MKT_Broker_Analysis_Measuring_Execution_Quality_in_a_Fragmented_Market(US)_March2015_report

Measuring Execution Quality
in a Fragmented Market (US)
MARKIT BROKER ANALYSIS
March 30, 2015

Measuring Execution Quality in a Fragmented Market (US) – March 30, 2015 2
Has Implementation Shortfall
exhausted its usefulness in
evaluating execution quality for large
equity orders?
This report is an update of a previously published report that focuses on brokers
and their ability to minimize the price impact required to execute an institutional size
order. The report introduces a unique set of execution quality metrics designed to fill
the gap left by ordinary TCA benchmark analysis. These measures pioneered by
Markit’s trading analytics group untangle the interaction of a client’s orders in the market
with the interactions of others in the market to isolate the cumulative price impact of
acquiring liquidity.
Of particular interest in this report are those orders that demand more than 5% of
a stock’s average daily trading volume. This study confirms that brokers are having
differing levels of success in leveraging their trading talent and technologies to minimize
the cost of acquiring liquidity. The report also reveals the glaring inadequacy of relying
on IS measures which effectively act as a proxy for execution interval momentum. Using
IS to judge execution quality is like using the visible coastline to understand the continent
that lies beyond it.

This report update applies a number of innovative measures developed to more
precisely evaluate the sell-side’s ability to deliver value to Markit clients through effective
liquidity management.
The simple VWAP and Implementation Shortfall benchmarks relied on by traditional TCA providers fail to deliver
any reliable insight into the efficient sourcing of liquidity. Both of these popular benchmark-driven measures are
overwhelmed by the noise of short-term market drift and the influence of competing orderflow in a security. Though
the influence of market related drift and short-term alpha require significant attention from traders, they are distinctly
different from judging the impact and effectiveness of a client’s liquidity sourcing for their own executions.
To better measure successful liquidity management,
Markit has developed four unique analysis metrics that
we use to examine each order in our universe:
—— Cumulative Liquidity Charge® (CLC)
—— % of Order/Overall Adverse Ticks
—— Order Average Trade Size (ATS) as a % of
Interval ATS
—— CLC Forecast Error
Our ongoing research into execution quality using
these metrics confirms that there are strategies and
execution providers that consistently deliver superior
trading outcomes. This achievement results in lower
realized cumulative price impacts, reduced adverse
tick exposure, high relative fill sizes and positive CLC
Forecast Errors. Consistent success across these
metrics requires that a firm optimize their trading
technology,address liquidity fragmentation and
neutralize aggressive trading competitors.
Introduction

High Frequency Trading competitors continue to capture the market’s imagination.
The introduction of decimalization, proliferation of
trading venues, material declines in latency and
increases in volume are just a few of the issues
that reinforce this fascination. Since liquidity is not
directly observable, precise definitions are elusive.
Consequently, the numbers used to characterize
and evaluate these topics are often inadequate and
occasionally deceptive. Since this report was first
issued in 2011, relative order sizes (measured as a
percent of ADTV) have shifted to smaller relative sizes
both in number of orders and the value of the orders as
can be seen from the Order and Value Distribution by
Liquidity charts below.
Of continued debate among traders is the appropriate
way to characterize the complex dynamics of individual
stock ‘liquidity’. The number, motivation, access,
investment horizons, capital and risk tolerance of
market participants materially influence market liquidity.
The most common liquidity measures are related to
trading volume, but others examine quote depth, trade
velocity and bid/ask spreads. Intuitively, liquid stocks
exhibit high trading volumes, large quote depths, high
numbers of transactions per unit of time and narrow
bid/ask spreads.
Order distribution by liquidity
Liquidity 2013 study Liquidity 2014 study
0-0.005% 0.005-0.05% 0.05-0.5% 0.5-5%
%oforders
5-10% 10-25% >25%
50%
45
40
35
30
25
20
15
10
5
Value distribution by liquidity
Liquidity 2013 study Liquidity 2014 study
0-0.005% 0.005-0.05% 0.05-0.5% 0.5-5% 5-10% 10-25% >25%
50%
45
40
35
30
25
20
15
10
5
%ofExecutionvalue
The challenging liquidity landscape

