{"id":"W2913440313","doi":"10.3390/jrfm13100244","title":"The Unusual Trading Volume and Earnings Surprises in China’s Market","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; China; Financial economics; Economics; Divergence (linguistics); Post-earnings-announcement drift; Volume (thermodynamics); Monetary economics; Business; Earnings response coefficient; Accounting; Law; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007268785,0.0002313685,0.0003116525,0.001463595,0.0003895099,0.001083145,0.0002685381,0.0004458631,0.001549471],"category_scores_gemma":[0.004593245,0.0001495686,0.0003135967,0.001145146,0.0005573542,0.001061795,0.0006110446,0.0004870841,0.0001530795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005146703,"about_ca_system_score_gemma":0.0003530151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004787241,"about_ca_topic_score_gemma":0.005074273,"domain_scores_codex":[0.9996355,0.000041352,0.00004256118,0.00005961244,0.0001393612,0.00008175609],"domain_scores_gemma":[0.9917042,0.002112141,0.004756225,0.0002717491,0.0004476865,0.0007079745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001050948,0.00004905465,0.9920121,0.00001752161,0.00006145554,0.0006469878,0.0002648672,0.001041501,0.0009416279,0.0006275121,0.0002507119,0.003981563],"study_design_scores_gemma":[0.000006433538,0.00005806292,0.9932426,0.000005140767,0.00002087743,0.0001787587,0.0001902293,0.005326862,0.0002274196,0.0004998525,0.0002321115,0.00001173725],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990838,0.00009231662,0.0001003782,0.00007206004,0.000005613746,0.000002127195,0.00004755797,0.000004158404,0.0005921012],"genre_scores_gemma":[0.9998022,0.00003543651,0.00001974386,0.0000070882,0.00001561211,7.815822e-7,0.00004923824,6.991447e-7,0.00006921325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004787241,"threshold_uncertainty_score":0.009518743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200614197830765,"score_gpt":0.1839618181350517,"score_spread":0.171955676156744,"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."}}