The Impact of Architectural Features on Global Equity Market Performance: How Harmful is Opacity for Trading Success?†

Peter L. Swan ‡

Joakim Westerholm §

December 21, 2004

Abstract

Utilizing a system of structural equations and a unique intra-day dataset for 33 major exchanges, we

evaluate several types of market architecture and numerous architectural design features to explain

relative trading costs, volatility, trade size and parcel numbers. We find that trading is sensitive to

transaction costs and minimum tick size, the limit order book design is ideal for all but small stocks;

transparency is generally preferable to opacity, and evidence of economies of scale and scope. We

rank the performance of every exchange relative to predicted best practice to conclude that all

exchanges have scope to improve performance drastically.

Keywords: Exchange trading systems, Architecture, Performance, Transparency, Decimalization

JEL Classification:G10, G15, G2

† Peter Swan gratefully acknowledges financial support from the Australian Research Council (ARC) DP0209729, and

Australian Professorial Fellowship and Joakim Westerholm from OKO BANK Group Research Foundation and the

Australian Capital Markets CRC. We gratefully acknowledge data provision from Reuters and SIRCA. We thank Jim

Berry, Ekkehart Boehmer, Zhian Chen, Gerald Garvey, David Feldman, Doug Foster, Peter Ho, Ron Masulis, George

Sofianos, Terry Walter and participants at the 16th Australasian Finance and Banking Conference 2003, ASX seminar,

Western Finance Association Conference, Vancouver, 2004, European Finance Association Conference, Maastricht, 2004,

Economics School Seminar, 2004, the executive committee of the Securities and Derivatives Industry Association (SDIA),

and 17th Australasian Finance and Banking Conference, keynote session, 2004, for comments and Mats Grankvist for

valuable assistance. The original title was “The Impact of Market Architectural Features on World Equity Market

Performance”.

© 2004 Copyright Peter L. Swan and Joakim Westerholm. All rights reserved. ‡ School of Banking and Finance, Faculty of Commerce and Economics, University of New South Wales, Sydney NSW

2052 Australia. Email: peter.swan@unsw.edu.au. § P. Joakim Westerholm, School of Business H69, University of Sydney, NSW 2006, Australia. Email:

j.westerholm@econ.usyd.edu.au.

In today’s increasingly competitive global environment for stock exchanges the survivors are likely

to be those exchanges that manage to improve their performance; creating markets with low

transaction costs, a higher dollar value of investor trades for the typical stock, with more stock

parcels trading. In short, the performance focus is on “traded value”, which is the product of the

average dollar value of parcels of a given stock and the number of parcels traded. Fundamental to

this approach is the notion that the demand for trading is downward sloping. Like any other product

or service, if we offer investors better terms on which to trade, not just transaction costs but every

conceivable dimension of service quality such as the appropriate degree of anonymity or

transparency, then more trading services will be demanded with more stock parcels trading for a

given parcel size. What then is the best strategy for the exchange or regulator to set to meet the

investor welfare goal of maximizing traded value, reflecting the requirements of diverse investors in

a global market? We attempt to address this question by establishing criteria for ranking the

performance of all stock exchanges and benchmarking world best practice in terms of market-

microstructure stock exchange architectural designs. One of the aims of our ranking is to encourage

exchanges to adopt the most efficient architectural designs, under the control of the exchange or

regulator, which we identify. Even the best currently performing exchanges should more than triple

traded value, weaker ones a multiple of this and in some cases add trillions of dollars to trading

activity currently discouraged by higher cost and less effective microstructure designs.

Additionally, we establish a market microstructure modeling approach that distinguishes between

the short- and long-run solutions to the system of equations describing the performance of all major

exchanges. We utilize the Hausman statistical test to show that microstructure models should

model relative transaction costs, volatility, the average value of trades and the number of trades as

endogenous within a system of structural equations using a two-stage or three-stage estimating

framework. In addition, we identify benefits from using daily panel data and a system of

endogenous equations as a basis for examining both the cross-sectional and time-series (event

study) implications of a whole host of market architectural design features. Transaction costs,

volatility, trade size and the number of trades are normally endogenous in most microstructure

models but existing econometric tests largely treat them as exogenous. Our simultaneous equation

approach overcomes the usual “endogeneity bias” or “errors in variables” problem, and by utilizing

a dynamic model with daily panel data overcome problems inherent in purely cross-sectional

modeling. In addition to modeling the full gamut of stocks traded using this approach, we focus on

the largest and smallest quintile to highlight the differences in required designs, and investigate

controversial design features that may particularly advantage or disadvantage large institutional

traders.

3

The most severe form of illiquidity occurs on non-trading days. Most analyses normally ignore

non-trading days with nothing recorded due to illiquidity. Nonetheless, like the dog that did not

bark in the Sherlock Holmes story, what does not happen can be more significant than what does.

We address many of the features of stock exchange architecture that alter the probability of trade

occurring to show that exchange features that drive successful trading in small stocks also promote

trading when otherwise a no-trading day would have been recorded.

As part of a series of robustness checks, we test the model’s predictions for many design features

that are almost purely cross-sectional and thus do not alter, against five major changes to

architectural design that we examine as event studies. Our findings are mostly supportive of the

largely cross-sectional predictions, meaning that there is a concordance between the cross-sectional

and time-series impact of architectural features. We also replicate the cross-sectional methodology

that has already received attention in the literature for examining traded value. Our findings

utilizing 4,271 individual stocks summarized on a cross-sectional basis are supportive of our

dynamic panel data analysis based on 1,268,188 daily observations and a larger number of stocks,

totaling 4,631. Out-of-sample predictions of all variables of interest are also extremely accurate

with explanatory power of up to 86% and relatively unbiased forecasts.

Finally, the impact factors derived by solving for the reduced form of equations explicitly

representing demand and supply for the components of traded value, incorporating all a priori

information from microstructure and economic theory, are vastly superior to conventional “least-

squares-no-restrictions” (LSNR) reduced forms estimated via ordinary least squares (OLS)

assuming no knowledge of the structural equations. Impact factors derived from the structural

model are extremely accurate in that they explain over 93 percent of the variation in traded value

expressed in levels across the 33 exchanges whereas the correlation between the predicted values

using LSNR and actual values is negative.

Choices that reduce transaction costs in the form of the effective spread are the basis of most market

microstructure empirical work and market design recommendations. Goettler et al. (2004) point

out, in the context of a simulated order book, that the unobservable “true” transactions cost

correlates negatively with the observed effective spread. Welfare significance arises from efficient

design that facilitates higher traded value. This is one of our motivations for going beyond the

traditional exclusive reliance on transaction costs to include all the components of traded value in

our model, with endogenous trading cost and volatility each feeding into the two components of

traded value. The traditional focus on observable trading costs alone could be misleading. We find

4

that the observed relative effective spread is a good, but not perfect, negative predictor of traded

value across 33 world exchanges.

Our findings are also strikingly inconsistent with Kyle (1985) in which “noise traders” with an

entirely inelastic demand for trading undertake the bulk of trading. Noise traders are more rational

that they might first appear, because, unlike their more informed brethren, they are discouraged by

higher trading costs. This explains why we would expect the proportion of relatively uninformed

traders to be higher in large, low-transaction-cost stocks with a higher proportion of public to

private information (Easley and O’Hara, 2004).

The present study analyzes the relationship between exchange performance and market architecture,

including factors that exchanges can alter and institutional/environmental features that are outside

their control. Utilizing the world’s most comprehensive intraday database with coverage of about

240 exchanges, our analysis of 33 of the more major exchanges, capped at 200 companies per

exchange, enables us to assess the impact of each design feature on exchange performance.

Participants desire to approach long-run optimal values in a partial adjustment framework with

geometric distributed lags. Hence, the long-run trading cost elasticity of demand for trading, after

taking into account trade size, appears moderate at – 0.47 while the short-run elasticity is relatively

inelastic within our dynamic system. Our structural equation approach also elucidates the actual

trading process: the way in which volatility detracts from trading in the long-run, with an elasticity

of – 0.54, which is similar in magnitude to the equivalent elasticity with respect to trading costs. A

larger trade size in dollar terms is associated with both higher trading costs and lower volatility.

These findings are in contrast to Brennan and Subrahanyam (1998), hereafter, B&Subra, although

the results are closer when we replicate their cross-sectional methodology.

Evidence is found of economies of scale and scope in the trading process with the relative trading

costs incurred for each trade declining 22 percent for every doubling of the number of trades and

largely unaffected by trade size since economies of scale are offset by more asymmetric information

and also by volatility. These findings on economies of scale and scope are generally consistent with

those of Hasan and Malkamaki (2001), and Hasan et al. (2003), even though they adopt a different

approach based on exchange costs recognized in accounting statements.

In conformity with theoretical predictions, if the number of shares traded in two stocks is the same

but one trades far more frequently than the other does, that stock will be cheaper to trade

(Madhavan, 1992). Smaller trades occur in lower-cost and more volatile markets, with both high

5

cost and high volatility inimical to the number of parcels traded as well as to traded value. There is

other evidence of scope economies with a doubling in the number of stocks listed raising traded

value by up to 92 percent. As we double the market capitalization of a company, a proxy for lower

asymmetric information, trading costs fall by 11 percent and traded value climbs by 34 percent due

to a larger endogenous trade size. Although it is not our primary focus here, the model suggests the

possible eventual demise of individual domestic exchanges and the creation of a single integrated

global market adopting world best practice to better reap these economies of scale and scope.

Would domestic exchanges continue to have a role? The setting and enforcing of listing

requirements could become their major focus.

Conditional on a range of design features relating to transparency and other matters, the market

design with the lowest realized volatility and highest traded value for the entire dataset is the

electronic “limit order book” (LOB) market. This provides some justification for Glosten’s (1994)

prediction that LOB markets will dominate in that they appear impervious to competition from

dealership markets. The electronic LOB improves on floor-traded markets such as the New York

Stock Exchange (NYSE), stocks with affirmative dealers such as the NYSE and smaller Euronext

stocks, and hybrid dealerships/electronic communication networks (ECNs) such as Nasdaq.

Electronic LOBs supplemented by affirmative dealers for typically smaller stocks have the lowest

overall trading costs. An exception to the out performance of LOBs is hybrid dealer markets for the

largest stock quintile. Despite higher trading costs, dealership/ECNs markets in this quintile reduce

volatility sufficiently to raise traded value. This finding is supportive of the predictions of game-

theoretic models such as Madhavan (1992) and Shin (1996) that emphasize the role of dealer

competition in reducing margins and coping with asymmetric information by absorbing

idiosyncratic risk. The possibility of coexistence between dealers and the LOB, with some degree

of specialization according to size, and search-informational considerations, is emphasized in the

theoretical models of Pagano (1989), Viswanathan and Wang (2002), Parlour and Seppi (2003) and

Snell and Tonks (2003).

The second exception to the out-performance of pure electronic LOBs is stocks trading in a LOB

with affirmative dealers for the smallest quintile of stocks. In addition, markets with exchange

floors, dealership markets and stocks with affirmative dealers are all better than electronic LOBs for

discouraging extreme illiquidity in the form of non-trading days. However, we do find that

affirmative dealers can absorb idiosyncratic risks associated with both large and small stocks and

both hybrid dealers and affirmative dealers ameliorate the problem of non-trading days on which a

6

stock is too illiquid to trade at all. Clearly, simple LOBs are deficient in terms of their ability to

discourage extreme illiquidity/non-trading.

Another striking finding from the study is that most transparent design features outperform opaque

features by reducing information asymmetry. Green et al. (2003) find that intermediaries exercise

more monopoly power in an opaque bond market, particularly with respect to smaller trades. An

implication of the market power hypothesis is that in markets that are more transparent, smaller

trades should receive many of the transaction cost benefits of large trades, since there is less

opportunity to exploit smaller traders. When we include interactions between the (partial) market-

depth transparency variable and trade size, our findings are not supportive of the market power

hypothesis. The execution costs of large trades relative to small trades improve in progressively

more transparent markets.

Reduced post-trade transparency, due to delayed reporting of block facilitating principle trades, is

harmful overall, and for all quintiles investigated, because it directly increases asymmetric

information, as well as promoting non-trading. A partial exception is an event study: the

introduction of delayed reporting of very large facilitated principal trades by the Australian Stock

Exchange (ASX) towards the end of our sample period. Its initial impact appears to have lowered

transaction costs but with an offsetting rise in volatility. Pre-trade disclosure of broker IDs to other

brokers and to investors is beneficial overall and for the largest stock quintile since it reduces

volatility, but disclosure can harm smaller stocks and increase the probability of non-trading. An

event study associated with the removal of pre-trade broker ID when Paris adopted Euronext rules

supports these findings. Broker ID opacity raises transaction costs by between 30 and 42 percent,

but leading to only a small decline in traded value. Our system of simultaneous equations provides

an explanation as to why many reforms which reduce asymmetric information and lower trading

costs do not impact significantly on traded value. An associated rise in volatility discourages

trading.

For the overall sample and the smallest quintile, revealing the depth of the LOB partially, leads to

both lower transaction costs and volatility, boosting traded value by 27 percent and discouraging

non-trading. For the largest stocks, partial revelation of depth appears to be harmful while there are

benefits from moving to full disclosure. At least partial revelation of depth encourages trading, as

opposed to days with no trades occurring. The only exception to the adverse impact of opacity is

iceberg orders, a form of pre-trade opacity that does not reveal the full depth of the LOB.

Surprisingly, these are beneficial overall and for encouraging trading when it might not otherwise

7

occur.

An opening call auction confers benefits to trading in the largest stocks during the day by

dramatically lowering volatility, despite the fact that it has an adverse impact on trading costs. This

is because the opening attracts more uninformed trading away from the regular trading mechanism,

raising asymmetric information during the day. The auction is also associated with a higher

probability of non-trading. The auction leads to lower volatility for the largest stocks, presumably

due to better price discovery. The strong overall adverse impact of the opening call simply reflects

the transfer of trading from the main exchange to the opening.

We control for the exchange environment in a number of ways. By classifying securities according

to their effective spread plus exchange taxes and charges, volatility, average trade size, number of

trades, the market to book ratio, and the market capitalization of each stock, we aim to determine

what type of market architecture is the most suitable for each market segment. These characteristics

also control for the nature of the stocks on each exchange. We introduce a range of proxies for

trading demand: the market capitalization of equity for each country, the number of listed

companies, aggregate income as measured by GDP, the population size each exchange draws on,

and the ability to arbitrage cross-listed stocks, as indicated by opening hours shared in common

with New York. We also control for shareholder rights conferred by the legal system and brokerage

fees for institutional investors on a country and an exchange-by-exchange basis, respectively. The

aim is to account for differences between exchanges in how well they have been able to establish

their position in the competitive global market place, overall, in extreme size quintiles and their

ability to discourage the severe problem of non-trading. Upstairs dealer markets grafted onto LOB

markets may in general filter out less informed trades, giving rise to a higher spread in the LOB.

For the largest stock quintile, which is most likely to be representative of upstairs trading, there is

no reduction in traded value because of a reduction in volatility.

The paper proceeds as follows: Section I provides a literature review while II introduces the

performance measures and III the market design features. Section IV outlines the data used.

Section V presents the model and empirical findings while Section VI concludes.

I. Literature Review

Perold and Sirri (1997) establish a variation in trading costs across international borders using

information about institutional investors’ intents, as well as executed orders, and are thus able to

measure implementation shortfalls and market impact costs. Domowitz et al. (2001) examine the

8

cross sectional variation in total trading costs using information on institutional trading on 42

exchanges. Low and falling transaction costs promote higher and rising stock turnover. These

studies do not directly associate market design and institutional features with exchange

performance. Venkataraman (2001) compares the Paris and the New York exchanges to conclude

that the New York floor trading system is superior to an electronic LOB. Jain (2002) examines the

impact of various market designs on liquidity and finds, based on sampled observations of daily

closing bid-ask spreads on 51 exchanges, that dealer-emphasis markets have higher transaction

costs than LOB markets when emerging markets are included.

Most previous literature analyzes one institutional characteristic at a time and compares two

exchanges. La Plante and Muscarella (1997) examine market impact costs for block trades and find

that liquidity provision for blocks is superior on the NYSE compared with Nasdaq. Chan and

Lakonishok (1997) compare institutional trading on the NYSE and Nasdaq. They find that smaller

stocks gain better execution on Nasdaq and larger ones on the NYSE. Bessembinder and Kaufman

(1997) find that both transaction costs and volatility is higher on Nasdaq than the NYSE for

comparable stocks.

