{"id":"W4252531249","doi":"10.2139/ssrn.1344879","title":"Institutional Herding and Information Cascades: Evidence from Daily Trades","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Herding; Financial economics; Business; Information cascade; Economics; Econometrics; Natural resource economics; Geography; Mathematics; Statistics","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":[0.003720197,0.0002235642,0.0005841912,0.001422045,0.0007846996,0.002049883,0.0005969472,0.001487562,0.006283741],"category_scores_gemma":[0.03266623,0.0003851224,0.0003028391,0.001758811,0.001398428,0.002230719,0.0007440099,0.001141602,0.0008179994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004520251,"about_ca_system_score_gemma":0.0002880484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005255689,"about_ca_topic_score_gemma":0.004692491,"domain_scores_codex":[0.9990668,0.0003895923,0.00007631948,0.0001519712,0.0001794085,0.0001359338],"domain_scores_gemma":[0.9115466,0.05974966,0.02016141,0.004904171,0.002325467,0.001312689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002115819,0.00113778,0.9114708,0.0002843817,0.0006742337,0.0006691698,0.004128565,0.007358433,0.001807488,0.01018559,0.004742792,0.05542487],"study_design_scores_gemma":[0.0002834609,0.000626062,0.9525542,0.0001328406,0.0004175714,0.0004400694,0.003840421,0.01784314,0.00109834,0.01847625,0.004195041,0.00009275626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949284,0.0004457407,0.0005800109,0.000433658,0.000008764511,0.00001391156,0.0002155669,0.000015039,0.003358938],"genre_scores_gemma":[0.9990379,0.000262033,0.0001577008,0.00004040636,0.00002147938,0.000005296852,0.0001174773,0.000003016755,0.0003547293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006283741,"threshold_uncertainty_score":0.02102119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976100250205414,"score_gpt":0.2770215611768693,"score_spread":0.2572605586748151,"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."}}