Anomalies Enhanced: The Value of Higher Frequency Information

56 Pages Posted: 1 Jul 2015 Last revised: 10 Feb 2017

Yufeng Han

UNCC

Dayong Huang

University of North Carolina (UNC) at Greensboro - Bryan School of Business & Economics

Guofu Zhou

Washington University in St. Louis - Olin School of Business

Date Written: November 26, 2016

Abstract

Many anomalies are based on low frequency attributes, such as annual characteristics, that ignore higher frequency information. In this paper, we provide a simple strategy to incorporate the higher frequency information. We find that there is significant economic value-added. For eight major anomalies, we find that the enhanced anomalies can double the average returns while having similar or lower risks. The results are robust to a number of controls.

Keywords: Anomaly, low frequency information, technical analysis

JEL Classification: G11, G23

Suggested Citation

Han, Yufeng and Huang, Dayong and Zhou, Guofu, Anomalies Enhanced: The Value of Higher Frequency Information (November 26, 2016). Available at SSRN: https://ssrn.com/abstract=2624650 or http://dx.doi.org/10.2139/ssrn.2624650

Yufeng Han

UNCC ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

Dayong Huang

University of North Carolina (UNC) at Greensboro - Bryan School of Business & Economics ( email )

401 Bryan Building
Greensboro, NC 27402-6179
United States

HOME PAGE: http://sites.google.com/a/uncg.edu/dayong-huang/

Guofu Zhou (Contact Author)

Washington University in St. Louis - Olin School of Business ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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