Should Retail Investors Listen to Social Media Analysts? Evidence from Text-Implied Beliefs

69 Pages Posted: 29 Mar 2021 Last revised: 13 Jun 2022

See all articles by Chukwuma Dim

Chukwuma Dim

Frankfurt School of Finance & Management

Date Written: December 17, 2020

Abstract

This paper uses machine learning to infer nonprofessional social media investment analysts' (SMAs) beliefs from their opinions on individual stocks. SMAs' average beliefs predict future abnormal returns and earnings surprises. However, there exists substantial heterogeneity in SMAs' ability to form beliefs that yield investment value. Some 13% high-skilled SMAs form beliefs that yield a sizeable one-week three-factor alpha of 61 bps, while the remaining 87% low-skilled SMAs generate only 6 bps. Firm and industry specializations are the most distinctive characteristics of high-skilled SMAs. When forming beliefs, SMAs extrapolate from past returns and herd on the consensus view of their peers. However, these seemingly behavioral biases do not result in systematically wrong beliefs.

Keywords: Nonprofessional analysts, Belief formation, Investor skill, Market efficiency, Herding, Extrapolation, Machine learning, Natural language processing

JEL Classification: G11, G12, G14

Suggested Citation

Dim, Chukwuma, Should Retail Investors Listen to Social Media Analysts? Evidence from Text-Implied Beliefs (December 17, 2020). Available at SSRN: https://ssrn.com/abstract=3813252 or http://dx.doi.org/10.2139/ssrn.3813252

Chukwuma Dim (Contact Author)

Frankfurt School of Finance & Management ( email )

Adickesallee 32-34
Frankfurt am Main, 60322
Germany

HOME PAGE: http://sites.google.com/view/chukwumadim/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
777
Abstract Views
2,723
rank
45,509
PlumX Metrics