{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006943925,0.0004483889,0.001129681,0.0006120574,0.0003638456,0.001061568,0.0008831426,0.0007670142,0.001419937],"category_scores_gemma":[0.001677183,0.000379173,0.0006401769,0.0006376223,0.0002947949,0.001266467,0.0005711074,0.0006614201,0.0002333104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004392613,"about_ca_system_score_gemma":0.0004539315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004022438,"about_ca_topic_score_gemma":0.003007285,"domain_scores_codex":[0.9997955,0.00006416263,0.00001550049,0.00004758477,0.00005408124,0.00002314254],"domain_scores_gemma":[0.9991382,0.0005927688,0.00005397738,0.00004000749,0.0001451072,0.00002993749],"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":[0.0002220637,0.0001047083,0.002485295,0.00004812912,0.0001261544,0.00006580742,0.00005396986,0.9386095,0.002240739,0.004518188,0.0004373403,0.05108809],"study_design_scores_gemma":[0.000003080821,0.000009515951,0.0001073379,7.165566e-7,0.00000532205,0.000003035034,0.000001316329,0.9994346,0.00005048607,0.000357992,0.00002467605,0.000001869236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3047055,0.0004043169,0.6892295,0.0003194253,0.0001161652,0.00003599398,0.0001123685,0.0003638153,0.004712773],"genre_scores_gemma":[0.9607124,0.0001956682,0.03613202,0.00005017196,0.00005633513,0.00007369057,0.0001143165,0.00002015853,0.002645275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004022438,"threshold_uncertainty_score":0.007998049,"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."}}