{"id":"W3125334144","doi":"10.1016/j.jfineco.2018.07.002","title":"Regulating dark trading: Order flow segmentation and market quality","year":2018,"lang":"en","type":"article","venue":"Journal of Financial Economics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dark liquidity; Market liquidity; Intermediation; Business; Order (exchange); Intermediary; High-frequency trading; Revenue; Monetary economics; Market maker; Liquidity crisis; Market segmentation; Economics; Finance; Marketing","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":[0.001315556,0.0002123724,0.0004714356,0.0005353749,0.0003988438,0.003651807,0.000449998,0.0006813746,0.005350938],"category_scores_gemma":[0.008852739,0.0003289554,0.0002555031,0.0003670958,0.001028649,0.00233932,0.001004757,0.000918058,0.0004980248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008702182,"about_ca_system_score_gemma":0.0006502663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009288476,"about_ca_topic_score_gemma":0.001208664,"domain_scores_codex":[0.9994739,0.0001240675,0.00003325866,0.000125463,0.0001010768,0.0001423705],"domain_scores_gemma":[0.9939029,0.002410376,0.00143216,0.0007242801,0.0005099078,0.00102034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006734337,0.002680348,0.1393406,0.0003586614,0.0005012186,0.0005290582,0.002649967,0.04086344,0.4482172,0.2071323,0.006408019,0.1445848],"study_design_scores_gemma":[0.0008349654,0.001172814,0.3455718,0.0001114347,0.0002698072,0.000303989,0.00141473,0.274811,0.06526557,0.3036526,0.00642575,0.0001655365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792345,0.0002468563,0.01195309,0.000441649,0.00004503229,0.00001572138,0.00008046331,0.0001067905,0.007875879],"genre_scores_gemma":[0.9985631,0.00003616131,0.0007326417,0.0000555554,0.00001566202,0.000002790427,0.00002592078,0.00002918855,0.0005390468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005350938,"threshold_uncertainty_score":0.01790071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03545596415784045,"score_gpt":0.2476716516884313,"score_spread":0.2122156875305909,"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."}}