market microstructure a practitioner’s guide.pdf

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Market Microstructure: A Practitioner’s GuideAnanth Madhavan

Knowledge of market microstructure—how investors’ latent or hiddendemands are ultimately translated into prices and volumes—has grownexplosively in recent years. This literature is of special interest topractitioners because of the rapid transformation of the marketenvironment by technology, regulation, and globalization. Yet, for the mostpart, the major theoretical insights and empirical results from academicresearch have not been readily accessible to practitioners. I discuss thepractical implications of the literature, with a focus on price formation,market structure, transparency, and applications to other areas of finance.

tudies of market microstructure analyze theprocess by which investors’ latent demandsare translated into executed trades. Interestin market microstructure is not new. De la

Vega’s (1688) description of trading practices, mar-ket manipulations, futures, and options trading onthe Amsterdam stock exchange remains a classic inthis field. Interest in markets and trading hasincreased enormously in recent years, however,because of the rapid structural, technological, andregulatory changes affecting the securities industryworldwide. Beyond immediate concerns abouttrading and market structure, microstructure hasconsiderable relevance in general for finance prac-titioners. Specifically, a central concept in micro-structure is that asset prices need not equal full-information expectations of value because of a vari-ety of frictions. Thus, the study of market micro-structure is closely related to the field ofinvestments, which studies the fundamental valuesof financial assets, and is of interest to portfoliomanagers and investment advisors. Moreover, dis-crepancies between price and value affect the leveland choice of corporate financing, providing a linkto the field of corporate finance.

Knowledge of microstructure has grown explo-sively in recent years as complex new models havebeen developed and rich intraday data from a vari-ety of sources have become available. Despite theirpractical value, however, many important theoreti-cal insights and empirical results from academicresearch are not readily accessible to practitioners.A sample of such topics would include models topredict transaction costs for traders or portfoliomanagers, limit-order models for intraday trading

strategies or automated market making, liquidity asa factor in asset returns and in portfolio risk, expla-nations of return anomalies associated with peri-odic index reconstitution, the effect displaying thelimit-order book may have on liquidity and volatil-ity, the choice between automated or floor tradingsystems, determinants of international capital mar-ket segmentation, and the link between pricing ofinitial public offerings (IPOs) and the activity ofsecondary-market dealers.

This article provides a practitioner-orientedreview of the literature to complement academicsurveys by Biais, Glosten, and Spatt (2002), Lyons(2001), Harris (2001), Madhavan (2000), Keim andMadhavan (1998), and O’Hara (1995). Any surveymust be selective, especially for the microstructureliterature, which comprises literally thousands ofresearch articles spanning decades. My 2000 paperprovides a more complete set of citations than I dohere. I should also emphasize that this article is nota survey of topics under current debate (e.g., Super-Montage). Such topics change frequently andreceive comprehensive coverage in press andindustry publications. Rather, I provide a selectivereview of the academic literature, with an emphasison the modern line of thought that focuses on infor-mation. The objective is to provide the reader witha conceptual framework that will prove valuable inattacking a variety of practical problems, both cur-rent and future.

The article is organized around four topics thatroughly correspond to the historical evolution ofmicrostructure: • first, price formation and price discovery—

including both static issues (such as the deter-minants of trading costs) and dynamic issues(such as the process by which prices come toimpound information over time). The goal is to

Ananth Madhavan is managing director at ITG Inc.,New York.

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look inside the “black box” by which latentdemands are translated into realized prices andvolumes;

• second, market structure and design issues,including the relationship between price for-mation and trading protocols. The focus is onhow various rules affect the black box and,hence, liquidity and market quality;

• third, information, especially market transpar-ency (i.e., the ability of market participants toobserve information about the trading pro-cess). This topic deals with how revelations ofthe workings of the black box affect the behav-ior of traders and their strategies;

• fourth, the interface of market microstructurewith corporate finance, asset pricing, and inter-national finance. Models of the black box pro-vide fresh perspectives on such topics as IPOunderpricing, portfolio risk, and foreignexchange movements.

Price Formation and DiscoveryPrice formation, the process by which prices cometo impound new information, is a fundamentaltopic in microstructure.

The Crucial Role of Market Makers. By vir-tue of their role as price setters, market makers area logical starting point for an exploration of theblack box within which a security market actuallyworks. In the traditional view, market makers pas-sively provide “immediacy,” the price of which isthe bid–ask spread. (Note that “spread” here refersnot solely to quoted bid–ask spreads—which havebeen typically small since decimalization in the U.S.market—but to effective spreads—that is, the truecost of a round-trip transaction for an average-sized trade.) Early empirical research confirmedthat effective bid–ask spreads are lower in higher-volume securities because dealers can achievefaster turnaround in inventory, which reduces theirrisk. Spreads are wider for riskier and less liquidsecurities. Later research provided a deeper under-standing of trading costs by explaining variation inbid–ask spreads as part of intraday price dynamics.This research showed that market makers are notsimply passive providers of immediacy but mustalso take an active role in price setting to rapidlyturn over inventory without accumulating signifi-cant positions on one side of the market. Exhibit 1illustrates this literature with a description of “Gar-man’s Logic.”

