{"id":"W2772601807","doi":"10.1016/j.insmatheco.2018.08.003","title":"Time-consistent mean–variance portfolio optimization: A numerical impulse control approach","year":2018,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Efficient frontier; Optimal control; Discretization; Portfolio investment; Portfolio; Investment strategy; Leverage (statistics); Economics; Mathematics; Computer science; Finance; Profit (economics); Microeconomics","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":[0.002792,0.001074722,0.001867711,0.00097349,0.0006164149,0.002302752,0.001662022,0.003755266,0.003230548],"category_scores_gemma":[0.01195944,0.001130073,0.001363718,0.0009451862,0.002325365,0.002255321,0.001985392,0.002658073,0.0003132528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049865,"about_ca_system_score_gemma":0.001754665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004564551,"about_ca_topic_score_gemma":0.002717878,"domain_scores_codex":[0.9992329,0.000424977,0.0000315629,0.0001138942,0.0001357924,0.000060863],"domain_scores_gemma":[0.9961032,0.002949717,0.0002820431,0.0001613427,0.0003573985,0.0001463113],"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":[0.00003359796,0.00003451215,0.0002109455,0.00003639679,0.00005933705,0.00004957357,0.00002836299,0.933269,0.0003737783,0.06147207,0.0004428712,0.00398956],"study_design_scores_gemma":[0.000005716435,0.000003678824,0.00001471723,0.000003164285,0.00000444376,0.000003711014,0.000002020356,0.992736,0.00003639898,0.007097335,0.0000891216,0.000003659984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01187138,0.0004161975,0.9812248,0.0007354863,0.0001584202,0.0000253598,0.00003136458,0.00008993281,0.005446992],"genre_scores_gemma":[0.7805951,0.0008820466,0.1997696,0.0005743543,0.0004907646,0.0002819507,0.000176694,0.0002438423,0.01698567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004564551,"threshold_uncertainty_score":0.01476568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606216036980704,"score_gpt":0.1981775041025498,"score_spread":0.1821153437327427,"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."}}