{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02368775,0.0002074387,0.0003566197,0.0006166631,0.0006434121,0.0003019359,0.0008959321,0.000154454,0.00003545851],"category_scores_gemma":[0.02033661,0.0001626839,0.0003155438,0.0009227151,0.00006165643,0.0003074529,0.0001075066,0.001516085,0.00001080456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008767028,"about_ca_system_score_gemma":0.003121754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003291252,"about_ca_topic_score_gemma":0.0005295917,"domain_scores_codex":[0.9948885,0.0005687047,0.0009966781,0.0005240398,0.0009015235,0.002120565],"domain_scores_gemma":[0.9951278,0.003231053,0.0004795479,0.0004350765,0.0006206951,0.0001058448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003420917,0.00003935393,0.01715872,0.000004064089,0.0001299502,0.000001312804,0.0002481466,0.000187153,0.001062772,0.0144,0.005520158,0.9609063],"study_design_scores_gemma":[0.001663341,0.000458074,0.002365047,0.00005997449,0.00007569431,0.0002427302,0.003451766,0.01408773,0.001054195,0.9415852,0.03474982,0.0002063645],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2026917,0.001252758,0.7861261,0.001262968,0.002423974,0.0004578694,0.000008938789,0.00008614179,0.00568956],"genre_scores_gemma":[0.944181,0.0002008538,0.02205434,0.0003174279,0.001081799,0.00004054715,0.0000037841,0.0000388945,0.03208141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9606999,"threshold_uncertainty_score":0.9879155,"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."}}