{"id":"W4224302811","doi":"10.3390/jrfm15050188","title":"Forecasting a Stock Trend Using Genetic Algorithm and Random Forest","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Stock (firearms); Stock market index; Classifier (UML); Computer science; Econometrics; Artificial intelligence; Data mining; Stock market; Economics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006705356,0.000146843,0.0004122952,0.0006214817,0.0006652137,0.0001605662,0.0003240249,0.00002877826,0.00004671866],"category_scores_gemma":[0.001633478,0.0001161299,0.0001310715,0.0006361938,0.00007092973,0.0001477587,0.0005760097,0.0002995381,2.578452e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005732651,"about_ca_system_score_gemma":0.00003538327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729722,"about_ca_topic_score_gemma":0.00001264176,"domain_scores_codex":[0.9971139,0.0005097868,0.0008380789,0.0002724988,0.001018746,0.0002470114],"domain_scores_gemma":[0.9976186,0.001099438,0.0008969309,0.0001762501,0.000092029,0.0001167513],"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":[0.0002402297,0.0000286778,0.02174662,0.000005902709,0.00001500816,0.0001721175,0.0005054155,0.003647978,0.000001540577,0.00005227072,0.0003975735,0.9731867],"study_design_scores_gemma":[0.007747726,0.001081976,0.3646296,0.00008720684,0.000424064,0.001959056,0.002602037,0.4089616,0.000004549308,0.08666068,0.1253619,0.000479558],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5065076,0.001077923,0.4913512,0.00002190326,0.0005933017,0.0001516388,0.00001291097,0.000004011148,0.0002795011],"genre_scores_gemma":[0.5770715,0.000253544,0.4220151,0.00007124439,0.000350859,0.000009270513,3.522916e-7,0.00001853102,0.0002096409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9727071,"threshold_uncertainty_score":0.5116353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07355580097656854,"score_gpt":0.3339804228058774,"score_spread":0.2604246218293088,"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."}}