{"id":"W4289225573","doi":"10.1007/s40745-022-00432-6","title":"Forecasting Directional Movement of Stock Prices using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Annals of Data Science","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Stock market; Computer science; Deep learning; Artificial intelligence; Stock market prediction; Word2vec; Machine learning; Stock (firearms); Sentiment analysis; Binary classification; Econometrics; Support vector machine; Economics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03551867,0.0001206024,0.0003049671,0.0007205129,0.001002682,0.0001090875,0.004279354,0.00001793784,0.0005330131],"category_scores_gemma":[0.02415333,0.000106064,0.00007007003,0.00429244,0.000600697,0.001832032,0.00497979,0.0002221691,0.000001511491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003906317,"about_ca_system_score_gemma":0.000359566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001911508,"about_ca_topic_score_gemma":0.00001134747,"domain_scores_codex":[0.9931129,0.000461034,0.0009026147,0.0008662941,0.004237912,0.0004193028],"domain_scores_gemma":[0.9937217,0.002974151,0.001217216,0.00117126,0.0008049736,0.0001106664],"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":[0.0001108544,0.0002014642,0.08964239,0.00002107399,0.0000291141,0.000005521533,0.001320243,0.07717986,0.0357974,0.0009731023,0.0008432002,0.7938758],"study_design_scores_gemma":[0.0001568708,0.000297523,0.03190312,0.00003288119,0.00001124485,0.00002858115,0.002036156,0.9414012,0.0116208,0.006838891,0.005456664,0.0002160582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9467461,0.0002603604,0.04678619,0.0001753328,0.0005572816,0.000199832,0.0001319504,0.00002266609,0.005120267],"genre_scores_gemma":[0.8796946,0.000005660424,0.1200111,0.0001022207,0.00004042004,0.000005549203,0.000007139396,0.000008339839,0.0001249926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8642213,"threshold_uncertainty_score":0.9931365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.609124789376396,"score_gpt":0.5163442024371268,"score_spread":0.09278058693926927,"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."}}