Actual markets do not conform to simple structures. Indeed, they typically involve more than one structure. What is important, therefore, is not the operation of any specific trading mechanism, but rather the rules by which trades occur. These rules dictate what can be traded, who can trade, when and how orders can be submitted, who may see or handle the order, and how orders are processed. The rules determine how market structures work, and thus how prices are formed. Since rules can affect the behaviour of prices, liquidity might also naturally depend on how a market is structured. Indeed, liquidity concerns may dictate the structure of the market. Drawing on the extensive body of research investigating the interaction between market structure and liquidity, the remainder of this paper focuses on two critical issues in the creation of liquidity: the impact of limit orders, and the effects of transparency.

Limit orders A wide variety of order types are found in securities markets. The most familiar type is a market order to buy or sell one round lot at the prevailing price. Other orders, such as “market-at-close”, “fill-or-kill” and “immediate-or-cancel” allow traders to control the timing, quantity or execution of their trades. By far the most common alternative type of order is a limit order specifying a price and a quantity at which a trade is to transact. Limit orders specify a price either above the current ask or below the current bid and await the movement of prices to become active. If the market is rising, the upward price movement triggers limit orders to sell; if the market is falling, the downward movement triggers limit orders to buy. Limit orders thus provide liquidity to the market. Limit order traders receive a better price than they would have if they had submitted a market order, but face the risk of non-execution and a winner’s curse problem. Whereas a market order executes with certainty, limit orders await the movement of prices to become active, ie a limit order is held in a “book” until either a matching order is entered or the order is cancelled. Moreover, because once posted their prices do not respond to the arrival of new information, limit orders are more likely to be executed when they are mispriced. Foucault (1999) finds that in deciding whether to submit a market order or post a limit order, traders’ main consideration is the volatility of an asset. In a volatile market, the probability of mispricing an asset is higher, and so limit order traders quote relatively wide bid-ask spreads. This raises the cost of market order trading, thereby increasing the incentive to use limit orders rather than market orders. But as a result of fewer market orders, the execution risk associated with limit orders increases. Order size may also influence investors’ choice between market and limit orders. Seppi (1997) concludes that small retail and large institutional investors prefer hybrid markets such as the NYSE, where specialists compete with limit orders to execute market orders.4 Mid-size investors, on the other hand, might prefer pure limit order markets such as electronic exchanges. According to Seppi, specialists will undercut limit order prices at the margin. Such undercutting lowers the probability that limit orders will execute, thus resulting in reduced depth in the book. Evidence in Sofianos (1995) of a U-shaped relationship between specialists’ total revenue and trade size suggests that specialists do indeed provide relatively more liquidity to small and large trades. The composition of order flows is a dynamic process, with investors’ preferred order type changing in response to developments over time. Goldstein and Kavajecz (2000) examine the behaviour of liquidity providers on the NYSE during periods of extreme volatility. They find that following a precipitous drop in equity prices, traders abandoned limit orders in favour of floor brokers. In particular, whereas specialists maintained narrow spreads and normal depth, liquidity drained out of the limit order book. Similarly, in foreign exchange markets, trading tends to move from electronic ordermatching markets to dealer markets during periods of market stress. Such dynamics raise the question of whether dealer markets handle information more efficiently than pure limit order markets. Another issue relating to limit orders is whether they can provide enough liquidity for every type of trade. The experience of limit order markets suggests not. For example, on the NYSE, the Toronto Stock Exchange and other exchanges with features of limit order markets, a substantial proportion of block trades - trades of 10,000 shares or more - are submitted to block traders or “upstairs market makers”, who form a syndicate of buyers to take the other side of the trade. One reason for using block traders rather than limit orders is that large transactions might be interpreted as signalling new information, and so move prices against the seller. Limit order systems are constantly evolving as new technologies are developed, and indeed OptiMark designed an electronic trading system that was supposed to minimise the impact that large orders had on price. OptiMark’s system ensured that orders remained anonymous until executed in full and was initially lauded as presaging the transformation of institutional trading. Despite the system’s advantages, however, it was poorly received by brokers and OptiMark ran into financial difficulties in mid-2000. Finally, there is the question of how much information about the limit order book is optimal. On the NYSE and a number of other exchanges, orders held in the specialist’s book are not common knowledge, although