Robust Measures of Earnings Surprises

• 成果类型: Article
• 署名作者: Chiang, Chin-Han; Dai, Wei; Fan, Jianqing; Hong, Harrison; Tu, Jun
• 署名单位: The World Bank; Princeton University; Capital University of Economics & Business; Columbia University; National Bureau of Economic Research; Singapore Management University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12746
• 发表日期: 2019
• 页码: 943-983
• 关键词: Career concerns; analysts

摘要

Event studies of market efficiency measure earnings surprises using the consensus error (CE), given as actual earnings minus the average professional forecast. If a subset of forecasts can be biased, the ideal but difficult to estimate parameter-dependent alternative to CE is a nonlinear filter of individual errors that adjusts for bias. We show that CE is a poor parameter-free approximation of this ideal measure. The fraction of misses on the same side (FOM), which discards the magnitude of misses, offers a far better approximation. FOM performs particularly well against CE in predicting the returns of U.S. stocks, where bias is potentially large.