The Order Distribution by Liquidity chart above shows
that buy-side traders have continued to parcel orders
into smaller and smaller trade sizes over the last few
years. Over 30% of orders in the 2014 sample are
between 0 and 0.005% of daily volume. This trend
confirms that desks are adding more active liquidity
management to their trading process. It also shows that
investment managers are disseminating orders to the
market in less chunky sizes as they continually become
aware of the dangers of information leakage and
predatory trading. Accordingly, the Value Distribution
by Liquidity above shows an increase in notional value
traded for orders ranging from 0. 5% - 5% of day’s
volume, while larger orders between 5% and 25% of
day’s volume saw a stable trend.
Trading volumes drive much of the perception of
liquidity in today’s equity market. Increasingly, traders
differentiate conceptually between the sources of the
transactions in the market. A quote from a few months
ago sums up the concerns on this issue, “Liquidity is
the depth of the market as expressed by the number
of unique shares available to transact. If the same
two people pass a thousand shares back and forth a
thousand times a minute, it looks like a million shares
of volume on the tape. But there are only a thousand
shares of liquidity. That distorts market signals.”
As the above quote suggests, the emergence of
high frequency trading operations with ultra-short
investment horizons has materially changed equity
market liquidity dynamics.
A new class of quasi market maker has evolved and
has successfully woven together the communication,
computation and automation advantages of
technology. These capabilities translate into a firm’s
ability to be on the inside bid and offer of thousands
of stocks across 50+ venues in the US. The latency
advantage offered by co-location allows HFT’s the
option of cancelling those quotes before they can be a
true source of liquidity. Recent statistics indicate that
the message to execution ratio may be 30:1 or higher.
It would be naive to not consider the P&L advantages
for a competitive low-latency trading firm who,
having identified offers lifted on one venue, wouldn’t
immediately cancel offers on other venues. Not
only would that apparent liquidity disappear, but
it’s conceivable that this signal, combined with
corroborating pattern recognition analysis, would
prompt a more aggressive bid in the stock in
anticipation of the trading demand . In this scenario,
the pattern of signals by an institutional buyer may
have converted a passive liquidity provider to an active
anticipatory competitor. These dynamics would
certainly tighten bid/ask spreads and increase volumes,
but would they improve actual liquidity or merely create
the illusion of liquidity? It is these dynamics that are
inspiring the development of increasingly sophisticated
trading technologies by the brokers that are competing
for agency orderflow.
THE CHALLENGING LIQUIDITY LANDSCAPE

Liquidity metrics in context
Most institutional trading desks understand that liquidity management must be
considered in the context of the investment goal itself.
The catalysts for the buy/sell decision are often of
primary importance since they often contain important
information about expectations for the future direction
of prices. Optimal liquidity management should
leverage these investment priorities to simultaneously
balance the exposure to the cumulative price impact of
their executions and the price drift experienced during
completion of the order. This reality propelled Markit
to develop a technique that accurately differentiates
the price impact of accessing liquidity from the price
drift related to trade timing. This innovation is a key
advancement in acquiring the numbers required to put
liquidity management into context within the overall
investment process.
In addition to measures related to deciphering the
path of prices over the execution period, there are
opportunities to directly examine the elements of
volume into context of what transpired over the
trading interval.
When properly conceived, such measures will help
better separate actionable liquidity from the illusion
of liquidity. For example, quotes that evaporate may
influence the average bid or ask size, but only trades
that actually occur are considered in calculating the
average trade size for a stock. Similarly, the bid/ask
spread can expand and contract from transitory quotes,
but actual spreads at execution and the price changes
that accompanied them are a measurable reality.
Ancillary metrics often associated with liquidity
management include individual fill rates and price
reversals that occur immediately following an execution
or the completion of an order. This type of analysis
is outside the scope of this report. You can request
the reports “Liquidity Charge® & Price Reversals: Is
High Frequency Trading Adding Insult to Injury?” and
“Evaluating Liquidity Capture: The TCAP Ratio” for
more details.

