{"id":"W3097753938","doi":"10.2139/ssrn.3396098","title":"Can Investors Fully Adjust for Known Biases in Management Earnings Forecasts?","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Earnings management; Earnings; Business; Econometrics; Economics; Actuarial science; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01145777,0.0007919826,0.0008698555,0.0008078708,0.0003551627,0.003403689,0.00104616,0.002174316,0.003212278],"category_scores_gemma":[0.1149174,0.0005486385,0.0005899832,0.0007526079,0.0005998425,0.006328115,0.001109969,0.001768304,0.00153962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006268441,"about_ca_system_score_gemma":0.001030646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004817266,"about_ca_topic_score_gemma":0.004362672,"domain_scores_codex":[0.9979135,0.0006021673,0.0002126911,0.0005335461,0.0004147992,0.0003231729],"domain_scores_gemma":[0.9592832,0.01909035,0.01165598,0.006595087,0.002795792,0.0005796182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001230474,0.000255167,0.5130062,0.0003609674,0.002080355,0.0005807455,0.0006969229,0.08527884,0.00457669,0.02977535,0.0272515,0.3349068],"study_design_scores_gemma":[0.0002931502,0.0003682598,0.3826398,0.0002200698,0.0007234529,0.0005876837,0.000621483,0.4136404,0.01011497,0.1765231,0.01401692,0.0002507547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7870564,0.003566959,0.1491898,0.02174835,0.001559963,0.0001388705,0.003138649,0.002129973,0.031471],"genre_scores_gemma":[0.9896479,0.0004522396,0.005425225,0.001098082,0.0004002982,0.00001406071,0.0005072032,0.00009000229,0.002365022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01145777,"threshold_uncertainty_score":0.06059521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02280012303886002,"score_gpt":0.2131075330108908,"score_spread":0.1903074099720308,"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."}}