{"id":"W4391743702","doi":"10.1093/jjfinec/nbag006","title":"Multi-Factor Timing with Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Guelph; University of Waterloo","funders":"","keywords":"Leverage (statistics); Artificial neural network; Computer science; Artificial intelligence; Deep learning; Profitability index; Machine learning; Task (project management); Deep neural networks; Factor (programming language); Recurrent neural network; Economics; Finance","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":[0.0008814155,0.0007938115,0.0006885953,0.0004503341,0.0002334306,0.0009667809,0.0009929399,0.001006661,0.00411251],"category_scores_gemma":[0.004251435,0.0004646307,0.0005173972,0.0005945915,0.0005855403,0.001755497,0.001043796,0.001575494,0.0006319492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008340271,"about_ca_system_score_gemma":0.0009577186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007510269,"about_ca_topic_score_gemma":0.007677404,"domain_scores_codex":[0.9997563,0.00005159109,0.00001451454,0.00007608906,0.00004665234,0.00005491764],"domain_scores_gemma":[0.9990436,0.0004691955,0.0001654856,0.0001159174,0.0001334313,0.00007229269],"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":[0.0001233169,0.00004409091,0.003266159,0.00004110618,0.0000588353,0.00009078864,0.00002124889,0.9094689,0.001176092,0.02131587,0.00203196,0.06236149],"study_design_scores_gemma":[0.00000274392,0.000006833927,0.0001287251,0.000003670581,0.000002830217,0.000005110644,0.000001144484,0.9922169,0.0002179699,0.007204446,0.0002069599,0.000002643351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07547735,0.0008756656,0.9170927,0.0007742557,0.0001304289,0.0000230721,0.0004931843,0.001173732,0.00395961],"genre_scores_gemma":[0.9448411,0.0003169407,0.04827273,0.0001600978,0.00009297952,0.00003997001,0.0005334194,0.00009717684,0.005645539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007510269,"threshold_uncertainty_score":0.01493311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0552835753777989,"score_gpt":0.232074640129933,"score_spread":0.1767910647521341,"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."}}