{"id":"W4409494094","doi":"10.1371/journal.pone.0320089","title":"A novel decision ensemble framework: Attention-customized BiLSTM and XGBoost for speculative stock price forecasting","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"King Saud University","keywords":"Computer science; Interpretability; Machine learning; Volatility (finance); Artificial intelligence; Stock market; Data mining; Econometrics; Economics","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006714767,0.0003026347,0.0008128117,0.0007025109,0.0005362564,0.000394076,0.0006219062,0.0002384266,0.0001209676],"category_scores_gemma":[0.1037262,0.000248738,0.0002044252,0.001679691,0.0001424211,0.0003166362,0.000500102,0.0003355679,0.00002222755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001214238,"about_ca_system_score_gemma":0.0001376513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001497048,"about_ca_topic_score_gemma":0.00002171453,"domain_scores_codex":[0.9957171,0.0002763291,0.001052335,0.001046528,0.001363172,0.0005444996],"domain_scores_gemma":[0.9596308,0.0377567,0.0005247445,0.0007890378,0.001128292,0.0001704449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005435716,0.003005986,0.06400661,0.0003147876,0.001135522,0.00001190587,0.00180852,0.0001804237,0.09436423,0.01707993,0.004307945,0.8083484],"study_design_scores_gemma":[0.006821883,0.0003922176,0.05282946,0.003065217,0.0005122581,0.00001861956,0.0007962353,0.4388648,0.007986524,0.4865269,0.001340661,0.0008451812],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3939648,0.0001158963,0.5995047,0.0003677715,0.0002117761,0.001055418,0.00002451756,0.00006227403,0.004692845],"genre_scores_gemma":[0.2442708,0.000006294345,0.7510626,0.000211994,0.000121975,0.0001577304,0.000003525359,0.00002919616,0.004135848],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8075032,"threshold_uncertainty_score":0.9999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503546357546435,"score_gpt":0.3997724608300081,"score_spread":0.1494178250753646,"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."}}