{"id":"W1942894251","doi":"10.48550/arxiv.0709.2209","title":"Topological Properties of Stock Networks Based on Random Matrix Theory in Financial Time Series","year":2007,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock (firearms); Stock market; Eigenvalues and eigenvectors; Random matrix; Econometrics; Network theory; Mathematics; Complex network; Topology (electrical circuits); Financial economics; Economics; Statistics; Combinatorics; Physics; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001534664,0.0003632327,0.001445099,0.0004569327,0.0000871163,0.00004495511,0.0004311814,0.0004932824,0.001416471],"category_scores_gemma":[0.0002987041,0.0003379128,0.0004768625,0.0003238724,0.0001863853,0.00007905774,0.0003091167,0.0005865337,0.0001958836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000124499,"about_ca_system_score_gemma":0.00006146665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007317057,"about_ca_topic_score_gemma":0.0001332748,"domain_scores_codex":[0.9974093,0.0001021548,0.001335349,0.0006700555,0.00007186087,0.0004112128],"domain_scores_gemma":[0.9983043,0.0001245772,0.0007369342,0.0007080053,0.00005873263,0.0000674405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004510832,0.000671023,0.7947311,0.0005998088,0.0003451261,0.00005964559,0.0005663342,0.1213227,0.00004902779,0.07561291,0.0006052629,0.0009262732],"study_design_scores_gemma":[0.007077973,0.001090915,0.7612243,0.001449272,0.0001624282,0.000007986203,0.0002582757,0.1670078,0.000430333,0.03754419,0.02076262,0.002983879],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829407,0.004818332,0.004540688,0.0002713203,0.0005267337,0.0006071836,0.00009722923,0.00005023383,0.006147517],"genre_scores_gemma":[0.9955471,0.0001246504,0.0001474882,0.0001300493,0.000293796,0.0000645905,0.00003829013,0.00003425886,0.003619747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04568509,"threshold_uncertainty_score":0.9999073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04486317621789301,"score_gpt":0.2286785654486728,"score_spread":0.1838153892307798,"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."}}