{"id":"W4389883043","doi":"10.32920/24625158","title":"Intelligent Probabilistic Risk Forecasting With Applications to Algorithmic Trading and Portfolio Optimization","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Portfolio optimization; Hidden Markov model; Kalman filter; Portfolio; Probabilistic logic; Computer science; EWMA chart; Volatility (finance); Trading strategy; Markov chain; Econometrics; Mathematical optimization; Artificial intelligence; Machine learning; Economics; Financial economics; Mathematics","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008593872,0.0004609124,0.0006848648,0.001111987,0.0003780543,0.0007950334,0.000993627,0.0002443746,0.000276275],"category_scores_gemma":[0.01253413,0.000340334,0.0001234902,0.001973747,0.0001337371,0.0001229164,0.001409595,0.0006531395,0.00004974652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001435589,"about_ca_system_score_gemma":0.0002329857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002050076,"about_ca_topic_score_gemma":0.0001387426,"domain_scores_codex":[0.9945471,0.0004775446,0.001280271,0.00185882,0.001361496,0.0004747504],"domain_scores_gemma":[0.9911435,0.005601138,0.0008825063,0.001293994,0.0007059368,0.0003729691],"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":[0.00003347883,0.00002703882,0.003498957,0.00004477391,0.00004741031,0.000006136823,0.0004738579,0.626709,0.000001628688,0.0003902212,0.001378942,0.3673885],"study_design_scores_gemma":[0.0001161269,0.00009682934,0.001452371,0.0001734345,0.00009870285,0.00004531835,0.0005653661,0.929133,0.00002468635,0.06700104,0.0008306428,0.0004625028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005766442,0.00004378487,0.9833876,0.0002628701,0.0003941627,0.003144563,0.00006857469,0.0003809195,0.006551111],"genre_scores_gemma":[0.04598433,0.00003158629,0.9494114,0.00005446654,0.0002560341,0.00145405,0.00003150398,0.0001004238,0.002676216],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.366926,"threshold_uncertainty_score":0.9999049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2201626135311801,"score_gpt":0.4038816305778379,"score_spread":0.1837190170466579,"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."}}