Navigating Liquidity

EUROPE September 2009

Disclosures available on www.cheuvreux.com

Navigating Liquidity 3

Making sense of liquidity fragmentation

Recent significant changes in the liquidity landscape

CHI-X has become well established as the leading MTF and No. 3execution venue in Europe. It is still extending its influence in less liquidstocks.

TURQUOISE has succeeded in recovering market share after March2009 and has kept its key strength: good coverage of indices. Itsperformance has also been confirmed by the increase in its percentage oftime at the EBBO. There are concerns about the future ownership anddirection of Turquoise due to current discussions of a potential sale.

BATS’s inverted pricing had a major effect on its market share, whichdoubled on the CAC 40 in the space of two weeks. However, taking intoaccount market share, stock coverage and percentage of time at theEBBO, its best performance has been in UK stocks.

NASDAQ OMX has every chance of succeeding as a newcomer.Indeed, an MTF that can improve its bid-ask spread and BBO presencecompared to others is immediately rewarded with an increase in marketshare.

Overall, the MTFs’ unique intra-day volume profiles tend todisappear. This can be attributed to more widespread use of SORs byinvestors. Conversely, slight differences at the opening and the close arelikely to continue to exist as long as investors remain uninterested in usingMTFs for fixing auctions. Key points when routing orders in a fragmented market

Focus on child orders: Before market fragmentation, large orderswere only temporally split, but now each child order has to be sent todifferent trading destinations according to the liquidity they offer.

Use a Smart Order Router (SOR): It has become obvious thatSORs are now the key component when accessing fragmented liquidity.The best way to use the liquidity available in alternate venues is to avoidcrossing a tick price on the main trading destination if volume is availableat the same price on another venue. Efficient SORs – such as ours at CACheuvreux – now source more than 40% of their liquidity outside primarymarkets.

Aggregate liquidity to minimise the impact of each order: An SORorder is like a new order type giving access to an aggregated view ofliquidity. As long as the average size of aggressive orders is larger thanthe size of passive orders, any decrease in the size of the former has noimpact on the ATS or on the number of deals.

Beware of duplicated liquidity: High frequency proprietary tradingtactics lead to duplicated quantities in order books, which makes theSOR's task more complex. Adding pegged and iceberg orders, "pre-trade transparency" is not a very useful pointer for guessing where liquidityreally is. Post-trade transparency gives far more information to investors.This makes dark pools far more attractive than they seemed somemonths ago.

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September 2009 Navigating Liquidity 3

Introduction

Over the past two years, MiFID and the financial crisis have drastically modified the European trading landscape. The expected fragmentation has occurred, as was the case in the U.S. two years after Reg NMS, but the financial environment has not been favourable to the creation of numerous MTFs, as some are finding it difficult to compete with the primary markets. We can nevertheless observe that Chi-X has succeeded in challenging the traditional exchanges, as it is now the third-largest trading destination in Europe based on numerous criteria. At the same time 70% of deals on the U.S. market stem from the constantly-growing population of high-frequency traders and market makers. These players are coming to Europe and could further modify this already-evolving landscape. CA Cheuvreux is now monitoring market liquidity for clients in its "Monthly Market Indicators" publication. These indicators allow you to follow and interpret local fragmentation trends. In contrast, our Navigating Liquidity studies are an invitation to gain a deeper understanding of crucial mechanisms. In this third issue, we will comment on market share trends since the publication of Navigating Liquidity 2, and look at mid cap stocks when needed. This will be followed by a focus on the impact of tick size on fragmentation. We will then analyse the main principles of smart routing of aggressive orders, and explain the splitting rates obtained by CA Cheuvreux's Smart Order Router, which gives our clients aggregated and enhanced access to European liquidity. The last section of this study presents a model explaining the decay in the market share of primary markets resulting from investors' objective and rational optimisation of their trades. About the authors

Charles-Albert Lehalle

Currently Head of Quantitative Research at CA Cheuvreux, Charles-Albert Lehalle also lectures at "Paris 6 (EL Karoui) Master of Finance" (Ecole Polytechnique, ESSEC, Ecole Normale Supérieure) and gives master classes in the Certificate in Quantitative Finance in London. He has also given lectures at numerous seminars and international conferences at MIT and the University of Edinburgh. With a Ph.D. in applied mathematics, Charles-Albert is an expert in stochastic processes, information theory and nonlinear control. He has published international papers on quantitative finance, real-time optimisation of high dimensional processes (with applications to Formula One, high-mix fabs, large plants, and aerospace), and learning theory.

Romain Burgot

Currently working as a statistician researcher in the CA Cheuvreux Quantitative Research team, Romain previously worked on microstructure questions for the final paper of his studies (ENSAE 2006), which was entitled "The comparison of VaR measures with high frequency data".

Execution Services Contacts

GENERAL HOTLINES: Paris: +33 1 41 89 80 88 London: +44 207 621 52 00 New York: +1 212 492 8850 Ian Peacock Jerry Lees Global Head of Execution Services Head of Alternative Execution Services +44 207 621 5144 / [email protected] +44 207 621 5281 / [email protected] SELL SIDE BUY SIDE Jonathan Carp Mark Freeman Head of Alternative Execution Sales Europe Head of Alternative Execution Sales to Buy Side +44 207 621 5244 / [email protected] +44 207 621 5285 / [email protected]

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CONTENTS

I— What changes have occurred recently in this fragmented liquidity landscape? 4

The MTF's specificities of intra-day volume profiles are tending to disappear 4

Chi-X continues to do well and to attract more and more flows 6 Turquoise has done a very good job and is recovering its market share 8 After taking into account BATS' temporary aggressive rebates, its

performance is disappointing 10

II— How can tick size be "optimal"? 12 Tick size is clearly linked to an MTF's market share 12 The potential dangers and benefits of a tick size reduction 13 Each stock needs its own tick size 14

III— How to route orders in a fragmented market? 16 Focus on atomic orders 16 Using a Smart Order Router (SOR) 17 Aggregate liquidity to minimise the impact of each order 18 Beware of duplicated liquidity 19

IV— Two results and a guess 21 Efficiency of CA Cheuvreux's SOR 21 A split driven by Chi-X efficiency 22 Fragmentation is a consequence of primary markets' variance 23

Appendices 25 Appendix 1: Glossary 25 Appendix 2: Methodology 26 Appendix 3: Lists of stocks 27 Appendix 4: Information on backbone latencies 29 Appendix 5: Liquidity metrics 30

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I— What changes have occurred recently in this fragmented liquidity landscape?

The MTF's specificities of intra-day volume profiles are tending to disappear

FIGURE 1: INTRA-DAY PROPORTION OF VOLUME FOR THE DIFFERENT FIGURE 2: INTRA-DAY PROPORTION OF VOLUME FOR THE EXCHANGES (CAC 40, FEBRUARY 2009) DIFFERENT EXCHANGES (FTSE 100, FEBRUARY 2009)

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In Navigating Liquidity 2, we highlighted that each trading destination had specific intra-day volume seasonality (i.e. the volume profile). The main facts worth noting were:

Only primary markets are able to provide enough liquidity at the opening;

Chi-X and BATS offer their best liquidity when the U.S. market is open;

Conversely, Turquoise does not really benefit from higher volumes after the U.S. markets open.

