Transaction Cost Analysis in Europe:Current and Best PracticesEuropean Survey, January 2007

1. Table of Contents

1 Table of Contents 1

2 Executive Summary 3

3 Background 5

3.1 Introduction 5

3.2 Components and Drivers of Transaction Costs 63.2.1 Explicit transaction costs 63.2.2 Implicit transaction costs 73.2.3 Explicit versus implicit costs 103.2.4 Execution methods and transaction costs 12

3.3 Measuring Transaction Costs 123.3.1 Benchmarking approach 133.3.2 Implementation shortfall 14

3.4 Pitfalls and Common Problems in Transaction 15Cost Analysis

3.4.1 Noise in measurement 153.4.2 Bias in estimation 153.4.3 Relevance of indicators 153.4.4 Gaming problems 163.4.5 Dealing with opportunity costs 16

3.5 A Critical Review of the Most Popular 16TCA Indicators

3.5.1 Spread midpoint benchmark 173.5.2 Volume weighted average price 173.5.3 Closing price benchmark 183.5.4 Average of the lowest, highest, opening 18and closing prices (LHOC)

3.5.5 Implementation shortfall 193.6 The EBEX Indicators 203.7 Presentation of the New Methodology and 20

Its Foundations

3.7.1 Objectives, principles and concepts 203.7.2 Detailed description of the indicators 213.7.3 Advantages of this method 243.7.4 Open questions and further developments 25

4 A Pan-European Survey

4.1 Methodology and Sample 274.1.1 Methodological approach 274.1.2 Analysis of the sample 27

4.2 Pre-trade Analysis 294.3 The Execution Process 314.4 Post-Trade Analysis 354.5 MiFID Readiness 37

5 References 39

6 About EDHEC Risk Advisory and 41the Authors

7 About the Sponsor 43

1

2. Executive Summary

The MiFID is establishing a very open and competitive

market for execution and other investment services.

The new requirement is for regulators to ensure that

the development of new execution venues, systematic

internalisation and the end of the concentration obligation

(ie the obligation in certain Member States to route all

negotiations through a central regulated market) will not

result in less efficient or transparent markets that could

harm the end investor.

Transaction cost Analysis (TCA) is obviously very likely to

form the cornerstone of any future academic and industry

development related to this new Best Execution obligation.

In this respect EDHEC Risk Advisory, sponsored by HSBC,

have conducted a pan-European survey aiming at better

understanding the current state of the industry when it

comes to assessing transaction costs and the level of

readiness in terms of complying with Article 21st.

EDHEC Risk Advisory questioned 127 buy-side firms

with the support of a structured questionnaire on a

pan-European basis. 26 responses were provided by

European hedge fund managers while 101 responses were

sent from traditional asset management firms giving us the

opportunity to analyse the difference between these two

fairly specific populations in terms of trading and execution.

Pre-trade analysis forms an essential part of ensuring that

best execution can be achieved. With only half of

respondents confirming the use of pre-trade analytics out

of 83 responses, it is striking to see that pre-trade analytics

may not have yet reached the desk of the majority of

buy-side firms. This situation is rendered even more

problematic in light of the nature of the forthcoming best

execution obligation, which encompasses a significant

obligation of means.

If pre-trade analysis allows the intermediary/trader to

optimally design its execution strategy, it is again striking to

note that situations where a trading desk decides to return

the order unfilled when execution conditions are not

favourable seems to be only possible with one traditional

asset manager out of seven, and one hedge fund

management company out of four. For the majority of our

respondents, once decided, the order will have to be

executed at all costs; the conditions usually attached to the

order may therefore be considered as null. This surprising

result confirms that changes are desirable not only for the

trader but more globally in the entire relationship between

the manager and the trader.

One very important pre-requisite for performing post-trade

transaction cost analysis and implementing an automated

processing of order flow is the use of technology from the

initiation of the order and its handling in a format that is

acceptable by most vendors and intermediaries. FIX (The

Financial Information eXchange protocol) is obviously

the natural response to this need and has seen a

tremendous development over the last five years. With 57

respondents out of 79 (72 per cent) confirming the use of

FIX to encode transactions and process them electronically,

the industry has clearly embraced the technology

revolution wholeheartedly.

This very rosy picture, however, has to be dealt with

carefully as only 60 per cent of hedge fund respondents

claim they are using FIX, which demonstrates that smaller

firms are slower to adopt the protocol, and probably the

digitalisation of their entire execution flow. More importantly

FIX Protocol today does not allow full encoding of all forms

of parameters that may be attached to an order; the survey

respondents indicated that less than 20 per cent transmitted

instructions to the intermediary with a specific price, volume

or benchmark objective, thereby making transaction cost

measurement potentially more difficult.

Finally, transaction cost analysis cannot be performed

without intraday timestamps being made available during

the entire flow of the order from the buy-side to the

intermediary and back to the issuer. It is therefore a concern

to see that two thirds of respondents may not be in a

position to document the time the order was responded to.

When it comes to assessing transaction costs post-trade,

a logical approach consists in estimating transaction costs

ex-ante and comparing the actual result of the intermediary

with the initial estimates. The fact that only one third

of respondents have taken that route confirms the

difficulties inherent in estimating transaction costs ex-ante,

probably because of a clear lack of consensus on models.

3

One of the consequences of this difficulty is the reliance on

the most common, but also the least reliable, benchmarks.

With 90 per cent of our respondents declaring use of VWAP,

it is clear that the absence of consensual methodology for

assessing transaction costs ex-post is resulting in the total

failure of all efforts made to assess the costs incurred by

the end investor.

The prominence of VWAP as an indicator may be justified

by the simplicity of its implementation and its apparent

ease of interpretation for the end user. It is nevertheless

probably not the most effective indicator of execution

quality, especially for large size transactions. The full report

published by EDHEC Risk Advisory analyses in detail

the most common TCA benchmarks and reviews the

many associated issues and pitfalls. To support further

development, an innovative framework (EBEX) for an

absolute measure of transaction cost is also suggested

within the report. This framework has been designed to

attempt putting a stop to the endless debate of what the

appropriate benchmark may be depending on the specifics

of the situation, and make execution quality (with regards

to price) comparable from one order to another, from one

intermediary to another.

In line with this current relatively poor approach to

measuring transaction cost, it is interesting to note that 40

per cent of respondents would not take into consideration

their ex-post transaction cost analysis when reviewing the

allocation made to their brokers. If the selection of an

intermediary obviously cannot solely rely on transaction

costs, it is striking to see that such a large number of firms

do not review the performances of their brokers according

to such an important quality factor. Once again, it is

interesting to note that a piece of regulation may induce

adverse effect by creating a sense of confidence best

execution has been achieved while the reality of the facts

can not be demonstrated.

One year from the implementation deadline, it is also

astonishing to see that 26 per cent of traditional asset

managers still believe the MiFID is not related to their

business or are not even aware of the Directive. Even worse

is the situation in hedge fund management firms, with half

of respondents being mistaken as to the importance of the

MiFID for their firm.

Defining a clear execution policy, documenting it and

informing the client of all details of this execution policy

forms a significant part of the duty of best execution as set

out by the MiFID. Once again, with one third of hedge

funds having failed to clear the path towards a documented

execution policy, and with one fund manager out of ten

having to look at the question, there seems to be room for

greater detailed awareness within certain business

functions within many buy side firms.

4

3. Background

3.1 Introduction

Recent industry press headlines on the MiFID ‘Markets in

Financial Instruments Directive’ demonstrate that it is not a

welcome new step in harmonisation of the European

Financial Services landscape, at least not according to the

loudest voices.

The dominant message remains that the MiFID is yet

another piece of heavy European regulation constraining an

industry that is tired of paying the price for regulatory red

tape. According to industry specialists, the cost of the

MiFID could reach as much as EUR9bn over the coming

years — GBP1.5bn for the City alone — and is likely to bring

the industry to its knees1.

The MiFID is the second step in the harmonisation of the

European capital markets industry and aims to adapt the

first Investment Services Directive (ISD 1, issued in 1993)

to the realities of the current market structure.

Part of the European Financial Services Plan (FSP),

the ‘MiFID’ (Directive 2004/39/EC, formerly know as

Investment Services Directive II) was ratified by the

European Union Parliament on April 21st 2004.

The directive forms the Level 1 regulation (under the

Lamfalussy procedure).

Level II of the Directive was issued on February 2nd 2006

and factored in the clarification elements provided by the

CESR (Committee for European Securities Regulators)

in response to a request for technical advice.

The second level comprises two important sections:

A ‘Commission Implementing Directive’ as regardsorganisational requirements and operating conditions. This Directive will be translated into local regulations as part of the level III

A ‘Commission Implementing Regulation’ as regards record-keeping, transaction reporting, markettransparency, admission of financial instruments totrading. This takes the form of common regulations forall member states

Implementation of the Directive is expected no later than

November 2007, after issuance of the level III regulations

(translated from the Directive in local regulations), to be

finalised in January 2007.

The April 2004 Markets in Financial Instruments Directive

introduces a significant new obligation for European

investment firms. This obligation lies in the now famous

Article 21 that comes in addition to article 19(1), which has

been at the heart of the arguments put forward by

opponents of the MiFID.

The MiFID is establishing a very open and competitive

market for execution and other investment services.

The new requirement is for regulators to ensure that the

development of new execution venues, systematic

internalisation and the end of the concentration obligation

(ie the obligation in certain Member States to route all

negotiations through a central regulated market) will not

result in less efficient or transparent markets that could

harm the end investor.

Within the 2004 Directive, the Best Execution obligation

has been defined as an obligation of means whereby

investment firms are required to have taken all reasonable

steps to obtain the best possible result for the client.

The Best Execution obligation is therefore structured

around three major principles:

i. an obligation of means to achieving the best net result

for the client, involving factors that determine whether

or not this best net result has been achieved pending

definition of the criteria and their relative importance

ii. documentation of an execution policy that includes the

execution venues and documentation of the parameters

that justify these choices

iii. an obligation for investment firms to demonstrate, at the

demand of the client, that execution has been carried

out in accordance with the agreed execution policy and

that the execution policy allows achievement of the best

possible result on a consistent basis

5

Transaction cost analysis (TCA) is therefore likely to form

the cornerstone of any future academic and industry

developments related to this new Best Execution

obligation. It is the objective of this survey to better

understand the current state of the industry when it comes

to assessing transaction costs.

3.2 Components and drivers of transaction costs

Transaction costs are significant costs of active

management. As claimed by Harris (2003), people cannot

manage what they cannot measure. TCA should be a

scorecard that helps investment managers to both assess

and understand how well they have traded and how they

can improve their global performance. The total portfolio

performance depends both on the investment decisions

and on the implementation of those decisions. On the one

hand, investment managers need to know how much the

implementation of a given trading strategy really costs.

Different trading strategies correspond to different

risk/cost trade-offs. On the other hand, a bad execution can

impact the total performance of even the best

decision. Therefore, to evaluate their intermediaries

(brokers/traders/algorithms), investment managers must

be able to measure their performance. This assessment

requires quantitative data that can be obtained through

TCA. As a whole, TCA is therefore a tool for monitoring the

relative performance both of different trading strategies and

of different intermediaries.

At first sight, TCA seems quite a complex task, essentially

because transaction costs include several components.

These are usually categorised into explicit and implicit

costs. People are often more aware of explicit costs, which

are the visible part of transaction costs. Figure 1 exhibits

the different cost components involved, which will be

described in detail below.

Figure 1: Typology of transaction costs

3.2.1 Explicit transaction costs

Brokerage commissions, market fees and taxes or stamp

duties are explicit costs. They are said to be explicit

because they are usually documented separately from the

trade price. In theory, they could be determined before the

execution of the trade. In practice, their measurement is

not so obvious because brokerage commissions are often

paid for bundled services and not only for order execution.

Research, analytics and trading technology are often

bundled services. An unbundling trend is today being

observed in Europe and the US. New unbundling of

commission regulations, which separates the payment for

deal execution and the payment for broker research,

will undoubtedly make the measurement of explicit

costs easier.

Explicit costs vary across different trading venues.

On average, higher volume markets exhibit the lowest

costs. Within the same trading venue, explicit costs differ

across intermediaries. For example, brokerage commissions

might be subject to volume discount. In the current trading

environment, competition across markets and among

brokers to attract more investors has led to significant

reduction in explicit costs.

