{"id":"W3120908908","doi":"10.48550/arxiv.2101.02287","title":"COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Coronavirus disease 2019 (COVID-19); Deep learning; Artificial neural network; Machine learning; Sentiment analysis; Stock (firearms); 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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004519049,0.0004883512,0.0006797581,0.0008207835,0.0006239988,0.000591701,0.001145949,0.0005663456,0.0003135957],"category_scores_gemma":[0.01844533,0.000510849,0.0003387106,0.001135572,0.0001903779,0.0009779136,0.00207196,0.0007915968,0.00002117314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007325422,"about_ca_system_score_gemma":0.0007061006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001324206,"about_ca_topic_score_gemma":0.00003849226,"domain_scores_codex":[0.9956528,0.0007979518,0.0009282374,0.001452745,0.0006634837,0.0005047825],"domain_scores_gemma":[0.9905159,0.005142768,0.001319227,0.001436304,0.001004803,0.0005809683],"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":[0.002103673,0.0002614358,0.03749217,0.0006930625,0.000349169,0.0001161895,0.002830227,0.8725767,0.00003261845,0.03102709,0.006285307,0.04623239],"study_design_scores_gemma":[0.001591362,0.0002022644,0.01158361,0.0001831861,0.0001965698,0.00001352822,0.002014027,0.7293604,0.00003968316,0.2423979,0.01178336,0.0006341618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2197846,0.00008162991,0.776284,0.0002148365,0.001132271,0.001403676,0.0001776474,0.0001764653,0.000744904],"genre_scores_gemma":[0.8796663,0.0003364167,0.1170344,0.001588345,0.0001806094,0.00003108678,0.0003426714,0.00003743709,0.0007827149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6598817,"threshold_uncertainty_score":0.9997343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1878050122855401,"score_gpt":0.3062335467336126,"score_spread":0.1184285344480725,"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."}}