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Comparing Figures 1 and 3, as well as Figures 2 and 4, shows that these specific aspects have disappeared. The most significant trend concerns Turquoise, whose volumes were almost uniformly distributed throughout the trading day, whereas they now follow the classic U-shape with a spike at the preopening period for U.S. markets (which is when most of the news and macro-economic figures are disclosed) and a substantial increase in volumes when U.S. cash equity markets open.

Intra-day volume seasonality is now much

the same on primary markets and MTFs

Turquoise's liquidity now looks more natural with respect to this seasonality. This is not surprising, as Figures 1 and 2 have been built using data from a time when there were still liquidity agreements with non-professional market makers. As this very specific population, who used to provide more than half of Turquoise's liquidity, is no longer present, this feature was likely to fade.

The intra-day volume profile of Chi-X has evolved to a lesser extent, it is merely due to the fact that Chi-X was already a mature MTF with sound liquidity sought after by numerous investors. Nevertheless, at the U.S. market open, it seemed that Chi-X was more sensitive to increases in the liquidity flow than the primary markets. This trend has disappeared and there are now very few differences between Chi-X and primary markets. To be more precise, there are only two slight differences: the first and the last 15 minutes of the continuous trading phase.

This is not surprising, and these differences will probably not disappear altogether, as there is a fundamental reason for this, namely the fixing auctions. These continue to be phases in which the primary markets still have a monopoly.

Concerning the opening fixing auction, this is evidence that in terms of price discovery, investors are more confident in the primary markets. The opening often reveals strong uncertainty about the price, as this is the time when the spread is the highest during the day. As there is no volume traded at an MTF opening auction, the latter's order book at the beginning of the continuous phase does not have good depth or a tight spread, and investors have to wait a while for it to fill with limit orders, to be able to execute on these venues.

These slight differencesat the opening and the

closing are likely tocontinue to exist as long

as investors are notinterested in using MTFs

for fixing auctions

These slight differences will continue to exist as long as MTFs do not manage to compete successfully for the flow at fixing auctions. This is not likely to happen until investors become convinced that the primary markets do not host the price formation process more efficiently than other execution venues.

In our view, this will be quite a long process, and investor behaviour will not change until primary markets attract less than half of the non-OTC volume.

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Chi-X continues to do well and to attract more and more flows

FIGURE 5: CHI-X MARKET SHARE (EXCLUDING FIXING AUCTIONS) FIGURE 6: CHI-X COVERAGE LIQUIDITY METRIC

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FIGURE 7: CHI-X PERCENTAGE OF TIME AT BEST BID AND OFFER FIGURE 8: CHI-X PERCENTAGE OF TIME AT BEST BID AND

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Figures 5-8 above show the main metrics enabling an analysis of how steadily Chi-X is continuing to spread its influence over Europe. The first metric is the usual market share, for which fixing auctions have not been taken into account, as there is no competition on these phases. The second is coverage, as introduced in Navigating Liquidity 2 and monitored in our Monthly Market Indicators. The last two metrics in these analyses represent:

Figure 7: The percentage of time that the best bid and best offer on Chi-X is as good as the best bid and offer among the primary markets and the other MTFs (the sum of this metric for all considered venues can be higher than 100 because different venues can share the best quotations).

Figure 8: The percentage of time that Chi-X has the best bid and offer, and has the greatest size on both sides among the primary markets and the other MTFs (the sum for all considered venues is less than 100, because venues never have the same best bid and offer as well as the same sizes at these prices).

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With regard to the main European indices (see Figure 5), Chi-X clearly gained market share from March to July, but the trend slowed down in August. The overall increase of market share ranges from 4% (CAC 40 and AEX) to 8% (FTSE 250). Furthermore, this market share increase has been achieved along with increasing coverage, confirming Chi-X's ability to offer good liquidity on all components of these indices, not just the more liquid stocks.

The analysis of percentage of time at the best EBBO is also flattering for Chi-X which, exception made of the DAX, shows a clear uptrend. If we displayed the percentage of time at the EBBO for primary markets, we would see that for the AEX, CAC 40 and FTSE 100, Chi-X's figures reached the same level as those of the primary markets in July. The DAX is an exception regarding this metric, as Chi-X has beaten XETRA in this field since April 2009. The decrease in August is directly linked to the decline in market share, which would surprise no one, as SOR technology cannot but have such an effect. The analysis of the percentage of time at the EBBO with the greatest size is a harder task as these figures may be decreasing for each execution venue. Hence it is important to note that this percentage of time has also been decreasing for primary markets from March to June.

FIGURE 9: CHI-X MARKET SHARE (EXCLUDING FIXING AUCTIONS) FIGURE 10: CHI-X COVERAGE LIQUIDITY METRIC

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The progression on less liquid stocks (see Figure 9) is very impressive. Except for the MDAX and AMX there was clear exponential growth in May and June. However, as noted above, this increase has also slowed down in the past two months. The overall increase in market share ranges from 2.2% (AMX and MDAX) to 9%: for the OMX Helsinki. Nevertheless, there is massive variance in these market shares, as shown by the erratic behaviour over the past two months. So one might have to be careful: it is unclear whether these market shares will noticeably increase very soon, or even if they will settle at this level.

Lastly, Chi-X is well established as the leading MTF and the No. 3 execution venue in Europe. It is still extending its influence in less liquid stocks, as well as other European indices such as Nordic ones. Nevertheless, this summer's figures in terms of market share and the percentage of time at the EBBO show that primary markets may have found a way to curb their decline. Whatever the reason for this break in Chi-X's progression in market share, it will be interesting to see whether this is just a lull or if there is indeed a trend reversal.

Chi-X is well established as the leading MTF and

the No. 3 execution venue in Europe. It is still

extending its influence in less liquid stocks

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Turquoise has done a very good job and is recovering its market share

FIGURE 11: TURQUOISE MARKET SHARE FIGURE 12: TURQUOISE MARKET SHARE (EXCLUDING FIXING AUCTIONS) – MAIN INDICES (EXCLUDING FIXING AUCTIONS) – OTHER INDICES

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FIGURE 13: TURQUOISE COVERAGE LIQUIDITY METRIC FIGURE 14: TURQUOISE COVERAGE LIQUIDITY METRIC

– MAIN INDICES – OTHER INDICES

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With regard to the main indices (see Figure 11), there are two cases to highlight:

For London and Switzerland, Turquoise has not merely recovered its market share, but already increased it by approximately 2%. Turquoise's market share on the FTSE 250, which had not really suffered from the end of the liquidity agreements, as it was near 1%, has seen an impressive increase of more than 5%.

For the CAC 40, AEX, BEL20 and DAX, the market share recovery has not been completed. The worst case has been the AEX, for which the loss is 5%; this was the index with the highest potential loss, as it represented the best market share. For these indices, the downward movement began at the end of August, and this trend continued in the first week of September. More work thus has to be carried out on these segments to curb this decline, and try to fully recover the lost market share.

With regard to Nordic indices and less liquid stocks, few conclusions can be drawn given the huge differences in market share. Nevertheless, Turquoise seems to have done a good job on these segments as well and gained significant market share.