Price may depart from expectations of value ifthe dealer is long or short relative to desired (target)inventory, giving rise to transitory price move-

ments during the day—and possibly over longerperiods. This intuition drives the models of inven-tory control developed by, among others, Madha-van and Smidt (1993). Figure 1 illustrates a typicalinventory model. As the dealer trades, the actualand desired inventory positions diverge, whichforces the dealer to adjust prices, lowering them ifthe position is long and raising them if it is shortrelative to desired inventory. Because setting pricesaway from fundamental value will result inexpected losses, inventory control implies the exist-ence of a bid–ask spread even if actual transactioncosts (i.e., the physical costs of trading) are negligi-ble. The spread is the narrowest when the dealer isat the desired inventory; it widens as inventorydeviations grow larger.

The model has some important practical impli-cations. First, dealers who are already long may bereluctant to take on additional inventory withoutdramatic temporary price reductions. Thus, priceeffects become progressively larger following asequence of trades on one side of the market. This

Exhibit 1. Garman’s Logic

Garman (1976) showed that dealer inventory must affectstock prices. The intuition can be easily explained with asimple example. Consider a pure dealer market where amarket maker, with finite capital, takes the opposite side ofall transactions. Suppose for the sake of argument that themarket maker sets price to equate demand and supply, sobuys and sells are equally likely. Consequently, inventory isequally likely to go up or down (i.e., it follows a random walkwith zero drift). Whereas inventory has zero drift, however,the variance of inventory is proportional to the number oftrades. Intuitively, if you flip a coin and win a dollar on headsand lose a dollar on tails, your net expected gain is zero,although your exposure is steadily increasing with the num-ber of coin flips. But if dealer capital is finite, eventual marketfailure is certain because the dealer’s long or short positionwill eventually exceed capital. Thus, to avoid such “ruin,”market makers must actively adjust price levels in relation toinventory.

Figure 1. Price and Deviation from Desired Inventory

Price

Ask Price

Bid Price

Midquote

0Deviation from Desired Inventory

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consideration is important for institutional traders,who typically break up their block trades over sev-eral trading sessions.

The second implication is that inventory con-siderations are also likely to affect market-impactcosts (i.e., price movements associated with trad-ing), which will be greater toward the end of the daybecause market makers must be compensated forbearing overnight risk. Indeed, Cushing andMadhavan (2001) documented significant increasesin market-impact costs at the close of the day. Fur-thermore, significant return reversals occur over-night and the subsequent day following indicationsof order imbalances in the closing period. Intu-itively, the concessions demanded by dealers aretemporary, so large price reversals from the close tothe open should occur once market makers havehad a chance to lay off excess inventory in othermarkets or hedge their risk. These transitory inven-tory effects represent a significant hidden tradingcost for traders who use market-on-close orders.

Third, because inventory effects are related tothe degree to which dealers are capital constrained,larger inventory effects might be observed for thesmaller dealers with less capital.

Finally, inventory models provide an addedrationale for reliance on dealers. Specifically, just asphysical marketplaces consolidate buyers and sell-ers in space, the market maker can be viewed as aninstitution to bring buyers and sellers together intime through the use of inventory. A buyer neednot wait for a seller to arrive but may simply buyfrom the dealer, who depletes his or her inventory.

Inventory is not the only consideration for adealer. An influential paper by Jack Treynor (pub-lished under the pseudonym of Walter Bagehot in1971) suggested the distinction between liquidity-motivated traders (who possess no special informa-tional advantages) and informed traders (who pos-sess private information about future value). Themarket maker loses to informed traders, on aver-age, but recoups the losses on trades with liquidity-motivated (“noise”) traders. Models of this type(asymmetric-information models) include those ofGlosten and Milgrom (1985) and Easley andO’Hara (1987). Exhibit 2 illustrates the Glosten–Milgrom arguments about asymmetrical informa-tion and bid–ask spreads.

Asymmetric-information models have impor-tant implications: (1) In addition to inventory andorder-processing components, the bid–ask spreadcontains an informational component because mar-ket makers must set a spread to compensate them-selves for losses to informed traders. (2) Without

noise traders, dealers would not be willing to pro-vide liquidity and markets would fail. (3) Given thepractical impossibility of identifying informed trad-ers (who are not necessarily insiders), prices adjustin the direction of money flow.

Empirical evidence on the extent to whichinformation traders affect the price process is com-plicated by the difficulties of identifying the effectsexplicitly because of asymmetrical information.

Both inventory and information models pre-dict that order flow will affect prices—but for dif-ferent reasons. In the traditional inventory model,order flow affects dealers’ positions and dealersadjust prices accordingly. In the information model,order flow acts as a signal about future value andcauses a revision in beliefs. Stoll (1989) proposed amethod to distinguish the two effects by usingtransaction data. But without inventory data, theresults of such indirect approaches are difficult toverify. Madhavan and Smidt developed a dynamic-programming model that incorporates inventory-control and asymmetric-information effects. Themarket maker acts as a dealer and as an activeinvestor. As a dealer, the market maker quotesprices that induce mean reversion toward inven-tory targets; as an active investor, the market makerperiodically adjusts the target levels toward whichinventories revert. The authors estimated the modelwith daily specialist inventory data and found evi-dence of both inventory and information effects.

Inventory and information effects also explainwhy “excess” volatility might be observed, in thesense that market prices appear to move more oftenthan is warranted by “fundamental” news aboutinterest rates, dividends, and so on. An interestingexample is provided in Exhibit 3.

