{"id":"W3008571904","doi":"10.1109/bigdata47090.2019.9005523","title":"Deep Learning for the Prediction of Stock Market Trends","year":2019,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictability; Computer science; Profitability index; Stock (firearms); Econometrics; Stock market; Deep learning; Usability; Stock market prediction; Artificial intelligence; Machine learning; Economics; Finance; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001163559,0.0006834473,0.000497415,0.00104267,0.0002164204,0.0006423769,0.0005345788,0.0005817464,0.001798501],"category_scores_gemma":[0.002699044,0.0002976277,0.0005016996,0.0009750795,0.000179454,0.0009959646,0.0004249164,0.00115056,0.000492439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006715943,"about_ca_system_score_gemma":0.0007802243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009740822,"about_ca_topic_score_gemma":0.009829745,"domain_scores_codex":[0.999763,0.00005028331,0.00002519988,0.00004407802,0.00007934664,0.00003821342],"domain_scores_gemma":[0.9993384,0.0003641385,0.0000863758,0.00003645462,0.000145497,0.0000291571],"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":[0.0002500637,0.0002515957,0.01125685,0.0001918051,0.0001923798,0.0001207846,0.00006789141,0.6767229,0.004085358,0.007143497,0.003677934,0.296039],"study_design_scores_gemma":[0.000004017893,0.00002237247,0.0006716688,0.00001064821,0.000008615129,0.000005878247,0.000004237582,0.9965579,0.0005966185,0.001775482,0.0003388434,0.000003715225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2528094,0.009617681,0.7237493,0.001937861,0.0003836341,0.0001187547,0.001541539,0.002374755,0.007467191],"genre_scores_gemma":[0.9232124,0.002058859,0.06905219,0.0001440053,0.0001057283,0.00007080479,0.001298348,0.00004998314,0.004007705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009740822,"threshold_uncertainty_score":0.01936829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1108897606695406,"score_gpt":0.3918158991053557,"score_spread":0.2809261384358151,"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."}}