The Impact of Market Architectural Features on World Equity Market Performance: A Structural Equation Approach† Peter L. Swan ‡ Joakim Westerholm § Draft: August 1, 2004 Abstract We evaluate several types of market architecture and numerous trading design features using a unique intra-day market microstructure dataset for 38 major exchanges making up 98 percent of the world’s market capitalization.
We estimate a system of four
simultaneous structural demand and supply equations with distributed geometric lags, to explain relative trading costs, volatility, average trade size and the number of trades. We find evidence that there are huge economies of scale and scope, trading is sensitive to transaction costs, the limit order book design is ideal for all but small stocks and transparency is generally preferable to opacity. We obtain long-run reduced-form impact factors, which we utilize to rank the performance of every exchange relative to predicted world best practice. We find evidence of the ability of every exchange to improve performance drastically, both overall and for each quintile of stock size. Keywords: Market microstructure, Exchange performance, Structural equations JEL Classification:G10, G15, G2
†
Peter Swan gratefully acknowledges financial support from the Australian Research Council (ARC) DP0209729 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, Doug Foster, Peter Ho, Ron Masulis, George Sofianos, Terry Walter and participants at the Australasian Banking and Finance Conference 2003, ASX seminar, and Western Finance Association Conference, Vancouver, 2004, for comments. © 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:
[email protected]. § P. Joakim Westerholm, School of Business H69, University of Sydney, NSW 2006, Australia. Email:
[email protected] .
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, deeper liquidity and a higher dollar value of investor trades for the typical stock. The goal is to provide seamless executions in all listed stocks under all market conditions. Both institutions and retail clients should be able to execute their orders at the best possible price without being subject to severe price impacts but at the same time promoting efficient price discovery. What then is the best strategy for the exchange or regulator to set to meet the trading requirements of diverse investors in a global market? We attempt to address these previously unasked questions by establishing criteria for ranking the performance of all stock exchanges and benchmarking world best practice in terms of marketmicrostructure stock exchange design. Several different models for restructuring of exchanges have been attempted, some more successful than others. Despite a paucity of hard empirical evidence, Parlour and Seppi (2003) suggest that regulators, exchanges and other market participants increasingly focus on market structure as a major determinant of liquidity. Perold and Sirri (1997) establish 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 or market impact costs. Domowitz, Glen and Madhavan (2000) find cross sectional variation in total trading costs and the composition of these costs using information on institutional trading on 42 exchanges. These studies do not directly associate market design and institutional features with exchange performance. 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 limit order book market when emerging markets are included. 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. Controversially, Goettler et al. (2004) point out, in the context of a simulated order book that the “true” transactions cost correlates negatively with the observed effective spread. Informed agents in a game-theoretic model can know the true cost of trading, like the “true” value of the asset, but can never be publicly observed or known to the
econometrician. Hence, either the observed spread has no association with trader welfare and the number or value of trades, or the association is positive, contrary to what the existing literature presumes.
Welfare significance arises from efficient
design that facilitates higher traded value, the product of trade size, and the number of trades. Contrary to the findings of the Goettler et al. (2004) model, we find that the observed relative effective spread is a very good, but not perfect, negative predictor of traded value across 38 world exchanges. Our findings are also strikingly inconsistent with the celebrated Kyle (1985) model in which “noise traders” with an entirely inelastic demand for trading undertake the bulk of trading. Noise traders are far more rational that they might first appear. The present study analyzes the relationship between exchange performance and market
architecture,
including
factors
that
exchanges
can
institutional/environmental features that are outside their control.
alter
and
Utilizing the
world’s most comprehensive intraday database for the first time, our analysis of 38 exchanges, including company-specific data for up to 250 companies per exchange, enables us to assess the incremental impact of each feature we study on exchange performance. Who are the winners and losers from each exchange design? We analyze how each feature interacts with trade size, and hence investor class (individual and institutional), to probe more deeply into why architecture matters. An efficient design must also encourage price discovery with the presence of informed traders raising the spread and transaction costs. Thus, the maximal traded value and gains from trade does not necessarily imply the lowest possible trading costs. In fact, we catalogue situations, as with iceberg orders for the largest stocks, in which a fall in the relative effective spread results in a fall in traded value, due to an associated rise in volatility that is harmful to large trades. Our approach integrates trading costs, market volatility, trade size and the number of trades into a system of four structural equations in which we treat all of these variables as endogenous.
The larger is the number of trades, the closer the
approximation to continuous trading. Frequently, researchers treat some of these variables as exogenous controls for trading costs, even though the policy under consideration will affect the degree of asymmetric information and potentially all four variables. Moreover, participants desire to approach long-run optimal values in a 3
partial adjustment framework with geometric distributed lags. Hence, the long-run price (trading cost) elasticity of demand for trading appears quite high at -0.63 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 harms trading costs and particularly harms larger trades, the mechanism by which both the number of trades and trade size impinge differentially on trading costs, and how trading costs determines the number of trades in both the short- and long-term. Strong evidence is found of economies of scale and scope in the trading process with trading costs declining 29 percent for every doubling of the number of trades and only 3.3 percent with respect to trade size since larger trades contain more asymmetric information. 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). A doubling of realized volatility raises trading costs by 5.4 percent but has a far more harmful effect on trade size with an 18 percent reduction. More splitting of orders occurs in shallower and more volatile markets. There is other evidence of scope economies with a doubling in the number of stocks listed lowering trading costs by 21 percent, the size of a trade falls by 31 percent but nonetheless, traded value rises by 44 percent due to a larger number of trades. As we double the market capitalization of a company, trading costs fall by nine percent but traded value climbs by 38 percent due to a larger endogenous trade size. Thus, the model predicts the eventual demise of individual domestic exchanges and the creation of a single integrated global market 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 focus. In terms of the market design that consistently exceeds any other, for the entire dataset and individual stock size quintiles, the floor trading system of the NYSE and Frankfurt is best. While the size and efficiency of the NYSE could be a contributing factor, such systems are relatively transparent in that the actions and identities of both the specialist and floor brokers are visible to other participants on the trading floor. Thus, not only is such transparency beneficial to uninformed traders, it is also beneficial overall. The next most highly rated system is a pure electronic limit order 4
book market, which does well overall and for both large and intermediate sized stocks. These results provide some justification for Glosten’s (1994) prediction that limit order book markets will dominate in that they appear impervious to competition from dealership markets, except perhaps as an upstairs adjunct. A stock trading in a limit order book market utilizing a dealer with an affirmative obligation to make an efficient market does better than a pure limit order book market for small stocks. Finally, relatively opaque hybrid dealer exchanges such as NASDAQ, where there is no obligation to make a market, perform the worst, not only overall, but also for each quintile segment. Consistent with these findings, NASDAQ is either bottom or poorly rated overall and for the different quintiles. Another striking finding from the study is that more transparent design features outperform opaque features. Hollifield et al. (2003) find that intermediaries exercise more monopoly power in opaque markets, particularly with respect to smaller trades. Consistent with this hypothesis, we find that spreads diminish with trade size, despite the greater risk of asymmetric information. 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 market-depth
transparency variables and trade size, our findings are supportive. The execution of small trades relative to large trades improves in progressively more transparent markets. Delayed reporting of block trades and iceberg orders, both of which reduce transparency, are not only harmful overall but also are harmful for most quintiles. The exception is iceberg orders for the smallest quintile. Increasing opaqueness for both features interacts with trade size to harm progressively large trades and institutional investors. Institutional investors are supposed to gain from such opaqueness. Partial disclosure of broker IDs is beneficial overall and for all but intermediate-sized stocks. Complete disclosure is only beneficial for large stocks. Consistent with this, as trade size increases, the benefits from complete disclosure increase.
Finally, after-hours trading facilities, and competition in the form of
fragmented markets, are beneficial overall. An after-hours facility is beneficial for all quintiles, while a fragmented market is beneficial for all but the largest quintile. 5
By classifying securities according to their effective spread plus exchange taxes and charges, volatility, average trade size, number of trades, and the market capitalization of each stock, we aim to determine what type of market architecture is the most suitable for each market segment, as well as controlling for the nature of the stocks traded on each exchange. We introduce a range of proxies for trading demand: exchange size, the number of listed companies, aggregate income as measured by GDP, the population size each exchange draws on, and the ability to trade in crosslisted stocks as indicated by opening hours shared in common with New York. We account for differences between exchanges in how well they have been able to establish their position in the competitive global market place, overall and in each size quintile, and the significance of the forces of scale and scope. For the overall sample and for all but small stocks, pure limit order books have lower volatility than floor markets, affirmative obligation dealers and dealer-hybrid markets including NASDAQ. This is not supportive of the predictions of game-theoretic models such as Shin (1996) that emphasize the role of dealer competition in reducing margins and coping with asymmetric information. The possibility of coexistence 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).1 Upstairs dealer markets grafted onto limit order book markets are beneficial overall but do not appear to be beneficial for large stocks. There is a reduction in trading costs for intermediate-sized stocks but due to volatility impacts, there is a slightly negative overall impact on traded value. Upstairs markets come into their own with large trades, as demonstrated by the very significant improvement when it interacts with trade size. We estimate world best practice in terms of traded value and provide a ranking of every exchange relative to best practice overall and for the top, intermediate and bottom quintiles. Robustness tests utilizing stocks with a greater
1
However, note that Fong et al. (2004) find only very limited empirical support for the very precise
predictions of the Viswanathan and Wang (2002) model.
6
uniformity of size suggest that our results are not dependent on focusing on the largest stocks on each exchange. We also identify the key features of major winners and losers. The paper proceeds as follows: Section I provides a more extensive literature review. Section II discusses the model, provides expectations for the empirical tests, and introduces the performance measures employed in this study. Section III defines the market design and institutional features we analyze in our study. Section IV outlines the data used. Section V predicts and then investigates the relationship between exchange performance and market architecture utilizing a very large and unique panel data set, after controlling for size of exchanges and size and liquidity of the traded stocks. Section VI concludes. I. Literature Review Most previous literature analyzes one institutional characteristic at a time and compares two exchanges.
Huang and Stoll (1996) compare spread differences
between NYSE (limit order book market in which the specialist sees the limit order book), and NASDAQ, (dealer market), and find higher spreads on NASDAQ. 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. One study examines a change in a single feature, display of broker IDs to other brokers, on a single exchange (Foucault et al., 2003). Few studies take a wider cross sectional approach. Even if a focused approach 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, while 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. 7
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 event studies or simple comparisons of (say) two exchanges may not always be robust to the stocks and periods used, due to changing market conditions and the impact of news. In our approach, trading on every exchange acts as a control for every other exchange with all the global relationships tied together by the underlying model and system of equations. Choudhry and Nanda (1991), Foster and George (1992), Madhavan (1995, 1996) 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 limit order books but not in dealer markets. Spreads should thus be lower under a limit order book system. Shin (1996) models 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 (1996), relying on changes made by the London Stock Exchange (LSE), found that delayed publication of block trades did not consistently reduce transaction costs and had little impact on spreads and prices. Grammig et al. (2001) find that uninformed traders prefer the non-anonymous traditional floor trading mechanism while informed traders prefer the relatively anonymous electronic limit order book system. The adverse selection cost component of trading costs is higher in the system attracting more informed trading. Madhavan et al. (2004) find some evidence of a decline in public liquidity of stocks on the Toronto Stock Exchange (TSE) following greater transparency of orders prior to trade in 1990. They attribute this to a greater propensity for “picking off” of orders viewed as “free options”. Boehmer and Saar (2004) analyze the introduction of pre-trade transparency to limit order book on the NYSE in January 2002. This was a response by the NYSE to the earlier introduction 8
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 limit order book. See also Simaan et al. (2002). Lee (1998) provides an extensive discussion of transparency issues. An equally controversial and related issue to market transparency is the minimum tick size. This is particularly so given the requirement by the Securities and Exchange Commission to require both the NASDAQ and NYSE to move from one-eighth to one-sixteenth of one dollar and then, finally, to decimalization of the minimum tick size. An important early contribution was by 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. (2004), and Bessembinder (2004). The consensus appears to be that quotes have fallen as a result, facilitating a larger number of smaller trades, but Chakravaarty et al. finds 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) and he finds no evidence of a liquidity decline. II. Model, Expectations and Performance Measures A1. Model and expectations for the empirical tests 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 six exchanges meeting this requirement;
(ii)
a pure public order driven electronic limit order book (e.g., the Australian Stock Exchange, ASX) which has, in addition to the limit order book, voluntary market-makers and possibly an upstairs dealer-market for exceptionally large institutional trades to which a value 0 is assigned to the 9
25 qualifying markets; (iii)
Stock_Affirmative_Dealer_Dumi, a variant on (ii) with designated dealers with an affirmative obligation to provide price continuity, limit volatility, and typically cross-subsidize less liquid stocks
(e.g., some Euronext
stocks in several European countries and all NYSE stocks) to which a value of 1 is assigned to qualifying stocks in the eight exchanges falling into this category. (iv)
Market_with_Exchange_Floor_Dumi, a traditional floor trading system (exclusive to NYSE and Frankfurt) to which a value of 1 is assigned.
