[ I N - D E P T H
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A R T I F I C I A L
T
he application of artifi-
cial intelligence (AI) and
machine learning (ML)
technologies in financial services is
being increasingly positioned at the
vanguard of technology-focused
industry discussion and strate-
gies, as discussions focus on the
practical implications of deploying
such tools beyond middle- and
back-office functions. As margins
are squeezed and costs continue to
rise in light of fee compression and
an increased regulatory environ-
ment, the buy-side is increasingly
turning to AI and ML in the search
for further efficiencies.
There can be no doubt that huge
increases in data and trading
volumes that the buy-side is now
transacting, is forcing the need for
new technologies. While taking
part in a panel discussion at this
year’s TradeTech Europe con-
ference, AXA Investment Man-
agement’s global head of trading,
Daniel Leon, told delegates that
his firm has no choice but to invest
in new technologies because his
trading desk simply cannot keep up
with the sheer amount of data and
information required to maintain
its trading activity.
“We are not able to do what we
used to do 20 years ago,” said Leon.
“Yes, you can have a specialist on
leverage loan, but on the big credit
market or medium and small-cap
I N T E L L I G E N C E ]
“We have to reconstitute the experience that the
trader used to have: What has traded, what was
the liquidity and what was the market impact.
You can’t do that on a comprehensive basis.”
DANIEL LEON, AXA INVESTMENT MANAGERS
you cannot have all that informa-
tion on one guy. We are trying as
well to solve problems that we used
to do a long time ago. For the more
vintage traders it used to be that
the trader would know the market
and what’s traded for one month,
what happened last week, they had
information and that’s what typical
trading used to be.
“But now we have to gain effi-
ciency, we have to trade so many
bonds that you can’t ask one trader
to remember everything, to know
that this sector last week had this
event. We have to reconstitute the
experience that the trader used to
have: What has traded, what was
the liquidity and what was the
market impact. You can’t do that on
a comprehensive basis.”
BlackRock’s global head of
trading, Supurna VedBrat, echoed
Leon’s sentiment on the impor-
tance of AI and data for the future
of the industry at this year’s US
Fixed Income Leaders Summit in
Philadelphia. Focusing on fixed
income markets, VedBrat told
“The world isn't that flat and there are certainly
unusual things that happen in life every day
that don't follow the patterns and I am not sure
that we are 100% there, where the AI is able to
interpret all of those black swan events and build
them into a model.”
IAN MAWDSLEY, REFINITIV
64 // TheTrade // Summer 2019
delegates that not only will AI be a
key element in the next evolution
of buy-side trading operations, but
it will likely morph the role of the
buy-side trader in the process.
“Data science and AI give us the
ability to truly augment human
intelligence with computing power,
and you are able to do that at scale.
I think it is going to materially
change trading strategies that
the buy-side uses. You don’t need
human intelligence to pick trades,
so you can automate a lot of that
flow and the trader is now much
more of a risk manager overseeing
that the market is working the way
we expect, and if not, they have the
ability to step in and correct it,”
VedBrat said.
Ahead of the curve
Research from TABB Group earlier
this year has, in fact, suggested that
the buy-side is slightly ahead of the
curve in terms of AI adoption com-
pared to the sell-side and exchange
operators. Over 80% of asset man-
agement respondents stated that
they were at least in the planning
or research phase of implementing
AI, compared to 73% of their sell-
side counterparts and exchange
operators. At the same time, more
than 60% of buy-siders said they
expect spending on AI to increase
over the course of this year.
According to the research, the
majority of asset managers agree
that actionable insight is the
biggest benefit of deploying AI