THETRADETECH DA I LY
in-depth
THE OFFICIAL NEWSPAPER OF TRADETECH 2019
THETRADETECH DAILY
in-depth
THE OFFICIAL NEWSPAPER OF TRADETECH 2019
Industry slams regulatory ploy to
shift trading volumes to lit venues Patience is a virtue when it comes
to machine learning say experts
MARKET PARTICIPANTS AGREED THAT TRADING VOLUMES HAVE FAILED TO SHIFT TO LIT VENUES UNDER MIFID II, BUT THE INDUSTRY EXPERTS TALK DOWN IMMEDIATE RESULTS FROM MACHINE LEARNING AND WARN THAT THERE ARE NO SHORT-
REGULATION HAS LED TO UNINTENDED POSITIVE DEVELOPMENTS IN THE FORM OF PERIODIC AUCTIONS. CUTS TO SUCCESS.
S
everal industry veterans have criticised
efforts by policy makers in Europe to force
more trading volumes from dark to lit venues
under MiFID II.
Senior market participants from European
exchanges and the sell-side agreed during a
panel discussion that the central aim of MiFID
II to shift activity to on-exchange venues was
misguided, although it has led to positive
innovation within the industry.
“I was surprised that the unintended conse-
quences of MiFID II have actually been positive.
Before MiFID II was implemented, we were all
convinced that it would have a negative effect
on markets, but instead we’ve seen positive
innovation driven by commercial need,” said
Richard Semark, CEO of UBS MTF.
“The policy’s objective has been to move
trading to lit markets, with no backing or basis
for that. But the reality that we see as market
participants is that lit venues are often not
the best place trade, so other venues have
come into the marketplace because we are still
committed to getting the best outcome for our
investors.”
Similarly, Mark Hemsley, president of ex-
change operator Cboe Europe, told delegates
that investors are constantly seeking low
impact execution, previously provided by broker
crossing networks, which have been shut down
under MiFID II. The closure of broker crossing
networks has seen a rise in the use of alterna-
tive trading venues such as periodic auctions
“The policy’s objective has been to move trading to lit
markets, with no backing or basis for that.”
RICHARD SEMARK, UBS MTF
and systematic internalisers (SIs).
“If you speak to the buy-side and brokers,
there’s always been a genuine need to execute
in low impact ways through dark pools or
broker crossing networks. Those venues have
clashed with regulators forcing the political
drive to move everything towards a lit environ-
ment,” Hemsley said.
“But the industry finds a way around that.
The problem we saw moving from MiFID I to
MiFID II was the incremental approaches to
dark trading, and the artificial double volume
caps have led to a surge in activity within the
SI environment and periodic auctions, ulti-
mately detracting from lit liquidity. Regulators
need to take a more fundamental approach.”
The regulatory aim to shift volumes to lit
markets was a keen topic of discussion at
TradeTech Europe this year, with Citigroup’s
head of European market structure, Jame
Baugh, stating on a panel session during the
first day of the event that navigating the new
trading landscape has been a huge effort for
the industry.
“A lot of innovation, time and resources
have been spent on looking at ways of sourc-
ing liquidity, connecting to SIs, looking at
periodic auctions and navigating the liquidity
that they can provide,” Baugh said. “The
unfortunate outcome is that the channel shift
has been fairly muted. When we look at the
percentage of business that has migrated to
lit venues, it has been relatively small.”
On lingering concerns about the UK’s impend-
ing exit from the European Union, some of the
panelists agreed that the long-term impact of
Brexit could be positive in terms of increasing
competition. But others reiterated the need
to find sufficient harmonisation with some
form of equivalence to preserve the UK’s right
outside of the EU to be more creative.
“It’s about finding that balance because ulti-
mately we want integrated capital markets.
I agree that increased competition will be
a positive, but we don’t want to jeopardise
equivalence,” Rob Boardman, CEO of Virtu Ex-
ecution Services for EMEA at Virtu Financial,
said.
C
apital markets firms that are looking to
implement machine learning and artificial
intelligence (AI) systems within their trading
processes must be prepared to undertake a
multi-year project that requires significant
patience before seeing results.
Industry experts taking part in a keynote inter-
view outlined how their firms had approached
machine learning projects and warned that
those expecting immediate results from such
endeavours would most likely be disappointed.
Antish Manna, head of execution research
at MAN GLG, part of MAN Group, said that the
firm went live with a machine learning-based
framework for order flow and broker allocation
last year.
“This framework effectively takes away the
need for humans to set an arbitrary target for
14
THETRADETECH DAILY
“There is a perception
that you can hire people
and have meaningful, AI-
based outcomes…it doesn’t
work that way.”
SHARY MUDASSIR, RBC CAPITAL MARKETS
‘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 conversation 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.”
Manna explained that although the hype
around machine learning has grown to a point
where expectations are now becoming unre-
alistic as to what the technology can achieve,
starting with a relatively simple element such as
broker allocation means the firm can build out
the framework to take on more expansive and
intuitive projects in future.
Representing the sell-side was Shary Mu-
dassir, co-head of global equities execution at
RBC Capital Markets, who agreed that industry
perceptions around machine learning were
often false, particularly around how long such
developments take to complete and the amount
of time it can take to acquire the required
expertise.
“There is a perception that you can hire people
and have meaningful, AI-based outcomes…
it doesn’t work that way. Real success with AI
requires very large teams,” he said.
“At RBC, we’ve got in our AI research team
over 100 AI scientists. Within the applied AI
space we have over 300 data scientists on the
bank side. On the equities execution team now,
for the most recent product we will be rolling
out at some point over the year, we have a team
of over 60 people, and these 60 people are
not all AI scientists – these are sales traders,
traders, quants, execution consultants, technol-
ogists…they have all come together over that
period of time to deliver on one big outcome.”
Addressing those unrealistic expectations,
Manna said that the majority of time spent
on machine learning projects is used to clean
data before research and development can 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.”
Issue 2
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