The pure limit order book thus becomes the standard of comparison for each market type. In a limit order book 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 nondesignated 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 entire exchange to score in two categories as both an affirmative dealer exchange and as a floor-based system. A2. Performance measures We measure exchange performance from four main aspects: transaction costs, volatility, average trade size and the number of trades. The product of the last two variables generates the dollar value of trades. Moreover, dividing this dollar value by market capitalization yields stock turnover. Apart from being of critical value to traders whose objective is to exchange, 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. The measures of transaction costs are calculated using intra-day, trade-by-trade data. Every time a trade occurs, a bidask spread is observed either as the difference between best bid and ask in a limit order book environment or as the difference between the quoted buy and sell price in 10
a dealership environment.2 As the primary transaction cost measure, we use the effective spread, which measures how far from the mid-point in the spread that trade execution occurs. We add information to the effective spread measure by weighting it by the size of the trade 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 weighted relative effective spread as follows: Trade value weighted relative effective spreadt = ⎧ ⎫ ⎡ ⎛ Askt + Bidt ⎞ ⎤ ⎪ ABS ⎢Trade Pricet − ⎜ ⎪ ⎟⎥ ⎡ 2 Trade valuet ⎤ ⎪ ⎪ ⎝ ⎠⎦ ⎣ ×⎢ 2× ∑ ⎨ ⎥⎬ , ⎛ Askt + Bidt ⎞ t0 ⎪ ⎣ Total traded value ⎦ ⎪ ⎜ ⎟ ⎪⎩ ⎪⎭ 2 ⎝ ⎠ tc
(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 and tc is the time when trading ceases for the day. 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). Brokerage is, to some degree, fixed, while market impact, which has both temporary and permanent components, can be difficult to measure accurately. 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.
Since 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 add the round-trip cost to the effective spread to obtain our estimate of overall trading cost.
2
In dealer markets, quotes are often only indicative so as to provide a degree of protection to the
dealers themselves.
11
Transaction costs are important for the performance of an exchange since lower transaction 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. A related 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. As our volatility measure, we use daily-realized volatility, calculated as the squared daily continuously compounded close-to-close return, see equation (2) below. Andersen et al. (2001) outline the advantages of using the realized volatility measure. They show that the model-free realized volatility is more accurate when the frequency of measure points increase and is thus a very suitable measure for aggregating intraday information. Furthermore, they find that standardized returns obtained by scaling returns with realized volatility are free of many of the problems with non-normality in the distribution. Realized volatilityi,t = [(ln(
ptc pt-1c
) ]2 ,
(2)
where tc is the time when trading ceases at the end of the trading day and t-1c is the time trading ceased the previous day. We also compute and experiment with the fiveminute and 15-minute standard deviation of returns computed from the intraday trades and quotes. These measures proved unsatisfactory relative to the realized volatility, especially for relatively illiquid stocks. Whether volatility or idiosyncratic risk is good or bad for a stock market is a debatable issue (see Pagano, 1989, for a theoretical model), which, fortunately, we are able to address. We conclude that volatility is unambiguously harmful. We explain two fundamental measures of trading activity, average trade size and the number of trades, the counterpart of trade duration or gap between trades. The dollar 12
value of the trade is simply the product of average trade size and the number of trades. We could attempt to allow for some double counting 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 data period. However, even with perhaps some limited doublecounting, markets such as NASDAQ perform particularly poorly even with traded value as the criterion. 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 a whole host of market architectural features is likely to be quite different on these two components of traded value. For example, a lower minimum tick size is expected to increase the number of trades but trade size is expected to fall as market depth declines in a shallower and more volatile market leading to ambiguous effects on traded value. While we do not specifically incorporate factors such as execution speed and rapidity of price discovery, for example, floor markets are typically much 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. Market Architectural 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 and institutional 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 actually endogenous and thus cannot, or 13
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 of endogenous variables using Two Stage Least Squares (2SLS) structural equation methods; see for example, Harris (1994) and Brennan and Subrahmanyam (1998). 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 income on the nature and magnitude of trading, as measured by GDP. (ii), the impact of the overall market size, as measured by population, and (iii), the number of trading hours overlap with the New York Stock Exchange (NYSE) to capture the ability to arbitrage ADRs and provide additional liquidity for European and North American exchanges. None of the Asian or Australasian exchanges has concurrent trading times and hence we assign a value of zero. However, we do take account of the presence of an after-hours facility possessed by some exchanges designed to achieve some commonality in trading times. Other institutional factors include, (iv), is the market, as defined by income and population, fragmented into several exchanges or concentrated into one exchange?; (v) if it is an electronic limit order book 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 SIRCA3 contains intra-day trade, quote, and volume information for all securities listed on all world stock exchanges with approximately 240 in total. We choose the 38 exchanges used in the study to provide a generalized cross sectional picture of world stock exchanges. Three are selected
3
This 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.
14
from North America, 19 from Europe, 11 from Asia, two from Oceania, two from South America and one from Africa. Table I lists the investigated exchanges, the country, the full name of the exchange, the number of stocks included in our study, the market capitalization of each exchange as of the start of 2000, and the classification of the exchange. The included exchanges represent 98 percent of the capitalization of stock exchanges that are members of the World Federation of Exchanges. We collect a consistent set of exchange information regarding market architecture and institutional feature variables for all 38 exchanges. The largest is the NYSE, a floor-trading system with a limit order book and affirmative dealers (“specialists”), followed by NASDAQ, Tokyo, which is a pure limit order book 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 select 250 common stocks with the highest value of securities traded during the period selected from the 38 exchanges. For the exchanges that have fewer listed companies than the target of 250, we include the total number of available listed companies. The average number of companies is 223, as reported in Table II. This selection process results in a balanced portfolio representing world common stock markets while still giving representation to smaller exchanges. The inclusion of up to 250 securities is broad enough to give a dataset with many different levels of activity and liquidity across the 38 exchanges and the 8,474 stocks and generally, we can match with Datastream to compute market capitalization and identify stock splits. We obtain intra-day trade-by-trade prices, numbers of trades and average trade size, and best bid-ask quotes or orders, whichever is applicable, for all 8,474 stocks and comparable exchange rate adjusted measures are calculated using intra-day data and presented as a daily time-series for each company. We add transaction taxes and exchange fees, expressed as relative measures on a round-trip basis, to the effective
15
spread calculations based on equation (1) above.4 Market capitalization, daily number of shares on issue and market to book ratio values are available in Datastream for 5,098 of the stocks, which then constitutes the final sample used in analysis. Datastream is also the source of close-to-close returns that form the basis of the realized volatility measure. This final sample represents approximately 37 percent of the world stock market capitalization at the start of the investigated period. 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. We collect the exchange specific information from the World Federation of Exchanges, Exchange Fact 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. We confirm all exchange information documented in the publicly available information sources through direct correspondence with the exchanges. Table II reports our classification of exchanges according to the four groups mentioned above. (Insert Table II about here) V. Empirical Findings A1. Descriptive statistics Table II reports means and ranks for the trade weighted relative effective spread with the addition of exchange fees and taxes, the realized volatility, the average trade size, the number of trades, and traded value per exchange. The reported measures are average stock level measures for each exchange. While some of the large exchanges such as NYSE, NASDAQ and LSE figure prominently in the rankings, so do limit order book hybrids with affirmative dealers such as Amsterdam and even electronic
4
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.
16
limit order book markets such as the ASX.
Interestingly, the highest volatility
exchanges are also the largest and apparently most successful dealership exchanges such as NASDAQ. These rankings incorporate a whole host of size and demand variables as well as market design. Hence, further analysis is required to identify and explain good architectural design. A2. Cross-sectional and time-series analysis We start with a pooled cross-sectional and time-series analysis on daily data, which we aggregate from our intra-day data. We thus include daily observations for the period March 2000 to end of October 2001 for each company for all 38 exchanges. The size of the dataset equals the number of analyzed companies for which there are sufficient information available times the number of included trading days, or 1,334,938 observations. Since some smaller stocks on smaller exchanges do not trade every day, the number of observations is smaller than the theoretical maximum. We 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 five exogenous continuous variables, the average stock market capitalization of each stock over the sample period, number of listed companies, income (GDP) for the country in which each exchange is located, together with its population. We compute the relative minimum tick size as 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. Subsequently, we carry out statistical specification tests, which confirm that, indeed, our four endogenous variables are truly endogenous. Taking logarithms of all nine 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 simple Box-Cox test confirms that the log specification for the endogenous variables describes them better. For each of the four endogenous variables, ytj , j ∈ (1,.., 4 ) , described below, we begin with a simple partial adjust geometric distributed lag model (e.g., Greene, 2003): 17
ytj − ytj−1 = (1 − λ j )( yt*, j − ytj−1 ) + ε t j , (3) in which the adjustment of the actual level is a proportion of the difference between the desired level, yt*, j , and the actual level in the previous day. The equations to estimate become:
ytj = a j (1 − λ j ) + λ j ytj−1 + β j (1 − λ j ) xtj + ε t j , (4) with short-run elasticity, β j (1 − λ j ) , and long run elasticity, β j . We estimate the system of partial adjustment structural simultaneous equations, (5a) to (5d), using Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), and Generalized Method of Moments (GMM) methods below, applied to the crosssectional and time-series data: Ln(Trans_Cost)i,t = β 01 (1 − λ 1 ) + λ 1 Ln(Trans_Cost)i,t-1 + β11 (1 − λ 1 ) Ln(Volat)i,t
+ β 21 (1 − λ 1 ) Ln(Tr_Size)i,t + β 31 (1 − λ 1 ) Ln(No_Tr)i,t + β 41 (1 − λ 1 ) Ln(Mkt_Cp_Cpy)i +
β51 (1 − λ 1 ) Ln(Mn_Tck)i,t + β 61 (1 − λ 1 ) ... β171 (1 − λ 1 ) Mkt_Arch_Dumi,t + ε 1 ,
(5a) Ln(Volat)i,t
=
β 02 (1 − λ 2 ) + λi Ln(Volat)i,t-1 + β12 (1 − λ 2 ) Ln(Mkt_Cp_Cpy)i +
β 22 (1 − λ 2 ) Ln(Mn_Tck)i,t
+ β32 (1 − λ 2 ) ... β142 (1 − λ 2 ) Mkt_Arch_Dumi,t+ ε 2 ,
(5b)
Ln(Tr_Size)i,t = β 03 (1 − λ 3 ) + λ 3 Ln(Tr_Size)i,t-1+ β13 (1 − λ 3 ) Ln(Volat)i,t+ β 23 (1 − λ 3 ) Ln(Mkt_Cp_Cpy)i + β33 (1 − λ 3 ) Ln(Mn_Tck)i,t+ β 43 (1 − λ 3 ) Ln(List_Cpy)i
+ β53 (1 − λ 3 ) Ln(GDP)i + β 63 (1 − λ 3 ) Ovrlp_NYi + β 73 (1 − λ 3 ) Ln(Popn)i,t +ε 3 ,
(5c)
and Ln(No_Tr)i,t= β 04 (1 − λ 4 ) + λ 4 Ln(No_Tr)i,t-1+ β14 Ln(Trans_Costs)i,t+
β 24 (1 − λ 4 ) Ln(Mn_Tick)i,t+ β34 (1 − λ 4 ) Ln(List_Cpy)i,t + β 44 (1 − λ 4 ) Ln(GDP)i+ β54 (1 − λ 4 ) Ovrlp_NYi+ β 64 (1 − λ 4 ) Ln(Popn)i,t+ ε 4 , 18
(5d) where the RHS explanatory variables, Volat, Tr_Size and No_Tr in (5a), Volat in (5c) and Trans_Costs in (5d) are endogenous. As the control variable for company size, Mkt_Cp_Cpyi, we use the average market capitalization of every individual stock included in the system of equations 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. All currency amounts have been converted to $US using the current exchange rate for that day or period.
The
company size variable controls for the expected lower effective spreads in stocks with a higher market capitalization. This is due to relatively less asymmetric information. As proxies for market size, we use the log of GNP measured in consistent $US in 1999, representing our opening period, and the log of population for 1999. The demand variable, Overlap_with_NYSEi, represents the number of regular trading hours shared in common between any given exchange and the NYSE. A greater overlap enhances the ability to arbitrage cross-listed stocks between the US and other American and European markets.
