This article first appeared in Professional Investor, the official journal of the CFA Society of the UK

1. 26 | The Journal of the CFA Society of the UK | www.cfauk.org
Feature | Professional Investor
Web users’ interactions and commentaries in the social media
space can reflect current opinions, views and experiences, and
therefore contain helpful information for market research.
Consumers who profess stronger positive affinity with a certain
brand are likely to have a higher customer lifetime value, a
predictor of the net present value of profits from a customer
over the entire future relationship with him/her. The evidence
so far is too little to demonstrate consistent results. However,
this new avenue demands further investigation with advanced
statistical analysis and larger scale application.
INTRODUCTION
In the early days when internet search algorithms were being
developed, who could have imagined that search data could be
used to predict the future? Yet here we are in 2012 witnessing
it. Many organisations have found that data extracted from
specific searches can predict – or at least model the future.
The Bank of England (BoE) is just one organisation that is
convinced that appropriately interpreted search data can act as
an indicator of future economic trends. In June 2011, a team
of researchers from the BoE released a report illustrating how
Fernan Flores asks whether the analysis of tweets or other social media
postings could be a useful predictor of market movements, as it has been
demonstrated in the case of Google search data.
Tweet: “@cfauk – is it true that tweets
can help predict a stock price movement?”

2. The Journal of the CFA Society of the UK | www.cfauk.org | 27
Professional Investor | Feature
results extracted from Google search data could predict changes
in unemployment and even house prices.
Being not a fan of social media sites, I had never used
Twitter, a micro blogging site, until I read an article describing
it as the new Google. As a market research analyst and
consequently a fan of Google, I was intrigued and registered for
Twitter to see what the buzz was about.
Twitter has indeed a search function that allows anyone to
browse through tweets, postings or status updates, sometimes in
real time. In fact, the research results by seeking out key words
from tweets proved to be very useful when I undertook some
competitive intelligence work for a client to check about its
competitor’s customer service. This was quite a revelation.
Web users’ interactions and commentaries in the social media
space can reflect current opinions, views and experiences and
therefore contain helpful information for market research.
Could the analysis of tweets or other social media postings be
a useful predictor of market movements though, as it has been
demonstrated in the case of Google search data?
Derwent Capital, a company which was originally established
as a hedge fund that used consumer tweets in its trading strategy
but has now repositioned itself as a technology provider giving
traders and investor access to its proprietary platform, said in an
article published in August 2011 that based on its research and
testing of randomly selected unstructured data from Twitter that
its algorithm, which helps classify a tweet into a sentiment (e.g.
alert, vital, happy), helped predict movements in liquid stocks.
A similar strategy was replicated by the University of
Manchester and Indiana University in a research paper
(Bollen, Mao, and Zeng, 2010), showing that Twitter data
analysed for sentiment predicted around 87.6% of the
movements in the Dow Jones industrial average. The study
was based on an assumption used in behavioural finance,
which states that “financial decisions are significantly driven
by emotion and mood… therefore, [it is] reasonable to assume
that the public mood and sentiment can drive stock market
values as much as news.”
ANALYSIS
In order to explain unstructured tweets, many social media
monitoring and analytics (SMMA) firms like Derwent Capital
have developed algorithms that categorise tweets (or any social
media postings) as positive, neutral or negative. The tweets are
further classified so that words that express stronger emotions
are classified at the extreme ends of a Likert scale such as the
ones illustrated in Chart 1 above.
The hypothesis that social media can be a strong indicator of
financial performance is based on the principle that consumers
who profess stronger positive affinity with a certain brand will
have a higher customer lifetime value, a predictor of the net
present value of profits from a customer over the entire future
relationship with him/her. If a brand or organisation has more
customers with stronger positive (or less negative) affinity, it
should have a positive financial outlook, which is reflected
through a strong stock performance.
