{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003152523,0.0004549697,0.0004572745,0.0006761608,0.0002432758,0.0006822349,0.0004968575,0.0006827384,0.001182829],"category_scores_gemma":[0.001174945,0.0002120695,0.000292668,0.001114071,0.0001317938,0.001125972,0.0002887449,0.0006134316,0.0003838801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002689702,"about_ca_system_score_gemma":0.0002815011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006472452,"about_ca_topic_score_gemma":0.003692125,"domain_scores_codex":[0.999761,0.00003116391,0.0000178302,0.00005138589,0.0001204016,0.00001812498],"domain_scores_gemma":[0.9998324,0.00005722835,0.00002023911,0.00001175078,0.00007153838,0.00000683417],"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":[0.0002019378,0.0001035951,0.005754964,0.0001711753,0.0001160654,0.000175108,0.00006529343,0.5480905,0.01099129,0.005445813,0.003869146,0.4250152],"study_design_scores_gemma":[0.000002530538,0.000008931926,0.0005203167,0.000003967481,0.000006520755,0.00001233258,0.0000028049,0.9972942,0.0007372815,0.001044279,0.0003624667,0.000004219409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08234116,0.00447619,0.9024889,0.0004461935,0.0003872916,0.00005699839,0.0004092535,0.001463349,0.007930631],"genre_scores_gemma":[0.8844576,0.002503292,0.1066091,0.0000735797,0.0003508578,0.00006292915,0.0005102215,0.00006640998,0.00536591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006472452,"threshold_uncertainty_score":0.01286954,"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."}}