Exhibit 2. Post-Trade Rationality and Bid–Ask Spreads

That bid–ask spreads contain a component attributable toasymmetrical information can easily be proved. Consider anextreme example with no inventory or transaction costs butin which some traders have information about future assetvalues. Based on public information, the dealer believes thatthe stock is worth $30. This dealer, however, is “post-traderational”; that is, if a trader buys 100 shares, the dealer knowsthat the probability that the asset is undervalued is greaterthan the probability it is overvalued. Why? Because informedtraders only participate on one side of the market. Suppose,for the sake of exposition, that (given that the dealer observesa buy of 100 shares) the expected asset value is $30.15, andassume, symmetrically, that the expected value of a sell of 100shares is $29.85. A post-trade-rational dealer will set the bidand ask prices at $29.85 and $30.15, good for 100 shares. Theseprices are free of “regret,” in the sense that after the trade, thedealer does not suffer a loss. The result is a nonzero bid–askspread driven purely by information effects.



Practical Implications of InformationTheories. An important application of the infor-mation models concerns the price movements asso-ciated with large trades. Given the precedingdiscussion, the price impact of a block trade can bedecomposed into permanent and temporary com-ponents, as in Keim and Madhavan (1996). Thepermanent component is the information effect(i.e., the amount by which traders revise their valueestimates based on the trade); the temporary com-ponent reflects the transitory discount needed toaccommodate the block. Figure 2 illustrates theeffects for a block sale. Let pt–h denote the pretradebenchmark, pt the trade price, and pt+k the post-trade benchmark price, where h and k are suitablychosen periods, typically half-hour periods. Thesolid line shows the price path over these time

periods; the dotted lines show the continuation ofthe benchmark price (top of the figure) and thetemporary price (bottom of the figure). The priceimpact of the trade, relative to the pretrade bench-mark, is manifested by the drop in price at the tradetime. For a sell order, we can represent the impactas pt – pt–h, a negative number usually. The priceimpact can, in turn, be decomposed into two com-ponents: (1) the permanent component, defined asπ = pt+k – pt–h (the difference between the post-trade price and the price before the trade), and (2)the temporary component, defined as τ = pt – pt+k,the difference between the trade price and the post-trade price, which is represented in Figure 2 by the“rebound” in the price following the trade.

Distinguishing the two components in post-trade analysis is valuable for traders and portfoliomanagers. For example, a passive index fund canavoid significant permanent effects by trading withcounterparties who know that it does not trade oninformation. Consequently, many equity marketshave created two economically distinct tradingmechanisms for large-block transactions. First, ablock can be sent directly to the “downstairs” mar-kets, such as the NYSE or Nasdaq. Second, a blocktrade can be directed to the “upstairs” market,where a block broker facilitates the trading processby locating counterparties to the trade and thenformally crossing the trade in accord with the reg-ulations of the primary market.

The upstairs market operates as a search bro-kerage where prices are determined through nego-tiation. Reputation plays a critical role in upstairsmarkets, where it allows traders who are knownnot to trade on private information to obtain betterprices than in an anonymous market. Liquidityproviders, especially institutional traders, arereluctant to submit large limit orders and thus offerfree options to traders using market orders. Thisproblem is especially significant in systems withopen limit-order books and small minimum priceincrements. Upstairs markets allow these traders toparticipate selectively in trades screened by blockbrokers, who avoid trades that may originate fromtraders with private information. Indeed, anec-dotal evidence from the Toronto Stock Exchange(TSE) suggests a migration of orders upstairs fol-lowing greater transparency in the primary market.Exhibit 4 illustrates an astute strategy based on useof the upstairs market.

Another application of the ideas in this sectionconcerns the determinants of the anomalous returnsassociated with stocks added to and deleted fromwidely used stock market indexes. Madhavan(forthcoming) documented economically and statis-tically significant abnormal returns associated with

Exhibit 3. Does Trading Create Volatility?

French and Roll (1986) found that, on an hourly basis, thevariance during trading periods is 20 times larger than thevariance during nontrading periods. One explanation is thatpublic information arrives more frequently during businesshours, when the exchanges are open. An alternative explana-tion is that order flow is required to move prices to equilib-rium levels. To distinguish between these explanations isdifficult, but a historical quirk in the form of weekday“exchange holidays” that the NYSE declared at one point (tocatch up on a backlog of paperwork) provides a solution.Because other markets and businesses were open, the publicinformation hypothesis predicted that the variance over thistwo-day period, beginning with the close the day before theexchange holiday, would be roughly double the variance ofreturns on a normal trading day. In fact, however, the vari-ance for the period of the weekday exchange holiday and thenext trading day was only 14 percent higher than the normalone-day return. This evidence suggests that trading itself isan important source of volatility; for markets to be efficient,someone has to make them efficient.

Figure 2. Price-Impact Components of a Block Sale

Price

Permanent

Temporary

Time

Trade Time


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the annual reconstitution of the Frank Russell Com-pany indexes from 1996 through 2002. Decompos-ing returns into permanent and temporary effectsprovides insights into such return anomalies. Spe-cifically, permanent changes in prices are attribut-able to shifts in liquidity associated with changes inindex membership, whereas temporary effects arerelated to transitory price pressure. The magnitudeof return reversals (temporary effects) followingindex rebalancing suggests that liquidity pressureshelp explain the return anomalies associated withthe annual reconstitution of the stock indexes of theFrank Russell Company.

Pretrade-Cost Estimation. Intraday modelsare essential for formulating accurate predictionsof trading costs. Traders use pretrade-cost modelsto evaluate alternative trading strategies and formbenchmarks for evaluating the post-trade perfor-mance of traders and brokers. Such models can beused as modules in autotrading strategies in whichcomputers automatically generate trades undercertain conditions.

