Underreaction, Overreaction, and Dynamic Autocorrelation of Stock Returns

48 Pages Posted: 13 Nov 2019 Last revised: 24 Dec 2019

See all articles by Guo Hongye

Guo Hongye

University of Pennsylvania

Date Written: November 16, 2019

Abstract

I document that in the US, the aggregate monthly stock returns correlate positively with past returns 2/3 of the time, and negatively 1/3 of the time. While the two arms of correlation are separately strong, they cancel with each other, leading to an average autocorrelation that is only weakly positive. I argue this pattern of aggregate return predictability will be generated if investors fail to see the time-varying autocorrelation structure of earnings news. In this model, investors act as if they have underreacted to past news 2/3 of the time, and overreacted to past news 1/3 of the time. I then look out-of-sample and find affirmative evidence in the cross section and the international stock markets. The paper shows that the traditional view on stock return autocorrelation misses important information, which is that it varies over time.

Keywords: Asset Pricing, Behavioral Finance, Earnings Announcement, Underreaction, Overreaction

JEL Classification: G02, G12

Suggested Citation

Hongye, Guo, Underreaction, Overreaction, and Dynamic Autocorrelation of Stock Returns (November 16, 2019). Available at SSRN: https://ssrn.com/abstract=3480863 or http://dx.doi.org/10.2139/ssrn.3480863

Guo Hongye (Contact Author)

University of Pennsylvania ( email )

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