{"id":"W3085796557","doi":"10.1108/jdqs-04-2010-b0001","title":"The Lead-Lag Relationship between the Stock Market and CDS Market in Korea","year":2010,"lang":"en","type":"article","venue":"Journal of Derivatives and Quantitative Studies 선물연구","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Stock market; Equity (law); Stock exchange; Lead–lag compensator; Lag; Monetary economics; Economics; Sample (material); Business; Financial economics; Econometrics; Finance; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.001752178,0.0001312894,0.0003888577,0.0001478316,0.0005971452,0.00007915507,0.0001401711,0.00005607992,0.00001260683],"category_scores_gemma":[0.003274034,0.00008138429,0.00007287205,0.0002704926,0.0006612095,0.0002621275,0.00008140232,0.0004530222,0.000001770445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002230116,"about_ca_system_score_gemma":0.00002075319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001808569,"about_ca_topic_score_gemma":0.0002179194,"domain_scores_codex":[0.9988649,0.00007332371,0.0006771421,0.0001491476,0.00005954288,0.000175921],"domain_scores_gemma":[0.9955365,0.00352726,0.000644123,0.0001240338,0.0001265052,0.00004157631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004733185,0.0000140037,0.8031211,0.00001015924,0.00009423239,0.000001434074,0.003360662,0.000001297568,0.000004389735,0.1890326,0.002411879,0.001900956],"study_design_scores_gemma":[0.0003500194,0.0001426989,0.8986838,0.00002331954,0.00001309187,0.00000557093,0.004117445,0.0001354557,0.000002190034,0.07765169,0.01878149,0.00009319891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722095,0.01632082,0.0008799094,0.006543709,0.000333235,0.0001587727,0.00004190306,0.000003116499,0.003509019],"genre_scores_gemma":[0.9936747,0.004123476,0.001489809,0.00002099031,0.0001617461,0.000007445951,5.86114e-7,0.000009806755,0.0005114169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1113809,"threshold_uncertainty_score":0.4592819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09118348488838972,"score_gpt":0.3216921691032038,"score_spread":0.230508684214814,"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."}}