{"id":"W2036679573","doi":"10.1109/72.935086","title":"Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Early stopping; Regularization (linguistics); Bayesian probability; Econometrics; Computer science; Artificial neural network; Generalization; Standard deviation; Black–Scholes model; Derivative (finance); Baseline (sea); Artificial intelligence; Mathematics; Statistics; Volatility (finance); Economics; Financial economics","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":[0.01211922,0.001059881,0.001279874,0.001071281,0.0004297696,0.001012582,0.001328972,0.001376171,0.0004080651],"category_scores_gemma":[0.02833907,0.0005205813,0.0007691453,0.001084979,0.0009594721,0.002637222,0.00105731,0.001813895,0.000128989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001297617,"about_ca_system_score_gemma":0.000927662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006879271,"about_ca_topic_score_gemma":0.007897867,"domain_scores_codex":[0.9979746,0.001124381,0.00011769,0.0001738108,0.0004329149,0.0001766938],"domain_scores_gemma":[0.9852496,0.01081463,0.001299596,0.001116861,0.001222853,0.0002964379],"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":[0.0004111606,0.0003147725,0.01052588,0.00004715907,0.0001678636,0.00004861728,0.00008421103,0.9191339,0.0008745967,0.003871257,0.00046602,0.06405464],"study_design_scores_gemma":[0.00001333595,0.00007049382,0.0009431649,0.000005781179,0.00001122267,0.000005796564,0.000005278562,0.9967501,0.0004906458,0.001646614,0.00005116404,0.000006298253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8076515,0.0008365859,0.1891028,0.0005867168,0.00004624088,0.00005164386,0.00007059241,0.0003171219,0.001336898],"genre_scores_gemma":[0.9629232,0.0001724613,0.03606737,0.0001016277,0.00002970341,0.00004025139,0.0001344508,0.00002686609,0.0005040203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01211922,"threshold_uncertainty_score":0.06409329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03771736784902873,"score_gpt":0.3041129530411983,"score_spread":0.2663955851921695,"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."}}