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The trend in coverage, which has always been one of Turquoise's strengths, is also very good. European indices, independently of the execution venues, have experienced such an evolution: looking at coverage on any execution venue, one can see that these figures are increasing, which means that activity is expanding in the less active components of European indices. Nevertheless, the end of liquidity agreements could have also meant an end to Turquoise's ability to offer liquidity on the whole index, but this did not happen.

The trend in coverage, which has always been

one of Turquoise's strengths, is also very

good

Furthermore, the newly captured market share on the AMX, MDAX, SBF 120\CAC 40 (i.e., the SBF 120 excluding the CAC 40, and sometimes known as the SBF 80) and OMX Stockholm started with the more liquid stocks on these indices, but has quickly spread to all the components, reaching coverage that is often close to that of the primary markets.

FIGURE 15: TURQUOISE PERCENTAGE OF TIME AT BEST BID AND OFFER

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Lastly, Turquoise's very good performance has also been confirmed by the increase in its percentage of time at the EBBO, which is clearly a key factor in gaining market share in an environment where more and more investors are using SORs. Special mention has to be made for the Swiss market, where the increase is impressive. There was a clear jump in Turquoise's market share on the SMI at the end of May: it virtually doubled in two weeks. It is also worth noting that the increase in Turquoise's percentage of time at the EBBO with the greatest size has increased significantly over the past six months, unlike that of primary markets and Chi-X.

Turquoise's good performance has also

been confirmed by the increase in its

percentage of time at the EBBO

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After taking into account BATS' temporary aggressive rebates, its performance is disappointing

FIGURE 16: BATS MARKET SHARE (EXCLUDING FIXING AUCTIONS) – FIGURE 17: BATS MARKET SHARE (EXCLUDING FIXING MAIN INDICES AUCTIONS) – OTHER INDICES

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FIGURE 18: BATS COVERAGE LIQUIDITY METRIC – MAIN INDICES FIGURE 19: BATS COVERAGE LIQUIDITY METRIC – OTHER

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At the end of March, Turquoise and BATS had similar market share on the main indices, apart from the FTSE 100 and SMI. With regard to the FTSE 100, Turquoise had 1% more than BATS, and for the SMI BATS's market share has never reached 1%. For the other indices, BATS was either slightly ahead or the difference was negligible.

Five months later, BATS has been beaten by Turquoise on all indices. The worst performances for BATS concern the AEX and FTSE 100 (if SMI is excluded from the analysis), for which the difference in market share has widened by 3%.

Maybe it is not fair to compare their performances, as their initial situations were not really the same. Turquoise had very high market share, which subsequently fell, so there were probably many more investors connected to Turquoise at the end of March than there were to BATS. Therefore, Turquoise merely had to attract better quotes to its order book, whereas BATS also had to find a way to convince more people to connect to it.

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To achieve this goal BATS chose to use inverted pricing, meaning that for each trade it had to pay something (receiving 0.2bp from the liquidity taker and paying 0.4bp to the liquidity maker). BATS used this inverted pricing for Euronext segments (French, Belgian and Dutch stocks) throughout June. This had the expected impact as its market share on the CAC 40 reached 8% in mid-June, a very good performance compared to its 4% market share at the end of May. Figure 17 shows that its market share on the SBF 120\CAC 40 also doubled. However, this had a more lukewarm effect on its AEX, AMX and BEL 20 market share. For example, the market share gain on the AEX during this period is clearly more abrupt but also rather close to that of the FTSE 100 and DAX.

BATS's inverted pricing had a major effect on its

market share, which doubled on the CAC 40

in the space of two weeks

Perhaps this last point is indeed good news, as it would mean that more investors trading on the whole European landscape have set up a connection to BATS, and that the growth in market share has resulted more from these new connections than from already connected investors directing their flow to BATS in order to benefit from the special pricing.

In any case, it is fairly disappointing to see that BATS's market share at the end of August is at virtually the same level as at end May. In addition, the stagnating coverage (except for the FTSE 100) is indeed (as we have said above, it has increased on every other venue) a regression. This shows that liquidity on BATS is concentrating on the more liquid stocks of the index (relative to other execution venues, which are gaining market share on less liquid stocks).

FIGURE 20: BATS PERCENTAGE OF TIME AT BEST BID AND OFFER

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Moreover, the comparison of percentage of time at the EBBO is once again largely in favour of Turquoise, and as is the case for market share, these figures have been on a downtrend since June, with the exception of the FTSE 100. Taking into account

market share, coverage, and percentage of time at the EBBO, the best performance for BATS

has involved UK stocks

Taking into account market share, coverage, and percentage of time at the EBBO, the best performance for BATS has involved UK stocks. The latter have been the target of its new inverted pricing in September. During the first week of September there was a significant increase in market share on the FTSE 100, whereas other indices were either stagnating or decreasing.



II— How can tick size be "optimal"?

In our first issue of Navigating Liquidity, we highlighted the connection between spread and tick size. To be more precise, the context was: speaking about execution cost, the bid-ask spread is an important component, and for some stocks, a large tick size can constrain the bid-ask spread to be large (as the spread can obviously not be smaller than the tick size). As an example, we highlighted the fact that Alcatel-Lucent had a smaller tick size on Chi-X than on Euronext, and that this enabled a smaller spread on Chi-X, even smaller during a whole day than the tick size on Euronext. This has consequences on an execution venue’s market share, as it is the exact aim of SOR technologies to benefit from such better quotes. We also illustrated with this specific example that there was no evidence of market depth decreasing following a reduction in the tick size.

Furthermore, we indicated how to calculate in which cases (which fees for two competing venues and which tick size) an investor had an incentive to quote a better price on an alternative venue than the one displayed in the primary market because he would earn the same amount as on the primary market and because it would also be beneficial for the participant that would fill his order with a market order.

Tick size is clearly linked to an MTF's market share

FIGURE 21: TURQUOISE MARKET SHARE FIGURE 22: BATS COVERAGE MARKET SHARE

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Such a situation has recently recurred, and has led to intense debates. This year, MTFs started working collaboratively in order to find a way to harmonise tick sizes in Europe. The LIBA (London Investment Banking Association) and the FESE (Federation of European Securities Exchanges) then joined these discussions. This led to the FESE tables, a list of four different tick size regimes that should become the standards in European trading.

At the beginning of June, Chi-X decided to implement smaller tick sizes for Danish, Norwegian, Spanish and Swedish stocks. The successful result of this change can easily be read on Figure 9. Then, on 8 June, Turquoise chose to reduce the tick sizes for five UK blue chip stocks. One week later, Turquoise implemented smaller tick sizes for five more UK stocks and five Italian stocks. On the same day, BATS followed in the steps of Chi-X and Turquoise, and implemented the same tick sizes as Chi-X (for Danish, Norwegian, Spanish and Swedish stocks) and Turquoise (for the UK and Italian stocks). The consequences, in terms of market share, of the step taken by Turquoise and BATS can easily be seen in Figures 21 and 22:



The venue that offers a tick size that best fits a stock is immediately rewarded with an increase in market share.

