{"id":"W2290730829","doi":"10.5539/jms.v6n1p206","title":"Stock Fundamentals Model Based on Genetic Algorithm-Rough Set","year":2016,"lang":"en","type":"article","venue":"Journal of Management and Sustainability","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Portfolio; Stock (firearms); Rough set; Econometrics; Nonlinear system; Investment (military); Genetic algorithm; Computer science; Set (abstract data type); Operations research; Mathematical optimization; Economics; Mathematics; Data mining; Financial economics; 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":[0.00069547,0.0006682872,0.001376649,0.001153292,0.0004586945,0.001662195,0.001822035,0.0009147858,0.002212884],"category_scores_gemma":[0.001336019,0.0004750671,0.001335711,0.001321633,0.0003741452,0.001243297,0.0005793119,0.0007902593,0.0002661171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251362,"about_ca_system_score_gemma":0.001616623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02957773,"about_ca_topic_score_gemma":0.01200166,"domain_scores_codex":[0.9995543,0.00009917972,0.00003288901,0.0001023744,0.0001474462,0.00006375403],"domain_scores_gemma":[0.9996446,0.0001796574,0.00005567272,0.00001680525,0.00008702099,0.0000162422],"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":[0.00001497442,0.000009836533,0.0005789591,0.0000286164,0.00003564209,0.00005117022,0.00002246804,0.9842995,0.0001979343,0.004842718,0.0003101848,0.009608029],"study_design_scores_gemma":[0.000005148772,0.000006736374,0.0001350738,0.000003164135,0.000009672019,0.000007075228,0.000003741431,0.9977726,0.00006779512,0.001838014,0.0001463642,0.000004539678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08829666,0.001120244,0.8981555,0.000713454,0.00009040401,0.0001380178,0.0004547391,0.0005037167,0.01052734],"genre_scores_gemma":[0.9034452,0.0009321568,0.08795852,0.00008348053,0.00005292578,0.0002847555,0.0005427071,0.00003482878,0.006665349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02957773,"threshold_uncertainty_score":0.05881113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0784352754071204,"score_gpt":0.3937311174544681,"score_spread":0.3152958420473477,"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."}}