{"id":"W4413057086","doi":"10.2139/ssrn.5349068","title":"&lt;p&gt;&lt;span&gt;Bay Street Meets Machine Learning:&amp;nbsp;Predicting Stock Risk Premium&lt;/span&gt;&lt;span&gt;&lt;/span&gt;&lt;/p&gt;","year":2025,"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":"Dalhousie University","funders":"","keywords":"Span (engineering); Life span; Attention span; Bay; Stock (firearms); Psychology; Medicine; Structural engineering; Engineering; Gerontology; Geology; Cognition; Neuroscience; Oceanography; Mechanical engineering","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":[0.001329264,0.0008359298,0.001104369,0.001259797,0.0009543215,0.002850896,0.0007913249,0.002496369,0.1697711],"category_scores_gemma":[0.007947647,0.0003618942,0.0003600434,0.00211411,0.0008187644,0.003202278,0.001395643,0.001678375,0.05853189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001392432,"about_ca_system_score_gemma":0.001044842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01545062,"about_ca_topic_score_gemma":0.0156229,"domain_scores_codex":[0.9995255,0.0001103182,0.00002503897,0.0001336844,0.0001558384,0.00004960511],"domain_scores_gemma":[0.9984698,0.0007279607,0.00009247526,0.0002257919,0.0003264334,0.0001575272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001850795,0.00009700962,0.003214138,0.0001715271,0.00005489586,0.0001377893,0.0001097352,0.007180721,0.001094375,0.07380877,0.5033504,0.4105957],"study_design_scores_gemma":[0.0001493196,0.0001131408,0.009046443,0.0002002896,0.00007741238,0.0002193848,0.0002488655,0.2093241,0.008702718,0.3526228,0.4192183,0.00007719461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05527879,0.008182123,0.2045399,0.1444221,0.01057925,0.0002353822,0.01942921,0.009050244,0.548283],"genre_scores_gemma":[0.4465214,0.004465335,0.07079529,0.002807244,0.003983831,0.0001622711,0.006491305,0.003974373,0.460799],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1697711,"threshold_uncertainty_score":0.5679411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516587514828014,"score_gpt":0.3478671111138478,"score_spread":0.3027012359655676,"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."}}