{"id":"W4401759234","doi":"10.3390/jrfm17080377","title":"Estimating Volatility of Saudi Stock Market Using Hybrid Dynamic Evolving Neural Fuzzy Inference System Models","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock market; Econometrics; Volatility (finance); Inference; Adaptive neuro fuzzy inference system; Computer science; Fuzzy inference system; Artificial neural network; Fuzzy inference; Fuzzy logic; Artificial intelligence; Economics; Machine learning; Fuzzy control system; 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":[0.01009486,0.000202113,0.0005803189,0.0006665406,0.0002046747,0.0003033127,0.0004822594,0.0000513507,0.00001842249],"category_scores_gemma":[0.004095864,0.0001533041,0.0002189465,0.0007253787,0.00008716782,0.0007075131,0.0003688034,0.0003695653,8.841284e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608124,"about_ca_system_score_gemma":0.00009339124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003809906,"about_ca_topic_score_gemma":0.000008756112,"domain_scores_codex":[0.9962246,0.0005170017,0.001475419,0.0003596989,0.001155566,0.0002677412],"domain_scores_gemma":[0.996108,0.002032957,0.001024448,0.0003213542,0.0004070682,0.0001061726],"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":[0.0001537076,0.00002855113,0.008086839,0.0004826783,0.00003805405,0.0001645565,0.0004829061,0.03045691,0.00002568287,0.0008135532,0.000496835,0.9587697],"study_design_scores_gemma":[0.0002089679,0.00007603464,0.0375069,0.0007802854,0.0001327857,0.00009854326,0.0002321304,0.9221275,0.000004246433,0.03858418,0.0001227183,0.0001257049],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4520402,0.0008800084,0.5449629,0.00001235256,0.001093543,0.0001247392,0.00001657635,0.00001413468,0.0008556021],"genre_scores_gemma":[0.8396958,0.00003842838,0.1600676,0.000005501181,0.0001094462,0.000001585617,1.790574e-7,0.00001201487,0.00006945509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.958644,"threshold_uncertainty_score":0.6251561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05905563827256832,"score_gpt":0.3579671447401533,"score_spread":0.298911506467585,"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."}}