{"id":"W1979891294","doi":"10.2139/ssrn.1722202","title":"Modeling Asset Prices for Algorithmic and High Frequency Trading","year":2010,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"High-frequency trading; Market liquidity; Algorithmic trading; Pairs trade; Tick size; Market microstructure; Volume-weighted average price; Dark liquidity; Econometrics; Financial economics; Alternative trading system; Trading strategy; Market maker; Economics; Profit (economics); Volatility (finance); Incentive; Stock market; Monetary economics; Finance; Microeconomics; Geography; 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":[],"consensus_categories":[],"category_scores_codex":[0.001348487,0.0001351829,0.0002393807,0.000147156,0.0002820858,0.0001586214,0.0001694933,0.00009717419,0.00003635993],"category_scores_gemma":[0.00007692973,0.000137861,0.00007898645,0.00007969837,0.00003529105,0.0004644278,0.00001348615,0.0009101886,0.000006957425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001378888,"about_ca_system_score_gemma":0.000245825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002416844,"about_ca_topic_score_gemma":0.0003738719,"domain_scores_codex":[0.998219,0.000005007643,0.0003902716,0.0002350765,0.00003005121,0.001120625],"domain_scores_gemma":[0.9995961,0.0000270959,0.0001828272,0.0001039762,0.00002862057,0.00006139409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007718415,0.00001831744,0.001329628,0.000007935548,0.00003863576,3.805968e-7,0.00005114577,0.00001500851,0.0001805396,0.9952609,0.00001974009,0.003070031],"study_design_scores_gemma":[0.0004737708,0.0001624009,0.000945089,0.000006070852,0.000007743313,0.00005559351,0.0001441376,0.02685561,0.00001156229,0.9702462,0.0009087413,0.000183079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8919759,0.005205931,0.09781872,0.0007891812,0.0007163511,0.000198005,0.00003226446,0.0000240331,0.003239593],"genre_scores_gemma":[0.9911883,0.003137193,0.004913046,0.00007343582,0.0004643077,0.00001620781,0.00000529984,0.00002266656,0.000179562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09921236,"threshold_uncertainty_score":0.5621808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660300461974772,"score_gpt":0.2148213146222095,"score_spread":0.1982183100024618,"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."}}