{"id":"W3182721994","doi":"10.1080/1540496x.2021.1941860","title":"Macro Factors and Bond Returns in China","year":2021,"lang":"en","type":"article","venue":"Emerging Markets Finance and Trade","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Economics; Government bond; Emerging markets; Bond; Predictive power; Macro; Bond market; Excess return; Financial market; Monetary economics; Sample (material); Financial economics; Monetary policy; Econometrics; Macroeconomics; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007571984,0.0002875581,0.000300376,0.001166013,0.0003635904,0.00109051,0.0002459982,0.0002641686,0.00158736],"category_scores_gemma":[0.00206102,0.000155714,0.0003828532,0.001485865,0.0002888184,0.0005845223,0.0004305341,0.0003798835,0.0001993262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009794711,"about_ca_system_score_gemma":0.0007451597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04519634,"about_ca_topic_score_gemma":0.03666226,"domain_scores_codex":[0.9998102,0.00002622271,0.00002202659,0.00004801182,0.00005030995,0.00004333744],"domain_scores_gemma":[0.9987782,0.0002302347,0.000514982,0.00008347864,0.0002090461,0.0001840334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005359602,0.00003692256,0.9846769,0.00001778328,0.00008519562,0.0002875802,0.0001760044,0.004043661,0.000411756,0.001288615,0.0004559132,0.008466192],"study_design_scores_gemma":[0.000004306302,0.00002667353,0.9843797,0.000007756949,0.00002992029,0.00004095294,0.0001264571,0.01392037,0.0001646964,0.0005278492,0.0007609511,0.0000102912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985184,0.0001906761,0.000196209,0.0001177273,0.000003841469,0.000003697663,0.0002542039,0.000007043462,0.0007081899],"genre_scores_gemma":[0.999253,0.000123371,0.00003919442,0.000007463013,0.000005765837,0.000001524664,0.0002156244,0.000001459955,0.0003527623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04519634,"threshold_uncertainty_score":0.08986652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516214761101322,"score_gpt":0.2056004779665291,"score_spread":0.1904383303555158,"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."}}