exchange structure by jain (2006)

7/29/2019 Exchange Structure by Jain (2006)

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Industrialization

Improving liquidity

through efficient stock

market structure and

operational design1

Pankaj JainSuzanne Downs Palmer Professor of Finance,

Fogelman College of Business and Economics,

University of Memphis

Abstract

This study is an analysis of the secondary market liquidity on

equity markets around the world. The role of operational

design of stock exchanges in enhancing liquidity is assessed.

The market structure within which exchanges operate is also

shown to affect the optimal operational design and liquidity.

Narrower tick sizes, designated market makers, centralized

limit order books, computerized trading, and strong share-

holder rights index all improve liquidity directly and indirect-

ly. Interaction effects among these features result in hybrid

auction-dealer systems outperforming pure limit order booksor quote-based dealer systems in the race for better liquidity.

1511 The paper is abstracted from Jain, P.K., 2003, Institutional Design and Liquidity

at Stock Exchanges around the World Available at SSRN:

http://ssrn.com/abstract=869253.


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Improving liquidity through efficient stock market structure and

operational design

The stock exchange industry has been in a process of major

transformation over the last ten years. Leading examples

include the big bangs in 1997 at the London Stock Exchange

(LSE) and the Frankfurt Stock Exchange (FSE), which have

now evolved into hybrid auction and dealer markets; com-

plete automation of the trading process by over 100

exchanges of the world2; the merger of exchanges in

Amsterdam, Brussels, and Paris in 2000 to form the

Euronext; the introduction of highly transparent

Supermontage in 2002 at Nasdaq and real OpenBook in 2006

at NYSE; the reduction of tick size on Nasdaq and NYSE from

eights to teens in the 1990s and then pennies in 2001; and

finally the demutualization of exchanges starting with

Stockholm in 1993 and continuing with New York Stock

Exchange (NYSE) in 2006, with many others in between.

A wide variety of trading mechanisms are available to the

exchanges both in terms of who provides liquidity and how

the trades are submitted and processed. Exchanges have

used quote-based dealer markets, open-outcry method, sin-

gle price-fixing call auctions, continuous double auctions,

specialist market makers, and pure electronic limit order

books. However, many of the worlds leading exchanges

including LSE, FSE, and NYSE allow for hybrid trading sys-

tems that combine two or more pure systems. Multiple

sources of liquidity, such as consolidated electronic public

order books combined with obligatory quotes by designated

market makers, have the potential to improve the efficiency

of the markets in different states of the economy.

Our goal in this paper is to analyze the association between

liquidity measures and the operational structure of an

exchange. Secondary market liquidity is the main product lineof any exchange3. The quality of this product is considered to

be the success factor in any stock exchanges strategic plan.

Several testable hypotheses on market quality emerge from

the theoretical models focusing on operational-design of

stock exchanges and the market structure within which they

operate. Against the null hypothesis of operation-design hav-

ing no effect, these models generate the alternative hypoth-

esis that operational-design does affect liquidity. For many

design-features, competing models predict opposite effects

of liquidity enhancement versus deterioration.

Viswanathan and Wang (2002) predict that risk neutral

investors prefer pure limit order book markets but that risk

averse traders prefer dealer markets. Glosten (1994) theo-

rizes that the limit order book provides the maximum liquidi-

ty and, therefore, is the optimal exchange-design. Parlour and

Seppi (2001) postulate that hybrid markets can compete and

co-exist with limit order book markets.

The rationale behind having designated market makers is

that they improve liquidity when the depth of the order book

is not sufficient or lacks synchronization. However, Black

(1995) predicts that market makers may become redundant

in high technology limit order markets and Rock (1996) goes

a step further in suggesting that market makers may in fact

disrupt trading in limit orders and induce second order

adverse selection. Whether market makers improve or lower

liquidity is, therefore, an empirical question. Similarly, the

effect of tick size on liquidity can be ambiguous. On one

hand, lower tick sizes reduce the cost of jumping the queue

of orders. Thus, more liquidity providers will find it feasible

to compete. On the other hand, this increased competition

will lower profitability and will drive away depth from the

markets. Also, the option to fragment versus consolidate the

order flow potentially has two opposing effects.

