ANALYSIS
THE RISE
OF HIGHFREQUENCY
INFORMATION
Will the early bird always catch the
alpha and how are firms trying to
compete in this rapidly evolving space?
BY CARLY MINSKY
I
n 1935, Albert Einstein was troubled by a paradox.
Quantum entanglement, a consequence of Heisenberg’s uncertainty principle, violated the theory of
relativity, since measuring one entangled particle
would instantaneously change the state of its entangled pair, even when they were separated in space.
“Spooky action at a distance” as Einstein termed it – or
instantaneous communication between entangled particles
– involves a mysterious kind of information exchange faster
than the speed of light. Later analysis confirmed Einstein’s
fears: accepting this special ‘internal’ communication also
requires accepting that it is possible for information to travel
faster than the speed of light beyond this special case. And
that would contradict one of Einstein’s most important
results from special relativity, that no information or matter
can travel faster than light.
Instantaneous information exchange would dramatically
alter capital markets activity, so much so that it’s hard to even
begin to flesh out what a quantum market would look like.
What would perfect efficiency mean?
Fortunately for Einstein, most later interpretations of
quantum entanglement propose ways to resolve the paradox
without accepting that information can travel faster than
light. We may never get near instantaneous information
exchange, and so with the speed of light as a theoretical
latency limit, the time it takes to receive information will
depend in part on the distance it has to travel. Among other
factors, it is this distance – or rather, the ability to reduce this
distance – that can be responsible for disparities between
high-frequency traders that leave some with the edge.
But it’s not just high-frequency traders with an interest
in low latency. Even strategies that don’t require high-speed
execution could gain an advantage by extracting value from
data quicker than others also using or with access to that
data. And, to state the obvious, latency doesn’t just depend
on distance. The arms race between high-frequency traders
wasn’t just a battle of location, but of technology. According
to Mark Israel, principal in the asset management advisory
business of PwC, a similar battle is playing out in the sphere
of what he terms ‘high-frequency information’ – the new
frontier.
“Historically, most firms would operate on a daily cycle,”
he says. “Now what has changed is the speed of information.
The news cycle is no longer one day. Hourly or every 15
minutes it seems to change.”
Israel distinguishes between high-frequency data –
whether structured or unstructured – and information. By
his usage, information is the valuable results of data that can
be used in decision-making or automated actions. “How
do you take all this raw data that is thrown at you continually throughout a 24-hour cycle and then come up with this
high-frequency information to gain insights and hopefully
take action?” he asks.
The problem – of being able to make use of data in realtime, or learn something valuable from data in as close to
real-time as possible – splits into two branches. On the one
hand, even structured data from exchanges requires a normalisation process in order to compare, join or analyse it
alongside another feed or dataset which may well use a different structure and/or different labels and identifiers.
On the other hand, it’s increasingly common to use and
extract value from unstructured data like news articles and
social media feeds. Depending on the type or domain of an
unstructured data set, a semantic network, natural language
processing or a method of fuzzy matching is required to turn
it into intelligence that is compatible with structured data or
automated decision-making processes.
Media coverage around real-time data usage in hedge
funds, and particularly unstructured feeds like social media,
began appearing in 2011 when researchers at Indiana
University published a study titled Twitter mood predicts the
stock market. Attention on the prospect of real-time intelligence gleaned from noisy, messy feeds only heightened in
the following years.
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In 2012, Derwent Capital Markets made better than average returns using twitter sentiment and promptly shut down.
In an incident the following year, the stocks of two companies
that were featured in fake news shared on twitter plummeted
– long before the term ‘fake news’ had even taken root in public consciousness. With the rise of AI and machine learning
techniques, advances in the ability to extract signals, even from
unstructured data are accelerating. The real-time data and
analytics space is fast becoming commoditised. Last month,
Winton Ventures, part of David Harding’s Winton Group,
announced an additional £3.7m funding for Ripjar, a real-time
business intelligence platform it had initially invested in just
eight months earlier. In early April Options IT announced the
creation of OGAxess, a real-time platform for news.
Back in 2015, Matthew Granade, chief of Point72’s newly
created data team, told Fortune: “You have this explosion of
other independent real-time sources. It’s a lot easier to get to
[on-the-ground] truth. Overall, I think this is a golden age
for new investment data sources.”
