{"id":"W3164985120","doi":"10.2139/ssrn.3170378","title":"Machine Learning and High-Frequency Algorithms during Batch Auctions","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Common value auction; Algorithm; Machine learning; Speech recognition; Mathematics; Statistics","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":[0.005683382,0.0006760539,0.001158236,0.0009028693,0.000810258,0.001848959,0.002212242,0.001415145,0.003414528],"category_scores_gemma":[0.02719551,0.0006443992,0.0005682264,0.001038605,0.0007759802,0.003291715,0.001082996,0.002135332,0.0004803199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008100418,"about_ca_system_score_gemma":0.001152255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003892903,"about_ca_topic_score_gemma":0.002857326,"domain_scores_codex":[0.9982604,0.0007597815,0.00008848534,0.0002556853,0.000406686,0.000228953],"domain_scores_gemma":[0.9788199,0.01750017,0.0008591791,0.00130607,0.001204879,0.0003097944],"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":[0.001513399,0.0003889494,0.005473189,0.000135257,0.0001011727,0.0001917863,0.0002199533,0.7458925,0.004627718,0.05261933,0.003335538,0.1855013],"study_design_scores_gemma":[0.00001526205,0.00002792172,0.0002840198,0.000003562981,0.000004619695,0.0000207162,0.000007665491,0.9924121,0.0004434808,0.00662034,0.0001565291,0.000003824329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1825649,0.0005166744,0.8114022,0.000502822,0.0002099181,0.00006895887,0.00007897749,0.0005245427,0.004130919],"genre_scores_gemma":[0.8663931,0.0001458971,0.1282059,0.00008526436,0.0002053323,0.00009269872,0.0001593504,0.0001491168,0.004563344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005683382,"threshold_uncertainty_score":0.03005695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03399959393607691,"score_gpt":0.3559520506933339,"score_spread":0.321952456757257,"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."}}