As the control variable for volatility and
idiosyncratic company risk, we use the log of Realized_Volatilityit, which is the square of the close-to-close daily stock return. This varies on a day-to-day basis. The List_Cpy is 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 top 250 listed stocks, we would expect a larger number of companies to lower costs and promote trading activity due to scale and scope economies. The Relative_Minimum_Tickit is 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 a time series 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 data period. 19
This
controversial and very substantial reduction in tick size was mandated by the Securities and Exchange Commission (SEC). The first equation, (5a), is an inverse supply equation describing the endogenous supply
of
trading
activity
in
terms
of
the
“price”
of
a
trade,
Relative_Transactions_Costsit, which is made up of the sum of the relative trade weighted effective spread, taxes, and exchange charges. The impact of information, reflected in the logarithm of realized volatility, feeds directly into transaction costs, and it enters via, Av_Trade_Sizeit. Since we expect larger trades to contain greater asymmetric information, a larger trade size should impinge adversely on trading costs. The third endogenous variable in the first equation is the log of the number of trades. If there are significant scale economies resulting from fixed costs, then its expected sign is negative. The average market capitalization of each stock over the data period is included as a size control. The first architectural design variable is the relative minimum tick size followed by 12 of the architectural and environmental dummies. In the volatility equation, the size control is included, along with the relative minimum tick size.
However, consistent with the idea that volatility is more
“fundamental” than the other endogenous variables; no endogenous variables feed into the volatility equation. We expect a positive association between the relative minimum tick size and both trading costs and volatility. We incorporate all of the 13 architectural and environmental features of exchange design into the volatility equation. The remaining two equations, (5c) and (5d), are endogenous “demand” equations for trading activity, with the demand for Average_Trade_Size described in (5c) and Number_of_Trades in (5d). The endogenous variable, realized volatility, impinges on trade size since trade size is likely to be sensitive to the nature of information contained in the order flow. The higher the idiosyncratic risk, the lower is the expected market depth and the smaller the expected trade size. Since trade size is essentially a descriptor for each trade, we assume that transaction costs do not impinge directly on this variable. Rather, we expect higher trading costs to impinge adversely on the number of trades. From a theoretical perspective at least, demand curves are downward sloping. Hence, the sign of the transaction cost elasticity in (5d)
20
should be negative. We
exclude
the
four
variables
representing
Number_of_Companies_Listed,
GDP,
the
demand
for
Population,
trading, and
Trading_Time_Overlap_with_NYSE, 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 to lower trade size but raise the number of trades, a higher population to reduce trade size while encouraging more trades. The impact of trading time overlap with New York should be beneficial to overall traded value but the split between numbers and size is hard to predict.
The
Market_Cap_Company control variable is included in (5c) as we logically expect larger companies to increase trade size. 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 are over-identified and thus we can then estimate them as a set of simultaneous equations (see, for example, Greene, 2003). A3. Market architectural design features We describe the included market architecture variables, commencing with transparency issues, by the following: Delayed_Reporting_Block_Tradesi takes the value 1 for 10 exchanges that allow block trades of a 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 activity and possibly higher trading costs since trading brokers gain a relative trading informational advantage. However, the literature reviewed in Section I above failed to find any gain from delayed reporting, in which case it is likely to have a harmful effect on both trading cost and traded value.
21
The Iceberg_Orders_Facility_Dumi takes the value 1 for 17 exchanges that allow orders disclosing only a fraction of the true size of the order, or so called ‘iceberg’ orders.5 The hidden part of the order cannot be executed before disclosed orders with the same limit price. If transparency relating to the depth of the limit order book reduces uncertainty and the impact of asymmetric information, then this feature could raise trading costs and reduce traded value. Full_Transparency_for_Investorsi takes the value 1 for eight exchanges that provide full
ex
ante
order-book
disclosure
to
all
investors,
while
Partial_Transparency_for_Investorsi takes the value 1 for the 18 exchanges that disclose the order-book partially to investors and for the exchanges that also provide full transparency.
That is, these transparency variables are additive and a fully
transparent market prior to the trade is defined to be also partly transparent. An order book is defined as partially disclosed if at least the three best price levels of orders are disclosed but not the full market depth. This issue is discussed at length in Section I. On one hand, there is the increased prospect of stale limit orders being picked off and, on the other, the success enjoyed by the NYSE with its Open Book system following its introduction in 2002. In previous order book disclosure variables we do not distinguish between order books that display broker identity to both brokers and investors (e.g., Toronto and South Korea for our sample period) to brokers only (e.g., the ASX) and those that do not display the identity at all (e.g., Euronext).
For this purpose we design the
variable, Broker_ID_Complete_Disclosureit, that takes the value 1 for the two exchanges that display broker identities with orders so that both institutions and brokers are informed, and, Broker_ID_Partial_Disclosureit, which takes the value 1 for the 26 exchanges that disclose broker IDs to brokers only and zero for the 10 exchanges that keep broker identities entirely anonymous. Once again, we proceed in an additive fashion by treating the two exchanges with complete disclosure as also
5
The ASX has an iceberg facility which differs significantly from the other exchanges in terms of the
treatment of time priority, and has thus been excluded from this variable and the exchange rankings based on our regression model.
22
satisfying partial disclosure. These variables also pick up in our time series the effect of some exchanges going from partial disclosure to keeping broker IDs entirely anonymous, for example, Paris Euronext. The sign of the partial disclosure dummy variable is potentially ambiguous. Not only does it signal greater transparency than complete anonymity, but it also introduces an informational bias in favour of brokerdealers which might exacerbate any monopolistic power with respect to small investors. Foucault et al. (2003) find some limited evidence that small investors might have benefited from the Paris Bourse becoming completely anonymous when it previously had partial disclosure. Knowledge of broker identity prior to a trade having taken place may make it easier to “front run” clients serviced by particular brokers. Upstairs_Market_Facility_in_LOB_Framework takes the value 1 for the 21 electronic limit order book 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, giving rise to higher trading costs downstairs. A study of the Australian Stock Exchange (ASX) upstairs market by Fong et al. (2004) finds no evidence that the downstairs market is adversely affected. We expect upstairs dealers adopting a search role to improve overall market efficiency. After_Hours_Trading_Facility takes the value 1 for 18 exchanges that provide after hours trading. We might expect after hours trading to provide greater ability to trade across time zones when other markets are open which should promote higher trading activity. Fragmented_Marketsi, takes the value one for 16 markets that have more than one exchange. Arnold et al. (1999) find that the merger of a number of regional US exchanges created higher volume and lower trading costs relative to a control group of non-merging exchanges. Since fragmented markets fail to take full advantage of economies of scale and scope, we expect trading costs to be higher and the value of trading lower. However, we cannot rule out the possibility that greater competition
23
may either lower costs and/or raise trading activity. A4. Two Stage Least Squares and 2SLS-GMM regression results In Tables III to VII we report estimates of the relationship between our four endogenous variables, our control and trading demand variables and our market architectural design variables. Table III presents the coefficient estimates and student t values for the OLS and 2SLS estimates of the four structural equations estimated over the entire dataset.
We employed 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. Tests showed unambiguously that 2SLS estimates are preferred to OLS, which ever set of variables we considered. However, 3SLS estimates (not reported) proved inferior to 2SLS estimates. A comparison of the OLS and 2SLS estimates in Table III reveals differences, extending to signs of key parameters such as the impact of volatility on trade size as well as magnitudes. (Insert Table III about here) We present our main results in Table IV in which 2SLS results in Table III for the entire data set are refined by the Generalized Least Squares (GMM) procedure implementing the Newey-West correction for heteroskedacticity and auto-correlation with a 21 day lag structure. Since there is no ideal specification for the lag length we experimented by raising the length until the incremental impact in terms of lowering of lowering student t values and refining coefficients was substantially lessened. Using the estimated GMM coefficients for the four linear (in logarithms) equations incorporating endogenous variables the linear equations are solved simultaneously to derive the reduced-form dynamic impact factors taking account of the interactions between the various supply and demand models. We rank every stock globally according to its market capitalization, and then divide equal numbers of stocks into five global size groups (quintiles). Since the number of non-trading days for smaller stocks is greater, fewer observations appear in the smallest quintile. Similar results for large stocks (quintile 1) are presented in Table V, intermediate stocks (quintile 3), Table VI and small stocks (quintile 5), Table VII. (Insert Tables IV to VII about here) 24
The overall fit of the four equations in Table IV is excellent with relatively high adjusted- R-Squareds for three of the four endogenous variables, ranging up to 88 percent for the log of the number of trades and down to 10.4 percent for the log of realized volatility. Since there are no endogenous variables explaining volatility and most researchers regard volatility is as relatively exogenous, the outcome for this variable is reasonable. Of the 60 parameters estimated, only two are not significant at the 1% or better level. Our comparatively large sample size may have contributed to this outcome. The exceedingly high value for the Hausman test indicates that the system of structural equations is superior to the simple OLS specification. The (1 − λ j ) partial adjustment coefficients based on each day’s trading range from as low as 12.3 percent for the log of trade number, 15.5 percent for log of trade size, 40.4 percent for log of relative trading costs and 71 percent for log of realized volatility. The huge 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 capture the long-run impact and may falsely conclude that trade numbers are unresponsive.
We found that the introduction of the partial
adjustment model eliminated first-order serial correlation, enhanced the explanatory power, made the system of equations exceedingly stable and amenable to observing the impact of one endogenous variable on another, and gave relatively consistent estimates using the same set of explanatory variables overall and across the quintiles. The results for the different trade sizes in Tables V to VII are remarkably similar with similarly high adjusted-R-Squareds, t statistics and even similar coefficients and impact factors. Examining the most fundamental of the endogenous variables first, utilizing Table IV, a 72.6 percent rise in volatility will be the consequence if NASDAQ replaces a limit order book market as the retention of more asymmetric information raises idiosyncratic risk. There will be two adverse consequences. Relative transaction costs will rise 5.4 percent and trade size will fall 18 percent for each doubling of volatility. Hence, volatility is unambiguously bad in this framework as it reduces market depth. The fall in trade size will deny economies of scale to the trading process leading to a further rise in trading costs at the rate of 3.3 percent. These cumulative rises in trading costs decrease trade numbers at the rate of 63 percent and 25
the decline in trade numbers denies even more scale economies to the trading process leading to an even greater adverse rise in trading costs at the rate of 29 percent. This process continues until we reach a new equilibrium. The impact columns, with coefficients in bold, display the long-run equilibrium solutions to the initial impetus of the introduction of a dealer market. Trading costs eventually rise by 57 percent; trade size falls by 13 percent, number of trades by 33.5 percent, and traded value by 47 percent.
We illustrate the relationship between the five endogenous variables
including traded value in Figure 1. (Place Figure 1 about here) Table IV shows that a lowering of the relative minimum tick size, due for example to decimalization, results is a direct and statistically significant lowering of trading costs and the relative effective spread at the rate of 0.0136. Due to the increased incentive to trade, realized volatility rises at the rate of 0.041. Since higher volatility reduces trading costs, this has an adverse impact on trading costs. The higher volatility and reduced depth for larger trades reduces trade size at the rate of 0.0168. This further increases trading costs, as there is a loss in scale economies of trading. However, the main effect of the fall in relative tick size is on the number of trades which goes up at the high rate of 0.0762. The direct and indirect effect of all these forces affecting trading costs is a negative impact at the rate of 0.0303, on trade size a negative impact of 0.0242 and a positive impact on trade numbers of 0.0762 with a net positive impact on traded value of 0.052. Hence, the overall impact of decimalization is favorable, both in terms of the fall in trading costs and rise in traded value. These improvements come about regardless of the fact that large institutional traders now break up a higher proportion of their trades into smaller units. The corresponding estimates from Tables V, VI and VII show that decimalization benefits all but the quintile of the largest stocks, with no change in traded value for the largest quintile. For this quintile, lower depth and higher volatility, which act to reduce trade size, exactly offset the gain in trade numbers due to lower spreads. The findings on the number of companies listed are of interest to exchange strategists. More listed companies encourage a smaller average trade size with an elasticity of 0.31 but the impact on trade numbers is both positive and more than twice as elastic at 0.76. These feed through into a decline in trading cost of – 0.21 and an overall rise in 26
traded value of 0.44. Exchanges in nations with high GDP experience far higher average trade sizes, with an income elasticity of trade size as high as 1.65 but the income elasticity for trade numbers is negative at -0.28, still leaving a substantial elasticity improving traded value of 1.36. A large population favors more trades with an elasticity of 0.58 but the trade size elasticity is sufficiently negative to lead to a substantially negative impact on traded value with an elasticity of -0.89. Exchanges do best by locating in large, rich countries like the US and not in poor populous countries such as China, India and Indonesia. Not only does overlap with the NYSE’s trading hours benefit European and American exchanges, but also the resulting trading activity in ADRs and cross-listed stocks appears to be largely uninformed, with a traded value gain (benefit from an additional hour) as high as 10 percent. Should pure electronic limit order book markets that have proliferated around the world introduce designated dealers with affirmative obligations into market segments or the entire market? The answer is clearly, no, except for the smallest quintile (Table VII). Should they shift over to becoming dealer emphasis hybrids? No. Not under any circumstances. The volatility equations in all tables but the smallest quintile (Table VII) shows that, conditional on other design variables, dealer hybrids such as NASDAQ are highly volatile.
