{"id":"W4409797029","doi":"10.1109/cifer64978.2025.10975737","title":"Non-Linear Data Representation with Machine Learning for Dynamic Covariance Based Financial Portfolio Optimization","year":2025,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Portfolio optimization; Computer science; Portfolio; Representation (politics); Artificial intelligence; Finance; Machine learning; Mathematical optimization; Mathematics; Economics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001666909,0.0008369145,0.0009718881,0.0006819025,0.0002888757,0.001286999,0.0008762146,0.0007663866,0.001227984],"category_scores_gemma":[0.005295197,0.0004818341,0.0007418685,0.0009131334,0.0005275735,0.00148923,0.001366074,0.001826932,0.0003067273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007130634,"about_ca_system_score_gemma":0.001125176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002725772,"about_ca_topic_score_gemma":0.002577583,"domain_scores_codex":[0.9994093,0.0002377698,0.00004564184,0.0001122953,0.0001508005,0.00004412626],"domain_scores_gemma":[0.9984755,0.000981563,0.0001754345,0.0001329295,0.0001953533,0.00003919037],"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":[0.00002768723,0.00005675712,0.0009436049,0.00005752828,0.00006784411,0.00003996757,0.0000372386,0.890815,0.001113868,0.02685469,0.0009789109,0.0790069],"study_design_scores_gemma":[9.34813e-7,0.000004320137,0.00004460248,0.00000233684,0.000001559188,0.000002768962,0.000001264714,0.9961072,0.0000786316,0.003625073,0.0001295226,0.000001828094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006446146,0.0003046271,0.9921935,0.0002089791,0.00002096338,0.00001717273,0.00003542818,0.0001341188,0.0006390979],"genre_scores_gemma":[0.5955991,0.001065355,0.3990153,0.0003163136,0.0001633575,0.0002747801,0.0004635291,0.0001329266,0.002969302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002725772,"threshold_uncertainty_score":0.008815527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127907152602497,"score_gpt":0.4377121595670743,"score_spread":0.3249214443068246,"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."}}