Interest in pretrade-cost models is also moti-vated by the fact that transaction costs can signifi-cantly erode investment performance. Specifically,the net alpha to an investment strategy is theexpected alpha less the product of turnover andtwo-way trading costs. Because alpha is linear intrade size but costs are typically nonlinear, manyinvestment managers use portfolio optimizers thatincorporate pretrade-cost models to avoid con-structing portfolios that consist of large positions inilliquid small-cap securities.

A successful model has three essential ingredi-ents: (1) Because most investors break their orders

into component trades, the model builder mustrecognize that current trades affect the prices offuture transactions by distinguishing between per-manent and temporary price impacts. (2) Costsdepend on stock-specific attributes (liquidity, vola-tility, price level, market) and order complexity(order size relative to average daily volume, tradinghorizon). And (3) because costs are a function ofstyle, no single cost estimate applies to any givenorder; rather, the model yields cost estimates thatvary with the aggressiveness with which the orderis presented to the market. In particular, an ordertraded quickly using market orders will typicallyincur higher costs than if it were traded passivelyover a long horizon using limit orders, upstairsmarkets, or crossing systems. Passive trading, how-ever, involves more risk from adverse price move-ments on the unexecuted portion of the order.

The first ingredient implies that a realisticmodel will have to be solved recursively, becausethe execution price of the last subblock of an orderdepends on how the last-but-one subblock wastraded, and so forth. In technical terms, the optimaltrade-break-up strategy and corresponding mini-mum expected cost is the solution to a stochasticdynamic-programming problem. The problem isstochastic because the future prices are uncertain;the analyst has conjectures about their means butrecognizes that other factors will affect the actualexecution prices. Dynamic programming, a mathe-matical technique, is needed because it providessolutions to multiperiod problems in which actionstoday affect rewards in the future. Examples ofmodels of this type include those in Almgren andChriss (1999) and Bertsemas and Lo (1999). In suchmodels, the price-impact function (i.e., the effect oftrade on price) is assumed to be linear. Thisassumption is made partly for analytical tractabil-ity but also because theoretical models (such asKyle’s 1985 model) derive linear equilibriums fromfundamental principles. A consideration of equalor greater importance is that when the permanentprice function is linear, investors cannot manipu-late the market by, say, buying small quantities andthen liquidating in one go at a future date. Thisargument does not apply to the temporary priceimpact, which is likely to be nonlinear (see Keimand Madhavan 1996). In practical terms, the modelbuilder who allows nonlinear price functions oftenruns into situations in which the recommendedtrade strategy involves some trades that are in theopposite direction to the desired side. This recom-mendation is often counterintuitive to traders andrisks the possibility of regulatory scrutiny. Thus, acommon practice is the imposition of a requirement

Exhibit 4. A Smart Passive Fund and the Upstairs Market

Keim (1999) analyzed the performance of the 9–10 Fund ofDimensional Fund Advisors (DFA). The 9–10 Fund is a pas-sive index fund that attempts to mimic the performance ofthe bottom two deciles of the NYSE by market capitalization.Keim reported that the 9–10 Fund’s mean return from itsinception in 1982 exceeded that of its benchmark by an aver-age of almost 250 bps without higher risk. This performancewould be envied by many actively managed funds. Keimshowed that the outperformance was largely a result ofDFA’s intelligent use of the upstairs market. Instead of imme-diately selling or buying shares when a stock moved into orout of the universe, DFA traded in the upstairs market byproviding liquidity when approached by block traders whoknew DFA’s strategy. Thus, DFA earned the liquidity pre-mium in the upstairs market, although it incurred sometracking error because it waited to buy or sell. The rewardsto earning the spread through upstairs trading—as opposedto incurring the price-impact costs in illiquid stocks—exceeded 204 bps annually.



that all parcels of the order be on the same side asthe order itself.

However, although most traders agree thatlinearity is too simplistic an assumption, they dis-agree over what form these functions really take.Are they concave (i.e., rising at a decreasing rate insize), convex (rising at an increasing rate), or linear?Figure 3 shows three possible shapes. Loeb’s 1983study of block quotations suggests a concave shapeto the functions. Similarly, Hasbrouck (1991) foundthat square-root transformations of volume fit wellin modeling price impacts. But others argue forconvex functions because available liquidity is ulti-mately limited. Madhavan and Cheng (1997) sug-gested that the resolution of this problem might befound in the fact that observed prices and volumesreflect the operation of two economic mechanisms.On the one hand, Keim and Madhavan argued thatthe price functions are concave for upstairs trades,where buyers and sellers are matched. Theyshowed that as block size increases, more counter-parties are contacted by the upstairs broker, whichcushions the price impact, so the costs of tradingare relatively low for large trades. These results areconsistent with Loeb’s findings from interviewswith block traders; he reported a concave price-impact function. Similar logic applies to trades inexternal crossing systems. Because of commissioncosts, upstairs trades are relatively uneconomicalfor small trades.

On the other hand, the price-impact functionfor market orders sent anonymously to downstairsmarkets might well be convex. On the NYSE, smallorders are executed through the SuperDot systemand if they are below the stated depth, have zeroprice impact; they may even benefit from priceimprovement. Medium-sized orders may execute

against the limit-order book or the specialist, pro-ducing some price impact. A very large trade willeat up all the liquidity on the book, and the special-ist may demand a large price concession to accom-modate the remainder of the trade from inventory,as is sometimes observed at the close if a large orderimbalance exists. The result is a convex price func-tion.

