Tweets and Trades: the Information Content of Stock Microblogs

Published date01 November 2014
Date01 November 2014
Tweets and Trades: the Information
Content of Stock Microblogs
Timm O. Sprenger, Andranik Tumasjan,
Philipp G. Sandner and Isabell M. Welpe
Technische Universität München, TUM School of Management, Germany
Microblogging forums (e.g., Twitter) have become a vibrant online platform for
exchanging stockrelated information. Using methods from computational
linguistics, we analyse roughly 250,000 stockrelated messages (socalled tweets)
on a daily basis. We nd an association between tweet sentiment and stock returns,
message volume and trading volume, as well as disagreement and volatility. In
contrast to previous related research, we also analyse the mechanism leading to an
efcient aggregation of information in microblogging forums. Our results
demonstrate that users providing above average investment advice are retweeted
(i.e., quoted) more often and have more followers, which amplies their share of
Keywords: Twitter,microblogging,stock market,investor sentiment,text classica-
tion,computational linguistics
JEL classification: G12, G14
1. Introduction
Scholars and practitioners alike increasingly call attention to the popularity of online
investment forums among investors and other nancial professionals (Antweiler and
Frank, 2004; BusinessWeek, 2009). Stock microblogging, mostly based on the social
networking service Twitter (, has recently been at the forefront of this
development. Some commentators have even described the conversations on this
platform as the modern version of traders shouting in the pits(BusinessWeek, 2009).
Twitter is a microblogging service allowing users to publish short messages with up to
140 characters, socalled tweets. These tweets are visible on a public message board
of the website or through various thirdparty applications. Users can subscribe to
We thank John Doukas (the editor of this Journal) and an anonymous referee for insightful
comments and suggestions that helped to improve this article. Correspondence: Timm O.
European Financial Management, Vol. 20, No. 5, 2014, 926957
doi: 10.1111/j.1468-036X.2013.12007.x
© 2013 John Wiley & Sons Ltd
(i.e., follow) a selection of favorite authors or search for messages containing a specic
key word (e.g., a stock symbol). The public timeline has turned into an extensive realtime
information stream of currently more than 340 million messages per day generated by
roughly 140 million active users (ZDNet, 2012). Many of these messages are dedicated to
the discussion of public companies and trading ideas. As a result, there are investors who
attribute their trading success to the information they nd on social media websites
and Twitterbased trading systems have been developed by nancial professionals to
alert users of sentimentbased investment opportunities (Jordan, 2010), and by academic
researchers to predict breakpoints in nancial time series (Vincent and Armstrong,
As yet, there is only little research examining whether and how microblogging
messages are related to nancial indicators. For instance, Sprenger et al. (2012)
investigate how different companyspecic news events published on Twitter (e.g.,
corporate governance or legal issues) are related to S&P 500 stock prices. Their
comprehensive study shows that the content of Twitter messages provides valuable
information with regard to the effects of different stockrelated news types on company
nancial indicators. In a related vein, Sprenger and Welpe (2011) demonstrate that joint
company mentions in Twitter messages predict their stockscomovement. Moreover,
their study indicates that this measure of company relatedness can also be used to
delineate homogenous industry groups.
