{"id":"W7130515823","doi":"10.5220/0013264200004568","title":"The Application of Machine Learning to Algorithmic Trading in Financial Markets","year":2024,"lang":"","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Financial market; Algorithmic trading; High-frequency trading; Key (lock); Order (exchange)","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":[0.02871419,0.0002924259,0.0004823979,0.0008468668,0.0003859102,0.000511857,0.001236739,0.0001684953,0.0005039478],"category_scores_gemma":[0.02355626,0.0001961349,0.0002220238,0.005084905,0.0001685774,0.0002401117,0.0003917324,0.0007591182,0.0001479119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001746195,"about_ca_system_score_gemma":0.0003583929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002480033,"about_ca_topic_score_gemma":0.0002893437,"domain_scores_codex":[0.9936653,0.001612557,0.001696749,0.001020012,0.001394,0.0006114334],"domain_scores_gemma":[0.9800445,0.01874404,0.0002398458,0.0006395513,0.0001732855,0.0001588054],"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":[0.0001529146,0.00002441441,0.002147636,0.000029696,0.000009803168,0.000007484115,0.001346782,0.0003313156,0.001042089,0.003086356,0.0009067325,0.9909148],"study_design_scores_gemma":[0.0001716829,0.0001298042,0.01994716,0.0001860878,0.00001518383,0.00001776703,0.0003690318,0.854683,0.0006802448,0.0137134,0.1098713,0.0002152715],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1131793,0.004433341,0.8467385,0.00293744,0.002779879,0.001442732,0.00001609214,0.0001055559,0.02836718],"genre_scores_gemma":[0.9545,0.0001030066,0.03748594,0.00006248204,0.0002222315,0.00008866627,0.000001054139,0.00003411037,0.0075025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906995,"threshold_uncertainty_score":0.9951823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04903677901760841,"score_gpt":0.3819709095370543,"score_spread":0.3329341305194459,"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."}}