about the pitfalls of Twitter, and they might point to recent gaffs by a
prominent South African politician as evidence that the dangers outweigh its
benefits. But such warnings have not stopped many others, most notably the
president of the United States, from tweeting on a regular basis: Twitter’s
user base creates more than 500m tweets a day, and it added about 2m new users
in the last quarter of 2016.
this wealth of information must have some value. Twitter, sadly for its
shareholders, struggles to turn such growth into profit: in the last quarter of
2016, revenue growth was only 1%. But because it captures public sentiment at a
very granular level, it has attracted the interest of both scientists and
entrepreneurs hoping to turn this information into public or private benefit.
The use of
social media for prediction is, of course, not a recent phenomenon. Google Flu
Trends, founded in 2008, used Google’s search engine to track the spread of flu
in 25 countries. But excitement about the project waned as it struggled to make
accurate predictions. A 2014 Nature paper noted the value of social media “big
data”, but warned that “we are far from a place where they can supplant more
traditional methods or theories”.
though, seems to attract increasing attention. Another 2014 paper uses Twitter
to predict crime. A 2015 paper shows how psychological language on Twitter
predicts heart disease mortality. Another 2015 paper shows how Twitter
sentiment predicts enrolment of Obamacare. A 2016 paper shows how Twitter could
be used to predict the 2015 UK general elections.
But it is,
understandably, the financial markets that have attracted the most attention. A
2016 paper by Eli Bartov (NYU Stern School of Business), Lucile Faurel (Arizona
State University) and Partha Mohanram (University of Toronto) shows how Twitter
can predict firm-level earnings and stock returns. They used a dataset of
nearly 1m corporate tweets by 3 662 firms between 2009 and 2012, all tweeted in
the nine-trading-day period leading to firms’ quarterly earnings announcements.
find, unsurprisingly, that the tweets successfully predict the company’s
forthcoming quarterly earnings, but find, surprisingly, that the tweets predict
the “immediate abnormal stock price reaction to the quarterly earnings
announcement”. These findings are more pronounced for firms in weaker
information environments, such as “smaller firms with lower analyst following
and lower institutional ownership”, and are not driven by concurrent
information from sources other than Twitter, such as press articles or web
sense that corporate communication provides information, but can public
sentiment on Twitter also inform market activity? A 2017 NBER Working Paper by
Vahid Gholampour (Bucknell University) and Eric van Wincop (University of
Virginia) answers this question by looking at the euro/dollar exchange rate.
with all Twitter messages that mention EURUSD in their text and that were
posted between 9 October 2013 and 11 March 2016. There were 268 770 of these
messages, or an average of 578 per day. What they hope to do is identify
whether informed opinions about future currency changes can actually predict
actual currency changes, so they eliminate all tweets that do not express a
sentiment about the future behaviour of the two currencies. This reduces the
sample to 43 tweets per day, or 27 557 in total.
classify each of these tweets as positive, neutral or negative using a detailed
financial lexicon that they developed to translate verbal tweets into opinions,
and create a Twitter Sentiment index for each day. They also split the sample
in two: those opinions expressed by individuals with more than 500 followers,
which they call the “informed opinion”, and those with fewer than 500
followers, which they call the “uninformed opinion”.
So what do
they find? It turns out that the 633 days of data they have is too short to
calculate the Sharpe ratio, a measure of the risk-adjusted return. The
annualised Sharpe ratio based on daily returns is 1.09 for the informed group
and -0.19 for the uninformed group. The Sharpe ratio of 1.09 for the informed
group is impressive, but it has a large standard error of 0.6. The 95%
confidence interval is therefore very wide, ranging from -0.09 to 2.27. They
then construct a model with a precise information structure, estimate the
parameters and then recalculate the Sharpe ratio to average at 1.68 with a 95%
confidence interval between 1.59 and 1.78. Success: “The large Sharpe ratios
that we have reported,” they conclude, “suggest that there are significant
gains from trading strategies based on Twitter Sentiment.”
If all this
sounds terribly complicated, that is exactly the point. Translating opinions
into numbers is not an easy undertaking, and discerning the “informed” opinions
from the noise is even less so. But there is no doubt that Twitter does offer
some useful, perhaps even lucrative, insights. Whoever can exploit that
knowledge first, stands to benefit most.
Johan Fourie is associate professor in economics at Stellenbosch University.
This article originally appeared in the 20
April edition of finweek. Buy
and download the magazine here.