{"id":"W3116613610","doi":"10.3390/econometrics9010001","title":"Regularized Maximum Diversification Investment Strategy","year":2020,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Sharpe ratio; Portfolio; Econometrics; Covariance matrix; Diversification (marketing strategy); Portfolio optimization; Mathematics; Market portfolio; Covariance; Investment strategy; Economics; Mathematical optimization; Computer science; Statistics; Capital asset pricing model; Financial economics; Finance; Business","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":[0.001215577,0.0005961751,0.001272507,0.0005591465,0.0001910546,0.0008327405,0.0009482341,0.001087471,0.002448461],"category_scores_gemma":[0.004194489,0.0002748692,0.000451398,0.000389662,0.0005940265,0.000758365,0.0006045587,0.0006552516,0.0004535425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000600234,"about_ca_system_score_gemma":0.0007881547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009125586,"about_ca_topic_score_gemma":0.0008176581,"domain_scores_codex":[0.9994753,0.0001895486,0.00002844432,0.0001058667,0.0001346735,0.00006621302],"domain_scores_gemma":[0.9989994,0.0005123683,0.0001465068,0.000108833,0.000169408,0.00006339975],"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":[0.0002154477,0.0001070231,0.001419389,0.0001004166,0.0001068697,0.00023587,0.00006966402,0.8322448,0.004161632,0.04974143,0.004267571,0.1073298],"study_design_scores_gemma":[0.00002031014,0.00003813552,0.0001928804,0.000009659142,0.000009493091,0.00004200401,0.000003757694,0.9885193,0.0005377138,0.01005035,0.0005703353,0.000006028056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08830944,0.0006188551,0.9001384,0.0004535588,0.00005487871,0.0001069581,0.0001183221,0.0004540479,0.009745494],"genre_scores_gemma":[0.829801,0.0002313774,0.1629353,0.0002833109,0.00005927842,0.0001721003,0.0001828176,0.000072929,0.00626193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002448461,"threshold_uncertainty_score":0.00819093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0938337129060445,"score_gpt":0.2110520457036113,"score_spread":0.1172183327975668,"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."}}