{"id":"W7116959534","doi":"10.2139/ssrn.5943491","title":"Forecasting Asset Returns with Sentiment-Enhanced FIGARCH Models","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Random forest; Volatility (finance); Financial market; Econometric model; Stock market; Commodity; Asset (computer security); Financial asset","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":[0.0008746548,0.0004472242,0.0006895058,0.000499165,0.0001461768,0.0008149403,0.0006250562,0.0008412805,0.001453227],"category_scores_gemma":[0.003141834,0.0002626284,0.0005437158,0.0005154863,0.0001601003,0.0007726173,0.000339916,0.0007467392,0.0003619893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003552951,"about_ca_system_score_gemma":0.0003914314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0073775,"about_ca_topic_score_gemma":0.008596252,"domain_scores_codex":[0.9998765,0.00005117877,0.000007697831,0.00002346342,0.00002110088,0.00002003924],"domain_scores_gemma":[0.9992969,0.00046642,0.00007770723,0.00004345359,0.00008476975,0.00003073795],"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":[0.0002088236,0.00007444494,0.004805035,0.00004040767,0.00008933843,0.00004950757,0.00002249485,0.9414746,0.001166245,0.004827417,0.001357112,0.04588461],"study_design_scores_gemma":[0.000004417761,0.000008208377,0.0002456124,0.000001231483,0.000003645448,0.000001990797,0.000001102364,0.9986758,0.00006181876,0.0009492671,0.00004545518,0.000001562216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6518688,0.001013449,0.3384373,0.0009372534,0.0002243116,0.00005653733,0.0009629443,0.0006330109,0.005866307],"genre_scores_gemma":[0.9744515,0.0002362247,0.02237368,0.00008280297,0.00008552949,0.0000224912,0.0005155907,0.0000239381,0.002208217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0073775,"threshold_uncertainty_score":0.01466912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09052736618702092,"score_gpt":0.3748782828817188,"score_spread":0.2843509166946978,"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."}}