{"id":"W3164753059","doi":"10.2139/ssrn.3305277","title":"Investigating Limit Order Book Features for Short-Term Price Prediction: A Machine Learning Approach","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Order book; Term (time); Limit (mathematics); Order (exchange); Computer science; High-frequency trading; Artificial intelligence; Financial market; Machine learning; Econometrics; Algorithmic trading; Financial economics; Economics; Mathematics; 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":[0.001133299,0.00059653,0.0008532367,0.001581391,0.0002750039,0.001536523,0.0006657233,0.0008024647,0.001585932],"category_scores_gemma":[0.005218212,0.000189522,0.0005456666,0.001222336,0.0002607002,0.001704994,0.0003697294,0.0009421576,0.0003888158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002454233,"about_ca_system_score_gemma":0.0003798014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00239689,"about_ca_topic_score_gemma":0.002027123,"domain_scores_codex":[0.9997632,0.00005641539,0.0000249138,0.00005268577,0.00006694958,0.00003580342],"domain_scores_gemma":[0.9966919,0.002432007,0.0002715023,0.0001874745,0.0003158557,0.0001012478],"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":[0.00121989,0.001364041,0.1083108,0.0002216591,0.0003278172,0.0005308191,0.0001530857,0.3308695,0.01745111,0.007215622,0.004021033,0.5283145],"study_design_scores_gemma":[0.000006633418,0.0000501951,0.006292569,0.000006773992,0.00001902682,0.00002479916,0.00001506334,0.9913896,0.0006605491,0.001411737,0.0001170779,0.000006011575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8076384,0.001439702,0.1864565,0.0005088202,0.00008107436,0.00005588038,0.0005055298,0.0005513529,0.002762706],"genre_scores_gemma":[0.9879518,0.0001966191,0.01081744,0.00002679737,0.00006834314,0.0000127814,0.0003153426,0.00001420033,0.0005967226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00239689,"threshold_uncertainty_score":0.005993485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06759535854628615,"score_gpt":0.3614725632963628,"score_spread":0.2938772047500767,"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."}}