{"id":"W2896262751","doi":"10.1109/fuzz-ieee.2018.8491523","title":"Fuzzy Logic-Based Data Analytics on Predicting the Effect of Hurricanes on the Stock Market","year":2018,"lang":"en","type":"article","venue":"","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stock market; Big data; Stock exchange; Profit (economics); Stock (firearms); Market maker; Stock market bubble; Computer science; Analytics; Fuzzy logic; Business; Financial economics; Data science; Economics; Finance; Data mining; Artificial intelligence; Microeconomics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03641781,0.000237335,0.0003845093,0.0001997322,0.0004426951,0.0001755425,0.003996569,0.0000811756,0.001576489],"category_scores_gemma":[0.09553882,0.00008609432,0.0001316135,0.001417936,0.0006094417,0.00009141802,0.0006509669,0.0002912072,0.0001044786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002831483,"about_ca_system_score_gemma":0.00007158114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005177491,"about_ca_topic_score_gemma":0.00004067621,"domain_scores_codex":[0.9926301,0.003630181,0.0006565187,0.0007088197,0.002032608,0.0003418345],"domain_scores_gemma":[0.9120055,0.08337034,0.0004940607,0.003798826,0.0002588962,0.00007241307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00199447,0.0001158203,0.1309292,0.00002074069,0.0001273055,0.00000632768,0.0002051461,0.0007884607,0.0001800178,0.003103471,0.5147167,0.3478124],"study_design_scores_gemma":[0.001470843,0.009166431,0.1388135,0.0002192729,0.0002490588,0.00001604849,0.0007782676,0.7916812,0.009802802,0.01922376,0.02799538,0.0005834259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4092289,0.00002789689,0.01348637,0.004681519,0.001099366,0.00113122,0.0001005619,0.0001216959,0.5701225],"genre_scores_gemma":[0.9917184,8.622808e-7,0.004000086,0.001116065,0.0003126289,0.0000147656,0.000003101567,0.0000188189,0.002815339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7908928,"threshold_uncertainty_score":0.9993362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2509882350628654,"score_gpt":0.4415236796144294,"score_spread":0.190535444551564,"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."}}