{"id":"W4389913520","doi":"10.32920/24625158.v1","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009591787,0.0006701461,0.0007234142,0.0007868898,0.0002899095,0.001296906,0.0006541668,0.0009661115,0.001710833],"category_scores_gemma":[0.004381958,0.000424604,0.0005874128,0.001396355,0.0007170311,0.001409399,0.0007451441,0.001299518,0.0003302538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006782283,"about_ca_system_score_gemma":0.0005353464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002850882,"about_ca_topic_score_gemma":0.001696553,"domain_scores_codex":[0.9995665,0.0001236586,0.00002730143,0.00007315857,0.0001823686,0.00002713044],"domain_scores_gemma":[0.9988483,0.0007877699,0.0001319351,0.0000733654,0.0001295995,0.00002900057],"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":[0.00001493892,0.00002147461,0.0007147086,0.00005131104,0.00003298615,0.00004166978,0.00004326901,0.7782699,0.0008380801,0.1334651,0.001700427,0.08480617],"study_design_scores_gemma":[0.000002021293,0.000005116668,0.0001188998,0.000006441119,0.000002620998,0.00001097455,0.000003654664,0.9375356,0.0001709442,0.06118396,0.0009539942,0.000005796938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006073127,0.001359442,0.9876136,0.0008795373,0.0001035721,0.00001135004,0.00004467564,0.000165763,0.003749028],"genre_scores_gemma":[0.6592871,0.005439657,0.3251842,0.0003287859,0.0009688179,0.0001172284,0.0003044935,0.0001212847,0.008248459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002850882,"threshold_uncertainty_score":0.005723298,"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."}}