Fragmentation increases competition by increasing the num-

ber of dealers, which in turn reduces transaction costs.

However, it splits the trading volume across trading venues

and decreases price competition between orders, thus

decreasing liquidity. On the transparency of order flow,Madhavan (1995) predicts that dealers in less transparent

(opaque) markets price more aggressively in early rounds to

attract informed traders. The information learned can be

used in later rounds to extract profits. In more transparent

markets, dealers have no such incentive or opportunity.

Pagano and Roell (1996), however, predict the opposite, i.e.,

increases in both ex-ante and ex-post transparency lower

152 - The journal of financial transformation2 See Jain (2005) for dates of automation and the impact of electronic trading on

cost of equity capital.

3 Listing of stocks and dissemination of information such as price and volume are

other important services provided by exchanges.


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operational design

spreads because it reduces the adverse selection problem

for the dealers.

Perhaps the most significant transformation in the stock

exchange industry is the replacement of manual floor trading

with the automated computerized model. Automation sub-

stantially reduces both the fixed and the variable costs of pro-

viding transaction services. The majority of empirical papers

report tremendous savings in market development, distance,

and order-processing costs. However, Venkataramans (2001)

finding that spreads are wider on the automated Paris Bourse

than the floor-based NYSE departs from the generally favor-

able view of automation.

We empirically test these hypotheses by generating various

liquidity performance measures and exploring their correla-

tions with each operational design variable. Our primary

measures of liquidity are quoted bid-ask spreads, effective

spreads, realized spreads, volatility, depth, and trading

turnover. Spreads and volatility are inverse measures of liq-

uidity whereas depth and turnover are direct measures. We

also supplement this list of liquidity measures with another

inverse measure called price impact of trades, which we obtain

from secondary sources, namely, Domowitz et al. (2001) and

Chiyachantana et al. (2004).

The results of this study can be summarized as follows. The

operational design of stock exchanges and the institutional

environment within which they operate are critical factors that

influence liquidity in the secondary markets. Spreads and

volatility are highest (representing low liquidity) in dealer

emphasis quote-driven markets, followed by those in pure

electronic-limit-order-books (LOB), and are lowest (best liquid-ity) in hybrid mechanisms4. Other operation-design features

matter as well. Lower tick sizes, the presence of specialists or

designated market makers, consolidation of order flow, and

computerized trade execution are all associated with lower

bid-ask spreads or higher liquidity. Standardized trading vol-

ume is higher on exchanges with computerized trade execu-

tion and on exchanges with centralized order flow. The liquid-

ity differences caused by trading mechanisms or other design

features are much larger in emerging countries than they are

in financially developed ones. Another important structural

change that is affecting the performance of the exchanges is

the demutualization of ownership after which the stock

exchanges become publicly-traded companies and are subject

to greater scrutiny and pressure for profitability. Although this

implies potential improvements in accounting profitability of

an exchange [Mendiola and Ohara (2003)], the impact on cost

of liquidity for investors is ambiguous. A pursuit for higher

profitability and revenues could stimulate the exchanges to

charge either a higher price for liquidity with lower volumes or

a lower price with higher volumes. Finally, improvements in

shareholders rights and speedier dissemination of insiders

private information can reduce the adverse selection problem

for liquidity providers and improve market quality.

Apart from their academic interest, these results carry policy

implications for companies, investors, exchange managers,

and lawmakers who want to increase fairness and efficiency

in securities markets. Better institutional design can improve

liquidity, which in turn could potentially reduce the cost of

equity for listed firms. By identifying better institutional fea-

tures, investors can reduce transaction costs and improve the

profitability of their investments. With better institutional fea-

tures, stock exchanges can become more competitive and

attract more investors for trading and more firms for listing

their stocks5.

