{"id":"W3126020388","doi":"","title":"When Herding and Contrarianism Foster Market Efficiency : A Financial Trading Experiment","year":2008,"lang":"en","type":"article","venue":"Warwick Research Archive Portal (University of Warwick)","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Herding; Herd behavior; Irrational number; Economics; Boom; Information cascade; Loss aversion; Asset (computer security); Econometrics; Financial market; Financial economics; Microeconomics; Market efficiency; Risk aversion (psychology); Efficient-market hypothesis; Expected utility hypothesis; Finance; Stock market; Computer science; Statistics; Mathematics","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":[0.003019373,0.0005543978,0.0006232113,0.0004031394,0.0005712667,0.001590147,0.0006542183,0.00171721,0.005699151],"category_scores_gemma":[0.009387289,0.0004335755,0.0002830529,0.0002341193,0.001422025,0.001559708,0.000945925,0.001487485,0.0004927405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002825247,"about_ca_system_score_gemma":0.0002732294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773765,"about_ca_topic_score_gemma":0.000247548,"domain_scores_codex":[0.9990771,0.0003280289,0.0001090195,0.0001904403,0.0001564471,0.0001389244],"domain_scores_gemma":[0.9900309,0.005365069,0.001913503,0.001460763,0.0001754907,0.001054313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.06295371,0.1040941,0.1159692,0.0009743675,0.0009498412,0.001739834,0.006704835,0.005902901,0.5515075,0.03577041,0.004877118,0.1085562],"study_design_scores_gemma":[0.01872692,0.1838681,0.3788722,0.0003624322,0.00128643,0.002460677,0.003375284,0.07154096,0.1839602,0.1399162,0.01511611,0.000514466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975222,0.00002950436,0.0004061658,0.0001334252,0.00001443986,0.00005912952,0.00003091602,0.00001472354,0.001789583],"genre_scores_gemma":[0.995627,0.00005463259,0.00213941,0.0002522213,0.00002179793,0.0002293807,0.00005759653,0.00001558283,0.001602275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005699151,"threshold_uncertainty_score":0.01906556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06841145724035191,"score_gpt":0.3109428824340457,"score_spread":0.2425314251936938,"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."}}