Two studies examine what at first blush appears to be a change in a single feature, pre-trade display

of broker IDs to other brokers, on the Paris exchange (Foucault et al., 2003; Comerton-Forde and

Frino, 2004). However, it is rare for an exchange just to change a single feature at one time. For

example, while the introduction of the Euronext trading system occurred in Amsterdam, Brussels,

and Paris on different dates, Paris moved to a more anonymous system with respect to broker IDs at

the same time that it implemented the standardized Euronext trading system. Few studies take a

wider cross sectional approach. Even if focusing exclusively on events gives more easily

interpretable results closer to the ceteris paribus ideal, a cross sectional study across a wider array

of exchanges should better address the problem that most exchanges differ by more than one

architectural or institutional characteristic. Event study evidence alone, while often useful, is

conditional on the complex array of policies in place on a given exchange and thus might be

unreliable as a guide for designing an ideal exchange. Similarly, the determination of an optimal

array of architectural features should account for complex interrelationships between architectural

and institutional characteristics, together with macro-economic features that generate trading

demand.

The findings of traditional event studies may not always be robust with the altering of one or more

architectural variables, or even a new trading platform introduced, due to endogenous changes in

9

asymmetric information, the size and number of trades, demand conditions and volatility. When

embedded in a system of equations with endogenous variables and a complex array of exogenous

controls correctly identified and modeled, event studies become more reliable. Trading on every

exchange acts as a control for every other exchange with all the global relationships tied together by

the underlying economic model and system of equations. Below, we illustrate with a series of five

architectural design changes modeled with new event methodology, the strength of our approach.

Occasionally the subject of event studies, and one of the most critical architectural decisions facing

exchanges, is the question of transparency, both pre- and post-trade. Chowdhry and Nanda (1991),

Forster and George (1992), Madhavan (1992, 1995) and Lyons (1996) address the transparency

issue from a theoretical perspective. In a similar vein, Pagano and Röell (1996) establish that

uninformed investors benefit from the greater transparency, which is inherent in auction markets

such as pure public LOBs but not in dealer markets. Spreads should thus be lower in a transparent

LOB with immediate reporting of all trades and revelation of broker IDs. Madhavan (1992) and

Shin (1996) model differential information in a game-theoretic setting in which dealer markets are

less prone to informational uncertainties than are decentralized order-driven markets. Bloomfield

and O’Hara (1999) find that spreads could be wider with greater transparency in an experimental

approach. Likewise, Flood et al. (1999) adopt an experimental approach.

Since both theoretical models and experimental markets are far from conclusive about the impact of

transparency on financial markets, we now turn to empirical studies. Gemmill (1994), relying on

several changes to post-trade transparency made by the London Stock Exchange (LSE), including a

90 minute delay and a 24 hour delay, found that delayed publication of block trades did not

consistently reduce transaction costs and did nothing to improve liquidity. Grammig et al. (2001)

find that uninformed traders prefer the non-anonymous traditional floor trading mechanism while

informed traders prefer a relatively anonymous electronic LOB system. The adverse selection cost

component of trading costs is higher in the system attracting more informed trading. If facilitating

principle traders are given time due to delayed reporting of blocks to unwind a position, this

introduces asymmetric information as a policy choice. Does such a policy give an “unfair”

advantage to large brokers at the expense of other market participants and, consequently, have a

detrimental effect on the liquidity of the entire market?

Madhavan et al. (2004) find evidence of a decline in public liquidity of stocks on the Toronto Stock

Exchange (TSE) following greater pre-trade transparency of orders in 1990. They attribute this to a

greater propensity for “picking off” of orders viewed as “free options”. Boehmer et al. (2004)

10

analyze the introduction of pre-trade transparency to LOB on the NYSE in January 2002. This was

a response by the NYSE to the earlier introduction of decimalization of the quote size. Prior to this

time, only the best bid and ask was visible. Contrary to the findings of Madhavan et al. (2004) for

the TSE, they find an increase in liquidity with additional orders attracted to the LOB. Lee (1998)

provides a discussion of transparency issues.

Another controversial issue is the minimum tick size, or the minimum dollar difference in the price

of a trade. A lower minimum tick may reduce market depth by as much as it lowers trading cost,

either leading to no change, or an adverse impact on the value of trading. Nasdaq and NYSE have

moved recently from one-eighth to one-sixteenth of one dollar and then, finally, to decimalization

of the minimum tick size in response to regulatory demands. An early contribution was Harris

(1994) who used simultaneous equation modeling to predict the effect of smaller tick sizes. The

recent empirical literature includes Goldstein and Kavajecz (2000), Graham et al. (2003),

Chakravarty et al. (2004a), and Bessembinder (2004). The consensus appears to be that quotes have

fallen because of decimalization, facilitating a larger number of smaller trades, but Chakravarty et

al. (2004a) find evidence that overall liquidity has fallen with reduced depth for larger trades and

lower overall liquidity. A small minimum tick size reduces the importance of price and time

priority and makes it possible for traders to “front-run” posted limit orders that may potentially have

information content. By contrast, Bessembinder’s (2004) findings support the earlier predictions

made by Harris (1994) with no evidence of a liquidity decline.

II. Trading Mechanisms and Performance Measures

A. Trading mechanisms

We consider the following types of trading mechanisms:

(i) Dealer_Hybrid_Dummyi, a hybrid market with continuous dealer presence and the

option of an order book (e.g., Nasdaq and associated ECNs) to which a value 1 is

assigned to the four exchanges meeting this requirement and 0 otherwise;

(ii) a pure public order driven electronic LOB (e.g., the Australian Stock Exchange, ASX)

which has, in addition to the LOB, voluntary market-makers and possibly an upstairs

dealer-market for exceptionally large institutional trades to which a value 0 is assigned

to the 16 qualifying markets;

11

(iii) Stock_Affirmative_Dealer_Dumi is a variant on (ii), with designated dealers with an

affirmative obligation to provide firm and continuous quotes and to trade “against the

wind” to limit volatility (e.g., some Euronext stocks in several European countries and

all NYSE stocks). A value of 1 is assigned to qualifying stocks in the 13 exchanges

falling into this category and 0 otherwise.

(iv) Market_with_Exchange_Floor_Dumi, a traditional floor trading system (exclusive to

NYSE and Frankfurt) to which a value of 1 is assigned and 0 otherwise.

The pure electronic LOB assigned a value of 0 thus becomes the standard of comparison for each

alternative market type. In a LOB market with designated market makers in some or all stocks,

entry of dealers is controlled but incumbents obtain privileged status such as the absence of trading

fees in exchange for obligations. Of course, the NYSE is unique in a number of ways. Every stock

has a designated dealer and that dealer is unique to that stock as the specialist. The specialist also

operates visibly on the trading floor. Under a pure electronic-order-book trading mechanism, entry

of non-designated market makers is free, but there are no concessions granted to or obligations

imposed on broker-dealers acting in this role. The NYSE is the only exchange to qualify in two

categories, as both an affirmative dealer and as a floor-based system.

B. Performance Measures

We measure exchange performance from four main aspects: transaction costs, volatility, average

dollar value of trade size and the number of trades, the counterpart of trade duration or gap between

trades. As mentioned, the product of the last two variables generates the dollar value of trades,

which is of welfare significance to traders. Deflating traded value by market capitalization yields

the stock turnover rate, a standard measure of liquidity. The more a trading system facilitates the

trading desires of participants due to superior design, the greater will be traded value expressed in

dollar terms. Apart from being of critical value to investors whose objective is to trade, the dollar

value of trades is of particular concern to most exchanges since, apart from listing fees, levies on

traded value are typically the primary source of exchange income. Exchanges that are cooperatives

of broker/dealers will not necessarily wish to maximize traded value since the exchange owners

may be able to extract rents from traders via designs that discourage trading relative to the optimal

trading design. Differing pressure from brokers may help to explain the diversity we find in market

architectural solutions, together with departures from best practice.

When an exchange employs an opening or closing auction mechanism, a single large trade is

12

recorded which distorts measures of average trade size and trading costs in the continuous market.

We thus exclude such trades from our trade dataset, which we compute from trade-by-trade data

over the period of normal trading, excluding both opening and closing calls. The measures of

transaction costs are calculated using intra-day, trade-by-trade data. Every time a trade occurs, a

bid-ask spread is observed, either as the difference between best bid and ask in a LOB environment

or as the difference between the quoted buy and sell price in a dealership environment.1 As the

primary transaction cost measure, we use the effective spread, which measures how far from the

mid-point of the spread trade execution occurs. It also includes the benefits of any “price

improvement” over the quoted spread. We add information to the effective spread measure by

weighting it by the number of shares in the parcel when we calculate the daily average. See Lee

(1993) and Chalmers and Kadlec (1998) for earlier applications of the effective spread.

We calculate the trade volume weighted relative effective spread as:

0

22

2

c

t ttt

t

t t t t

Ask BidABS Trade Pricetrade volume

Ask Bid total trade volume=

⎧ ⎫⎡ + ⎤⎛ ⎞−⎪ ⎪⎜ ⎟⎢ ⎥ ⎡ ⎤⎪ ⎪⎝ ⎠⎣ ⎦⎨ ⎬⎢ ⎥+⎛ ⎞ ⎣ ⎦⎪ ⎪⎜ ⎟⎪ ⎪⎝ ⎠⎩ ⎭

∑ , (1)

where to is the time when regular trading commences during a trading day following an opening

algorithm, t is time when a trade is executed, tc is the time when trading ceases for the day, and

trade volume refers to the number of shares traded rather than the traded value. We follow

convention by doubling the effective spread on a single trade to compute the round-trip cost.

A smaller spread indicates lower transaction costs. There are, however, five major components of

transaction costs: brokerage, bid-ask spread, market impact, exchange fees and taxes (stamp duty),

as well as the “short-fall” cost of an inability to make a desired trade. If we knew the entire size of

each share parcel, together with time-stamps for the first and last shares in the parcel, we would be

in a better position to measure accurately market impact costs. The effective bid-ask spread is one

way to take into account the market impact effect. Since we calculate the trade weighted effective

spread, the size of the executed trade has an impact on the spread. Because government–imposed

exchange taxes and transaction fees paid to the exchange all add to trading costs, we obtain these

for exchanges where they are significant (e.g., the LSE and China) and add the round-trip cost to the

1 In dealer markets, quotes are often only indicative to provide a degree of protection to the dealers themselves.

13

effective spread to obtain our estimate of trading cost. Brokerage charges will clearly impinge

adversely on traded value and are thus included in the equations explaining the components of this

value. We exclude brokerage directly from our transaction cost measure, partly because we lack

information on a trade-by-trade or stock-by-stock basis but, more importantly, because we are

interested in how architectural design features determine the effective spread.

Transaction costs are important for the performance of an exchange since lower transactions cost

induce a higher level of trading activity. The responsiveness of trading to trading costs and the

impact of taxes such as stamp duty on trading activity is an important and controversial issue.

Increasingly, global fund managers have discretion about where trade execution occurs. Pulatkonak

and Sofianos (1999) show that the allocation of trades in US cross-listed stocks responds to the

relative transaction costs in the different global exchanges markets. As global competition

intensifies, exchanges are motivated to lower execution costs.

As our volatility measure, we use daily volatility, calculated as the squared daily continuously

compounded close-to-close return:

Volatilityi,t = ( )1

)ct 2

ct

p[(ln( ]

p −

, (2)

where tc is the time when trading ceases at the end of the trading day and ( )1c

t − is the time trading

ceased the previous day. This measure is a proxy for realized volatility computed from intraday

data described by Andersen et al. (2001). We also experimented with the five-minute and 15-

minute standard deviation of returns computed from the intraday trades and quotes. These

measures proved unsatisfactory relative to our volatility measure, especially for relatively illiquid

stocks subject to thin trading.

We could attempt to allow for some double counting of trades in dealer markets such as Nasdaq, but

do not do so because of the difficulty of making reasonably precise estimates across a number of

markets where the degree of double counting is declining over our sample period. However, even

with some limited double counting, markets such as Nasdaq are not rated highly. Hence, we do not

believe that any double counting unfairly biases our results in favor of dealer markets.

By breaking up traded value into its two natural and distinct components, we are able to analyze the

impact of asymmetric information on trading costs since information is more likely to be contained

in larger trades. Moreover, the impact of market architectural features is likely to be quite different

on these two components of traded value. For example, lower trading costs will result in more

14

trades but the impact on trade size could go either way. While we do not specifically incorporate

factors such as execution speed and rapidity of price discovery, for example, floor markets are

typically slower than dealer markets; we believe these are implicit in our traded value method.

Ceteris paribus, faster execution systems are likely to encourage a greater value of trading.

III. Architectural, Governance and Institutional Features

Market architectural features included in this study focus on the type of trading mechanism used

and on the features provided to the market participants using the mechanism. Apart from

categorizing exchanges according to the four basic types described in Section II above, there are a

large number of other architectural features related to trading systems and rules. We include

estimation with a selection of such variables to assist stock exchanges in improving their market

architectural design and to test a variety of theoretical models.

In this study, our focus is the efficiency of trading systems with different designs. Many

microstructure studies actually or potentially encounter the problem of simultaneity bias as most of

the “explanatory” variables such as transaction costs, volatility, size, and numbers of trades are

endogenous and thus cannot, or should not, be used as explanatory variables in standard regression

models. This may make it virtually impossible for empirical studies that encounter these problems

to appropriately test theoretical models and provide policy guidance. However, several studies

address issues arising from endogenous variables using two stage least squares (2SLS) structural

equation methods; see for example, Harris (1994) and B&Subra. These methods, like ours, are

similar to an instrumental variable model in which the instruments are exogenous variables. It

therefore helps to mitigate the errors-in-variables problem.

Some special variables that relate to issues of governance seem to belong naturally in all four

structural equations as they affect both supply and demand. The most notable of these is the La

Porta et al. (1998a) and (1998b) definition of countries that support shareholder rights. Shareholder

rights, including supportive legal systems, should, by improving the climate in which trade takes

place, lower transactions costs, lower volatility, raise the dollar value of trade size and possibly

increase the number of parcels traded over and above the increase due to lowered trading costs. A

country with shareholder rights is assigned a dummy variable 1, and one that does not is assigned a

value of 0. We incorporate a second governance variable, the market to book ratio defined at the

individual stock level rather than the exchange or country level. The higher is this ratio the better

managerial ability for the firm concerned and the more it qualifies as a growth stock. It is also the

15

inverse of the Fama-French risk factor and bankruptcy risk is low. We predict that transaction costs

should be lower for these reasons, and volatility should be higher as the valuation of growth stocks

is more difficult. The direct impact on trade size and trade numbers is harder to predict a priori.

We experimented with country variables representing the quality of regulatory regimes, anti-

corruption measures and the enforcement of the rule of law from World Bank sources (Kaufmann,

et al., 2003) but these were not included because better-managed economies according to these

criteria appear to have more poorly performing stock markets with higher transactions costs. They

also do not relate directly to the functioning of stock markets, unlike, for example, shareholder

rights.

We define a structural model with a supply of transactional services, which we invert to express

trading costs as a function of control and market architectural variables, together with a downward

sloping demand for transactional services that reflects demand factors impinging on an exchange.

We describe institutional features such as: (i) the impact of the aggregate equity market

capitalization of all exchanges affiliated with the World Federation of Stock Exchanges in each

country on the nature and magnitude of trading; (ii) similarly, the impact of income, as measured by

GDP; and (iii), the impact of the overall market size, as measured by population. We also consider

(iv), the number of trading hours overlap with the New York Stock Exchange (NYSE) to capture

the ability to arbitrage American Depository Receipts (ADRs) and provide additional liquidity for

European and North American exchanges. None of the Asian or Oceania exchanges has concurrent

trading times and hence we assign a value of zero. Other institutional factors include, (v), if it is an

electronic LOB market, does it have an upstairs dealer market facility for block trades? Another

included size control variable is the total number of stocks listed on the exchange, although the

number of listed stocks will reflect more than simply size.

