A DV E RTOR IAL
aggregating the executions that are done at
the same time and then averaging. 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 ag-
gregating fills across multiple order books.
Perhaps a broker got the 1,000 shares by si-
multaneously removing 200 shares from five
venues. The average fill size would be 200
shares (or less depending on the structure
of the order books). But the broker captured
1,000 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
liquidity distributed across different venues
than it is from single venue. Again, it is easy
to measure this and reward the broker with
a larger liquidity capture metric, i.e. 1,000
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 re-
moved by incoming marketable orders. The
broker’s passive order could be filled all at
once, be filled over time, be partially filled or
go unfilled. Again, the broker has no control
over when incoming orders arrive, so why
would we think that measuring the average
fill size would be a good indicator of broker
performance? The broker does have control
over which venue they choose to post on,
and in some cases, they may choose to post
simultaneously on several venues.
A better performance metric would be
the time that it takes to get filled. We could
transform to volume time to allow compar-
isons between stocks that have different
liquidity characteristics. This would measure
the opportunity cost of getting filled passively
rather than crossing the spread and getting
an immediate fill. 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 booked their order and
on other venues where the broker may not
have placed a passive order.
To rectify the situation, 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
three milliseconds. We would then measure
the passive fills differently, looking at the
opportunity cost of booking passive orders
on a venue or group of venue.
Henry Yegerman, global head of sales,
LiquidMetrix
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. Luckily there
are alternatives that are straightforward to
compute and that provide deeper insights
into a broker’s performance.
THETRADE
LEADERS IN TRADING
AWARDS 2018
22 November 2018 | Savoy, London
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