Broker Commissions

Exchange Fees

Taxes/Stamp Duties

Bid–Ask Spread

Market Impact

Operational Opportunity Costs

Timing Opportunity Costs

Missed Trade Opportunity Costs

High

Low

Low

High

Awareness Impact

Implicit costs Explicit costs

6

Broker

Manager

Responsibility

Interestingly, clearing and settlement costs are usually not

included in the calculation of explicit costs, even though

they impact the global portfolio performance. As both types

of costs can affect the economics of the trade in an

environment offering multiple execution venues, they

should be taken into account when performing TCA.

3.2.2 Implicit transaction costs

Transaction costs go beyond brokerage commissions, fees

and taxes. Implicit costs represent the invisible part of

transaction costs that cannot be measured ex-ante because

they are included in the trade price. They depend mainly

on the trade characteristics relative to the prevailing market

conditions. These costs are usually decomposed into

their component parts of spread, market impact and

opportunity costs.

3.2.2.1 Spread

When taking liquidity (by buying at the best ask or selling at

the best bid), traders pay the spread (assuming the

valuation of the asset at liquidation price). The latter is a

compensation for the costs incurred by the liquidity

provider. In the microstructure literature, three kinds of cost

are usually associated with the bid–ask spread:

The order processing cost

The inventory control

The adverse selection cost

The order processing cost is a compensation for supplying

an immediacy service to the market (Demsetz 1968). The

ability to trade immediately rather than to have to wait for

the opposite trade provides certainty for market

participants. Liquidity is thus provided at that cost.

The inventory control cost is a compensation for the risk of

bearing unwanted inventories (Ho and Stoll 1981).

Accommodating other market participants’ trades makes

the liquidity providers deviate from their optimal inventory

based on their own risk-return preference. To restore their

optimal position, they adjust their bid-and-ask prices to

attract and/or avoid certain trades.

The adverse selection cost is a compensation for the risk of

trading against informed traders (Copeland and Galai 1983).

Informed traders have a certain amount of private

information that allows them to know or better estimate

the true value of a security. As the liquidity providers lose

when they trade with informed traders, they widen their

bid–ask spread for all market participants to cover their

potential losses.

The size of the spread varies across trading venues and

across stocks. Figure 2 illustrates the variation of the

volume weighted bid–ask spread size across several major

European stock exchanges for orders of all size. For a given

trading venue, the spread fluctuates across stocks. In

general, spreads are negatively related to market

capitalisation and liquidity while they are positively related

to volatility and information asymmetry. The spread size

can also vary over time according to trading conditions. In a

limit order book system, the spread mechanically widens

after a large trade consuming more than the quantity

available at the best opposite quote. Market participants

are, overall, sensitive to the prevailing market conditions,

especially the spread. Many of the models analysing

traders’ behaviour provide evidence that market

participants tend to take (supply) liquidity when the spread

is narrow (large).

Figure 2: Weighted average bid–ask spreads

Source: various public and proprietary sources,

EDHEC-Risk Advisory

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

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7

3.2.2.2 Market impact

Market impact, also called price impact, is the price to pay

for consuming the liquidity available on the market beyond

the best quote. The market impact represents the

additional cost incurred by the trader when execution

transaction volumes of size exceed the quantity available at

the best bid/ask.

In other words, it is the price shift exceeding the spread

that is due to the trade size. Consequently, the main

determinants of market impact are the trade size and the

market liquidity. The market impact is a positive function of

the trade size and a negative function of the liquidity

available on the trading venue. For a given level of liquidity,

the market impact increases with the trade size. For a given

trade size, the market impact increases with the lack

of liquidity.

There are two reasons for explaining why trades affect

prices. First, if the trade is large relative to the available

liquidity, the trade mechanically shifts the market price

towards less favourable prices because it needs to

consume several quotes in the order book. This mechanical

price impact is transitory until the liquidity is replenished.

Secondly, trades can impact prices when they are

perceived as motivated by new information. When the

trade brings new information to the market place, the

related price impact is permanent.

To briefly illustrate how trades affect prices, let us take a

simple numerical example. We first assume that, at a given

time, the limit order book looks as it does in Table 1.

We then consider the arrival of a market buy order for a

quantity of 500 shares. Such a buy order will be

immediately executed in full, by walking up the order book.

As a consequence, the current best ask (52.00) will

disappear, the new best ask will become 52.20 for a

quantity of 350 shares (550-(500-300)), and the bid–ask

spread will be enlarged (2.20 instead of 2.00). Since the buy

order walks up the order book, it impacts the market. If it

had specified a quantity smaller than the depth available at

the best ask (300 shares), it would not have an impact on

prices. Obviously, this example does not take the time

dimension into consideration, as the absorption of

quantities available at the best bid/ask are likely to result in

new orders placed in the order book.

Table 1: Example of a limit order book

Figures 3 and 4 illustrate the relationship between market

impact and trade size or liquidity. They are built upon

statistics based on the Sinopia Asset Management

databases spanning 16 developed countries and refer to

trades executed during the period 1999–2000. Figure 3

shows the relationship between market impact and the

relative trade size. The latter is defined as the size of trade

(expressed in number of shares) divided by the daily

average volume for the stock. Market impact appears to

increase dramatically from the fourth class of size, when

the trade size approaches or exceeds 1 per cent of the daily

average volume. Figure 4 focuses on the relationship

between market impact and liquidity. Market liquidity here

is estimated by the turnover, defined as the ratio of the

average daily volume to the total number of shares

outstanding. As we can see, the lack of liquidity starts to be

expensive when the turnover falls below 0.026 per cent in

the sample. Rather than the actual levels, which vary

according to markets and stocks, the reader will analyse the

relationship between the average impact and the relative

size of the orders or turnover.

BID ASK

Quantity Price Price Quantity

400 50.00 52.00 300

500 49.60 52.20 550

250 49.50 52.30 315

300 49.30 52.60 170

800 49.00 53.00 400

8

Figure 3: Average impact by relative trade size

Source: Boussema et al (2002)

Figure 4: Average impact by turnover

Source: Boussema et al (2002)

3.2.2.3 Opportunity costs (price appreciation

related to delays)

The decision to trade and the actual trade do not usually

take place at the same time. As market prices are not

static, they can move for or against the proposed trade

(price appreciation). The costs related to the effect of time

on prices during the delay required to trade are opportunity

costs. They arise when prices move between the time the

trading decision is made and the time the order is filled.

At each stage between the investment decision and the

execution of the trade, there exists the potential for delays

that could positively or negatively affect the performance.

Opportunity costs depend consequently on the speed

of execution.

Three different components can be identified in

opportunity costs:

When the delay required to trade is operational andunintended (transmission delay between buy and sell sides for example), we refer to operational

opportunity costs

When the delay results from market timing under the control of the broker (for example, the broker splits the order into small lots over a period to minimise market impact), we refer to market timing opportunity costs

We refer to missed trade opportunity costs when traders fail to fill their orders. Some trades may not be fully completed either because price movements have led to the cancellation of the initial trading decision orbecause there is no more security available. If a predetermined trading strategy is not completed, the resulting opportunity cost can be expensive. Failing to trade can be costly for the end investor, who will have missed the opportunity to make an investment in the security requested

It is worth noticing that the contribution of opportunity

costs to total implicit costs is not independent of market

impact. Attempting to reduce one can lead to the increase

of the other. For example, splitting large orders over time to

reduce market impact can lead to larger opportunity costs

and vice versa. This issue is summarised in Figure 5 which

brings the problem back to the optimisation of a trading

strategy and the creation of on an efficient frontier that

best adjusts the level of risk with the performance of the

transaction (performance being measured as the

minimisation of the implicit costs).

Market Impact

Relative Size

0.00%

0.15%

0.20%

0.25%

0.30%

0.10%

0.05%

Class 1 Class 2 Class 3 Class 4 Class 5 Class 6

* The classes are defined as follows:Class 1: Relative Size < 0.05%; Class 2: 0.05% =<Relative Size < 0.2%; Class 3: 0.2%=< Relative Size < 0.4%; Class 4: 0.4% =< Relative Size < 1%; Class 5: 1% =< RelativeSize < 5%; Class 6: Relative Size >= 5%

0.15%

0.10%

0.05%

0.00%Class 1 Class 2 Class 3

Turnover

Class 4 Class 5

Market Impact

* Share’s Turnover = Average volume traded during the previous month / Number of shares outstanding. The classes of liquidity are defined as follows:Class 1: Turnover < 0.019%; Class 2: 0.019% =< Turnover < 0.026%; Class 3: 0.026% =< Turnover < 0.034% Class 4: 0.034% =< Turnover < 0.046%; Class 5: Turnover >= 0.046%

9

Figure 5: Relationship between market impact and

opportunity costs

Source: Giraud (2004)

3.2.3 Explicit versus implicit costs

Several studies have been conducted on the relative

importance of explicit and implicit transaction costs.

Most of them have focused on the costs of equity trading

for institutional investors. Several conclusions can be drawn

from them.

First, transaction costs have dramatically declined over

recent years, essentially due to technological innovations

and increased competition both across trading venues and

across intermediaries. This decline is reported by Domowitz

et al (2001) in an analysis of equity trading costs across a

sample of 42 countries. Boussema et al (2001) also

document this decrease in transaction costs. For a sample

of trades included in the Sinopia Asset Management

database, the authors find that total transaction costs

reached about 0.38 per cent over the period 1996-1999

while they fell to 0.18 per cent for the period 1999-2000.

We observe a similar finding in Munck (2005), a more

recent study devoted to trading costs on the larger stock

exchanges of Europe and northern countries. The author

shows that total transaction costs have been declining over

the last few years. He attributes this decrease to the

significant drop in explicit costs. Figure 6 shows that the

explicit costs of trading on the OMX exchanges, Euronext

and Deutsche Börse have fallen over the past eight years

and are fairly clustered. The higher explicit costs on the

London Stock Exchange are due to a special stamp duty of

50 basis points on all buy trades. Figure 7 exhibits the

pattern of implicit costs over the same period. Although

fluctuating, the tendency of clustering in the cost levels

across the different exchanges is present. At the end of the

year 2004, implicit costs ranged from 10 to 15 basis points.

Figure 6: Explicit transaction costs

Source: Munck (2005)

Figure 7: Implicit transaction costs

Source: Munck (2005)

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Market Impact and opportunity cost

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Market Impact and opportunity cost

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Market Impact and opportunity cost

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Cos

t

Time

Opportunity Cost Market Impact

Implicit Cost

Market Impact and opportunity cost

45

4035

3025

20

15

105

0

Ba

sis

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ints

4Q97

2Q98

4Q98

2Q99

4Q99

2Q00

4Q00

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4Q01

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4Q03

2Q04

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London (I) Frankfurt Euronext (II) OMX Exchanges (III)

Ba

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45

40

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30

25

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London (I) Frankfurt Euronext (II) OMX Exchanges (III)

10

Secondly, transaction costs vary across and within regions.

While declining, they remain economically significant,

especially in emerging markets. According to Domowitz et

al (2001), over the third quarter of 2000, total transaction

costs ranged from 22 basis points in the Netherlands to

184 basis points in Venezuela, with a cross-country mean of

about 60 basis points. Boussema et al (2001) also compare

transaction costs between developed and emerging

markets. In their sample of trades, explicit costs (including

only brokerage commissions) average 0.15 per cent in

developed markets against 0.61 per cent in emerging

markets. This phenomenon is similar for implicit costs

(market impact and timing costs): they are about 0.23

per cent in developed markets against approximately 0.58

per cent in emerging markets. Checking for a possible

correlation between the cost level and the use of trading

systems, Munck (2005) identifies the market system

turnover as a statistically significant explanatory variable for

both explicit and implicit costs. According to his results,

both costs of trading depreciate as the system activity

appreciates. Figure 8 illustrates this relationship for the

total transaction costs.

Figure 8: Total transaction costs versus market

system turnover

Source: Munck (2005)

Thirdly, the composition of transaction costs can vary

across trading venues. Extracted from Munck (2005),

Figure 9 illustrates the composition of the total trading

costs in 2004 for the European exchanges included in the

sample. Except on the OMX Helsinki market, explicit costs

represent the highest proportion of transaction costs, with

an average of approximately 60 basis points. This cost

composition is generally the opposite in US trading venues

where implicit costs tend to be higher than explicit ones.

This phenomenon is illustrated in Figure 10.

Figure 9: Composition of transaction costs

Source: Munck (2005)

Figure 10: Equity transaction costs in US markets

Source: Domowitz et al (2001)

To

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sts

(b

asis

po

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)

45

40

35

30

25

20

15

10

5

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50.000 100.000 150.000 200.000 250.000

Avg. turnover ($m)

100%90%80%70%60%50%40%30%20%10%0%

Sh

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Fran

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elsinki

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tock

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Explicit trading costs Implicit trading costs

across sections 2004

Average

40

35

30

25

20

15

10

5

0NYSE AMEX Nasdaq World

(One way, in basis points, for Q3 2000)

11

3.2.4 Execution methods and transaction costs

Transaction costs also vary according to the execution

methods that are adopted. There exist two main kinds of

techniques for executing an order: agency trading or

principal trading. Both methods differ in terms of the risk

sharing between the investor and the broker. In fact, the

choice between agency trades and principal trades involves

a trade-off between a low certain explicit cost and an

uncertain implicit cost.