In general, traders who have the choice willselect the lowest-cost mechanism (Madhavan andCheng), so the proportion of upstairs trades willrise with size. Unfortunately, publicly availabledatabases (the Trade and Quote database, forexample) do not distinguish between upstairs anddownstairs trades, so the true cost of large tradesdirected to the downstairs market is underesti-mated. The result is, as Figure 4 illustrates, that aconcave price function appears to best fit the data.The dashed line shows the price impact in thedownstairs (convex) market, and the dotted lineshows the price impact in the upstairs (concave)market; the observed relationship is the lowerenvelope of the two curves, the solid line. In addi-tion, a concave relationship might emerge if anorder that moved prices substantially inducedother traders to enter the market as counterpartiesto supply liquidity.

Market Structure and Design“Market architecture” designates the set of rulesgoverning the trading process. Many academicstudies have shown that market structure mattersby affecting the speed and quality of price discov-ery, liquidity, and the cost of trading. Market archi-tecture is determined by choices regarding a varietyof attributes, including the following:• Degree of continuity. Periodic systems allow

trading only at specific points in time, whereas

Figure 3. Price-Impact Functions

Price Impact

ConvexLinear

Concave

Trade Size0

Figure 4. Upstairs and Downstairs Trading Costs

Cost

Upstairs

Downstairs

Trade Size0


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continuous systems allow trading at any timewhile the market is open.

• Dealer presence. Auction or order-driven mar-kets feature trade between public investorswithout dealer intermediation; in a dealer (orquote-driven) market, a market maker takesthe opposite side of every transaction.

• Price discovery. Some markets provide indepen-dent price discovery; others use prices deter-mined in another market as the basis fortransactions.

• Automation. Markets vary considerably in theextent of automation; floor trading and screen-based electronic systems are at oppositeextremes. The technology of order submissionis less important, however, than the protocolsgoverning trading.

• Order forms. The kinds of order forms permit-ted include market, limit, stop, upstairscrosses, and hidden.

• Protocols. Protocols are the rules regarding pro-gram trading, choice of minimum tick, trade-by-trade price continuity, when to halt trading,and circuit breakers and any special rulesadopted for opens, reopens, and closes.

• Pre- and post-trade transparency. The quantityand quality of information provided to marketparticipants during the trading process consti-tutes pretrade and post-trade transparency.Nontransparent markets provide little in theway of indicated prices or quotes. Transparentmarkets often provide a great deal of relevantinformation before trades (quotes, depths, etc.)and after trades (actual prices, volumes, etc.).

• Information dissemination. Markets differ in theextent of information dissemination (to bro-kers, customers, or the public) and the speed ofinformation dissemination (real time ordelayed feed).

• Anonymity. A crucial factor, the anonymityafforded by a market takes dimensions ranging

from hidden orders to zero disclosure regard-ing trader identities.

• Off-market trading. Some markets permit off-exchange trading and/or trading after hours,and some do not.Trading systems, a sampling of which is shown

in Table 1, exhibit considerable heterogeneity intheir architecture. For example, automated limit-order-book systems of the type used by the TSE andParis Bourse offer continuous trading with highdegrees of transparency (i.e., public display of cur-rent and away limit orders) without reliance ondealers. The foreign exchange market and corpo-rate junk bond market rely heavily on dealers toprovide continuity but offer little transparency;other dealer markets (Nasdaq, the London StockExchange) offer moderate degrees of transparency.Some exchanges have fairly strict trade-to-tradeprice continuity requirements; others, such as theChicago Board of Trade, allow prices to movefreely. Most organized markets also have formalprocedures to halt trading in the event of large pricemovements. Crossing systems, such as POSIT, donot currently offer independent price discovery butcross orders at the midpoint of the quotes in theprimary market.

How do such differences affect price formationand the costs of trading?

Practical Issues in Market Design. Thediversity of systems has spurred considerable theo-retical research. Early investigators recognized thepresence of strong network externalities. For exam-ple, high volume implies a short holding period formarket makers and, hence, low inventory-controlcosts. If volume is initially split equally between twomarkets and the initial volume allocation is per-turbed slightly, the higher-volume market willenjoy reduced costs and volume will migrate to thismarket. In the long run, volume migration will leadto consolidation of the two markets into a singlemarket. The inclusion of information in this model

Table 1. Variation in Real-World Trading Systems

Architecture Element

Typical ECN

NYSE Open

Market

NYSE Intraday Trading

Paris Bourse POSIT

Chicago Board of

Trade

Foreign Exchange

Market

Continuous trading × × × × ×Dealer presence × × ×Price discovery × × × × × ×Automation × × ×Anonymity × × × ×Pretrade quotes × × × ×Post-trade reports × × × × × ×



only serves to confirm this prediction. With asym-metrical information, rational, informed traderswill split their orders between the two markets,providing incentives for liquidity traders to consol-idate their trading. Intuitively, if two markets arecombined into one, the fraction of informed tradingvolume will drop, resulting in a narrowing ofspreads. Even if the information signals are sym-metrical, if they are diverse, then pooling orders willprovide prices that are informationally more effi-cient than decentralized trading among fragmentedmarkets. Indeed, even when multiple markets coex-ist, the primary market is often the source of all pricediscovery (as shown by Hasbrouck 1995), with thesatellite markets merely matching quotes.

