Dataminr is a social media information provider that aims to overcome challenges of disseminating data from sources like Twitter to assist with trading decisions. Using proprietary algorithms, it can alert clients early to breaking news from social media that may affect financial positions. The algorithms categorize users and tweets, approximate locations, and tag signals to topics to filter meaningful information for subscribers in real-time. While some events spread on social media before traditional sources, the company focuses on delivering qualitative news from on-the-ground sources that may be missed elsewhere.

1. iwww.profit-loss.com I Reprint: May/June 2015
Using Social Media
Is the Wait Over For
Useful Social Media Information Discovery?
US-based social media information provider Dataminr is the latest entrant aiming to overcome a number
of the challenges associated with disseminating the flood of data from various sources, including Twitter,
to assist with trading decisions, writes Nicola Tavendale.
T
here are 350-400 million people in social media talking
about events as they happen, according to Pradeep
Menon, chief revenue officer at Dataminr. “The scale of
that is huge,” he says. “The big challenge is that in that huge
volume of data, how do you pick the one or two signals that are
important for you?”
Founded in 2009, the firm offers finance feeds to hedge
funds and investment banks intended to provide actionable
signals in real-time, often from on-the-ground sources that may
be missed by mainstream news feeds. For example, on 17 April
the firm is said to have alerted its clients that Greek banks
abroad were being advised to exit Greek debt at 07:34 (UK
time). Other sources signalled the same event 11 minutes
later, by which time the EURUSD had dropped 30 pips.
The proprietary algorithms can alert trading desks and finance
professionals early to breaking news that may affect positions in
their portfolios, Menon claims. Key information is tweeted in real
time, picked up by the algos and then the “nugget of
information” is available to any subscribers to that relevant topic.
In order to achieve this, the algorithms use over 30 factors;
one of the factors is the source of information, where every
Twitter user is categorised into 12 different categories like
market commentator, government, major blogs, corporates,
etc. Another factor is the geo-location of the person. Less than
2% of Twitter users opt to geo-locate themselves, but Dataminr
claims it can approximate the locations of 65-70% of tweets.
Once a tweet has been deemed important, the next level in
Dataminr is the taxonomy associated with the signal – where a
signal is tagged to several broad topics, financial sectors or
individual corporates. “As a user you can say you are interested
in particular topics or sectors,” says Menon. “As and when
signals are detected in these topics we deliver them into users’
workflow via an API, mobile app, chat, web portal or by email.”
Menon adds, however, that in order to achieve this, the firm
has to have access to all the Twitter data happening in real
time and the science to be able to pick out the signal from the
noise and send it out to subscribers. In the current climate,
such a service could appeal to traders on the desk, portfolio
managers, analysts, COOs and risk managers, to name a few.
Furthermore, all the signals have different strengths (i.e., a
signal with a ‘flash’ is very important, perhaps received once a
month, with around 10 ‘urgent’ signals in a week, 10 ‘notables’
in a day and about 100 regular signals in a day). “But it’s not
about keyword matching as you would miss something
completely,” says Menon.
Dataminr has around 40 financial clients so far from major
organisations and hedge funds, but is expanding outside of the
US. Yet according to Menon, the aim is not just to beat
mainstream news sources such as Bloomberg but to deliver news
that sometimes happens on the ground that is not covered by the
news wires but could still be important for trading purposes.
The firm is also thinking of incorporating other data sets in
addition to Twitter such as real-time satellite information. While
it currently has a presence across the US, it recently reached
out to clients in London and continental Europe and will be
expanding into Asia.
Yet Menon also says it’s important to realise that Dataminr
does not generate content or give viewpoints. “In the case of
the SNB de-coupling there was no information on Twitter ahead
of traditional sources,” he says. “But there are plenty of cases
where the opposite is true and events break on Twitter first.”
The finance world is also keen to source a numerical output
from social media, something Dataminr says its backtesting
has proven inconclusive. “To date, we have focused on the
qualitative signals, we just haven’t gone into quantative
content as yet,” adds Menon.
In the coming months, the firm will be building analytics around
the real-time signals. This will be useful for clients looking to
incorporate historical patterns and correlations into the decision
making process, such as the ability to be alerted when a certain
cluster has a 5% increase in standard deviation from the mean.
“A sell-side institution could couple their internal research
with views from social media to derive some insightful
strategies to specific clients ,” Menon explains. “We supply
events as they break on the ground and you choose how to
incorporate that into your workflow.”
ISSN: 1467-2650 © 2015 P&L Services Ltd. All Rights Reserved. This article first appeared in: Profit & Loss Magazine, May/June 2015 | issue 159 | vol. 16