Our empirical findings clearly contradict those
theoretical models of dealer activity in which dealers without an affirmative obligation to make a market are voluntary risk-bearers absorbing idiosyncratic risk. Our evidence is largely to the contrary. 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 with a corresponding fall in traded value. Iceberg order facilities that act to disguise the depth of the limit order book have the effect of raising uncertainty and thus appear to lead to an even more pronounced rise in trading costs and fall in traded value. However, we find a possible exception for the smallest stocks (Table VII). By contrast, initiatives such as the NYSE’s OpenBook, which ex ante reveals the full depth of the limit order book to investors, resolves uncertainty about the depth of the limit order book leading to considerable falls in trading costs, falls in volatility and rises in trade size. The initiative also reduces the ability of the specialist to exercise 27
monopolistic power against smaller investors. The overall positive impact on traded value is not only sizeable, but reflects the additive gains from both partial and total transparency. The final transparency issue we address is perhaps the most controversial. Should exchanges require brokers and dealers to reveal their identity prior to trade? Institutional brokers acting for relatively informed clients are often vehemently opposed to either partial disclosure to other brokers, or the more radical step of complete disclosure to investors as well as brokers, because it allegedly facilitates “front-running” of orders. Our findings are mixed. Most exchanges only disclose to other brokers but, when they do so, trading costs fall and traded value rises, not only overall but also for every trade size. Note that we have included the two open-outcry exchanges in this category in that the specialist and floor brokers know the identity of other floor brokers. Admittedly based on a small sample of two exchanges, complete disclosure to investors is beneficial for large stocks but not for intermediate or small stocks. This is due to excessive volatility. Alternatively, the regression is picking up the fact that one of the two countries that do disclose over our sample period, Korea, is either the first or the second most volatile market in the world, depending on whether predicted or actual values are used.
Investor benefits from Broker ID
disclosure to other brokers seem to diminish with trade size. One possibility is that large informed traders are being front-run by benefiting small traders, even though the market as a whole gains in terms of traded value. Contrary to our inability to show a clear advantage to dealer orientated hybrid markets, the structural model does identify a market segment in which the role of the dealer is entirely beneficial to the overall market efficiency. A dealer upstairs market grafted on to an electronic limit order book, or limit order book hybrid, is associated with a fall in trading costs in the downstairs market. This fall gives rise to a relatively small rise in overall traded value. Supportive evidence provided by Fong et al. (2004) suggests that these upstairs dealers provide a search for counterparties role rather than provision of insurance. At first blush, our findings with respect to the presence of after-hours trading are so strong as not to be fully believable. Far from fragmenting liquidity over the day and raising costs, they appear to substantially lower trading costs and raise trade size to 28
produce a significant overall gain to traded value. This architectural feature plays a similar favorable role to overlap with the New York trading time. It suggests that dealers enable large institutional trading to take place between relatively isolated (in terms of the time zone) markets and London and New York at times when the main market is not open. Table V shows, not surprisingly, that the beneficial effects are even stronger for the largest quintile. Finally, fragmentation in the form of multiple exchange trading venues gives rise to higher trading costs, but essentially because of higher volatility and the loss of economies of scale due to a smaller number of trades. However, a higher average trade size compensates for the loss in trade numbers, leading to a net gain in traded value. The counter-argument to this is that our size controls do not fully work, and that countries with multiple venues, including the US, India and China, tend to be larger. Of course, there are many exceptions to these examples and we believe that our demand model with a high overall fit works exceptionally well. A5. Which architectural features do institutional investors most 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 nine different architectural dummy variables and trade size, which are inserted one at a time into the relative trading cost and volatility equations in the model underlying Table IV. In an effort to reduce possible multicollinearity problems, we introduce the interaction effects separately in estimates of the structural equations using the full dataset. (Insert Table VIII about here) The first two sets of results reported in Table VIII shows that large traders are the main beneficiaries of full and partial disclosure of Broker IDs. Transaction costs fall and traded value increases with trade size, as indicated by the signs of the interaction variables. It would appear that some knowledge of who else may be trading assists the trading of large blocks. These results downplay the hypothesis that display of broker IDs leads to front-running of large informed orders by small investors. 29
A theory with considerable currency argues that transparency of the limit order book is harmful because it facilitates picking-off of visible “stale” limit orders that provide “free” options to large, well-informed market order placers. A consequence would be a fall in the depth of the limit order book market. We include interaction terms between partial and full revelation of the depth of the limit order book and trade size. Trading costs increase and traded value falls as trade size increases. Hence, limit order book transparency is most beneficial to the small investor, who may be less subject to monopolistic dealer powers in more transparent markets. However, the higher incremental cost of trading large parcels relative to small trades in transparent markets implies a greater incentive to split up large trades. The overall gain to trading in transparent markets belies the “picking-off” argument and supports the idea that the bargaining power of small traders has increased relative to institutional investors. Not surprisingly, large trades and traders are the main beneficiaries of upstairs dealer markets, complementing the limit order book.
The ability of these interaction
variables to yield sensible results when the prediction is clear-cut, speaks to the soundness of the methodology. Two opacity measures explicitly designed to benefit large institutional traders, block delay and iceberg orders, have 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. Finally, both forms of dealerships, affirmative dealers and regular dealers such as NASDAQ, have rising costs and falling traded value as trade size increases. We expect this result for affirmative dealers, whose major role in most European markets is to increase liquidity in smaller stocks. There is also a surprising contrast between regular dealer markets and dealers in upstairs limit order book markets, with the latter facilitating large trades, presumably via a search role.
A6. Simulated adoption of best-practice reforms In Table IX, we simulate the adoption of world best practice to achieve the maximum traded value using the reduced-form dynamic impact factors computed from the 2SLS-GMM coefficient estimates for the entire stock sample from Table IV. We 30
first predict and rank the overall performance of all 38 exchanges based on all variables, with respect to traded value (column 1). Not surprisingly, the three largest exchanges, New York, NASDAQ and Tokyo, fill the top three positions.
Our
modeling predicts that even New York’s performance would more than double by the adoption of best practice. We then predict traded value based on architectural features entirely under the control of the exchanges in question (Columns 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. Tokyo and Osaka fill the top two positions followed by three relatively smaller exchanges, Korea, Luxembourg and Helsinki.
All are relatively simple and straightforward
electronic limit order book markets. According to these simulations, most exchanges could considerably lower trading costs and increase traded value by implementing world best practice. In the remaining four columns of Table IX, we report the predicted trading cost, volatility, trade size and trade numbers for these ranked exchanges using all the impact factors reported in Table IV. The intercept for every performance measure has been set such that New York’s performance is representative of its typical stocks.
Hence, we normalize by setting actual and
predicted values for New York to be the same. In Table X we extend the rankings to large, medium and small quintiles with the same normalization with respect to NYSE quintiles. Tokyo, a relatively large limit order book market, does best overall and in the medium category. The much more substantial differences between best-practice and the performance of most exchanges dealing in small stocks suggests that it is much harder to design and implement optimal architectural policies for stocks that do not contribute a great deal to exchanges by way of trading fees. (Place Tables IX and X about here) In Table XI, we report all the input variables for the best-practice exchange, the exchange which is top-ranked for architectural features (Tokyo), the unrestricted highest-ranked exchange (New York), a highly ranked limit order book market (Australia), and a large but poorly ranked dealer market (NASDAQ). Tokyo does well because it does not deliberately restrict the transparency of the limit order book via iceberg orders or block delay but it could further improve transparency. It also 31
has the benefit of a small minimum tick size and relatively highly valued stocks. During the period of the study, New York moved to decimalization and subsequently introduced full disclosure of the limit order book. Were we to rank it now, it could well be number one. Figure 2 illustrates how the move to best practice for the policy variables directly under the control of NYSE works within the model. Equilibrium occurs where trading costs (supply) cuts the downward-sloping trade-number demand curve.
To obtain trade value, project a line vertically downwards from the
equilibrium until it cuts the traded value line. With the move to best practice, trading costs fall, in the process doubling trade numbers with no diminution in trade size. (Place Table XI and Figure 2 about here) The ASX does not perform quite as well as the top-ranked limit order book markets because stock prices are quite low relative to even the small (decimalized) minimum tick.
Moreover, it has implemented a measure that increases opacity, despite
revealing the full depth of the limit order book. However, the ASX does rank higher than NASDAQ, which has the disadvantage of being a relatively opaque dealer market, lacking any affirmative obligations. Note both the great prominence of the ASX in Table X for the largest quintile stocks, where in terms of predicted traded value and best-practice score, it rates within the top few, and in the medium and small-sized stocks in which its rank is relatively poor. The introduction of affirmative dealers, or possibly discreet auctions every 90 seconds as in Taiwan, could greatly assist the very large number of small stocks listed by the exchange. A minimum tick size of one-tenth of a cent would also improve trading in the majority of stocks that are low-priced, as would relaxation of draconian upstairs market access rules. VI. Conclusions
Using a unique intra-day dataset including the top 250 securities listed on 38 representative exchanges for the period between start of March 2000 to end of October 2001, we construct a simultaneous structural equation model of global trading by stock exchanges making up 98 percent of the world’s market capitalization. The essence of the model is that there exists a downward-sloping function describing the endogenous demand for equity trading in terms of relative transaction costs and a 32
host of macro-economic demand factors. The endogenous relative cost of trading depends on numerous architectural design features, chosen by each of the 38 exchanges.
The endogenous market volatility reflecting idiosyncratic risk drives
down the endogenously chosen trade size, with more splitting of orders. The main endogenous impact on trading cost is the number of trades.
These enjoy huge
economies of scale. In turn, the long-run demand for trading (number of trades) is relatively transaction cost sensitive. We estimate the set of four simultaneous equations using 2SLS and GMM and the solutions to set of equations are reduced-form dynamic impact factors. We use the impact factors to evaluate numerous policies ranging from the optimal degree of transparency, performance comparisons of electronic limit order books, floor-trading exchanges, affirmative dealers and conventional dealer markets. electronic limit order books generally perform very well. generally improves market performance.
We find that
Greater transparency
We use the model to predict the
performance of every exchange according to trading costs and 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.