Agency trading is the most common execution method.

When orders are sent to the market through an agency, the

investor bears all the risks associated with the trade.

The agency assumes no market risk, so implicit costs fall to

the investor. In practice, the investor sends an order to the

broker, specifying the name of the security and the number

of shares to buy or sell. The agency then executes the order

and the speed of execution depends mainly on the order

size and the available liquidity. In some cases, the investor

can give the agency execution constraints in terms of price

(closing price, daily VWAP, etc) or in terms of volume

(percentage of the daily volume, for example). The average

brokerage fees for this execution method are quite low.

In the case of principal trading, the market risk is passed

from the investor to the broker. In practice, the investor

sends the broker only some parameters of the trade: the

amount to be invested, trade direction, name and weight of

the securities in the basket, etc. Names and quantities are

unknown to the broker when the order is sent but the

broker commits to buying or selling the basket at the prices

available on the market at a specified time decided by the

two parties. The entirety of the information about the trade

is given to the broker only after the specified reference

time. In such an execution technique, the entire market risk

is transferred to the broker. As a consequence, brokerage

fees are larger than for agency trades. In fact, they will

depend on the level of risk associated with the execution of

the basket.

Both execution methods and their implications are

summarised in Table 2.

Table 2: Summary of execution methods for

the investor

3.3 Measuring transaction costs

Measuring transaction costs is vitally important, not only

because it represents one of the most important

quantitative measure of trading quality, but also because it

can be analysed in respect of total portfolio performances.

Two different objectives can be sought when assessing

transaction costs:

– What was the actual cost of trading and how does it

impact upon portfolio performances?

– How do intermediaries, traders, algorithm measure up?

In both cases, transaction cost analysis provides elements

of information that will allow a response to the question.

Transaction cost analysis is about the cost of individual

trades, not about the overall cost of the market. Two kinds

of measures can be used to assess transaction costs for a

specific trade. Ex-ante measures forecast the cost of a

trade not yet done while ex-post measures estimate the

cost of a completed trade. Actually, ex-ante measurement

models are usually estimated using ex-post transaction

costs. After a brief description of ex-ante transaction cost

measurement, we will focus on ex-post transaction

cost measurement.

EXECUTIONMETHOD

CHARACTERISTICS ADVANTAGE DISADVANTAGE

Agency trade Release of an orderspecifying thesecurity and thequantity

Execution on themarket with orwithout a target(closing price, VWAP,etc)

Low fees Market risk

(implicit costs)

Principal trade Determination of abasket: size,securities, etc

Commitment of thebroker to trade thebasket at apredetermined price;

Any other form ofRisk trade

No marketrisk

Higher fees

12

Ex-ante TCA relies on both explicit and implicit information

to predict transaction costs. Explicit information refers to

explicit transaction costs (fees, commission, etc) and

currently displayed market conditions. In transparent

markets, the prevailing liquidity conditions can be assessed

through the displayed limit order book or market makers’

quotations. When they trade less size than the displayed

market depth, investors can quite confidently estimate

their market impact if the liquidity available does not vary in

the meantime. When price improvement is possible on the

execution venue (hidden volume, specialists, floor brokers,

etc), the estimated transaction costs can even

overestimate the real transaction costs incurred by the

investor. The accuracy of such estimation becomes much

harder, and even impossible in some cases, when the order

size exceeds the contemporaneous market depth.

As well as explicit information, ex-ante TCA uses

information about the previous implicit transaction costs.

Ex-ante analysis assumes that the market impact of future

trades can be predicted from the market impact of previous

orders. This assumption is valid when liquidity conditions do

not change. Specifically, econometric regression models

are developed to explain post transaction costs by using

trade-specific variables (order size, price limit, etc),

market-related variables (spread, displayed depth, recent

volume, recent price change, etc) and stock-specific

variables (average volume, capitalisation, volatility, etc).

For performing ex-post TCA, two approaches exist: the

benchmarking method and the implementation shortfall.

Both are the most frequently used approaches applied in

the industry to enable assessment of the execution quality

of specific trades. Each one is described in detail below.

3.3.1 Benchmarking approach

A benchmark is a reference against which transaction costs

can be measured in a relative way. The benchmarking

approach significantly developed due to the need for

specific references to be factored into the measure of

transaction costs in order to account for the specificities of

each order (size, stock turnover) and ensure a fair evaluation

of the trader’s performances.

This method consists in computing the signed difference

between the average price obtained for the trade and a

benchmark price. For a buy, the transaction cost indicator is

defined as the trade price minus the benchmark price. For

a sell, the transaction cost indicator is defined as the

benchmark price minus the trade price. The choice of the

benchmark price is crucial in such an approach. On the one

hand, the benchmark price should be easy to obtain or

compute. On the other hand, the benchmark price should

be ideal, in the sense that it should enable an estimation of

the price that would have been observed if the trade had

not taken place. In this case, the difference between this

price and the trade price would be due entirely to the trade.

Depending on which benchmark price is used, different

kinds of transaction cost indicators exist. We can classify

them into three categories.

3.3.1.1 Absolute indicators without consideration

of time

These indicators are the most convenient to compute

because they do not require order time stamping to be

considered. In other words, these indicators do not take

into account the accurate time at which the trade is

decided or executed.

The most frequent indicators are based on the following

benchmark prices:

Daily VWAP: the volume weighted average price of the day

Daily LHOC: the average of the lowest, highest, openingand closing prices of the day

T-1 Close: last night closing price

T Close: closing price of the day

T Open: opening price of the day

Midpoint H/L: the median of the highest and lowestprices of the day

13

3.3.1.2 Time-related indicators based on market

data only

Unlike the previous category, these indicators are

dependent on the time at which either the trade decision is

made or the trade is completed. They rely on a benchmark

price that is computed around or at the time the order is

sent to the broker (release time) or the trade is executed

(execution time).

The most frequent indicators are based on the following

benchmark prices:

Ask: last ask price before execution

Bid: last bid price before execution

Last: last trade price before execution

Midpoint Bid-Ask: last Bid-Ask average

Next Mid Bid-Ask: next Bid-Ask average

Available VWAP: the VWAP computed from the market opening to the order/trade time

Interval VWAP: the VWAP calculated over a fixed time interval around the order release time or the trade execution time

Multi-day VWAP: the VWAP computed over a certain period of time (n days) around the trade execution time

3.3.1.3 Time-related indicators based on models

The growing importance of transaction costs has generated

several market impact models developed by brokers and

third-party services providers. The most famous include the

ITG, Barra and Plexus market impact models, which provide

support in searching for optimal trading solutions. The risk

inventory model, which directly relates transaction costs to

the liquidity provider’s risk of carrying excess inventories, is

also commonly used in the industry. Freyre-Sanders et al

(2004) offer a very complete description of the implicit

transaction cost indicators delivered by these models.

3.3.2 Implementation shortfall

The principle behind this approach is very simple to

understand. It consists in assessing the impact of trading

on portfolio returns by computing the difference between

the net returns on a paper portfolio and those on a real

portfolio. The first stage is hence to build and price a paper

portfolio at the time of the trade decision. This portfolio will

be an imaginary holding consisting of all the security

positions the investor decides to have. These positions are

assumed to be acquired at the price that prevailed on the

market at the time it was decided to hold them.

The resulting paper portfolio does not incur any cost. In the

opposite, the corresponding actual portfolio will incur all

the trading costs. Therefore, the next stage of the

implementation shortfall is to calculate the difference

between the paper portfolio and the actual portfolio.

This measurement is thus performed after the trade(s).

Figure 11 gives a summary overview of the approach.

Figure 11: Principle of the implementation shortfall

Source: Plexus Group

The biggest advantage of this method is that it includes all

the components of transaction costs, from explicit to

implicit costs. This is the reason why the implementation

shortfall approach is often preferred to other methods in the

industry. As we may see in Figure 12, even opportunity

costs are included if the order is partially filled or not

executed at all.

Figure 12: Implementation shortfall components

Source: Giraud (2004)

Settlement costs

Taxes, stamp duties

Cost of bundled services

Execution costs (broker)

Execution costs (market)

Market impact

Opportunity cost

Bid/ask spread

Explicit Costs

Net Proceeds of the transaction

Price at which trade decision is taken

Implementation Shortfall

Implicit Costs

14

The key element here is the choice of the reference price

for valuing the paper portfolio. On this point, the

implementation shortfall is somewhat similar to the

benchmarking method. The spread midpoint at the time of

the trading decision is the price of reference most

frequently used, essentially because it provides an

easy-to-interpret measure of transaction cost.

However, varying the reference price allows one to

emphasise different aspects of transaction costs. If the

benchmark price used is the market price prevailing at the

time the order is sent to the broker (release time), one can

compute the implementation shortfall over the execution

window only, excluding operational opportunity cost.

Choosing a post-trade reference price allows one to control

the information content of the trade. The implementation

shortfall measure can also be adjusted for the impact of the

market trend on the portfolio value.

When properly implemented through the difference

between a paper and actual portfolio, implementation

shortfall correctly allows one to estimate the cost of

missed trades, as it takes into consideration the time

horizon of the investment decision.

3.4 Pitfalls and common problems in transactioncost analysis

Transaction cost indicators are numerous and their quality

often relies on the choice of the right benchmark price to

compute them. Identifying the appropriate benchmark price

is not always as easy as it seems. The ‘ideal’ transaction

cost indicator should be accurate, unbiased, relevant and

not liable to gaming strategies. It should also properly deal

with opportunity costs. This definition emphasises five

issues that arise when measuring transaction costs:

Noise in measurement

Bias in estimation

Relevance of indicators

Gaming problems

Dealing with opportunity costs

When investors want to gauge the quality of their

transaction cost indicators, they should consider each of

these issues. We explain them in detail below.

3.4.1 Noise in measurement

Indicators are noisy when the time gap between the

execution of the trade and the determination of the

benchmark price is large. A benchmark price identified

around the time of the execution seems more suitable for

assessing the quality of the trade execution. Hu (2004)

classifies measures of implicit transaction costs into three

categories regarding the benchmark prices used. Pre-trade

measures use prices prior to the trade. During-trade

measures use some kind of average price over the trading

horizon. Post-trade measures use prices after the trade.

Hu empirically shows that a pre-trade measure can be

broken down into a market movement component and a

during-trade measure. As the market movement component

is dominant in pre-trade measures, Hu suggests that

during-trade measures are less noisy for gauging the

quality of an execution.

3.4.2 Bias in estimation

Indicators are biased when they depend on how or why the

trade is done. According to Harris (2003), biases may arise

when trading decisions depend on past price changes or

when traders are well informed about future price changes.

Some benchmark prices can deliver indicators that will be

systematically high or low according to whether the

investor pursues a momentum or a contrary strategy.

For example, the opening price delivers indicators that are

easily biased. Momentum traders overestimate their

transaction costs because they buy (sell) when prices have

risen (fallen), so that the opening price is low (high). In the

opposite scenario, contrarian traders buy (sell) when prices

have fallen (risen), so that the opening price is high (low).

This leads to an underestimation of their transaction costs.

3.4.3 Relevance of indicators

First, TCA assumes that the benchmark price is an

appropriate reference price for the value of the security.

The only ‘true’ value of a security is the price at which an

actual trade was made. Sometimes, the benchmark price is

15

not this price. For example, the spread midpoint can be a

price at which no trade has taken place. Furthermore,

market fragmentation and proliferation of liquidity pools

make the determination of the right benchmark price more

difficult. When a security is traded at the same time on

various execution venues, which price among the coexisting

ones can be defined as the best value for the security?

Secondly, the benchmark price should be an appropriate

reference price regarding the difficulty level of the trade.

In other words, the benchmark price should be adjusted for

the size of the order relative to the liquidity of the security

traded. Indeed, the spread midpoint or the best quote at

the time of the trading decision may not be relevant given

the order size. It can also be the case with the last market

price, if the size of the last trade was small.

Thirdly, the appropriate benchmark price should also

consider the investor’s requirements. Investors can set, in

relation to their broker, constraints related to timing,

execution probability, price or volume. For example, using

the closing price as benchmark for a trade that the broker

has to execute quickly because the investor is impatient

cannot be suitable, especially when the order is released in

the morning.

