Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-25T07:29:00.265Z Has data issue: false hasContentIssue false
  • English
  • Français

Stock Return Asymmetry: Beyond Skewness

Published online by Cambridge University Press:  14 March 2019

Lei Jiang ,
Ke Wu ,
Guofu Zhou Open the ORCID record for Guofu Zhou [Opens in a new window]  and
Yifeng Zhu
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

Acharya, V. V., and Pedersen, L. H.. “Asset Pricing with Liquidity Risk.” Journal of Financial Economics, 77 (2005), 375–410.Google Scholar
Amaya, D.; Christoffersen, P.; Jacobs, K.; and Vasquez, A.. “Does Realized Skewness Predict the Cross-Section of Equity Returns?” Journal of Financial Economics, 118 (2015), 135–167.Google Scholar
Amihud, Y.“Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.” Journal of Financial Markets, 5 (2002), 31–56.Google Scholar
An, L.; Wang, H.; Wang, J.; and Yu, J.. “Lottery-Related Anomalies: The Role of Reference-Dependent Preferences.” Management Science, forthcoming (2019).Google Scholar
Ang, A.; Hodrick, R. J.; Xing, Y.; and Zhang, X.. “The Cross-Section of Volatility and Expected Returns.” Journal of Finance, 61 (2006), 259–299.Google Scholar
Arditti, F. D.“Another Look at Mutual Fund Performance.” Journal of Financial and Quantitative Analysis, 6 (1971), 909–912.Google Scholar
Backus, D.; Boyarchenko, N.; and Chernov, M.. “Term Structures of Asset Prices and Returns.” Journal of Financial Economics, 129 (2018), 1–23.Google Scholar
Baker, M., and Wurgler, J.. “Investor Sentiment and the Cross-Section of Stock Returns.” Journal of Finance, 61 (2006), 1645–1680.Google Scholar
Baker, M., and Wurgler, J.. “Investor Sentiment in the Stock Market.” Journal of Economic Perspectives, 21 (2007), 129–152.Google Scholar
Bakshi, G., and Chabi-Yo, F.. “New Entropy Restrictions and the Quest for Better Specified Asset Pricing Models.” Journal of Financial and Quantitative Analysis, forthcoming (2019).Google Scholar
Bali, T. G.; Cakici, N.; and Whitelaw, R. F.. “Maxing Out: Stocks as Lotteries and the Cross-Section of Expected Returns.” Journal of Financial Economics, 99 (2011), 427–446.Google Scholar
Bali, T. G.; Engle, R. F.; and Murray, S.. Empirical Asset Pricing: The Cross Section of Stock Returns. Hoboken, NJ: Wiley (2016).Google Scholar
Barberis, N., and Huang, M.. “Stocks as Lotteries: The Implications of Probability Weighting for Security Prices.” American Economic Review, 98 (2008), 2066–2100.Google Scholar
Boyer, B.; Mitton, T.; and Vorkink, K.. “Expected Idiosyncratic Skewness.” Review of Financial Studies, 23 (2010), 169–202.Google Scholar
Brennan, M. J.; Chordia, T.; and Subrahmanyam, A.. “Alternative Factor Specifications, Security Characteristics, and the Cross-Section of Expected Stock Returns.” Journal of Financial Economics, 49 (1998), 345–373.Google Scholar
Campbell, J. Y.; Hilscher, J.; and Szilagyi, J.. “In Search of Distress Risk.” Journal of Finance, 63 (2008), 2899–2939.Google Scholar
Campbell, J. Y.; Hilscher, J.; and Szilagyi, J.. “Predicting Financial Distress and the Performance of Distressed Stocks.” Journal of Investment Management, 9 (2011), 14–34.Google Scholar
Chabi-Yo, F.“Pricing Kernels with Stochastic Skewness and Volatility Risk.” Management Science, 58 (2012), 624–640.Google Scholar
Chabi-Yo, F., and Colacito, R.. “The Term Structures of Co-Entropy in International Financial Markets.” Management Science, forthcoming (2019).Google Scholar
Conrad, J.; Dittmar, R. F.; and Ghysels, E.. “Ex Ante Skewness and Expected Stock Returns.” Journal of Finance, 68 (2013), 85–124.Google Scholar
D’Agostino, R. B.“Transformation to Normality of the Null Distribution of g1.” Biometrika, 57 (1970), 679–681.Google Scholar
Dittmar, R. F.“Nonlinear Pricing Kernels, Kurtosis Preference, and Evidence from the Cross Section of Equity Returns.” Journal of Finance, 57 (2002), 369–403.Google Scholar
Fama, E. F., and French, K. R.. “The Cross-Section of Expected Stock Returns.” Journal of Finance, 47 (1992), 427–465.Google Scholar
Fama, E. F., and French, K. R.. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, 33 (1993), 3–56.Google Scholar
Fama, E. F., and French, K. R.. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, 116 (2015), 1–22.Google Scholar
Gao, X., and Ritter, J. R.. “The Marketing of Seasoned Equity Offerings.” Journal of Financial Economics, 97 (2010), 33–52.Google Scholar
Ghysels, E.; Plazzi, A.; and Valkanov, R.. “Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry.” Journal of Finance, 71 (2016), 2145–2192.Google Scholar
