{"id":"W2117943682","doi":"10.5267/j.msl.2013.12.036","title":"Asset management using genetic algorithm: Evidence from Tehran Stock Exchange","year":2014,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock exchange; Computer science; Stock (firearms); Econometrics; Algorithm; Business; Finance; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01485278,0.0004126643,0.0004421303,0.001541723,0.0008650924,0.00138846,0.004764886,0.0000547598,0.0004892363],"category_scores_gemma":[0.00131818,0.0003519845,0.000167259,0.004234218,0.001045382,0.001226776,0.002192908,0.0002106487,0.0004119123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003320819,"about_ca_system_score_gemma":0.00001350853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000164085,"about_ca_topic_score_gemma":0.000006520339,"domain_scores_codex":[0.989746,0.0007956027,0.0009074494,0.002164937,0.005234552,0.001151424],"domain_scores_gemma":[0.9953733,0.001342394,0.00048539,0.002375874,0.0001015081,0.0003215324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001419385,0.00003516265,0.01367466,0.00002360423,0.00003456393,0.00007413553,0.0002590257,0.0033139,0.002182386,0.0001520464,0.007963676,0.9722726],"study_design_scores_gemma":[0.0006135291,0.00007982041,0.6185133,0.0002966679,0.0001371788,0.00001173752,0.0003588639,0.3520308,0.0004489766,0.006630011,0.02005911,0.0008199012],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2149117,0.00004932785,0.7746918,0.002185262,0.001680586,0.0007140175,0.000005604263,0.0001242254,0.005637426],"genre_scores_gemma":[0.1647297,0.0000209922,0.8293651,0.004467243,0.0003007232,0.00006853022,0.000001618744,0.00003557088,0.001010507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9714527,"threshold_uncertainty_score":0.9998932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1536048845918196,"score_gpt":0.3992309352867092,"score_spread":0.2456260506948896,"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."}}