The price impact for liquidity
Cumulative Liquidity Charge®
Few would argue with the fact that institutional size liquidity comes at a price.
Unfortunately, few practitioners have accurate insight
into the size of the price concession, how it accumulates
and when it’s incurred. It’s our experience that once this
information gap is filled, traders act to fine-tune trading
strategies and ensure that their agency execution
providers are efficiently managing their liquidity
management responsibilities.
The ability to accurately calculate the size of the price
concession requires that the standard implementation
shortfall measure has to be broken into its constituent
parts which we refer to as the Cumulative Liquidity
Charge® and Timing Consequence®. An order’s
Cumulative Liquidity Charge isolates the cumulative
price impact specifically resulting from an order’s
individual executions. The calculation examines the
market conditions related to each individual execution
that comprises a completed order. This analysis
is accomplished by matching each time-stamped
execution from a given order to the trade and quote
data for the security. Any price concessions (adverse
ticks) directly related to the executions are identified
and accumulated over the life of the order. These
cumulative price concessions provide a unique
insight into the liquidity dynamics that existed during
the execution interval. A more detailed review of
this innovative calculation technique is available in
Markit’s December of 2008 report titled “Anatomy of a
Footprint”.
It stands to reason that the larger the relative size
of spreads the greater the potential exposure to
cumulative price changes for the execution of an
institutional size order. To properly account for this
phenomenon we developed a Spread-Adjusted
Cumulative Liquidity Charge. Normalizing the
Cumulative Liquidity Charge by dividing it by the
Spread Cost of each stock traded is an effective way to
assure that the measure is contextualized to the stock
specific properties that directly influence the price
impact of completing an order. It also enhances our
ability to make standardized comparisons of broker
performance. For example, a broker with a Spread-
Adjusted CLC of 200% on average pays the equivalent
of twice the bid/offer spread on every share across the
entire order, while a broker that pays 25% required less
than a quarter of the spread cumulative concession to
execute the order. We use this measure to rank the
brokers in the study.
Spread-Adjusted Cumulative Liquidity
Charge: The Cumulative Liquidity Charge
normalized by the Spread Cost.
Cumulative Liquidity Charge
Spread Cost
Spread - Adjusted CLC =
Implementation Shortfall: The total
slippage between Arrival Price and
Execution Price. This is calculated for each
execution and aggregated into the total
Implementation Shortfall. This is the sum
of two parts: Cumulative Liquidity Charge
and Timing Consequence.
Cumulative Liquidity Charge®
(CLC):
The total impact cost that the trader
had on the market. This is the sum of
the Cumulative Spread Charge and
the Liquidity Premium. The calculation
accounts for the cumulative effect of
liquidity concessions made throughout the
execution horizon and is a measure of the
trader’s ‘footprint’ in the name.
Spread Cost: The bid/ask spread as a
percentage of the stock price.
B/A Spread
Stock Price
Spread Cost =