IV. Data

The original data provided by Reuters to SIRCA2 contains intra-day trade, quote, and volume

information for all securities listed on all world stock exchanges. We choose the 33 exchanges used

in the study to provide a generalized cross sectional picture of world stock exchanges. Three are

selected from North America, 15 from Europe, 10 from Asia, two from Oceania, two from South

2This is an exclusive arrangement with SIRCA, Securities Industry Research Centre of Asia-Pacific, which represents a

consortium of 25 universities, to receive and store all Reuters trading information.

16

America and one from Africa. We used NYSE Trades and Quotes (TAQ) data for NYSE and

Nasdaq stocks. We collected consistent exchange specific information from the International

Federation of Stock Exchanges, Annual Year Books, the official Internet home pages of the

exchanges, and exchange rulebooks published by the stock exchanges. Demarchi and Foucault

(2000) is the source of the European market designs while Naik and Yadav (2003) is the source of

the latest changes in market design for the London Stock Exchange. The Australian Stock

Exchange (2003) provides a microstructure survey of major exchanges for the period adjacent to

our data sample. We confirm as much exchange information documented in the publicly available

information sources through direct correspondence with the exchanges as we can. The most

difficult part of the exercise was not only identifying architectural features that changed over our

sample period, but also the actual date of the change and the stocks to which the change is

applicable. We obtain brokerage fees for institutional investors on an exchange-by-exchange basis

from Chakravarty et al. (2004b) and add-in comparable estimates for North American exchanges

based on a Plexus AIMR report. We also collect institutional and country-based information on

population and GDP from World Bank sources.

Table I lists the investigated exchanges, the country, the full name of the exchange, the number of

stocks listed on each exchange, the market capitalization of each exchange as of the start of 2000,

and the classification of the exchange according to our schema. The included exchanges represent

96 percent of the capitalization of stock exchanges that are members of the World Federation of

Exchanges. We collected a consistent set of exchange information regarding market architecture

and institutional feature variables for all 33 exchanges. We also collected data on a number of

smaller exchanges but in the end, we excluded these because we could not be certain of our

coverage and data quality. The largest included exchange is the NYSE, a floor-trading system with

a LOB and affirmative dealers (“specialists”), followed by Nasdaq, Tokyo, which is a pure LOB

system, and LSE.

INSERT TABLE I ABOUT HERE

Reuters intra-day trading and bid-ask spread data is extracted for the period between start of March

2000 to end of October 2001. We selected up to 200 common stocks with the highest value of

securities traded during the period selected from 31 exchanges, excluding the two US exchanges.

We obtained Thomson Financial Datastream data on share numbers, stock splits and close-to-close

returns for as many stocks for which the data is available, within the limitation of the top 200 stocks

by traded value. To prevent over-representation of two of the world’s largest exchanges, NYSE and

17

Nasdaq, we capped our inclusion at 200 of the largest stocks by traded value from each exchange,

with NYSE Trades and Quotes (TAQ), University of Chicago Center for Research into Security

Prices (CRSP) and Standard & Poors North American data Compustat as our sources. For smaller

exchanges, we included the total number of available listed companies. The average number of

included companies per exchange is 140. This selection process resulted in a relatively balanced

portfolio representing world common stock markets while still giving representation to smaller

exchanges.

We obtained intra-day trade-by-trade prices, numbers of trades and average trade size expressed in

dollars, and best bid-ask quotes or orders, whichever is applicable, for all included stocks,

calculated comparable exchange rate adjusted measures using intra-day data, and present them as a

daily time-series for each company. We expressed the series for the average value of each daily

trade in each stock in all 33 exchanges in USD of the day. We added transaction taxes and exchange

fees, expressed as relative measures on a round-trip basis, to the effective spread calculations based

on equation (1) above.3 This final sample represents approximately 74 percent of the market

capitalization of the included exchanges at the start of the investigated period or 71.8 percent of

world market capitalization. For further details, see Table AI in the Appendix.

Since our market capitalization control might not fully capture the effect of size differences between

stocks on large and small exchanges, we carry out a range of robustness checks on sample sets with

more uniform stock sizes such as global quintile size rankings of stocks. The quintile of the largest

stocks consists of 1,046 stocks and the smallest quintile, 1,075 stocks. See Table AI for further

details.

V. Model and Empirical Findings

A. Descriptive Statistics

Table II reports means and ranks for each of the 33 exchanges for six daily averages per stock: the

trade weighted relative effective spread with the addition of exchange fees and taxes, the realized

volatility, the trade size per stock expressed in USD, the number of trades, the traded value per

exchange and the average relative minimum tick. The latter is defined as the minimum tick size for

3 Jim Thames of Arrowstreet Capital provided information on exchange fees and taxes for international exchanges, as

well as the overlap in trading hours with the NYSE.

18

each stock within a given price range deflated by its closing price. The reported measures are

average stock level measures of our included stocks for each exchange. While some of the large

exchanges such as NYSE, Nasdaq and London figure prominently in the rankings, so do LOB

hybrids with affirmative dealers such as Amsterdam and even electronic LOB markets such as the

ASX. The large trade size value for exchanges such as New York, with 200 of the largest stocks,

reflects the absence of smaller NYSE stocks in our capped sample. Interestingly, the highest

volatility exchanges include the largest and apparently most successful dealership exchange,

Nasdaq, and also a smaller LOB exchange, Korea while some of the lowest include countries such

as New Zealand and Singapore. The ranking by relative minimum tick size puts volatile countries

such as Korea with the lowest relative tick size. It is no coincidence to find that exchanges that

perform relatively poorly based on purely architectural features, such as New Zealand and

Singapore, have the highest relative tick size. While these countries with high relative tick sizes

have decimal pricing, an unwillingness to reduce tick size in the face of low stock prices per share

hampers exchange performance. These rankings incorporate a whole host of demand and

environmental variables as well as market design. Hence, further analysis is required to identify

and explain good architectural design.

INSERT TABLE II ABOUT HERE

Table III provides descriptive statistics for the 13 non-dummy variables and the correlation matrix

for the same continuous variables, but replacing the market to book ratio that has extremely low

correlations with all the other variables with the shareholder rights dummy. The high negative

correlations between transaction costs and trade size, trade numbers, market capitalization for

stocks and for the country as a whole not only indicates a strongly declining demand curve for

traded value, but also why transactions costs and the traded value variable need to be treated as

endogenous. The correlation matrix for the architectural dummies (not-reported) shows far lower

levels of correlation for most of the design items. The high significance levels for most of the

estimated coefficients in subsequent tables would suggest that multicollinearity is not a significant

problem.

INSERT TABLE III ABOUT HERE

19

B. Cross-Sectional and Time-Series Analysis

We start with a pooled cross-sectional and time-series analysis on daily data, which we have

aggregated from intra-day data. We thus include daily observations for the period March 2000 to

end of October 2001 for each company for all 33 exchanges. The size of the dataset depends on the

number of analyzed companies available and the number of included trading days, or 1,268,188

observations. Since some smaller stocks on smaller exchanges do not trade every day, the number

of observations is smaller than the theoretical maximum. Thus, there are 365,139 trading days for

the top quintile but only 208,680 for the bottom, indicating that illiquidity due to non-trading is a

serious problem confronting exchange design. We compute the relative minimum tick size based

on the stock’s minimum tick deflated by the stock’s closing price from Datastream for that

stock/day, capturing both the days and stocks in which both the New York Stock Exchange (NYSE)

and Nasdaq adopted decimalization of tick sizes during our sample period. We also compute the

skewness and kurtosis of the four endogenous variables, relative trading cost, daily volatility, daily

average trade size, and daily number of trades. We do the same for six exogenous continuous

variables, the average stock market capitalization of each stock over the sample period, number of

listed companies, total equity market capitalization for the country in which each exchange is

located, income (GDP) for the country, together with its population and the minimum relative tick

size. Subsequently, we carry out statistical specification tests, which confirm that, indeed, our four

endogenous variables are truly endogenous. Taking logs of all 12 continuous variables reduces

both skewness and kurtosis and enables us to create a simple linear in logarithms structure for our

system of equations that is easily solved for the set of reduced-form impact factors eliminating all

endogenous variables. A Box-Cox test confirms that the log specification for the endogenous

variables describes them better.

For each of the four endogenous variables, jty , ( )1,.., 4j ∈ , described below, we begin with a partial-

adjustment geometric distributed lag model (e.g., Greene, 2003):

( )( )*,1 11j j j j j j

t t t t ty y y yλ ε− −− = − − + , (3)

in which the adjustment of the actual level is a proportion of the difference between the desired

level, *, jty , and the actual level in the previous day. The equations to estimate become:

( ) ( )11 1j j j j j j j j jt t t ty a y xλ λ β λ ε−= − + + − + , (4)

20

with short-run elasticity, ( )1j jβ λ− , and long-run elasticity, jβ .

We estimate the system of partial adjustment structural simultaneous equations, (5a) to (5d), in the

logs of relative trading costs, volatility, dollar trade size and number of trades using OLS, 2SLS,

and Generalized Method of Moments (GMM) methods (Hansen, 1982) below, applied to the daily

panel (cross-sectional and time-series) data:

TransCti,t = 10β ( )11 λ− + 1λ TranCti,t-1 + 1

1β ( )11 λ− Volati,t + 12β ( )11 λ− TrSizei,t + 1

3β ( )11 λ− NoTri,t

+ 14β ( )11 λ− MktCpCyi+ 1

5β ( )11 λ− MnTcki,t + 16β ( )11 λ− ... 1

16β ( )11 λ− MktArcDumi,t

+ ( )1 117 1 iSTβ λ− + 1

itε , (5a)

Volati,t.= 20β ( )21 λ− + 2λ Volati,t-1+ 2

1β ( )21 λ− MktCpCyi+ 22β ( )21 λ− MnTcki,t

+ 23β ( )21 λ− ... 2

13β ( )21 λ− MktArchDumi,t+ ( )2 214 1 iSTβ λ− + 2

itε , (5b)

TrSizei,t = 3

0β ( )31 λ− + 3λ TrSizei,t-1+ 31β ( )31 λ− TranCti,t + 3

2β ( )31 λ− Volati,t+ 33β ( )31 λ− MktCpCyi

+ 34β ( )31 λ− MnTcki,t+ 3

5β ( )31 λ− ListCyi + 36β ( )31 λ− GDPi + 3

7β ( )31 λ− OvrlpNYi+ 38β ( )31 λ− Popi,t

+ 39β ( )31 λ− iMCapCtry + 3

10β ( )31 λ− iBrok + 3itε , (5c)

and

NoTri,t = 40β ( )41 λ− + 4λ No_Tri,t-1+ 4

1β Tran_Cti,t + 42β ( )41 λ− Volati,t + 4

3β ( )41 λ− Mn_Ticki,t

+ 44β ( )41 λ− List_Cyi,t+ 4

5β ( )41 λ− GDPi + 46β ( )41 λ− Ovrlp_NYi + 4

7β ( )41 λ− Popi,t

+ 48β ( )41 λ− iMCapCtry + 4

9β ( )41 λ− iBrok + 4itε , (5d)

where the RHS explanatory variables, log of volatility, Volat, log of average trade size, TrSize and

log of the number of trades, NoTr, in (5a), log of transaction costs, TranCt, and Volat in (5c) and

TranCt and Volat in (5d) are endogenous and the logs are taken of all continuous variables. The

institutional brokerage fee, Brokeragei, expressed as a percent and comparable in magnitude to

transactions cost, is included as an explanatory variable in both (5c) and (5d). Additionally, the two

governance related variables, shareholder rights and log of market to book are included in all four

equations.

The structure of our model is relatively traditional but with some embellishments. On the

assumption of negative exponential utility with absolute risk aversion, Glosten (1989) builds a

model with informed traders. B&Subra extend this model to solve for the average dollar value of

trade size when the trader has private knowledge of his endowment that he wishes to hedge and the

21

asset payoff to the traded security that he wishes to exploit. The structure of our model has

something in common with B&Subra, including a simultaneous framework, although they confine

their empirical work to a cross-section of NYSE stocks.

The first equation, (5a), is a relatively conventional inverse supply equation describing the

endogenous supply of trading activity in terms of the “price” of a trade,

LogRelativeTransactionsCostsit, which is made up of the sum of the relative trade weighted

effective spread, taxes, and exchange charges. We follow both B&Subra and Bollen et al. (2004) in

including the log of trade size expressed in dollars in the trading cost (5a) expression, together with

the log of the number of trades, to capture the fixed costs of trading. If there are economies of scale

due to fixed costs then the signs of the trade size and number of trades variables should be negative.

It is conventional in practically all trading cost regressions including B&Subra to include a

volatility measure with an expected positive sign. The larger the stock, as given by its size,

market_capitalization_company (MktCpCyi), the more likely public information will be available.

Therefore, stock market capitalization proxies for trading interest by uninformed traders with an

expected negative sign (B&Subra).

We use the average market capitalization over the period of the study. This is to avoid having

market capitalization serve as a proxy for returns, if allowed to vary on a day-to-day basis.

Conversion of all currency amounts to USD takes place using the current exchange rate for that day

or period. We expect the log of trading costs to be increasing in the log of the relative minimum

tick, where the relative dollar minimum tick is the minimum tick deflated by stock price. When

there is no variation in the minimum tick size, as in Harris (1994), Harris shows that it is important

to include inverse stock price, as do Bollen et al. (2004). We recognize that a portion of the trading

cost variable, namely stamp duty, for example, at the rate of 0.5 percent for UK stocks, and

exchange charges, is exogenous. These are included as the log of these charges in the variable, SDi,

which we include in both (5a) and 5(b) so as to account for the exogenous component.

In the volatility equation, (5b), the size proxy for uninformed trading is included, along with the

relative minimum tick size. However, consistent with the existing literature such as B&Subra and

the idea that volatility is more “fundamental” than the other endogenous variables; no endogenous

variables feed into the volatility equation. We incorporate the 16 architectural and environmental

features of exchange design into the volatility equation. Our approach thus departs from B&Subra

in that we allow volatility to be endogenous and for market design factors to affect it.

The remaining two equations, (5c) and (5d), are endogenous “demand” equations for trading

22

activity, with the demand for the log of AverageTradeSize described in (5c) and log of the

NumberofTrades in (5d). In common with B&Subra, equation (5c) includes the effect of the log of

stock size, which we expect to be positive as uninformed traders have less need to split orders and

the impact of trading costs, which we would expect to be negative, according to the B&Subra

model. We also include relative trading costs in (5d) with the expectation of a negative sign. Both

equations include the log of relative tick size that we would also expect to raise costs and lower

trade size even though the impact of changes in tick size is highly controversial. The general point

made by their model is that trade size, and by implication, trade numbers, are diminishing in

variables which proxy for illiquidity and adverse selection such as trading costs. We also follow

B&Subra in including the log of volatility and do so in both (5c) and (5d). B&Subra find that the

sign of volatility is positive in both the OLS and 2SLS framework. Both trade size and numbers are

likely to be sensitive to the nature of information contained in the order flow and reflected in

volatility. From an alternative perspective, the higher the idiosyncratic risk, the lower is the

expected market depth and the smaller the expected trade size and trade number. This view

contrasts with the findings of B&Subra.

The fact that we are estimating our model in an international context enables us to identify for the

first time supply and demand relationships that do not form part of existing models since demand in

a given jurisdiction is taken as a given. As proxies for market size, we use the log of GNP

measured in consistent USD in 1999, representing our opening period, and the log of population for

1999. Similarly, we take the log of the sum of the equity market capitalization of all exchanges for

1999, converted into USD. OverlapwithNYSEi, represents the log of the number of regular trading

hours shared between any given exchange and the NYSE. Since a greater overlap enhances the

ability to arbitrage cross-listed stocks between the US and other North American and European

markets, we regard it as a demand variable. The greater the overlap, the higher should be the

demand.

The ListCy is the log of the number of listed companies on each exchange at the start of the

investigated period. More than one interpretation of this variable is possible. It could be purely a

demand variable proxying for market size. It could acts as a control variable measuring the ability

of the exchange to attract listings or potentially also, the ease/severity of listing requirements.

Conditional on a given average market capitalization for the included stocks, we would expect a

larger number of companies to lower costs and promote trading activity due to scale and scope

economies.