To prove this relationship at a basic level, I plotted the
proportion of positive and negative sentiments against
the closing stock price of Apple (see Figures 1 and 2) and
Microsoft (see Figures 3 and 4). Because of the volatility of
the data, especially the sentiments, I used the data’s three-day
moving average standardised with z-scores in order to
compare the movements in the stock price and the sentiments
more evenly.
Chart 1: Likert Scale
“Financial decisions are significantly
driven by emotion and mood…therefore,
it is reasonable to assume that the public
mood and sentiment can drive stock
market values as much as news.”
Apple annoys me!
I will never buy an
iPhone again.
My iPhone is
getting
problematic.
My iPhone is
working ok.
I enjoy using
my iPhone.
I love my new
iPhone! I strongly
recommend that
everyone buys one too!
1 2 3 4 5
Positive sentimentsNegative sentiments

3. 28 | The Journal of the CFA Society of the UK | www.cfauk.org
Apple (Jan - Dec 2011)
Microsoft (Jan - Dec* 2011)
Microsoft (Jan - Dec* 2011)
Apple (Dec 2011 - Jan 2012)
Feature | Professional Investor
As can be seen in Figures 1 and 2, the correlation coefficient
between the stock price and sentiments is very weak for Apple
and actually counter-intuitive as the positive sentiments trend
is negatively correlated with the stock price.
For Microsoft, a relationship seems to exist especially for positive
sentiments. As highlighted in Figures 3 and 4, there are days when
either the positive or negative sentiments clearly moved along with
the changes in stock price (as highlighted by the blue vertical lines).
While the accuracy of the technology developed by SMMA
firms in data mining has considerably improved over the years,
removing spam or filtering only relevant information remains a
challenge with the best technology achieving only an accuracy
level of between 75%-85% and the majority achieving an
accuracy level of between 50%-60%.
A deep-dive analysis of Apple verbatims reveals that a
considerable number of statements analysed refers to either
apple, the fruit, or apple juice. It is therefore not surprising
that the relationship between the sentiments and Apple’s stock
price hardly exists at all.
In contrast, the data mining technology has more accurately
analysed Microsoft given the uniqueness of the brand as
a term. The resulting correlation for Microsoft over a year,
however, remains weak. It could be possible that while
verbatims for Apple include irrelevant information, analysis
for Microsoft may have excluded tweets that refer to Microsoft
but have been omitted because consumers may have used their
own jargon when spelling the brand or have unintentionally
misspelled it (e.g. MS, Macrosoft, Mikrosoft, Microsof).
Figures 3 and 4:
Microsoft full year 2011 (positive and negative sentiments)
Apple (Jan - Dec 2011)
Apple (Jan - Dec 2011)
Microsoft (Jan - Dec* 2011)
Microsoft (Jan - Dec* 2011)
Apple (Dec 2011 - Jan 2012)
Source: Yahoo! Finance and Twitter
Figures 3 and 4. Z-scores of Microsoft’s closing stock price in NASDAQ versus z-scores of positive sentiments (Figure 3) and
negative sentiments (Figure 4) from Twitter. (Note that z-scores of negative sentiments are shown in reverse order as a decrease
in negative sentiment is expected to have a positive impact while an increase in negative sentiment is expected to have a negative
impact on a stock’s performance.)
Figures 1 and 2:
Apple full year 2011 (positive and negative sentiments)
Apple (Jan - Dec 2011)
Apple (Jan - Dec 2011)
Microsoft (Jan - Dec* 2011)
Microsoft (Jan - Dec* 2011)
Apple (Dec 2011 - Jan 2012)
Source: Yahoo! Finance and Twitter
Figures 1 and 2. Z-scores of Apple’s closing stock price in NASDAQ versus z-scores of positive sentiments (Figure 1) and negative
sentiments (Figure 2) from Twitter. (Note that z-scores of negative sentiments are shown in reverse order as a decrease in negative
sentiment is expected to have a positive impact while an increase in negative sentiment is expected to have a negative impact on a
stock’s performance.)