Adverse ticks
Insight into a key driver of trading costs
In addition to accurately calculating the charge for liquidity, a few properly devised tick-
based measures fill in the gaps in the liquidity management landscape.
These measures focus on execution management
by putting the fill size and price changes experienced
during the order’s execution into context. An often
overlooked element fundamental to the dynamics of
liquidity for a stock is the security’s tick structure. Tick
structure relates to the path price changes can take
from trade to trade. There are three paths a security
can take from the last trade: an uptick, a down tick or
a zero tick (the same price as the last execution). The
path and distribution of these price moves have a
material influence on the cumulative price impact of
an order.
Of the many liquidity related measures available on
Markit’s Trade EQM trading analytics platform, the
ability to compare an order percent of adverse ticks to
that of the overall tick structure of the stock during the
trade is truly enlightening (AdvTick%). Adverse ticks,
defined as an uptick on a buy or a downtick on a sell,
on the individual trades that make up a larger order are
what accumulate to create price impact. Interestingly,
the majority of equity trades occur at the previous
trade price (on a zero tick); there is no price impact
for the liquidity. For some large capitalization stocks
the number of zero ticks on any trading day routinely
approaches 90% of all trades. Our research has
consistently confirmed that strategies that can avoid
adverse ticks routinely deliver the best
execution results.
% Order Adverse Ticks: The percent
of all fills in the order that were incurred
on adverse ticks. An adverse tick is
defined as buying on an up-tick or
selling on a down-tick.
% Order Adverse Ticks =
Total Order Adverse Tick Executions
Total Order Executions
% Overall Adverse Ticks =
Total Adjusted Market Adverse Tick Executions
Total Adjusted Market Executions
Average Trade Size as a % of Market
Average Trade Size (ATS%Mkt): The
broker’s average trade size for the order
as a percentage of the average trade
size in the market within the order’s
execution horizon.
ATS % Mkt =
Order Size
Total Order Executions
Market Average Trade Size
CLC Forecast Error: The error term between the Cumulative Liquidity Charge and the Liquidity
Charge Estimate. A positive (negative) value indicates outperformance (underperfomrance) versus
the estimate. The broker’s average trade size for the order as a percentage of the average trade size
in the market within the order’s execution horizon.
% Overall Adverse Ticks: The
percent of all market fills during the
order’s execution horizon that were
adverse ticks (less the order’s adverse
ticks and executions).

Average trade size
One aspect of fill quality
Markit’s October 2009 report “The Risks of Flying under
the Radar” and our November 2009 report “Beware
of the VWAP Trap” both illustrate the risk of cutting
large orders up into too many individual trades. Both
reports illustrate the signaling risks that can occur when
automated strategies ‘over-parcel’ to adhere to the
VWAP strategy parameters or when they are realizing
low fill rates. Smaller fill sizes increase the number
of executions required to complete an order, which
increases the probability of incurring price concessions
for liquidity. Also, it often lengthens execution
timeframes and the risk of information signaling.
The calculation of the Average Trade Size for an order is
simply the total shares traded divided by the number of
individual executions required to complete it. The same
calculation is done for the overall trading activity in the
stock for the same execution interval. Comparing these
numbers provides insight into the average fill size of the
market versus the order over the execution interval.
An average fill size below the market average could
indicate that the order strategy or algorithm is over
parceling given the average liquidity of the stock, or that
it is executing on less liquid venues. We have developed
more granular fill rate measures that improve upon
this simple metric, but unfortunately they require more
extensive data tagging that only a subset of our client
base requires of their execution providers. For more
information please see “Evaluating Liquidity Capture:
The TCAP Ratio”.

CLC Forecast Error
An accurately contextualized view of execution quality
After nearly a decade of research, Markit introduced a new forecasting model designed
to accurately forecast the cumulative price change required to execute a specific size
order over a specific time frame.
The model is a departure from the ubiquitous ‘impact’
models that rely simply on the stock’s historic price
volatility to approximate a wide range of possible
outcomes. The technique breaks new ground by
considering the non-linearity of order size, the changing
nature of bid/ask spreads and the dynamic nature of
a stock’s ‘tick risk’. A stock’s tick risk is related to the
probability of an individual execution occurring on an
adverse tick, for example an uptick on a buy order.
Since price impact, at its most basic level, is created by
the cumulative costs of crossing the spread to obtain
liquidity, this model accurately reflects the actual
mechanics of trading.
The bottom-up approach of this model provides a
uniquely synchronized comparison for each order,
rather than relying on a categorized set of actual
client trades. This approach allows us to account for
the changing nature of the cost drivers on a day to
day basis.
Since these accurate forecasts are based on both the
most recent market data and the specific trade size
and time horizon of an order, they become a perfectly
calibrated benchmark for broker evaluations. For more
information on the model, please see “Markit’s Impact
Cost Model: Unparalleled Accuracy”, “Slaying the Myth
of Equity Impact Forecast Models” and “Enhanced
Liquidity Risk Estimates for VaR Models”.
We Spread-Adjusted the CLC Forecast Error
performance to assure that brokers trading the most
difficult orders (large orders in stocks with large relative
spreads) are not unduly penalized, further improving
the quality of the broker comparisons.

