{"id":"W3124572353","doi":"10.4236/ti.2013.41b010","title":"Cross-Sectional Estimation Biases in Risk Premia and Ze-ro-Beta Excess Returns","year":2013,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"BETA (programming language); Economics; Estimation; Risk premium; Excess return; Econometrics; Computer science; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002641884,0.0001439575,0.0002350829,0.0004608152,0.0001424435,0.000104547,0.0001011739,0.0002451175,0.0002182289],"category_scores_gemma":[0.000150932,0.0001465686,0.00002220198,0.0002555508,0.0003889304,0.0004507386,0.00008299606,0.0002047718,0.0000682457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005820626,"about_ca_system_score_gemma":0.00001464162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000485337,"about_ca_topic_score_gemma":0.00004428938,"domain_scores_codex":[0.9989251,0.00001135799,0.0004210595,0.0003721755,0.00002548528,0.0002448273],"domain_scores_gemma":[0.9995037,0.00003998521,0.0002051059,0.0001843323,0.00001810113,0.00004874224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003860096,0.00002844109,0.5298265,0.00001226053,0.000008675416,7.342043e-7,0.00004394472,0.00001926677,0.00001476147,0.4692867,0.0001426742,0.0006122559],"study_design_scores_gemma":[0.0002766715,0.00008329958,0.6044925,0.00001314075,0.000001700002,0.000004616811,0.00002634776,0.002428397,0.0001238481,0.3912534,0.001187659,0.0001083989],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847807,0.003191002,0.00004775565,0.0005910907,0.0001278754,0.0003049118,0.0000329984,0.00007057994,0.01085314],"genre_scores_gemma":[0.9972634,0.000737,0.001103323,0.0003510989,0.00002121484,0.000134987,0.00001341081,0.00000973938,0.0003658624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07803334,"threshold_uncertainty_score":0.5976893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02842987359192381,"score_gpt":0.2402907582565682,"score_spread":0.2118608846646444,"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."}}