{"id":"W3094918329","doi":"10.3390/jrfm13110265","title":"Neural Network Models for Empirical Finance","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council","keywords":"Computer science; Machine learning; Artificial intelligence; Overfitting; Model selection; Artificial neural network; Leverage (statistics); Deep learning; Hyperparameter; Dropout (neural networks); Context (archaeology); Regularization (linguistics)","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":[0.001419659,0.001085804,0.0009910965,0.001024664,0.0003051909,0.001533922,0.001171469,0.001638995,0.006005525],"category_scores_gemma":[0.004370195,0.0002859301,0.0005311764,0.001859579,0.0007593351,0.001654775,0.0009953447,0.002572841,0.001603018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104012,"about_ca_system_score_gemma":0.001114747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005086876,"about_ca_topic_score_gemma":0.00410139,"domain_scores_codex":[0.9995282,0.0002181638,0.00002340376,0.00006082237,0.00014174,0.00002768079],"domain_scores_gemma":[0.999029,0.0006319855,0.0000729543,0.00009132211,0.0001429375,0.00003172212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002302462,0.00003315627,0.0008702854,0.0002874761,0.00008400043,0.0000902682,0.00007848391,0.1590523,0.0002710788,0.7571415,0.01812921,0.06393921],"study_design_scores_gemma":[0.00000836694,0.00001189729,0.0003876023,0.000114551,0.0000134383,0.00003770915,0.00002047934,0.3608123,0.00006912476,0.6044882,0.03402299,0.0000133352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007076224,0.06930697,0.8675423,0.008193562,0.001219994,0.00007978343,0.001457872,0.0005721024,0.04455119],"genre_scores_gemma":[0.5129637,0.1201362,0.2774911,0.00186292,0.004099393,0.0009978364,0.003623694,0.000414906,0.07841032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006005525,"threshold_uncertainty_score":0.02009046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1404369829245352,"score_gpt":0.3837848372831986,"score_spread":0.2433478543586633,"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."}}