The venue that offers a tick size that best fits a

stock is immediately rewarded with an

increase in market share

LIBA and FESE organised a conference call on 16 June, during which MTFs and exchanges agreed not to implement smaller tick sizes until a meeting on 30 June. On 22 June, the LSE, Chi-X, Turquoise, BATS Europe and Nasdaq OMX Europe standardised the tick sizes where differences had appeared.

On 30 June, a FESE press release announced that the MTFs were now committed to adopting the same tables as the domestic venues, and to make no further moves in their tick size regimes outside the timetable agreed for each market.

This led to the harmonisation of tick sizes on the BEL 20, AEX 25, AMX 25, CAC 40, CAC next 20, SBF 120 and PSI 20 (all of these indices are traded on Euronext) on 16 July.

The potential dangers and benefits of a tick size reduction Reducing tick size, for

an intensively traded stock that has a spread

mostly equal to one tick, will reduce the realised

spread

Indeed, there has been quite extensive academic literature in the past decade on the subject of optimal tick size, and studies have rarely reached the same conclusion. The only consensus is that reducing tick size, for an intensively traded stock that has a spread mostly equal to one tick, will reduce the realised spread. And this is a good thing, because the realised spread is part of execution costs. Some observers have therefore predicted that such a reduction in the tick size would increase market turnover as execution costs were lowered.

But there are arguments against tick size reduction, among which:

It would trivialise time priority and thus lead to higher negotiation costs, as there would be a higher range of possible prices;

It would reduce market depth, meaning that for a large order, it would be more expensive to execute in one go. This is very hard to assess because of the lack of good measures of 'resiliency', i.e. the market’s ability to quickly renew the liquidity that has just disappeared.

It is also frequently stated that reducing the tick size will reduce the profitability of limit market orders. It is true that if the tick size is large and you are relatively sure you can fill your order, you might have an incentive to post a limit order. But conversely, as everyone will be thinking the same way, you will have huge volume on the best bid and offer, so the probability of your order being filled in a reasonable time will be reduced. What will the investor really gain from using limit orders? Reducing tick size may

lead to the use of more hidden orders, making

the potential depth loss even harder to evaluate

Furthermore, many studies have shown that it was unclear whether market depth was lowered, but it was clear that more hidden orders such as icebergs were used in small tick size contexts.

This is a part of the very difficult question raised by tick size: dynamic and hidden liquidity versus static displayed liquidity. The former definitely requires more research and monitoring to be exploited.

But even more so, some studies have concluded that lowering tick size (where appropriate) would reduce volatility, some have said that it would increase the profitability of front-running strategies, and finally some people have warned about more executions increasing execution costs or the complexity of gathering data.

Many people have ideas on the subject, but no one has given a satisfactory answer to this difficult question. Markets have thus globally reduced tick size in the past decade in an attempt to lower the spread, but are careful about the potential detrimental effects on liquidity.



Each stock needs its own tick size

FIGURE 23: LSE RELATIVE VWAS ON FTSE 100 (AUGUST 2009) FIGURE 24: EURONEXT RELATIVE VWAS ON CAC 40 (AUGUST 2009)

102

103

101

101

102

100

101

FIGURE 25: BORSA ITALIANA RELATIVE VWAS ON MIB 30 FIGURE 26: XETRA RELATIVE VWAS ON DAX (AUGUST 2009)

(AUGUST 2009)

101

101

101

102

100

101


In Figures 23 to 26, the VWAP is plotted on the x-axis, and the VWAS (volume weighted average spread) divided by the VWAP (and expressed in basis points) is plotted on the y-axis, with both axes on a log scale. Furthermore, one point stands for one stock on one day of August, and we have used colour to indicate the stock’s turnover on that particular day: the redder the point, the higher the turnover. We also added the tick size for the various price ranges divided by the price; this is the black line that sets the lower boundary of the spread.

What is very clear from these charts is that exchanges do not agree on optimal tick size. Euronext has the lowest one, followed by XETRA, and then the London Stock Exchange Group (the Italian stock exchange is part of the London stock exchange group).

The consequence is also fairly clear: the London Stock Exchange Group’s tick policy constrains the spread to be higher than it would be otherwise. Once again, we are not claiming that this is a bad thing for overall liquidity, but rather that if one would like to reduce spreads then there is a way to do so.



Regardless of the methods used to determine optimal tick size, it is likely that no one can give a universal grid (dependent on the price range, anyone would agree on this point). Indeed, it has to depend on the liquidity of the stock (the higher the turnover, the lower the spread), and it would need to be reviewed on a frequent basis, as volatility is determinant for the spread and would inevitably change the optimal tick size.

A good tick size not only depends on the

price of the stock, but also on the

liquidity of the stock and its volatility

In addition, the higher the tick size, the more crucial it is to be part of the queuing process on each price tick. Thus, it is worth waiting when tick size is high, whereas it is efficient to simply take the liquidity aggressively when the spread is very low because of a small tick size. From this point of view, "Fill or kill" or similar orders are more suited to small tick stocks than to large tick stocks. This means that penalising such very fast order types (for instance via a fee schedule) is equivalent to increasing the tick size.



III— How to route orders in a fragmented market?

Focus on atomic orders

FIGURE 27: MARKET IMPACT VS. MARKET RISK FIGURE 28: OPTIMAL TRADING CURVES

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1

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5

Pct of the day

Ris

k co

st (

bp)

Exec of 50,000 shares for FTE.PA

Market risk

Market impactOverall risk


Most large orders are now temporally split by traders using decision support tools and trading algorithms. It is well known that this split has to follow a few rules:

On the one hand, the market impact of orders has to be estimated (no temporal split leading to a large order that would impact the market and depreciate the price obtained);

On the other hand, the market risk has to be considered: waiting too long can expose the order to opposite moves in the price.

A subtle balance must be found between these two effects. Practitioners and academics have proposed using a parameter called "risk aversion", whose meaning is similar to that used in portfolio allocation: if you fear risk (high risk aversion), you will accept paying a high market impact to reduce your exposure to future risk. If you are ready to accept risk (low risk aversion), you will accept being exposed to market risk and try to use time to reduce your market impact. Figure 27 shows how the market impact and the market risk vary with the time you take to trade a given number of shares (horizontal axis). The black line is the combined risk: for this risk aversion parameter, we see the line decreasing first (it is worth waiting) before increasing (the market risk after 10% of the day does not balance the expected gain in market impact).

Because the market risk and the market impact are not linear functions of the time and the quantity to be traded, and because volume and volatility experience the usual intra-day variation, solving this optimisation leads to optimal trading curves that are not straight lines, like the one used by CA Cheuvreux’s algorithms. Figure 28 shows three trading curves corresponding to three different market conditions (see "Rigorous Strategic Trading: Balanced Portfolio and Mean-Reversion", The Journal of Trading, Vol. 4, No. 3. (2009), pp. 40-46, by Charles-Albert Lehalle).

Before market fragmentation, large orders were only temporally split according to such optimisation schemes, but now each temporal slice itself (each "atomic order", also called "child order", "slice" or "occurrence") has to be split and sent to different trading destinations according to the liquidity they offer. This part of the study is dedicated to an explanation of the mechanisms that take place during this spatial split.



