{"id":"W4412958056","doi":"10.3390/jrfm18080435","title":"Development of Financial Indicator Set for Automotive Stock Performance Prediction Using Adaptive Neuro-Fuzzy Inference System","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Adaptive neuro fuzzy inference system; Neuro-fuzzy; Automotive industry; Inference system; Computer science; Artificial intelligence; Fuzzy inference system; Stock (firearms); Machine learning; Data mining; Econometrics; Fuzzy logic; Engineering; Mathematics; Fuzzy control system","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":[0.005534946,0.0001763965,0.000508139,0.0008643916,0.0003664421,0.00006497435,0.0004197783,0.00009220753,0.000002887924],"category_scores_gemma":[0.004143753,0.0001399352,0.0001261983,0.0007662107,0.00008300003,0.0002788844,0.0002466811,0.0002281468,8.363421e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001740823,"about_ca_system_score_gemma":0.0004297645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005045887,"about_ca_topic_score_gemma":0.000005492457,"domain_scores_codex":[0.9971264,0.0002168753,0.001361553,0.0002972863,0.000767523,0.000230352],"domain_scores_gemma":[0.9965307,0.001045384,0.001520669,0.0001936682,0.0006357513,0.00007382766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009265865,0.00006852235,0.03686672,0.0001726855,0.00004105008,0.000007267639,0.001842216,0.001151717,0.00002861296,0.003332286,0.0007097697,0.9548526],"study_design_scores_gemma":[0.001659802,0.000541464,0.9532169,0.0008619172,0.0002205064,0.00001351812,0.001237084,0.02226084,0.0005598577,0.004256917,0.01497241,0.0001987491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5352083,0.00006283788,0.4633006,0.000007966057,0.0008011006,0.0003024228,0.00002665758,0.000007069328,0.0002829554],"genre_scores_gemma":[0.8408133,0.00004329272,0.1589446,0.00002702138,0.00009980644,0.00001399885,5.320193e-7,0.000006485766,0.00005090396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9546538,"threshold_uncertainty_score":0.570639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07170805020073914,"score_gpt":0.3481770486993302,"score_spread":0.2764689984985911,"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."}}