23

We exclude the five variables representing the demand for trading, NumberofCompaniesListed,

CountryEquityMarketCapitalization, GDP, Population, and TradingTimeOverlapwithNYSE, from

the supply equations, (5a) and (5b), and include them in both the trade size equation (5c) and in the

explicit demand equation (5d). We expect more listed companies on an exchange, and a higher

equity market capitalization and population for the country to raise traded value. The impact of

trading time overlap with New York should be beneficial to traded value since it permits arbitrage

between the stock and ADR. The MarketCapCompany proxy for uninformed trading interest is

included in (5c) as we expect a higher trade size the more important is public relative to private

information. However, we exclude it from (5d) to ensure that both equations are over-identified.

Since volatility and relative trade size incorporate the effects of all the architectural decisions and

feed into trade size and numbers respectively, we drop all the architectural dummies from (5c) and

(5d). These exclusions are sufficient to ensure that all four equations meet the order and rank

condition and thus we can then estimate them as a set of simultaneous equations (see, for example,

Greene, 2003). We subsequently show that introducing architectural dummies directly into

equations (5c) and (5d) leads to exceedingly poor predictions.

C. Market Architectural Design Features

We describe the included market architecture variables by the following:

Upstairs_Market_Facility_in_LOB_Framework takes the value 1 for the 20 electronic LOB

exchanges that have an upstairs trading facility in which dealers negotiating over the phone can

compete with the downstairs market. The traditional view based on the theoretical model of Seppi

(1990), and supported empirically by Madhavan and Chen (1997) and Bessembinder and

Venkataraman (2004), is that trades with more information are screened out of the upstairs market,

presumably giving rise to higher trading costs downstairs.

The Relative_Minimum_Tickit is the log of the minimum change in stock price allowed by the

exchange deflated by the daily closing price of the stock to reflect the differential impact of tick size

on “large” high-priced stocks with a small relative minimum tick and smaller low-priced stocks

with high relative minimum ticks. There is also an “event” element in that both the NYSE and

Nasdaq reduced the minimum tick size from one-sixteenth of a dollar to only one cent during our

sample period. We base an event study on the dates at which decimalization occurred for the two

exchanges and different stocks.

Delayed_Reporting_Block_Tradesi takes the value 1 for 11 exchanges that allow block trades of a

24

certain size to be reported with a delay, to help market makers or other facilitators dispose of larger

orders. Hence, we might anticipate that exchanges with this provision should have higher trading

costs since trading brokers gain a relative trading informational advantage and thus add to the extent

of asymmetric information. In conformity, the literature reviewed in Section I above failed to find

any gain from post-trade opacity. Hence it is likely to have a harmful effect on trading cost and

thus indirectly on traded value. On September 24, 2001, the Australian Stock Exchange (ASX)

introduced a scheme to permit delayed reporting of up to 24 hours of very large principle-facilitated

block trades (Australian Stock Exchange, 2004). We utilize the model to examine this as an event.

The Iceberg_Orders_Facility_Dumi takes the value 1 for 18 exchanges that allow orders disclosing

only a fraction of the true size of the order, or so called ‘iceberg’ orders.4 For the majority of

exchanges the hidden part of the order cannot be executed before disclosed orders with the same

limit price. If icebergs provide some protection to LOB traders despite the loss of time priority so

that larger orders are placed, then the depth of the LOB is enhanced with a lower effective spread

and volatility and higher traded value.

Partial_Depth_for_Investorsi takes the value 1 for the 23 exchanges that disclose the order-book

partially to investors with up to five price steps and for the exchanges that also provide the full

depth. That is, these depth variables are additive, and a market displaying the full depth prior to the

trade is defined to be also partly displayed. Full_Depth _for_Investorsi takes the value 1 for eight

exchanges that provide full ex ante pre-trade order-book disclosure to all investors or alternatively,

ten or more price steps. The pre-trade depth disclosure issue is discussed in Section I.

The dummy variable, Broker_ID_Disclosureit, takes the value 1 for the 25 exchanges that display

broker identities with orders so that brokers are informed. In addition, two exchanges, Brussels and

Paris, changed from the display of broker IDs to opacity during the time period of our study and we

take account of the time series impact of these changes.

There have been several studies of events associated with pre-trade display of broker IDs.

Comerton-Forde and Frino (2004) find a significant decline in the bid-ask spread and increase in

volume when Korea revealed the identity of the largest five traders on 25 October 1999. Foucault

et al. (2003) find in a comparison of pre- and post-event means that the only significant change is a

4 The ASX has an iceberg facility which differs from the other exchanges in that it recognizes time priority.

Nevertheless, it has been included in this variable and the exchange rankings based on our regression model.

25

reduction in volatility when it no longer displayed broker IDs after it became part of Euronext on

April 23, 2001. The study did not attempt to control for the new Euronext trading system as such.

They attribute changes to limit order placers becoming more aggressive under anonymity.

Likewise, Comerton-Forde and Frino (2004) also find a reduction in volatility around the Paris

event and also a decline in trading costs, but an even a more pronounced decline in trade volume

suggests that the policy was harmful. Once again, there is no control for the Euronext trading

system. Simaan et al. (2003) compare Nasdaq market makers quoting on Nasdaq’s quote montage

which reveals broker identities and on ECNs with anonymous limit order placement.

How can one account for the difference between that of Korea, with the fall in spreads due to

introducing transparency, and the apparent decline in both spreads and volatility in Paris with

opacity? A possible explanation is that the fall in spreads and volatility in Paris had nothing to do

with the increased anonymity but rather was due to the introduction of the Euronext trading system

in Paris on that date, Brussels shortly afterwards on May 21 and Amsterdam on October 29, 2001.

This last event involved no change in broker ID anonymity. To test for this we include a Euronext

dummy, which takes the value 0 prior to the three critical (but separate) dates for the three

exchanges and 1 afterward. We also carry out an event study for the Paris adoption of ID

anonymity with and without a Euronext dummy.

Opening_Call_Auction takes the value 1 for the 25 exchanges with an opening call auction and zero

for the remainder. One exchange, Singapore, introduced an opening and closing call on August 21,

2000, during our data period. Based on the Singaporean experience, there is evidence that an

opening call auction improves price discovery for large stocks by concentrating trading interest

(Comerton-Forde et al., 2003).5 If so, it might lower subsequent volatility. Its impact on

subsequent trading costs will depend on whether it draws more informed or uninformed trades away

from regular trading. The opening call is likely to reduce trade in the regular market by diverting it

to the call auction. We utilize our model to investigate the impact of this event.

5 See also Madhavan (1992) for a theoretical treatment.

26

D. OLS, 2SLS and 2SLS-GMM Regression Results

In Table IV we report long-run impact factors for traded value in column 5, estimated using the

unrestricted LSNR without the aid of a structural model. In a traditional fashion, all 22 exogenous

variables and dummies are regressed on the four endogenous variables. Despite the high R-Squared

of about 88 percent in columns 3 and 4, the signs appear to be largely inconsistent. For example, an

architectural feature that reduces trading cost should raise traded value and vice versa. This is not

the case for dealer markets, upstairs facilities, exchange floors, iceberg orders, broker ID disclosure,

the Euronext dummy or transaction taxes. Moreover, when we use these impact factors to predict

traded value for the 33 exchanges within sample, so that the fit should be good, the results are very

poor with a slightly negative correlation between the actual and predicted values in levels rather

than logs. Hence, the findings overwhelmingly reject the LSNR means of estimating impact

factors.

INSERT TABLE IV ABOUT HERE

The long-run coefficient estimates and student t values for the OLS estimates for the four structural

equations, (5a) to (5d), estimated over the entire dataset (not shown) give similar results for trade

size as B&Subra with size diminishing in transaction costs and increasing in volatility. However,

these signs are reversed in the 2SLS estimation. The 2SLS results for the entire data set are refined

by the GMM Newey-West (1987) correction for heteroskedasticity and autocorrelation with a 21

period lag structure.6 We report these in Table V as our main results. We employ the Hausman

specification test (see, for example, Greene, 2003) to see if individually and collectively the four

endogenous variable, relative transaction costs, volatility, average trade size and number of trades,

are truly endogenous. The 2SLS estimates reported proved superior to both OLS and 3SLS

estimates, according to the Hausman test. These test results confirm that the four dependent

variables in our system of equations are indeed endogenous. Although the coefficient is relatively

small, trade size is larger the higher is transactions costs. The negative elasticity with respect to

volatility is high.

INSERT TABLE V ABOUT HERE

Using the estimated GMM coefficients for the four linear (in logarithms) structural equations

incorporating endogenous variables, the linear (in logarithms) equations are solved simultaneously

6 We experimented by doubling the lag length but coefficients were not significantly affected.

27

to derive the reduced-form impact factors, taking account of the interactions between the various

supply and demand variables. We rank every stock globally according to its market capitalization,

and then divide equal numbers of stocks into five equal global size groups based on the number of

stocks (quintiles). Similar results for large stocks (quintile 1) are presented in Table VI and small

stocks (quintile 5), in Table VII. Not surprisingly, a large portion of the overall global market

capitalization is in Quintile 1.

INSERT TABLES VI AND VII ABOUT HERE

The overall fit of the four equations in Table V is excellent with relatively high Adjusted-R-

Squareds for three of the four endogenous variables, ranging up to 87 percent for the log of the

number of trades (92 percent in Table VI) and down to nine percent for the log of realized volatility.

Since there are no endogenous variables explaining volatility and most researchers regard volatility

as relatively exogenous, the outcome for this variable is reasonable. Of the 66 parameters

estimated, all but four are significant at the 1 percent significance level or better. The (1 jλ− )

partial adjustment deflator coefficients based on each day’s trading range from as low as 10 percent

for the log of trade number, 13 percent for the log of trade size, 26 percent for log of relative trading

costs and 72 percent for log of realized volatility. The considerable discrepancy between short- and

long-run trade number transaction cost and other elasticities means that most studies, which confine

themselves to short periods around an event, will not necessarily capture the long-run impact and

may falsely conclude that trading costs, trade numbers and other variables are relatively

unresponsive. The partial adjustment model eliminates first-order serial correlation, enhances

explanatory power, makes the system of equations stable and amenable to observing the impact of

one endogenous variable on another, and provides consistent estimates using the same set of

explanatory variables overall and for quintiles. The results for the stock size extremes in Tables VI

and VII are remarkably similar with high Adjusted-R-Squareds, t statistics and even mostly, but not

entirely, similar coefficients and impact factors.

Examining the exogenous variables in Table V, a doubling of the country’s market capitalization

increases traded value by 32 percent. Doubling the number of listed companies improves traded

value with an increase of 92 percent. As expected, a higher company market capitalization

indicates lower asymmetric information as uninformed traders typically have access to more

information about larger companies. Trading costs fall 11 percent and traded value improves with

an elasticity of 35 percent for each doubling of size. Not only does overlap with the NYSE’s

trading hours benefit European and American exchanges, but also there is a traded-value elasticity

28

(benefit from an additional hour) of 8 percent.

Dealership hybrids such as Nasdaq do not perform as well for the overall sample relative to LOB

markets or for the smallest stocks. This is despite Nasdaq’s high ranking for most performance

variables in Table II above, due to capping the number of stocks at 200. Of course, the performance

measure is conditional on all the exogenous controls. Their superior performance for the largest

stocks despite higher costs is due to the ability of dealers to absorb idiosyncratic risk/volatility.

Acting as an upstairs market in conjunction with a LOB, dealers increase asymmetric information

for the overall sample but reduce it for the largest quintile. The ability to absorb risk by facilitating

principle trades lowers volatility and raises traded value for this category. It is thus supportive of

the hybrid market theory of Viswanathan and Wang (2002). Stocks with affirmative dealers acting

in conjunction with a LOB are only beneficial in terms of traded value for the smallest quintile of

stocks. These dealers reduce asymmetric information and lower volatility. However, affirmative

dealers do reduce trading costs for the overall sample and the two extreme quintiles. Admittedly,

with only a sample of two, we find no support for floor traded markets per se.

A lowering of the relative minimum tick size, due for example to decimalization, results is a direct

and statistically significant lowering of the relative effective spread when the effects of the

endogenous variables are taken into account. Volatility significantly increases in an offsetting

fashion. However, the direct effects of the fall in minimum tick on both trade size and trade

numbers are significantly positive, leading to an overall favorable effect, which outweighs the direct

effects on volatility. The corresponding estimates from Tables VI and VII show that decimalization

has no net effect on the largest stock quintile, in part due to higher volatility, but substantially

benefits the lowest quintile.

With respect to block delays, our findings strongly support earlier findings such as those by

Gemmill (1994), which found no benefit from trade opacity in the form of delayed reporting of

block trades. Delayed reporting raises transaction costs overall but the effect is very small for both

large and small stocks, due to an increase in asymmetric information. The harmful effect on traded

value is due to the associated rise in volatility. Iceberg order facilities that act to disguise the true

depth of the LOB seem to be beneficial.

In general, ex ante display of the first five steps of the depth of the LOB lowers trading costs by

reducing asymmetric information. Clearly, disclosure reduces the private incentive to collect

information on people’s trading intentions. It also encourages more trading by a reduction in

volatility. There is, however, some variation across the different quintiles between partial and full

29

disclosure. Hence, initiatives such as the NYSE’s OpenBook, which came after our sample period

and ex ante reveals the full depth of the LOB to investors, should be beneficial.

The final transparency issue we address is perhaps the most controversial. Should

exchanges/regulators require brokers and dealers to reveal their identity prior to trade? Institutional

brokers acting for relatively informed clients are sometimes vehemently opposed to disclosure

because it may facilitate “front-running” of orders. Our findings are unambiguous overall and for

large stocks. Ex ante revelation of broker ID significantly reduces asymmetric information. This

lowers trading costs and volatility and is thus beneficial to traded value even though informed

traders would not support it. Furthermore, it strongly suggests that it is costly for informed traders

to disguise their trading by utilizing multiple brokers. Harm occurs to the smallest quintile of

stocks by the revelation of broker ID.

An opening call market is harmful overall, for trading away from the opening. It raises transaction

costs in the main market overall and for the two quintiles by attracting uninformed traders to the

opening and leaving a residue of more informed traders during the day. The largest quintile of

stocks receives a benefit despite higher trading costs because superior price discovery at the open

sufficiently lowers volatility so that traded value is higher. Finally, the substantial taxes on trades in

UK and Chinese stocks raise trading costs, as expected, and have a depressing effect on traded

value, even though the turnover rate on Chinese stocks remains high by world standards.

E. Which Architectural Features Do Institutional Investors Appreciate?

Our structural equations, so far, have revealed a lot about the impact of design features on stocks of

varying sizes, but not a great deal about how design features such as transparency impact

differentially on large trades and traders (institutional investors) and smaller trades and traders

(individual investors). We correct this deficiency in Table VIII, which reports the interaction effect

between three different architectural dummy variables and trade size, which are inserted one at a

time into the relative trading cost and volatility equations in the 2SLS GMM model for the entire

dataset underlying Table V. In an effort to reduce possible multicollinearity problems, we introduce

the interaction effects separately in estimates of the structural equations using the full dataset.

A post-trade opacity measure explicitly designed to benefit large institutional traders, block delay,

has the opposite effect, with transaction costs increasing and traded value falling with increases in

trade size. Large institutional traders would thus be better off trading in markets that are more

transparent. One theory argues that transparency of the LOB is harmful because it facilitates

30

picking-off of visible “stale” limit orders that provide “free” options to large, well-informed market

order placers (Madhaven et al., 2004). A consequence would be a fall in the depth of the LOB

market. We include an interaction term between partial depth of the LOB and trade size. Trading

costs reduce as trade size increases meaning that institutional investors benefit from transparency,

but higher volatility ensures that the net effect of higher trade size is negligible.

The final set of results reported in Table VIII shows that large traders are the main beneficiaries of

disclosure of Broker IDs. Transaction costs fall and traded value increases with trade size, as

indicated by the signs of the interaction variables.