Despite strong arguments for consolidationoccurring, however, many markets are fragmentedand remain so for long periods of time. This puzzlehas two aspects: (1) the failure of a single market toconsolidate trading in time and (2) the failure ofdiverse markets to consolidate in space (or cyber-space) by sharing information on prices, quotes, andorder flows. As for the first issue, theory suggeststhat multilateral trading systems (such as single-price call auctions) are efficient mechanisms toaggregate diverse information. Consequently,researchers are interested in how call auctions oper-ate and whether such systems can be used morewidely to trade securities. The information aggrega-tion argument suggests that call auctions are espe-cially valuable when uncertainty over fundamentalsis large and market failure is a possibility. Indeed,many continuous markets use single-price auctionmechanisms when uncertainty is large, such as at theopen, close, or reopening after a trading halt. Yet,most trading systems are continuous and bilateral(not periodic and multilateral), which suggests thatthe benefits of being able to trade immediately atknown prices are extremely important.

With regard to the second issue, although con-solidated markets pool information, whether theywill be more efficient than fragmented markets isnot clear; for example, efficiency will diminish ifsome traders develop reputations based on theirtrading histories. Indeed, one argument cited forthe growth of electronic crossing markets is thattraditional markets offer too much informationabout a trader’s identity and motivations for trade.Models emphasizing asymmetrical informationprovide some rationale for the success of off-market competitors in attracting order flow fromprimary markets. Technology may eventuallyresolve the fragmentation debate. Today, a varietyof systems are being built to route orders intelli-gently to the most liquid market centers.

Automated Auctions. Within the class ofcontinuous markets are the designated dealers orlimit-order markets without intermediaries. Pureauction markets can be structured as batch (single-price) auctions or, more commonly, as automatedlimit-order-book markets. Examples of automatedauctions are electronic communications networks(such ECNs as Island) and the Paris Bourse. With alimit order, an investor associates a price withevery order so that the order will execute only if theinvestor receives that price or better. Clearly, allorders can be viewed as limit orders; a market buyorder is simply a limit buy order for which the limitprice is the current ask price or higher. Conse-quently, recent models of limit-order executionhave generated considerable interest. Exhibit 5describes two approaches to estimating limit-ordermodels. Such models can help a trader evaluatetrading strategies, can be used within an autotrad-ing strategy to automatically submit or cancel limitorders, or can form the basis for automated marketmaking.

A limit-order provider is offering free optionsthat can be exercised if circumstances change. Con-sequently, the limit-order trader needs to expendresources to monitor the market—a function thatmay be costly. Perhaps for this reason, dealers of

Exhibit 5. Estimating Limit-Order Models

Limit-order models provide the probabilities of a limit orderbeing hit as a function of the limit price and other variables.Two types of limit-order models exist—first passage time(FPT) models and econometric models.

FPT models estimate the probability a limit order will beexecuted on the basis of the properties of the stock-priceprocess; typically, these models assume stock prices followsome type of random walk. To get a feel for these models,consider an extremely simple example. Assume for simplic-ity that the midquote return over the next 10 minutes isnormally distributed and that price changes are independent.If the current price is, say, $50, the probability that the mid-quote will cross a given limit price in a prespecified period oftime is straightforward to compute from the cumulative nor-mal distribution.

But although FPT models are analytically convenient, theyare not especially realistic. Econometric models (see Lo,MacKinlay, and Zhang 2001) offer more realism because theycan accommodate large numbers of explanatory variables.But estimating econometric limit-order models presentsproblems. Specifically, unless one knows the investor’s strat-egy for canceling unfilled limit orders, estimating the proba-bility of execution is difficult because only executions forfilled orders can be observed. Statistical techniques must beused to handle such “censoring”—for example, survivalanalysis to model the true time to execution. In particular, ifT is the random execution time, one models the probabilitythat T < t as a function of a vector of variables, includingwhere in the current bid–ask spread the limit price is located,current depth, recent price changes, and other such variables.


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some form or another who carry out the monitoringfor the trader arise so often in auction markets.

Limit-order models provide insights into theconsequences of introducing decimalization andchanging the minimum tick. Strictly speaking, dec-imalization refers to the quoting of stock prices indecimals rather than fractions, such as eighths orsixteenths. Proponents of decimalization in the U.S.markets noted that it would allow investors to com-pare prices more quickly than could be done whenfractions were used, thereby facilitating competi-tion, and would also promote the integration ofU.S. and foreign markets. They often mistakenlycomputed large cost savings to investors becausequoted spreads were deemed likely to fall dramat-ically under decimalization. The minimum tick is aseparate issue, although it is often associated withdecimalization in the literature, that concerns thesmallest increment in which stock prices can bequoted. For example, a system could have decimalpricing but a minimum tick of 5 cents or 2 cents.From an economic perspective, what is relevant isthe minimum tick, not the units of measurement ofstock prices.

If the minimum tick is reduced in a market, theprofits from supplying liquidity in the market(assuming a constant book) go down. The result isa reduction in liquidity at prices away from the bestbid or offer. The quoted spread itself may fall,however, because of competition. Thus, a reductionin the minimum tick may reduce overall marketliquidity (see Harris 1998 for a discussion of thisand related points). Recent empirical evidence sug-gests that quoted spreads were reduced followingdecimalization in the United States, together witha reduction in quoted depth. In other countries,decimalization and transparency have had similareffects—benefiting small, retail traders but reduc-ing visible liquidity and inducing institutionaltraders to use alternative venues, such as theupstairs market or low-cost crossing systems.

Intermarket comparisons are difficult becausereal-world market structures are more complexthan simple models suggest. The NYSE, for exam-ple, has elements of both auction and dealer mar-kets, whereas SuperMontage moves the Nasdaqcloser to an auction model. Furthermore, the defi-nition and measurement of market quality raiseserious empirical issues. For example, use of theusual measure of trading costs (or illiquidity),namely, the quoted bid–ask spread, creates prob-lems because quoted spreads capture only a smallportion of a trader’s actual execution costs.

