{"id":"W2085444045","doi":"10.1016/j.omega.2011.07.008","title":"Stock index forecasting based on a hybrid model","year":2011,"lang":"en","type":"article","venue":"Omega","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":377,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Lanzhou University","keywords":"Autoregressive integrated moving average; Exponential smoothing; Index (typography); Autoregressive model; Artificial neural network; Computer science; Stock market index; Moving average; Time series; Econometrics; Stock market; Statistics; Mathematics; Artificial intelligence; Machine learning","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005703243,0.0002333103,0.000338848,0.0005628301,0.0002121768,0.0001242757,0.001001029,0.00007870749,0.0006251857],"category_scores_gemma":[0.01195242,0.0001792255,0.0001823949,0.0008029329,0.00009539187,0.0002192017,0.0001735652,0.0002803099,0.0001813378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006364092,"about_ca_system_score_gemma":0.000144404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002653838,"about_ca_topic_score_gemma":0.00001001719,"domain_scores_codex":[0.9963889,0.000356801,0.0005981966,0.0007057293,0.001468027,0.0004824086],"domain_scores_gemma":[0.9955524,0.002615012,0.0003068922,0.001057251,0.0002583188,0.0002101077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006220411,0.0002190681,0.05462619,0.000008541487,0.00001655166,0.00007637643,0.0007355348,0.01494771,0.0001088899,0.0005835555,0.01856288,0.9094927],"study_design_scores_gemma":[0.0004305015,0.0001446612,0.005614806,0.00002752071,0.000006837601,0.00001496544,0.00003472966,0.9494491,0.0007568322,0.04181946,0.001482763,0.0002178507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3106948,0.000007887091,0.4659243,0.0000782596,0.000568128,0.0002270829,0.00001322449,0.0001192295,0.2223671],"genre_scores_gemma":[0.870268,1.360534e-7,0.1249392,0.0004808702,0.00007168357,0.0000311197,0.000001063424,0.00003170076,0.004176203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9345013,"threshold_uncertainty_score":0.9963703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3318230884209961,"score_gpt":0.388666807430418,"score_spread":0.05684371900942187,"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."}}