A few recent studies have made a rst step in exploring whether the content of Twitter
may help predict macroeconomic market indicators. For instance, Bollen et al. (2011)
investigated whether collective mood states on Twitter are related to the value of the Dow
Jones Industrial Average (DJIA). They nd that certain mood states are indeed predictive
of the DJIA closing values. In a similar vein, Zhang et al. (2010) explored the relationship
between hope and fear on the one hand and the Dow Jones, NASDAQ, and S&P 500 on
the other hand. Their results indicate that the level of tweet emotionality was signicantly
related to all three aggregated indicators. However, while these studies offer a rst
indication of the relationship between tweets and aggregated nancial indicators, they are
also limited in a number of ways. First, both studies use randomised subsamples of all
available tweets of the Twitter message stream. Since the majority of these messages may
arguably not be related to stock market topics, we cannot infer whether the stockspecic
information contained in tweets is indeed associated with these indicators. Second, both
studies only explored the relationship between aggregate sentiment measures and
aggregate stock market indices, which does not allow us to draw conclusions on whether
the information contained in stockrelated messages is related to the performance of
individual stocks. Since Das and Chen (2007) found the relationship between aggregated
sentiment and index returns to be much stronger than the correlation for individual stocks,
a more conservative approach focusing on the specic domain of stockrelated messages
and their relationship with the market prices of publicly traded companies (rather than
relying on aggregated indices) is needed. Third, and most importantly, there is as yet no
research investigating the mechanism underlying the link between social media message
sentiment (e.g., tweet sentiment) and market prices. As a result, we do not know how
information diffuses in social media in general, and Twitter microblogging in particular,
leading to efcient information processing.
Our study addresses these limitations of prior related work and makes the following
three main contributions to the literature. First, unlike previous related research (e.g.,
Bollen et al., 2010; Zhang et al., 2010), we analyse only microblogging messages with a
© 2013 John Wiley & Sons Ltd
Tweets and Trades 927
direct reference to the stock market (i.e., we analyse only explicit stock microblogging
messages rather than all available Twitter messages as in prior studies) which allows us to
determine the predictive validity of stock microblogs without noisethat is unrelated to
the stock market, as has been the case in prior studies (e.g., Bollen et al., 2010; Zhang
et al., 2010).
Second, to the best of our knowledge, our study is the rst to comprehensively explore
the information content of stock microblogs with respect to individual stocks rather than
aggregate stock market indices. In contrast to related previous studies, this study is able to
go beyond the analysis of relatively simple measures of online activity (e.g., message
volume or word counts), instead leveraging an innovative methodology from
computational linguistics to evaluate the actual message content and sentiment.
Moreover, this study replicates and extends similar research in the context of internet
message boards without some of the previous methodological limitations (e.g., sample
selection, timeframe; Antweiler and Frank, 2004). We analyse a more comprehensive set
of stocks over the course of 6 months with fairly stable nancial market activity.
Third, in contrast to the existing research that has merely shown a correlation of online
message content with nancial market indicators (e.g., Antweiler and Frank, 2004; Bollen
et al., 2010; Zhang et al., 2010), our study goes beyond this correlational evidence by
providing an explanation for the underlying mechanism that leads to the efcient
aggregation of information in stock microblogging forums. Unlike these previous studies,
we explicitly exploit the structure of the microblogging forum Twitter,which allows us to
empirically explore theories of social inuence concerning the diffusion and processing of
information in the context of a nancial community, which is not possible in traditional
message boards (e.g., Antweiler and Frank, 2004).
In particular, the present study takes the following steps in extending the existing
literature on the relationship between social media content and stock performance using
the microblog forum Twitter. First, for comparability with prior related research (e.g.,
Antweiler and Frank, 2004), our study examines the relationship between the most
important and heavily studied market features return, trading volume, and volatility and
the corresponding tweet features message sentiment (i.e., bullishness),
message volume,
and the level of agreement among postings. Second, we empirically explore the
mechanism behind the efcient aggregation of information in microblogging forums.
Specically, we investigate the association between the quality of investment advice and
the level of mentions, the rate of retweets, and the authorsfollowership.
2. Related Work and Research Questions
2.1 Introduction to the research of online stock forums
One of the most intriguing sources of unofcial and qualitative information is the vast
amount of usergenerated content online. In the context of the stock market, internet
forums dedicated to nancial topics, such as internet stock message boards like Yahoo!
Finance, deserve special attention. A number of previous studies have investigated the
relationship between stock message boards and nancial markets. Wysocki (1998) was
the rst to investigate internet stock message boards. For the 50 most frequently discussed
We use the terms sentiment and bullishness interchangeably.
© 2013 John Wiley & Sons Ltd
928 T.O. Sprenger, A. Tumasjan, P. G. Sandner and I. M. Welpe

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