{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002752504,0.0009010561,0.002492249,0.00083257,0.0006187231,0.002056316,0.002854899,0.003217463,0.005144173],"category_scores_gemma":[0.008889679,0.0008660045,0.001488626,0.001455659,0.001511164,0.003199034,0.001418559,0.002686323,0.0007653182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643147,"about_ca_system_score_gemma":0.001687533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008502921,"about_ca_topic_score_gemma":0.006335886,"domain_scores_codex":[0.9990209,0.0004401631,0.00005015632,0.0002043405,0.0001424896,0.0001419367],"domain_scores_gemma":[0.9960533,0.002849943,0.0003656656,0.0002370175,0.0003292784,0.0001646633],"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":[0.0001220039,0.00007024227,0.001134976,0.000100659,0.00009357016,0.0002030224,0.0001366565,0.8149032,0.001225289,0.1714214,0.001078461,0.009510438],"study_design_scores_gemma":[0.000008190154,0.000007986204,0.0001002481,0.000003761864,0.000009223036,0.0000124672,0.000002818632,0.9821539,0.0000418678,0.01753054,0.0001219999,0.000006836672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05555218,0.000844643,0.9389813,0.001068708,0.0001683485,0.00004916862,0.000340115,0.0001746847,0.002820995],"genre_scores_gemma":[0.9262249,0.001096806,0.05861636,0.0003284763,0.0002258907,0.0001733399,0.0005002088,0.00008461335,0.01274934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008502921,"threshold_uncertainty_score":0.01720899,"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."}}