Conference Dailys TRADETech FX Daily 2018 Wrap-up | Page 12

THETRADETECHFX DA I LY
in-depth
TECHNOLOGY
Ignore the hype and focus on
machine learning objectives,
say buy-side tech experts
INDUSTRY BUZZ AROUND MACHINE LEARNING AND AI MUST BE SET ASIDE IN FAVOUR OF TRANSPARENCY AND INTERPRET-
ABILITY ACCORDING TO TRADETECH FX SPEAKERS.
A
dvancements in machine learning and
artificial intelligence (AI) may be improving
trading strategies, but at the cost of transpar-
ency and interpretability, according to panellists
at this TradeTech FX Europe.
Ian McWilliams, investment analyst at
Aberdeen Asset Management, detailed how
the understanding of what machine learning
technologies are capable of is being distorted by
a lack of understanding and exaggeration.
“I joke that when you are advertising exter-
nally you say AI, but inside you say machine
learning and actually you are just doing logistic
“The interesting thing we need to
think about as an industry and
maybe where attitudes need to
change is around interpretability
of the models.”
IAN MCWILLIAMS, ABERDEEN
ASSET MANAGEMENT
12
THETRADETECHFX DAILY
Issue 2
regression and things like that,” he said. “I don’t
think that’s disingenuous, maybe it’s a bit of
hyperbole, but it’s not wrong in terms of defi-
nitions, 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 machine learning such as deep
learning, image recognition and natural lan-
guage processing (NLP) are distorting expecta-
tions 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 mod-
els, which is a big question 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 generally expect a very causal, A to B
explanation, but that often defeats the point
of these very complex 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.”
Saeed Amen, founder of trading consultancy
Cuemacro and veteran of developed systematic
trading strategies for Lehman Brothers and
Nomura, extolled the virtues of simplicity when
approaching the adoption of trading strategies
based on machine learning technologies.
“The question to ask when you are thinking
about using machine learning is: What are
you trying to achieve, and can you use logistic
regression or linear regression, for example, as
sufficient for your task?” Amen said. “I would
always try to go for the simplest tool necessary.
There needs to be a rationale for the complexity
involved in the trading strategy.”