{"id":"W4416448426","doi":"10.1016/j.asoc.2025.114302","title":"A stable technical feature with GRU-CNN-GA fusion","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sharpe ratio; Feature (linguistics); Weighting; Stability (learning theory); Bayesian probability; Convergence (economics); Pattern recognition (psychology); Artificial neural network; Overfitting","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":[0.006004506,0.0002759887,0.0005116198,0.0003875592,0.000601797,0.0003434641,0.001084762,0.00020531,0.00008119296],"category_scores_gemma":[0.002922859,0.0001964037,0.00009546815,0.00264131,0.0001558982,0.00008162375,0.0008825441,0.0006061939,0.00007348089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009311775,"about_ca_system_score_gemma":0.0001882449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001361948,"about_ca_topic_score_gemma":0.00001636041,"domain_scores_codex":[0.9964792,0.0002173178,0.0005765388,0.000957697,0.001203368,0.0005659163],"domain_scores_gemma":[0.9919608,0.006428111,0.0003001427,0.0009328168,0.0002594747,0.0001186681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003357275,0.0001030847,0.01099929,0.00002928649,0.00003951969,0.00002060863,0.0003746176,0.003472381,0.007557799,0.01891442,0.04386637,0.9142869],"study_design_scores_gemma":[0.006772696,0.0005165469,0.193251,0.00122256,0.000251583,0.0002403194,0.003526498,0.1487886,0.01015454,0.2714866,0.3608139,0.002975192],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09392969,0.00008377808,0.7332702,0.0007306053,0.0003772717,0.0004864278,0.000001854441,0.0004195067,0.1707006],"genre_scores_gemma":[0.7127933,5.591093e-7,0.2838586,0.0005715847,0.00008885635,0.00001253952,0.000002236887,0.00002024804,0.002652089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9113117,"threshold_uncertainty_score":0.8009109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0420554900257216,"score_gpt":0.3617790895153358,"score_spread":0.3197235994896142,"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."}}