Apple (Jan - Dec 2011)
Microsoft (Jan - Dec* 2011)
Microsoft (Jan - Dec* 2011)
Apple (Dec 2011 - Jan 2012)
r = -0.26
r = 0.39
r = 0.14
r = -0.26

4. I conducted regression analysis and made various
combinations of analysis accounting for potential lag,
comparing the weighted average score of all sentiments (i.e.
rating extremely positive statements a 5, a relatively positive
statement a 4, a neutral statement a 3, a relatively negative
statement a 2 and an extremely negative statement a 1) and
comparing the net sentiment (i.e. the resulting proportion of
sentiments when negative is deducted from positive) but none
of the resulting analysis proved that the sentiments have a
strong relationship with a brand’s stock price.
With some effort, I manually cleaned hundreds of Apple
tweets (i.e. removing tweets that refer to apple, the fruit, or
apple juice) from December 2011 until January 2012. The
resulting comparison as shown in Figure 5 illustrates that
tweets that are more accurately filtered can potentially be
more effective in predicting a brand’s stock price, achieving a
correlation coefficient of 0.85.
CONCLUSION
While manually cleaned Twitter sentiments, at least for Apple
in this example, shows that consumer sentiment movements
movements can have a strong correlation to a company’s
stock price movements, the evidence so far is too little to
demonstrate consistent results. Clearly, this new avenue
consisting of exploitating Twitter or other social media websites
demands further investigation with advanced statistical analysis
and application on a larger scale to ascertain the relationship
between the two data sets.
With the rapid progress of technology in this field, especially
with search algorithms becoming more and more clever, it
is likely that the capability to demonstrate a correlation will
improve across time.
Can this work for non-consumer brands (e.g. BHP Billiton)?
Can sentiments on brands really have an impact on the stock
price of the company that owns them (e.g. PG tips, Bovril
and Persil owned by Unilever)? Can tweets from non-English
speaking countries and consumers, which are continuously
increasing in share as a proportion of total global tweets,
weaken or strengthen the relationship between sentiments and
stock price? These are just a few of the questions that we have
not even begun to address. Yet as technology develops, this will
spread into other compatible areas, geographies and cultures.
Given these issues, using tweets or any social media data
for trading strategy needs further exploration to strengthen
the case for it. But perhaps, based on Everett Rogers’ theory
of “Diffusion of Innovation” this may not be necessary for
innovators and early adopters – the consumer segments
which adopt technology ahead of the rest of the population.
Given the speed of technological innovation in data mining,
combined with advanced statistical analysis, I am confident
that using social media as a highly reliable predictor of stock
price movements can be achieved much sooner than expected.
However, when this point happens and when everyone else
starts to use insights from tweet sentiments for trading, then
the opportunity for arbitrage will have disappeared. ■
The Journal of the CFA Society of the UK | www.cfauk.org | 29
Professional Investor | Feature
Profile
Fernan Flores
Fernan Flores is a freelance market research
analyst and director at Zapienza, a Canary
Wharf-based market research consulting
firm that specialises in the technology and
finance sectors, which he established after
completing his MBA degree from the
Cambridge Judge Business School. Apart
from the technology and finance sectors, he
also does a considerable amount of work in
the not-for-profit sector and specialises in the
deployment of technology to solve healthcare
issues in developing markets. He has passed
the Level I exam of the CFA Program and
is a member of the CFA UK marketing and
communications committee.
Source: Yahoo! Finance and Twitter
Figure 5:
Apple 2 months December 2011 - January 2012
Microsoft (Jan - Dec* 2011)
Microsoft (Jan - Dec* 2011)
Apple (Dec 2011 - Jan 2012)
Source: Yahoo! Finance and Twitter
Figure 5. Z-scores of Apple’s closing stock price in
NASDAQ versus z-scores of positive sentiments using
data that are further filtered manually.
r = -0.85