Goulding, C.“Pricing Implications of Clearing a Skewed Asset from the Market.” Working Paper, Michigan State University (2017).Google Scholar
Granger, C. W.; Maasoumi, E.; and Racine, J.. “A Dependence Metric for Possibly Nonlinear Processes.” Journal of Time Series Analysis, 25 (2004), 649–669.Google Scholar
Grinblatt, M., and Han, B.. “Prospect Theory, Mental Accounting, and Momentum.” Journal of Financial Economics, 78 (2005), 311–339.Google Scholar
Gupta, A. K., and Nadarajah, S.. Handbook of Beta Distribution and Its Applications. Boca Raton, FL: CRC Press (2004).Google Scholar
Han, B.; Hirshleifer, D. A.; and Walden, J.. “Social Transmission Bias and Investor Behavior.” Working Paper 24281, National Bureau of Economic Research (2018).Google Scholar
Harvey, C. R., and Siddique, A.. “Conditional Skewness in Asset Pricing Tests.” Journal of Finance, 55 (2000), 1263–1295.Google Scholar
Horowitz, J. L.“The Bootstrap.” Handbook of Econometrics, Vol. 5, Maddala, G. S., Rao, C. R., and Vinod, H. D., eds. Amsterdam, Netherlands: Elsevier Science BV (2001).Google Scholar
Huang, D.; Jiang, F.; Tu, J.; and Zhou, G.. “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns.” Review of Financial Studies, 28 (2015), 791–837.Google Scholar
Jegadeesh, N.“Evidence of Predictable Behavior of Security Returns.” Journal of Finance, 45 (1990), 881–898.Google Scholar
Jegadeesh, N., and Titman, S.. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, 48 (1993), 65–91.Google Scholar
Jiang, L.; Wu, K.; and Zhou, G.. “Asymmetry in Stock Comovements: An Entropy Approach.” Journal of Financial and Quantitative Analysis, 53 (2018), 1479–1507.Google Scholar
Kelly, B., and Jiang, H.. “Tail Risk and Asset Prices.” Review of Financial Studies, 27 (2014), 2841–2871.Google Scholar
Kullback, S., and Leibler, R. A.. “On Information and Sufficiency.” Annals of Mathematical Statistics, 22 (1951), 79–86.Google Scholar
Kumar, A.“Who Gambles in the Stock Market?” Journal of Finance, 64 (2009), 1889–1933.Google Scholar
Lehmann, B. N.“Fads, Martingales, and Market Efficiency.” Quarterly Journal of Economics, 105 (1990), 1–28.Google Scholar
Li, Q., and Racine, J. S.. Nonparametric Econometrics: Theory and Practice. Princeton, NJ: Princeton University Press (2007).Google Scholar
Maasoumi, E., and Racine, J.. “A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes.” Econometric Reviews, 28 (2008), 246–261.Google Scholar
Newey, W. K., and West, K. D.. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55 (1987), 703–708.Google Scholar
Ohlson, J. A.“Financial Ratios and the Probabilistic Prediction of Bankruptcy.” Journal of Accounting Research, 18 (1980), 109–131.Google Scholar
Pástor, L., and Stambaugh, R. F.. “Liquidity Risk and Expected Stock Returns.” Journal of Political Economy, 111 (2003), 642–685.Google Scholar
Pástor, L.; Stambaugh, R. F.; and Taylor, L. A.. “Do Funds Make More When They Trade More?” Journal of Finance, 72 (2017), 1483–1528.Google Scholar
Pham-Gia, T.; Turkkan, N.; and Eng, P.. “Bayesian Analysis of the Difference of Two Proportions.” Communications in Statistics—Theory and Methods, 22 (1993), 1755–1771.Google Scholar
Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; and Flannery, B. P.. Numerical Recipes: The Art of Scientific Computing. 3rd ed. New York, NY: Cambridge University Press (2007).Google Scholar
Racine, J. S.“Consistent Significance Testing for Nonparametric Regression.” Journal of Business and Economic Statistics, 15 (1997), 369–378.Google Scholar
Racine, J. S., and Maasoumi, E.. “A Versatile and Robust Metric Entropy Test of Time-Reversibility, and Other Hypotheses.” Journal of Econometrics, 138 (2007), 547–567.Google Scholar
Stambaugh, R. F.; Yu, J.; and Yuan, Y.. “The Short of It: Investor Sentiment and Anomalies.” Journal of Financial Economics, 104 (2012), 288–302.Google Scholar
Stambaugh, R. F.; Yu, J.; and Yuan, Y.. “Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle.” Journal of Finance, 70 (2015), 1903–1948.Google Scholar
Tversky, A., and Kahneman, D.. “Advances in Prospect Theory: Cumulative Representation of Uncertainty.” Journal of Risk and Uncertainty, 5 (1992), 297–323.Google Scholar
Xing, Y.; Zhang, X.; and Zhao, R.. “What Does the Individual Option Volatility Smirk Tell Us about Future Equity Returns?” Journal of Financial and Quantitative Analysis, 45 (2010), 641–662.Google Scholar
Zhang, Y.“Individual Skewness and the Cross-Section of Average Stock Returns.” Working Paper, Yale University (2005).Google Scholar
Supplementary material: File

Jiang et al. supplementary material

Jiang et al. supplementary material

Download Jiang et al. supplementary material(File)
File 184.4 KB