3.4.4 Gaming problems

Gaming indicators is possible when intermediaries can

estimate the benchmark price that will be used to measure

transaction costs and can choose to postpone the trade to

obtain a new benchmark price. In fact, when they have

discretion over the timing of their trades, intermediaries

can game any indicator for which the benchmark price

depends on the timing of their trades.

Harris (2003) describes several situations in which brokers

can game their evaluations. For example, brokers who have

discretion over how aggressively they fill their orders can

easily game an indicator based on the spread midpoint

prevailing at the time of the trade. To game this measure,

brokers always supply liquidity and never take it.

Consequently, they always buy at the ‘bid’ or sell at the

‘ask’ and their estimated transaction cost indicators will

always be negative. This behaviour can generate significant

opportunity costs.

3.4.5 Dealing with opportunity costs

Opportunity costs are real transaction costs. However,

most indicators do not deal with them in an appropriate

way. First, missed trade opportunity costs are often

ignored. Nevertheless, failing to trade can be costly.

To assess the opportunity cost of an uncompleted trade,

traders should use as a benchmark the average price at

which the trade would have taken place if it had been

completed. Identifying such a price is not at all easy, owing

in particular to the potential market impact of the trade.

Next, most indicators do not distinguish between market

impact and opportunity costs. Varying the time at which the

benchmark price is defined allows for measuring market

impact separately from operational and market timing

opportunity costs. However, this requires a large amount of

data about orders and trades. In practice, investment firms

can face the difficulty that time-stamped information on

individual trades may be incomplete. For example,

time-stamped data can be only available from the time of

the release to the broker. In this case, no data including

decision time is available, so the operational opportunity

cost cannot be measured. Another possibility is that

time-stamped information is not gathered in one place but

held across different systems and perhaps not within the

same firm.

3.5 A critical review of the most popular TCA indicators

No transaction cost indicator seems to be perfect and

therefore offer a standardised framework for the easy

assessment of the performance of any execution. This is

partially due to the attendant history. Mostly, TCA indicators

were tailored to answer specific issues about individual

aspects rather than to address the quality of the entire

trading process. In fact, various problems that have been

described in the previous section can restrict or complicate

both their implementation and interpretation. The reference

price used when evaluating transaction costs determines

what is actually measured. Different benchmark prices can

therefore serve different purposes. Even if they have

certain advantages, most benchmark prices can also have

serious drawbacks.

16

3.5.1 Spread midpoint benchmark

The indicator based on the spread midpoint relies on the

signed difference between the trade price and the average

of the bid-and-ask at a given time. Varying the time at

which the spread midpoint is determined delivers

various indicators.

The effective spread relies on the quotation midpoint

prevailing at the time of the trade execution. The effective

spread is twice the liquidity premium, which is the signed

difference between the trade price and the time-of-trade

midpoint. When the trade is completed at the quoted price

(a buy at the ask/a sell at the bid), the effective spread

equals the quoted spread. When the trade is completed

inside the spread owing to price improvement, the effective

spread is smaller than the quoted spread. When the trade

is completed outside the spread (the large order is filled at

prices outside the best quote), the effective spread is larger

than the quoted spread.

The realised spread is based on a post-trade quotation

midpoint. It equals twice the signed difference between

the trade price and the midpoint observed at a specified

time after the trade. The realised spread equals the

effective spread only when the quotation midpoint does not

change over the measurement interval.

Spread midpoint indicators are very popular, essentially

because they are very easy both to implement and

interpret. The cost of a buy at the ‘ask’ (or a sell at the ‘bid’)

is one half of the spread. However, they present

several limitations.

First, these indicators do not indicate whether the trade is

well timed. The consideration of trade-timing is important

for investors who give their intermediaries discretion over

the timing of their trades. In this situation, investors expect

that their brokers will use both their experience and skills to

use predictable short-term price movements. Buys are

cheaper than sells when dealers lower prices to move

excess inventory or when prices are depressed in response

to a large uninformed seller. Transaction cost indicators can

help investors to check if their brokers use discretion in an

appropriate way. In general, trade-timing effects are best

measured when the benchmark price does not depend on

the time of the trade. When the benchmark price relies on

the time of the trade, trade-timing effects will be best

measured when the time gap between the execution and

the determination of the benchmark price is long. As a

consequence, the consideration of trade-timing effects is

the opposite of the consideration of accuracy (absence of

noise) in measurement.

Secondly, the spread midpoint at the time of the trade can

deliver a poor indicator for large orders completed through

multiple trades. When orders are split into smaller lots, the

indicator should estimate the total cost of executing the

entire order and not simply make the sum of the cost of

each lot. As the first lots impact upon the market,

they make the remaining lots more expensive. An indicator

based on the spread midpoint benchmark price will

underestimate the total transaction costs if a different

midpoint is used for each trade.

Thirdly, intermediaries can game effective spread indicators.

As shown in the previous section, always supplying liquidity

and never taking it is a way of gaming these indicators.

Brokers can also game them because the benchmark price

depends on when they execute the trade. The trade may be

deferred until the quoted spread becomes narrower.

3.5.2 Volume weighted average price

The volume weighted average price (VWAP) indicator is

defined as the signed difference between the trade price

and the market volume weighted average price computed

over a given interval. The most frequently used indicator

relies on the daily VWAP, ie the volume weighted average

of all prices in the trading day. Nowadays, almost all data

vendors as well as some trading venues compute and

diffuse the daily VWAP in real time. However, the window

measurement can be shorter (interval/available VWAP) or

longer than one day (multiday VWAP). The VWAP-related

indicator is widely used in the industry, mainly because of

its easy interpretation: it indicates whether the trader

received a higher or lower price than did the ‘average

trader’ of the measurement interval. Nevertheless, the

VWAP benchmark is not always appropriate for the correct

assessment of transaction costs.

17

First, VWAP-related indicators can be noisy since they

assume that the same benchmark price is the reference

price for all trades that take place within the window

measurement. Consequently, the larger the window is, the

noisier the indicator can be.

Secondly, indicators based on VWAP become less useful if

the individual trade being analysed is the dominant trade in

the measurement interval. They can even equal zero if the

trade is the only one made during the time window. This

issue is particularly relevant for less liquid securities and/or

thin markets.

Thirdly, the VWAP benchmark delivers biased indicators in

some circumstances. When using daily VWAP or available

VWAP, momentum traders estimate positive transaction

costs while contrarian traders obtain negative transaction

costs. Biased indicators are also possible for large orders

that are split to be filled. In general, biases arise when the

benchmark price depends on the multiple trades required

to fill a large order. Since the price impact of each trade

affects the VWAP, any VWAP-related indicator will offer a

biased measure of transaction cost for the large order.

Fourthly, gaming problems can arise with the VWAP

benchmark, which is partially determined by the point at

which the broker decides to execute the trade. The trade

may be deferred to obtain a new measurement interval.

Intermediaries can also ensure that their transaction cost

estimate will tend to zero by spreading the execution of the

trade over the window measurement, so that the volume

weighted average price of the trade is equal to the

market VWAP.

3.5.3 Closing price benchmark

The indicator based on the closing price is calculated as the

signed difference between the trade price and the closing

price of the day. This price benchmark is very attractive in

the industry and presents several advantages.

First, closing prices are easy to get and require little data

treatment. All other popular indicators require intraday

market trade data (VWAP, LHOC) or quotation data (spread

midpoint), although the indicator relying on the closing

price only needs summary daily market data. Furthermore,

many investment firms prefer to use closing prices as

benchmarks because they already value their portfolios at

these prices.

Next, as the determination of the benchmark price is made

after the time of the trade, the closing price indicator

cannot in general be gamed. The exception comes from

brokers who have timing discretion to execute the trade

and who trade only at the close. By doing this, they ensure

that their transaction cost estimate will be zero.

Another advantage of using the closing price to assess

transaction cost is that indicators are not biased in efficient

markets. However, such indicators can be noisy when the

time gap between the execution of the trade and the

market close is long. The daily close is seldom the right

reference price for a trade completed early in the session.

In this case, the opening price should be preferred to

minimise noise.

3.5.4 Average of the lowest, highest, opening andclosing prices (LHOC)

The indicator relying on the LHOC makes the signed

difference between the trade price and the average of the

lowest, highest, opening and closing prices of the day.

This indicator is widely recognised in the industry, even if it

raises two serious issues.

First, as a simple average of prices irrespective of the

traded quantities, the LHOC-related indicator loses the

dimension of market depth included in the VWAP. In fact,

the LHOC benchmark price cannot be one at which a trade

of the required size could have been done.

Secondly, the LHOC benchmark price provides transaction

cost indicators that greatly depend on opening and closing

prices (50 per cent of the measure), which cannot always

be considered relevant as references. The opening (closing)

price produces very noisy transaction cost estimates for

orders filled at the end (start) of the day.

The only advantage of indicators based on the LHOC is that

they cannot be gamed. Indeed, intermediaries cannot

estimate the benchmark price in advance and make the

related indicator equal to zero.

18

3.5.5 Implementation shortfall

The method of paper portfolios is very popular in the

industry, in particular because it offers a framework for

measuring all components of trading costs, from explicit to

implicit costs. However, this approach has both advantages

and disadvantages.

The main advantage of the implementation shortfall is that

it delivers transaction cost indicators that cannot be gamed

by intermediaries. This is related to the fact that the

benchmark price is determined before the order is sent to

the broker. If the broker delays or avoids the execution of

the order, he will automatically generate opportunity costs

that will appear in the implementation shortfall.

Furthermore, the implementation shortfall method is

suitable for a large order that requires multiple trades to be

filled. As the benchmark price is determined before the

order has an impact on market prices, the resulting

indicator is unbiased.

The main disadvantage of this approach is that it requires a

large amount of data, ie, intraday quotation data as well as

decision time and order size data. Collecting all this data

and dealing with it can be difficult or quite costly for

investment firms. In practice, time-stamped information on

individual trades may be incomplete or not gathered in one

place but held across different systems and perhaps not

within the same firm. Therefore, the implementation

shortfall indirectly requires that data acquisition and

treatment costs are not a consideration.

Finally, the implementation shortfall is, in a way, similar to

the benchmarking approach; a price of reference is needed

to value the paper portfolio. According to the benchmark

price that is used, we can face some of the problems that

have been described previously, namely:

Noise in measurement

Relevance of indicators

Dealing with opportunity costs

Table 3 provides a summary of the advantages,

disadvantages and data requirements for the TCA

indicators that have been analysed. As can be seen in the

table, none offers a satisfying method for assessing the

quality of execution.

INDICATOR BENCHMARK PRICES MAIN ADVANTAGES MAIN DISADVANTAGES MARKET DATA REQUIREMENTS

Spread midpoint Time-of-trade midpoint

Post-trade midpoint

Easy interpretation,

implementation

No trade-timing consideration

Biased for split orders

Gaming problems

Intraday quotation data

VWAP Daily/Multi-days VWAP

Interval/Available VWAP

Easy interpretation,

implementation

Noisy for large windows

Biased for split orders

Biased for some strategies

Gaming problems

Daily VWAP, intraday trade data

Closing price Daily close Easy implementation

Few gaming problems

Noisy for trades completed at the start of the day Daily market data

LHOC Average of the LHOC prices No gaming problem No trade-size consideration

Noisy due to the weight of opening and closingprices

Intraday trade data

Implementation shortfall Decision-time midpoint

Release-time midpoint

Post-trade midpoint

No gaming problem

No bias

Heavy implementation (decision time and orderdata)

(Choice of the benchmark)

Intraday quotation data

Table 3: Summary of the most popular TCA indicators

19

3.6 The EBEX indicators

The absence of a standardised framework to enable an

easy assessment of the quality of the whole trading

process becomes problematic under the Markets in

Financial Instruments Directive (MiFID). Indeed, with this

new piece of European regulation, traders are going to

enter an environment where they will have to demonstrate

that they have executed under the best possible conditions

for their customers while taking into consideration

potential multiple liquidity pools. This section is devoted

to a proposed methodology that fills the gap by

offering a unified framework for measuring the quality of

execution ex-post.

We present the new tools for measuring the quality of

execution as part of a peer group review and identifying

whether the intermediary (broker/trader/algorithm) has

implemented the execution too aggressively or too slowly.

In fact, we will see that this new approach relies on two

indicators that allow an easy comparison of a large universe

of trades and provide insightful information not only about

the final performance (the absolute EBEX indicator)

but about the possible justification of the performance

(the directional EBEX indicator). Having exposed the

objectives and principles behind this new methodology, we

then describe in detail how both indicators are built, as well

as how they can be interpreted.

