[ 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
|
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
I N T E L L I G E N C E ]
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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