INSERT TABLE VIII ABOUT HERE

F. Exchange Design to Promote Trading and Prevent Non-Trading

With slightly more stocks in the smallest quintile relative to the largest, one might expect more

observations in Table VII relative to Table VI but the proportion is only 58 percent, due principally

to non-trading. Since there are no observations, researchers typically ignore non-trading days

altogether. However, non-trading is a symptom of severe illiquidity risk which appears to be

reflected in investors demanding abnormally high returns in compensation for the inability to trade

when desired (Liu, 2004). Hence, it is important to adopt exchange designs which mitigate the

problem of non-trading. To investigate this on a stock-by-stock basis for a cross-section of 4,285

stocks, we compute the log of the proportion of the ratio of trading days to exchange open days

deflated by the ratio of non-trading days to open days. To ensure a finite ratio, if a stock always

trades, we subtract 1 from the number of trading days. This transformation enables us to estimate

the probability of a trade-day occurring with a normally distributed variable in Table IX. We then

use our market architectural features and company market capitalization to explore design features

that affect the probability of trading. There is remarkable agreement between the findings for the

smallest quintile (Table VII) and the probability of trading occurring. Both sets of findings indicate

that low brokerage fees, shareholder rights, absence of an upstairs facility, affirmative dealers, a low

relative tick size, full post-trade transparency, an iceberg order facility, partial revelation of order

book depth, no broker ID disclosure, and no call auction improve the liquidity of relatively illiquid

stocks.

INSERT TABLE IX ABOUT HERE

G. Five Event Studies

Table X reports summaries of the results of the five event studies described in Section C above.

The first two events, NYSE and Nasdaq decimalization, had to be estimated over the entire period

31

since the model was poorly estimated over a short period either side of the event. The trading cost

dummy variables indicate a substantial reduction in transaction cost at the time of the introduction

with a reduction in volatility for the NYSE and increase for Nasdaq. Traded value responded more

favorably on the NYSE than on Nasdaq. While the event study and Table V show the same

favorable direction, the magnitude of the improvement is greater with respect to the event study.

INSERT TABLE X ABOUT HERE

The introduction of a reporting delay for very large facilitated principle trades in Australia reduces

trading costs but with an offsetting rise in volatility, based on a two-month period pre- and post- the

event. The net impact on traded value was thus negligible. This is less unfavorable than the overall

adverse findings for block delay shown in Table V above. Perhaps what appears to be similar to

schemes on the LSE and elsewhere is subtly different. Moreover, the period examined in Australia

was the beginning of a trial period so that brokers may have been especially cautious.

We report the event study for the removal of the pre-trade display of broker ID in Paris over a two-

month period pre- and post-event, both with and without the dummy for the introduction of the

Euronext system. The dummy variable for opacity takes the value 1 during the period broker IDs

were displayed and 0 after the move to opacity. The first set of results shows that the move to

opacity on the Paris Exchange severely raised trading costs and in doing so harmed the exchange.

These results support our earlier overall findings in Table V. Even without controlling for the

effects of the Euronext trading system, opacity significantly raised transaction costs, but there was a

fall in volatility so that the impact on traded value was negligible. Finally, the introduction of the

opening call auction in Singapore substantially reduced volatility and improved exchange

performance, just as occurred for large stocks on all exchanges with an opening call auction (see

Table VI and Comerton-Forde et al., 2003). We conclude that, with the possible exception of

Australia’s not so adverse experience with post-trade opacity, the event study findings support the

earlier overall findings.

G. Cross-Sectional Robustness Check

In their study of a single market that does not incorporate institutional features, B&Subra aggregate

trades to obtain a single cross-sectional observation for each stock. While this not appropriate for

our study because of the changes in architectural features which took place at various times, it is of

interest to see how many of our findings from the dynamic daily structural model survive in this

framework, as reported in Table XI. With respect to the endogenous volatility variable, our results

are now more similar to B&Subra in that a higher volatility encourages a larger trade size, but trade

32

size does not fall with higher transaction costs as it did in B&Subra. Examining the transaction cost

equation, the main difference is that broker ID disclosure and Euronext are insignificant, with the

signs of other coefficients the same where coefficients are significant.

Most of the traded value impact factors for the architectural designs are contrary to the impact one

would expect from the signs of the trading costs coefficients in the first column of the Table, and

contrary to the impact factors in the daily panel data estimation, Table V. The poor predictions

resulting from the cross-sectional model principally arise because of the change in sign on the

volatility term in the trade size regression relative to the negative sign estimated from the panel data

in Table V. We conclude that the dynamic model estimated utilizing 2SLS and daily panel data in

Table V is superior to the cross-sectional estimation in Table XI. Moreover, these is a slightly

lower correlation between actual and predicted traded values for exchanges using the cross-

sectional impact factors, compared with coefficients estimated from panel data.

INSERT TABLE XI ABOUT HERE

H. Out of Sample Predictive Performance

While some models fit the data well and even provide good predictions within the sample period, a

much tougher test of any model is to predict out of sample. In Table XII we report the performance

of the main model of Table V when forecasting out of sample. We re-estimate the model for the

entire dataset but dropping the last six-month’s observations for all trades. We then use the

coefficients from the re-estimated model to predict values for all four dependent variables over the

omitted six-months of data. We make one-day-ahead forecasts, due to the single-day lag structure

of the model, to obtain the predicted values by daily updating. We then estimate the linear

relationship between observed and predicted values. The performance is excellent with only very

small reductions in Adjusted R-Squareds, and the predictions are relatively unbiased with the slopes

of the linear relationship between observed and predicted close to unity.

INSERT TABLE XII ABOUT HERE

I. Simulated Adoption of Best-Practice Reforms

In Table XIII, we simulate the adoption of world best practice to achieve the maximum traded value

for a typical stock using the reduced-form dynamic impact factors computed from the 2SLS-GMM

coefficient estimates for the entire stock sample from Table V. We first predict and rank the overall

performance of all 33 exchanges based on all variables, with respect to traded value (Column 1).

Unlike the deficient LSNR reduced form model of Table IV, the correlation between observed and

predicted log of Traded Value is very high at 93 percent. We normalize the intercept such that the

33

actual and predicted values for New York based on Table V are the same. Not surprisingly, the four

largest exchanges fill the top four positions. Our modeling predicts that even New York’s

performance could increase about many fold by the adoption of best practice but this would require

an ability to alter macro-economic and other variables and thus lies outside the ability of an

exchange to adopt all the reforms. We then predict traded value based on architectural features

entirely under the control of the exchanges in question (Column 2) and rank all exchanges

accordingly. In an “apples with apples” comparison, we strip large exchanges of their considerable

economies of scale and scope advantages, advantages stemming from low brokerage charges, and

even the system of legal protection and governance such as shareholder rights. Korea, Budapest

and Tokyo fill the top three positions. While Korea and Tokyo are electronic LOBs, Budapest is a

small dealer market. Even the top-ranked exchange, Korea, could improve its performance by a

factor of 3.6 by adopting best practice. The respective exchanges or regulators control all the

architectural features that are the basis of the ranking. According to these simulations, most

exchanges could considerably lower trading costs and increase traded value. Particularly critical for

the rankings is the relative minimum tick size. It is not a coincidence that the top-ranked exchanges

have exceeding low relative minimum tick sizes, due in part to very low currency values relative to

the average stock price. It would seem that there are benefits from previous hyperinflation without

subsequent currency reform, so long as exchanges are reluctant to adopt tick sizes of one-tenth or

one-hundredth of a cent!

If the three environmental/governance variables, shareholder rights, brokerage fees and book-to-

market, are included as architectural variables then Tokyo moves to first place with New York and

Nasdaq high up in seventh and eighth place respectively. The alterations to the rankings illustrate

the importance of goverance variables, but they are not as amenable to change as pure

microstructure/architectural variables.

INSERT TABLE XIII ABOUT HERE

In Table XIV, we report all the input variables for the best practice and ten representative

exchanges to show the driving forces behind the rankings. Korea does best because of its very low

relative tick size and high transparency, as does Tokyo. During the period of the study, New York

moved to decimalization and subsequently introduced full disclosure of the LOB. Figure 1

illustrates how the move to best practice for the policy variables directly under the control of NYSE

works within the model. Equilibrium occurs where the steeply downward-sloping iso-elastic trade-

number demand schedule cuts the moderately downward-sloping iso-elastic relative transaction

costs (supply) schedule from above. To obtain traded value, project a line vertically from the

34

equilibrium point until it cuts the relevant traded-value line, either for actual transaction costs or for

best practice. With the move to best-practice, trading costs fall, in the process doubling trade

numbers with a substantial increase in traded value.

INSERT TABLE XIV and FIGURE 1 ABOUT HERE

The ASX does not perform quite as well as the top-ranked LOB markets because stock prices are

quite low relative to even the small (decimalized) minimum tick. In fact, the relative minimum tick

size is over 2,500 times higher than on the exchange with the best design. However, the ASX does

rank higher than Nasdaq, which has the disadvantage of being a relatively opaque dealer market,

lacking any affirmative obligations. The introduction of affirmative dealers, or possibly discreet

auctions every 90 seconds as in Taiwan, could assist the very large number of small stocks listed by

the exchange. However, we have not explicitly investigated the replacement of a LOB by a series

of call auctions during the day. Our analysis of the opening call indicates some possible adverse

effects. A minimum tick size of one-tenth to one-hundredth of a cent, would also improve trading

in the majority of stocks that are low-priced.

VI. Conclusions

We estimate a set of four simultaneous equations explaining transaction costs, volatility, trade size

and number of trades for a large and representative sample of stocks and major stock markets and

the solutions to the set of equations are reduced-form impact factors. We use the impact factors to

evaluate numerous policies ranging from the optimal degree of transparency, performance

comparisons of electronic LOBs, floor-trading exchanges, affirmative dealers and conventional

dealer markets. We find that electronic LOBs generally perform very well. Greater transparency

generally improves market performance. We use the model to predict the performance of every

exchange according to traded value and relative to the model’s prediction of world best practice.

No exchange has, to date, adopted a set of ideal policies, leaving considerable scope for every

exchange to improve. The big gap we have identified between the ideal and reality might well

indicate that the forces of global, and even domestic, competition have yet to make their full impact

felt. The shortfalls in actual performance relative to best practice could also be due to ignorance or

the reluctance of brokers to adopt more transparent designs with lower minimum tick sizes, which

some may believe threaten their ability to earn rents. However, our story is an optimistic one. The

potential upside from reform is great.

Appendix: Supplementary Table {PLACE TABLE AI ABOUT HERE}

35

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39

Table I: The 33 Investigated Exchanges at the beginning of 2000

Exchange Country Exchange Full Name No Lst Cmp MCap Exch Trading System1 Amsterdam Netherlands Euronext nl 387 695,196 LOB Affirm Dealers 0.672 Australia Australia ASX 1,287 427,655 LOB3 Brussels Belgium Euronext be 268 184,136 LOB Affirm Dealers 0.924 Budapest Hungary Budapest stock exchange 66 16,980 Hybrid Dealer Emph5 Frankfurt Germany Frankfurt, Deutsche Börse Grp. 851 1,432,167 LOB Affirm Dealers 1.006 Germany Germany Xetra, Deutsche Börse Group. 851 incld above LOB Affirm Dealers 0.707 Helsinki Finland HEX 150 349,394 LOB8 Hong Kong Hong kong Hong Kong Stock Exchange 708 609,090 LOB9 Jakarta Indonesia Jakarta stock exchange 276 64,045 LOB

10 Johannesburg South Africa Johannesburg Stock Exchange 668 180,463 LOB11 Korea Korea Korea stock exchange 712 306,128 LOB12 Lima Peru Bolsa de valores de Lima 239 12,092 Hybrid Dealer Emph13 London UK London stock exchange 2,274 2,855,351 LOB Affirm Dealers 0.2414 Luxembourg Luxembourg Luxembourg stock exchange 277 35,939 LOB Affirm Dealers 0.0015 Milan Italy Borsa Italiana 270 728,240 LOB Affirm Dealers 1.0016 Nasdaq USA Nasdaq 4,829 5,204,620 Hybrid Dealer Emph17 India India National stock exchange of India 1,243 261,133 LOB18 New York USA NYSE 3,025 11,437,597 LOB Affirm Dealers 1.0019 New Zealand New Zealand New Zealand Stock Exchange 172 27,827 LOB20 Osaka Japan Osaka securities exchange 1,281 91,589 LOB21 Oslo Norway Oslo bors 215 63,695 LOB Affirm Dealers 0.0022 Paris France Euronext fr 1,144 1,496,938 LOB Affirm Dealers 0.6723 Sao Paulo Brazil Sao Paulo stock exchange 487 227,962 Hybrid Dealer Emph24 Singapore Singapore Singapore exchange 399 198,040 LOB25 Bankok Thailand the stock exchange of Thailand 392 57,177 LOB26 Shanghai China Shanghai stock exchange 551 175,857 LOB27 Shenzhen China Shenzhen stock exchange 463 142,317 LOB28 Stockholm Sweden Stockholms borsen 300 373,278 LOB Affirm Dealers 0.0029 Switzerland Switzerland Swiss exchange 412 693,133 LOB Affirm Dealers 0.0030 Tel-Aviv Israel the Tel-Aviv stock exchange 654 63,472 LOB31 Toronto Canada Toronto stock exchange 1,456 789,180 LOB32 Tokyo Japan Tokyo stock exchange 1,935 4,463,298 LOB33 Warsaw Poland Warsaw stock exchange 221 29,577 LOB Affirm Dealers 0.00

Total 28,463 33,693,563Average 863 1,052,924Total World Market 35,079,835Share of Total World Market 96.0%

These exchanges collectively make up 96 percent of the world’s market capitalization displaying the name, country, full name, number of stocks included, and the nature of the trading system. The included exchanges are listed in alphabetical order, exchange, country, exchange full name, selected number of common stock for our sample, market capitalization of the exchange in million USD at the start of 2000, and classification of the type of trading system are reported, including Limit Order Book (LOB) markets and LOB markets with affirmative dealers, typically in small stocks. We show the proportion of stocks included in our sample with affirmative dealers. Sources: World Stock Exchange Federation and Thomson Financial Datastream. The total world market capitalization includes member exchanges of the World Securities Exchange Federation.

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Table II: Ranks from Best to Worst of the Mean per Share by Exchange for the Endogenous Performance Variables and Relative Minimum Tick Size

Rank Trans Costs % Volatility x 1,000 Av Tr Size USD No of Trades Tr Value USDm Rel Mn Tk x1,0001 Nasdaq 0.1106 New Zeal 0.0254 New Y 66,657 Nasdaq 2,243 New Y 88.60097 Korea 0.000112 New York 0.1526 Sao P 0.0311 Amster 58,225 New Y 1,329 Nasdaq 46.02758 Budap 0.000523 Tokyo 0.2573 Singa 0.0404 London 53,447 London 642 London 34.29068 Tokyo 0.000614 Milan 0.3913 Lima 0.0415 Tokyo 30,760 Korea 381 Amster 6.69048 Osaka 0.000775 Australia 0.4026 Bangk 0.0488 Nasdaq 20,516 Paris 223 Tokyo 5.99512 Jakarta 0.002136 India 0.4026 Brussels 0.0507 Sao P 16,002 Tokyo 195 Korea 4.44511 Stock 0.010087 Paris 0.4701 Johann 0.0591 Osaka 15,288 Toronto 190 Toronto 2.34868 Switzer 0.010308 Amsterdam 0.4773 Luxem 0.0648 Frank 14,162 Stock 168 Paris 2.04759 Luxem 0.01546