Although it presents many advantages, the new

methodology also raises significant questions that will

have to be dealt with. We complete this chapter by

addressing these open questions and presenting

further developments.

3.7 Presentation of the new methodology and its foundations

From the analysis carried out in the previous section, we

know that existing TCA approaches, especially the

benchmarking methods and the implementation shortfall,

provide interesting information about the quality of

individual executions. However, we also know that various

problems and pitfalls can restrict or complicate both their

use and their interpretation.

In our opinion, the most significant issue is the absence of

a standardised framework such that the entirety of the

trading process can be readily gauged. While each popular

indicator might be considered optimal for specific situations

or conditions, no common measure of the execution quality

can be easily aggregated at broker, trader or algorithm level

across a series of trades in order to determine its overall

performance. This becomes problematic in an environment

where traders will have to demonstrate that they have

executed at the best possible price for their customers

while taking into consideration potential multiple

trading venues.

The methodology that will be described addresses this

specific issue by offering a unified framework for

measuring the quality of execution as part of a peer group

review and characterising how the broker, trader or

algorithm has implemented the execution.

3.7.1 Objectives, principles and concepts

The methodology that we propose for measuring the quality

of execution is based on two fundamental principles.

First, the average trade price is the primary component of

the quality of an execution, even if other dimensions exist.

The price includes all the implicit costs related to the

execution, with the exception of the missed trade

opportunity cost. The reader will acknowledge that other

explicit costs (brokerage fees, stamp duties, and IT and

operational costs) can be assessed separately on a global

basis rather than trade by trade. Finally, qualitative

elements such as timeliness and speed of information flow

are further elements of importance that cannot be included

in a systematic quantitative framework.

Secondly, we consider that the best reference for

assessing the quality of a price ex-post is the universe of all

trades relative to the same security, executed on the

available trading venues, under similar timing and

constraints. Assuming a model can tell us what the ‘best

price’ would have been, the question of whether the trade

would have been processed differently carries a significant

level of model risk on which we do not want to depend.

20

As can be understood, our methodology involves peer

group analysis, which offers the most interesting areas of

development under the MiFID. Indeed, harmonisation of

post-trade reporting requirements for regulated exchanges,

multilateral trading facilities, OTC markets and systematic

internalisers will allow for transaction databases to be built

that will support the development of peer group analysis.

Our approach provides a relatively simple answer to the

following natural question:

‘Given a transaction handed over to a broker, trader or

algorithm and executed for a given price at times that are

recorded under given time constraints, to what extent have

other brokers, traders or algorithms executed comparable

volumes to this transaction, either before or after this

transaction, at a better or equal price?’

The answer to this question can be split into four important

elements:

The time at which the order is handed over (release time)

to an intermediary (a broker, a trader or an algorithm) is the

first point of reference; the time at which the order is

entirely filled (execution time of the last lot referring to the

order in case of splitting) is the second point of reference.

With reference to timing, it is equally important to know

what deadline was given to the trader/algorithm/broker to

implement the instruction.

The size of ‘competing trades’ is not important as such; the

relevant measure is how many times a volume comparable

to the order has been executed at a better or equal price,

which is the first measure of the quality of the price

obtained. The price has to be compared with small trades

executed at equal or better prices (the broker, trader, or

algorithm could have split the order better) as well as with

larger trades (the order could have been grouped with a

larger flow of orders to be executed en bloc if such trading

capability is offered).

Volumes traded beforehand at a better or equal price allow

one to measure whether the broker, trader or algorithm has

been too patient.

Volumes traded afterwards at a better or equal price allow

one to measure whether the broker, trader or algorithm has

been too aggressive.

Based on these elements, our methodology enables

measurement of the quality of execution as part of a peer

group review and determination of whether the broker,

trader or algorithm has implemented the execution too

aggressively or too slowly. Specifically, this approach relies

on two indicators:

Absolute EBEX indicator — measures the quality ofexecution in a peer group review

Directional EBEX indicator — determines whether the broker, trader or algorithm has implemented the execution too slowly or too aggressively

In other words, the first indicator assesses the quality of

execution itself while the second indicator provides

information about why the quality of execution is

as observed.

3.7.2 Detailed description of the indicators

Our two indicators rely on the same philosophy and are

easy to both compute and interpret. In the interests of

convenience, we will begin with a presentation of the

second indicator.

3.7.2.1 Directional EBEX

3.7.2.1.a Definition and components

Directional Estimated Best Execution for an order

indicates how the broker (or any other intermediary,

trader or algorithm) could have traded over time to

provide a better execution. This indicator results from the

combination of two sub-indicators that respectively

measure the volumes traded at a better or equal price

before and after the execution of the trade. Specifically,

the directional EBEX indicator for order i is computed

as follows:

)(S

V

)(S

V

NBBEX

i

M

1mjm,

i

N

1n

APPjn,

ji,

i

∑

∑

=

=

≥

=

)(S

V

)(S

V

NBBEX

i

M

1mjm,

i

N

1n

APPjn,

ji,

i

∑

∑

=

=

≤

=

21

In both equations, each element is defined as follows:

NBBEXi,j is the number of better executions for order i during the time interval j

j is the interval between the time the broker receives order i and the time order i is completely filled

Si is the size of order I

APi is the average trade price obtained for order I

N is the number of trades at a price equal to or betterthan APi during time interval j

is the size of trade n at a price equal to or higher (lower) than APi during interval j

M is the total number of trades during the time interval j; M ≥ N

is the size of trade m during time interval j

NABEXi,t stands for Number of After-Better Executions

for order i over the time interval t. This component can

be defined as a ratio between the aggregate volumes

traded at a price equal to or better than the average trade

price of order i divided by the size of order i and the

aggregate volumes without consideration of price

divided by the size of order i. This ratio is computed over

the interval t which starts at the time order i is

completely filled (execution time) and which ends at the

market close of the day.

The mathematical notations referring to NABEXi,t are

given below, for sell orders and buy orders respectively:

In both equations, each element is defined as follows:

NABEXi,t is the number of better executions for order i during the time interval t

t is the interval between the time order i is completely filled and the next market close

Si is the size of order I

APi is the average trade price obtained for order I

N is the number of trades at a price equal to or betterthan APi during time interval t

is the size of trade n at a price equal to orhigher (lower) than APi during interval t

M is the total number of trades during the time interval t; M ≥ N

is the size of trade m during time interval t

3.7.2.1.b Interpretation

Now that both components of the directional EBEX

indicator have been presented, we can focus on how

they can be interpreted with respect to characterising

the timing of the trade. This interpretation is very easy to

make because both range from zero to one, given the

way they are built. Reference to Figure 13 will help

understand how the interpretation can be made.

Figure13: Interpretation of directional EBEX

components

jm,V

)(S

V

)(S

V

NABEX

i

M

1mtm,

i

N

1n

APPtn,

ti,

i

∑

∑

=

=

≥

=

)(S

V

)(S

V

NABEX

i

M

1mtm,

i

N

1n

APPtn,

ti,

i

∑

∑

=

=

≤

=

( ) iAPPjn,V ≤≥

( ) iAPPtn,V ≤≥

tm,V

TO BE IMPROVED

BEST

POOR

TO BE IMPROVED

Broker should

be more aggressive!

Broker should be more patient!

NB

BEX

i

NABEXi

0

1

1

Order i execution

22

NBBEX is close to zero when few traders secured a

price equal to or better than the order before its

execution. In this case, the quality of execution can be

said to be high. The intermediary did a good job because

few traders obtained equal or better prices. In the

opposite case scenario, NBBEX is close to one when

many traders got an equal or better price before the

order execution time. Hence, the quality of execution is

low and the intermediary should have been more

aggressive. Indeed, the intermediary would have had

more opportunities to trade at equal or better prices

before the order execution time.

The way we can interpret NABEX is similar, except that

we focus on what happens after the order execution

time. NABEX is close to zero when few traders obtained

an equal or better price after the execution of the order.

The quality of execution is then high because few

traders did as well as or better than the intermediary did.

NABEX is close to one when many traders obtained

equal or better prices. In this case, the intermediary dida

bad job in the sense that more patience should have

been shown. The intermediary would have had more

opportunities to trade at a better price after the order

execution time.

A direct comparison of both indicators delivers our

directional EBEX indicator, for which the interpretation is

even easier. The goal of this indicator is to give

information about how the intermediary could have

traded over time to provide a better execution. Given its

construction, a simple difference between NBBEX and

NABEX, the directional EBEX indicator can range from

–1 to +1. Consequently, it can be interpreted as follows.

Directional EBEX has a negative value when NBBEX is

lower than NABEX. In this situation, we can say that the

intermediary should have been more patient because

more opportunities would have arisen to enable trading

at a better price after, as opposed to before, the order

execution. In the opposite case scenario, directional

EBEX is positive when NBBEX is larger than NABEX.

In this case, the intermediary should have been more

aggressive because more opportunities would have

arisen to trade at a better price before rather than after

the order execution.

The specific situation where NBBEX is just equal to

NABEX corresponds to a directional EBEX of zero.

This means that there were as many equal or better

executions before as there were after the execution of

the order. In such a case, the intermediary could have

traded at any other time to provide a better execution.

This specific case can also refer to the outstanding

situation where both NBBEX and NABEX tend to zero.

This should mean that the intermediary chose exactly

the right moment to trade.

Figure 14: Interpretation of the directional EBEX

indicator

3.7.2.2 Absolute EBEX

3.7.2.2.a Definition

The absolute indicator of Estimated Best Execution for

an order is defined as the difference between one and a

ratio between the aggregate volumes traded at a price

equal to or better than the average trade price obtained

for the order divided by the order size and the aggregate

volumes without consideration of price divided by the

size of the order. The ratio is then computed over the

interval running from the time the broker receives the

order (release time) to the next market close.

Specifically, the absolute EBEX indicator for order i is

calculated as follows, for a buy and a sell respectively.

ti,ji,idir, NABEXNBBEXEBEX −=

NBBEXi,j = NABEXi,t

NBBEXi,j < NABEXi,t NBBEXi,j > NABEXi,tMany or no better executions

before and after the broker’s trade!

-1 0 + 1

23

In both equations, each element is defined as follows:

EBEXabs,i is the absolute best execution indicator for order i during the trading day

day is the interval between the time the broker receives order I and the next market close

Si is the size of order I

APi is the average trade price obtained for order I

N is the number of trades at a price equal to or betterthan APi during the time interval

is the size of trade n at a price equal to or higher (lower) than APi during interval day

M is the total number of trades during the time interval day; M ≥ N

is the size of trade m during the time intervalday

3.7.2.2.b Interpretation

Given the way it is built, the absolute EBEX indicator can

only take values between zero and one. This makes the

interpretation very easy, as illustrated in Figure 15.

Figure 15: Interpretation of the absolute EBEX

indicator

3.7.3 Advantages of this method

As may have been noticed in the presentation of the

preliminary study, our method presents several

advantages, especially in comparison with the existing

approaches used in TCA. The main advantages that we have

identified can be summarised as follows.

Easy computation and interpretation The method that

we propose relies on two indicators. Both are easy to

understand, calculate and interpret. Compared with

indicators based on sophisticated models and resembling

black boxes, this simplicity is a real advantage for any

investors who want to assess their intermediaries.

Standardised framework Our method offers a unified

framework to enable easy assessment of the quality of

execution across a series of trades aggregated at any level.

Neither a specific indicator per trade nor a benchmark price

depending on the trade characteristics is needed.

Whatever the trade, the comparison has to be done with

the other trades relative to the same security around the

same time. This standardised approach allows an easy

comparative analysis of performance across several

brokers, traders or algorithms to be made.

Absolute measure of execution quality The absolute

EBEX indicator delivers an absolute measure of the quality

of execution for a trade. This measure is a score lying

between zero (bad execution) and one (good execution).

As a consequence, the interpretation is straightforward and

objective. Furthermore, this absolute measure, by varying

from zero to one, can be readily used to impart clear

objectives to an intermediary. For example, one could

expect an active trader not to execute trades in the lowest

quartile, or to have a median absolute EBEX indicator

larger than 0.5 to justify the use of an active market

timing strategy.

Trade-timing consideration When investors give their

intermediaries discretion over the timing of their trades,

they like to assess whether their trades are well timed.

Trade-timing consideration is included in our method thanks

to the directional EBEX indicator. This offers an easy-to-

interpret measure, varying from –1 (too aggressive) to 1

)(S

V

)(S

V

1EBEX

i

M

1mdaym,

i

N

1n

APPdayn,

iabs,

i

∑

∑

=

=

≤

−=

)(S

V

)(S

V

1EBEX

i

M

1mdaym,

i

N

1n

APPdayn,

iabs,

i

∑

∑

=

=

≥

−=

( ) iAPPdayn,V ≤≥

daym,V

0 1

The closer to 0 EBEXabs is, the worse the execution!

