{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008163109,0.0007282437,0.0006488999,0.0007901751,0.0003317945,0.001156059,0.001089513,0.0007724827,0.001433974],"category_scores_gemma":[0.0009681236,0.0003041968,0.0006125129,0.0008327858,0.0002490392,0.001106211,0.0003894598,0.0006384465,0.0002756555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006869546,"about_ca_system_score_gemma":0.0008704963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007101645,"about_ca_topic_score_gemma":0.005331884,"domain_scores_codex":[0.9996915,0.00008763178,0.00002333973,0.00007043996,0.00009458074,0.00003244167],"domain_scores_gemma":[0.9996741,0.0001481142,0.00005685127,0.000007986571,0.00009645508,0.00001654077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009399719,0.00009503746,0.006689142,0.0001096743,0.0001318604,0.000278987,0.00006959777,0.9320516,0.001210193,0.01215723,0.001468691,0.04564397],"study_design_scores_gemma":[0.000003749398,0.00001710635,0.0004638727,0.000004512658,0.00001325713,0.00001340527,0.000004397361,0.9978377,0.0001195466,0.001281862,0.0002357699,0.000004786962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2245312,0.001783306,0.7587851,0.001014187,0.0002123419,0.0002021232,0.0005205068,0.000520213,0.01243104],"genre_scores_gemma":[0.9617418,0.0008567668,0.03000595,0.00006882368,0.0001023661,0.000202296,0.0003618184,0.00001618205,0.006643955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007101645,"threshold_uncertainty_score":0.01412064,"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."}}