{"id":"W2023875268","doi":"10.5430/air.v4n2p13","title":"Reproduce stylized facts of artificial financial market and comparison with real data","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","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":"Stylized fact; Volatility clustering; Financial market; Computational finance; Volatility (finance); Big data; Explication; Market data; Cluster analysis; Order (exchange); Finance; Economics; Financial economics; Computer science; Artificial intelligence; Autoregressive conditional heteroskedasticity; Data mining; Macroeconomics","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":[0.005848107,0.0001667275,0.0006685118,0.0004270622,0.0002032748,0.0001773768,0.0006841136,0.0001033669,0.0004889203],"category_scores_gemma":[0.001595262,0.0001622362,0.000050672,0.001117059,0.0004669287,0.000330145,0.0005148446,0.0003223509,0.0002332713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006989729,"about_ca_system_score_gemma":0.000173889,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01119422,"about_ca_topic_score_gemma":0.004523877,"domain_scores_codex":[0.9969881,0.0001449878,0.001130508,0.0009218929,0.0002844294,0.0005300341],"domain_scores_gemma":[0.997454,0.0002351067,0.0003217931,0.001358414,0.0003937602,0.0002368636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002182321,0.0009518684,0.07479762,0.0001838257,0.0002683203,0.00004215377,0.005105532,0.0004869781,0.0006392743,0.7879032,0.01233529,0.1151036],"study_design_scores_gemma":[0.0005541338,0.002819624,0.02162142,0.0002303871,0.00007837112,0.00003053999,0.02271575,0.3609072,0.0145466,0.4820197,0.09263211,0.001844191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581189,0.001846088,0.01171391,0.00130055,0.0002921421,0.000745247,0.0003663915,0.0000502276,0.02556658],"genre_scores_gemma":[0.9978383,0.0001192011,0.001105828,0.000007731796,0.0002324832,0.00001473319,0.00003841061,0.00002189982,0.0006213952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3604202,"threshold_uncertainty_score":0.9953903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4761785879836369,"score_gpt":0.4068856807643041,"score_spread":0.06929290721933284,"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."}}