Broker performance analysis
Order difficulty categories
There is increasing interest in the impact of advancements in order routing technology
on a client’s largest, most important and hardest to trade orders.
To shed some light on this issue we’ve conditioned our
execution quality analysis on daily trading volume (DTV)
as a proxy for order difficulty. The orderflow we analyze
on behalf of clients is dominated by orders below 0.5%
of DTV. For that reason we created seven liquidity
categories that approximate the distribution of orders
across our execution universe.
The analysis is based on Markit client data from
October 2013 to December 2014. The execution data
represents more than 13 million orders made up of
more than 226 million executions totaling more than
54 billion shares. We limited the analysis to 14 firms,
the top executing firms in our universe along with a
couple of the top performing minority brokers in the
sample. We have substituted the Greek alphabet for
the broker names as we’ve done in previous reports.
These comparisons are constructed to provide insight
into the broad performance differences between the
top execution providers; we encourage our clients to
combine these insights with their own execution
quality rankings from Markit’s Trade EQM trading
analytics platform.
The first three categories represent the least
challenging orders over the period. Table 1 (see Page
12) (0% - 0.005% DTV) is sorted by the Spread-
Adjusted CLC metric and represents the truly small
orders, providing an overview of orders that are often
completed in a few executions. These executions are
often smaller than the resting liquidity of the market,
which results in lower average trade sizes than the
market (ATS % Mkt). Given the consistency in the size
of the orders, the average trade size is below the market
for all the brokers in the survey. Since these small
orders are often executed with expediency, the percent
of adverse ticks for the orders tend to be much higher
than those of the market over the execution interval
(positive Net % Adverse Ticks). Broker Lambda had
only 12% adverse ticks higher than the market while
the worst experience was 48%. Consistent with the
small demand for liquidity and limited number of child
executions for this category, most of the brokers’
average CLC’s are generally between 1-3 bps.
The new ‘Spread-Adjusted CLC Forecast Error’
column in Table 1 highlights that some brokers slightly
improved on the model’s expectation of price impact.
Of course, the nominal values of the estimates for
these orders are so small that the impact of the error is
negligible. The issue with the Implementation Shortfall
(IS) benchmark becomes evident as the magnitude of
the ‘Intra-trade Momentum’ translates directly into the
realized IS performance. The numbers clearly illustrate
the challenge of using the IS benchmark as a metric
to study liquidity management given the dominant
influence of a stock’s short-term price drift, even for
executions with the shortest execution intervals.
The slightly larger order sizes in Table 2 (0.005% -
0.05%) and Table 3 (0.05% - 0.5%) on Page 12 begin
to illustrate a wider divergence in broker performance
across the categories. In Table 2, the larger order
sizes use an increased amount of the market’s resting
liquidity as execution sizes for many brokers exceed
100% of the interval average trade size. The adverse
tick performance remains consistent with the earlier
category as the ‘get it done’ philosophy for the still small
orders takes a toll on the relative tick performance.
The advantage of the Spread-Adjusted calculations
is revealed in Tables 2 and 3 (see Table 3 on Page
12) as the variability in Spread Cost increases across
the orders executed by each of the brokers. There
is a perceptible increase in the size and variability in
the CLC’s across the two categories, with the nominal
CLC’s tripling in Table 3. This effect is confirmed by
the Spread-Adjusted CLC’s as well. Broker Zeta
and Alpha continue to perform impressively in these
two categories and the basic relative performance
rankings of the brokers remains intact across the three
least liquidity-demanding categories of orders.

