{"id":"W1964439841","doi":"10.5430/air.v1n2p198","title":"Role of soft computing techniques in predicting stock market direction","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Soft computing; Stock market; Computer science; Popularity; Chaotic; Financial market; Econometrics; Data mining; Finance; Artificial intelligence; Economics; Artificial neural network","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":[0.001554623,0.0006469579,0.0006736138,0.002103099,0.000327197,0.002558651,0.0005264633,0.0007751302,0.001441666],"category_scores_gemma":[0.006221204,0.0002393644,0.0006587681,0.001875357,0.000665525,0.001965363,0.0006026375,0.001087465,0.0004385988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004063437,"about_ca_system_score_gemma":0.0005715311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001214759,"about_ca_topic_score_gemma":0.001115293,"domain_scores_codex":[0.9992265,0.0002596737,0.00007421244,0.00009908972,0.0003036231,0.00003696172],"domain_scores_gemma":[0.9959381,0.002977602,0.0003233073,0.0001587109,0.0005158569,0.00008647611],"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":[0.0001787997,0.0002289687,0.01319082,0.000931707,0.0003112557,0.0003426182,0.0003423915,0.2223614,0.01095935,0.03898696,0.002239892,0.7099258],"study_design_scores_gemma":[0.00001392592,0.0001682848,0.00438457,0.0002835616,0.00008050312,0.0001768077,0.000237592,0.9436873,0.007552356,0.03846086,0.004895929,0.00005827376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.112674,0.0307148,0.8258602,0.003496601,0.000809011,0.0001538265,0.0002020917,0.0004758741,0.02561357],"genre_scores_gemma":[0.7990849,0.01803684,0.1776223,0.0003224334,0.0005824437,0.00007821612,0.0001300063,0.00004429092,0.004098683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002558651,"threshold_uncertainty_score":0.008221745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.369288030854201,"score_gpt":0.5345690801322399,"score_spread":0.1652810492780389,"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."}}