{"id":"W4323543296","doi":"10.1117/12.2670200","title":"Algorithm trading strategy based on GARCH and LSTM models","year":2023,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Volatility (finance); Implied volatility; Volatility smile; Volatility risk premium; Volatility swap; Forward volatility; Econometrics; Autoregressive conditional heteroskedasticity; Stochastic volatility; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007993112,0.0001362361,0.0002235626,0.0005605591,0.0001599432,0.0002627775,0.0003835935,0.00006951565,0.0005853181],"category_scores_gemma":[0.002115095,0.00009574116,0.00006639594,0.001465323,0.00007100009,0.0001830026,0.00008482232,0.0001478743,0.0001300171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002001064,"about_ca_system_score_gemma":0.00005335671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002960832,"about_ca_topic_score_gemma":0.000005923387,"domain_scores_codex":[0.99712,0.0004316272,0.0003650062,0.0005603519,0.001188863,0.0003341018],"domain_scores_gemma":[0.9918563,0.007387856,0.000067503,0.0004392885,0.00009790252,0.0001511834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001596516,0.00001247662,0.0002105976,0.000001579037,0.000002765544,0.00001305161,0.00007736745,0.006937425,0.00006001401,0.0009559942,0.003997418,0.9877154],"study_design_scores_gemma":[0.0002582,0.00009114227,0.003243764,0.000007974547,0.000002410457,0.000004093723,0.0002278432,0.8592899,0.0001866753,0.1359604,0.0006213216,0.0001061974],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06967869,0.00001451722,0.7409191,0.0006353312,0.0003074859,0.0002047922,0.00001161608,0.0002867309,0.1879418],"genre_scores_gemma":[0.8013382,0.000003195313,0.1833485,0.0003916492,0.00009126331,0.00001960027,0.000002558505,0.00002542312,0.01477963],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9876091,"threshold_uncertainty_score":0.6408823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3414754334803329,"score_gpt":0.4559460534389583,"score_spread":0.1144706199586253,"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."}}