How to Gauge Investor Behavior? A Comparison of Online Investor Sentiment Measures

42 Pages Posted: 12 Jul 2019 Last revised: 6 Jan 2021

Date Written: January 5, 2020

Abstract

Given the increasing interest in and the growing number of publicly available methods
to estimate investor sentiment from social media platforms, researchers and practitioners
alike are facing one crucial question – which is best to gauge investor sentiment? We compare
the performance of daily investor sentiment measures estimated from Twitter and
StockTwits short messages by publicly available dictionary and machine learning based
methods for a large sample of stocks. To determine their relevance for financial applications,
these investor sentiment measures are compared by their effects on the cross-section
of stocks (i) within a Fama-MacBeth (1973) regression framework applied to a measure
of retail investors’ order imbalances and (ii) by their ability to forecast abnormal returns
in a model-free portfolio sorting exercise. Interestingly, we find that investor sentiment
measures based on finance-specific dictionaries do not only have a greater impact on
retail investors’ order imbalances than measures based on machine learning approaches
but also perform very well compared to the latter in our asset pricing application.

Keywords: Investor Sentiment, Twitter, StockTwits, Stock Returns, Portfolio returns

JEL Classification: G11, G17, G40

Suggested Citation

Ballinari, Daniele and Behrendt, Simon, How to Gauge Investor Behavior? A Comparison of Online Investor Sentiment Measures (January 5, 2020). Available at SSRN: https://ssrn.com/abstract=3418436 or http://dx.doi.org/10.2139/ssrn.3418436

Daniele Ballinari (Contact Author)

University of Basel ( email )

Petersplatz 1
Basel, CH-4003
Switzerland

Simon Behrendt

Zeppelin University ( email )

Am Seemooser Horn 20
Friedrichshafen, Lake Constance 88045
Germany

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