{"id":"W4403943671","doi":"10.1080/1350486x.2024.2410200","title":"A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization","year":2024,"lang":"en","type":"article","venue":"Applied Mathematical Finance","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Portfolio; Portfolio optimization; Artificial intelligence; Mathematical optimization; Economics; Financial economics; 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":[0.001042617,0.001133942,0.0007830988,0.0004684609,0.0003301921,0.0009911408,0.001230172,0.001676568,0.003907196],"category_scores_gemma":[0.002461758,0.0003946943,0.0006645911,0.0006421651,0.0008602943,0.001693947,0.00131925,0.002259976,0.0003824981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009206728,"about_ca_system_score_gemma":0.000764649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00200247,"about_ca_topic_score_gemma":0.002864892,"domain_scores_codex":[0.9996137,0.0001477575,0.00001837062,0.00009033667,0.00008698105,0.00004290065],"domain_scores_gemma":[0.9994066,0.0003680443,0.00006760232,0.00004674377,0.00007933984,0.0000317619],"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":[0.00001256185,0.0000206765,0.000157727,0.00003536022,0.00003425289,0.00004218227,0.00001908228,0.9384412,0.0007468383,0.04341656,0.0006208424,0.01645284],"study_design_scores_gemma":[0.00000157018,0.000007974381,0.00002099201,0.00000286508,0.000003736063,0.00000968733,0.000001651902,0.9880051,0.000128552,0.01140043,0.0004149903,0.000002442967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002789542,0.0003400319,0.9933599,0.0002677841,0.00004594131,0.00001303243,0.00002800639,0.00005608357,0.00309973],"genre_scores_gemma":[0.5183214,0.001878923,0.4613339,0.0005983514,0.0004411712,0.0003316043,0.0002187377,0.0002071746,0.01666888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003907196,"threshold_uncertainty_score":0.01307088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126188981377649,"score_gpt":0.221825971318164,"score_spread":0.2092070731803991,"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."}}