{"id":"W2120554959","doi":"10.1109/grc.2007.78","title":"Use of Neural Networks in Forecasting Financial Market","year":2007,"lang":"en","type":"article","venue":"2007 IEEE International Conference on Granular Computing (GRC 2007)","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Artificial neural network; Computer science; Simple (philosophy); Black–Scholes model; Point (geometry); Financial market; Call option; Index (typography); Artificial intelligence; Econometrics; Finance; Economics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001152086,0.0005760701,0.000424287,0.0009293553,0.0001945714,0.000818201,0.0004354051,0.0007173903,0.0005240027],"category_scores_gemma":[0.004251106,0.0002333494,0.0002222828,0.0007179606,0.0002940283,0.001184758,0.0003163799,0.0005525037,0.0001332166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005387859,"about_ca_system_score_gemma":0.0002750721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006254931,"about_ca_topic_score_gemma":0.005006969,"domain_scores_codex":[0.9997186,0.0001014942,0.00002490785,0.00004830958,0.00008571006,0.00002098221],"domain_scores_gemma":[0.9989881,0.0006825294,0.0001076289,0.00004421262,0.0001542077,0.00002328413],"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":[0.0001425296,0.00007552627,0.006659884,0.00006501452,0.00009254953,0.00007578776,0.00003720277,0.8778951,0.002087728,0.00339491,0.0004717193,0.1090021],"study_design_scores_gemma":[0.000003013152,0.000009538109,0.0005298404,0.000005454845,0.000004584976,0.000003917495,0.000003831204,0.9976993,0.0003661429,0.001275793,0.00009457545,0.00000391419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5174511,0.005161632,0.4682595,0.001051381,0.0002511466,0.00009215953,0.0002754636,0.0007663941,0.006691251],"genre_scores_gemma":[0.9614135,0.0008397573,0.03664116,0.00005363803,0.00004496639,0.00002749766,0.00008043242,0.00001081412,0.0008883385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006254931,"threshold_uncertainty_score":0.01243705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2432475768998181,"score_gpt":0.403417189259239,"score_spread":0.1601696123594209,"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."}}