{"id":"W1501524595","doi":"10.1007/978-3-642-23336-4_9","title":"An Order-Driven Agent-Based Artificial Stock Market to Analyze Liquidity Costs of Market Orders in the Taiwan Stock Market","year":2011,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Market liquidity; Stock market; Order (exchange); Market maker; Business; Financial economics; Market depth; Primary market; Market microstructure; Econometrics; Economics; Finance; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001776666,0.0005221872,0.001321968,0.001042315,0.0001714999,0.00006530682,0.0009321602,0.0002047656,0.004933232],"category_scores_gemma":[0.0003146323,0.0005232797,0.0003098958,0.0006310874,0.0003931867,0.0001272934,0.000234508,0.0004176092,0.00008546429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000428381,"about_ca_system_score_gemma":0.0001221946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001343661,"about_ca_topic_score_gemma":0.007585927,"domain_scores_codex":[0.9962092,0.0001495319,0.002014447,0.0009487878,0.0002633446,0.0004147026],"domain_scores_gemma":[0.9970492,0.0007099789,0.0009868571,0.0006989718,0.0004587635,0.00009619182],"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":[0.001479296,0.0009259511,0.01368839,0.0007667431,0.002187602,0.0001302328,0.006553188,0.2950185,7.668422e-7,0.610908,0.0481279,0.02021338],"study_design_scores_gemma":[0.000589876,0.00158081,0.01813492,0.001068406,0.0001956557,0.00001183583,0.003598684,0.5190175,0.000003731288,0.3893535,0.06381745,0.002627599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01209627,0.01087439,0.2531206,0.00305933,0.003118456,0.006018017,0.004751758,0.0001309631,0.7068302],"genre_scores_gemma":[0.9800213,0.0003882384,0.004111136,0.0004384656,0.0002212117,0.0001562119,0.0001336429,0.00008534507,0.01444447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.967925,"threshold_uncertainty_score":0.9997219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1206755424766039,"score_gpt":0.3159524952276304,"score_spread":0.1952769527510265,"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."}}