{"id":"W2185930465","doi":"10.2139/ssrn.2304337","title":"Need for Speed? Low Latency Trading and Adverse Selection","year":2013,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Calgary","funders":"","keywords":"Adverse selection; Latency (audio); Business; Selection (genetic algorithm); Computer science; Telecommunications; Actuarial science; Artificial intelligence","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":[0.003921594,0.0003957624,0.001064063,0.0007151905,0.0008462915,0.004789,0.0009209403,0.002296313,0.01796038],"category_scores_gemma":[0.04665488,0.0004662935,0.0004753719,0.0006748199,0.002386525,0.01062632,0.001657666,0.002932434,0.001419369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005182368,"about_ca_system_score_gemma":0.0006262533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007535786,"about_ca_topic_score_gemma":0.0005664647,"domain_scores_codex":[0.9988995,0.0003399201,0.00007236433,0.0002238945,0.0002432554,0.000220999],"domain_scores_gemma":[0.9582146,0.02862249,0.006206142,0.004076639,0.00153913,0.001341089],"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":[0.001051767,0.0001780138,0.0352409,0.0002320032,0.000202888,0.001104962,0.0006526399,0.02564576,0.004226143,0.8412967,0.008655871,0.08151232],"study_design_scores_gemma":[0.0001068618,0.0001112709,0.006244825,0.00002601221,0.00005065166,0.0005442181,0.0002423529,0.0308345,0.0006173978,0.9581401,0.003037642,0.00004415679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.745982,0.005111533,0.1319149,0.03164323,0.001001156,0.00007565524,0.0002858048,0.0006826071,0.08330315],"genre_scores_gemma":[0.9921451,0.0004387521,0.002164701,0.0005229897,0.0005495144,0.00001038383,0.00001907581,0.00004152878,0.004107961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01796038,"threshold_uncertainty_score":0.06008345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345992256633882,"score_gpt":0.1957868152770086,"score_spread":0.1823268927106698,"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."}}