{"id":"W2315773907","doi":"10.3934/jimo.2016002","title":"Consumption-portfolio optimization and filtering in a hidden Markov-modulated asset price model","year":2016,"lang":"en","type":"article","venue":"Journal of Industrial and Management Optimization","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Portfolio optimization; Portfolio; Computer science; Hidden Markov model; Asset (computer security); Consumption (sociology); Markov chain; Markov model; Econometrics; Financial economics; Artificial intelligence; Economics; Machine learning; Computer security","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":[0.002256472,0.0008770633,0.001536483,0.0004526022,0.0003381633,0.001466932,0.001159845,0.0020426,0.001823508],"category_scores_gemma":[0.006973398,0.0006688193,0.0009798855,0.0007208486,0.001368986,0.001822834,0.001037203,0.001365888,0.0001839885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348622,"about_ca_system_score_gemma":0.001072652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006471029,"about_ca_topic_score_gemma":0.003005454,"domain_scores_codex":[0.9991608,0.0003614238,0.00003859337,0.000189223,0.0001379362,0.0001120826],"domain_scores_gemma":[0.9965758,0.002624752,0.0004064406,0.0001071508,0.0001782662,0.0001075418],"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":[0.00007008521,0.00004564396,0.001106012,0.00005906276,0.00007277964,0.0002233035,0.00006507769,0.8988151,0.0009064188,0.09308556,0.0003286478,0.005222379],"study_design_scores_gemma":[0.00001012924,0.00001117184,0.0001197164,0.000003351087,0.00000920406,0.000007414123,0.000003690291,0.9859099,0.00009416323,0.01375158,0.00007452384,0.000005139052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1065404,0.0005197357,0.8883207,0.001011207,0.00005347792,0.00003416303,0.0001296397,0.00008647735,0.003304187],"genre_scores_gemma":[0.9588105,0.0005036336,0.03438904,0.0001005466,0.00007568714,0.0000879821,0.0001400975,0.0000261157,0.005866389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006471029,"threshold_uncertainty_score":0.01286674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0455568099035439,"score_gpt":0.2293351093488359,"score_spread":0.1837782994452919,"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."}}