33
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Markets LLC, TX, USA. Naik, Y. Narayan and Pradeep K. Yadav, Trading costs of public investors with obligatory and voluntary market-making: Evidence from market reforms, Working Paper London School of Business and University of Strathclyde. Pagano, Marco, 1989, Trading Volume and Asset Liquidity, Quarterly Journal of Economics 104, 255-274. Pagano M. and A. Roell, 1996, Transparency and liquidity: A comparison of auction and dealer markets with informed trading, Journal of Finance 51 (2), pp. 579-611. Pagano, M. and B. Stiel, 1996, Equity trading I: The evolution of European trading system, The European equity markets: The state of the union and an agenda for the millennium, A report of European capital market institute, 1-58. Palomino, F., 2001, Informational efficiency: ranking markets, Economic Theory, 18, pp. 683-700. Parlour, C. A. and D. J. Seppi, 2003, Liquidity-based competition for order flow, The Review of Financial Studies 16 (2), 301-343. Perold A. F. and E.R. Sirri, 1997, The cost of international equity trading, Working paper 97-012, Harvard Business School - Research Division. Porter, D., Weaver, D., 1998, Post-Trade Transparency on Nasdaq’s National Market System, Journal of Financial Economics 50, 231-252. Pulatkonak, M. and G. Sofianos (1999), The distribution of global trading in NYSElisted non-US stocks, Working Paper, New York Stock Exchange. Seppi, Duane J., 1990, Equilibrium Block Trading and Asymmetric Information, Journal of Finance 45 (March), 73-94. Shin, H. S., 1996, The robustness of trading systems to higher-order uncertainty, Review of Economic Studies 63, pp. 39-59. Simaan, Y., Weaver, D., Whitcomb D., 2002, Market Maker Quotation Behavior and Pre-Trade Transparency, Journal of Finance 50, 2147-1267. Snell, A. and I. Tonks, 2003, A theoretical analysis of institutional investors’ trading costs in auction and dealer markets, Economic Journal 113, pp. 576-597. Stoll, H. R., 1998, Reconsidering the affirmative obligation of market makers, Financial Analyst Journal 54, pp 72-82. Viswanathan, S. and James J. D. Wang, 2002, Market architecture: limit-order books versus dealership markets, Journal of Financial Markets 5, 127-167. Table I: The 38 investigated exchanges that collectively make up 98 percent of the world’s market capitalization displaying the name, country, full name, number of stocks included, and the nature of the trading system
37
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Exchange Country Amsterdam Netherlands Australia Australia Brussels Belgium Budapest Hungary Frankfurt Germany Germany Germany Helsinki Finland Hong Kong Hong kong Istanbul Turkey Jakarta Indonesia Johannesburg South Africa Korea Korea Lisbon Portugal Lima Peru London UK Luxembourg Luxembourg Madrid Spain Milan Italy Nasdaq USA India India New York USA New Zealand New Zealand Osaka Japan Oslo Norway Paris France Sao Paulo Brazil Singapore Singapore Bankok Thailand Shanghai China Shenzhen China Stockholm Sweden Switzerland Switzerland Tel-Aviv Israel Tallinn Estonia Toronto Canada Tokyo Japan Vienna Austria Warsaw Poland Total Average Total World Market Share of Total World Market
Exchange Full Name Euronext nl ASX Euronext be Budapest stock exchange Frankfurt, Deutsche Börse Group. Xetra, Deutsche Börse Group. HEX Hong Kong Stock Exchange Istanbul stock exchange Jakarta stock exchange Johannesburg Stock Exchange Korea stock exchange Euronext pt Bolsa de valores de Lima London stock exchange Luxembourg stock exchange Bolsa de valores de Madrid Borsa Italiana Nasdaq National stock exchange of India NYSE New Zealand Stock Exchange Osaka securities exchange Oslo bors Euronext fr Sao Paulo stock exchange Singapore exchange the stock exchange of Thailand Shanghai stock exchange Shenzhen stock exchange Stockholms borsen Swiss exchange the Tel-Aviv stock exchange Tallinn stock exchange Toronto stock exchange Tokyo stock exchange Wiener borse Warsaw stock exchange
Sel Com Stock 250 250 250 250 250 250 249 201 250 250 250 250 101 178 250 212 27 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 8 250 250 12 250 8488 223
MCap Exch beg 2000 695,196 427,655 184,136 16,980 1,432,167 349,394 609,090 112,716 64,045 180,463 306,128 68,147 12,092 2,855,351 35,939 431,649 728,240 5,204,620 261,133 11,437,597 27,827 91,589 63,695 1,496,938 227,962 198,040 57,177 175,857 142,317 373,278 693,133 63,472 1,868 789,180 4,463,298 33,023 29,577 34,340,967 928,134 35,079,835 97.89%
Trading System LOB Affirmative Dealers 0.67 LOB LOB Affirmative Dealers 0.92 Hybrid Dealer Emph LOB Affirmative Dealers 1.00 LOB Affirmative Dealers 0.70 LOB LOB LOB LOB LOB LOB LOB Affirmative Dealers 0.00 Hybrid Dealer Emph LOB Affirmative Dealers 0.24 LOB Affirmative Dealers 0.00 LOB LOB Affirmative Dealers 1.00 Hybrid Dealer Emph LOB LOB Affirmative Dealers 1.00 LOB LOB LOB Affirmative Dealers 0.00 LOB Affirmative Dealers 0.67 Hybrid Dealer Emph LOB LOB LOB LOB LOB Affirmative Dealers 0.00 LOB Affirmative Dealers 0.00 LOB Hybrid Dealer Emph LOB LOB LOB Affirmative Dealers 0.00 LOB Affirmative Dealers 0.00
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 $US at the start of 2000, and classification of the type of trading system are reported. Sources: World Stock Exchange Federation and Thompson’s DataStream. The total world market capitalization includes member exchanges of the World Securities Exchange Federation.
38
Table II: Ranks of mean per share endogenous variables, daily relative 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 38 world stock exchanges, March 1, 2000 – October 31, 2001. RANK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
TRANSACTIONS COSTS New York 0.001528 Tokyo 0.003299 Amsterdam 0.004113 Nasdaq 0.004125 Australia 0.004244 Milan 0.004246 Lisbon 0.004270 Shenzhen 0.004399 Shanghai 0.004417 Paris 0.004556 India 0.004748 Toronto 0.005002 Osaka 0.006381 Korea 0.007549 Frankfurt 0.008467 Stockholm 0.008584 Switzerland 0.008847 Warsaw 0.009472 Tallinn 0.009508 Vienna 0.009746 Germany 0.010046 Brussels 0.010405 London 0.010850 Johannesburg 0.011391 Singapore 0.013038 Istanbul 0.013733 Oslo 0.013932 Tel-Aviv 0.014914 Hong Kong 0.015146 New Zealand 0.015246 Luxembourg 0.015535 Helsinki 0.015634 Bangkok 0.016477 Budapest 0.017925 Madrid 0.020178 Sao Paulo 0.022016 Lima 0.024783 Jakarta 0.028456
REAL VOLAT x1,000 Nasdaq 0.7974 Korea 0.5055 Frankfurt 0.2886 Germany 0.2531 Istanbul 0.2479 New York 0.1597 Oslo 0.1565 Tokyo 0.1523 Paris 0.1442 India 0.1435 Amsterdam 0.1378 London 0.1214 Budapest 0.1136 Hong Kong 0.1088 Shenzhen 0.1045 Stockholm 0.0968 Osaka 0.0963 Toronto 0.0926 Sao Paulo 0.0828 Milan 0.0824 Warsaw 0.0799 Australia 0.0778 Tel-Aviv 0.0734 Switzerland 0.0654 Johannesburg 0.0598 Shanghai 0.0557 Jakarta 0.0531 Bangkok 0.0522 Tallinn 0.0516 Brussels 0.0503 Singapore 0.0450 Lisbon 0.0418 Luxembourg 0.0269 Vienna 0.0266 New Zealand 0.0263 Lima 0.0250 Helsinki 0.0198 Madrid 0.0172
AVERAGE TRADE SIZE New York 90,850 London 61,656 Amsterdam 60,688 Tokyo 36,659 Nasdaq 21,879 Frankfurt 20,271 Switzerland 17,530 Osaka 15,844 Milan 12,655 Sao Paulo 11,707 Luxembourg 11,455 Stockholm 10,539 Shenzhen 10,454 Germany 9,713 Paris 9,656 Australia 9,610 Johannesburg 7,800 Korea 7,567 Shanghai 6,992 Oslo 5,713 Brussels 5,305 Helsinki 5,018 Singapore 4,332 Tallinn 3,824 Tel-Aviv 3,741 Madrid 3,717 Vienna 3,321 Hong Kong 2,910 Lima 2,483 New Zealand 2,296 Budapest 1,250 Toronto 823 India 792 Warsaw 751 Lisbon 208 Jakarta 60 Bangkok 13 Istanbul 2.67
NUMBER OF TRADES Nasdaq 3962 New York 955 London 646 Korea 486 Paris 312 Tokyo 263 Shanghai 261 Milan 252 Istanbul 228 Toronto 208 Stockholm 201 Amsterdam 193 Shenzhen 170 India 147 Australia 119 Germany 75 Sao Paulo 66 Hong Kong 55 Frankfurt 54 Budapest 45 Johannesburg 44 Osaka 39 Singapore 35 Bangkok 34 Warsaw 34 Jakarta 29 Oslo 27 Switzerland 22 Tel-Aviv 20 Brussels 17 Helsinki 16 Tallinn 8 New Zealand 7 Lima 7 Lisbon 3 Vienna 3 Luxembourg 2 Madrid 2
TRADED VALUE New York 86,734,874 Nasdaq 86,681,271 London 39,837,084 Amsterdam 11,704,952 Tokyo 9,638,673 Korea 3,676,873 Milan 3,194,828 Paris 3,015,209 Stockholm 2,125,927 Shanghai 1,826,374 Shenzhen 1,777,980 Australia 1,143,640 Frankfurt 1,097,636 Sao Paulo 771,900 Germany 732,220 Osaka 611,997 Switzerland 388,043 Johannesburg 340,395 Toronto 170,903 Hong Kong 159,523 Oslo 154,059 Singapore 150,344 India 118,660 Brussels 89,006 Helsinki 80,727 Tel-Aviv 73,773 Budapest 56,042 Tallinn 29,947 Luxembourg 26,260 Warsaw 25,261 Lima 17,304 New Zealand 16,053 Vienna 8,696 Madrid 7,897 Jakarta 1,732 Lisbon 704 Istanbul 610 Bangkok 437
The table reports the means and ranks for five 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, the average daily realized volatility, the average daily trade size, the average daily number of trades, and the average daily traded value expressed in US$ of the day. The exchanges are sorted in the order of lowest to highest trading costs, the highest to the lowest volatility, average trade size, number of trades per stock, and traded value per stock. The tradeweighted 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 250 stocks (or available listed common stocks) using available shares on issue data from Datastream and then aggregated to daily average measures for the exchange and then converted to $US 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 1,334,938 daily intraday summaries, are representative of typical stocks traded on these exchanges, and are directly comparable between exchanges.
39
Table III: The impact of market architectural and institutional feature variables on the log of daily relative trading costs, made up of the sum of the trade value weighted relative effective spread, exchange charges and trading taxes, daily realized volatility, average trade size, and number of trades. ENDOGENOUS IN 2SLS
LN(TRAN COSTS) OLS 2SLS
LN(REAL VOLATY) OLS 2SLS
LN(TRADE SIZE) OLS 2SLS
ln(Trans Costs) ln(Realized Volatility) ln(Av Trade Size) ln (No Trades) EXOGENOUS Lagged Dependent Variable Intercept ln(Market Cap Company) ln(Relative Tick Size)
0.0259 (134.52) -0.0234 (70.02) -0.1286 (-286.03)
0.0489 (62.08) -0.0223 (-48.01) -0.1478 (-239.77)
0.5445 (755.66) -0.3756 (55.97) -0.0330 (90.18) 0.0129 (39.92)
0.5275 (631.23) -0.1846 (18.48) -0.0315 (81.64) 0.0159 (47.87)
0.0149 (42.68)
0.2601 (308.18) -8.1521 (273.29) 0.0636 (44.56) -0.0156 (11.00)
ln(No Companies Listed) ln(GDP) Hours in New York Time Zone ln(Population)
0.2601 (308.18) -8.1521 (273.29) 0.0636 (44.56) -0.0156 (11.00)
LN(NO. TRADES) OLS 2SLS -0.0884 -0.0723 (-124.72) (-65.54)
-0.0417 (-35.90)
0.7506 0.7497 0.8620 (1,309.07) (1,293.71) (1,990.58) -3.8468 -4.8289 -1.2241 (93.72) (105.67) (53.48) 0.0981 0.1002 (154.48) (155.86) 0.0041 -0.0007 -0.0068 (7.60) (1.26) (-22.99) 0.0041 -0.0765 0.0929 (7.60) (31.87) (67.45) 0.4316 0.4528 -0.0022 (130.48) (134.49) (1.30) -0.02196 -0.01767 0.0167 (34.62) (27.36) (47.01) -0.40465 -0.41644 0.0465 (161.95) (164.33) (36.76)
0.1238 0.1336 0.1443 0.1443 (67.67) (70.41) (18.39) (18.39) Iceberg Order Facility 0.0227 0.0142 0.0142 0.0142 (14.34) (8.65) (2.07) (2.07) Upstairs Fac LOB Market -0.0018 -0.0104 0.3159 0.3159 (0.88) (4.96) (36.88) (36.88) Hybrid Mkt (Dealer Emphasis) 0.2931 0.3025 0.6511 0.6511 (103.28) (102.66) (53.57) (53.57) Stocks with Affirmative Dealer -0.0270 -0.0290 -0.0695 -0.0695 (12.74) (13.57) (7.28) (7.28) Market with Exchange Floor -0.2084 -0.2412 (0.19) (-0.24) (55.44) (55.44) (10.92) (55.44) Full Transparency Orderbk Investor -0.0653 -0.0745 -0.4074 -0.4074 (27.41) (30.25) (39.82) (39.82) Partial Transpy Orderbk Investor -0.0450 -0.0439 -0.3437 -0.3437 (29.95) (28.75) (51.31) (51.31) Broker ID Complete Disclosure 0.1260 0.1289 0.6536 0.6536 (41.92) (42.16) (49.06) (49.06) Broker ID Partial Disclosure -0.0572 -0.0519 -0.2278 -0.2278 (31.30) (27.38) (28.48) (28.48) After Hours Trading Facility -0.0986 -0.0888 -0.0741 -0.0741 -0.11364 -0.12288 (61.69) (54.76) (-10.57) (-10.57) (42.73) (45.64) Fragmented Markets 0.027093 0.035329 0.3032 0.3032 0.360495 0.363946 (16.86) (21.54) (43.69) (43.69) (81.53) (81.48) Adjusted R-Squared 0.6325 0.6284 0.1041 0.1041 0.7642 0.7595 Root Mean Sq Error 0.6617 0.6654 2.981 2.981 1.2356 1.248 Number of Observations 1,308,711 Hausman statistic OLS vs. 2SLS 5,120 Student t values shown in brackets with parameter estimates for endogenous variables shown in bold.