Using a Smart Order Router (SOR)

FIGURE 29: FUNCTIONAL DIAGRAM OF A SMART ORDER ROUTER

Source: Crédit Agricole Cheuvreux Quantitative Research.

Assuming that each "slice" is sent at the better instant given the information available to the trader and his decision support systems, assuming also that this slice has a well-defined "limit price" and exact quantity, it is possible to focus on the best way to split the quantity among available trading venues. It is obvious that such a split will be optimal as:

It obtains a price better than or equal to the asking price;

It finishes the order as soon as possible (because the temporal aspect has been solved at a higher level).

The best way to use the liquidity available in alternate venues is to avoid crossing a tick price on the main trading destination if volume is available at the same price on another venue: it will accelerate the execution, improve the price and minimise the footprint of the overall execution in the market.

The standard device to achieve such an optimisation is named a Smart Order Router (SOR). Other tactics such as multi-destination icebergs, liquidity seekers, etc., can also be used given that users of these tactics are aware of the overall strategy that led to this order being sent at this time and at this price. The specific feature of an SOR is that it is an agnostic device: it takes a limit or market order and splits it with advanced knowledge of the state of the liquidity on the trading destinations that are ready to trade. An SOR order is like a new type of order giving access to an aggregated view of a large set of trading venues.

Crédit Agricole Cheuvreux’s SOR can be customised for different uses, mainly via a selection of trading destinations and some split options. The better the capabilities of each trading destination are understood, the better an SOR will be able to comply with clients' needs. The knowledge shared in the Navigating Liquidity studies has been used substantially to fine-tune our SOR.

Incoming orders

1. Listen to market data

SOR

2. Take the decision

4. Take exec into account

3. Send orders

Venue 1

Venue 2

5. Log for post trade

Venue N

An optimal split will accelerate the

execution, improve the price and minimise its

footprint

A SOR order is a new type of order giving

access to an aggregated view of a set of trading venues



An SOR is not only a "fire and forget" device: it also needs to be able to prove that every decision made is in accordance with the agreed-upon "execution policy". To comply with MiFID, the choices made by the SOR and the relevance of the execution policies must also be reviewed. Such a trading tool thus needs to be properly connected to large data warehouse recording policies, snapshots of the state of the market, decisions made and executions obtained.

Figure 29 is a simplified view of the components of an SOR: the decision-making part has to fit in with the market listeners, and the SOR has to be able to adjust its decisions with respect to feedback from the market.

Aggregate liquidity to minimise the impact of each order

FIGURE 30: INDICATORS OF THE AVERAGE TRADING SIZE (ATS) FIGURE 31: INDICATORS OF THE AVERAGE TRADING SIZE (ATS) AND OF THE DAILY NUMBER OF TRADES FOR THE FTSE 100 AND OF THE DAILY NUMBER OF TRADES FOR THE CAC 40

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Indicator of the ATS700

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08/09


Figures 30 and 31 show that while the average trading size (ATS) of an order has decreased overall since early 2007, the daily number of deals has increased regularly. This is a well-known consequence of the development of electronic trading.

While the acceleration of the decrease of the ATS due to MiFID and the financial crisis can easily be seen, the slope of the increase of the daily number of deals did not change with MiFID. This probably stems from the fact that the current average trading size is conditioned by the size of passive orders. As long as the average size of aggressive orders is larger than the size of posted orders, any decrease in the size of aggressive orders has no impact on the ATS or on the number of deals.

As long as the size of aggressive orders is

larger than the size of passive orders, any

decrease in the size of the former has no

impact on the number For instance, if an aggressive order of 150 consumes two posted orders of 100 and 50 at the same limit price (say 10.00), two deals will occur and the ATS for these two transactions will be 75, which is the average size of the two passive orders.

Therefore, these charts have to be read as indicators of the behaviour of passive orders: with market fragmentation, the ATS goes down, and moreover, passive orders are now spread over more trading destinations. An SOR’s main task is thus to be able to build a consolidated view of all these small quantities to avoid a dramatic decrease in execution quality.

Going back to the previous example: if the two posted orders are no longer on the same trading venue, if the sender of the marketable order (say it is a buy) does not use an SOR, he will need to take quantity at a higher price after 100.



Beware of duplicated liquidity

FIGURE 32: SIMPLIFIED DIAGRAM OF TRAJECTORIES OF MESSAGES


The very first issue of Navigating Liquidity placed the emphasis on market makers (or traders with an activity very similar to market makers, such as high frequency arbitrageurs) that become heavily very involved in MTFs when they are created.

Their typical activity is to be present simultaneously at the bid and ask of the same security on several trading destinations. They agree to take on some market risk (because they maintain an inventory rather than immediately unwinding their positions), and are "rewarded" by a fraction of the market bid-ask spread. The maker / taker fee schedule of many MTFs helps high frequency traders be profitable. These HF traders invested substantially in technology, and each one generally focuses on a small subset of instruments whose behaviour is compliant with their trading robots.

Once market makers have posted liquidity on the same side of the order books on several trading venues, their inventory-keeping strategies often imply that as soon as one of those orders is executed, all the remaining ones are immediately cancelled. This leads to "duplicated" quantities in the order books, which makes the SOR’s task more complex.

Adding pegged and iceberg orders, it is now clear that so-called "pre-trade transparency" is not very useful to guess where liquidity really is. Post-trade transparency (i.e. trade reports) give far more information to investors. This makes small dark pools (with no pre-trade transparency but clear post-trade transparency) more attractive than some months ago.

Figure 32 gives keys to understanding how latency is an issue in such a context: it is a simplified view of an order’s trajectory. An SOR’s goal is to obtain an execution that is in line with the information used to take its decision:

The first step is to build an aggregated view of the market data; this view is usually built on the fly at the rhythm of the updates coming from the market;

Then the SOR has to compute its decision according to its tactic. This is usually very fast because the only limiting factor is the clock of the server it runs on.

After that, the orders sent have to go from the SOR to the trading venues. If the trading venues are close to each other, the SOR can be positioned in the same area, but this is not always the case and physical distance cannot be crossed instantaneously.

Incoming orders

Broker systems (including

SORs)

Matching Engine

Trading venue

gateway Outgoing

executions

Market data

Orders

Executions

Backbone / hardware

Priority / queuing management

High-frequency prop trade tactics lead to

duplicated quantities in order books that makes

the SOR’s task more complex

"Pre-trade transparency" is not so useful to guess

where liquidity really is



For instance, it takes light around 1.14ms to travel the 350km distance between Paris and London; the Eurostar’s route would take 1.6ms at the speed of light, while in a usual backbone, a message needs around 4ms to cover the same distance (see Figure A4 in Appendix 4 for more information on the latency of worldwide roundtrips).

Then the orders have to reach the matching engines of the trading destinations. Depending on its type, an order will not have the same priority (for instance, inserting a new order will have a better rank than a modified order).

Finally, the execution reports have to be returned to the SOR.

All these steps give an idea of the different "latency arbitrages" that can occur during the life of an order. The efficiency of an

SOR cannot be stated in terms of latency but in terms of the results

obtained: the higher the quantities executed

on MTFs, the more efficient the SOR

Nevertheless, the efficiency of an SOR cannot be stated in terms of latency but in terms of the results obtained: for well-defined orders and given that the price obtained is better than or equal to the one that could have been obtained on the main market only, the higher the quantities executed on MTFs, the more efficient the SOR.