Data sources

Our hand-collected data contains rich details about the opera-

tional-design features of 51 leading stock exchanges in the

world for which closing bid-ask spreads are available from theBloomberg Financial Services archives and NYSEs Trades and

Quotes (TAQ) databases. The stock exchanges in our sample

represent over 90% of the worlds equity market capitalization.

The period of our analysis is from January 2000 to April 2000.

To fully understand the trading mechanism and overall oper-

ational design, we conduct a detailed survey of each

1534 On DLR markets dealers quotes are the primary source of liquidity, on LOB mar-

kets investors limit orders are the primary source. On HYB markets these two

sources of liquidity compete with each other and designated market makers have

obligations to maintain orderly markets and execute orders from their own

account when necessary.

5 Previous literature has documented various instances where trading turnover is

found to be very sensitive to trading costs and market structure. See for example

Pagano and Stiel (1996) who document that in 1989, French order handling rules

made block trades unattractive and as a result a majority of block trades in French

stocks were executed anonymously on the London SEAQ-International exchange.


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operational design

exchange through email, telephone, and letter correspon-

dence. Secondary sources of information were collected from

the Internet, stock exchange sites, directories, handbooks,

and reports of capital market institutes like the International

Financial Reviews (1997-2003) Handbook of World Stock &

Commodity Exchanges, the Saloman Smith Barneys Guide to

World Equity Markets, Bloomberg Financial Services

Exchange Profiles, and Morgan Stanley Capital Internationals

Handbook of World Equity and Derivative Exchanges. All

information was cross checked from two or more sources to

verify its authenticity.

Operational-design classification

The exchanges use a continuum of possible operational design

features. First, the trading mechanism can be a pure limit

order book (LOB), a hybrid system with an order book and des-

ignated market makers, or a dealer-quote driven system.

Second, we set the indicator variable designated market-

maker equal to one if the exchange employs liquidity

providers who are obliged to provide binding bid and ask

quotes for some minimum quantity. Our third variable is per-

centage tick size, which is a continuous variable defined as

exchange specified minimum tick size divided by price of the

stock. Fourth, we define markets as consolidated if all domes-

tic trades in any stock in the country are executed at a single

venue or pass through a single execution system. On the other

hand, if the same stock can be traded on multiple trading ven-

ues within the country, we classify the market as fragmented.

Note that some stocks listed domestically on consolidated sys-

tems may be cross-listed on foreign exchanges and be global-

ly fragmented. Our focus is on within country effects and we

classify these exchanges as consolidated. Our fifth opera-

tional-design variable is transparency of the trading process. Ifthe details of the order flow, such as price and quantity sched-

ules on the demand as well as the supply sides, are displayed

to the public we classify the exchange as being transparent;

otherwise we call it opaque. The sixth operational design vari-

able focuses on technology. We classify an exchange as auto-

mated if trades are executed electronically with algorithms

based on price and time priorities or if the trades can be exe-

cuted by hitting dealers quotes on the screen without requir-

ing any further manual intervention. Next we focus on the

ownership structure of an exchange as the seventh variable.

An exchange is classified as demutualized if the shareholders

base includes the public at large instead of being restricted to

broker-members only. Finally, we model shareholder rights

and information environment as the eighth structural variable.

We add the shareholder rights index of La Porta et al. (1998)

and the dummy for enforcement against insider trading from

Bhattacharya and Daouk (2002) to obtain our informational

environment index value for each country. These range from 0

to 6, with six being the best in terms of shareholders rights

and informational transparency.

In the sample of 51 exchanges, 20% are dealer-emphasis

markets, 51% are pure limit order markets, and 29% are

hybrid exchanges. If we weigh the exchanges by their market

capitalization, we find that the dealer-emphasis system rep-

resents 23% of market capitalization, pure limit order book

account for 28%, and hybrid markets for 50%. In our sample,

63% of the exchanges operate in consolidated markets and

the remaining in fragmented markets; 41% have full ex-ante

transparency of order flow; 86% of exchanges have trading

systems with automatic execution of trades; 63% are owned

broker-members; and 51% operate in markets that are classi-

fied as developed by Morgan Stanley Capital International.

