{"id":"W4415434060","doi":"10.2139/ssrn.5636031","title":"Aligning Multilingual News for Stock Return Prediction","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Stock (firearms); Context (archaeology); Data collection","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":[0.00179942,0.001197181,0.0007277046,0.00594212,0.001094822,0.002377797,0.0005879435,0.001173775,0.007356425],"category_scores_gemma":[0.008919532,0.0004302332,0.0008715376,0.004643695,0.0003872728,0.003319992,0.002040589,0.001248225,0.007179006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004317622,"about_ca_system_score_gemma":0.001218654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00727019,"about_ca_topic_score_gemma":0.01045482,"domain_scores_codex":[0.9986161,0.0004052657,0.0001350151,0.0003927831,0.0002749967,0.000175795],"domain_scores_gemma":[0.9953222,0.00206951,0.0003733089,0.0005735602,0.001486087,0.0001753926],"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":[0.003028101,0.001144065,0.04303123,0.0006504164,0.0005451214,0.001133028,0.00103681,0.01806208,0.0414743,0.002881531,0.03172227,0.8552911],"study_design_scores_gemma":[0.000365348,0.001262692,0.0706558,0.0003566263,0.002160912,0.001320967,0.004642673,0.7310739,0.0770383,0.01947327,0.0912946,0.0003549669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6372853,0.007584278,0.278115,0.002500712,0.003544788,0.0003487565,0.02365515,0.009708815,0.03725718],"genre_scores_gemma":[0.8444272,0.002562139,0.1078373,0.0003708162,0.001807253,0.0002375307,0.02992296,0.0009267882,0.0119081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007356425,"threshold_uncertainty_score":0.02460974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06715346177952777,"score_gpt":0.4147563736178241,"score_spread":0.3476029118382963,"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."}}