{"id":"W4280561173","doi":"10.1016/j.jedc.2022.104438","title":"Machine learning and speed in high-frequency trading","year":2022,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Science and Technology Planning Project of Guangdong Province; Australian Research Council; National Office for Philosophy and Social Sciences; Special Project for Research and Development in Key areas of Guangdong Province; Chinese University of Hong Kong; Tianjin University; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Sun Yat-sen University; University of Technology Sydney","keywords":"High-frequency trading; Market liquidity; Algorithmic trading; Trading strategy; Pairs trade; Profitability index; Electronic trading; Trading turret; Dark liquidity; Economics; Order book; Flash trading; Market microstructure; Financial market; Alternative trading system; Industrial organization; Computer science; Order (exchange); Open outcry; Monetary economics; Financial economics; Finance","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":[],"consensus_categories":[],"category_scores_codex":[0.0009216212,0.000109445,0.0006157894,0.0003785897,0.0001440257,0.00007881857,0.0001157947,0.0000282124,0.0005151045],"category_scores_gemma":[0.0000211157,0.0001256487,0.0001072662,0.00007771326,0.00002534197,0.0001623835,0.00005054436,0.0002904425,0.000002948482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717491,"about_ca_system_score_gemma":0.00001886226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153021,"about_ca_topic_score_gemma":0.0003730337,"domain_scores_codex":[0.9987383,0.00003334904,0.000858407,0.0001791439,0.00002380512,0.0001670019],"domain_scores_gemma":[0.9990212,0.00006423079,0.0007555949,0.00007606522,0.0000100999,0.0000728138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007488698,0.00003961475,0.6064242,0.00001512976,0.0002187302,0.00003036646,0.0001937801,0.005021734,0.00002813705,0.3858251,0.00001432008,0.002113999],"study_design_scores_gemma":[0.003180011,0.0002836764,0.02556273,0.000009004806,0.00002767239,0.0001660707,0.0004624299,0.9216977,2.379955e-7,0.04517937,0.003207073,0.000224019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881694,0.006994368,0.0009224589,0.0009892849,0.0003302555,0.00008688058,0.00012503,0.000004079543,0.002378209],"genre_scores_gemma":[0.9989251,0.0003724268,0.00008331131,0.00005604707,0.00009717084,0.000002288813,0.000004270426,0.00001425648,0.0004451638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.916676,"threshold_uncertainty_score":0.5640033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00830678390032978,"score_gpt":0.1866106992338664,"score_spread":0.1783039153335366,"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."}}