Control variables

Apart from the operational-design variables discussed above,

secondary market liquidity will vary across stocks based on

country- and firm-specific characteristics. We must, there-

fore, control for such other determinants of liquidity in our

experimental design. Our control variables include the levelof economic development of a country, age of the stock

exchange, size of the equity market, accounting standards,

ownership concentration, and depth of the markets. Morgan

Stanley Capital Internationals (MSCI) provides a classifica-

tion of markets, based on economic developments, as either

developed or emerging countries. The ages of all stock

exchanges are computed from the year of establishment to

154 - The journal of financial transformation


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operational design

the year 2000, where the establishment year is obtained

from Jain (2005). Exchange ages range from 1 year to 400

years; with the oldest stock exchange being in Germany. The

total market capitalization (in billions of U.S. dollars) for each

exchange is calculated as being the sum of the market capi-

talizations of all firms listed on that exchange. In regressions,

the logged value of this market capitalization is used. The

aggregate market capitalization exceeds twenty-five trillion

dollars. The exchange with the highest market capitalization

is NYSE with more than U.S.$8 trillion. Accounting practices

around the world are obtained from a survey conducted by

the Center for International Financial Analysis and Research

Inc. The survey tells us how strict the disclosure and report-

ing requirements are about subsidiaries, reserves, off-bal-

ance sheet items, insider transactions, etc. We construct an

accounting standards index by adding one for each item

required to be disclosed by firms in a given country. The high-

est possible value of the index is 50. The actual values range

from 12.5 for Greece to 39 for the U.S. Higher disclosure and

frequent reporting reduce the information asymmetry for liq-

uidity suppliers. Therefore, higher values of this variable are

expected to improve liquidity and lower spreads. The next

control variable is ownership concentration, which is defined

as the percentage of shares held closely by the companys

officers, directors, and their immediate families, shares held

in trusts, shares held by another corporation (except shares

held in a fiduciary capacity by financial institutions), shares

held by pension benefit plans, and shares held by individuals

who hold 5% or more of the outstanding shares. Ownership

concentration ranges from 8% for U.S. firms to 78.1% for the

Czech Republic. Greater concentration of ownership reduces

the float and trading turnover in the market. Therefore, this

variable is expected to lower liquidity and increase spreads.

Liquidity performance measures

Bid, ask, and transaction prices at the close of each day from

January 1st, 2000 to April 30th, 2000 are collected from the

Bloomberg Financial Services archives and from NYSEs

Trades and Quotes (TAQ) database. We focus on the 25 stocks

with highest market capitalizations, which represent on aver-

age 75.80% of the total market capitalization of all stocks in

the 51 sample exchanges. The selection is based on exchange

of primary listings, and therefore, cross-listings through ADRs

do not get included. Our seven measures of liquidity per-

formance are quoted spreads, effective spreads, realized

spreads, Rolls (1984) implied spreads, price impact of trades,

volatility of returns, and trading turnover. Quoted spreads are

computed as the difference between the closing ask price and

the closing bid price divided by the bid-ask midpoint for 25

securities with the highest market capitalization on each of

the 51 stock exchanges. Percentage effective spreads are

computed as twice the difference between actual transaction

price and quote midpoint divided by quote midpoint at the

close of each day.

We filter out potential data errors by removing approximate-

ly 1.5% of the observations with spreads that are negative or

are higher than 100%. NYSE has the lowest percentage quot-

ed spreads (0.20%) and percentage effective spreads

(0.10%) on the top 25 securities listed on the exchange.

Ukraine has the highest closing quoted spreads (15.34%) and

effective spreads (14.47%) followed by Bermuda. Effective

spreads are lower than quoted spreads on 34 exchanges due

to price improvement by specialist, dealers, market makers,

or other liquidity providers. On the remaining 17 exchanges,

the effective spreads are larger than the quoted spreads,

indicating that even small retail trades have significant price

impacts in these markets. Volatility of returns is lowest in

Switzerland and highest in Ukraine for the top 25 stocks.

