{"id":"W1990344701","doi":"10.1007/s10852-012-9214-4","title":"A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation","year":2012,"lang":"en","type":"article","venue":"Journal of Mathematical Modelling and Algorithms in Operations Research","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Markov chain; Hidden Markov model; Hidden semi-Markov model; Asset (computer security); Portfolio; Markov model; Asset allocation; Econometrics; Variable-order Markov model; Computer science; Benchmark (surveying); Discrete time and continuous time; Order (exchange); Portfolio optimization; Markov property; Multivariate statistics; Mathematical optimization; Economics; Mathematics; Finance; Statistics; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002903722,0.00009515626,0.0003158448,0.0003502306,0.0002010926,0.00009296375,0.0001658702,0.0001088805,0.00002184271],"category_scores_gemma":[0.000406611,0.00008952893,0.00005367341,0.0003786486,0.00007052522,0.0003052875,0.00003814476,0.0003076349,0.00001741959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008169252,"about_ca_system_score_gemma":0.00006464202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002313587,"about_ca_topic_score_gemma":0.000002318229,"domain_scores_codex":[0.9986058,0.00001402759,0.0007709135,0.0001593172,0.0001196507,0.0003302687],"domain_scores_gemma":[0.998951,0.0002691481,0.00009979668,0.0001440234,0.0004080477,0.0001280276],"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":[0.00001776731,0.0003635983,0.00002946512,0.00007313914,0.00001995305,3.311947e-7,0.001026291,0.08702314,0.00002457045,0.9090081,0.00005687357,0.002356759],"study_design_scores_gemma":[0.0002664553,0.0000378234,0.00003002468,0.00003505882,0.000003345982,0.000005705017,0.00005632403,0.6655294,0.000008399279,0.3338534,0.0001064741,0.00006759079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01830614,0.00122397,0.9780185,0.001687557,0.00006414266,0.0003132144,0.00003134874,0.000005518043,0.0003496445],"genre_scores_gemma":[0.642739,0.0001801542,0.3563947,0.00002907305,0.0001925689,0.00009617465,0.000005405845,0.00001583604,0.0003470913],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6244329,"threshold_uncertainty_score":0.3650884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488583176428479,"score_gpt":0.3559526080630695,"score_spread":0.2070942904202216,"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."}}