As the bar is raised, it is no longer sufficient just to ask ‘can
we learn something useful from this data’ but ‘how fast can
we learn something useful?’
Nick Elprin, CEO of Domino Data Lab, recognises this
inherent problem with generating alpha from new information sources; it’s not always new. “All alpha sources
erode over time as information and techniques disseminate
through the market,” he says. “We’ve seen that the firms that
generate alpha and survive long-term are constantly innovating, investing in technology and developing new strategies.”
Israel calls this “the lifecycle of information” and believes
an arms race around faster data processing is already underway in response to alpha-erosion.
This applies also to market data; optimising the practical
value of real-time exchange data with better hardware and software to lower latency, improve connectivity and deliver data in
a usable form is not a new phenomenon. But the cost of being
at the cutting-edge has been prohibitive for all but a few players.
Tech vendors are now looking to fill that gap in the market. On 18 April Colt Technology Services announced a new
hardware solution for low-latency market feed normalisation
which would be more accessible for firms unable to make
huge technological investments.
The product, developed with NovaSparks, leverages the
low latency of FPGA hardware but as a mutualised offering for all clients, with a customisable software rack on top.
Although the software – which can be used for symbol
remapping or metadata calculations – does decrease latency,
it allows Colt to roll out the normalisation hardware at a lower
price than for a hard-programmed product designed for individual client needs. Since clients can customise the software,
they don’t need specialised developers who can reprogramme
the FPGA’s processing functionality when needed.
Ralph Achkar, Colt’s capital markets product director,
explains that most firms could not justify the cost of an
FPGA product to improve latency, unless they were in a very
active market processing huge amounts of data. But these
firms did express interest in an FPGA product without the
prohibitive costs. He says: “You get firms who are very latency sensitive and they will only take dedicated FPGAs, like the
big high frequency traders. But then you have everybody else
who are interested in this latency profile, and we can fill their
needs with this new offering.”
On the other end of the spectrum, the need for better computing power and low latency, but at a low cost without compromising much flexibility, is driving some into the cloud.
Although there are plenty of examples of cloud-based historic
data products, Achkar has yet to come across real-time data
providers running wholly in a cloud environment, or cloud
providers developing real-time feed functionality.
“With historic data you don’t have thousands of events per
second happening that you need to direct into and consume
from this virtual environment,” he explains.
“That is changing – the cloud providers are taking note and
a lot of people are looking at the question of how do I input
massive data into the cloud and how to I export massive data
out of the cloud. Historic data is simpler – you only have the
output from the cloud to solve, or consumption in the cloud if
users are doing analysis within the cloud environment. But the
next step is real time data.”
Verne Global, a data centre in Iceland,
The news cycle is no longer
is exploring partnerships to develop new
products that can accommodate advances
one day. Hourly or every 15
in “near-real-time data and analytics”.
minutes it seems to change”
Stef Weegels, business development
Mark Israel, PwC
director for finance and capital markets,
believes the future of data analytics is
through hybrid cloud solutions, where private cloud environments in a data centre like theirs are predominantly used
alongside burst capability in a public cloud solution for peak
demand. According to Weegels, Iceland will be a popular data
centre location due to lower costs.
“There are indeed opportunities to make this cheaper,
particularly from a power and energy security perspective,”
he says. “Iceland is unique in this regard with its abundance
of low cost geothermal and hydro-electric energy sources.
Iceland also benefits from a very reliable, modern, industrialscale grid network, with vast amounts of power available and
untapped, leading not only to very low power costs from a
TCO perspective, but also to a predictable energy supply for
the future.”
Not everyone is convinced that technological solutions
for data processing latency are really that valuable. Elprin, of
Domino Data Lab, compares trading to “grabbing pennies
out of a fountain”.
He says: “Speeding up latency by milliseconds lets you beat
the other hands to the next penny in the fountain. But speeding up research through collaboration lets you find complete
undiscovered fountains before anyone else does. And deploying new models faster is like running over to the new fountain
before anyone else gets there.”
Even in Elprin’s metaphor, speed is of the essence.
Undeniably, the future moves very fast indeed.
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