Korea, Nasdaq, Taiwan, and France have the highest trading

turnovers in the sample, and Luxembourg has the lowest.

Research design empirical resultsFirst we conduct a univariate analysis focusing on one opera-

tional design feature at a time and then we perform a regres-

sion to identify the incremental impact of the various design-

variables. For the univariate analysis we divide the sample into

groups of exchanges with common characteristics for the

given operational-design variable. For example, based on the

trading mechanism the sample is divided into dealer-empha-

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operational design

sis, pure limit order book, and hybrid markets and the average

effective spread in these subsamples is 4.13%, 1.97%, and

1.17%, respectively. Other liquidity measures are ranked simi-

larly, indicating that hybrid markets provide the best liquidity,

followed by limit order books, whereas pure dealer markets

have the lowest liquidity. Based on computerization of the

trading process, the sample is divided into automated markets

which offer far superior liquidity, with average effective

spreads of 1.87%, compared to markets requiring manual

intervention, where spreads are much higher at 4.66%.

Transparency of order flow is the next operational-design

variable used to divide the sample into two. Perhaps contrary

to intuition, full quote transparency is not a desirable charac-

teristic because it results in lower liquidity and higher effec-

tive spreads of 2.67%, whereas opaque market spreads are

1.54%. Demutualization of ownership appears to be a positive

design-feature, which provides better liquidity with lower

effective spreads of 1.68% vis--vis 2.53% for mutually-

owned exchanges. Choosing between centralized (competing

orders) and fragmented order flows (competing venues), the

regulators are advised to adopt the former. The average

effective spread is 1.76% in countries with a single trading

venue and 2.98% in countries where order flow is split

among multiple trading venues.

Finally, liquidity is also affected by the informational environ-

ment. If insider trading is prohibited by law and such laws are

rigorously enforced, then liquidity providers do not have any

informational advantage and are willing to provide a higher

level of liquidity; the result being a lower spread of 1.37% ver-

sus 4.05% for markets where no such enforcement of insid-

er trading prohibitions takes place. The univariate analyses ofquoted spreads, realized spreads, volatility, Rolls implied

spreads, and price impact all generate the same results about

the choice of operational design variable as the ones

obtained above with effective spreads.

The incremental effect of the optimal operational-design

choices identified above are expected to vary across devel-

oped markets, which have inherently high liquidity with aver-

age effective spreads being 1.15%, and emerging markets

with wider spreads of 3.38%. Therefore, we use a two-step

procedure in which the sample exchanges are first catego-

rized into developed or emerging markets and then within

each category split again based on the operational-design

features discussed above. Indeed, the implications of the

operational-design choices are more dramatic in emerging

markets. For instance, the average effective spreads in pure

dealer, pure limit order book, and hybrid systems are 2.02%,

0.94%, and 0.95%, respectively, in developed markets and

6.55%, 2.89%, and 2.15%, respectively, in emerging markets.

Having established the linkages between several operational-

design choices and liquidity performance of stock exchanges,

we now perform several regression analyses to gauge the rel-

ative importance and incremental effects of each variable of

choice in an integrated framework. In each regression, the

dependent variable is one of the liquidity measures and the

explanatory variables include all operational-design variables

and other country-specific and firm-specific control variables.

Furthermore, the liquidity measures are themselves interde-

pendent to some extent. For example, liquidity providers will

demand a higher spread if a stocks volatility is very high or

turnover is very low. An appropriate econometric technique

under this situation is a simultaneous system-of-equations

model, which can be estimated with a two stage least squares

method.

