{"id":"W2238458842","doi":"","title":"A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines","year":2012,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial neural network; Computer science; Technical analysis; Artificial intelligence; Machine learning; Point (geometry); Stock (firearms); Selection (genetic algorithm); Mathematics; Engineering; Economics","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":[0.002817039,0.0001270367,0.0005169441,0.0001068259,0.0001022846,0.00005645933,0.00008812604,0.00005799872,0.00001873305],"category_scores_gemma":[0.00037426,0.0001040926,0.00007975791,0.0003167261,0.00008186125,0.0001629503,0.00006096004,0.000087767,1.744167e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002555567,"about_ca_system_score_gemma":0.00002269957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001325786,"about_ca_topic_score_gemma":0.000009343325,"domain_scores_codex":[0.998794,0.0001145242,0.0005297379,0.0003114264,0.00007889426,0.0001714286],"domain_scores_gemma":[0.9988663,0.0005100953,0.0003601774,0.0001709081,0.0000362017,0.00005628645],"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":[0.0001897567,0.0001713764,0.1123405,0.0003044739,0.00008651747,3.691188e-7,0.0000261068,0.1496406,0.000206514,0.006013792,0.000694297,0.7303257],"study_design_scores_gemma":[0.00007517224,0.00005892563,0.008171435,0.00008347451,0.0001134416,0.000009650542,0.000001016173,0.9903312,0.000008143381,0.0005359295,0.000506893,0.0001046771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895788,0.01497524,0.0946707,0.0001484617,0.0001065817,0.0002790804,0.00005831582,0.000007394258,0.0001754179],"genre_scores_gemma":[0.974174,0.00709194,0.01843741,0.00021554,0.000042413,0.000007863439,0.000004891277,0.000007941655,0.00001802172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8406907,"threshold_uncertainty_score":0.4244775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09286186149733378,"score_gpt":0.3688163432816846,"score_spread":0.2759544817843508,"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."}}