{"id":"W3017026466","doi":"10.3390/jrfm13040075","title":"Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations","year":2020,"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":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stylized fact; Portfolio; Financial market; Portfolio optimization; Trading strategy; Algorithmic trading; Asset (computer security); Capital market; Asset allocation; Business; Economics; Financial economics; Finance; Computer 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":[0.0005578164,0.0001585639,0.0007790682,0.0008492977,0.0001399638,0.00005480293,0.0001297056,0.0000706614,0.0004760658],"category_scores_gemma":[0.0002493358,0.0001565696,0.0005135156,0.001280621,0.00003303761,0.000168729,0.00005725298,0.00013522,0.000004162582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000722272,"about_ca_system_score_gemma":0.00001981267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001815079,"about_ca_topic_score_gemma":0.00007337083,"domain_scores_codex":[0.9982808,0.00004461061,0.00116344,0.0002345924,0.0001056028,0.0001709105],"domain_scores_gemma":[0.9981974,0.0000544608,0.001381697,0.0001682364,0.0000931894,0.0001050028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002181754,0.0008333395,0.5895159,0.0001846576,0.003086786,0.00005669815,0.001825627,0.2498509,0.00009414859,0.08935485,0.001919759,0.06109556],"study_design_scores_gemma":[0.0004801764,0.0001623359,0.8135876,0.00001987653,0.000587017,7.431855e-7,0.00003747652,0.1776793,0.000003135653,0.002331849,0.004968832,0.0001416154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8724968,0.0003549693,0.1259284,0.00007722899,0.0001562813,0.000140404,0.0004349478,0.00000506287,0.0004058176],"genre_scores_gemma":[0.9976053,0.0001919394,0.001913727,0.00004959864,0.0001850445,7.879545e-7,0.000009635848,0.00001053994,0.00003345344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240718,"threshold_uncertainty_score":0.6384723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03691490904235478,"score_gpt":0.2519252087602832,"score_spread":0.2150102997179284,"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."}}