Reading the tea leaves: Model uncertainty, robust forecasts, and the autocorrelation of analysts' forecast errors

• 成果类型: Article
• 署名作者: Linnainmaaa, Juhani T.; Torous, Walter; Yae, James
• 署名单位: University of Southern California; National Bureau of Economic Research; Massachusetts Institute of Technology (MIT); University of Houston System; University of Houston
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2015.08.020
• 发表日期: 2016
• 页码: 42-64
• 关键词: model uncertainty; parameter uncertainty; forecasting; Robustness; Financial analysts

摘要

We put forward a model in which analysts are uncertain about a firm's earnings process. Faced with the possibility of using a misspecified model, analysts issue forecasts that are robust to model misspecification. We estimate that this mechanism explains approximately 60% of the autocorrelation in analysts' forecast errors. The remainder stems from the cross-sectional variation in mean forecast errors and in analysts' estimation errors of the persistence of earnings growth shocks. Consistent with our model, we find that analysts learn about some features of the earnings process but not others, and this learning reduces, but does not eliminate, the autocorrelation of forecast errors as firms age. Other potential explanations for the autocorrelation of analyst forecast errors are rejected. Our model of robust forecasting applies not only to analysts' forecasts but also to all model-based forecasts. (C) 2016 Published by Elsevier B.V.