10 Toronto 0.5484 Austral 0.0701 Switzer 12,851 Milan 160 Milan 1.92707 Paris 0.0252611 Osaka 0.6178 Tel-Aviv 0.0750 Toronto 12,380 Shenz 128 Stock 1.65584 Oslo 0.0268612 Frankfurt 0.8866 Jakarta 0.0755 Milan 12,046 Amster 115 Shenz 1.20238 Frank 0.0285114 Switzerland 1.0129 Shang 0.0832 Korea 11,670 Shang 101 Frank 0.65737 Brussels 0.0292415 Shanghai 1.0483 Milan 0.0875 Stock 9,863 India 78 Austral 0.64554 Sao P 0.0329516 Shenzhen 1.0637 Amster 0.0880 Shenz 9,386 Austral 75 Shang 0.48340 Amster 0.0398318 Johannesburg 1.1407 Warsaw 0.0887 Paris 9,166 Germ 48 Germ 0.32241 Tel-Aviv 0.0402219 Warsaw 1.1722 Switzer 0.0976 Austral 8,623 Frank 46 Johann 0.25870 India 0.0424320 Korea 1.1793 Shenz 0.1045 Luxem 8,221 Johann 39 Sao P 0.21490 Germ 0.0431721 Stockholm 1.1801 Helsinki 0.1106 Oslo 7,942 Singa 33 Switzer 0.17899 Toronto 0.0435422 London 1.5535 Hong K 0.1114 Germ 6,724 Bangk 29 Osaka 0.15867 New Y 0.0586523 Tel-Aviv 1.5920 Toronto 0.1245 Johann 6,664 Warsaw 28 Oslo 0.14715 Warsaw 0.0708624 Germany 1.6264 London 0.1266 Hong K 5,646 Hong K 21 Singa 0.13449 Shang 0.0842425 Oslo 1.6790 Paris 0.1369 Shang 4,780 Jakarta 19 Hong K 0.11654 Johann 0.0860026 Singapore 1.8846 Budap 0.1384 Brussels 4,602 Oslo 19 Brussels 0.08074 Bangk 0.0922727 Budapest 1.9032 New Y 0.1508 Helsinki 4,119 Brussels 18 Helsinki 0.04758 Shenz 0.0928328 Hong Kong 1.9125 Oslo 0.1593 Singa 4,056 Switzer 14 Tel-Aviv 0.04586 Nasdaq 0.0965029 Brussels 1.9762 India 0.1749 Tel-Aviv 3,669 Budap 14 India 0.03874 London 0.1033730 Bangkok 2.3336 Tokyo 0.1877 Lima 2,458 Sao P 13 Warsaw 0.02018 Helsinki 0.1447031 New Zealand 2.3374 Stock 0.1986 New Zeal 1,971 Tel-Aviv 12 Budap 0.01306 Milan 0.1537832 Luxembourg 2.4030 Frank 0.3043 Budap 951 Helsinki 12 Luxem 0.01170 Austral 0.2922733 Helsinki 3.5530 Germ 0.3111 Warsaw 712 Osaka 10 New Zeal 0.01006 Lima 0.3345835 Jakarta 3.8912 Osaka 0.3260 Jakarta 510 New Zea 5 Jakarta 0.00956 Hong K 0.5059336 Sao Paulo 5.8226 Nasdaq 0.5592 India 497 Lima 3 Lima 0.00657 New Zeal 0.8355338 Lima 6.4922 Korea 0.6858 Bangk 14 Luxem 1 Bangk 0.00041 Singa 1.87768

The daily relative round-trip trading costs made up of trade-weighted relative effective spread plus exchange charges and taxes, daily realized volatility, daily average trade size, daily number of trades and daily traded value for a representative stock traded on each of 33 world stock exchanges, March 1, 2000 – October 31, 2001. The table reports the means and ranks for six measures of exchange performance, the average daily relative transaction costs, made up of the trade weighted relative effective spread, exchange charges and taxes (stamp duties) imposed on trading, expressed as a percentage; the average daily realized close to close volatility times 1,000; the average daily trade size expressed in USD Million of the day; the average daily number of trades, the average daily traded value expressed in USD Million of the day; and the relative minimum tick size times 1,000. The exchanges are sorted in the order of lowest to highest trading costs, the lowest to highest volatility, highest to lowest average dollar trade size, number of trades per stock, and traded value per stock, and lowest to highest relative minimum tick size. The trade-weighted relative effective spreads, average trade size, number of trades and traded value are computed from intra-day trades an quotes as reported by Reuters for the top 200 stocks (or available listed common stocks) using available shares on issue data from Thomson Financial Datastream and then aggregated to daily average measures for the exchange and then converted to USD using the relevant daily exchange rate. Realized volatility is computed from the close-to-close daily return for each stock as reported by Datastream. These relative measures represent exchange summaries of 1,268,188 daily intraday summaries, are representative of typical stocks traded on these exchanges, and are directly comparable between exchanges. Data for NYSE and Nasdaq is computed from NYSE TAQ data supplemented by CRSP data on the number of shares on issue and capitalization changes.

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Table III: Descriptive Statistics and Pearson Moment Correlation Matrix for Continuous Variables

Panel A: Descriptive Statistics

Panel A: Descriptive StatisticsVariable Mean Median Min Max Std Dev NTrans Costs % 0.708436 0.820000 0.004 60.65 3.59 1,268,188Realized Volatility 0.000123 0.000235 0 2.9382 3.06 1,268,188Av Trade Size 6,890 9,314 0 246,507,703 7.88 1,268,188No Trades 98 103 1 350,528 7.31 1,268,188Mcap Stock m 916 814 0 487,818 10.58 1,268,188Brokerage Fee % 0.40 0.28 0.16 1.00 0.00 1,268,188GDP m 816,484 718,968 14,569 9,363,791 4.56 1,268,188Popn m 50.88 58.89 0.43 1,266.84 5.47 1,268,188Comps_Listed 728 700 62 4,829 2.59 1,268,188MktCap Country m 624,834 427,655 12,092 16,732,963 6.28 1,268,188Hours US Time Zone 0 0.50 0 6.50 7.94 1,268,188Market to Book 1.17 2.39 0 3,493.08 3.70 1,268,188Rel Tick Size % 0.037496 0.050327 0 100.00 2.42 1,268,188

Panel B: Correlation Matrix

Variable Trans Real Trade No MCap GDP Popn Comp Mktcap US Time Rel Share Brok Costs Volat Size Trades Stock Listed Country Zone Tick Size Rights

Trans Costs 1Realized Volatility -0.059 1Av Trade Size -0.445 0.103 1No Trades -0.595 0.218 0.430 1MCap Stock -0.564 0.114 0.662 0.573 1GDP -0.509 0.139 0.317 0.580 0.506 1Popn -0.298 0.092 0.032 0.415 0.249 0.885 1Comps_Listed -0.538 0.128 0.420 0.578 0.515 0.765 0.531 1Mktcap Country -0.607 0.155 0.589 0.606 0.669 0.796 0.459 0.837 1Hours US Time Zone -0.236 0.092 0.387 0.257 0.413 0.163 -0.072 0.104 0.391 1Rel Tick Size 0.210 -0.085 -0.227 -0.121 -0.232 -0.188 -0.197 -0.053 -0.141 0.056 1Shareholder Rights -0.328 0.010 0.254 0.217 0.137 0.139 -0.008 0.542 0.343 -0.115 0.180 1Brokerage Fee 0.259 -0.059 -0.651 -0.201 -0.477 -0.280 0.061 -0.294 -0.563 -0.314 -0.061 -0.208 1 Descriptive statistics are shown for all continuous variables together with the correlation matrix. However, one dummy variable, Shareholder Rights, replaces Market to Book in the correlation matrix.

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Table IV: Long Run Least Squares Non-Restricted (LSNR) Reduced Form Estimates of Traded Value Impact Factors

EXOGENOUS 1. Tr Cost 2. Volat 3. Tr Size 4. Tr No 5 = 3 + 4 Lagged Depend Variable 0.7312 0.2978 0.7752 0.8605 (1,878.6) (337.2) (1,434.6) (4,027) ln(Mcap Country) (-0.20) 0.0684 -0.2531 -0.2052 -0.4583 (-42.72) (6.46) (31.31) (18.50) ln(GDP) -0.3207 0.0261 -0.2057 -1.1048 -1.3106 (21.79) (32.16) (8.34) (89.26) ln(Popn) 0.1055 -0.1728 -0.183 -1.4319 -1.6149 (11.34) (7.75) (11.96) (70.16) ln(Comps_Listed) 0.3739 0.5038 0.8814 -1.1048 -0.2235 (45.39) (27.66) (64.01) (60.10) ln(Mktcap_Compy) -0.2097 -0.0520 0.3389 -0.2052 0.1337 (169.76) (20.83) (164.34) (18.50) Ln(Brokerage Fees) -0.2593 0.1268 -1.4564 1.1794 -0.2771 (36.14) (7.87) (119.59) (69.40) Ln(Hours in US Time Zone) 0.0224 0.0261 0.063 0.0122 0.0747 (68.18) (32.16) (102.25) (14.82) Ln(Market to Book) 0.0017 0.0057 -0.0065 -0.0058 -0.0123 (8.25) (10.97) (17.28) (10.98) Shareholder Rights -0.2527 -0.2016 -0.1148 0.2301 0.1153 (68.88) (27.71) (19.21) (29.10 Hybrid Mkt (Dealer Emphasis) 0.4929 0.5227 0.2914 -0.2538 0.0376 (32.94) (14.71) (10.74) (-7.05) Upstairs Fac LOB Market 0.4584 0.5502 1.2476 -0.4538 0.7938 (50.61) (24.68) (71.24) (-20.00) Stocks with Affirm Dealer 0.1114 -0.4337 0.3120 -0.8651 -0.5532 (15.61) (26.34) (25.32) (54.59) Market with Exchange Floor 0.4307 0.1757 1.01 -0.4245 0.5864 (32.74) (6.16) (46.02) (14.57) ln(Rel Tick Size) 0.0830 -0.1033 -0.0921 0.0389 -0.0532 (91.63) (49.24) (60.49) (18.90) Delayed Report Blk Trades 0.2831 0.3197 -0.2616 -0.1591 -0.4207 (57.97) (27.95) (-30.99) (-13.71) Iceberg Order Facility -0.4767 0.0505 -1.1641 -0.4480 -1.6121 (55.99) (3.08) (-88.36) (25.44) Partial Depth Odrbk Invest -0.3107 -0.7107 -0.3183 0.7763 0.4580 (47.98) (47.71) (-30.92) (50.11) Full Depth Odrbk Invest -0.2460 -0.0853 0.8462 0.8832 1.7294 (33.01) (4.72) (65.69) (46.46) Broker ID Disclose -0.0637 0.7167 -0.2750 -0.3861 -0.6611 (10.05) (39.94) (-24.60) (22.55) Euronext Dummy -0.1430 0.6417 -0.254786 -0.2833 -0.5381 (10.49) (20.55) (11.67) (8.79) Open Call Auction 0.5341 -0.4780 -1.0999 -0.6783 -1.7782 (66.91) (21.58) (79.04) (29.33) Ln(Transn Tax Exch Charges) 0.30224 0.1125 0.1033 0.0570 0.1603 (80.22) (13.92) (16.96) (6.42) Intercept 10.2697 -12.56 4.03 -35.749 -31.719 (80.35) (41.0) (18.47) (124.31) Adjusted R-Squared 0.8089 0.0898 0.8719 0.8831 Root Mean Sq Error 0.5507 2.9489 0.7388 0.6799 Number of Observations (000) 1,268.19

Impact factors are found by regressing every exogenous variable on the four endogenous variables using OLS with GMM Newey-West estimation with a one period lag. Student t-values are shown in brackets. All coefficients (except for the lagged dependent variable) are converted to the long-run by deflating by (1 minus the coefficient of the lagged dependent variable).

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Table V: The Overall Long-Run Impact of Market Features on the Daily Performance Logarithm of: Trans Cost Real Volat Trade Size No Trades Coeff Impact Coeff Impact Coeff Impact Coeff Impact Elsticity Endogenous ln(Trans Costs) 0.0649 -0.5374 -0.4725 (6.31) (51.20) ln(Realized Volat) 0.0007 -0.1164 -0.4227 -0.5391 (0.28) (19.11) (46.97) ln(Av Trade Size) -0.0098 (3.50) ln (No Trades) -0.2189 (70.14) Exogenous Trd Val Lagged Depend Var 0.7366 0.2798 0.8746 0.9012 Impact (410.1) (132.4) (523.5) (1,278) ln(Mcap Country) -0.0340 0.1773 0.1773 0.1475 0.1475 0.3248 (13.41) (10.48) ln(GDP) -0.1177 -1.0560 -1.0560 0.5848 0.5848 -0.4712 (26.35) (16.03) ln(Popn) 0.0380 0.491 0.4912 -0.1954 -0.1954 0.2958 (20.53) (9.22) ln(Comps_Listed) -0.0552 0.7001 0.7001 0.2207 0.2207 0.9208 (28.70) (10.44) ln(Mktcap_Compy) -0.1109 -0.1139 0.0414 0.0414 0.3163 0.3043 0.0421 0.3464 (44.29) (11.12) (40.65) Ln(Brokerage Fees) -0.0780 -1.7070 -1.7070 0.4325 0.4325 -1.2744 (54.75) (22.26) Ln(Hours US Time Zn) -0.0054 0.057 0.0571 0.0220 0.0220 0.0791 (44.99) (20.06) Ln(Market to Book) -0.0023 0.0022 0.0056 0.0056 0.0029 0.0021 -0.0206 -0.0218 -0.0197 (2.82) (3.2) (1.60) (10.17) Shareholder Rights -0.5708 -0.5472 -0.1784 -0.1784 0.2476 0.2314 -0.1191 0.2630 0.4944 (67.80) (11.1) (11.49) (5.40) Hybrd Mkt (Dlr Emph) 0.4703 0.4706 0.3778 0.3778 -0.0134 -0.4124 -0.4258 (30.23) (10.67) Upstairs Fac LOB Mkt 0.2034 0.2036 0.3123 0.3123 -0.0232 -0.2413 -0.2645 (19.64) (14.71) Stocks-Affirm Dealer -0.1321 -0.1320 0.1393 0.1393 -0.0248 0.0121 -0.0127 (-12.10) (5.60) Mkt- Exchange Floor 0.3338 0.3339 0.1544 0.1544 0.0037 -0.2447 -0.2410 (18.27) (3.95) ln(Rel Tick Size) 0.0897 0.0952 -0.0930 -0.0930 -0.1506 -0.1340 -0.0188 -0.0277 -0.1617 (56.73) (27.67) (40.65) (-5.45) Delay Rept Blk Trds 0.1819 0.1820 0.1573 0.1573 -0.0065 -0.1642 -0.1707 (25.00) (9.84) Iceberg Order Fac -0.1511 -0.1513 -0.3251 -0.3251 0.0280 0.2186 0.2467 (13.79) (-16.13) Ptl Depth Odrbk Inv -0.1808 -0.1811 -0.3365 -0.3365 0.0274 0.2394 0.2668 (16.98) (15.19) Full Depth Odrbk Inv 0.0516 0.0516 0.1264 0.1264 -0.0114 -0.0811 -0.0925 (5.36) (6.80) Broker ID Disclose 0.0605 0.0602 -0.4994 -0.4994 0.0620 0.1786 0.2406 (6.41) (21.21) Euronext Dummy -0.1402 -0.1408 -0.7803 -0.7803 0.0817 0.4052 0.4869 (6.36) (16.03) Open Call Auction 0.4142 0.4142 -0.0265 -0.0265 0.0300 -0.2114 -0.1814 (35.51) (1.08) Ln(Tr Tax Exch Chgs) 0.32671 0.3265 -0.2216 -0.2216 0.0470 -0.0819 -0.0349 (75.45) (28.35) Intercept 1.0779 4.8653 -11.79 -11.789 0.55 1.995 -17.363 -12.959 -10.964 (21.91) (137.1) (1.69) (51.94) Adjusted R-Squared 0.8114 0.0956 0.8649 0.8719 Root Mean Sq Error 0.5471 2.9395 0.7587 0.7117 Number of Obs (000) 1,268.19 Hausman OLS v 2SLS 37,448

The equations “explaining” the four stock exchange performance variables are estimated using a Generalized Method of Moments (GMM) Newey-West procedure with a 21 period lag structure from Two Stage Least Squares estimates. Student t-values are in brackets. A four equation cross-sectional and time series estimation of values for all daily share observations during the period, March 1, 2000 – October 31, 2001, is undertaken using a partial-adjustment geometric lag model. We compute the long-run impact factors making up the reduced form equations as a function of only exogenous variables by solving the set of simultaneous linear equations. All but four of the 66 coefficients are significant at the 1% level or better. The Hausaman test shows that 2SLS is superior to OLS.