IV— Two results and a guess

Efficiency of CA Cheuvreux's SOR

FIGURE 33: RANGE OF FIGURE 34: COMPARISON OF MARKET SHARES FIGURE 35: COMPARISON OF MARKET SHARES OBTAINED PRICE (UPPER) WITH THE SPLIT OBTAINED (LOWER) FOR (UPPER) WITH THE SPLIT OBTAINED (LOWER) FOR

IMPROVEMENTS (IN BP) THE FTSE 100 THE CAC 40

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BATSCHI-XLSENASDAQ OMXTURQUOISE

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BATSCHI-XEURONEXTNASDAQ OMXTURQUOISE


As explained above, splitting an aggressive order on several trading destinations enables access to the same level of price for a larger volume, instead of going to the next tick of price on the primary trading destination.

The efficiency of such a split can be quantified by the part of the execution coming from alternative venues as long as a price improvement is obtained.

Figure 33 gives the range of price improvement obtained by CA Cheuvreux's SOR on all its aggressive orders from 1 January to end August 2009 on the DAX, CAC 40, AEX and FTSE 100 indices. This shows that the median of the improvement is higher than 1 basis point and the probability of an improvement between 0.8bp and 1.4bp is high. Client orders can even obtain a 1.8bp improvement. This is a realistic and very good performance, as it has to be compared with less than half of the effective tick size. An order that would consume two price ticks should have a size of around 5 ATS (~EUR50k on the CAC 40), and if the entire quantity needed could be found on the first limit in an alternative venue, the price improvement for a stock of EUR20 with a 0.005 tick size would be 1.25bp.

The median of the improvement is higher than

1 basis point; an improvement of 1.8bp is

not rare

Moreover, the SOR’s efficiency can be seen in its split rate: the SOR finds more than 50% of its liquidity outside primary markets, meaning that being more concentrated on primary venues would have a negative impact on the price. One of the explanations of the split rate obtained is that because more agents are posting on cheap venues, consuming liquidity on such venues for one order increases the probability of being able to find liquidity again for the next one.

The SOR’s efficiency can be seen in its split rate: the SOR finds more than 50%

of its liquidity outside the primary markets



A split driven by Chi-X efficiency

FIGURE 36: EXPLANATION OF CA CHEUVREUX'S AGGRESSIVE SPLIT

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CAC 40 outside EURONEXTPredictorFTSE 100 outside LSEPredictor


Figure 36 shows the performance of a model explaining the rate of execution by our SOR outside the primary market for the CAC 40 and FTSE 100 indices.

The model obtained is linear and uses as inputs the Cheuvreux-TAG market monthly indicators;

The less the LSE is present on the BBO with the greatest quantity, the fewer executions occur on primary markets;

The smaller the Chi-X bid-ask spread, the fewer executions go to the primary markets;

The performance of Chi-X and the

decreased efficiency of the LSE is a sufficient information about the

fragmentation

The more Chi-X is present on the bid-ask spread, the fewer executions go to the primary markets.

The split rate obtained is very close to the effective execution rate. This means that the month-by-month performance of Chi-X and the decreased efficiency of the LSE give sufficient information about the fragmentation to be able to explain the splitting rate outside primary markets.



Fragmentation is a consequence of primary markets' variance

FIGURE 37: SIMULATION OF A PRIMARY MARKET WITH SMALL FIGURE 38: SIMULATION OF A PRIMARY MARKET WITH LARGE VARIANCE VARIANCE

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

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Main Altern. ( 17)Altern. 2 ( 7)

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Main Altern. ( 17)Altern. 2 ( 7)


The preceding sections analysed the optimal split for aggressive orders, and have shown that a natural consequence of the availability of quantities at the best bid and offer on alternative venues was that the bigger the quantity sent to MTFs, the more efficient the execution. For passive orders, the question is more complex, because the prices and quantities of many of these are often reassessed. In such a context, it would be nonsensical to produce aggregated statistics for passive orders, because such data would mix orders that have overly divergent goals.

Nevertheless, we obtained a very interesting result on splitting passive orders that is worth sharing. To give a clear idea of what was obtained, we will use a simulated instrument and say that it is traded on three venues:

Its primary venue, concentrating 76% of the flows;

A main alternative venue, with 17% of the flows;

And another "small" alternative venue, obtaining 7% of the flows.

The illustrations come from large-scale simulations and are backed by theoretical calculations.

The important point here is that the average size of the investor we will study is lower than the average size traded on the market.

Figure 37 shows how the optimal split can be learned from the data: the horizontal axis is time (in the real world, the more investors use SORs and rational tactics, the faster this time line will be covered) and the vertical line is the optimal split rate. A "learning phase", during which investors are trying to guess how to split optimally, can be observed. This come from the fact that pre-trade transparency is not really good on current trading venues (iceberg, duplicated and pegged orders), so execution reports have to be used: the most reliable source of information is the messages you get back from the venues when you send them an order. It takes time the learn the optimal split from them.

The most reliable source of information is

trade reportsAs expected, in such a low variance context, the optimal split for an investor using an efficient SOR, as seen at the end of the time line of Figure 37, respects the market shares of the various venues.



Figure 38 is far more informative: the market shares of the three simulated trading destinations are the same as in Figure 37, but with a larger variance. The variance quantifies the regularity of the flow that a venue can guarantee. For instance, let’s assume one venue can ensure you a flow of execution between 60 and 120 (with an average of 90), while the range of the flow for another lies between 20 and 25 (the average is 22.5). Given that the size of your orders is around 80, your best split is 60 for venue A and 20 for venue B and you are sure to have the order filled. This leads to a rate of 75% for venue A when its market share is 80%.

In Figure 37, the best split for an investor with orders that are on average smaller than the market is 50% for the exchange that has a 76% market share, 40% for the alternative one and 10% for the smallest: this obviously transfers flows from the main venue to MTFs.

This brief illustration backs the simulation, which is in line with theoretical results: investors whose average order size is lower than or equal to the market average will fear variance of the flows. All studies have shown that variance is proportional with market share, so the main markets automatically have more variance than mid-players like Chi-X or Turquoise. As a result, most of the investors send Chi-X more than its market share.

Investors whose average size is lower than or equal to the

market average will fear variance of flows; this

automatically leads to a situation of eroding

market share for the primary markets

An important remark is that given the shape of the distribution of the volumes going to the market, a significant percentage of investors have an average size lower than the overall market. The only situation where no investor has an average size lower than the overall average is when all investors have exactly the same order size… Another important point is that investors with an average size larger than the market have no reason to send more than their market share to primary markets: this automatically leads to a situation of eroding market share for the main players (some investors send less than their market share, others send the market share: the combination of these factors is a decrease in market share for the next month).

This explanation fits perfectly with the observations: the main market loses market share each month, especially when the flow it provides is volatile (as is the case for the LSE).



Appendices

Appendix 1: Glossary Price Formation Process The price formation process covers all events occurring during trading that result in a constantly evolving market price. Such events are, for instance: insertion of new orders or cancellation of orders, or matching of opposite orders.