The adjusted R-square is 30.77% in the regression, with aver-

age effective spread as the dependent variable, and 89,460

daily observations. The operational-design characteristics of

stock exchanges possess significant explanatory powers inthe liquidity regressions. We rank the indicator variables for

the institutional features by the magnitude of their standard-

ized coefficients. Consolidation of order flow through a limit

order book, automatic execution of trades, and lower relative

tick size has the maximum positive impact on liquidity per-

formance. Designated market makers lower trading costs,

whereas market fragmentation seems to widen the spreads



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operational design

and reduce liquidity. The coefficients on these variables are

statistically and economically significant. For instance, pres-

ence of limit order book reduces the effective spreads by

about 1.40%. This compares with the minimum percentage

effective spread of 0.10% and average of 2.13% across all

exchanges. Complete absence of limit order books in the

world markets can cost the investors an extra U.S.$210 billion

on an annual trading turnover of over U.S.$15 trillion around

the world.

The impact on quoted spreads is even more dramatic. The

impact of other operational-design variables lower relative

tick size, consolidation of order flow, and fully computerized

trade execution is also positive and economically significant.

Excessive ex-ante transparency of order flow appears to drive

away liquidity providers and increase spreads because too

much transparency can elevate concerns about front running

and jumping the queue. Results on ownership are mixed.

Univariate statistics discussed earlier suggest that demutual-

ized exchanges have better liquidity. Turnover regressions also

point to higher volumes on demutualized exchanges, but

spread regressions indicate otherwise. Of course, mutual ver-

sus demutualized ownership structure may also matter in

areas other than spreads and volume, such as security inno-

vation and technology adoption. Better shareholder protec-

tions and informational environment are associated with

lower spreads or improved liquidity. Volatility of returns is

lower in consolidated limit order books and increases with

market fragmentation. Volatility is higher on newer exchanges

and lower on older exchanges. Trading turnover is significant-

ly higher in the emerging markets. Automation and aggrega-

tion of orders in a consolidated limit order book increases

trading volumes.

As mentioned earlier, the regression system controls for

interdependencies among the various liquidity measures. The

coefficients on these interdependent measures might them-

selves be of interest. We find that effective spreads increase

with volatility of returns, and decrease with market capital-

ization of a firm and trading turnover. Higher quoted or effec-

tive spreads widen the transaction cost band and lower the

incentive for trading and the trading turnover. Finally, we

examine the association between quoted spreads and quoted

depths. Data on quoted depths is available for a subset 33

exchanges from Bloomberg Financial services. Exchanges

with higher spreads seem to have shallower depth as well.

Thus both measures indicate a lower liquidity on such

exchanges.

We carry out several additional regressions by introducing

interactive variables. The main results discussed so far are

found to be robust and some new insights are obtained. When

we introduce interaction between market maker dummy and

economic development dummy, it is apparent that the pres-

ence of market makers is more important for emerging mar-

kets than for developed markets. This result has important

implications for stock exchanges in emerging markets, which

have abundantly adopted the pure limit order book model

from the Paris Bourse, even though this system may not be

optimal for less liquid securities. Assigning designated mar-

ket makers to securities in addition to the electronic limit

order book appears to offer definite gains in liquidity in

emerging market stocks. Similarly, when we integrate other

operational-design variables with economic development of

the country, we find that the effects are sharper in emerging

countries.

The results discussed thus far are quite robust to alternative

specifications and subsamples. Results are qualitatively simi-

lar if we estimate ordinary least squares Whites hetero-

skedasticity correction for standard errors instead of two

stage least squares. Regressions excluding the endogenous

variables, subsample of only the top 10 stocks from eachexchange, a fixed effects model with an intercept and 50

dummy variables to represent the 51 exchanges, separate

month-wise regressions for the four sample months, separate

region-wise regressions for developed and emerging markets,

regression with 51 observations only by averaging across

exchanges, subsamples excluding the most liquid and the

least liquid exchanges (possibly outliers), subsamples exclud-

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operational design

ing NYSE, and subsample of firms constituting the top 15% of

market capitalization of each exchange, all point to the same

choices for optimal operational design.

Many large multinational firms cross-list their stocks on mul-

tiple exchanges. Such a move can have two opposite effects

on domestic market liquidity. On the one hand, order flow

migration to a foreign exchange can reduce liquidity in the

domestic market. On the other hand greater intermarket

competition and increased international visibility of the firm

can improve liquidity in the domestic market. Our analysis

suggests that these two effects are either insignificant or

they cancel each other out. The average effective spreads for

the stocks that are listed only domestically and stocks that

are cross-listed on international exchange are not statistical-

ly different from each other. In the regression framework too,

when we test the cross-listing effect by including an indicator

variable its coefficient is statistically insignificant.

