{"id":"W4388591154","doi":"10.1016/j.frl.2023.104715","title":"Price limit relaxation and stock price crash risk: Evidence from China","year":2023,"lang":"en","type":"article","venue":"Finance research letters","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"China Scholarship Council","keywords":"Economics; Hoarding (animal behavior); Stock price; Mid price; Crash; Econometrics; Limit price; Stock (firearms); Financial economics; Monetary economics; Limit (mathematics); Cost price; Price level; Computer science; Mathematics; Medicine","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":[0.0007274331,0.0003332295,0.0003831396,0.00137359,0.0007454576,0.000872696,0.0005227562,0.0005125407,0.002109792],"category_scores_gemma":[0.002604829,0.0002250339,0.000429736,0.002261135,0.0008772755,0.0006185521,0.0006196863,0.0005196302,0.0001578329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008913904,"about_ca_system_score_gemma":0.001263978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0807215,"about_ca_topic_score_gemma":0.07646105,"domain_scores_codex":[0.9997302,0.00004098945,0.00002839292,0.00005086593,0.00007617529,0.00007343823],"domain_scores_gemma":[0.9966087,0.000692286,0.001430531,0.000266405,0.0005362931,0.0004657246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001679242,0.0000533066,0.9944229,0.00001980515,0.00008797519,0.0002599388,0.0003226751,0.0001500736,0.0002491187,0.0002178834,0.0001975913,0.003850812],"study_design_scores_gemma":[0.00001129678,0.00004840994,0.9988915,0.000003367255,0.00005359075,0.00007357177,0.0002377548,0.0003082423,0.00007802361,0.0000919157,0.0001977663,0.000004469975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989719,0.0002460511,0.00003263135,0.0001098102,0.000001869301,0.000003565802,0.0001254452,0.000001268432,0.0005073961],"genre_scores_gemma":[0.9992937,0.0002285511,0.00001567317,0.00002711168,0.000009194407,0.000001720905,0.0002419676,6.818249e-7,0.0001814842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0807215,"threshold_uncertainty_score":0.1605033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09969751264918772,"score_gpt":0.2971403597991211,"score_spread":0.1974428471499334,"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."}}