{"id":"W2793495915","doi":"10.5539/ijef.v10n4p101","title":"The Impact of Securities Margin Trading on Chinese Stock Market","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Volatility (finance); Econometrics; Economics; Financial economics; Stock market; Stock exchange; Margin (machine learning); Trading strategy; Computer science; Finance","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":[0.000939699,0.000290276,0.0003351449,0.0004845195,0.0004125166,0.00127022,0.0002754724,0.0005433168,0.00190767],"category_scores_gemma":[0.005160742,0.0001426667,0.0004303761,0.0003872979,0.0005533511,0.001502866,0.0006081927,0.0006019033,0.0001285861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007477585,"about_ca_system_score_gemma":0.0007285484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008855052,"about_ca_topic_score_gemma":0.006030726,"domain_scores_codex":[0.9995105,0.0001030742,0.00003596519,0.00007666494,0.0001433289,0.0001303968],"domain_scores_gemma":[0.9983339,0.0006132244,0.0005092781,0.0001190754,0.000224854,0.0001998476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00236811,0.0005024317,0.7312665,0.0002285893,0.0004529985,0.004829552,0.0007907609,0.1061525,0.0267224,0.0391284,0.003374942,0.08418284],"study_design_scores_gemma":[0.0001115289,0.000526842,0.6911079,0.00003506622,0.0003813127,0.0003905677,0.0005987685,0.2831025,0.006136628,0.01530373,0.002215481,0.00008963989],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955705,0.0003846857,0.0009527002,0.0002917403,0.00002898679,0.000004636785,0.00004937189,0.00002673797,0.002690793],"genre_scores_gemma":[0.9995571,0.00008689014,0.00007457582,0.00001836018,0.00001493012,8.469023e-7,0.00002674296,0.000002787228,0.0002178532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008855052,"threshold_uncertainty_score":0.01760703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.063610515299298,"score_gpt":0.3917036164175854,"score_spread":0.3280931011182874,"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."}}