ORDER DIFFICULTY CATEGORIES

In Table 4 (0.5% - 5%) the category represents a
significant change in the liquidity demands of the
orders. The Spread Costs rise across most of the
brokers, as more small capitalization stocks are
included in the data. The variety of execution
techniques increases as these orders increasingly
benefit from more ‘block’ style of executions. This is
borne out in the substantial shift in average trade sizes
relative to the market (ATS % Mkt). The execution
periods also lengthen due to the liquidity demands and
the adverse tick spreads begin to decline for certain
brokers as order instructions are of an increasingly
participatory nature.
The CLC price impact measures begin to discriminate
between those brokers who are consistently delivering
the smallest execution ‘footprints’. Broker Zeta had
the minimum price impact as measured by the CLC
benchmark and Alpha’s impact was in line with the
average. Broker Delta and Pi had positive average
CLC Forecast Errors, indicating that they consistently
delivered lower price impacts than the liquidity
circumstances surrounding their orders would suggest.
Most institutional trading desks approach trades that
exceed 5% of a day’s trading volume in a stock with
increased attention. This increased focus may be
the result of the trade being a major shift in portfolio
exposure, either a position acquired over a significant
period to be liquidated or a new position expected to
generate significant excess return. The more visible
nature of these orders is usually a call to arms to use the
best brokers, order routing technologies and liquidity
management strategies.
Table 5 (5% - 10%) illustrates that broker Gamma,
Lambda and Delta delivered impressively low average
price impacts and succeeded across most of the
execution quality metrics in 5% - 10% DTV. Broker
Alpha and Pi also did very well relative to the CLC
estimates. Though down the list on the price impact
performance measures, broker Zeta delivered high
quality liquidity management relative to the expected
difficulty of the orders in this DTV category.

Tables 6 (10% - 25%) and Table 7 (> 25%) on Page
14 and 15 respectively, represent the highest demand
for liquidity orders in our study. The significant demand
for liquidity of these orders resulted in the largest and
most varied cumulative price impact performance as
well. It is of significant interest that 2 of the brokers
(Alpha and Delta), who all performed well in the other
categories, had consistent success with the most
difficult orders as well. A deeper look at the liquidity
management metrics suggests that they have taken
different paths to deliver these results. Broker Delta,
who has delivered good performance across all of the
DTV categories, had an average trade size much larger
than the market for the category.
The challenge this creates with large DTV orders is
that it increases the number of executions required
to complete the order, relative to a strategy that
encourages larger prints. A greater number of
transactions often translates into more risk of crossing
the spread for liquidity and possibly increases the risk
of information signaling. Broker Alpha seems to have
avoided this risk given the exposure to adverse ticks,
the low Spread-Adjusted CLC levels and the good IS
performance in both categories.

Broker Mu’s large average trade size in both categories
suggests that they engaged in a more traditional block
trading strategy. This technique had the benefit of
limiting their exposure to adverse ticks and reducing
their exposure to the negative momentum encountered
during their execution intervals. Out of the top
cumulative price impact performers, broker Detla and
Gamma delivered the best performance relative to the
expected difficulty of the orders they executed.
This consistent ability to deliver better than forecast
execution results, without consistently having the
lowest cumulative price impacts, suggests that broker
Gamma may be receiving a larger share of hardest to
trade names in our study. Distinguishing these kinds
of circumstances reveals why well designed liquidity
management metrics are crucial to appreciating the
many dimensions of execution quality.