0.8652 (1,861.48) -1.3007 (55.96)
-0.0079 (-26.03) 0.0948 (68.62) 0.0033 (1.93) 0.0166 (46.60) 0.0451 (35.54)
Delayed Report Block Trades
0.046054 (31.02) -0.0892 (35.49) 0.8781 0.6966
0.045479 (30.62) -0.09815 (38.38) 0.8781 0.6968
The short-run cross section and time series coefficient estimates during the period, March 1, 2000 – October 31, 2001, using both Ordinary Least Squares (OLS) and Two Stage Least Squares (2SLS) structural equation estimation with partial adjustment towards the desired long-run equilibrium with geometric lags, are displayed. The table reports coefficients and t-values for variables in Equations (5a) to (5d) in a panel data estimation.
40
Table IV: The long-run impact of market architectural and institutional feature variables on the daily relative trading costs, made up of the trade value weighted relative effective spread and exchange fees and taxes, daily realized volatility, average daily trade size and the daily number of trades. ENDOGENOUS
LN(TRANSN COSTS) Coeff Impact
LN(REAL VOLATY) Coeff Impact
LN(TRADE SIZE) Coeff Impact
ln(Trans Costs) ln(Realized Volatility) ln(Av Trade Size) ln (No Trades)
LN(NO.TRADES) Coeff Impact
LN(TRAD VAL) Impact
-0.6275 (61.59) 0.0535 (23.26) -0.0332 (20.00) -0.2890 (105.34)
-0.1816 (20.57)
EXOGENOUS Lagged Depend Variable Intercept ln(Mktcap_Compy) ln(Rel Tick Size)
0.5955 (245.0) -0.8799 (25.15) -0.0862 (49.94) 0.0136 (10.11)
0.9192 -0.0929 0.0304
ln(Comps_Listed)
-0.2078
ln(GDP)
0.0269
Hours in US Time Zone
-0.0413
ln(Popn)
-0.1177
0.2900 (155.5) -11.43 (130.9) 0.1074 (27.22) -0.0410 (11.36)
-11.4308 0.1074 -0.0410
0.8449 (448.3) -17.69 (52.58) 0.3750 (42.14) 0.0168 (3.58) -0.3147 (17.36) 1.6454 (59.27) -0.051 (10.90) -1.469 (64.31)
-15.6121
0.8771 (1,167) -6.3121 (31.73)
0.3555 0.0243 -0.3147 1.6454 -0.0506
-21.3721
0.0541
0.4096
-0.0762
-0.0520
0.7552
0.4405
-0.2820
1.3634
0.1489
0.0983
0.5759
-0.8932
0.2514 0.2595 0.1526 0.1526 -0.0277 -0.1577 (38.89) (8.08) 0.1015 0.0127 -0.0661 Iceberg Order Facility 0.1053 -0.0702 -0.0702 (17.35) (4.47) -0.1139 0.3248 -0.0590 0.0824 Upstairs Fac LOB Market -0.1313 0.3248 (16.80) (16.59) 0.5724 0.7260 -0.1319 -0.3348 0.7260 Hybrid Mkt (Dealer Emphasis) 0.5336 (48.23) (24.73) 0.0079 0.0767 -0.0139 -0.0024 Stocks with Affirm Dealer 0.0038 0.0767 (0.46) (3.08) -0.4634 0.0332 -0.0060 0.2919 Market with Exchange Floor -0.4652 0.0332 (26.67) (0.79) -0.1216 0.0842 0.0607 Full Transpy Odrbk Invest -0.0967 -0.4636 -0.4636 (10.63) (19.95) -0.0673 0.0977 0.0241 Partial Transpy Odrbk Invest -0.0385 -0.5377 -0.5377 (6.88) (33.68) Broker ID Complete Disclose 0.1017 0.1504 0.9090 0.9090 -0.1651 -0.0638 (10.69) (32.46) -0.0758 0.0547 0.0374 Broker ID Partial Disclose -0.0596 -0.3012 -0.3012 (7.79) (14.09) -0.2148 -0.2786 0.4528 After Hours Trad Facility -0.1110 -0.0491 -0.0491 -0.2876 0.3831 (19.56) (3.11) (14.96) (27.23) 0.2639 1.3557 -1.0713 Fragmented Markets -0.01382 0.2659 0.2639 1.4037 -1.0800 (2.27) (16.32) (38.02) (45.86) Adjusted R-Squared 0.6298 0.1025 0.7564 0.8779 Root Mean Sq Error 0.6642 2.9838 1.2561 0.6973 Number of Observations 1,308,711 Hausman statistic OLS vs. 2S 5,120 Dynamic long-run reduced-form impact factors from the solution to the set of four simultaneous equations are displayed in bold Student t values are reported in brackets below the estimated coefficients and adjusted for heteroskedacticity and auto-correlation with a 21 period lag structure using the Newey-West Procedure
-0.1855
Delayed Report Blk Trades
-1.4690
-0.0677 (23.28) 0.7552 (55.60) -0.2820 (19.22) 0.1489 (50.23) 0.5759 (51.64)
-5.7600
-0.0533 0.0234 -0.4667 -0.0163 0.2859 0.1449 0.1218 -0.2290 0.0921 0.1742 0.2844
The equations are estimated using a Generalized Method of Moments (GMM) Newey-West procedure with a 21 period lag structure from the Two Stage Least Squares estimates reported in Table III. 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, has been undertaken using a partial-adjustment geometric lag model. The long-run impact factors making up the reduced form equations as a function of only exogenous variables are computed by solving the four–equation set of simultaneous linear equations. The four endogenous variables are all treated as endogenous wherever they appear as explanatory variables in the 2SLS regressions. All but two coefficients are significant at the 1% level or better.
41
Table V: The long-run impact of market architectural and institutional feature variables on the log of daily trading costs, volatility, average trade size, and number of trades for Quintile 1, made up of the largest global stocks by market capitalization. ENDOGENOUS
LN(TRANS COSTS) Coeff Impact
LN(REAL VOLAT.) Coeff Impact
LN(TRADE SIZE) Coeff Impact
ln(Trans Costs) ln(Realized Volatility) ln(Av Trade Size) ln (No Trades) EXOGENOUS Lagged Depend Variable
0.0618 (13.68) -0.0567 (17.59) -0.2793 (57.77) 0.4798 (90.7) -1.2248 (9.73) -0.0180 (3.78) 0.1251 (29.78)
LN(NO.TRADES) Coeff Impact -0.1591 (6.41)
LN(TRAD VAL) Impact
-0.3593 (17.53)
0.2432
0.0368
0.0040
1.6402
1.8744
-1.9050
-1.1202
0.1105
0.0742
2.2479
1.4326
0.4828 0.0651 -0.0786 0.4940 -0.1811 -0.1811 (18.13) (-3.34) Iceberg Order Facility -0.1226 -0.1131 0.1530 0.1530 -0.0550 0.0195 (8.35) (4.16) 0.7588 0.4812 -0.1729 -0.1160 Upstairs Fac LOB Market 0.7291 0.4812 (25.49) (6.89) 1.0082 0.6396 -0.2298 -0.1541 Hybrid Mkt (Dealer Emphasis) 0.9687 0.6396 (26.67) (8.09) Stocks with Affirm Dealer 0.2090 0.2082 -0.0137 -0.0137 0.0049 -0.0333 (12.85) (0.31) -0.8681 0.0975 0.1354 Market with Exchange Floor -0.8513 -0.2714 -0.2714 (16.15) (2.53) -0.1462 0.2146 0.0174 Full Transpy Odrbk Invest -0.1093 -0.5972 -0.5972 (4.09) (10.46) -0.0845 0.2580 0.0064 Partial Transpy Odrbk Invest -0.0402 -0.7180 -0.7180 (2.55) (18.91) -0.6186 0.0249 -0.0089 0.0987 Broker ID Complete Disclose -0.6201 0.0249 (17.91) (0.35) -0.0938 0.0231 0.0143 Broker ID Partial Disclose -0.0899 -0.0644 -0.0644 (5.94) (1.55) -1.1977 0.4572 1.1640 After Hours Trad Facility -0.8691 -0.3927 -0.3927 0.3161 1.0258 (44.64) (-8.76) (5.51) (16.55) 1.2296 1.0985 -0.0522 -2.8964 Fragmented Markets 0.38951 1.0985 0.3425 -2.8344 (22.11) (26.70) (4.27) (27.32) Adjusted R-Squared 0.5627 0.1108 0.5851 0.8927 Root Mean Sq Error 0.7331 2.5027 0.9002 0.6111 Number of Observations 331,958 Hausman statistic OLSvs.2SLS 3,649 Dynamic long-run reduced-form impact factors from the solution to the set of four simultaneous equations are displayed in bold Student t values are reported in brackets below the estimated coefficients and adjusted for heteroskedacticity and auto-correlation with a 21 period lag structure using the Newey-West Procedure
-0.0135
ln(Rel Tick Size)
0.1147
ln(Comps_Listed)
-0.4714
ln(GDP)
0.4876
Hours in US Time Zone
-0.0288
ln(Popn)
-0.5816
-0.1309 0.0864
Delayed Report Blk Trades
-4.6597
0.9197 (575) 7.8203 (10.68)
0.0029
-0.0370
-5.0485
0.8744 (219.6) -6.47 (8.10) 0.1933 (7.92) -0.0018 (0.13) 0.2341 (5.19) 0.7849 (9.84) -0.036 (3.16) -0.815 (10.54)
3.3554
ln(Mktcap_Compy)
-3.3538
0.2502 (61.4) -5.05 (17.6) -0.1309 (11.75) 0.0864 (9.34)
8.0151
Intercept
0.2403 -0.0328 0.2341 0.7849 -0.0363 -0.8152
0.0567 (5.32) 1.6402 (29.82) -1.9050 (22.78) 0.1105 (9.31) 2.2479 (25.34)
-0.0355 -0.2889 -0.3839 -0.0283 0.2330 0.2320 0.2644 0.0897 0.0374 1.6212 -2.9486
Four equation cross-sectional and time series 2SLS GMM 21 lag estimation takes place during 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 trade-weighted relative effective spread and log of realized volatility, log of trade size and log of the number of trades. All but four coefficients are significant at the 1% level or better.
