@article{c35914a9d28b46b78878088e498201cd,
title = "Robust Measures of Earnings Surprises",
abstract = "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.",
author = "Chiang, {Chin Han} and Wei Dai and Jianqing Fan and Harrison Hong and Jun Tu",
note = "Funding Information: ∗Chin-Han Chiang is with World Bank Group. Wei Dai is with Princeton University. Jianqing Fan is with Princeton University and International School of Economics and Management, Capital University of Economics and Business. Harrison Hong is with Columbia University and NBER. Jun Tu is with Singapore Management University. The authors thank Ken Singleton (Editor) and anonymous referees for many helpful comments. They also thank Bruce Grundy; Hongjun Yan; and seminar participants at the European Finance Association Meetings 2015, Western Finance Association 2015 Meetings, China International Finance Conference 2015, Financial Management Association 2015 Meetings, Singapore Management University, Seoul National University, and Emory University. Jun Tu acknowledges support from Sim Kee Boon Institute of Financial Economics. This paper was previously titled “When Everyone Misses on the Same Side: Debiased Earnings Surprises and Stock Returns.” The authors have no material financial or nonfinancial interests related to this research, as identified in the Journal of Finance{\textquoteright}s disclosure policy. Publisher Copyright: {\textcopyright} 2018 the American Finance Association",
year = "2019",
month = apr,
doi = "10.1111/jofi.12746",
language = "English (US)",
volume = "74",
pages = "943--983",
journal = "Journal of Finance",
issn = "0022-1082",
publisher = "Wiley-Blackwell",
number = "2",
}