{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001347498,0.0005464316,0.0008565465,0.001732286,0.0003754951,0.0004992368,0.0006860521,0.0008353911,0.0007074289],"category_scores_gemma":[0.00289369,0.0003104246,0.000811905,0.001218489,0.0002328002,0.000682943,0.0002466984,0.0005414963,0.0002207652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006761173,"about_ca_system_score_gemma":0.001010933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02526137,"about_ca_topic_score_gemma":0.01695674,"domain_scores_codex":[0.9996713,0.0001017115,0.00002058124,0.00007972354,0.00007045824,0.00005623998],"domain_scores_gemma":[0.9990435,0.000623236,0.00009132196,0.00003669545,0.0001758797,0.00002927581],"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":[0.00009500034,0.00007722501,0.005252655,0.0000286687,0.00006748595,0.00006883038,0.00003227548,0.8746102,0.001349836,0.001341918,0.0005606851,0.1165151],"study_design_scores_gemma":[0.000003864844,0.00001576355,0.0002880504,0.000002479095,0.000005344426,0.000006306751,0.000003239618,0.9990501,0.0001249558,0.0004266131,0.00007104059,0.000002335689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3067731,0.0007959086,0.6879898,0.0003672907,0.00007956502,0.0001420665,0.0002915146,0.001302969,0.002257693],"genre_scores_gemma":[0.7319574,0.0002976455,0.2657971,0.00007832726,0.00004315917,0.0001169439,0.0004199349,0.00004284708,0.001246563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02526137,"threshold_uncertainty_score":0.05022866,"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."}}