[ I N - D E P T H
placements, even back then, going
through the framework and we
are rolling it out globally as well,”
Venkatraman said on the webinar
alongside Refinitiv. “In many ways
we have been there and we've been
optimising that wheel over time.
We are trying to think about this
more holistically. It's really about
being data-driven in the sense that
wherever we look at all functions
of trading, there are different
aspects to it and it is about trying
to leverage that data in the most
appropriate way.”
Taking part in a keynote dis-
cussion at this year’s TradeTech
conference, Antish Manna, head of
execution research at MAN GLG,
said that the firm went live with a
machine learning-based frame-
work for order flow and broker
allocation last year.
“This framework effectively
takes away the need for human to
set an arbitrary target for ‘my first
three brokers are going to get this
amount of flow’ and continuously
updating that target to having a
machine that automatically does
that”, Manna explained.
“The beauty of it is that it
becomes a very clean conversa-
tion with our brokers; they know
how we are doing things and that
they will get more flow, and this
machinery also adapts to changing
market conditions.”
Man and machine
Despite all of the potential ben-
efits that may be realised, there
are significant obstacles when
it comes to deploying AI or ML
processes, mainly in the form of
compliance hurdles, transpar-
ency concerns and the build vs.
buy dilemma that most firms will
consider at some point during im-
plementation. Firms are urged that
they must engage with compliance
departments when undertak-
|
A R T I F I C I A L
ing any technology project, and
the importance of continuously
assessing the model to overcome
some of those transparency barri-
ers is paramount.
The adoption and successful use
of AI or ML comes with a signif-
icant resource cost attached, and
as such, firms that expecting to
realise quick results will be sorely
disappointed unless they are pre-
pared to play the long game.
Addressing these unrealistic
expectations, MAN GLG’s Manna
said that the majority of time
spent on machine learning proj-
ects is used to clean data before
research and development can
I N T E L L I G E N C E ]
“Technically there is a bit of
truth that everything could be
automated, there is no doubt
that most processes could be run
without a human being, certainly
within our industry,” Mawdsley
explained. “The point is that the
world isn't that flat and there are
certainly unusual things that hap-
pen in life every day that don't fol-
low 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.
“There is an element that says
the human brain is trained to
deal with these outliers, and the
“On the machine learning and AI side of things,
problems are best solved by teams of people,
because you need the challenge, rigour and time
to learn and fail, learn and try again; that process
takes a lot of time.”
ANTISH MANNA, MAN GLG
take place, and that those firms
that are only now starting their
journey with machine learning
should be not expect to see results
in the short-term.
“The truth is, it is a fallacy and
it takes a huge amount of time to
build a framework where you can
deliver things at scale that work,”
he said. “On the machine learning
and AI side of things, problems
are best solved by teams of people,
because you need the challenge,
rigour and time to learn and fail,
learn and try again; that process
takes a lot of time.”
Another significant challenge
is in finding the right balance be-
tween man and machine, as mar-
ket participants and technologists
attempt to dispel the myth that AI
and ML is even close to replacing
the human buy-side trader.
machine giving help to follow
those patterns is probably where
you want to be… It's about using
technology to make more informed
trading decisions and as such
that means not fully automating
everything at all and ensuring that
we are bringing in an element of
human intelligence, but we are
presenting people with options.”
As data becomes more readily
available and the size of that data
continues to grow, there is little
doubt that asset managers imple-
menting AI and ML technologies
are at the forefront of the future
of buy-side trading, and this is
happening now. As some funds
struggle to adapt to the changing
trading landscape, others are ready
and willing to seize the opportu-
nity using AI and ML to overhaul
traditional trading processes.
Issue 60 // TheTradeNews.com // 67