42
Table VI: The long-run impact of market architectural and institutional feature variables on the log of daily trading costs, volatility, average trade size, and number of trades for Quintile 3, made up of mid-size global stocks by market capitalization. ENDOGENOUS
LN(TRANS COSTS) Coeff Impact
LN(REAL VOLAT.) Coeff Impact
LN(TRADE SIZE) Coeff Impact
ln(Trans Costs) ln(Realized Volatility) ln(Av Trade Size) ln (No Trades) EXOGENOUS Lagged Depend Variable
0.0951 (18.33) -0.0339 (9.40) -0.2553 (45.18) 0.5455 (110.3) 5.4529 (24.42) -0.3893 (33.17) 0.0212 (7.29)
LN(NO.TRADES) Coeff Impact -0.4488 (22.98)
LN(TRAD VAL) Impact
-0.2147 (19.66)
0.9636
-0.0418
-0.0847
0.4335
0.1807
-0.5654
0.5720
0.0939
0.0602
0.7814
-0.1702
0.1016 0.4129 -0.0886 -0.0280 0.0623 0.4129 (5.60) (12.76) Iceberg Order Facility 0.2596 0.2354 -0.2546 -0.2546 0.0547 -0.1165 (16.87) (-6.67) -0.1462 0.4542 -0.0975 0.0850 Upstairs Fac LOB Market -0.1894 0.4542 (12.70) (12.05) 0.2046 0.1593 -0.0342 -0.0850 Hybrid Mkt (Dealer Emphasis) 0.1895 0.1593 (6.56) (2.36) Stocks with Affirm Dealer -0.1212 -0.0514 0.7339 0.7339 -0.1576 0.0544 (-6.92) (14.89) -0.3844 0.2787 -0.0598 0.1844 Market with Exchange Floor -0.4109 0.2787 (14.26) (3.20) -0.0712 0.0620 0.0196 Full Transpy Odrbk Invest -0.0437 -0.2888 -0.2888 (2.88) (7.15) -0.1851 0.0396 0.0752 Partial Transpy Odrbk Invest -0.1675 -0.1846 -0.1846 (13.39) (5.22) 0.2997 0.8529 -0.1831 -0.0981 Broker ID Complete Disclose 0.2186 0.8529 (10.26) (13.62) 0.0789 0.0331 -0.0071 -0.0340 Broker ID Partial Disclose 0.0758 0.0331 (5.58) (0.80) -0.1849 -0.3067 0.3650 After Hours Trad Facility -0.1035 -0.1191 -0.1191 -0.3323 0.3185 (6.74) (-2.88) (-12.60) (14.00) 0.1726 0.8236 -0.6684 Fragmented Markets -0.03386 0.1279 0.1726 0.8607 -0.6835 (2.38) (4.63) (18.70) (17.90) Adjusted R-Squared 0.5195 0.0834 0.5368 0.8038 Root Mean Sq Error 0.5585 2.8128 1.0215 0.6318 Number of Observations 268,123 Hausman statistic OLSvs.2SLS 3,069 Dynamic long-run reduced-form impact factors from the solution to the set of four simultaneous equations are displayed in bold Student t values are reported in brackets below the estimated coefficients and adjusted for heteroskedacticity and auto-correlation with a 21 period lag structure using the Newey-West Procedure
-0.1166
ln(Rel Tick Size)
0.0287
ln(Comps_Listed)
-0.1021
ln(GDP)
0.1058
Hours in US Time Zone
-0.0228
ln(Popn)
-0.1673
-0.5771 -0.0240
Delayed Report Blk Trades
-19.74
0.8277 (433) 0.7609 (2.43)
0.1747
-0.4667
1.6036
0.7529 (133.0) -19.39 (24.29) 0.6650 (18.42) -0.0481 (7.21) -0.2527 (12.40) 1.1374 (31.02) -0.034 (4.76) -0.952 (30.19)
-21.42
ln(Mktcap_Compy)
6.0683
0.2290 (69.2) 1.60 (2.7) -0.5771 (19.32) -0.0240 (3.04)
-1.6866
Intercept
0.7889 -0.0429 -0.2527 1.1374 -0.0337 -0.9517
-0.0323 (-6.35) 0.4335 (24.89) -0.5654 (23.90) 0.0939 (15.89) 0.7814 (37.37)
-0.0619 -0.0125 -0.1192 -0.1031 0.1246 0.0816 0.1148 -0.2812 -0.0411 0.0582 0.1553
Four equation cross-sectional and time series 2SLS GMM 21 lag estimation takes place during 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 trade-weighted relative effective spread and log of realized volatility, log of trade size and log of the number of trades. All but one coefficient is significant at the 1% level or better.
43
Table VII: The long-run impact of market architectural and institutional feature variables on the log of daily trading costs, volatility, average trade size per stock, and number of trades and number of trades per stock for Quintile 5, made up of the smallest global stocks by market capitalization. ENDOGENOUS
LN(TRANS COSTS) Coeff Impact
LN(REAL VOLAT.) Coeff Impact
LN(TRADE SIZE) Coeff Impact
ln(Trans Costs) ln(Realized Volatility) ln(Av Trade Size) ln (No Trades) EXOGENOUS Lagged Depend Variable Intercept ln(Mktcap_Compy) ln(Rel Tick Size)
0.0296 (6.44) -0.0137 (4.71) -0.2537 (44.43) 0.4755 (71.7) -1.0129 (8.02) -0.0786 (11.87) 0.0955 (25.01)
LN(NO.TRADES) Coeff Impact -0.5122 (29.39)
LN(TRAD VAL) Impact
0.0033 (0.19)
4.5777 -0.0782 0.1117
0.2387 (66.7) -12.58 (37.1) 0.1739 (8.99) -0.1669 (14.44)
-12.5756 0.1739
-15.6211 0.3422
0.3825
0.4186
-0.0993
0.9949
3.5144
-0.0641
0.4378
-0.3055
-3.1070
0.8180 -1.2006 -1.2006 0.7825 -0.0039 -0.4190 (33.81) (-17.11) -0.2072 -0.2078 -0.0215 -0.0215 -0.0001 0.1061 (10.68) (-0.38) Upstairs Fac LOB Market 0.3773 -1.2538 -1.2538 0.3402 -0.0041 -0.1932 (17.34) (-17.76) Hybrid Mkt (Dealer Emphasis) 1.1559 1.1212 -1.1709 -1.1709 -0.0038 -0.5920 (30.63) (11.47) Stocks with Affirm Dealer -0.6481 -2.7307 -2.7307 -0.7289 -0.0089 0.3319 (-12.40) (12.51) Market with Exchange Floor -1.6182 -1.5513 2.2629 2.2629 0.0074 0.8288 (17.66) (7.51) Full Transpy Odrbk Invest -0.4813 0.4443 -0.4681 0.4443 0.0015 0.2465 (12.37) (3.82) Partial Transpy Odrbk Invest -0.1728 -0.2129 -1.3546 -1.3546 -0.0044 0.0885 (9.33) (27.06) Broker ID Complete Disclose 0.6765 0.7643 2.9669 2.9669 0.0097 -0.3465 (16.10) (21.76) Broker ID Partial Disclose -0.1430 -0.1058 1.2568 1.2568 0.0041 0.0732 (5.02) (13.77) After Hours Trad Facility -0.1508 -0.0523 0.0399 0.0399 -0.8781 -0.8780 -0.3364 -0.2592 (9.17) (0.76) (-18.47) (11.24) Fragmented Markets 0.08609 0.1099 -1.4898 -1.4898 4.1306 4.1257 -0.4905 -0.5346 (3.22) (-21.08) (35.88) (11.62) Adjusted R-Squared 0.4446 0.1026 0.6818 0.7509 Root Mean Sq Error 0.8199 3.8248 2.0695 0.912 Number of Observations 176,015 Hausman statistic OLSvs.2SLS 1,331 Dynamic long-run reduced-form impact factors from the solution to the set of four simultaneous equations are displayed in bold Student t values are reported in brackets below the estimated coefficients and adjusted for heteroskedacticity and auto-correlation with a 21 period lag structure using the Newey-West Procedure
-0.4229
ln(GDP)
-0.2869
Hours in US Time Zone
0.0094
ln(Popn)
0.1158
Delayed Report Blk Trades Iceberg Order Facility
-0.5179 2.5195 0.5019 -2.8015
-0.0869 (-15.39) 0.4186 (13.52) 0.9949 (22.04) -0.0641 (7.81) -0.3055 (13.39)
-37.7659
-0.0684
-0.0991
0.0674
0.7517 (287) -22.6636 -22.1448 (32.51) 0.0403 -0.1358
ln(Comps_Listed)
-0.1669
0.7273 (174.9) -15.58 (10.74) 0.3416 (14.81) 0.0680 (6.62) -0.5179 (9.23) 2.5195 (29.18) 0.502 (28.12) -2.801 (50.32)
0.1060 -0.1973 -0.5958 0.3230 0.8362 0.2479 0.0841 -0.3368 0.0773 -1.1371 3.5911
Four equation cross-sectional and time series 2SLS GMM 21 lag estimation takes place during 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 trade-weighted relative effective spread and log of realized volatility, log of trade size and log of the number of trades. All but one coefficient is significant at the 1% level or better.
44
TABLE VIII: Summary of nine separate architectural variable interaction effects with trade size. The main effects of trade size, the architectural variable in question, and the interaction effect are reported, together with all the impact factors. NINE MICROSTRUCTURE*SIZE INTERACT Broker ID Partial Disclose ln(Av Trade Size) Main Effect Interaction Broker ID_Partial Dis*Trade_Size Broker ID Partial Disclose Main Effect Broker ID Complete Disclose ln(Av Trade Size) Main Effect Interaction Broker ID_Full Dis*Trade_Size Broker ID Complete Disclose Main Effect Partial Transpy Odrbk Invest ln(Av Trade Size) Main Effect Interaction Part_Trans*Trade_Size Partial Transpy Odrbk Invest Main Effect Full Transpy Odrbk Invest ln(Av Trade Size) Main Effect Interaction Full_Trans*Trade_Size Full Transpy Odrbk Invest Upstairs Fac LOB Market ln(Av Trade Size) Main Effect Interaction Upstairs_Fac*Trade_Size Upstairs Fac LOB Market Delayed Report Blk Trades ln(Av Trade Size) Main Effect Interaction Block_Del*Trade_Size Delayed Report Blk Trades Main Efect Iceberg Order Facility ln(Av Trade Size) Main Effect Interaction Iceberg_Odr*Trade_Size Iceberg Order Facility Main Effect Stocks with Affirm Dealer ln(Av Trade Size) Main Effect Interaction Affirmative_Dealer*Trade_Size Stocks with Affirm Dealer Hybrid Mkt (Dealer Emphasis) ln(Av Trade Size) Main Effect Interaction Dealer_Hyb*Trade_Size Hybrid Mkt (Dealer Emphasis)
LN(TRANSN COSTS) Impact Coeff 0.0564 (12.65) -0.1007 (23.35) 0.8561 (20.57) -0.0423 (54.02) -0.0987 (32.81) 1.1823 (41.55) -0.1736 (119.14) 0.2675 (100.66) -2.2584 (103.75) -0.0723 (86.66) 0.4437 (70.78) -4.2179 (73.14) 0.0212 (17.68) -0.2279 (74.95) 1.8402 (73.13) -0.2445 (92.48) 0.3093 (77.03) -2.2749 (67.94) -0.2445 (92.48) 0.3093 (77.03) 0.2291 (51.96) -0.0476 (57.07) -0.0303 (4.33) 0.2226 (3.39) -0.0646 (75.41) 0.2701 (38.99) -1.8473 (28.86)
-0.0940 0.7705
-0.1058 1.3378
0.2672 -2.3090
0.3572 -3.4855
-0.2105 1.7318
0.3179 -2.3216
0.3179 0.2417
-0.0878 0.7505
0.2518 -1.5788
LN(REAL VOLAT) Impact Coeff
LN(TR SIZE) LN(NO TR) LN(TR VAL) Impact Impact Impact
0.0683
0.0380
0.0541
0.0921
-0.8699
-0.4835
-0.4602
-0.9437
-0.0697 (8.41) 1.5246 (19.45)
-0.0697
-0.0393
0.0589
0.0197
1.5246
0.8592
-0.7060
0.1531
-0.0023 (0.59) -0.4455 (13.36)
-0.0023
-0.0013
-0.1597
-0.1610
-0.4455
-0.2510
1.3487
1.0976
-0.6734 (42.32) 5.7022 (38.44)
-0.6734
-0.3795
-0.2650
-0.6445
5.7022
3.2134
2.5188
5.7323
0.1712 (32.05) -1.0670 (22.21)
0.1712
0.0965
0.1361
0.2326
-1.0670
-0.6013
-1.0989
-1.7002
0.1019
0.0574
-0.1847
-0.1273
-0.5548
-0.3126
1.3586
1.0459
0.1019 (31.75) 0.1494 (14.75)
0.1019
0.0574
-0.1847
-0.1273
0.1494
0.0842
-0.1368
-0.0526
-0.5689 (33.63) 5.2241 (32.93)
-0.5689
-0.3206
0.0181
-0.3025
5.2241
2.9440
-0.1329
2.8111
-0.1640 (9.41) 2.4046 (14.76)
-0.1640
-0.0924
-0.1613
-0.2537
2.4046
1.3551
1.1032
2.4583
0.0683 (32.47) -0.8699 (42.51)
0.1019 (31.75) -0.5548 (21.44)
Table IX: Predictions of relative trading cost and average traded value performance per stock, including best practice for 38 world exchanges and based on overall impact factors, Table IV.
