{"id":"W2071185898","doi":"10.1016/j.procs.2014.05.189","title":"Distance-based High-frequency Trading","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; High-frequency trading; Algorithmic trading; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01477399,0.0002283809,0.000343808,0.0006154617,0.000589907,0.0009313803,0.003432827,0.00005478073,0.00009178757],"category_scores_gemma":[0.0111474,0.0001726439,0.00009619439,0.004111477,0.0008641201,0.0008610971,0.0003111981,0.0001995669,0.0001319811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193255,"about_ca_system_score_gemma":0.0004350361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001442591,"about_ca_topic_score_gemma":0.000006700171,"domain_scores_codex":[0.9942852,0.0002675016,0.0006433723,0.001305017,0.002795629,0.000703303],"domain_scores_gemma":[0.9940555,0.003494037,0.0003234274,0.001090262,0.0006787948,0.0003579907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001430834,0.00005134916,0.03842305,0.00001313961,0.000002206388,0.000004292894,0.000407069,0.0006985597,0.001914849,0.02867908,0.001112869,0.9286792],"study_design_scores_gemma":[0.0003675121,0.0001433168,0.06547838,0.00004376895,0.000004701786,0.00001682779,0.000007471598,0.7858769,0.003177776,0.1423307,0.00219947,0.0003531332],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262669,0.00001735224,0.8655822,0.0006192439,0.002276622,0.0001635109,0.000001426495,0.0001687981,0.004904008],"genre_scores_gemma":[0.601772,1.750688e-7,0.3974453,0.0004146634,0.0002887057,0.00001164403,2.682139e-7,0.000008521928,0.00005877834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9283261,"threshold_uncertainty_score":0.9971821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07523439673813767,"score_gpt":0.3562722514232231,"score_spread":0.2810378546850854,"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."}}