Abstract

https://ssrn.com/abstract=2624650
 


 



Anomalies Enhanced: The Value of Higher Frequency Information


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

August 7, 2015


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.

Number of Pages in PDF File: 56

Keywords: Anomaly, low frequency information, technical analysis

JEL Classification: G11, G23


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Date posted: July 1, 2015 ; Last revised: November 29, 2016

Suggested Citation

Han, Yufeng and Huang, Dayong and Zhou, Guofu, Anomalies Enhanced: The Value of Higher Frequency Information (August 7, 2015). Available at SSRN: https://ssrn.com/abstract=2624650 or http://dx.doi.org/10.2139/ssrn.2624650

Contact Information

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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