{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003318275,0.00008449084,0.0002031918,0.0001772275,0.00008137029,0.0001673225,0.0007056217,0.00002858035,0.0000898669],"category_scores_gemma":[0.002301761,0.00004755914,0.0001649776,0.00007639496,0.0001841907,0.0002095254,0.00006794961,0.0001085011,0.000001940111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006182896,"about_ca_system_score_gemma":0.00008160594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001201699,"about_ca_topic_score_gemma":0.00001379252,"domain_scores_codex":[0.9988692,0.00008193574,0.0006385625,0.0001144281,0.0001979081,0.00009795302],"domain_scores_gemma":[0.9960859,0.002395036,0.0008900771,0.0001401023,0.0004599417,0.00002890179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003005249,0.0001032662,0.06887425,0.000002149134,0.000343551,0.00001536134,0.001291432,0.002498316,0.00006626454,0.027907,0.02601423,0.8698789],"study_design_scores_gemma":[0.0007879557,0.0007732114,0.565839,0.00007608511,0.000005019528,0.0002715259,0.00009819469,0.1313108,0.0001372301,0.2828207,0.01774938,0.0001308788],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876601,0.0002192904,0.0003087173,0.000494563,0.001184436,0.00003193308,0.00002473445,8.099468e-7,0.01007541],"genre_scores_gemma":[0.996847,0.0006718935,0.001497439,0.00003424881,0.000441581,6.212468e-7,1.035369e-7,0.0000053838,0.0005016864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8697481,"threshold_uncertainty_score":0.2755589,"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."}}