{"id":"W3199773792","doi":"10.2139/ssrn.3710491","title":"The Conduits of Price Discovery: A Machine Learning Approach","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Electrical conduit; Computer science; Telecommunications","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":[0.02729971,0.0009623505,0.003369346,0.004123816,0.002695498,0.00875188,0.005733866,0.007563807,0.006683265],"category_scores_gemma":[0.160448,0.001382893,0.001481096,0.003747065,0.01080359,0.02092223,0.006181364,0.009108049,0.0008504742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001987657,"about_ca_system_score_gemma":0.002843753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0016569,"about_ca_topic_score_gemma":0.001182623,"domain_scores_codex":[0.9871749,0.00657345,0.0006730608,0.002110678,0.002873569,0.000594352],"domain_scores_gemma":[0.7073458,0.2681436,0.008315181,0.01016742,0.004609463,0.001418586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004375931,0.0002035542,0.00817916,0.0003239417,0.0002269188,0.0005800336,0.0004551012,0.05768599,0.0004517633,0.8268266,0.005789973,0.0988394],"study_design_scores_gemma":[0.00004514095,0.0000567731,0.0003908224,0.00006063532,0.00003328707,0.0001167216,0.00004392091,0.3356082,0.0003913263,0.6616972,0.001531054,0.00002490586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05147343,0.005282232,0.8942087,0.03424744,0.0005564695,0.0001500427,0.0003690474,0.0004129072,0.01329965],"genre_scores_gemma":[0.8279996,0.003480668,0.1531467,0.003033036,0.002934003,0.0003319935,0.0003248706,0.0001823564,0.008566689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02729971,"threshold_uncertainty_score":0.1443765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0735767431848387,"score_gpt":0.3509866965892209,"score_spread":0.2774099534043822,"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."}}