{"id":"W2790110130","doi":"10.1016/j.irfa.2018.04.001","title":"Long memory in financial markets: A heterogeneous agent model perspective","year":2018,"lang":"en","type":"article","venue":"International Review of Financial Analysis","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Stylized fact; Capital asset pricing model; Economics; Financial market; Rational expectations; Rational agent; Agent-based model; Asset (computer security); Financial economics; Efficient-market hypothesis; Microeconomics; Econometrics; Computer science; Finance; Stock market; Macroeconomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002422172,0.0008341642,0.002235841,0.001164105,0.0006960406,0.004151372,0.002834731,0.00459664,0.003586509],"category_scores_gemma":[0.01066871,0.0006413235,0.0009626301,0.001487852,0.002541799,0.008007205,0.001656535,0.003520933,0.0003400413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169208,"about_ca_system_score_gemma":0.0009655686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003195415,"about_ca_topic_score_gemma":0.001728131,"domain_scores_codex":[0.9992954,0.0003209991,0.00004191817,0.0001487682,0.0001019839,0.00009095562],"domain_scores_gemma":[0.9918579,0.006019035,0.001057111,0.0003325606,0.0003844244,0.0003489422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004493312,0.00007371404,0.001556651,0.0002283194,0.000206232,0.0004207666,0.0002591752,0.1068539,0.0005009396,0.8785943,0.002090381,0.009170742],"study_design_scores_gemma":[0.00002959496,0.00003269344,0.0006622942,0.00005620107,0.0000758691,0.000101192,0.00007306656,0.2458642,0.00007551604,0.7511053,0.00189567,0.00002833561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1572907,0.0717626,0.6990333,0.03154056,0.001273219,0.00007551974,0.0005720257,0.0002603388,0.03819183],"genre_scores_gemma":[0.943193,0.02454353,0.01696257,0.001113961,0.002445125,0.00008096196,0.000167463,0.00005584434,0.01143743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00459664,"threshold_uncertainty_score":0.01280981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209129109969941,"score_gpt":0.2627185905902139,"score_spread":0.2406272994905144,"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."}}