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Table VI: The Long-Run Impact of Market Architectural Feature Variables on the Daily Performance Measures for the Largest Stocks, Quintile 1

Logarithm of: Trans Cost Real Volat Trade Size No Trades Tr Val Coeff Impact Coeff Impact Coeff Impact Coeff Impact Elsticity Endogenous ln(Trans Costs) 0.0445 -0.9409 -0.8964 (2.26) (33.56) ln(Realized Volat) 0.0321 -0.2950 -0.4823 -0.7772 (5.60) (16.15) (18.80) ln(Av Trade Size) 0.0030 (0.60) ln (No Trades) -0.2983 (46.83) Exogenous Trd Val Lagged Depend Var 0.7209 0.2539 0.9175 0.9463 Impact (245.1) (60.5) (273.4) (833) ln(Mcap Country) -0.2231 0.7580 0.7580 0.7556 0.7556 1.5137 (13.41) (13.52) ln(GDP) 0.7625 -1.0583 -1.0583 -2.5669 -2.5669 -3.6252 (7.76) (19.40) ln(Popn) -0.5081 0.409 0.4095 1.7075 1.7075 2.1169 (4.15) (18.32) ln(Comps_Listed) -0.1432 -0.4270 -0.4270 0.4756 0.4756 0.0487 (6.20) (7.32) ln(Mktcap_Compy) 0.0276 0.0270 -0.0364 -0.0364 0.2091 0.2211 -0.0084 0.2127 (5.52) (3.99) (10.52) Ln(Brokerage Fees) 0.4869 -1.2434 -1.2434 -1.6450 -1.6450 -2.8885 (11.93) (-15.88) Ln(Hours US Time Zn) -0.0252 0.079 0.0786 0.0851 0.0851 0.1638 (18.89) (20.00) Ln(Market to Book) -0.0027 0.0118 0.0211 0.0211 -0.0080 -0.0144 -0.0463 -0.0541 -0.0684 (2.41) (8.6) (2.26) (12.26) Shareholder Rights -0.5379 -0.7523 0.0853 0.0853 1.1308 1.0817 0.7393 1.2043 2.2860 (23.40) (2.1) (12.37) (8.20) Hybrd Mkt (Dlr Emph) 0.7167 0.6834 -1.0377 -1.0377 0.3380 -0.1739 0.1641 (21.07) (11.68) Upstairs Fac LOB Mkt 0.4053 0.3904 -0.4637 -0.4637 0.1548 -0.1577 -0.0029 (13.84) (-5.47) Stocks-Affirm Dealer -0.0570 -0.0475 0.2938 0.2938 -0.0892 -0.0881 -0.1773 (-3.49) (6.09) Mkt- Exchange Floor 0.4571 0.4703 0.4122 0.4122 -0.1013 -0.6288 -0.7301 (10.85) (3.63) ln(Rel Tick Size) 0.1711 0.1009 -0.0625 -0.0625 -0.1216 -0.0955 0.2277 0.0968 0.0013 (37.63) (9.06) (10.66) (17.38) Delay Rept Blk Trds 0.0230 0.0395 0.5129 0.5129 -0.1503 -0.2690 -0.4193 (1.24) (14.94) Iceberg Order Fac -0.5511 -0.5508 0.0106 0.0106 -0.0276 0.5134 0.4858 (19.37) (0.23) Ptl Depth Odrbk Inv 0.1167 0.1483 0.9841 0.9841 -0.2851 -0.5844 -0.8695 (4.72) (15.27) Full Depth Odrbk Inv 0.2108 0.1927 -0.5633 -0.5633 0.1755 0.0734 0.2489 (8.58) (13.11) Broker ID Disclose 0.0693 0.0290 -1.2534 -1.2534 0.3728 0.5393 0.9121 (3.23) (20.45) Euronext Dummy -0.1783 -0.2347 -1.7568 -1.7568 0.5103 1.0150 1.5253 (5.06) (21.54) Open Call Auction 0.2607 0.2127 -1.4962 -1.4962 0.4530 0.4763 0.9292 (6.02) (11.71) Ln(Tr Tax Exch Chgs) 0.31854 0.3130 -0.1736 -0.1736 0.0654 -0.2160 -0.1506 (29.26) (8.69) Intercept -0.9337 -2.1200 -8.00 -8.004 -2.78 -0.462 3.088 7.8263 7.364 (6.20) (23.9) (3.78) (3.73) Adjusted R-Squared 0.8036 0.0863 0.7145 0.9223 Root Mean Sq Error 0.5461 2.4859 0.6075 0.532 Number of Obs 356,139 Hausman OLS v 2SLS 3,691

Quintile 1 consists of the largest stocks by market capitalization. Four equation cross-sectional and time series 2SLS GMM 21 lag estimation for the period, March 1, 2000 – October 31, 2001 using a partial-adjustment distributed geometric lag model. The coefficients are estimated simultaneously in a linear two stage least squares estimation with the endogenous variables, log of the relative transaction costs and log of realized volatility, log of trade size and log of the number of trades. Student t-values are in brackets. All but six of the 66 coefficients are significant at the 1% level or better. The Hausaman test shows that 2SLS is superior to OLS.

45

Table VII: The Long-Run Impact of Market Architectural Feature Variables on the Log of Daily Performance Measures for the Smallest Stocks, Quintile 5

Logarithm of: Trans Cost Real Volat Trade Size No Trades Coeff Impact Coeff Impact Coeff Impact Coeff Impact Elsticity Endogenous ln(Trans Costs) 0.1701 -0.5850 -0.4149 (6.16) (35.02) ln(Realized Volat) 0.0209 -0.1349 -0.1370 -0.2720 (4.22) (10.32) (15.73) ln(Av Trade Size) 0.0302 (4.98) ln (No Trades) -0.2659 (37.95) Exogenous Trd Val Lagged Depend Var 0.6248 0.2257 0.8508 0.7652 Impact (111.1) (69.6) (306.3) (314) ln(Mcap Country) -0.0367 0.9174 0.9174 0.2422 0.2422 1.1596 (26.59) (13.47) ln(GDP) -0.5106 -3.6379 -3.6379 1.5070 1.5070 -2.1309 (20.93) (20.37) ln(Popn) 0.2823 1.374 1.3745 -0.9056 -0.9056 0.4688 (16.04) (22.77) ln(Comps_Listed) 0.1652 1.7568 1.7568 -0.4216 -0.4216 1.3352 (17.62) (10.42) ln(Mktcap_Compy) -0.0660 -0.0552 0.1969 0.1969 0.2193 0.1815 0.0116 0.1931 (7.75) (9.86) (9.12) Ln(Brokerage Fees) -0.3295 -1.5423 -1.5423 1.0639 1.0639 -0.4784 (22.13) (27.04) Ln(Hours US Time Zn) 0.0087 0.104 0.1045 -0.0207 -0.0207 0.0838 (22.28) (9.93) Ln(Market to Book) -0.0165 -0.0145 -0.0352 -0.0352 0.0360 0.0380 -0.0060 0.0085 0.0465 (8.51) (7.0) (6.45) (1.66) Shareholder Rights -1.1660 -1.1562 0.3164 0.3164 1.6147 1.3736 0.1715 0.8103 2.1839 (40.45) (5.5) (23.08) (4.25) Hybrd Mkt (Dlr Emph) 0.7372 0.7212 -0.7648 -0.7648 0.2286 -0.3265 -0.0979 (17.73) (8.47) Upstairs Fac LOB Mkt 0.3379 0.3527 0.7076 0.7076 -0.0380 -0.2946 -0.3326 (10.39) (13.02) Stocks-Affirm Dealer -1.1071 -1.1345 -1.3061 -1.3061 -0.0121 0.8267 0.8146 (-14.37) (8.57) Mkt- Exchange Floor 0.0441 0.0860 2.0025 2.0025 -0.2627 -0.3002 -0.5629 (0.34) (3.66) ln(Rel Tick Size) 0.1659 0.1502 -0.2410 -0.2410 -0.5577 -0.4970 -0.0232 -0.0873 -0.5842 (30.67) (20.77) (37.17) (-3.38) Delay Rept Blk Trds 0.0370 0.0530 0.7639 0.7639 -0.0968 -0.1263 -0.2231 (1.24) (10.91) Iceberg Order Fac 0.0568 0.0386 -0.8704 -0.8704 0.1271 0.0860 0.2131 (2.45) (-18.91) Ptl Depth Odrbk Inv -0.6245 -0.6391 -0.6977 -0.6977 -0.0121 0.4610 0.4489 (23.41) (12.38) Full Depth Odrbk Inv -0.0504 -0.0320 0.8767 0.8767 -0.1269 -0.0906 -0.2175 (1.63) (11.81) Broker ID Disclose -0.0850 -0.0717 0.6386 0.6386 -0.1006 -0.0378 -0.1384 (2.03) (6.23) Euronext Dummy -0.1400 -0.1402 -0.0084 -0.0084 -0.0227 0.0830 0.0604 (0.93) (0.03) Open Call Auction 0.4420 0.4426 0.0290 0.0290 0.0713 -0.2626 -0.1913 (23.45) (0.67) Ln(Tr Tax Exch Chgs) 0.33666 0.3206 -0.7656 -0.7656 0.1606 -0.0920 0.0685 (24.98) (26.23) Intercept 1.7288 8.2132 -20.99 -20.985 28.13 31.251 -22.843 -20.9788 10.272 (8.42) (51.1) (12.84) (23.54) Adjusted R-Squared 0.6031 0.0982 0.8435 0.6859 Root Mean Sq Error 0.6581 3.7227 0.9248 0.9109 Number of Obs 208,680 Hausman OLS v 2SLS 5,578

Quintile 5 consists of the smallest stocks by market capitalization. Four equation cross-sectional and time series 2SLS GMM 21 lag estimation for the period, March 1, 2000 – October 31, 2001 using a partial-adjustment distributed geometric lag model. The coefficients are estimated simultaneously in a linear two stage least squares estimation with the endogenous variables, log of the relative transaction costs and log of realized volatility, log of trade size and log of the number of trades. Student t-values are in brackets. All but eight of the 66 coefficients are significant at the 1% level or better. The Hausaman test showed that 2SLS is superior to OLS. The smaller number of observations for the 5th relative to the 1st quintile is due to non-trading.

46

Table VIII: Winners and Losers-Impact of Trade Size and Three Market Architectural Features

Logarithm of: Trans Cost Real Volat Trd Size No Trdes Trd ValCoeff Impact Coeff Impact Impact Impact Impact

Delayed Report Blk Tradesln(Av Trade Size) Main Effect -0.1956

(82.11)Interaction Block_Del*Trade_Size 0.2010 0.2011 -0.0117 -0.0117 0.0267 -0.1010 -0.0742

(45.46) (2.43)Delayed Report Blk Trades Main Efect -1.5802 -1.5847 0.3635 0.3635 -0.3098 0.6345 0.3246

(39.30) (8.20)Partial Transpy Odrbk Investln(Av Trade Size) Main Effect 0.0385

(6.03)Interaction Part_Trans*Trade_Size -0.0493 -0.0488 0.0216 0.0216 -0.0164 0.0139 -0.0025

(6.99) (5.04)Partial Transpy Odrbk Invest Main Effect 0.2233 0.2107 -0.6142 -0.6142 0.2632 0.2394 0.5026

(3.68) (16.39)Broker ID Disclosureln(Av Trade Size) Main Effect 0.0802

(10.10)Interaction Broker ID_Full Dis*Trade_Size -0.0993 -0.1004 -0.0805 -0.0805 0.0150 0.0998 0.1147

(10.58) (17.48)Broker ID Complete Disclose Main Effect 0.9669 0.9717 0.3654 0.3654 0.0169 -0.7251 -0.7082

(10.95) (8.48) The three interaction terms between the specified architectural dummy and dollar trade size are included one at a time in the model specified by Table V utilizing 2SLS and Newey-West in GMM and the complete model re-estimated. Only the two main effects of the two variables and their interaction are reported in Table VIII for each of the three sets of full estimations.

47

Table IX: Explaining the Log of the Proportion of the Ratio of Trading Days to Non-Trading Days with Cross-

Sectional Observations ln(Mktcap_Compy) 0.0228

(1.36)Ln(Brokerage Fee) 0.2806

(2.38)Ln(Market to Book) -0.0344

(4.93)Shareholder Rights 1.6100

(17.67)Hybrid Mkt (Dealer Emphasis) 0.6040

(3.00)Upstairs Fac LOB Market -0.9159

(6.13)Stocks with Affirm Dealer 1.1180

(14.46)Market with Exchange Floor 1.5773

(8.49)ln(Rel Tick Size) -0.2368

(13.36)Delayed Report Blk Trades -0.2478

(3.09)Iceberg Order Facility 1.3167

(13.77)Partial Depth Odrbk Invest 1.8848

(17.96)Full Depth Odrbk Invest -0.1591

(1.32)Broker ID Disclose -0.2895

(3.52)Euronext Dummy 2.1079

(8.26)Open Call Auction -0.6840

(5.32)Ln(Transn Tax Exch Charges) -0.0694

(1.68)Intercept 1.5409

(2.40)Adjusted R-Squared 0.3122Root Mean Sq Error 1.7123Number of Observations 4,285

Cross-sectional observations estimated with OLS with GMM and Newey-West correction for heteroskedasticity. A single equation cross-sectional estimation is made for all stocks using the mean daily share values computed over the period, March 1, 2000 – October 31, 2001. The dependent variable is the log of the ratio of the probability of a trading day occurring for each stock deflated by the probability of a trading day not occurring. If a stock traded on every day the exchange was open, 1 was deducted from the number of trading days prior to deflating by the number of days for which the exchange was open to compute the probability of a trading day occurring. The transformation ensures that the dependent variable satisfies the requirements for an OLS regression in that it is normally distributed.

48

Table X: Summary of Five Event Studies Logarithm of: Trans Cost Real Volat Trade Size No Trades Trd Val

Coeff Impact Coeff Impact Coeff Impact Coeff Impact ImpactEvents 1 and 2: New York and Nasdaq Decimalization Events estimated Over Entire Period

NYSE Decimalization Dummy -0.75 -0.7539 -0.94 -0.9376 0.2727 0.8244 1.097129 Jan 2001 (33.51) (69.65)Nasdaq Decimalization Dummy -1.42 -1.4199 0.08 0.0848 -0.3974 0.7585 0.36119 Apr 2001 (60.59) (4.26)Adjusted R-Squared 0.8134 0.0821 0.8588 0.8705Root Mean Sq Error 0.5464 2.9389 0.7757 0.716Number of Observations 1,266,579 Hausman static OLS vs 2SLS 38,276

Event 3: Australia Introduction of Block Delay for Facilitated Trades estimated Over Period Two Months Prior and Two Months Post

Australia Block Delay Dummy -0.3425 -0.3410 0.1181 0.1181 -0.1137 0.1108 -0.002924 Sept 2001 (11.25) (2.12)Adjusted R-Squared 0.8124 0.0916 0.8586 0.8677 Root Mean Sq Error 0.5479 2.9237 0.7764 0.7236 Number of Observations 1,266,579 Hausman static OLS vs 2SLS 233

Event 4: Paris Broker ID Display Event Estimated Over Period two Months Prior and Two Months PostWith Euro Event Dummy:Paris Broker ID Display Dummy -0.4158 -0.4149 0.1065 0.1065 -0.1242 0.1557 0.031523 Apr 2001 (40.47) (5.00)Adjusted R-Squared 0.8122 0.0917 0.8585 0.8677 Root Mean Sq Error 0.548 2.9235 0.7764 0.7237 Number of Observations 1,266,579 Hausman static OLS vs 2SLS 33,985

Without Euro Event Dummy:Paris Broker ID Display Dummy -0.2985 -0.2970 0.0941 0.0941 -0.0931 0.1030 0.009923 Apr 2001 (31.96) (4.73)Adjusted R-Squared 0.8128 0.0918 0.8585 0.8679 Root Mean Sq Error 0.5472 2.9234 0.7766 0.7232 Number of Observations 1,266,579 Hausman static OLS vs 2SLS 31,971

Event 5: Singapore Introduction of Opening Call Auction Event Dummy Estimated Over Entire Period

Singapore Opening Call Dummy 0.11 0.1202 -5.19 -5.1864 2.2686 2.7727 5.041321 Aug 2000 (3.22) (86.03)Adjusted R-Squared 0.8116 0.0774 0.8585 0.8693 Root Mean Sq Error 0.549 2.9465 0.7765 0.7192 Number of Observations 1,266,579 Hausman static OLS vs 2SLS 38,022

The full 2SLS model using GMM and Newey-West estimation, as specified by Table V, using the full data set is estimated for each event (or pair of events in the case of decimalization) with the exchange concerned excluded from the architectural dummy in each case. An event dummy which takes the value 0 prior to the event and 1 afterwards (order is reversed in the case of the Paris event) replaces the architectural dummy in the transactions cost and volatility equations. Only the event dummy coefficients are reported. The full period is used when the model could not be estimated over a two-month period pre- and post- the event.