Walrasian Equilibrium An equilibrium between consumers and producers to set a perfect match between supply and demand.

Trading Destinations MiFID removes domestic exchange concentration rules and recognises three trading destinations: Regulated Markets (RMs), Multilateral Trading Facilities (MTFs) and Systematic Internalisers (SIs). Everything else is over-the-counter (OTC).

Primary Market Also called new issue market. Securities are issued for the first time in this market. In this study, EURONEXT PARIS, the LONDON STOCK EXCHANGE and XETRA are examples of primary markets.

Multilateral Trading Facility (MTF) A multilateral system operated by an investment firm or a market operator which brings together multiple third-party buying and selling interests in financial instruments in a way that results in a contract. Chi-X, Turquoise and BATS are the MTFs studied here.

Dark Pools A trading destination not disclosing its order book. Trades are often reported with a delay. These venues are said to be adapted to trade large quantities without leaving a footprint in the market.

Smart Order Router A device routing an order across a given set of trading destinations according to a disclosed execution policy. It can split an order into smaller ones to spray all available destinations if needed.

Execution Costs Execution costs are a mixture of fees, bid-ask spread, price impact, market impact, opportunity risk and market risk.

Market Risk Measurement of the uncertainty in the evolution of the price.

Price impact Impact of the volume of an aggressive order on the obtained average price.

Market Impact Possibly persistent impact of the volume of an aggressive order on the market price.

Opportunity Risk Price of missing a transaction or obtaining a deteriorated price by not having placed an order on the adequate trading destination.

Tick Size The minimal difference allowed between two different prices. It is defined by the trading rules of each trading destination.

VWAS: Volume Weighted Average Spread The mean of the bid-ask spread at each trade, weighted by the volume of the trade.

Execution Benchmark Benchmarks define the target to be achieved during the trading of a large order.

Implementation Shortfall The average price of the order is compared to the market price before the beginning of trading.

PVOL: Percentage of Volume The volume curve of the order compared to that of the market for the same stock.

VWAP: Volume Weighted Average Price The mean of traded prices weighted by traded volume. Cross trades are often excluded from this computation.



Appendix 2: Methodology Once again, the scope of this study is broader than that of the previous one. We have added another trading destination: NASDAQ OMX.

We have also included certain indices when needed to provide some clues about how fragmentation is spreading to mid caps.

We decided to use data from March to August to be in line with our monthly "market liquidity indicators", unless explicitly stated.

Our data comes from our tick by tick database containing all market deals synchronised with the best bid and offer (prices and quantities) on a large set of stocks, and five limit-order books on a subset of those stocks.

This database is very comprehensive as it stores information about the trading phase, the trading destination and the type of deal (lit or dark).

What are we looking at?

Electronic markets have two main kinds of trading rules:

Rules for fixing auctions: after a pre-auction period, during which all agents post orders as liquidity providers, a "Walrasian" equilibrium is instantaneously found to fix a price. All sellers that post a price lower than the fixing price and all buyers that post a higher price will have their orders executed.

Rules for continuous auctions: a Limit Order Book (LOB) consists of orders posted by liquidity providers that did not find a counterpart. The highest buying price of the LOB and the lowest selling price are called best bid and best ask prices. Liquidity consumers have to post buy orders higher than the best ask, or sell orders lower than the best bid, thus generating a trade.

When talking about liquidity, investors can be divided into two subsets: liquidity providers, who are waiting for a counterpart and take on the risk of a reversal of markets (i.e. the "market risk") and liquidity consumers, who want to trade now, without taking any market risk. The latter will buy their need for certainty from the former, paying them the "price impact" of their trades.


Market data natively provides an indicator for each kind of behaviour: the turnover over the previous 15 minutes is a good proxy for the flow of liquidity consumers (i.e. investors seeking to trade immediately) and the bid-ask spread is an indicator of the price of this urgency because this is the amount they agree to pay to trade immediately.

Another indicator is market risk. It is known that there is no way to estimate this with perfect accuracy, but a common measurement of the uncertainty part of price moves is volatility. As its measurement is based on traded prices, it characterises liquidity consumers. It also influences the behaviour of liquidity providers since the premium they demand has to take into account the market risk they have to assume.




Appendix 3: Lists of stocks

Composition of indices used in the whole study:

CAC 40 Stocks

ACCOR ACCP.PA SAINT GOBAIN SGOB.PA AIR LIQUIDE AIRP.PA SCHNEIDER ELECTRIC SCHN.PA ALCATEL-LUCENT ALUA.PA VINCI (EX. SGE) SGEF.PA AXA AXAF.PA STMICROELECTRONICS STM.PA BNP PARIBAS ACT. A BNPP.PA SOCIETE GENERALE SOGN.PA BOUYGUES BOUY.PA TOTAL SA TOTF.PA CAPGEMINI CAPP.PA UNIBAIL-RODAMCO UNBP.PA CARREFOUR CARR.PA VALLOUREC VLLP.PA DANONE DANO.PA AIR FRANCE –KLM AIRF.PA ESSILOR INTL. ESSI.PA DEXIA SA DEXI.BR FRANCE TELECOM FTE.PA SANOFI-AVENTIS SASY.PA L'OREAL OREP.PA ARCELORMITTAL ISPA.AS LAFARGE LAFP.PA EADS EAD.PA LAGARDERE S.C.A. LAGA.PA VEOLIA ENVIRONNEMENT VIE.PA LVMH LVMH.PA VIVENDI VIV.PA MICHELIN MICP.PA CREDIT AGRICOLE CAGR.PA PERNOD RICARD PERP.PA GDF SUEZ GSZ.PA PEUGEOT PEUP.PA ALSTOM ALSO.PA PPR PRTP.PA EDF EDF.PA RENAULT RENA.PA SUEZ ENVIRONNEMENT SEVI.PA