45
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Traded Value Architectural Score All impact var $USM Architect variables only Best Practice 199.3640 Best Practice 4.7391 New York 86.7349 Tokyo 2.7863 Nasdaq 32.9891 Osaka 2.4977 Tokyo 12.4702 Korea 2.4499 London 9.8885 Luxembourg 2.2100 Frankfurt 7.8063 Helsinki 2.1232 Osaka 4.8257 Johannesburg 2.0652 Toronto 4.2007 New York 2.0618 Paris 2.5257 Oslo 1.9406 Germany 2.0310 Brussels 1.9107 Madrid 1.8527 Paris 1.8698 Amsterdam 1.0619 Australia 1.8649 Australia 1.0318 New Zealand 1.8437 Switzerland 0.9802 London 1.8339 Milan 0.8576 Shanghai 1.8298 Shanghai 0.6762 Shenzhen 1.8222 Korea 0.6318 Warsaw 1.7538 Brussels 0.5724 Frankfurt 1.7271 Shenzhen 0.5161 Istanbul 1.7190 Sao Paulo 0.3978 Amsterdam 1.7163 Stockholm 0.3625 Bangkok 1.6601 Johannesburg 0.2738 India 1.6437 Hong Kong 0.2344 Toronto 1.6321 Singapore 0.1608 Lisbon 1.6289 Oslo 0.1606 Switzerland 1.6189 Luxembourg 0.1474 Jakarta 1.5884 Tel-Aviv 0.1439 Vienna 1.5796 Helsinki 0.1160 Madrid 1.5573 Vienna 0.0957 Tel-Aviv 1.5408 India 0.0940 Hong Kong 1.4969 Bangkok 0.0536 Budapest 1.4733 New Zealand 0.0531 Stockholm 1.4662 Warsaw 0.0479 Singapore 1.3941 Lima 0.0344 Germany 1.3412 Jakarta 0.0326 Sao Paulo 1.3029 Lisbon 0.0262 Milan 1.2037 Budapest 0.0156 Tallinn 1.1952 Tallinn 0.0016 Lima 1.1819 Istanbul 0.0013 Nasdaq 1.0086
Tr Cost Volatility Tr Size Tr No US$ х 1,000 US$M 0.000708 0.0844 0.0914 2,182 0.002322 0.1043 0.0423 295 0.003118 0.1517 0.0245 197 0.004417 0.2057 0.0056 112 0.007077 0.0328 0.0074 20 0.006230 0.0437 0.0033 35 0.005305 0.0324 0.0050 55 0.001528 0.1597 0.0909 955 0.006635 0.0783 0.0042 38 0.004730 0.1017 0.0073 79 0.003271 0.1244 0.0114 222 0.004063 0.0361 0.0057 182 0.007979 0.0365 0.0022 24 0.003286 0.0685 0.0469 211 0.003626 0.0745 0.0033 203 0.004027 0.0725 0.0035 148 0.006905 0.0412 0.0008 59 0.003451 0.1480 0.0599 130 0.004557 0.0966 0.0000 258 0.004168 0.1477 0.0089 119 0.007230 0.0318 0.0004 120 0.005090 0.0800 0.0005 183 0.003919 0.1345 0.0183 229 0.009530 0.0579 0.0008 31 0.005195 0.1236 0.0146 67 0.009562 0.0923 0.0006 50 0.010210 0.0536 0.0049 20 0.005160 0.1064 0.0331 56 0.008045 0.0422 0.0028 52 0.006069 0.0805 0.0042 56 0.015045 0.0832 0.0009 17 0.006917 0.0880 0.0051 70 0.008407 0.0423 0.0060 27 0.006781 0.1216 0.0351 58 0.007310 0.0980 0.0031 128 0.007719 0.0895 0.0121 71 0.028799 0.0615 0.0009 2 0.008553 0.0416 0.0003 104 0.004004 0.2794 0.0468 705
The actual average and predicted values for New York are precisely the same due to normalization.
46
Table X: Rankings of predicted average traded value per stock relative to best-practice for all exchanges using all impact variables and only architectural variables for large, medium and small stock size quintiles, based on the long run impact factors presented in Tables V to VII.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Large Stocks Traded Value All impact var $USM Rank, Arch var only Best Practice 261.23 Best Practice 11.720 New York 137.66 London 7.922 Nasdaq 132.53 Toronto 6.525 Australia 112.81 Bangkok 6.336 London 67.52 Australia 6.227 Bangkok 36.18 New York 6.176 Tokyo 25.01 Korea 5.303 Paris 22.39 Helsinki 4.991 Toronto 16.19 Tokyo 4.886 India 11.58 New Zealand 4.850 Shanghai 10.16 Tallinn 4.391 Amsterdam 9.26 Sao Paulo 4.361 Osaka 6.01 Milan 4.260 Lima 6.00 Osaka 3.747 Milan 5.79 Oslo 3.673 Sao Paulo 5.08 Lisbon 3.651 Shenzhen 4.63 Brussels 3.579 Tel-Aviv 3.86 Paris 3.559 Brussels 3.16 Amsterdam 3.477 Korea 2.83 Nasdaq 3.236 Hong Kong 2.21 Johannesburg 1.657 Switzerland 1.99 Tel-Aviv 1.293 Johannesburg 1.94 Singapore 1.281 Frankfurt 1.81 Luxembourg 1.189 Warsaw 1.52 Frankfurt 1.174 Stockholm 1.29 Lima 1.137 Singapore 1.20 Madrid 1.010 Oslo 0.92 India 1.006 New Zealand 0.88 Stockholm 0.987 Madrid 0.86 Shenzhen 0.985 Helsinki 0.79 Shanghai 0.985 Germany 0.68 Jakarta 0.981 Jakarta 0.55 Vienna 0.970 Lisbon 0.38 Switzerland 0.963 Luxembourg 0.20 Warsaw 0.949 Vienna 0.09 Germany 0.883 Budapest 0.09 Budapest 0.876 Tallinn 0.01 Hong Kong 0.760
Medium Size Stocks Traded Value All impact var $USM Rank, Arch var only Best Practice 238.608 Best Practice 4.674 New York 86.735 Tokyo 3.061 Nasdaq 29.762 Osaka 2.785 London 21.345 Korea 2.721 Tokyo 16.469 Budapest 2.712 Frankfurt 12.332 Luxembourg 2.310 Paris 3.929 Switzerland 2.192 Madrid 3.323 Johannesburg 2.122 Osaka 2.949 Jakarta 2.113 Toronto 2.454 London 1.997 Switzerland 2.373 Helsinki 1.965 Amsterdam 1.961 Shanghai 1.933 Milan 1.805 Shenzhen 1.920 Germany 1.587 Istanbul 1.898 Shanghai 1.433 Oslo 1.893 Shenzhen 1.063 India 1.856 Sao Paulo 0.728 Stockholm 1.855 Australia 0.727 Tel-Aviv 1.850 Brussels 0.582 Warsaw 1.850 Stockholm 0.380 Sao Paulo 1.816 Korea 0.370 Paris 1.803 Johannesburg 0.369 Australia 1.790 India 0.169 Vienna 1.784 Hong Kong 0.097 Amsterdam 1.782 Luxembourg 0.079 Brussels 1.720 Tel-Aviv 0.076 Bangkok 1.709 Oslo 0.075 Madrid 1.700 Singapore 0.066 New York 1.699 Lima 0.052 Germany 1.651 Helsinki 0.049 Frankfurt 1.636 Vienna 0.036 Lima 1.623 Jakarta 0.035 New Zealand 1.601 Istanbul 0.035 Singapore 1.581 Warsaw 0.031 Tallinn 1.578 Bangkok 0.028 Nasdaq 1.576 Budapest 0.028 Lisbon 1.548 New Zealand 0.020 Hong Kong 1.515 Tallinn 0.008 Toronto 1.485 Lisbon 0.003 Milan 1.479
Small Stocks Traded Value All impact var $USM Rank, Arch var only Best Practice 668.566 Best Practice 13.648 New York 57.922 Frankfurt 4.380 Frankfurt 10.846 Luxembourg 2.531 Nasdaq 9.545 Johannesburg 2.439 Toronto 0.856 Vienna 2.083 Germany 0.435 Switzerland 1.885 London 0.348 India 1.841 Madrid 0.312 Warsaw 1.764 Tokyo 0.249 Madrid 1.714 Osaka 0.131 Shanghai 1.563 Switzerland 0.025 Shenzhen 1.555 Paris 0.012 Istanbul 1.554 Stockholm 0.011 Budapest 1.504 Luxembourg 0.010 Jakarta 1.471 Sao Paulo 0.009 Singapore 1.435 Korea 0.007 Germany 1.378 Milan 0.006 Stockholm 1.324 Amsterdam 0.006 Tel-Aviv 1.310 Brussels 0.005 Hong Kong 1.295 Johannesburg 0.005 Lima 1.192 Vienna 0.004 New York 1.182 Singapore 0.002 Brussels 0.746 Shanghai 0.001 Tokyo 0.693 Hong Kong 0.001 Paris 0.675 Shenzhen 0.001 Osaka 0.648 Australia 0.001 Amsterdam 0.621 Oslo 0.001 Korea 0.565 Helsinki 0.001 Oslo 0.555 Tel-Aviv 0.000 London 0.505 New Zealand 0.000 Milan 0.495 Lisbon 0.000 Helsinki 0.485 Lima 0.000 New Zealand 0.480 Jakarta 0.000 Lisbon 0.449 India 0.000 Bangkok 0.440 Budapest 0.000 Sao Paulo 0.389 Warsaw 0.000 Australia 0.388 Bangkok 0.000 Toronto 0.350 Tallinn 0.000 Tallinn 0.347 Istanbul 0.000 Nasdaq 0.313
Note that Istanbul has been excluded from the rankings of the largest quintile due to an underrepresentation of large global stocks. In addition, the predicted traded values have been normalized in such a way that the actual and predicted values for New York are the same.
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Table XI: Architectural features of best-practice, top-ranked and representative exchanges for the entire dataset with best-practice defined by the highest impact factors for traded value per stock in Table IV. Exchange Best Practice Architectural Score 4.7391 Predicted Traded Value US$M 199.4 Predicted No. of Trades 2,182 Predicted Trade Size US$M 0.0914 Predicted Trading Cost $ 0.00070812 Architectural Features in Score 0 Block Delay Dummy 1 Investor Full Transparency 1 Investor Partial Transparency a Iceberg Orders 0 1 After Hours Facility 0 Dealer Hybrid 0 Affirmative Dealer Stock 1 Floor Trading Exchange 0 Broker ID Full Disclosure 1 Broker ID Partial Disclosure b Upstairs Dealer LOB Market 1 0.00000109 Relative Minimum Tick Excluded from Architectural Score 1 Fragmentation Dummy Av Market Cap Listed Comp $M 39,254 No. of Listed Companies 3,025 GDP $M 9,290,375 Population M 276 Hours in New York Time Zone 6.5
Tokyo 2.7863 12.5 295 0.0423 0.00232209
New York 2.0618 86.7 955 0.0909 0.00152831
Australia NASDAQ 1.9670 1.0086 1.1 33.0 195 705 0.0056 0.0468 0.00367037 0.00400377
0 0 1 0 1 0 0 0 0 1 0 0.00000750
1 0 0 0 1 0 1 1 0 1 0 0.00076167
1 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0.00279479 0.00109172
1 7,924 1,935 2,941,674 127 0
1 39,254 3,025 9,290,375 276 6.5
0 2,387 1,287 414,360 19 0
1 15,746 4,829 9,290,375 276 6.5
a
Note that while the ASX has an iceberg order facility it differs significantly in terms of time preference from all the other exchanges with such a facility. Hence, this attribute dummy has been assigned a 0 value, both in the regressions, rankings and above.
b
The NYSE does have both a limit order book 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.
48
Figure 1: Examining the most fundamental of the endogenous variables first, utilizing
Table IV, a 72.6 percent rise in volatility will be the consequence if NASDAQ replaces a limit order book market. There will be two adverse consequences. Relative transaction costs will rise 5 percent and trade size will fall 18 percent for each doubling of volatility. Hence, volatility is unambiguously bad in this framework as it reduces market depth. The fall in trade size will deny economies of scale to the trading process leading to a further rise in trading costs at the rate of 3 percent. These cumulative rises in trading costs decrease trade numbers at the rate of 63 percent and the decline in trade numbers denies even more scale economies to the trading process leading to an even greater adverse rise in trading costs at the rate of 29 percent. This process continues until we reach a new equilibrium. Volatility rises 73 percent, transaction costs rise 57 percent. Trade size falls 13 percent, number of trades, 33 percent and traded value, 47 percent.
TRANS COST
-0.63
-0.29
0.05
-0.03
σ NO. TRADES VOLATILITY
σ -0.18
TRADED VALUE
49
TRADE SIZE
Figure 2: 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 resulting fall in transaction costs shifts trading costs down, resulting in a movement to the right around the constant elasticity demand curve. The movement to best practice leaves trade size virtually unaffected so that the same traded value schedule describes the new and far higher traded value prediction shown on the RHS vertical axis. The relationships are dawn to scale. 0.00300
Traded Value $M
Trading Cost $
0.00250 Best-Practice and Actual Traded Value Schedule
0.00200
Best-Practice Traded Value
300
250
200
Demand Schedule
NYSE Trading Cost
0.00150
150
NYSE Traded Value
0.00100
100
Best-Practice Trading Cost
0.00050
50
Number of Trades
0
50 5 60 5 70 5 80 5 90 10 5 0 11 5 0 12 5 0 13 5 0 14 5 0 15 5 0 16 5 0 17 5 0 18 5 0 19 5 0 20 5 0 21 5 0 22 5 0 23 5 0 24 5 0 25 5 05
0.00000
50
