{"id":"W3136514097","doi":"10.3390/jrfm14030119","title":"Machine Learning in Futures Markets","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Futures contract; Statistical arbitrage; Portfolio; Transaction cost; Computer science; Trading strategy; Robustness (evolution); Artificial intelligence; Equity (law); Econometrics; Machine learning; Profitability index; Sample (material); Economics; Financial economics; Capital asset pricing model; Finance; Arbitrage pricing theory; Risk arbitrage","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004968679,0.0004486676,0.0006920637,0.001018834,0.0004817558,0.00148571,0.0008009113,0.001220342,0.001627946],"category_scores_gemma":[0.018881,0.0002692016,0.0004295574,0.001155336,0.001386801,0.001859635,0.0009268687,0.001845926,0.0003502991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008054674,"about_ca_system_score_gemma":0.0007262899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002071062,"about_ca_topic_score_gemma":0.001121767,"domain_scores_codex":[0.9986363,0.0007688364,0.0000670482,0.0001804412,0.0002783267,0.00006907376],"domain_scores_gemma":[0.9897525,0.008415675,0.0005462599,0.0005479569,0.0006107549,0.0001268687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008840764,0.000117305,0.007505356,0.0001629137,0.0001007199,0.0002402115,0.0002141342,0.4952027,0.0009420853,0.3505755,0.00392084,0.1409299],"study_design_scores_gemma":[0.000009515691,0.00002224484,0.0007983199,0.00002123324,0.000003562974,0.00003417496,0.0000185687,0.8394643,0.0002847975,0.1577698,0.00156305,0.00001046474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1205595,0.008701305,0.8454831,0.009474087,0.0004088718,0.00006774721,0.0002266311,0.0004720513,0.01460674],"genre_scores_gemma":[0.871,0.002829848,0.1203901,0.0004949899,0.0007183046,0.00009433484,0.0001939435,0.00005632636,0.004222063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004968679,"threshold_uncertainty_score":0.02627718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03246668782222147,"score_gpt":0.3312906390468505,"score_spread":0.2988239512246291,"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."}}