{"id":"W2055503689","doi":"10.5539/mas.v3n12p28","title":"Prediction of Stock Market Index Movement by Ten Data Mining Techniques","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Naive Bayes classifier; Machine learning; Linear discriminant analysis; Data mining; Stock market; Stock market index; Artificial neural network; Quadratic classifier; Econometrics; Pattern recognition (psychology); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001482377,0.0007740987,0.0007109386,0.003183534,0.00028162,0.0006562681,0.0004981319,0.000498097,0.0004449986],"category_scores_gemma":[0.003243299,0.0002178092,0.000859222,0.002057567,0.0001805036,0.0007134526,0.0003488781,0.0005210291,0.0001856876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004417938,"about_ca_system_score_gemma":0.0005327635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003209394,"about_ca_topic_score_gemma":0.002753635,"domain_scores_codex":[0.9994358,0.00008858986,0.00009304701,0.00008194675,0.000254078,0.00004641792],"domain_scores_gemma":[0.9983305,0.0007906407,0.0002477068,0.00009151431,0.000486523,0.00005312134],"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":[0.0005190335,0.0008825829,0.07987497,0.0003767939,0.0003812674,0.000189808,0.0001470126,0.1358684,0.01659338,0.001285106,0.001262989,0.7626186],"study_design_scores_gemma":[0.00003069565,0.0003067123,0.01762975,0.00004155982,0.0001087337,0.0001014771,0.0000850797,0.9698948,0.009927398,0.0009656831,0.0008824033,0.00002568617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6729518,0.001175172,0.319728,0.0004145202,0.00008615544,0.0003619356,0.0007970644,0.0009200326,0.003565419],"genre_scores_gemma":[0.7947166,0.0007876139,0.2025848,0.00002984265,0.00002880634,0.0001805565,0.0008880955,0.00001524155,0.0007684713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003209394,"threshold_uncertainty_score":0.00783968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.151126865433362,"score_gpt":0.3896164916834791,"score_spread":0.2384896262501171,"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."}}