FTSE 100 Stocks

DIAGEO PLC DGE.L STANDARD CHARTERED PLC STAN.L BARCLAYS PLC BARC.L HAMMERSON PLC HMSO.L LLOYDS BANKING GROUP PLC LLOY.L LIBERTY INTERNATIONAL PLC LII.L MARKS & SPENCER GROUP PLC MKS.L LONMIN PLC LMI.L UNILEVER PLC ULVR.L REXAM PLC REX.L BRITISH AIRWAYS PLC BAY.L SCHRODERS PLC SDR.L BP PLC BP.L SERCO GROUP PLC SRP.L PRUDENTIAL PLC PRU.L SEVERN TRENT PLC SVT.L RSA INSURANCE GROUP PLC RSA.L SHIRE PLC SHP.L VODAFONE GROUP PLC VOD.L WM MORRISON SUPERMARKETS MRW.L CADBURY PLC CBRY.L INTERNATIONAL POWER PLC IPR.L KINGFISHER PLC KGF.L OLD MUTUAL PLC OML.L BAE SYSTEMS PLC BAES.L PENNON GROUP PLC PNN.L WPP PLC WPP.L ANGLO AMERICAN PLC AAL.L ASTRAZENECA PLC AZN.L SCHRODERS PLC-NON VOTING SDRt.L LEGAL & GENERAL GROUP PLC LGEN.L AMEC PLC AMEC.L PEARSON PLC PSON.L ANTOFAGASTA PLC ANTO.L TESCO PLC TSCO.L CAIRN ENERGY PLC CNE.L SMITHS GROUP PLC SMIN.L TULLOW OIL PLC TLW.L CABLE & WIRELESS PLC CW.L COBHAM PLC COB.L HSBC HOLDINGS PLC HSBA.L AMLIN PLC AML.L LAND SECURITIES GROUP PLC LAND.L ICAP PLC IAP.L NEXT PLC NXT.L BALFOUR BEATTY PLC BALF.L ROYAL BANK OF SCOTLAND GROUP RBS.L AUTONOMY CORP PLC AUTN.L BG GROUP PLC BG.L CARNIVAL PLC CCL.L NATIONAL GRID PLC NG.L GLAXOSMITHKLINE PLC GSK.L RIO TINTO PLC RIO.L COMPASS GROUP PLC CPG.L THOMSON REUTERS PLC TRIL.L FRIENDS PROVIDENT PLC FP.L SAINSBURY (J) PLC SBRY.L BT GROUP PLC BT.L AVIVA PLC AV.L XSTRATA PLC XTA.L SMITH & NEPHEW PLC SN.L FOREIGN & COLONIAL INVEST TR FRCL.L BRITISH SKY BROADCASTING GRO BSY.L INTERTEK GROUP PLC ITRK.L SCOTTISH & SOUTHERN ENERGY SSE.L ALLIANCE TRUST PLC ATST.L INVENSYS PLC ISYS.L RANDGOLD RESOURCES LTD RRS.L



FTSE 100 Stocks

RECKITT BENCKISER GROUP PLC RB.L INTERCONTINENTAL HOTELS GROU IHG.L REED ELSEVIER PLC REL.L VEDANTA RESOURCES PLC VED.L BHP BILLITON PLC BLT.L G4S PLC GFS.L BRITISH AMERICAN TOBACCO PLC BATS.L ADMIRAL GROUP PLC ADML.L ROLLS-ROYCE GROUP PLC RR.L ROYAL DUTCH SHELL PLC-A SHS RDSa.L SAGE GROUP PLC/THE SGE.L INMARSAT PLC ISA.L IMPERIAL TOBACCO GROUP PLC IMT.L ROYAL DUTCH SHELL PLC-B SHS RDSb.L JOHNSON MATTHEY PLC JMAT.L PETROFAC LTD PFC.L ASSOCIATED BRITISH FOODS PLC ABF.L KAZAKHMYS PLC KAZ.L BRITISH LAND CO PLC BLND.L DRAX GROUP PLC DRX.L BUNZL PLC BNZL.L STANDARD LIFE PLC SL.L CAPITA GROUP PLC CPI.L EXPERIAN PLC EXPN.L CENTRICA PLC CNA.L HOME RETAIL GROUP HOME.L MAN GROUP PLC EMG.L THOMAS COOK GROUP PLC TCG.L UNITED UTILITIES GROUP PLC UU.L TUI TRAVEL PLC TT.L WHITBREAD PLC WTB.L EURASIAN NATURAL RESOURCES ENRC.L SABMILLER PLC SAB.L FRESNILLO PLC FRES.L


DAX Stocks

ADIDAS AG ADSG.DE LINDE AG LING.DE ALLIANZ SE ALVG.DE MAN AG MANG.DE BASF SE BASF.DE MERCK KGAA MRCG.DE BAYER AG BAYG.DE METRO AG MEOG.DE BAYERISCHE MOTOREN WERKE AG BMWG.DE MUENCHENER RUECKVER AG-REG MUVGn.DE BEIERSDORF AG BEIG.DE SALZGITTER AG SZGG.DE COMMERZBANK AG CBKG.DE RWE AG RWEG.DE DEUTSCHE BANK AG -REG DBKGn.DE SAP AG SAPG.DE DEUTSCHE LUFTHANSA-REG LHAG.DE SIEMENS AG SIEGn.DE DEUTSCHE TELEKOM AG-REG DTEGn.DE E.ON AG EONGn.DE FRESENIUS AG -VZO- FREG_p.DE VOLKSWAGEN AG VOWG.DE FRESENIUS MEDICAL CARE AG & CO FMEG.DE DAIMLER AG DAIGn.DE HANNOVER RUECKVERSICHERUNGS HNRGn.DE THYSSENKRUPP AG TKAG.DE HENKEL AG+CO.KGAA VZO HNKG_p.DE DEUTSCHE POST AG-REG DPWGn.DE K+S AG SDFG.DE DEUTSCHE BOERSE AG DB1Gn.DE


BEL 20 Stocks

BEKAERT NV BEKB.BR ACKERMANS & VAN HAAREN ACKB.BR COLRUYT SA COLR.BR BEFIMMO S.C.A. BEFB.BR DELHAIZE GROUP DELB.BR COFINIMMO COFB.BR DEXIA SA DEXI.BR CNP -CIE NATL A PORTEFEUILLE NAT.BR FORTIS FOR.BR OMEGA PHARMA SA OMEP.BR SOLVAY SA SOLB.BR ANHEUSER-BUSCH INBEV NV INTB.BR UCB SA UCB.BR GROUPE BRUXELLES LAMBERT SA GBLB.BR UMICORE UMI.BR BELGACOM SA BCOM.BR KBC GROEP NV KBC.BR GDF SUEZ GSZ.PA MOBISTAR SA MSTAR.BR TELENET GROUP HOLDING NV TNET.BR



Appendix 4: Information on backbone latencies

FIGURE A4: INDICATIVE TIMES FOR DATA ROUNDTRIPS

Source: Hibernia Atlantic (courtesy of Roderick Beck)



Appendix 5: Liquidity metrics

MARKET SHARE LIQUIDITY METRIC, AUGUST 2009

Index Primary market Chi-X Turquoise BATS

63.4% 22.5% 9.3% 4.7%FTSE 100 69.8% 18.1% 7.3% 4.7%CAC 40 70.8% 19.0% 6.0% 4.2%DAX 70.1% 18.8% 7.3% 3.9%AEX 80.5% 12.8% 3.7% 3.0%BEL 20 81.0% 10.8% 8.2% N/ASMI

VWAS AND VWAS EFICIENCY LIQUIDITY METRICS, AUGUST 2009

VWAS (BASIS POINTS)


6 bp 5.8 bp 13 bp N/ACAC 40 7.1 bp 7.3 bp 15.9 bp N/AFTSE 100 6.8 bp 4.4 bp 11 bp 20.4 bpDAX

7 bp 6.4 bp 12.7 bp 20.5 bpAEX

FIXING AUCTION LIQUIDITY METRIC, AUGUST 2009

AEX BEL 20 CAC 40 DAX FTSE 100

5.3 9.2 5.5 5.2 6.3

COVERAGE LIQUIDITY METRIC, AUGUST 2009


FTSE 100 42% 41% 47% 43% AEX 48% 41% 51% 35% CAC 40 59% 53% 60% 46% DAX 50% 46% 53% 52%




Documents

Navigating Liquidity