{"id":"W4387223121","doi":"10.1016/j.ijforecast.2023.09.001","title":"The profitability of lead–lag arbitrage at high frequency","year":2023,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Statistical arbitrage; Lag; Lagging; Predictability; Arbitrage; High-frequency trading; Profitability index; Econometrics; Trading strategy; Lead–lag compensator; Price discovery; Pairs trade; Profit (economics); Lead (geology); Order (exchange); Algorithmic trading; Economics; Computer science; Financial economics; Arbitrage pricing theory; Capital asset pricing model; Risk arbitrage; Microeconomics; Alternative trading system; Statistics; Finance; Futures contract; Mathematics","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":[0.001105031,0.00007512534,0.0001913596,0.0001507313,0.00009704255,0.00005145635,0.0003759114,0.00003654825,0.00006625203],"category_scores_gemma":[0.000782533,0.00005919031,0.0001376194,0.0001484909,0.00009714008,0.000250371,0.00007125919,0.0001431458,0.00003388439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001147421,"about_ca_system_score_gemma":0.00003916363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009046783,"about_ca_topic_score_gemma":0.00001740719,"domain_scores_codex":[0.9987887,0.00001325922,0.0008368316,0.0001009689,0.000110148,0.0001501027],"domain_scores_gemma":[0.9985099,0.0002119554,0.0009589451,0.00009453516,0.0001990371,0.00002558888],"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":[0.0001347196,0.00006858041,0.2091625,0.00003546587,0.0002244415,0.00005679829,0.0005206651,0.0002546291,0.0008841329,0.7783313,0.002386107,0.007940605],"study_design_scores_gemma":[0.000822279,0.000237607,0.3761677,0.0001423944,0.000008798964,0.00007869843,0.0002452466,0.003235655,0.001830146,0.6097092,0.007316583,0.0002056525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750056,0.0004645808,0.0001410158,0.001590322,0.0017464,0.00005984123,0.00004979121,0.000008557521,0.02093387],"genre_scores_gemma":[0.9984697,0.000185213,0.0005614678,0.00004381967,0.0002679082,0.000002713486,0.00000377116,0.000008390335,0.0004569899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1686221,"threshold_uncertainty_score":0.2413711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05944428443614971,"score_gpt":0.2452565094830188,"score_spread":0.1858122250468691,"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."}}