Among all the “similar” trades of the interval day, most got a price equal to or better than the trade priceof the broker. Terrible performance.

The closer to 1 EBEXabs is, the better the execution!

Among all the ‘ similar‘ trades of the interval day, few got a price equal to or better than the trade priceof the broker. Excellent performance.

24

(too slow), to gauge whether the intermediary is exercising

discretion in an appropriate way.

No gaming problem Our method involves a peer group

analysis which is performed post-trade. Therefore,

this approach cannot be subject to gaming strategies from

intermediaries.

3.7.4 Open questions and further developments

Although presenting a lot of advantages, the new method

that we propose also raises significant questions that will

have to be dealt with. Each of them is discussed below as

well as the answers that we have thus far come up with.

How can one ensure that a complete universe of trades is

used for the peer group comparison?

In theory, our method relies on a peer group analysis that

considers all the trades relative to the same security and

executed on all the available trading venues. In practice,

this approach can suffer from operational difficulties.

We have seen in the previous chapter that it can be difficult

for investment firms to collect intraday market data,

especially for less liquid securities and/or OTC trades.

However, these operational difficulties are expected to

decline or disappear under the MiFID. Although there is no

industry infrastructure today for consolidating data about all

trades executed, or otherwise, on a regulated market,

recent developments suggest that data vendors and

exchanges are likely to develop such offerings. This will

allow peer group analysis to be carried out on the most

relevant universe of trades.

Is a peer group analysis relevant in very illiquid securities?

Both the absolute EBEX indicator and the directional EBEX

indicator provide a strong framework, even for very illiquid

securities, provided one accepts that the tickets printed

during the measurement window represent a relevant

measure of the value of the security. Even if only two

trades are booked during a period, our approach allows for

the price received to be assessed in relation to the only

other relevant information available.

How can one incorporate the possible constraints set by

the investor into such an analysis?

This last question is probably the most difficult to address.

Even though our absolute EBEX indicator remains a very

valid indicator of the absolute quality of execution,

constraints imposed on the intermediary might result in the

indicator giving an unfair picture. This can be the case when

the price target set by the investor is a poor objective or

when the volume constraint results in trading opportunities

being missed.

An appropriate approach would be to first assess the overall

execution process with the two EBEX indicators without

taking constraints into consideration and then fine-tune the

analysis with another adequate indicator reflecting the

performance regarding the constraint imposed on the

intermediary. To measure this last performance, we could

focus on the analysis of the dispersion of prices obtained.

We could then build a third indicator in order to define for

the trade a position between the target set by the investor

(price target if such is the objective, VWAP if the trade has

to be executed in line with participation, etc.) and the worst

price of the measurement period given the direction of

trade. Dealing with such an additional indicator requires

that investment firms are able to collect order data that

includes the constraints imposed on the intermediary.

The validity of our methodology remains complete with

regard to a best execution obligation as the EBEX indicators

measure the execution performance in absolute terms

based on the price obtained (hence indirectly the total

proceeds of the trade as required by the regulator) while a

unified approach for assessing the dispersion around a

target price would allow the constraints imposed on the

intermediary to be taken into consideration and the

performance of the intermediary ‘net’ of the constraints

given to be measured precisely.

What remains important to understand is that EBEX will

always measure the final quality of the execution, whatever

the constraints are. When constraints are imposed, EBEX

might demonstrate that a constraint led to a significantly

poor price, even though the constraint has been fully

respected (eg if the objective of trading in line with volumes

— participation algorithm — is fully respected, EBEX would

still analyse the absolute quality of the final price obtained).

25

4. A Pan-European Survey

In order to provide some perspective on current practices

in European buy-side firms, we have conducted a

significant survey in the most significant buy-side trading

teams over the second quarter of 2006. The objective of

this exercise is to better understand the realities of how

buy-side firms perceive the importance of transaction cost

analysis and how they currently operate in that area.

4.1 Methodology and Sample

4.1.1 Methodological approach

Following extensive research, an online questionnaire was

sent out to 2,600 contacts in European buy-side firms

(traditional asset managers and hedge fund managers).

The questionnaire was sent by means of electronic mailing

and followed by an intensive campaign of direct calls to

ensure a high level of responses was obtained in order to

increase the relevance of the data gathered.

127 responses were received and processed by the

EDHEC Risk team. 26 responses were provided by

European hedge fund managers while 101 responses were

sent from traditional asset management firms giving us the

opportunity to analyse the difference between these two

fairly specific populations in terms of trading and execution.

4.1.2 Analysis of the sample

The focus of this survey was strictly restricted to European

institutions and, for global firms, we contacted European

branches or operating business lines. Questionnaires were

sent to the largest sample possible; all 25 EU countries

were represented in our list of recipients.

Respondents within the hedge fund industry mainly came

from London and Paris, with a significant majority sourced

from the European capital of hedge funds: the London

west-end. No country other than UK, France and Germany

are represented in our sample but this does not cause a

significant bias, as the vast majority of European hedge

funds actually operate from the UK.

Responses from the traditional asset management industry

mainly came from the UK, France and Germany, which

represent the three European countries where asset

management firms are heavily present in numbers. But

nevertheless, we received significant responses from the

four other countries where the asset management industry

is well developed: Italy, Switzerland, the Netherlands

and Spain.

Question 1: What is your main country of operations?

An initial analysis of our respondents suggests our sample

does not suffer from any significant geographical bias and

our analysis can therefore be deemed to provide an

accurate reflection of the European buy-side industry.

In terms of assets under management, we asked the

approximate size of assets that were managed by the

firms, gross of any leverage (ie we actually summed up the

assets provided by investors to be managed through the

various strategies).

In our sample, the average assets under management of

traditional managers are EUR70bn, with a median of

EUR83bn. This clearly indicates that the respondents in our

survey adequately represent both the largest and the

smallest extremes of the industry.

With regards to our hedge fund respondents, average

assets under management are EUR117m, with a median of

EUR111m, which once again confirms our sample as an

adequate representation of both the smallest and the

largest extremes of the industry.

3

4

5

6

10

37

1

4

2136

0 10 20 30 40 50 60

Spain

The Netherlands

Switzerland

Italy

Germany

France

United Kingdom

Traditional Asset Managers Hedge Fund Managers

27

Figure 16: Hedge fund industry distribution of AUM

Source: EDHEC Risk Advisory

With an industry average of EUR153m (compared to an

average AUM of EUR117m in our sample), our sample

exhibits a slight bias towards smaller hedge funds that we

have to keep in mind when analyzing respondents in the

hedge fund industry.

Question 2: What are your assets invested in?

Transaction Cost Analysis has mainly developed within the

equity world, more specifically for listed securities. TCA in

other areas is developing but still faces a significant number

of issues:

– bonds: market maker industry with no centralised

source intraday tick data

– listed derivatives: conceptual issues related to the fact

that variations in price can largely be explained by

variations in the underlying, rendering the separation of

market impact and opportunity costs fairly complex

– other securities: low volumes and market driven by the

liquidity provider

– OTC markets: absence of normalisation of contracts and

difficulties in sourcing and centralising transaction data

Interestingly enough, the area where transaction cost

analysis is most developed today (listed blue chips) is likely

to be the one where it is least needed as market efficiency

tends to push market impact down, while market makers’

market and illiquid securities/contracts are exchanged in

more ‘obscure’ markets where spreads are likely to be

maintained wide.

The asset class that is most traded by our respondents is

without any doubt that of listed equities and other

securities. With nearly half of hedge fund investments

related to equities and 40 per cent of traditional managers’

investments limited to listed equities, the importance of

TCA for equities is confirmed, as volumes in these markets

are expected to be very significant.

Government and corporate bonds represent the second

and third largest share of our respondents’ trading activity,

which demonstrates the importance of developing market

data and transaction cost analysis tools dedicated to these

specific markets.

Obviously, most firms, especially hedge funds, tend to use

an increasing number of forms of derivatives in replacement

or addition to their investment in securities. As such, the

necessity to formalise transaction cost analysis for derivatives

contracts is likely to become a major challenge for the

industry in the coming year. This challenge will require:

– conceptual barriers to be eradicated

– market and transaction data to be made available as has

been done for cash markets

– OTC market transparency to increase and post-trade

reporting to develop

Finally, we asked where our respondents’ investments

were directed in terms of the geographic zone. The

dominant investment zone for our European managers is

the Americas (32 per cent), followed by the Euro zone

(23 per cent) and South East Asia (23 per cent).

Industry

0%5%

10%15%20%25%30%35%40%

0-25 25-50 50-100 100-200 200-500 500+

Assets Under Management (€ Million)

Foreign Exchange and Cash

15%

Commodities 7%

Equities39%

Government Bonds26%

Corporate Bonds13%

Foreign Exchange and Cash

5%

Commodities 12%

Equities49%

Government Bonds12%

Corporate Bonds22%

Traditional Asset Managers Hedge Fund Managers

28

Question 3: Where are your assets invested?

It is interesting at this stage to note that other zones such

as Russia, China and the Middle East represent a total of

22 per cent, which is far from negligible when one takes

into consideration the very fragmented nature of these

markets and the importance of actively assessing

transaction costs in those markets that have not all proven

to provide levels of efficiency as high as the European

and US markets. When we add to this the European and

American emerging markets, which exhibit the same

fragmentation and possible lack of efficiency, this amounts

to 29 per cent of all investments.

Once again, it is important to stress that most existing

pre- and post-trade transaction cost analysis models

impose stringent efficiency conditions. With one third of

European buy-side transactions taking place outside the

most liquid trading venues, this pre-requisite is likely not to

be fulfilled in those markets where TCA is probably the

most useful. It will be reassuring for the end investor to see

that the buy-side firm can demonstrate best execution for

trades performed on Euronext Paris or the NASDAQ and

that substantial investments have been made to ensure

that algorithms and traders have achieved the best

reduction in spread in these markets where spreads are

very narrow. At the end of the day, the difference in

portfolio performance might well arise from a smaller

number of transactions made on those emerging markets

where no intraday tick data is available to optimise

transactions or where the levels of liquidity simply imply

very high transaction costs.

It is very important at this stage that buy-side firms

maintain a pragmatic and honest approach to measuring

and demonstrating best execution so as not to provide the

end investor with a false sense of confidence while serious

issues arise on overseas markets or on the corporate

bond market.

4.2 Pre-trade analysis

Pre-trade analysis forms an essential part of ensuring that

best execution can be achieved. Faced with a client’s

requirement for the execution of a transaction in a specific

security, for a certain quantity, within time constraints and

very often within the boundaries of a series of additional

volume constraints or price objectives, the trader must

make a certain number of decisions in order to address

this request:

– Review the available liquidity pools and select the

optimal route

– Review market conditions and define an execution

strategy

Pre-trade analytics help the trader to find a response to both

questions and are mainly constructed around market models

and historical statistical information. Pre-trade analytics are

an essential tool to ensure the price can be obtained on a

very liquid market and that the optimal strategy can be

designed for working orders on less liquid markets, where

the information provided to the market when executing part

of the transaction is likely to become a disadvantage.

Question 4: Do you use pre-trade transaction cost

analysis tools?

83 firms responded to this question

China 2%

Russia 4%

North America29%

Euro emerging4%

Eurozone19%

SE Asia23%

Middle East7% America

emerging3%

India9%

Yes54%

No46%

29

With only half of respondents confirming the use of

pre-trade analytics out of only 83 responses, it is striking to

see that pre-trade analytics have not reached the desk of

the majority of buy-side firms. This situation is rendered

even more problematic in light of the forthcoming

obligation of best execution which is laid down in the MiFID

regulatory change that has now been agreed upon.

Two elements of analysis can explain why only half of

respondents do not use such pre-trade analytics:

– The responsibility of trading is fully outsourced and the

asset manager therefore expects the intermediary to

take responsibility for handling the execution strategy.

It would however be wise for buy-side firms to establish,

before the trade is sent to the intermediary, what market

impact can be expected given the constraints put on the

order and the market conditions. This pre-trade analysis

would form the first essential step to assessing the

actual performance of the intermediary post-trade

– With a growing number of orders being fully processed

by electronic means through algorithms (see question

9), respondents may feel no pre-trade analytics are used

when, in fact, most algorithms base their logic on a deep

pre-trade analysis and a market model that takes into

consideration specific market conditions at the time they

receive the instruction

Question 5: Which pre-trade transaction cost analysis tool

do you use? (One response only)

Those who use TCA tools use (by order of importance)

– Internal tool 14 responses

– ITG 12 responses

– Other 11 responses

– Plexus/Inalytics 8 responses

From the fifth question asked, it seems clear that pre-trade

analytics remain part of the ‘know-how’ buy-side firms

want to keep control over. With a quarter of respondents

explaining that pre-trade analytics were developed

internally, only two specific vendors were referred to by the

other respondents, revealing what is probably a weak

product offer in this domain.

