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Stock Return Asymmetry: Beyond Skewness

Published online by Cambridge University Press:  14 March 2019

Lei Jiang,
Ke Wu,
Guofu ZhouOpen the ORCID record for Guofu Zhou[Opens in a new window] and
Yifeng Zhu
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Abstract

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In this article, we propose two asymmetry measures for stock returns. Unlike the popular skewness measure, our measures are based on the distribution function of the data rather than just the third central moment. We present empirical evidence that the greater upside asymmetries calculated using our new measures imply lower average returns in the cross section of stocks. In contrast, when using the skewness measure, the relationship between asymmetry and returns is inconclusive.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 55 , Issue 2 , March 2020, pp. 357 - 386
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2019 

Footnotes

1

We thank Tarun Chordia, Philip Dybvig, Christian Goulding, Amit Goyal, Bing Han, Fuwei Jiang, Ying Jiang, Raymond Kan, Wenjin Kang, Hong Liu, Tingjun Liu, Laura Xiaolei Liu, Esfandiar Maasoumi, George Panayotov, Tao Shen, Qi Sun, Aurelio Vasquez, Baolian Wang, Hao Wang, Quan Wen, Baozhong Yang, Tao Zha, and Yingzi Zhu, as well as seminar/conference participants at Case Western Reserve University, Central University of Finance and Economics, Emory University, ITAM, Renmin University of China, San Francisco State University, Shanghai Tech University, Shanghai University of Finance and Economics, South University of Science and Technology of China, Tongji University, Tsinghua University, Washington University in St. Louis, the 2015 China Finance Review International Conference, the 2016 Midwest Finance Association (MFA) Annual Conference, the 2016 China International Conference in Finance (CICF), the 2016 Society for Financial Econometrics (SoFiE) Conference, the 2016 Financial Management Association (FMA) Annual Meeting, and the 2016 World Finance Conference for helpful comments and especially Jennifer Conrad (the editor) and Fousseni Chabi-Yo (the referee) for their many insightful and detailed comments that have substantially improved the article. Wu acknowledges financial support from the National Natural Science Foundation of China (NNSFC) (No. 71803187). Zhu appreciates financial support from the NNSFC (Nos. 71872195 and 71702205). Jiang gratefully acknowledges financial support from the AXA Research Fund, the Tsinghua University Initiative Scientific Research Program (20151080398), and the NNSFC (No. 71572091). The research is supported by Tsinghua National Laboratory for Information Science and Technology.

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