{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002544904,0.0003018196,0.0004242292,0.0002204831,0.0005974009,0.0002890765,0.0008953635,0.0001062075,0.0001794326],"category_scores_gemma":[0.0003084823,0.0001832065,0.00005933725,0.001961882,0.0001987303,0.0004692169,0.0001063286,0.0002064908,0.0001524465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006548073,"about_ca_system_score_gemma":0.0001188152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000271786,"about_ca_topic_score_gemma":0.0001096294,"domain_scores_codex":[0.9961531,0.0004626002,0.0008213616,0.0008505575,0.001194562,0.0005178772],"domain_scores_gemma":[0.9959287,0.001195752,0.0008921288,0.001163895,0.000591904,0.000227616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001570654,0.0006465856,0.4988707,0.000166434,0.0004015468,0.00004509146,0.02357291,0.01849621,0.0003956624,0.01438651,0.04567038,0.3957773],"study_design_scores_gemma":[0.003843105,0.002674393,0.06670389,0.001063404,0.0001694127,0.002296306,0.01348708,0.6413955,0.0009082323,0.006495441,0.257572,0.003391301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04485315,0.0005754802,0.8809019,0.0001446948,0.0003800606,0.004122963,0.0000112776,0.0002832513,0.0687272],"genre_scores_gemma":[0.8569217,0.000001491742,0.1371796,0.00008870746,0.0006445322,0.003062573,0.00001022469,0.00005895863,0.002032231],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8120685,"threshold_uncertainty_score":0.7470946,"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."}}