{"id":"W2761983349","doi":"10.1007/s10690-017-9232-3","title":"Internal Market Efficiency, Market Co-movement, and Cross-Market Efficiency: The Case of Hong Kong and Shanghai Stock Markets","year":2017,"lang":"en","type":"article","venue":"Asia-Pacific Financial Markets","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Financial crisis; Stock market; Market depth; Factor market; Domestic market; Market efficiency; Business; Monetary economics; Market microstructure; Financial system; Order (exchange); Economics; Market economy; Financial economics; Finance; Geography; International trade","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":[0.002071877,0.0003702322,0.0008642883,0.001433818,0.0009063981,0.004206518,0.0007108177,0.0008048586,0.00293341],"category_scores_gemma":[0.005652686,0.0002981258,0.0007147483,0.001583611,0.00204619,0.003358222,0.001202171,0.0007211606,0.000119148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002454396,"about_ca_system_score_gemma":0.001044133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08442397,"about_ca_topic_score_gemma":0.04912995,"domain_scores_codex":[0.9997106,0.00007558707,0.00001524722,0.00003585012,0.00003296821,0.0001296317],"domain_scores_gemma":[0.9966954,0.001564095,0.0007166398,0.0002152958,0.0003993488,0.0004091364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008200333,0.0002741354,0.4304042,0.0001697116,0.0008969908,0.004928286,0.00266065,0.2477949,0.001963237,0.2833079,0.003711744,0.02306836],"study_design_scores_gemma":[0.0001640392,0.0001234769,0.3753404,0.00005766133,0.0004549494,0.0004907272,0.004965759,0.5575319,0.001237419,0.05820088,0.001325063,0.0001076758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994892,0.0003750113,0.0009798981,0.0002279064,0.000004492952,0.00000969584,0.00003728013,0.000009086579,0.00346452],"genre_scores_gemma":[0.9994287,0.00009765784,0.00008049511,0.000006854682,0.00000427679,0.000001574694,0.00001913955,0.000001972426,0.0003592809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08442397,"threshold_uncertainty_score":0.1678651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446332327084378,"score_gpt":0.2508408913214777,"score_spread":0.2363775680506339,"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."}}