The interpretation of results may also be subjected to endo-

geniety of the choice of operational design. For instance, if

exchanges with lower liquidity endogenously choose dealer-

based mechanisms fearing even worse liquidity in other sys-

tems, then our analysis could be problematic. However, a

careful examination of the distribution of operational-design

variables rules out this possibility. For example, emerging

markets in general have higher spreads (lower liquidity) than

developed markets. If exchanges with lower liquidity endoge-

nously choose dealer-based mechanisms, then we should see

a dominance of dealer-based exchanges in emerging mar-

kets. However, the proportion of dealer-emphasis exchanges

is roughly the same, about 20%, in both developed and

emerging countries. Therefore, the current interpretation ofresults seems appropriate.

Like most empirical research projects, one needs to consider

some limitations of this study when interpreting the results.

Our results may be affected by the fact that stocks in the

sample not only trade on different types of exchanges but

could have different levels of informational transparency and

inventory carrying costs. Moreover, our analysis applies to

the largest stocks in each country and the role of institution-

al features could be less or more important for smaller

stocks. Other trading rules and characteristics, such as com-

missions, interdealer trading, preferencing of trades, etc.,

may also differ across exchanges. Of course, we take these

factors into consideration, to the extend that it is practical, by

using control variables like market capitalization, economic

development, shareholders rights, insider trading enforce-

ment, age of the exchange, accounting practices, ownership

structure of firms, and quoted depth on the sample

exchanges. The key results are robust and easily survive

these controls.

The practical applications of our findings also depend on sev-

eral additional factors. When policy makers choose a particu-

lar aspect of institutional design, the performance measures

analyzed by us may not be the only criteria. In fact, Clayton

et al. (2000) find that a countrys economic development,

degree of competition, extent of economic freedom, size of

economy, availability of technology, and its legal system are

important determinants of formation and structure (trading

system) of international exchanges. Nevertheless, liquidity

comparisons and competition has stimulated most exchanges

worldwide to significantly alter their structural and opera-

tional-design features. In the future too, liquidity considera-

tions will continue to be a key driver in shaping the stock

exchange industry.

Conclusions

We study the impact of structure and operational design of

stock exchanges on their liquidity performance. Our empirical

analysis is based on a comprehensive sample of the top twen-ty-five stocks from each of the 51 leading stock exchanges

across the world, and captures over 90% of the global equi-

ty market capitalization and a wide spectrum of operational

designs.

Our study identifies the structure and operational design fea-

tures of stock exchanges that are associated with high levels



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operational design

of liquidity. Dealer-emphasis systems have the lowest liquidi-

ty and investors face high spreads and volatility on such

exchanges. Pure limit order systems (LOB) offer compara-

tively better liquidity. The best liquidity is on hybrid systems

(HYB), which combine designated dealers with a limit order

book. Higher liquidity is also obtained with additional opera-

tional-design features, such as a consolidated limit order

book, designated market makers, and full automation of trad-

ing processes. The liquidity improving role of designated mar-

ket makers is more pronounced in the less liquid emerging

markets. Large mandatory tick sizes and order-flow fragmen-

tation within a country also adversely affect liquidity.

Additionally, operational-design features also affect volatility

and trading turnover.

These results can be useful from several perspectives.

Regulators and government policymakers can create incen-

tives for stock exchange owners and managers to choose the

optimal operational design. Exchanges themselves can com-

pete more effectively as they are armed with better insights

into the effects of operational design on liquidity. Listed com-

panies and their shareholders are both interested in higher

levels of liquidity for their stocks. Hence, a push for the opti-

mal stock exchange design could come from them when they

express their choices about a preferred listing or trading

venue.

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exchange structure by jain (2006)

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