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L E A D E R S H I P
two outcomes – in both cases the
broker got us our 1,000 shares.
We can easily fix this problem by
aggregating the executions that are
done at the same time, and then
compute the average size. When
a broker removes 10 orders of 100
shares at the same time, it should be
equivalent to the broker getting an
average fill size of 1,000 shares, not
100 shares. It is very easy to make
this adjustment and is much fairer
to the broker being measured.
We can also extend this concept
by aggregating fills across multiple
order books. Maybe a broker got
the 1,000 shares by simultaneously
removing 400 shares from five ven-
ues. The average fill size would be
400 shares (or less depending on
the structure of the order books).
But the broker captured 2,000
shares of liquidity and should get
credit for that, possibly even more
credit than when executing versus
a single order book because it is
more difficult to capture passive li-
quidity distributed across different
venues than it is from single venue.
Again, it is easy to measure this and
reward the broker with a larger
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S P O N S O R E D
liquidity capture metric, i.e. 2,000
shares in this example.
Next, we can consider the case
where a broker posts 1,000 shares
passively in either a lit order book
or a dark pool. The broker has
no control over when liquidity is
removed by incoming marketable
orders. The broker’s passive order
could be filled all at once, receive a
series of partial fills over time or go
unfilled. Again, the broker has no
control over when incoming mar-
ketable (active) orders arrive, so
why would we think that measur-
ing the average fill size would be
a good measure of broker perfor-
mance? The broker does have con-
trol over which venue they choose
to post on, and in some cases, they
may choose to post simultaneously
on several venues.
A much better performance met-
ric would be the time that it takes
to get filled. We could transform
to volume time to factor in the
prevailing market conditions. We
could also measure how much
volume traded at the same price
(or better if we are in a dark pool)
on both the venue the broker has
B Y
L I Q U I D M E T R I X ]
booked their order and on other
venues where the broker may not
have placed a passive order. Did
the broker choose the right venue
to post?
To provide more relevant liquid-
ity capture analytics, we would
first consider the active fills and
aggregate them by time (down to
the millisecond or better) to get
an effective liquidity capture that
recognises all fills that occur at the
same time, whether it is 10 fills of
100 or one fill of 1,000. We could
also specify a time window, for
example all fills within 300 micro-
seconds. We would then measure
the passive fills differently, looking
at the opportunity cost of booking
passive orders on a venue or group
of venue.
It is a complex market structure
and applying simplistic metrics
such as average trade size does
not adequately measure what it
purports to measure, i.e. a broker’s
ability to capture liquidity. Luck-
ily there are alternatives that are
straightforward to compute and
that provide deeper insights into a
broker’s performance.
Figure 1 On the left, we show a chart that stacks individual fills of various sizes for each time. The average size is shown
in the title and is a result of averaging all the individual fills without aggregating by time. On the right, we aggregate
the all the volume that is stacked and then compute the average. If there is more than one fill in a time window, the
average on the right will be larger than the average on the left. This method can be used to compute the average fill
size of active volume to measure a broker’s ability to capture passive liquidity.
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