{"id":"W4389482633","doi":"10.54254/2755-2721/27/20230133","title":"Features of realized volatility analysis and return predicting based on LGBM and RNN model","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Volatility (finance); Econometrics; Market liquidity; Computer science; Financial market; Stochastic volatility; Volatility swap; Monte Carlo method; Implied volatility; Economics; Finance; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001716935,0.0001053608,0.0002562953,0.0004611324,0.0000778115,0.00005733483,0.00007922148,0.00004626585,0.000004191132],"category_scores_gemma":[0.0008650669,0.00008968858,0.00003866504,0.001027929,0.00003959753,0.0000408148,0.00007008558,0.00009469924,2.353154e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006311024,"about_ca_system_score_gemma":0.00001711477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008435305,"about_ca_topic_score_gemma":0.000001717172,"domain_scores_codex":[0.9987617,0.00003454153,0.0002862275,0.0003170699,0.0004858984,0.0001146237],"domain_scores_gemma":[0.996092,0.003551698,0.00009177275,0.000126283,0.00006991813,0.00006830016],"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":[0.00004364655,0.000004641865,0.006503303,0.00001726261,0.00004103849,5.387116e-7,0.0001964848,0.962652,0.0003142815,0.002004234,0.00003714799,0.02818537],"study_design_scores_gemma":[0.0001952918,0.000008793934,0.2449536,0.000008128538,0.00002913738,7.822308e-7,0.00003287181,0.743049,0.00003920006,0.0116219,0.000004234717,0.00005710953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5753964,0.00002205499,0.4234994,0.00005459635,0.00002121233,0.00007673844,0.00001725094,0.00005803256,0.0008542373],"genre_scores_gemma":[0.9329368,0.000001645829,0.06697363,0.00002341087,0.00001478629,0.00000741484,0.00001145359,0.000006960025,0.00002389346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3575404,"threshold_uncertainty_score":0.3657394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04130003512919719,"score_gpt":0.3281966808681954,"score_spread":0.2868966457389983,"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."}}