{"id":"W7108697186","doi":"10.3390/jrfm18120685","title":"Deep Learning and Transformer Architectures for Volatility Forecasting: Evidence from U.S. Equity Indices","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Overfitting; Proxy (statistics); Realized variance; Portfolio; Equity (law); Financial market; Deep learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020998,0.0005203065,0.0002054742,0.0004961832,0.000168542,0.0007197298,0.0005550885,0.0005141579,0.0009101453],"category_scores_gemma":[0.008074111,0.0001907772,0.0003525801,0.0006419968,0.0003039629,0.001590459,0.0005420092,0.001105401,0.000293079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006352458,"about_ca_system_score_gemma":0.0005341824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01250684,"about_ca_topic_score_gemma":0.01101119,"domain_scores_codex":[0.9996613,0.0001279835,0.00002468009,0.00006516978,0.00008754803,0.00003324149],"domain_scores_gemma":[0.997566,0.001549542,0.0002253192,0.0001693005,0.0004029552,0.00008691154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009468776,0.0005320227,0.1567101,0.0003527718,0.000437627,0.0001777726,0.0002022105,0.3886724,0.001963226,0.008673233,0.007799826,0.4335319],"study_design_scores_gemma":[0.00004503971,0.0003413431,0.03297894,0.00009340797,0.0001192932,0.00005453902,0.0001156474,0.9516332,0.002394153,0.009937122,0.002263162,0.00002423187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9602497,0.006560169,0.02211998,0.002505901,0.00009512751,0.00002588267,0.0008027108,0.0002607382,0.007379838],"genre_scores_gemma":[0.9951143,0.001411254,0.002525422,0.00008006192,0.00001907418,0.000005140238,0.0004362578,0.00000933604,0.0003990706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01250684,"threshold_uncertainty_score":0.02486807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08065633700935425,"score_gpt":0.3917159943596644,"score_spread":0.3110596573503102,"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."}}