{"id":"W1968179824","doi":"10.1007/s11142-012-9219-2","title":"Removing predictable analyst forecast errors to improve implied cost of equity estimates","year":2013,"lang":"en","type":"article","venue":"Review of Accounting Studies","topic":"Auditing, Earnings Management, Governance","field":"Business, Management and Accounting","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Implicit cost; Econometrics; Equity (law); Economics; Cash flow; Cost of equity; Cost of capital; Actuarial science; Financial economics; Cost estimate; Finance; Microeconomics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001639198,0.000388874,0.001227651,0.0002603662,0.0002492346,0.0001378454,0.0006634887,0.00004778436,0.0002010248],"category_scores_gemma":[0.02397955,0.0003333812,0.000225656,0.001185255,0.0001449716,0.001422596,0.002056498,0.0001712407,0.0001984144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009641327,"about_ca_system_score_gemma":0.00002652316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071404,"about_ca_topic_score_gemma":0.00006469759,"domain_scores_codex":[0.9969432,0.00001347606,0.001216118,0.0005286711,0.0006924677,0.0006060777],"domain_scores_gemma":[0.9832329,0.0001956921,0.01472571,0.0005538905,0.001270775,0.00002104817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002643006,0.0001787457,0.1390282,0.1428412,0.001197001,0.000005740434,0.0002974331,0.0003512766,0.005040239,0.004626221,0.09947571,0.6069318],"study_design_scores_gemma":[0.002937072,0.0002506325,0.3513847,0.1760316,0.005373008,0.00000877526,0.005817071,0.009124282,0.003059868,0.00663984,0.4352329,0.004140247],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6779619,0.1664488,0.03485464,0.009184247,0.002563123,0.01389144,0.00007798713,0.001014974,0.09400285],"genre_scores_gemma":[0.98396,0.008454693,0.003344585,0.002900007,0.0006144678,0.0002760287,0.00001933455,0.00007853706,0.0003523689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6027915,"threshold_uncertainty_score":0.9999118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02657253152385102,"score_gpt":0.3012248582581464,"score_spread":0.2746523267342953,"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."}}