{"id":"W4235269769","doi":"10.32920/ryerson.14668224","title":"Short term stock price forecasting with application of neural network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stock market; Stock price; Stock (firearms); Artificial neural network; Cost price; Econometrics; Stock market bubble; Principal component analysis; Financial economics; Time series; Economics; Computer science; Artificial intelligence; Machine learning; Engineering; Series (stratigraphy)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005580887,0.0002018536,0.0003609815,0.00002544485,0.00002294422,0.00002556863,0.0001366415,0.0001235802,0.000005376098],"category_scores_gemma":[0.00000366926,0.0001765197,0.00006814176,0.00009910238,0.00001136055,0.00004637466,0.00007845831,0.0002809278,5.871169e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004345199,"about_ca_system_score_gemma":0.00001073065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002334017,"about_ca_topic_score_gemma":0.00006031733,"domain_scores_codex":[0.9990838,0.0000132202,0.0003012324,0.0002412553,0.000149781,0.0002106812],"domain_scores_gemma":[0.9993789,0.00003757191,0.00006531808,0.000397013,0.00007653046,0.00004473487],"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":[0.000006986644,0.000004752513,0.004981232,0.0002854607,0.0000674992,0.0000034484,0.00006128674,0.969632,0.0008720433,0.00003453505,0.00001730752,0.02403349],"study_design_scores_gemma":[0.0001068237,0.00001412191,0.005026586,0.0001460866,0.00003584966,0.0000176429,0.00007089672,0.9939224,0.0002816321,0.0000279993,0.0001083313,0.0002416395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2317157,0.0005260151,0.7574999,0.000004599207,0.0002519799,0.0005561957,0.000004167211,0.0002582097,0.009183261],"genre_scores_gemma":[0.9940301,0.00000901111,0.005349325,0.000005767748,0.0003194575,0.0001072353,0.00005193426,0.0000465849,0.00008060922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7623144,"threshold_uncertainty_score":0.7198266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561011542685982,"score_gpt":0.2192271640473035,"score_spread":0.2036170486204436,"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."}}