Time Variation in Extrapolation and Anomalies

49 Pages Posted: 23 Apr 2020 Last revised: 22 Jun 2021

See all articles by Wei He

Wei He

Southwestern University of Finance and Economics (SWUFE) - Institute of Chinese Financial Studies (ICFS)

Yuehan Wang

Tsinghua University, PBC School of Finance, Students

Jianfeng Yu

Tsinghua University - PBC School of Finance

Date Written: March 29, 2020

Abstract

We find that the degree of extrapolative weighting in investors' belief (DOX) proposed by Cassella and Gulen (2018) has strong predictive power for a broad set of overreaction-related anomalies in the stock market. The average return spread of these anomalies is about 0.81% per month following high DOX periods, and -0.22% per month following low DOX periods. In sharp contrast, DOX has opposite, but weaker, predictive power for under-reaction-related anomalies. In addition, the predictive power of DOX is robust after controlling for a broad set of economic forces including investor sentiment and the consumption surplus ratio. Moreover, most of the DOX effect on long-short anomaly returns derives from the short legs of these overreaction-related anomalies, suggesting that time variation in DOX leads to more time variation in overpricing than in under-pricing, probably because of short-sale impediments.

Keywords: Extrapolation, Overreaction, Underreaction, Mispricing, Factor

JEL Classification: G12

Suggested Citation

He, Wei and Wang, Yuehan and Yu, Jianfeng, Time Variation in Extrapolation and Anomalies (March 29, 2020). PBCSF-NIFR Research Paper, Available at SSRN: https://ssrn.com/abstract=3564119 or http://dx.doi.org/10.2139/ssrn.3564119

Wei He

Southwestern University of Finance and Economics (SWUFE) - Institute of Chinese Financial Studies (ICFS) ( email )

Chengdu
China

Yuehan Wang

Tsinghua University, PBC School of Finance, Students ( email )

No. 43, Chengfu Road
Beijing 100083
China

Jianfeng Yu (Contact Author)

Tsinghua University - PBC School of Finance ( email )

No. 43, Chengdu Road
Haidian District
Beijing 100083
China

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