[ I N - D E P T H
LIS too. Systematic Internalisers,
however, are very different beasts
and it is taking much longer for
the buy-side to get comfortable
interacting with them.
Bank and ELP type SIs are
obviously very different from each
other and within each of those cat-
egories there are also distinct dif-
ferences. Within the ELP category
you have SIs which make markets
off the back of their own strategies,
others that make markets off the
back of their exchange-traded fund
(ETF) business, and then the more
high-frequency trading (HFT) type
SIs that don’t want to hold their
risk for long at all and look to un-
wind reasonably quickly. You’ll see
different prices, reversion rates and
different sizes from each of those.
It’s important to understand that
it’s not simply a case of “SIs are
good” or “SIs are bad” and “an ELP
SI is good” or “an ELP SI is bad”.
You need to understand what their
models are, what they offer, how
they are different and which best
suits your trading strategy. We met
with all of the ELP SIs that we have
switched on, as well as ones that we
decided we didn’t want to interact
with due to them not providing the
type of liquidity that we wanted
to access. We tried to understand
their models as best as an outsider
ever can, because obviously there
is a lot information that they don’t
necessarily want to share with us.
We obtained as much data as pos-
sible from the SIs themselves, but
also from brokers who had already
interacted with them both in their
SI format, but also when they were
operating within their BCNs last
year. A difficult component to this
is that if you just use broker data
on SIs, then one ELP SI could look
very good according to one broker’s
data, but then look very different
on another broker’s data due to the
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way each interact with their liquidity. That’s difficult
for the buy-side to manage, and that’s why we try to get
as much data from the SIs themselves as well so that
we can question the brokers on their smart order router
logic.
We have interacted with a number of SIs this year,
both bank and ELP style. Now that we have our own
data set we continue to analyse each liquidity source
as this is a constantly developing picture. The data will
ultimately decide which of the venues thrive and which
cease to exist. The more data we get, the easier it will
become for the buy-side to select SIs to interact with.
BS: I think it’s important to understand that this isn’t
a static landscape. As one comes to an opinion based
upon a selection of data, that doesn’t necessarily mean
that particular experience will hold in the future. I
think you see changes in individual participants or
destinations that arise as the market evolves. In times
of volatility, for example, you will see some changes in
strategy and the providers may begin to model more
on those that are interacting with them. So market par-
ticipants are feeling each other out
in a way to try and understand and
then responding. With that in mind,
it can be difficult to make static
decisions around the use of SIs in
general, or individual providers.
At Jefferies, we have scoring
mechanisms that we apply to each
trading venue we trade with - that
can be ELP SIs, broker SIs, even lit
order books - for a dynamic quanti-
tative model that ranks each venue
in real-time. When it comes to the
time we need to trade, we know
from our short-term experiences
what to expect in our outcome. We
have actually seen a number of the
market maker pools that we trade
on drop below some of the regular
lit markets in terms of their rela-
tive priority. We aren’t triggered
or incentivised by the fact that it’s
Ben Springett
free to trade on these venues for
head of European electronic and
brokers, and we don’t have a cost
program trading, Jefferies
“It’s important to understand that this isn’t a
static landscape.”
BEN SPRINGETT
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