{"id":"W2765740150","doi":"10.5539/ijef.v9n11p100","title":"Stock Market Prediction Performance of Neural Networks: A Literature Review","year":2017,"lang":"en","type":"review","venue":"International Journal of Economics and Finance","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mean squared error; Artificial neural network; Mean absolute error; Absolute deviation; Econometrics; Profitability index; Benchmark (surveying); Stock market prediction; Statistics; Computer science; Stock market; Mathematics; Machine learning; Economics; Finance","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":[0.005561443,0.0002528713,0.001659867,0.0003929185,0.00006887184,0.0002847526,0.001843054,0.0001750681,0.00003632677],"category_scores_gemma":[0.002518494,0.0001810399,0.0005949315,0.0001325138,0.0001076825,0.0006170501,0.0002443704,0.0005104067,0.00000136624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007905554,"about_ca_system_score_gemma":0.0001916346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.576762e-7,"about_ca_topic_score_gemma":9.981219e-7,"domain_scores_codex":[0.9967159,0.0002327774,0.002270528,0.0003101219,0.0003300339,0.0001406502],"domain_scores_gemma":[0.9907919,0.001061942,0.006721809,0.0004450303,0.0009278036,0.0000514744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004512368,0.00001205457,0.00007348298,0.0008508392,0.00009708317,0.00001263895,0.000008634435,0.0003692996,2.319944e-9,0.0001281071,0.003812869,0.9945899],"study_design_scores_gemma":[0.0001559151,0.0001123443,0.0002901874,0.04305556,0.0001120472,0.001297352,6.735942e-7,0.03344331,3.58176e-8,0.0004155906,0.9209909,0.0001260231],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000411128,0.9945946,0.0001369729,0.0001203725,0.00350004,0.0001928846,0.0001396992,0.000001531379,0.0009028211],"genre_scores_gemma":[0.0001473141,0.9965168,0.001710737,0.00005613805,0.0007585553,0.000006670646,0.000006680499,0.00001706717,0.0007800474],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9944639,"threshold_uncertainty_score":0.7382591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1427371250307418,"score_gpt":0.4150117060324179,"score_spread":0.2722745810016761,"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."}}