{"id":"W3153624456","doi":"10.1016/j.ribaf.2021.101419","title":"The impact of COVID-19 on the stock market crash risk in China","year":2021,"lang":"en","type":"article","venue":"Research in International Business and Finance","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":190,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Skewness; Autoregressive conditional heteroskedasticity; Stock market crash; Crash; Stock market; Equity (law); Econometrics; China; Economics; Stock market index; Pandemic; Coronavirus disease 2019 (COVID-19); Index (typography); Financial economics; Proxy (statistics); Business; Statistics; Volatility (finance); Geography; Medicine; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003586202,0.0001005081,0.0002036462,0.0003128918,0.0001603228,0.0001093787,0.0004438543,0.00006408893,0.0002513855],"category_scores_gemma":[0.009358352,0.00007079606,0.00005735624,0.001026896,0.0002094604,0.0001340423,0.0002060267,0.0004563558,0.00001787301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000518906,"about_ca_system_score_gemma":0.0003769791,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007474724,"about_ca_topic_score_gemma":0.0006503408,"domain_scores_codex":[0.9986714,0.0001301162,0.0004166355,0.0003107347,0.0001507017,0.0003203936],"domain_scores_gemma":[0.9973996,0.001888994,0.0001854414,0.0003376068,0.0001457567,0.00004256125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002441427,0.000184091,0.8623379,0.00003751124,0.00003814285,0.00004223592,0.00046063,0.005217426,0.00001316144,0.1194282,0.006547996,0.005448595],"study_design_scores_gemma":[0.0005173428,0.00002664012,0.8912998,0.00005411017,3.148634e-7,0.000004615124,0.00003761204,0.01127836,0.000007249904,0.07619388,0.02050658,0.00007345468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670712,0.001945235,0.0002308575,0.02008761,0.0001809959,0.0002308237,0.0002487019,0.00000386753,0.01000067],"genre_scores_gemma":[0.9852002,0.01350598,0.00001792374,0.0001197319,0.00005500006,0.00004476124,0.000005444865,0.000009793254,0.001041185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0432343,"threshold_uncertainty_score":0.9991346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1008443444949387,"score_gpt":0.3810602242572835,"score_spread":0.2802158797623448,"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."}}