Robust Measures of Earnings Surprises

52 Pages Posted: 29 Jul 2014 Last revised: 6 May 2018

See all articles by Chin-Han Chiang

Chin-Han Chiang

Independent

Wei Dai

Princeton University - Department of Operations Research & Financial Engineering (ORFE)

Jianqing Fan

Princeton University - Bendheim Center for Finance

Harrison G. Hong

Columbia University, Graduate School of Arts and Sciences, Department of Economics; National Bureau of Economic Research (NBER)

Jun Tu

Singapore Management University

Date Written: May 3, 2016

Abstract

Event studies of market efficiency measure an earnings surprise with the consensus error (CE), defined as earnings minus the average of professional forecasts. 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 for this ideal measure. The fraction of misses on the same side FOM, by discarding the magnitude of misses, offers a far better approximation. FOM performs particularly well against CE in predicting the returns of US stocks, where bias is potentially large, than that of international stocks.

Keywords: Event Studies, Efficient Markets, Earnings Announcements, Post-Earnings Announcement Drift

JEL Classification: G1, G12

Suggested Citation

Chiang, Chin-Han and Dai, Wei and Fan, Jianqing and Hong, Harrison G. and Tu, Jun, Robust Measures of Earnings Surprises (May 3, 2016). Journal of Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2473366 or http://dx.doi.org/10.2139/ssrn.2473366

Chin-Han Chiang

Independent

No Address Available

Wei Dai

Princeton University - Department of Operations Research & Financial Engineering (ORFE) ( email )

Sherrerd Hall, Charlton Street
Princeton, NJ 08544
United States

Jianqing Fan

Princeton University - Bendheim Center for Finance ( email )

26 Prospect Avenue
Princeton, NJ 08540
United States
609-258-7924 (Phone)
609-258-8551 (Fax)

HOME PAGE: http://orfe.princeton.edu/~jqfan/

Harrison G. Hong (Contact Author)

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Jun Tu

Singapore Management University ( email )

Li Ka Shing Library
70 Stamford Road
Singapore 178901, 178899
Singapore

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