The early literature argued that competitionamong market makers on the Nasdaq systemwould result in lower spreads than in a specialist

system of the type used by the NYSE, but the oppo-site seems to be the case, even after such factors ascompany age, company size, risk, and price levelhave been controlled for. One explanation, pro-vided by Christie and Schultz (1994), is that dealerson Nasdaq implicitly collude to set spreads widerthan those justified by competition. Institutionalpractices such as order-flow preferencing (i.e.,directing order flow to preferred brokers) and softdollar payments limit the ability and willingness ofdealers to compete with one another on the basis ofprice and may explain why spreads are largedespite easy entry into market making.

Tests of market structure theories face a seriousproblem—the absence of high-quality data thatwould allow researchers to pose “what if” ques-tions. However, some interesting natural experi-ments have been reported. For example, in the late1990s, the Tel-Aviv Stock Exchange moved somestocks from periodic trading to continuous trading,which allowed researchers to investigate the effectsof market structure on asset values by using thestocks that did not move as a control. Amihud,Mendelson, and Lauterbach (1997) documentedlarge increases in asset values for the stocks thatmoved to continuous trading. But such naturalinstances for testing are few and far between.

Compounding the problem is the fact thattraders adjust their strategies in response to marketprotocols and information, so assessing the impactof market protocols is difficult. Furthermore,empirical studies are limited by the scarcity of largesamples of events to study. An additional obstacleto empirical research is that the changes in struc-ture that markets make are often responses to per-ceived problems or competitor actions. Suchchanges are often accompanied by design alter-ations in other dimensions, such as a switch toautomation or greater transparency.

A promising alternative to market-initiatedchanges is laboratory or experimental studies thatallow tests of subtle theoretical predictions aboutmarket design. For example, in a laboratory studyconducted by Bloomfield and O’Hara (2000),human subjects trading in artificial marketsallowed the researchers to examine the effects ofvarious changes in protocols on measures of mar-ket quality. A key advantage of such laboratoryresearch is that researchers can accurately measurequality metrics (e.g., deviation of price from intrin-sic value, speed of convergence to full-informationprices) that cannot ordinarily be observed with realdata. Further benefits of laboratory experiments infinance are discussed in Exhibit 6.

Practical issues of market design are central tothe subject of market microstructure. Although



researchers have learned a great deal, they havecome to no uniform view on which structures offerthe greatest liquidity and lowest trading costs.Given the considerable complexity of real-worldmarket structures, however, this diversity is hardlysurprising. Ultimate decisions on market structureare likely to be decided by the markets on the basisof factors that have less to do with information thanmost economists believe.

The factor I would single out as most influen-tial is a practical one, namely, the need for auto-mation and electronic trading to handle theincreasingly high volumes of trading. Althoughthis factor will favor the increased use of electronictrading systems, it does not imply the demise oftraditional floor-based systems. The point to keepin mind is that what ultimately matters is not themedium of communication between the investorand the market but the protocols that translate theorder into a realized transaction.

InformationMany of the informational issues about marketmicrostructure concern transparency and disclo-sure. Market transparency has been defined (see,for example, O’Hara) as the ability of market par-ticipants to observe information about the tradingprocess. Information in this context can be knowl-edge about prices, quotes, volumes, the sources oforder flow, or the identities of market participants.

A useful way to think about transparency,which has many aspects, is to divide it into pretradeand post-trade dimensions. Pretrade transparencyrefers to the wide dissemination of current bid and

ask quotations, depths (and possibly also informa-tion about limit orders away from the best prices),and other pertinent trade-related information, suchas the existence of large order imbalances. Post-trade transparency refers to the public and timelydisclosure of information on past trades, includingexecution time, volume, price, and possibly infor-mation about buyer and seller identifications.

Issues of Market Transparency. Both pre-and post-trade transparency issues have been cen-tral to some recent policy debates. For example, thedelayed reporting of large trades in London hasbeen cited as a factor in intermarket competitionand order-flow migration.

In addition, transparency is a major factor indebates about floor systems versus electronic sys-tems. Floor systems, such as the Chicago futuresmarkets, generally do not display customer limitorders unless they represent the best quote. In con-trast, electronic limit-order-book systems, such asthe TSE’s Computer Assisted Trading System(CATS) and the Paris Bourse’s Cotation Assistée enContinu (CAC) system typically disseminate notonly the current quotes but also information onlimit orders away from the best quotes. The trendworldwide has been toward greater transparency.(The NYSE recently created OpenBook, a real-timeview of the specialist’s limit-order book.)

The practical importance of market transpar-ency has given rise to a great deal of theoretical andempirical research. Several authors have examinedthe effect of disclosing information about the iden-tity of traders or their motives for trading. Theseissues arise in many different contexts, including• post-trade transparency and reporting,• predisclosure of intentions to trade (known as

“sunshine trading”) or the revelation of orderimbalances at the open or during a trading halt,

• dual-capacity trading, in which brokers canalso act as dealers,

• front running, when brokers trade ahead ofcustomer orders,

• upstairs and off-exchange trading,• hidden limit orders in automated trading sys-

tems,• counterparty trade disclosure, and• the choice of floor-based or automated trading

systems.In a totally automated trading system, where

the components of order flow cannot be distin-guished, transparency is not an issue. Most floor-based trading systems, however, offer some degreeof transparency regarding the composition of orderflow. For example, for trading on the NYSE, theidentity of the broker submitting an order may

Exhibit 6. Experimental Finance

The ability to frame controlled experiments in laboratorymarkets allows researchers to analyze complex informationeffects. The obvious focus is on metrics, such as the bid–askspread, market depth or liquidity, and volatility. But anexperimental study can also address variables that might nototherwise be possible to observe, including data on traders’estimates of value over time, their beliefs regarding the dis-persion of “true” prices, and the trading profits of variousclasses of traders (e.g., informed versus uninformed, specu-lators versus hedgers).

