{"id":"W3122732568","doi":"10.18280/isi.250608","title":"An Optimized Machine Learning Model for Stock Trend Anticipation","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Stock market; Machine learning; Mean absolute percentage error; Support vector machine; Financial crisis; Stock (firearms); Curse of dimensionality; Feature selection; Anticipation (artificial intelligence); Econometrics; Artificial neural network; Economics; Engineering; Context (archaeology)","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004205627,0.0002261995,0.0004102196,0.0003780229,0.0005005545,0.0007140136,0.0005623425,0.0001325447,0.00009602992],"category_scores_gemma":[0.01622279,0.0001935706,0.0001503261,0.0008593461,0.00008592295,0.004425423,0.00008650418,0.0001916921,0.00004752133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092758,"about_ca_system_score_gemma":0.00009446558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001873368,"about_ca_topic_score_gemma":0.000009345865,"domain_scores_codex":[0.9968478,0.0003924121,0.001253834,0.0003045349,0.000846714,0.0003547229],"domain_scores_gemma":[0.9969909,0.001079563,0.00081158,0.0003396953,0.0005488398,0.0002294394],"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":[0.0005005935,0.00001589297,0.0017254,0.0000546094,0.00001656772,3.166546e-7,0.01998865,0.4809763,0.0002945968,0.0005176444,0.0004590759,0.4954504],"study_design_scores_gemma":[0.0009483451,0.0002932055,0.001078179,0.00002113692,0.00002183462,0.000005111503,0.0006371998,0.9883704,0.000271688,0.006089807,0.002035326,0.0002277246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1011273,0.00002416641,0.8939558,0.0001539104,0.000174554,0.0005494253,0.00006626169,0.000249739,0.003698816],"genre_scores_gemma":[0.7884181,0.000002772549,0.2107838,0.0003203672,0.00008028404,0.00008780728,0.0001757439,0.0000185424,0.0001125706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6872908,"threshold_uncertainty_score":0.992064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1438491986427413,"score_gpt":0.3806031637869058,"score_spread":0.2367539651441646,"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."}}