It is true to say that although post-trade analytics have

developed tremendously over the last five years, there is

still a lack of academic and industry consensus on which

models are offering the most reliable analysis pre-trade;

such models can be counted on one hand.

Only 18 buy-side firms responded to our question related to

the mechanism behind their pre-trade analytics. As could

be expected, systems based on market historical data are

the most frequent. The fact that 10 respondents to this

question mentioned the arrival price (price of the security at

the time of transfer of the instruction to the intermediary)

demonstrates that implementation shortfall or benchmarks

based on the arrival price and estimated market impact are

the most widely used methods.

It is also very interesting to notice that only two

respondents mentioned that their pre-trade analytics were

based on actual order book data, confirming that the use of

historical models dominate, while real-time predictive

algorithms that take into consideration the specific market

conditions at the time of arrival are not yet the norm.

Question 6: What is this pre-trade analysis tool based on?

(One response only)

18 responses to this question

18

2

7

10

0 2 4 6 8 10 12 14 16 18 20

Historical market data

Order book data

Predic tive models

Arrival price

30

With the very strong development of advanced and

sophisticated algorithms for handling execution, we are

likely to see the emergence of new forms of pre-trade

analytics that will allow us to better analyse the impact of a

given transaction on the market order book ex-ante, taking

into account historical market information at tick level but

also exploiting the very content of the order book in real

time. These new tools will very quickly become the norm

for most buy-side firms, as they will enable either the

definition of the optimal execution strategy or improved

control of the quality of the execution. All in all, these new

developments will constitute an important element in

response to the new regulatory requirements imposed

under MiFID.

Question 7: Can orders be returned to the manager if not

feasible within certain cost limits?

101 responses to this question

However, the most optimal execution strategy may not

always result in transaction costs that are acceptable for

the portfolio manager. In certain market circumstances,

it may well be more interesting for the asset management

company to decide to postpone the execution or replace

the transaction in a given security by a derivative

instrument or a replacement security that is likely to

provide exposure to the same risk/return profile.

The situation where a trading desk decides to return the

order unfilled seems to be only possible with one traditional

asset manager out of seven, and one hedge fund

management company out of four. For nearly one quarter of

respondents, once decided, the order will have to be

executed at all costs; the conditions usually attached to the

order may therefore be considered as null.

Question 8: Does the trading desk have responsibility for

advising alternative execution strategy?

106 responses to this question

More reassuring is the fact that the vast majority of our

respondents leave the trading desk with the responsibility

of advising an alternative trading strategy when the

order cannot be filled within the relative price and

time conditions. The clear separation between portfolio

management and trading is therefore confirmed.

This should reassure the investor, as skills required to

manage and to trade are very distinct and very rarely in the

hands of a single person.

4.3 The execution process

A week does not go by in London without a conference or

a special report on the tremendous development of

algorithmic trading, confirming that we are experiencing,

if not a trend, then at least an interesting fashion wave in

the industry.

Algorithms are currently being marketed mostly by sell-side

firms in order to provide a response to the low value,

low impact trades that most asset management firms are

willing to see processed at a very negligible cost.

But algorithmic trading also encompasses automated

trading and is very often a key element of the investment

strategies that hedge funds are pursuing. When it is not for

trading by pairs, or by basket, most hedge funds tend to

rely on computers to get their trade lists executed as

smoothly as possible on the market with a minimised, or at

least contained and pre-determined, cost.

No18% No

25%

Yes82%

Yes75%

Traditional Asset Managers Hedge Fund Managers

No16%

Yes84%

31

Our survey does not reveal any surprises, with 55

respondents out of 82 (67 per cent) confirming the use of

algorithms. Unsurprisingly, this rate is even higher for

hedge fund respondents — an impressive 76 per cent

Question 9: Do you use algorithms?

These rates are not very different from those observed

recently as part of other execution-related surveys.

They confirm the trend highlighted in our recent Best

Execution Survey, carried out during the summer of 20051:

Figure 17: For what proportion of orders do you use

algorithmic trading systems, such as automatic VWAP

or pairs execution?

While a high number of firms do use algorithms, it must be

said that the same study showed that only 3 per cent of

their volumes were actually processed through algorithms

on the buy-side, confirming that we are only at the

beginning of the trend.

Question 10: Who provides these algorithms?

59 responses to this question

Question 10 confirms that nearly half of our buy-side

respondents use algorithms provided by their

intermediaries. Very few internal trading desks on the

buy-side actually have the capacity to develop and maintain

such algorithms internally, as they remain fairly expensive

in terms of R&D and technology investments. It is also

interesting to note the emergence of technology vendors

as a significant source of provision for such algorithms,

demonstrating once more that the entire competitive

landscape is being re-shaped and the relationship between

buy-side, sell-side and providers is evolving at a very

fast pace.

Question 11: Is the risk department involved in the

design/validation of these algorithms?

Developing algorithms is a complex initiative that can

possibly generate a significant amount of risks that used to

be addressed by the intermediary. From the risks related to

models or the data feeding these models, buy-side firms

No36%

No24%

Yes64%

Yes76%

Traditional Asset Managers Hedge Fund Managers

38%

14%

20%

18%

10%

41%

13%

21%

24%

1%

0% 10% 20% 30% 40% 50%

<5%

5-10%

10-20%

20-50%

>50%

2004

2005

1EDHEC Risk Advisory Best Execution Survey 2006: MiFID Best Execution

Obligation, Article 21: ‘A Step into the 21st Century’

Internally developed

22%A technology

provider29%

Your broker49%

No 14%

Yes 86%

32

also carry the risk of not keeping enough control on algorithms

that would operate on the market and possibly generate

orders outside of the market at a potential major loss. The

fact that the risk department needs to be involved and such

scenarios be assessed seriously before an algorithm is put

in production is a possible explanation for the persistently

low level of algorithms being developed internally.

One very important pre-requisite for performing post-trade

transaction cost analysis and implementing an automated

processing of order flow is the use of technology from the

initiation of the order and its handling in a format that is

acceptable by most vendors and intermediaries.

FIX is obviously the natural response to this need and has

seen a tremendous development over the last five years.

Detailed information about the purpose and genesis of the

FIX protocol is provided in Box 1.

Box 1: FIX PROTOCOL LIMITED2

The Financial Information eXchange (‘FIX’) Protocol is a

series of messaging specifications for the electronic

communication of trade-related messages. It has been

developed through the collaboration of banks, broker-

dealers, exchanges, industry utilities and associations,

institutional investors and information technology

providers from around the world. These market

participants share a vision of a common, global language

for the automated trading of financial instruments.

FIX is the industry-driven messaging standard that is

changing the face of the global financial services sector,

as firms use the protocol to transact in an electronic,

transparent, cost-efficient and timely manner. FIX is open

and free, but it is not software. Rather, FIX is a

specification around which software developers can

create commercial or open-source software, as they see

fit. As the market’s leading trade-communications

protocol, FIX is integral to many order management and

trading systems.

Yet its power is unobtrusive, as users of these systems

can benefit from FIX without knowing the language itself.

Background

Since its inception in 1992 as a bilateral communications

framework for equity trading between Fidelity

Investments and Salomon Brothers, FIX has become the

de-facto messaging standard for pre-trade and trade

communication globally within the Equity markets, and is

now experiencing rapid expansion into the post-trade

space, supporting Straight-Through-Processing (STP)

from Indication-of-Interest (IOI) to Allocations and

Confirmations. From this foundation, the protocol is

gathering increased momentum, as it continues to

expand across the Foreign Exchange, Fixed Income and

Derivative markets.

The significant adoption of FIX within the financial

services community was empirically highlighted by the

FIX Global Survey conducted by TowerGroup at the end of

last year. The results cited that 75 per cent of buy-side and

80 per cent of sell-side firms interviewed currently use

FIX for electronic trading, and that both groups plan to

focus substantial efforts on expanding their FIX usage to

over 93 per cent, as well as leveraging FIX across

additional asset classes by 2008. The survey also revealed

that FIX is developing a key role within the post-trade

space, as over 80 per cent of buy-side firms and over

95 per cent of sell-side firms surveyed currently support,

or plan to support, FIX for allocations.

Further to this, FIX is gaining increased attention within

the exchanges community as over three quarters of all

exchanges surveyed supported a FIX interface, with the

majority handling over 25 per cent of their total trading

volume via FIX.

Organisation

The success of the FIX Protocol is primarily due to the

voluntary efforts of its member firms from the buy-side,

sell-side, vendor and exchange communities who

work together to help achieve the FIX Protocol Limited

(FPL) mission statement: ‘To improve the global trading

process by defining, managing and promoting an

open protocol for real-time, electronic communication

33

between industry participants, while complementing

industry standards’.

Technical and business professionals from the FPL

member firms coordinate their activities and organise

their work through a series of committees,

subcommittees, and working groups, all overseen by a

Global Steering Committee that aims to ensure

consistency of protocol application as it is extended into

new markets, asset classes, and phases of the trade

lifecycle. In addition to rigorous engineering and technical

efforts undertaken to ensure the ongoing applicability of

the protocol to financial market systems, the FIX Protocol

benefits from energetic educational and marketing efforts

that seek to keep the specification viable, relevant and

responsive to the needs of market participants.

2For further information about FPL please visit the organisation’s website

at www.fixprotocol.org or contact Daniella Baker, the FPL Marketing and

Communications Manager by e-mail at Daniella.Baker@fixprotocol.org or

by telephone at 44 0 207 556 7668

Figures provided by TowerGroup are confirmed by this

year’s survey with an overall number of 57 respondents out

of 79 (72 per cent) confirming the use of FIX to encode

transactions and process them electronically.

Question 12: Are your trades encoded in FIX format?

79 responses to this question

This very rosy picture, however, has to be dealt with

carefully, as only 60 per cent of hedge fund respondents

claim they are using FIX, which demonstrates that

smaller firms have not yet fully embraced the protocol,

and probably the digitalisation of their entire execution flow.

More importantly, FIX Protocol today does not allow

full encoding of all specific instructions that can be attached

to an order and, as seen in the following question,

only 20 per cent of instructions are transmitted to

the intermediary with a specific price, volume or

benchmark objective.

It is extremely important to make certain that such specific

instructions are adequately stored with the initial record of

the transaction, as any post-trade transaction cost analysis

will require such specific constraints to be factored in and

taken into account in order to provide a fair judgement of

the intermediary’s performance. Assessing the market

impact and performing a peer group analysis of an order

when a VWAP objective or a ‘trade with volume’ constraint

has been imposed onto the broker would be totally unfair.

There is probably significant room for development of the

FIX Protocol to allow for all types of constraints and

objectives to be attached to instructions and be commonly

recognised by trading systems and intermediaries’

connectivity. This work, however, can only be achieved by

means of public consultation and joint working groups so as

to allow for the highest number of situations to be dealt

with while still allowing for sufficient flexibility to be left in

the protocol to allow for continuous innovation.

Question 13: Do you record specific instructions?

No20%

Yes80%

No40%

Yes60%

Traditional Asset Managers Hedge Fund Managers

Yes; 19%

Yes; 6%

Yes; 15%

Yes; 21%

Yes; 9%

No; 81%

No; 94%

No; 85%

No; 79%

No; 91%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Price objective

Volume objective

Benchmark

Deadline

Other specificinstruction

34

Finally, transaction cost analysis cannot be performed

without intraday timestamps being made available during

the entire flow of the order from the buy-side to the

intermediary and back to the issuer. It is more worrying to

see that two thirds of respondents are not in a position to

document the time the order was responded to (sent back

to the issuer), and that one third of buy-side firms do not

measure the time the order is transmitted to the

intermediary. This last timestamp is absolutely vital not

only to be in a position to determine who from the buy-side

or the intermediary causes the opportunity cost to rise

but, more importantly, to determine the window of time

that was available to the intermediary to perform his duty.

More obvious is the fact that 15 per cent of respondents

only track the time the investment decision is made, which

is an absolute pre-requisite for a serious measurement of

the implementation shortfall and an assessment of the full

extent of opportunity costs that impact the final

performance of the trade.

Question 14: Do you record timestamps?

