Mood Swings and Insufficient Information Acquisition: A Study on Cross-Section of Stock Returns

57 Pages Posted: 9 May 2018 Last revised: 19 Jun 2021

See all articles by Jiatao Liu

Jiatao Liu

Cass Business School

Ian W. Marsh

City University London - Sir John Cass Business School

Date Written: April 23, 2019

Abstract

This paper studies mood, measured by Twitter messages, which causes investors' insufficient acquisition of information about assets and the implications of asset pricing. Using a Twitter-based mood measure, we find that mood swings are negatively predictive of investors' acquisition of earnings-related information when seeking to learn about companies' performance. Therefore, we argue that this bias effect contributes to the explanation of classical (unconditional) pricing models' failures. Conducting tests on cross-sectional stock returns, we show that stocks that are more sensitive to mood earn a higher expected excess return than less mood-sensitive stocks. Sorting stocks to construct the risk factor portfolio based on mood betas as sensitivity to mood risk, we are the first to quantify the risk premium (0.56% per month) by holding stocks subject to mood risk. Our results are consistent with the theoretical prediction that investors mistakenly use mood as information rather than learning enough fundamental information about assets, thereby inducing mispricing in asset valuation.

Keywords: Information Acquisition; Mood; Mood Beta; Risk Premium; Anomalies

JEL Classification: G12, G14, G41

Suggested Citation

Liu, Jiatao and Marsh, Ian William, Mood Swings and Insufficient Information Acquisition: A Study on Cross-Section of Stock Returns (April 23, 2019). Available at SSRN: https://ssrn.com/abstract=3170954 or http://dx.doi.org/10.2139/ssrn.3170954

Jiatao Liu (Contact Author)

Cass Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
Great Britain

Ian William Marsh

City University London - Sir John Cass Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 20 7040 5121 (Phone)
+44 20 7040 8881 (Fax)

HOME PAGE: http://www.cass.city.ac.uk/faculty/i.marsh

Do you want regular updates from SSRN on Twitter?

Paper statistics

Downloads
246
Abstract Views
1,242
rank
170,464
PlumX Metrics