{"id":"W2943337529","doi":"10.33423/jabe.v20i1.312","title":"Announcement Effects of Macro Economic Variables on Stock Market Returns and Volatility- A Neural Network Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Volatility (finance); Econometrics; Economics; Stock market; Stock (firearms); Macro; Unemployment; Financial economics; Computer science; Macroeconomics; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001930571,0.000483192,0.0004450926,0.0007782885,0.0002001573,0.0008857455,0.0004940131,0.0007316289,0.001342978],"category_scores_gemma":[0.01052348,0.0003096168,0.0006068008,0.0007128946,0.000398341,0.001064474,0.0004756709,0.0009491317,0.0001411469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005163468,"about_ca_system_score_gemma":0.0003849646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00673281,"about_ca_topic_score_gemma":0.005012526,"domain_scores_codex":[0.9995009,0.0002525015,0.00002798312,0.0000725671,0.00008544602,0.00006062324],"domain_scores_gemma":[0.9934453,0.005584318,0.0004724227,0.0001297271,0.0002689576,0.00009933977],"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":[0.0006563745,0.0006314478,0.1956478,0.0001644392,0.0008246186,0.0004235679,0.0001897544,0.723941,0.005650273,0.01303266,0.00065921,0.05817887],"study_design_scores_gemma":[0.000009879604,0.00005875269,0.01814967,0.000007011931,0.0000663495,0.00001202883,0.00001880227,0.9787314,0.0006255608,0.002230652,0.00007912858,0.00001086549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615857,0.0006529969,0.03385803,0.0007797803,0.00003787109,0.00002681997,0.0001935775,0.00005294673,0.002812325],"genre_scores_gemma":[0.9963095,0.00037593,0.002572718,0.00003220562,0.00003135492,0.00001522464,0.00006420453,0.000005163037,0.0005936885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00673281,"threshold_uncertainty_score":0.01338726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04035859596387329,"score_gpt":0.2966028860353493,"score_spread":0.256244290071476,"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."}}