{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005188711,0.0004054524,0.0009953741,0.00145096,0.000526529,0.002029926,0.0009490779,0.001340435,0.002168289],"category_scores_gemma":[0.05245016,0.0004576862,0.0004119072,0.001175089,0.001479179,0.005391363,0.001072197,0.001610346,0.0001963249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007283374,"about_ca_system_score_gemma":0.0004845959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770676,"about_ca_topic_score_gemma":0.0009168286,"domain_scores_codex":[0.9990553,0.0004728077,0.00006620249,0.0001440733,0.000171646,0.00008999462],"domain_scores_gemma":[0.9116067,0.08257478,0.002067618,0.00173645,0.001606698,0.0004077452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008449823,0.000271281,0.03123817,0.0001895481,0.0002113532,0.0002562208,0.0003891284,0.6541731,0.002434484,0.1867939,0.002136776,0.121061],"study_design_scores_gemma":[0.00001540926,0.00002799172,0.002704719,0.00001057582,0.00001341891,0.00004971392,0.00002699535,0.9170041,0.0003678405,0.07958347,0.0001841339,0.00001157994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7764587,0.003980595,0.2104284,0.002823678,0.0002825367,0.0000257446,0.0001063763,0.0002670692,0.005627018],"genre_scores_gemma":[0.9894387,0.0003813092,0.008950361,0.00005828092,0.0001175511,0.000008735858,0.00002790424,0.00003228114,0.0009847721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005188711,"threshold_uncertainty_score":0.02744091,"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."}}