{"id":"W3125823266","doi":"","title":"Money, Velocity, and the Stock Market","year":2016,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Divisia index; Economics; Stock market; Money supply; Volatility (finance); Financial market; Autoregressive conditional heteroskedasticity; Divisia monetary aggregates index; Econometrics; Financial economics; Stock (firearms); Monetary economics; Monetary policy; Open market operation; Finance","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":[0.0006683421,0.0002460899,0.0002481884,0.001030344,0.0002246418,0.001272963,0.00021922,0.0004117966,0.001527479],"category_scores_gemma":[0.008233906,0.0001055446,0.000149401,0.001114561,0.0006503426,0.001421005,0.0004756135,0.0006387588,0.0001447846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000328439,"about_ca_system_score_gemma":0.0002714984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002068731,"about_ca_topic_score_gemma":0.001172613,"domain_scores_codex":[0.9998469,0.00005996555,0.000008084678,0.00002692183,0.00004046733,0.00001750892],"domain_scores_gemma":[0.9952312,0.003366537,0.0009485459,0.0001095841,0.0001872304,0.0001568477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004665703,0.0002620597,0.4878332,0.0001817314,0.0003936826,0.0007419678,0.001068403,0.05401944,0.002968866,0.3402344,0.0037295,0.1081003],"study_design_scores_gemma":[0.00004246182,0.0002328697,0.4773743,0.0001269403,0.0001099327,0.0003984122,0.0005515778,0.1145924,0.0009702924,0.3974558,0.0080672,0.00007778572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967963,0.008992542,0.008362234,0.002395821,0.00008517073,0.000006566046,0.0001635371,0.00004557487,0.01198566],"genre_scores_gemma":[0.9972585,0.001327185,0.0004502675,0.00003705449,0.0001333171,0.000002155068,0.00004844496,0.000006267663,0.0007367926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002068731,"threshold_uncertainty_score":0.005109966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07461628991229124,"score_gpt":0.2901647927871994,"score_spread":0.2155485028749081,"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."}}