the specialist may choose to allow traders to view the book. By contrast, on electronic exchanges the order limit book is usually transparent. Madhavan and Panchapagesan (2000) find that on the NYSE the ability to observe the evolution of the book conveys valuable information to the specialist. In particular, specialists use information from the order book to set a more efficient opening price than the price that would prevail if all orders - both market and limit orders - were considered. Coppejans and Domowitz (1999) examine a pure limit order market and conclude that the trading process is influenced only by the flow of orders, not the stock of orders on the book. The book is not irrelevant; flows, after all, are changes in stocks. But in a market with an open book, the book per se does not appear to contain information on the value of the asset being traded. While helping us to understand how price formation occurs in actual markets, the results of these empirical studies do not imply that one particular market structure provides for more efficient price discovery than another. The experimental methods discussed below offer more meaningful insight into such hypothetical questions. Transparency As the information-based microstructure models demonstrated, the information available in the trading process can affect the trading strategies of market participants. It thus follows that the market equilibrium depends on the degree of transparency, ie the ability of market participants to observe the information in the trading process. Consider the previous discussion of the limit order book. If the book were known only to the market maker (as on the NYSE), then the market maker, as well as the informed and uninformed traders, would behave differently than if the book were common knowledge (as in the market examined by Coppejans and Domowitz). The openness of the book is but one of many differences in the degree of transparency across markets. The breadth of trade data reported and even the timeliness of the reported data can also differ tremendously. Some markets such as bond dealers provide only pre-trade information, meaning that quote data are made available but not transactions data. Other markets require post-trade transparency, ensuring that the price and quantity of trades are observable. The NYSE and NASDAQ, for example, are required to report immediately all quotes and trades. At the other extreme, trades handled “off board” - trades executed outside of the United States after US markets close - need not even be acknowledged. Differences in transparency may play a significant role in the creation of liquidity. As a factor in traders’ strategic decisions, transparency can influence their willingness to participate in the trading process. In 6 BIS Papers No 2 the United Kingdom, for example, the Financial Services Authority allows the reporting of large trades to be delayed for a period of time because it believes that immediate disclosure would expose market makers to undue risk as they unwound their positions and so discourage them from providing liquidity. Transparency is also a crucial consideration in the competition among markets for trading volume, and thus in the prospects for further fragmentation of liquidity. Bloomfield and O’Hara (1999, 2000) use laboratory experiments to address some of these issues. Their experiments include multiple dealers operating under varying degrees of transparency and traders with differing trade motivations. A key finding is that low-transparency dealers are more likely to set the highest bid and the lowest ask (inside quotes) in early rounds of trading, in order to capture more order flow (Graph 3). The information learned from the order flow allows low-transparency dealers to quote narrower spreads than their more transparent competitors and to avoid money-losing trades. This informational advantage declines with repeated rounds of trading because lowtransparency dealers reveal their information through their choices of quotes. Moreover, as trade progresses and individual dealers learn from trade outcomes, spreads for all dealers decline (Graph 3). Trading gains follow a pattern similar to spreads. Wide spreads in early rounds result in large gains because traders in need of liquidity are forced to buy at high prices and to sell at low prices (Graph 4). Gains then decline in concert with the decline in spreads. Notably, neither high- nor low-transparency dealers earn money at outside quotes, ie bids that are lower than the highest bid and asks that are higher than the lowest ask. Even though trades at outside quotes are executed at more favourable prices, dealer profits are eliminated by the higher likelihood of transacting with an informed trader. At inside quotes, the proportion of total trades coming from informed traders is approximately 10%, but at outside quotes, the proportion rises to 70% (Graph 4). Liquidity traders’ preference to transact at the best available quote results in this higher degree of adverse selection at outside quotes. Interestingly, traders do not behave strategically in these experiments. Concern about the possible impact of a trade in one round on prices in future rounds might be expected to lead traders to pay a premium to conceal their trades by trading with low-transparency dealers. Recall that this concern was one of the motivations behind the design of the OptiMark trading system. As Graph 4 shows, however, informed traders are as equally likely to transact with low-transparency dealers as with hightransparency dealers. In this setting, transparency seems to have a greater impact on dealer behaviour than on trader behaviour.