Summary graphs
The overall performance, measured by the average
Spread-Adjusted cumulative price impact of each
broker’s executions across each of the DTV categories,
is illustrated in Graph 1. It is obvious that the top
executing brokers are tightly grouped in the easiest
to manage liquidity categories. As the order difficulty
increases the brokers begin to differentiate themselves,
as most practitioners would expect. This graph also
illustrates the performance consistency of many of the
top brokers. Our initial investigations of the top brokers
confirm that significant technology, expertise and
infrastructure investments have been made by each
of the firms.
Broker Alpha’s performance across the largest DTV
categories is remarkable. Brokers Gamma makes
significant performance gains in the middle ground of
the liquidity categories where most institutional activity
occurs. Brokers Nu and Sigma drop precipitously to
the bottom ranking in the most difficult order category.
This suggests that certain trading technologies that
succeed for a majority of institutional orders may not be
designed for the liquidity management requirements of
high DTV orderflow.
Graph 1 - Spread-Adjusted CLC by Broker and Order Liquidity.
0%
-600
-200
-400
-1400
-1200
-800
-1000
Alpha Beta Delta Epsilon Eta Gamma Iota Kappa Lambda Mu Nu Pi Sigma Zeta
0-0.005% 0.005- 0.05% 0.05-5% 5-10% 10-25% 10-25% >25%
Spread-AdjustedCLC(Bid/OfferSpreadUnits)
% of Day's Volume
Graph 2 - Spread-Adjusted CLC Forecast Error by Broker and Order Liquidity.
1000%
400
200
800
600
-400
-600
-800
0
-200
Alpha Beta Delta Epsilon Eta Gamma Iota Kappa Lambda Mu Nu Pi Sigma Zeta
0-0.005% 0.005- 0.05% 0.05-5% 5-10% 10-25% 10-25% >25%
Spread-AdjustedCLCForecastError(Bid/OfferSpreadUnits)
% of Day's Volume

Graph 2 (see Page 16) illustrates the overall results
of the brokers with regard to their Spread-Adjusted
CLC Forecast Error performance over the analysis
period. The value of introducing the price impact
forecast error is evident as it validates the performance
of some of the top liquidity management brokers
(Alpha & Pi). The measure also provides an avenue to
address the contextual challenge that haunts all relative
comparisons in trading analysis.
Brokers Pi and Alpha consistently delivered high quality
execution results relative to the expected difficulty
of the orders as the orders crossed the 0.5% DTV
threshold into the most difficult DTV categories. These
results, especially as they relate to the use of algorithms
and other strategy related techniques, would be missed
by all other metrics, especially evaluation based on the
IS benchmark.
SUMMARY GRAPHS

Performance consistency
Graphs 3 through 4 (see Page 18-19) illustrate the
quarterly average results for the top and bottom 3
brokers based on the Spread-Adjusted CLC rankings
for the DTV category displayed. The same brokers
were re-used for the quarterly time series graphs of
the Spread-Adjusted CLC Forecast Errors. The graphs
provide an interesting insight into the changing nature
of each broker’s performance across the liquidity
management metrics over time. Graph 3 (see Page
18) focuses on the 0.5% - 5% DTV order category
and confirms that the top broker, Alpha and Gamma,
have maintained leadership at the top end of the
performance scale over time.
Graph 4 (see Page 19), which details the CLC
Forecast Errors, confirms the positive performance
characteristics of the top brokers. It also provides an
indication that broker Delta may be providing more
value than the price impact metrics alone may reveal.
This insight was confirmed by our fieldwork as broker
Delta has been bringing new technologies on-line with
the goal of enhancing their performance with larger
institutional orders.
Graphs 5 & 6 reveal the quarterly time series for the
aggregation of the top DTV categories, everything
above 5% DTV is included in the results. Graph 5
confirms the performance characteristics of broker
Alpha. Graph 6 shows the significant value added by
broker Delta’s liquidity management strategies relative
to the price impacts expected from these most difficult
orders. The time series performance of the least
successful brokers highlights the performance volatility
that often surrounds the execution of the industry’s
most complicated orders. It also confirms the necessity
to systematically conduct this kind of analysis over
time to develop the rich datasets required for accurate
interpretation of the results.
Graph 3 - Time Series of Spread-Adjusted CLC for Top 3 & Bottom 3 Performing Brokers in Orders Between
0.5% and 5% of Day’s Volume.
0%
-150
-50
-100
-200
-250
Alpha Beta Delta Gamma Nu Pi
Q4
2014
Q3
2014
Q2
2014
Q1
2014
Q4
2013

