FOREIGN EXCHANGE
Our desk trades a high number
of tickets across various locations
globally.
Since we are a small team, we try
to automate as much trading flow
as possible. Today, around 90% of
our trades are executed box-to-box,
with traders using our exception
management tools to monitor trade
flow efficiently and accurately. Each
investment programme has its own
alpha profile, so an execution strategy
is needed for each of them.
Due to the nature of our business, we
use execution algorithms where we
can to execute our flow.
The algorithms used need to
complement the strategies’ alpha
profiles. We use input from both our
execution research team and the
dealing desk to come up with the most
suitable algorithm for each strategy’s
risk profile. We find that algorithms
allow seamless market access with
extremely auditable pre- and post-
trade Transaction Cost Analysis (TCA).
We use a combination of both
proprietary algorithms and broker/
bank provided algos.
A trader’s day at Aspect is extremely
varied and changes day to day, but the
figure below gives a rough idea of how
I split my time:
40
To what extent is your role dependent
on data and do you believe that any
forms of premium data-feeds are or
potentially may become a ‘weapon’
of choice to compete in the future of
capital markets?
Data is the new Gold! Our whole
systematic approach from signal
generation to trade execution is data
driven. At Aspect, data is key as we rely
on computers to identify investment
opportunities. We are extremely
diligent with the data that we collect
and use: it has to pass through various
stages of review and approval (including
from our legal department) before it can
be used.
Data will continue to be consumed in
many forms and will most definitely
continue to enhance the investment
process. On the trading desk, new forms
of data give us better granularity in pre-
and post-trade TCA, especially in more
opaque markets like FX Forwards, IRS
etc.
Thoughts on Algorithms?
There are only a few types of distinct
algos out there, currently pegged, time-
slice, VWAP, limit based, implementation
shortfall and, more recently, basket
algorithms. I think that these algos will
www.buysideintel.com
continue to develop as clients’ needs
adapt to the ever-changing landscape,
particularly in FX markets. External algos
will always have the challenge of having
to cater for a wide range of needs
and alpha decay profiles for instance,
whereas internally developed algos can
factor this information in right from the
start. Bank algos can be useful when the
price of developing in-house or buying
an entire Execution Management
System (EMS) is too expensive. You
will also need to weigh up the cost of
manual/naive execution vs the cost of
technology enhancements.
The Holy Grail of every market player
is to have the ability to reliably forecast
the future price at a given horizon. I
don’t know of any external algo out
there currently who offer this sort of
alpha capture. In truth, if they have
predicting power, will they pass it on to
3rd parties?
Which areas of technology innovation
do you foresee will make a significant
change to buy-side trading in the
next five years?
It is more and more common to see buy-
side firms seeking to eliminate ‘keying
risk’ when executing orders and this area
will continue to grow quickly. We built
our own internal Order Management
System (OMS) and EMS a few years
ago, but we have now partnered with
Quod Financial who will be handling the
EMS-side going forward. Maintaining
your own EMS systems can be costly
and can consume a lot of developers’
time, which could be spent on more
important tasks like strategic projects
benefitting the firm’s wider objectives.
Connectivity craftsmanship is also
expensive and time consuming from a
research point of view. When creating
your connectivity, you need to consider
the microstructure and quirks of each
venue. This task takes time, is expensive
and changes all the time. So, the build
vs buy conundrum will continue over
the next few years.
Algorithms will continue to improve.
Tier 1 dealers have long used real-time
visualisations to monitor their own
hedging behaviour, plots paid/given
Winter/Spring 2020