49

Table XI: Cross-Sectional 2SLS GMM Simultaneous Equation Regression Estimation for Entire Sample

Endogenous Trans Cost Real Volat Trade Size No Trades Trd Val Coeff Impact Coeff Impact Coeff Impact Coeff Impact Elsticity ln(Trans Costs) 0.3796 -0.6836 -0.3039 (6.45) (14.81) ln(Realized Volat) 0.3823 0.3823 (5.33) ln(Av Trade Size) -0.0376 (2.79) ln (No Trades) -0.4096 (38.99) Exogenous Impact ln(Mcap Country) -0.0415 0.1736 0.1736 0.0855 0.0855 0.2591 (5.30) (2.55) ln(GDP) -0.1270 -1.2480 -1.2480 0.4245 0.4245 -0.8235 (8.17) (3.96) ln(Popn) 0.0258 0.552 0.5515 -0.1136 -0.1136 0.4380 (6.17) (1.83) ln(Comps_Listed) -0.2059 1.0258 1.0258 0.4086 0.4086 1.4344 (16.81) (6.62) ln(Mktcap_Compy) -0.0766 -0.0888 0.0406 0.0406 0.3269 0.3134 0.0523 0.3657 (9.57) (3.03) (16.24) Ln(Brok Fees) -0.0795 -1.7257 -1.7257 0.3525 0.3525 -1.3732 (18.04) (5.95) Ln(Hrs US Time Zn) -0.0083 0.060 0.0602 0.0148 0.0148 0.0749 (18.87) (4.76) Ln(Market to Book) -0.0127 -0.0020 0.0008 0.0008 0.0110 0.0065 -0.0270 -0.0183 -0.0118 (4.84) (0.1) (1.84) (4.69) Shareholder Rights -0.4356 -0.3103 0.2912 0.2912 0.1439 0.0898 -0.3189 -0.0212 0.0686 (16.43) (5.1) (1.86) (4.63) Hyb Mkt (Dealer Emp) 0.8361 0.8361 0.8715 0.8715 0.6505 -0.5715 0.0790 (15.04) (8.26) Upsts Fac LOB Mkt 0.2293 0.2293 0.4893 0.4893 0.2741 -0.1568 0.1174 (5.61) (6.19) Stocks-Affirm Dealer -0.0869 -0.0869 0.1338 0.1338 0.0182 0.0594 0.0776 (-2.70) (1.86) Mkt- Exchange Floor 0.4265 0.4265 0.4955 0.4955 0.3513 -0.2915 0.0598 (6.91) (4.62) ln(Rel Tick Size) 0.0530 0.0349 -0.1592 -0.1592 -0.1268 -0.1675 0.0559 0.0196 -0.1479 (10.31) (13.79) (9.80) (5.34) Delay Rept Blk Trds 0.2948 0.2948 0.2780 0.2780 0.2182 -0.2015 0.0167 (14.76) (5.50) Iceberg Order Fac -0.3088 -0.3088 -0.1197 -0.1197 -0.1630 0.2111 0.0481 (8.95) (-1.59) Ptl Depth Odrbk Inv -0.1432 -0.1432 -0.5897 -0.5897 -0.2798 0.0979 -0.1819 (4.34) (7.93) Full Dpth Odrbk Inv 0.2165 0.2165 0.2126 0.2126 0.1635 -0.1480 0.0155 (7.66) (3.43) Broker ID Discl -0.0370 -0.0370 -0.0957 -0.0957 -0.0506 0.0253 -0.0253 (1.31) (1.53) Euronext Dummy 0.0772 0.0772 0.0168 0.0168 0.0357 -0.0528 -0.0171 (0.71) (0.07) Open Call Auction 0.3635 0.3635 -0.2492 -0.2492 0.0428 -0.2485 -0.2057 (10.51) (3.04) Ln(Tr Tax Exch Chg) 0.20708 0.2071 -0.0395 -0.0395 0.0635 -0.1416 -0.0780 (23.52) (1.87) Intercept 0.1513 4.3910 -11.56 -11.564 10.09 5.730 -11.276 -11.3798 -5.649 (1.36) (46.9) (4.94) (10.69) Adjusted R-Squared 0.7247 0.1524 0.5698 0.5739 Root Mean Sq Error 0.6212 1.4353 1.3413 1.3119 Number of Obs (000) 4,271 Hausman OLS v 2SLS 452

The equations “explaining” the four stock exchange performance variables are estimated using a Generalized Method of Moments (GMM) Newey-West procedure from Two Stage Least Squares estimates. Student t-values are in brackets. A four equation cross-sectional estimation is made for all stocks using the mean daily share values computed over the period, March 1, 2000 – October 31, 2001. We compute the impact factors making up the reduced form equations as a function of only exogenous variables by solving the set of simultaneous linear equations. All but 12 of the 60 coefficients are significant at the 1% level or better. The Hausaman test shows that 2SLS is superior to OLS. Note that the number of stocks with sufficient observations to qualify for the cross-sectional analysis is slightly lower than for the panel data analysis, Table V.

50

Table XII: Out-of-Sample Predictions based on the Model Underlying Table V

Actual vs Predicted Observations Intercept Slope RMSE R2 Ln(Transaction Costs) 396,911 0.11086 1.0249 0.6272 0.8281 (26.16) (1,382.85) Ln(Realized Volatility) 396,911 -0.7242 0.9362 2.9641 0.0788 (15.77) (184.32) Ln(Trade Size) 396,911 0.2733 0.9636 0.79114 0.8542 (49.00) (1,525.13) Ln(Trade Number) 396,911 0.11607 0.9703 0.79114 0.8592 (37.21) (1,556) Estimation period: March 1, 2000 to April 30, 2001 Forecast Period: June 1, 2001 to October 31, 2001

The four equation 2SLS Model with GMM and Newey-West underlying Table V and utilizing the full panel daily dataset is estimated over a 14-month period and then the resulting set of coefficients is used to forecast the four dependent variables out-of-sample over the next six-month period. We make one-day-ahead forecasts, due to the single-day lag structure of the model, to obtain the predicted values by daily updating. The actual dependent variables are then regressed against a constant term and the predicted values over the six-month period.

51

Table XIII: Predictions of Average Traded Value Performance per Stock, Including Best Practice, for 33 Exchanges divided into an Overall Score and an Architectural Score based solely on Design Features under

Exchange Control All Impact Variables Architecture VariablesBest 1,864.937 Best 100.00

1 New York 88.601 Korea 28.032 Nasdaq 69.037 Budapest 24.293 Tokyo 34.638 Tokyo 23.214 London 18.996 Jakarta 19.135 Osaka 8.441 Osaka 17.146 Frankfurt 7.713 Brussels 17.067 Toronto 5.402 Warsaw 16.528 Paris 4.286 Luxembourg 16.069 Amsterdam 2.843 Sao Paulo 15.91

10 Germany 2.283 Paris 14.9111 Brussels 1.684 Stockholm 14.5112 Switzerland 1.654 Bangkok 14.5013 Stockholm 1.508 India 13.9914 Milan 1.356 Tel-Aviv 12.9615 Johannesburg 1.351 Johannesburg 12.3916 Australia 0.845 Oslo 12.3417 Sao Paulo 0.765 Amsterdam 11.8418 Shanghai 0.664 Toronto 11.4619 Luxembourg 0.655 Switzerland 11.3320 Oslo 0.547 Shanghai 10.4721 Hong Kong 0.399 Frankfurt 10.4222 Shenzhen 0.384 Shenzhen 10.3123 Korea 0.328 Australia 9.8724 Helsinki 0.291 Helsinki 9.5925 India 0.211 Milan 9.3426 Singapore 0.117 New York 8.5927 New Zealand 0.109 New Zealand 8.4328 Tel-Aviv 0.099 Nasdaq 8.0529 Warsaw 0.050 Lima 7.7930 Lima 0.035 Germany 7.4931 Jakarta 0.032 Hong Kong 6.6132 Bangkok 0.031 London 5.1933 Budapest 0.025 Singapore 5.11

Predicted overall traded values for each exchange in USD for all stocks together with the predicted best-practice values using the full structural equation reduced-form impact factors from Table V and the architectural impact factors alone. We exclude shareholder rights, market to book, brokerage fees, and taxes and exchange charges from the architectural score, as well as the various demand variables, and the best-practice value has been set equal to 100. The actual average and predicted values for New York using all exogenous factors are precisely the same due to normalization.

Table XIV: Architectural Features of Best-Practice, Top-Ranked and Nine Representative Exchanges for the Entire Dataset

Exchange Best Korea Tokyo Paris Stockholm Toronto Shanghai Australia New York Nasdaq London

Architectural Score (M$US Traded Value) 100.00 28.03 23.21 14.91 14.49 11.46 10.47 9.87 8.56 8.05 5.19Predicted Traded Value 1864.94 0.328 34.64 4.29 1.51 5.40 0.66 0.85 88.60 69.04 19.00Actual Average Traded Value Stock $M / Day 4.445 6.00 2.04 1.66 2.35 0.48 0.65 88.62 46.03 34.29

Included in Architectural Score Relative Minimum Tick Size % 0.00011 0.00011 0.00061 0.02526 0.01008 0.04354 0.08424 0.29227 0.05865 0.09650 0.10337Delayed Reporting of Facilitated Blocks 0 0 0 1 1 1 0 0.069 0 0 1Iceberg Order Facility 1 0 0 1 1 1 0 1 0 1 0Upstairs Market Facility LOB market 0 1 1 1 1 1 1 1 0 0 0Hybrid Market Dealers No Affirmative Obl. 0 0 0 0 0 0 0 0 0 1 1Liquidity Providers with Affirmative Obl. 0 0 0 0.687 0 0 0 0 1 0 0.300Trading Floor 0 0 0 0 0 0 0 0 1 0 0Full Display of Orderbook Depth 0 1 0 0 1 1 0 1 0 0 1Partial Display of Orderbook Depth 1 1 1 1 1 1 1 1 0 0 1Disclosure of Broker Identity in Orderbook 1 1 1 0.639 1 1 1 1 1 0 0Open Call Auction 0 1 1 1 1 1 1 1 1 0 1Cross Border Merged Markets: Euronext 1 0 0 0.362 0 0 0 0 0 0 0

Excluded from Architectural ScoreShareholder Rights High 1 0 1 0 0 1 0 1 1 1 1Exchange Fees and Stamp Duty % Roundtrip 0.004 0.300 0.020 0.020 0.020 0.020 0.900 0.020 0.004 0.004 0.500Institutional Brokerage Fees % Roundtrip 0.160 0.680 0.160 0.280 0.280 0.506 0.260 0.460 0.240 0.240 0.280Market Capitalisation of Country $M 306,128 4,554,886 1,496,938 373,278 801,363 318,173 427,655 16,732,963 16,732,963 2,855,351Av Market Cap Sample Comp $M 240 5,299 2,726 616 1,458 736 611 25,937 4,499 11,058Market to Book Ratio Sample Comp 0.86 1.68 0.1876 3.01 1.34 6.04 0.63 3.34 5.05 0.07No. of Listed Companies 712 1,935 487 300 1,456 819 1,287 3,025 4,829 2,274GDP $M 618,184 2,960,217 1,372,044 184,064 718,968 4,813,984 415,251 9,363,790 9,363,790 1,280,619Population M 46.5 126.5 58.9 8.9 30.9 1,266.8 18.7 276.2 276.2 58.7Hours in New York Time Zone 0 0 2 4.5 6.5 0 0 6.5 6.5 2 The Table is based on the entire dataset using the impact factors for traded value from Table V for the entire dataset with best practice defined by the highest score across all 33 exchanges for traded value per stock. It shows how some of the exchange performance scores reported in Table 13 have been calculated. aNote that while the ASX is treated has having an iceberg order facility, it differs significantly in terms of time preference from all the other exchanges with such a facility. bThe NYSE does have both a LOB and an upstairs facility, but as it is classified as a floor market, these features are captured by this dummy rather than by a separate upstairs dummy. cLondon faces a 0.5 percent stamp duty on the round trip for UK stocks, wherever traded. A lot of its trading consists of European and US stocks for which there is no tax applicable so long as a foreign-domiciled corporation issued the stock. The Chinese exchanges also faced high stamp duty and compulsory exchange charges over the period of the study.

Figure 1: Simulating the model to show the effect on trading costs, number of trades and traded value of a movement by the NYSE to best practice traded value.

The initial equilibrium occurs where the iso-elastic NYSE demand schedule for traded parcels cuts the iso-elastic transaction costs schedule from above with the equilibrium relative transactions cost shown on the LHS axis and the equilibrium traded-value shown on the RHS axis. The resulting fall in transaction costs due to the movement to best–practice design shifts the iso-elastic transaction costs schedule down, resulting in a movement to the right around the constant elasticity demand curve with the number of trades almost doubling. The movement to best-practice multiplies trade size over ten times so that the new traded value schedule lies far above the initial valuation line and describes the new and far higher traded value prediction shown on the RHS vertical axis. The relationships are drawn to scale.

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

1,129 1,229 1,329 1,429 1,529 1,629 1,729 1,829 1,929 2,029 2,129 2,229 2,329 2,429 2,529 2,629 2,729 2,8290

200

400

600

800

1,000

1,200

1,400

NYSE Demand Schedule (No. of Trades)

Best-Practice Traded Value Schedule

Actual NYSE Traded Value Schedule

Best-Practice Transaction Cost Schedule

Actual NYSE Transaction Cost Schedule

Traded Value $MRelative Transaction Costs %

Number of Trades per Stock per Day

Best-Practice Traded Value

Actual Traded Value

Actual Relative Transasctions Cost

54

Table AI: Details of Full Sample and Large and Small Quintiles

Exchange No Stocks Mkt Cap USDm Coverage of Quintile Large Quintile Large Quintile Small Quintile SmallFull Smple Average 2000-01 Exch Mcap % No Stocks Av Mcap No Stocks Av Mcap M$US

1 Amsterdam 73 608,170 91.1 26 14,7052 Australia 155 343,552 87.7 18 10,018 10 6.683 Brussels 83 157,759 86.1 25 18,872 9 4.654 Budapest 42 11,641 91.1 1 4,809 27 8.525 Frankfurt 190 1,132,248 90.0 110 30,107 2 0.716 Germany 146 519,897 41.3 29 13,850 2 11.617 Helsinki 150 250,177 90.0 5 14,249 74 20.938 Hong Kong 181 243,654 42.0 8 13,193 90 34.049 Jakarta 200 23,620 62.2 158 17.79

10 Johannesburg 119 122,504 90.0 11 7,300 17 28.8311 Korea 200 139,670 64.6 4 11,018 41 61.6612 Lima 59 4,671 44.3 11 29,928 35 4.5113 London 139 1,827,104 71.8 133 13,639 3 14.7514 Luxembourg 17 8,548 27.4 6 8.3915 Milan 90 607,221 90.0 28 14,316 1 76.5716 Nasdaq 200 2,566,143 66.7 93 11,327 17 India 153 86,120 54.8 5 7,781 70 29.5518 New York 200 8,312,291 73.3 200 25,937 19 New Zealand 159 20,191 95.0 6 10,980 106 13.7720 Osaka 119 52,429 95.0 21 10,989 8 14.6521 Oslo 176 62,989 95.0 10 6,951 61 27.5822 Paris 179 1,294,820 87.8 69 20,185 3 8.5923 Sao Paulo 28 41,730 19.6 2 10,828 6 26.5024 Singapore 198 95,916 61.2 6 5,764 97 39.2825 Bangkok 200 38,164 93.6 132 21.3826 Shanghai 200 220,871 79.4 5 7,599 27 Shenzhen 171 99,239 50.3 28 Stockholm 114 205,431 65.7 17 7,048 10 51.6629 Switzerland 142 603,847 90.0 52 30,573 4 52.5830 Tel-Aviv 111 45,623 73.9 2 5,481 43 29.5731 Toronto 157 572,758 79.2 36 8,330 2 13.7932 Tokyo 200 1,756,505 53.4 113 9,883 33 Warsaw 80 14,386 49.5 58 22.08

Total/Average 4,631 22,089,891 73.7 1,046 13,416 1,075 24