Graph 4 - Time Series of Spread-Adjusted CLC Forecast Errors for Top 3 & Bottom 3 Performing Brokers in
Orders Between 0.5% and 5% of Day’s Volume.
100%
-50
50
0
-250
-200
-100
-150
Q4
2014
Q3
2014
Q2
2014
Q1
2014
Q4
2013
Graph 5 - Time Series of Spread-Adjusted CLC for Top 3 & Bottom 3 Performing Brokers in Orders Greater
than 5% of Day’s Volume.
0%
-600
-200
-400
-1200
-1000
-800
-1400
Q4
2014
Q3
2014
Q2
2014
Q1
2014
Q4
2013
-1600
Graph 6 - Time Series of Spread-Adjusted CLC Forecast Error for Top 3 & Bottom 3 Performing Brokers in
Orders Greater than 5% of Day’s Volume.
800%
500
700
600
200
300
400
100
Q4
2014
Q3
2014
Q2
2014
Q1
2014
Q4
2013
0
-100
PERFORMANCE CONSISTENCY

Conclusion
The significant investments in trading technology made by the top equity execution
providers should translate into improved execution quality for institutional equity orders.
As brokers compete for the orderflow that will provide
a return on their investment, they spend great effort
to describe their innovations but rarely are they in the
position to share definitive results. Our investigation
into execution quality is designed to assist in making
those comparisons; comparisons that are instrumental
in assuring that the industry’s increasingly engineered
solutions are achieving the desired results. This study
confirms that a subset of brokers are consistently
delivering high quality executions. We also found that
those successes transcend the most liquid orders and
extend to the most challenging equity orders, those
orders from 5% to 25% of a day’s trading volume.
The metrics introduced in this report are specifically
designed to improve the evaluation of liquidity
management. The measures focus on understanding
an execution provider’s ability to minimize the
cumulative price impact of acquiring the liquidity
required to complete an order. Our research confirms
that success in limiting these direct costs also reduces
exposure to information signaling and unnecessarily
long execution intervals. This study also illustrates the
significant compromise of relying solely on traditional
benchmarks like Implementation Shortfall (IS) to
evaluate broker execution quality, especially the ability
to manage high percent of DTV orders.
The analysis confirmed that 3 brokers out of 14 in the
study are delivering consistently high quality execution
results across many of the DTV categories. Two in
particular, broker Alpha and Delta are accomplishing
this success in materially different ways. Brokers Pi
has performance that suggests they have superior
algorithmic decision schemes that force liquidity
charges to the most benign part of the execution
horizon, the end.
We maintain that these comparisons are constructed to
provide insight into the broad performance differences
between the top execution providers; we encourage
our clients to work with our analyst team to combine
these insights with their own execution quality rankings
from Markit’s Trade EQM trading analytics platform.
03/30/15
More information
For more information on the products
and services from Markit, please contact
us at sales@markit.com or call one of
our sales offices:
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New York +1 212 931 4900
Amsterdam +31 20 50 25 800
Boulder +1 303 417 9999
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Frankfurt +49 69 299 868 100
Hong Kong +852 3478 3948
Tokyo +81 3 6402 0130
Toronto +1 416 777 4485
Singapore +65 6922 4200
Sydney +61 2 8076 1100
markit.com
® Markit makes no warranty, expressed or implied, as to accuracy, completeness or timeliness, or as to the results to be obtained by recipients of the products and services described herein, and shall
not in any way be liable for any inaccuracies, errors or omissions herein. Copyright © 2015, Markit Group Limited and Leading Risk. All rights reserved. Any unauthorised use, disclosure, reproduction
or dissemination, in full or in part, in any media or by any means, without the prior written permission of Markit Group Limited is strictly prohibited.

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