Conference Dailys TRADETech FX Daily 2019: Wrap-up | Page 14
THETRADETECHFX DA I LY
in-depth
Buy-side bursts bubble on
artificial intelligence hype
PANEL OF BUY-SIDE PRACTITIONERS AT THIS YEAR’S TRADETECH FX EUROPE CONFERENCE DISPEL THE
MYTHS OF PREVALENT USE OF AI BUT EMPHASISE WORK AROUND BRINGING DATA UP TO SCRATCH.
T
he hyperbole surrounding the use and
potential benefits of artificial intelli-
gence (AI) for the financial markets does not
portray an accurate representation of the
industry, according to panelists at TradeTech
FX Europe.
A panel of buy-side speakers said that
while there has been progress made with
various AI use cases and systems in produc-
tion by certain quantitative hedge funds, the
majority of firms, particularly among hedge
funds, are still only in the evaluation or
research phase.
The reality, according to Rafael Molinero,
CEO of Molinero Capital, is that most firms
are using AI on an ad-hoc basis but the
hype around AI means the truth becomes
obscured, while Richard Bateson, director
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THETRADETECHFX DAILY
Wrap-up
at Bateson Asset Management, said that the
larger buy-side funds with more resources
are perhaps in more advanced positions but
AI still only represents a fraction of what
they are doing.
However, there are areas in which AI holds
potential to improve processes and Sunil
Patel, a senior trader at APG Asset Manage-
ment, highlighted how his firm is currently
working on using AI within its pre-trade
analytics, although he qualified that it will
only be adopted “if there are clear patterns
that can be discerned”.
While there are aspects to machine
learning and AI that are already in use in
the industry for algo wheels and broker
allocation, Bateson said that there is a lot of
potential for AI to become more ingrained
within execution processes, at least where
available data was concerned. However, he
did acknowledge that there are challenges
with data work as well.
“It’s not just the cleaning of the data that
presents an issue, it’s the synchronisation
if you are using alternative data. What date
was it published? If you take something off
Bloomberg, some of it has been back-dated
and cleaned after the event it was actually
published. That’s a big issue,” he comment-
ed.
APG’s Patil highlighted the mundane side
of using AI, stating that “80% of the job is
cleaning the data” and commenting that
such work requires a significant investment
of time and resources to complete before AI
systems can even be started up.