{"id":"W2890431446","doi":"10.2139/ssrn.3244821","title":"Applying Deep Learning to Derivatives Valuation","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Valuation (finance); Deep learning; Artificial intelligence; Economics; Business; Computer science; 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":[0.0009770064,0.0004148708,0.0005433031,0.0005259239,0.0002930681,0.001433426,0.000549923,0.0008553674,0.004537066],"category_scores_gemma":[0.006544301,0.0003037068,0.0002965347,0.0006454238,0.000896475,0.002270384,0.001208981,0.001693208,0.0002602398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009400191,"about_ca_system_score_gemma":0.0008662757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003279443,"about_ca_topic_score_gemma":0.003297531,"domain_scores_codex":[0.9997991,0.00007770463,0.00001219397,0.00003051309,0.0000515051,0.00002903648],"domain_scores_gemma":[0.9988713,0.0006527224,0.00009247779,0.0001322956,0.0001591203,0.00009211338],"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":[0.0001423043,0.0001348449,0.002955774,0.0001167899,0.00006151359,0.0001967697,0.00008893074,0.558755,0.002269606,0.2770598,0.003720703,0.1544979],"study_design_scores_gemma":[0.000005033529,0.0000112667,0.0001863038,0.000008779669,0.000003931221,0.00001241383,0.000006716286,0.8648638,0.0003460817,0.1339215,0.0006301114,0.000004048047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1314267,0.001261618,0.8499629,0.002240775,0.0003126086,0.00002574656,0.0001466864,0.0003721187,0.01425074],"genre_scores_gemma":[0.9566584,0.0004938844,0.0341557,0.000119521,0.0001036355,0.00001303891,0.00006908869,0.00005342524,0.008333342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004537066,"threshold_uncertainty_score":0.01517802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606729645929624,"score_gpt":0.2744772459413095,"score_spread":0.2584099494820133,"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."}}