681,0.0004453676,0.0003208192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00027921,0.0002384286,0.003321795,0.0002074163,0.0001959658,0.0001164691,0.0001368823,0.1929237,0.002953109,0.7392307,0.002088708,0.05830748],"study_design_scores_gemma":[0.0000377033,0.00007268216,0.000661301,0.00002019774,0.00004085768,0.00004303156,0.00001677911,0.7362791,0.0005429853,0.2614369,0.0008302337,0.00001809126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04826649,0.00109499,0.946171,0.0007509022,0.0001120896,0.00002942454,0.00009100736,0.00009713152,0.003387006],"genre_scores_gemma":[0.9227579,0.001350203,0.05801335,0.0001460823,0.0005759333,0.00008706867,0.0001890421,0.00006919472,0.01681122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00496941,"threshold_uncertainty_score":0.02628106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03967872453143073,"score_gpt":0.2520735324185054,"score_spread":0.2123948078870747,"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."}}