{"id":"W2898660943","doi":"10.3390/jrfm11040076","title":"Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Stock (firearms); Stock market; Financial economics; Granger causality; Economics; Autoregressive model; Stock market bubble; Inventory turnover; Econometrics; Stock exchange; Monetary economics; Business; Finance; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001473733,0.0002193503,0.0005919078,0.0004338754,0.0002433121,0.0001039332,0.0001223161,0.0001057531,0.0001209886],"category_scores_gemma":[0.0001924815,0.0002137531,0.00008678407,0.0003269727,0.0001492099,0.000435439,0.000086961,0.0003765064,0.000003432938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087815,"about_ca_system_score_gemma":0.00002676663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003339168,"about_ca_topic_score_gemma":0.001393737,"domain_scores_codex":[0.9982747,0.00003969708,0.00089101,0.0003697238,0.00008913205,0.0003357536],"domain_scores_gemma":[0.9989661,0.00006638336,0.0005949987,0.0001811239,0.00007227065,0.0001191537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009467225,0.0001482891,0.8668031,0.0001018116,0.00004094257,0.000772716,0.002543905,0.00002118427,6.384452e-7,0.008920001,0.001543978,0.1181567],"study_design_scores_gemma":[0.002687426,0.0006898804,0.9173235,0.0001380869,0.00005532758,0.0002924589,0.0003228547,0.03599869,0.000002299891,0.01444205,0.02766826,0.0003792131],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9596502,0.001186007,0.03575836,0.00005781951,0.0004280669,0.0002737271,0.00004949891,0.000006846036,0.002589487],"genre_scores_gemma":[0.9958091,0.0005892097,0.002479242,0.00004231587,0.0009127695,0.000003894003,6.08244e-7,0.0000180296,0.0001447715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1177775,"threshold_uncertainty_score":0.8716596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678016701922964,"score_gpt":0.2108592969794736,"score_spread":0.194079129960244,"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."}}