{"id":"W2768301016","doi":"10.1016/j.jedc.2017.11.002","title":"Moment matching machine learning methods for risk management of large variable annuity portfolios","year":2017,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Portfolio; Computer science; Liberian dollar; Valuation (finance); Monte Carlo method; Econometrics; Variable (mathematics); Project portfolio management; Machine learning; Actuarial science; Economics; Artificial intelligence; Mathematical optimization; Mathematics; Finance; Project management; Statistics","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":[0.006917723,0.0007108868,0.001962593,0.001698425,0.0005863922,0.001394228,0.002086482,0.001859927,0.00348119],"category_scores_gemma":[0.02248619,0.0009383811,0.001169713,0.0017882,0.0009201075,0.002188794,0.001735923,0.002229773,0.0005166405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146642,"about_ca_system_score_gemma":0.001459938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004723432,"about_ca_topic_score_gemma":0.003543978,"domain_scores_codex":[0.9987802,0.0007076304,0.00007704457,0.0001763125,0.0001645597,0.00009423759],"domain_scores_gemma":[0.9858332,0.0119126,0.0007903326,0.0005490052,0.000677914,0.0002370332],"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":[0.0001157333,0.0001064411,0.001799309,0.00008857795,0.0001489134,0.00005132447,0.00006377494,0.8643518,0.0004620119,0.04903984,0.001430837,0.08234143],"study_design_scores_gemma":[0.00000563399,0.000008246492,0.0001193826,0.000004992058,0.000006484318,0.000004396162,0.000002871456,0.9860612,0.00005662877,0.01358146,0.0001435744,0.000005110182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01186954,0.0006906175,0.9864825,0.0002644166,0.00003619988,0.000027634,0.00006414384,0.0001721834,0.000392884],"genre_scores_gemma":[0.6420515,0.002038056,0.3458473,0.0002434887,0.0004818511,0.0003903046,0.0005616855,0.0002488322,0.008136948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006917723,"threshold_uncertainty_score":0.03658491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035025769941447,"score_gpt":0.3418259321824908,"score_spread":0.3314756744830764,"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."}}