{"id":"W35804612","doi":"","title":"Forecasting the Taiwan Stock Market with a Novel Momentum-based Fuzzy Time-series","year":2012,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Stock market index; Series (stratigraphy); Fuzzy logic; Stock exchange; Time series; Stock market; Index (typography); Econometrics; Artificial neural network; Composite index; Data mining; Artificial intelligence; Machine learning; Mathematics; Finance; Economics; Geography","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":[0.0005090888,0.0004774714,0.0005415649,0.0007728187,0.0002765362,0.0008427163,0.000777496,0.0004716316,0.0004871046],"category_scores_gemma":[0.001031826,0.0002278597,0.0006036211,0.0006683981,0.0002239687,0.0008852443,0.0003339932,0.0004641883,0.0000931343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006398067,"about_ca_system_score_gemma":0.0006148664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009541163,"about_ca_topic_score_gemma":0.007232746,"domain_scores_codex":[0.9997993,0.00002843016,0.00001782123,0.000057854,0.00008004993,0.00001640274],"domain_scores_gemma":[0.9997694,0.000082446,0.00004671154,0.00001753491,0.0000707062,0.00001318713],"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":[0.0001724587,0.0001857468,0.008702901,0.0001197655,0.0001443271,0.0002427201,0.0001218022,0.8466998,0.007382715,0.007929189,0.001140321,0.1271582],"study_design_scores_gemma":[0.000003383252,0.00001556091,0.0003828908,0.000002080606,0.000007880081,0.00001293541,0.000003208534,0.9988075,0.000294769,0.0003431773,0.0001232417,0.000003308789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2337023,0.0007570684,0.7608634,0.0004294433,0.0001197573,0.00008751071,0.0002657896,0.0004087684,0.003366055],"genre_scores_gemma":[0.9504118,0.0004309897,0.04769449,0.00003165374,0.00005163298,0.00005927733,0.0001790082,0.000007316706,0.001133844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009541163,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08946385883837525,"score_gpt":0.3211491252234754,"score_spread":0.2316852663851001,"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."}}