{"id":"W2904897621","doi":"10.3390/jrfm11040088","title":"Friendship of Stock Market Indices: A Cluster-Based Investigation of Stock Markets","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Magyar Tudományos Akadémia","keywords":"Stock (firearms); Cluster analysis; Econometrics; Stock market index; Covariance; Equity (law); Stock market; Financial economics; Economics; Mathematics; Statistics; Geography; Political science","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":[0.001329304,0.0001275347,0.0005661921,0.0005889644,0.00008240098,0.00002625551,0.0001881653,0.00006509687,0.0002624494],"category_scores_gemma":[0.0001091712,0.0001245048,0.0001866834,0.0004206496,0.0001265186,0.0001515522,0.00007347644,0.000106003,0.00000417024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003620712,"about_ca_system_score_gemma":0.00002323629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002024894,"about_ca_topic_score_gemma":0.00007735259,"domain_scores_codex":[0.9983798,0.00004810042,0.00114236,0.0001736138,0.0001031529,0.0001530075],"domain_scores_gemma":[0.9974715,0.00006429749,0.002050866,0.0002058478,0.0001331357,0.00007432744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001503553,0.0003053108,0.8538595,0.00106386,0.0004783303,0.00001737471,0.002314547,0.000146011,0.00001840371,0.06099552,0.006966553,0.07233106],"study_design_scores_gemma":[0.00184247,0.0007812592,0.9152223,0.0002064814,0.0001451218,0.000003975444,0.0002702703,0.003338698,0.00005307385,0.01683709,0.06108219,0.0002169979],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577374,0.001215796,0.03281691,0.00008802454,0.0003203276,0.0002007757,0.00009772661,0.000004031747,0.007518978],"genre_scores_gemma":[0.9967873,0.0002921869,0.002562536,0.00004780117,0.0001573363,0.00000329626,0.000001926014,0.00001006611,0.0001375198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07211406,"threshold_uncertainty_score":0.5077159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171850355005079,"score_gpt":0.2055556666949972,"score_spread":0.1883706311944893,"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."}}