{"id":"W2021938316","doi":"10.1016/j.eswa.2011.04.222","title":"Forecasting stock indices with back propagation neural network","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":484,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education; Lanzhou University","keywords":"Artificial neural network; Stock (firearms); Computer science; Backpropagation; Stock market index; Stock price; Composite index; Econometrics; Index (typography); Stock market; Data mining; Artificial intelligence; Series (stratigraphy); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009728516,0.0007283429,0.0008533912,0.0009375727,0.0002190925,0.0008073431,0.0005871444,0.0008615156,0.0009011805],"category_scores_gemma":[0.003191705,0.0004306741,0.0004957065,0.000918054,0.0002428366,0.001152377,0.0002976637,0.001094734,0.0002849069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004380316,"about_ca_system_score_gemma":0.0004724663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01102965,"about_ca_topic_score_gemma":0.008671816,"domain_scores_codex":[0.9997633,0.00004842567,0.00002217381,0.00004496515,0.0000957803,0.00002533082],"domain_scores_gemma":[0.9989876,0.0005990404,0.0001015974,0.00005295755,0.0002350442,0.00002367604],"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":[0.0002419959,0.0001879747,0.003963503,0.00008698916,0.0001473876,0.00007185492,0.00002836062,0.8191891,0.003956237,0.001439444,0.001135936,0.1695512],"study_design_scores_gemma":[0.000003750787,0.00000594306,0.0002064749,0.000001386225,0.000006881196,0.000001889348,7.909078e-7,0.9991791,0.0002943814,0.0002680455,0.0000295344,0.00000179163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2446136,0.001854577,0.7485633,0.0003460116,0.0003359133,0.00007228553,0.0001679398,0.001173858,0.002872535],"genre_scores_gemma":[0.8657986,0.0008204019,0.1291046,0.00006511412,0.0001289265,0.00006173913,0.0002472796,0.000044691,0.003728665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01102965,"threshold_uncertainty_score":0.02193093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189853560597106,"score_gpt":0.3608030537518224,"score_spread":0.1709494931547164,"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."}}