[ 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
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“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
[ I N - D E P T H
technology, followed by increased
efficiency and automation, strategy
selection and risk management.
However, there is a false percep-
tion can sometimes be that AI and
ML are relatively new to institu-
tional trading; the truth is that both
buy- and sell-side organisations
have been exploring, developing
and implementing such technolo-
gies for many years now.
“The key takeaway from all of
this is that most capital market par-
ticipants are bullish on the use of
AI and big data in the near future.
It is high on the change agenda
at most firms, with the main use
case being around the investment
process, but also in trade execution
and operations,” the research from
TABB Group concluded.
As with most technology trends
though, hyperbole has a way of
dominating the discussion. Similar-
ly to the way blockchain exploded
into the financial markets’ con-
sciousness in 2016,
AI and ML have become industry
buzzwords, or at the very least a
misleading shorthand, that risks
overstating practical applications.
Ian McWilliams, investment
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A R T I F I C I A L
analyst at Aberdeen Asset Manage-
ment, detailed how the under-
standing of what ML technologies
are capable of is being distorted by
a lack of understanding and exag-
geration, during a panel discussion
at TradeTech FX Europe at the end
of last year.
“I joke that when you are adver-
tising externally you say AI, but in-
side you say machine learning and
actually you are just doing logistic
regression and things like that,” he
said. “I don’t think that’s disingen-
uous, maybe it’s a bit of hyperbole,
but it’s not wrong in terms of
definitions, because when we talk
about machine learning it really is
anything where you are getting an
algorithm to learn from data.”
“We’re taking a lot of market
signals and sentiment signals,
forecasting what markets are going
to do in the future and using those
to build trading strategies.”
McWilliams explained that the
hype around elements of ML such
as deep learning, image recognition
and natural language processing
(NLP) are distorting expectations
around what are essentially tools
to better model data for trading
strategy decisions, particularly
when it comes to conversations
with fund managers.
“The interesting thing we
need to think about as an
industry and maybe where
attitudes need to change is
around interpretability of the
models, which is a big ques-
tion in a lot of areas, not just
finance,” he said.
“Whenever we come out
with a trade a question we get
asked by the traditional fund
managers is ‘Why is it making
that trade?’ and they gener-
ally expect a very causal, A to B
explanation, but that often defeats
the point of these very complex
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algorithms. The middle ground is
not good enough to just say that
the algorithm says to do it, so we
are doing it, but there needs to be
more conversation between the
quant people and more traditional
people to understand there is a
trade-off there.”
Beyond the middle-office
As asset managers continue to
experiment with AI and ML, the
goal has always been to automate
manual and often repetitive tasks
for greater efficiency and cost
savings, freeing up time for traders
to focus on more pressing tasks or
complex order flow. But, accord-
ing to market participants and
technologists, the use of AI and
ML elements are now permeating
into more intricate parts of the
business. AI and ML are beginning
to show value when it comes to
pricing and seeking liquidity, chal-
lenges that are often highlighted
by buy-side traders in the current
market conditions.
“The simple trade automation,
the idea of creating rules to take
some of the more liquid or easier to
trade orders off the books, makes
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