{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06789365,0.0001517541,0.0003549727,0.001430121,0.000292754,0.0001493577,0.000945678,0.0001613252,0.0005492253],"category_scores_gemma":[0.04961738,0.0001322552,0.0001044136,0.003920306,0.000387622,0.0004881936,0.0005732068,0.0007419412,0.00009061579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001615137,"about_ca_system_score_gemma":0.000117711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000673433,"about_ca_topic_score_gemma":0.0002002964,"domain_scores_codex":[0.9914136,0.003105983,0.001364009,0.0005321361,0.002557203,0.001027051],"domain_scores_gemma":[0.9825357,0.01542317,0.0002751677,0.000622616,0.0009519123,0.0001914084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009382651,0.0001325872,0.1474444,0.000007893326,0.000004288678,8.562919e-7,0.001693727,0.00007148468,0.0127289,0.001359302,0.0002038406,0.8362589],"study_design_scores_gemma":[0.00002870099,0.0002911576,0.08702049,0.0002160568,0.000005846029,0.00001938864,0.01396029,0.2356432,0.4764875,0.1833548,0.002636961,0.0003355737],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8511201,0.0003724838,0.09086806,0.000161023,0.0004602583,0.0007355065,0.000004474944,0.0001219803,0.0561561],"genre_scores_gemma":[0.9823033,0.000008650518,0.01698657,0.000006930189,0.0003493256,0.00002744146,5.671953e-7,0.00002051017,0.0002967499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8359233,"threshold_uncertainty_score":0.9597996,"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."}}