4.4 Post-trade analysis

Post-trade transaction cost analysis is obviously a key step

in demonstrating that all efforts have been made to obtain

the best possible result for the client. Ex-post TCA is

important both for assessing the pertinence of the

execution strategy and improving execution performances

over time, but also allows an independent assessment of

the quality of intermediaries, algorithms or traders.

As seen earlier in this study, assessing the quality of

execution is rendered extremely difficult by the very nature

of the changing situation within the order book and the

complex task of identifying an appropriate benchmark.

A logical approach consists in estimating transaction costs

ex-ante and comparing the actual result of the intermediary

with the initial estimates. The fact that only one third of

respondents have taken that route confirms the difficulties

inherent in determining transaction costs ex-ante.

Question 15: Do you assess execution performance with

pre-trade estimates?

98 responses to this question

One of the consequences of this difficulty is the reliance

on the most common, but also, as seen earlier, the least

reliable, benchmarks. With 90 per cent of our respondents

declaring use of VWAP, it is clear that the absence of

consensual methodology for assessing transaction costs

ex-post is resulting in the total failure of all efforts made to

assess the costs incurred by the end investor.

The prominence of VWAP as an indicator may be justified

by the simplicity of its implementation and its apparent

ease of use for the end user. It is nevertheless probably

one of the worst indicators of execution quality, especially

for large size transactions.

Yes; 15%

Yes; 76%

Yes; 63%

Yes; 94%

Yes; 33%

No; 85%

No; 24%

No; 37%

No; 6%

No; 67%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Investment DecisionTime

Arrival time at thetrading desk

Issue to the intermediary

Execution Time

Response Time

Yes No

No 62%

Yes38%

35

Question 16: What benchmark do you use?

54 responses to this question

Question 17: Do you construct bespoke benchmarks for

each trade depending on constraints imposed on time,

volume or implementation strategy?

55 responses to this question

As we have seen before, for orders that are accompanied

by specific instructions, it is very important to measure

transaction costs using a benchmark that is dedicated and

specific to the instructions attached. This is a key step to

providing a fair assessment of the intermediary or trader

and probably the only way to avoid all the pitfalls discussed

in the previous section.

With less than a third of respondents constructing bespoke

benchmarks, it seems the industry is still far from having an

authoritative framework for measuring transaction costs

fairly. This can probably be explained by the lack of

information available on trades (timestamps, instructions)

but more importantly by the absence of academic or

industry consensus on a methodology that could cope with

the most frequent case encountered.

Question 18: Do you review the performances of your

broker according to these benchmarks?

29 responses to this question

In line with this relatively poor approach to measuring

transaction cost analysis, it is interesting to note that

40 per cent of respondents would not take into

consideration their analysis in reviewing the allocation

made to their brokers. If the selection of an intermediary

obviously cannot solely rely on transaction costs, it is

striking to see that such a large number of firms do not

review the performances of their brokers according to such

an important quality factor.

Question 19: Do you review the performances of your

algorithms according to these benchmarks?

18 responses to this question

6

7

12

12

34

35

48

0 10 20 30 40 50 60

OHLC

% of volume participation

CLOSE or OPEN

TWAP

Arrival Time

Time VWAP

VWAP

No 69%

Yes31%

No 38%

Yes62%

No 61%

Yes39%

36

Similarly, while the use of algorithms is developing at a very

fast pace, very few studies exist that actually assess

the absolute and relative quality of these trading systems.

In the absence of clear opinion on algorithms, buy-side

firms do not feel seriously concerned by the performance

of the algorithms used: two thirds of respondents to our

panel (only 18 responses) do not actually review the

performances of the algorithms employed.

Finally, we wanted to better understand which companies

have been most successful in providing post-trade

transaction cost analysis systems to buy-side clients. It is

interesting to note that, based on the 51 responses

received, most buy-side firms are using technology

provided by sell-side firms. The significant step recently

made by intermediaries in this space in acquiring nearly all

independent TCA providers simply shows how strategic it

is — and will be — for sell-side firms to be pro-active in

providing adequate post-trade TCA services to their clients,

and thereby controlling all aspects of the quality control

made on their services.

Question 20: How do you perform transaction

cost analysis?

Those who use TCA tools use (by order of importance)

– Plexus / Inalytics 9 responses

– Internal tool 8 responses

– ITG 6 responses

– BECS 6 responses

– Elkins McSherry 4 responses

– GSCS 4 responses

– Abel Noser 3 responses

– Other 11 responses

4.5 MiFID readiness

The MiFID is the second step in the harmonisation of the

European capital markets industry and aims to adapt the

first Investment Services Directive (ISD 1, issued in 1993)

to the realities of the current market structure.

Part of the European Financial Services Plan (FSP),

the ‘MiFID’ (Directive 2004/39/EC, formerly known as

Investment Services Directive II) was ratified by the

European Union Parliament on April 21st 2004. The

directive forms a Level one regulation (under the

Lamfalussy procedure).

Level II of the Directive was issued on February 2nd 2006

and factored in the clarification elements provided by the

CESR (Committee for European Securities Regulators) in

response to a request for technical advice.

The MiFID introduces passport rights for firms willing to

operate in the European capital market industry and

focuses operating obligations around 6 major themes:

Investor classification (professionals or not)

Conflicts of interests

Best Execution Obligation

Regulated markets, MTF (Multilateral Trading Facilities) and internalisation operating standards

Pre-trade and post-trade disclosure

Reporting and record keeping

In a nutshell, the MiFID sweeps away the very concept of

central exchange and obligation of order concentration as it

currently exists in several European countries. It introduces

a passportable operating framework for execution services

that can be provided by regulated exchanges or multilateral

trading facilities, or internalised inside the financial

institution itself.

37

The opening of the execution landscape to full competition

is balanced by a series of obligations that intend to

increase transparency and client protection in order to

maintain European markets in a situation where the price

discovery mechanism remains efficient and fair and

where the markets’ integrity is guaranteed despite an

inevitable fragmentation.

Question 21: Are you fully aware of the forthcoming

Directive on Markets in Financial Instruments?

79 firms responded to this question (62 traditional asset

managers, 17 hedge fund managers).

79 responses to this question

Nearly one year from the implementation deadline

(November 2007), it is striking to see that 26 per cent of

traditional asset managers still believe the MiFID is not

related to their business or are not even aware of the

Directive. Even worse is the situation within hedge fund

management firms with half of respondents mistaken

about the importance of the MiFID for their firm.

Question 22: Have you started defining an execution policy

for your firm?

63 firms responded to this question (47 traditional asset

managers, 14 hedge fund managers).

63 responses to this question

Defining a clear execution policy, documenting it and

informing the client of all details of this execution policy

forms a significant part of the duty of best execution as set

out by the MiFID. Once again, with one third of hedge

funds having failed to clear the path towards a documented

execution policy, and with one fund manager out of ten

having to look at the question, there seems to be

substantial room for education and improvement on

the buy-side

Not related to my business

11%

Yes, fullybriefed

31%

Yes, aware43%

No, not aware15%

Not related to my business

24%

Yes, fullybriefed

40%

Yes, aware12%

No, not aware24%

Traditional Asset Managers Hedge Fund Managers

No, not looked atthe question

9%

Yes, beingdefined

26%

Yes, fullyenforced

65%

Traditional Asset Managers Hedge Fund Managers

No, not looked atthe question

29%

Yes, beingdefined

21%

Yes, fullyenforced

50%

38

Biais, B., P. Hillion and C. Spatt, 1995, ‘An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse’, Journal of Finance, 50(5), pp 1665-1689

Boussema, M., A. Bueno and P. Sequier, 2001, ‘Transaction Costs and Trading Strategies: An empiricalanalysis on global equity markets’, Working Paper

Boussema, M., A. Bueno and P. Sequier, 2002, ‘Transaction Costs and Trading Strategies’, in Best Execution: Executing Transactions in Security Marketson Behalf of Investors, a collection of essays, EuropeanAsset Management Association

Copeland, T. and D. Galai, 1983, ‘Information Effects and the Bid-Ask Spread’, Journal of Finance, 38, pp 1457-1469

Demsetz, H., 1968, ‘The Cost of Trading’, Quarterly Journal of Economics, 82, pp 33-53

Domowitz, I., J. Glen and A. Madhavan, 2001, ‘Global Equity Trading Costs’, Working Paper

Freyre-Sanders, A., R. Guobuzaite and K. Byrne, 2004, ‘A Review of Trading Cost Models: Reducing Transaction Costs’, Journal of Investing, pp 93-115, fall

Giraud, J.R., 2004, ‘Best Execution for Buy-Side Firms:A Challenging Issue, a Promising Debate, a Regulatory Challenge’, European Survey on Investment Managers’ Practices, 23pp, June

Griffiths, M.D., B.F. Smith, D.A. Turnbull and R.W.

White, 2000, ‘The Costs and Determinants of Order Aggressiveness’, Journal of Financial Economics, 56, pp 65-88

Harris, L., 2003, Trading & Exchanges: Market Microstructure for Practitioners, New York: Oxford University Press, 617pp

Ho, T. and H. Stoll, 1981, ‘Optimal Dealer Pricing Under Transactions and Return Uncertainty’, Journal of Financial Economics, 9, pp 47-73

Hu, G., 2004, ‘Measures of Implicit Trading Costs and Buy–Sell Asymmetry’, Working Paper, Boston College

Munck, N.H., 2005, ‘When Transactions Went High-Tech: A Cross-Sectional Study of Equity Trading Costs in the Light of More Sophisticated Trading Systems’, SSRN Working Paper

Ranaldo, A., 2004, ‘Order Aggressiveness in LimitOrder Book Markets’, Journal of Financial Markets, 7, pp 53-74

5. References

39

EDHEC, which is celebrating its centenary in 2006, is one

of the top five business schools in France owing to the high

quality of its academic staff (100 permanent lecturers from

France and abroad) and its privileged relationship with

professionals. Drawing on its extensive knowledge of the

professional environment, EDHEC concentrates its research

on themes that satisfy the needs of professionals and

pursues an active research policy in the field of finance.

The EDHEC Risk and Asset Management Research Centre’s

team of 33 researchers implements six industry-sponsored

programs focusing on asset allocation and risk management

in the traditional and alternative investment universes.

EDHEC Risk Advisory is the consulting arm of EDHEC’s

Risk and Asset Management Research Centre. Launched in

2003 and managed by Jean-René Giraud, EDHEC Risk

Advisory has developed a series of service offerings aimed

at supporting buy-side financial institutions as well as their

service providers, both in the traditional long-only and

alternative universes.

Present in Paris, London and Nice, the company is

leveraging an international network of industry and

academic professionals that provide the flexibility and

creativity required to manage the major challenges the

industry will face in the coming years.

EDHEC Risk Advisory focuses on issues related to the

management of risk (market, credit, operational, compliance

risks), from strategy, regulation, governance and organisation

to package selection and system implementation.

Jean-René Giraud is C.E.O. of EDHEC Risk Advisory, the

consultancy arm of the EDHEC Risk and Asset

Management Research Centre for which he is also Director

of Development.

Prior to joining EDHEC, Jean-René spent several years

at various positions within investment banks and

management consultancy firms in London. He began his

career in Paris, where he supported the development of a

software company specialised in portfolio management

and led the client advisory activity of the firm.

Jean-René is a frequent speaker at industry and academic

conferences on subjects including risk management,

alternative investments, market microstructure and

market regulation.

Catherine D’Hondt is an associate professor at EDHEC

Business School in Lille with her primary research area in

market microstructure and a special focus on traders’

behaviour and order submission strategies. Having had the

opportunity to present most of her empirical works at

several high-quality international conferences, she currently

teaches in the area of financial markets and assets.

6. About EDHEC Risk Advisoryand the authors

41

HSBC Corporate, Investment Banking and Markets (CIBM)

CIBM provides tailored financial products and services to

major government, corporate and institutional clients.

Within client-focused business lines, global banking, global

markets and global transaction banking, CIBM offers a full

range of capabilities, including foreign exchange, equity

capital markets, equity sales and trading, fixed

income, derivatives, risk advisory, investment banking

financing, investment banking advisory, payments and

cash management, trade services and securities

services. For more information on CIBM, please visit

www.cibm.hsbc.com.

HSBC Holdings plc

HSBC Holdings plc serves over 125 million customers

worldwide through some 9,500 offices in 76 countries and

territories in Europe, the Asia-Pacific region, the Americas,

the Middle East and Africa. With assets of USD1,502 billion

as at 31 December 2005, HSBC is one of the world’s

largest banking and financial services organisations. HSBC

is marketed worldwide as ‘the world’s local bank’.

7. About the sponsor

1 Sources: Accenture, ATOS Consulting, BT Radianz 2005

43