{"id":"W4400584404","doi":"10.2139/ssrn.4890466","title":"Robust Stock Index Return Predictions Using Deep Learning *","year":2024,"lang":"en","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":"Kellogg's (Canada)","funders":"","keywords":"Index (typography); Econometrics; Stock (firearms); Stock market index; Artificial intelligence; Financial economics; Computer science; Economics; Stock market; Engineering; Geography; World Wide Web","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":[0.0009826944,0.0007469014,0.0007066925,0.0005313727,0.0002012855,0.001050372,0.0008512301,0.001027813,0.002682679],"category_scores_gemma":[0.004788561,0.0004947749,0.0004688078,0.0004517393,0.0003116612,0.001132574,0.0009867775,0.001433893,0.001087649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004213981,"about_ca_system_score_gemma":0.0007275628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003610055,"about_ca_topic_score_gemma":0.003677871,"domain_scores_codex":[0.9997534,0.00004585846,0.00001487118,0.00007362331,0.00006869269,0.000043535],"domain_scores_gemma":[0.9986386,0.0006680431,0.000142627,0.0002299967,0.000258221,0.00006254113],"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":[0.0003666552,0.0001759722,0.003758064,0.00006817275,0.0001418259,0.00008784295,0.00002050952,0.7048681,0.008703455,0.004718875,0.005095856,0.2719947],"study_design_scores_gemma":[0.000003042709,0.000007910052,0.0001783377,0.000001691132,0.000003194137,0.000003345566,7.231761e-7,0.9980729,0.0006509672,0.001008376,0.00006779056,0.000001810538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1880817,0.0008908795,0.8011864,0.000990081,0.0003241164,0.00004937495,0.0006576368,0.003040821,0.004778915],"genre_scores_gemma":[0.9362304,0.000211127,0.05670027,0.0001673512,0.0001500782,0.00003342079,0.0008181968,0.0001515952,0.005537535],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003610055,"threshold_uncertainty_score":0.008974493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179324572208644,"score_gpt":0.3886197587198238,"score_spread":0.2706873014989594,"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."}}