{"id":"W3041125167","doi":"10.32732/jmo.2020.12.2.84","title":"Using Machine Learning Algorithms on Prediction of Stock Price","year":2020,"lang":"en","type":"article","venue":"Journal of Modeling and Optimization","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Machine learning; Stock price; Artificial intelligence; Convolutional neural network; Support vector machine; Stock (firearms); Artificial neural network; Predictive modelling; Long short term memory; Regression; Econometrics; Recurrent neural network; Economics; Series (stratigraphy); Statistics; Mathematics; Engineering","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":[0.002436584,0.0009579234,0.0009141403,0.001438611,0.0002842669,0.0009840843,0.0007173708,0.001132292,0.0007356875],"category_scores_gemma":[0.01018842,0.0002493878,0.000437634,0.001880536,0.0004970055,0.001671606,0.0005887547,0.001089086,0.0003331435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349868,"about_ca_system_score_gemma":0.000558923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003607319,"about_ca_topic_score_gemma":0.002327814,"domain_scores_codex":[0.9990065,0.0004328437,0.00008385819,0.0001752608,0.0002534136,0.00004821038],"domain_scores_gemma":[0.995154,0.003895126,0.0003053654,0.0001992683,0.0004069424,0.00003937101],"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":[0.00006595611,0.0001050844,0.007589418,0.0001415727,0.0001450262,0.00007792405,0.00003895881,0.7956761,0.0009097136,0.009314249,0.001351648,0.1845844],"study_design_scores_gemma":[0.000002348286,0.0000138274,0.0004473101,0.00001291463,0.00000642652,0.000008662158,0.00000408035,0.9961516,0.0002762987,0.002844174,0.0002283913,0.000003933506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09280702,0.009628923,0.889855,0.001427394,0.000268939,0.00005641441,0.0002372107,0.000766144,0.004953041],"genre_scores_gemma":[0.7987501,0.006975477,0.1905401,0.0002602098,0.0004728176,0.0001083035,0.0006375899,0.00006967439,0.002185633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003607319,"threshold_uncertainty_score":0.01288599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2418288859743529,"score_gpt":0.3925471487420468,"score_spread":0.150718262767694,"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."}}