Bloomfield and O’Hara used experimental markets toanalyze changes in disclosure rules. In their study, lab partic-ipants faced various disclosure regimes, and in some exper-iments, dealers (markets) could decide whether theypreferred transparency or not. Bloomfield and O’Hara foundthat transparency has a large impact on market outcomes.

Several other interesting findings have emerged from labmarkets. For example, generating price bubbles is quite easy,even if market participants are aware of bounds on funda-mental value. Interestingly, prices in auction markets neednot always converge to full-information values; agents maylearn incorrectly and settle prices at the “wrong” value.


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provide valuable information about the source andmotivation for a trade.

Theoretical modelers have reached mixed con-clusions about the effects of transparency. In somemodels (e.g., O’Hara), transparency can reduceproblems of adverse selection, and thus spreads, byallowing dealers to screen out traders likely to haveprivate information. Other models (e.g., Madhavan1996), however, indicate that transparency can exac-erbate price volatility. Intuitively, disclosing infor-mation should allow investors to better estimate theextent of noise trading, thus increasing the market’svulnerability to asymmetric-information effects.Essentially, noise is necessary for markets to oper-ate, and disclosure robs the market of this lubrica-tion. Contrary to popular belief, Madhavan (1996)showed that the potentially adverse effects of trans-parency are likely to be greatest in thin markets.

These results have important implications forpolicies on, for example, the choice between floor-based systems and fully automated (typicallyanonymous) trading systems. Specifically, supposetraders obtain better information on the portion oforder flow that is price inelastic on an exchangefloor than in an automated trading system. Floor-based systems may be more transparent becausetraders can observe the identities of the brokerssubmitting orders and make inferences about themotivations of the initiators of those orders. Unlessa system is explicitly designed to function in anonanonymous fashion, such inferences areextremely difficult in a system with electronic ordersubmission. In this case, traditional exchange floorsmay be preferred over automated systems foractive issues whereas the opposite may be true forinactive issues. Finally, the results of this branch ofresearch provide insights into why some liquidity-based traders avoid sunshine trades even if theycould benefit from reputation signaling.

In one model (Madhavan 1996), nondisclosurebenefits large institutional traders whose orders arefilled in multiple trades by reducing their expectedexecution costs but imposes costs on short-termnoise traders. With the benefit of nondisclosure, thelarge institutions can break up their trades overtime without others front-running them andthereby raising their trading costs. A large trade canbe successfully broken up without attracting toomuch attention and, hence, moving the price in thedirection of the trade. Nondisclosure also benefitsdealers by reducing price competition. The impli-cation is that, faced with a choice between a high-disclosure market and a low-disclosure market, anuninformed institutional trader will prefer to trade

in the more opaque market. This model suggeststhat one danger of too much transparency is thattraders might migrate to other venues, includingoff-exchange or after-hours trading.

Empirical Research on Transparency andDisclosure. In terms of post-trade transparency,late-trade reporting on the London Stock Exchangeand Nasdaq has generated extensive discussion.Research suggests that increased post-trade trans-parency increases volumes and lowers spreads (seethe discussion in Madhavan 2000). This findingmakes sense because immediate and accurate post-trade reports on volumes and prices reduce marketuncertainty. A validation effect may also be atwork; a trader who observes a large trade is morewilling to believe that the price of that trade isrepresentative or fair.

In terms of pretrade reporting, few “naturalexperiments” suggest that too much transparencyis detrimental. For example, Madhavan, Porter,and Weaver (2002) found a decrease in liquidityassociated with the display of the limit-order bookon the TSE after controlling for volume, volatility,and price. Limit-order traders are apparently reluc-tant to submit orders in a highly transparent systembecause these orders essentially represent freeoptions to other traders.

Transparency is a complicated subject, butrecent research provides several revealing insights.First, there is broad agreement that both pre- andpost-trade transparency matter; both affect liquid-ity and price efficiency (O’Hara; Madhavan 1996).Second, greater transparency, both pre- and post-trade, is generally associated with more informa-tive prices (O’Hara; Bloomfield and O’Hara).Third, complete transparency is not always “bene-ficial” to the operation of the market. Indeed, manystudies demonstrate that too much pretrade trans-parency can actually reduce liquidity because trad-ers are unwilling to reveal their intentions to trade(Madhavan, Porter, and Weaver). Also, too muchpost-trade transparency can induce fragmentationas traders seek off-market venues for their trades.Finally, changes in transparency are likely to bene-fit one group of traders at the expense of others, asdiscussed in Madhavan (forthcoming). Traderswith private information prefer anonymous trad-ing systems, whereas liquidity traders, especiallythose who can credibly claim their trades are notinformation motivated (e.g., passive index funds),prefer greater disclosure. Consequently, no marketstructure will be uniformly preferred by all tradersand dealers.



Applications to Other Areas The recognition that microstructure matters forasset values, liquidity, trading costs, and price effi-ciency is relatively recent. This section providesexamples of some of the applications of microstruc-ture research to other areas of finance—asset pric-ing, corporate finance, and international finance.

Asset Pr ic ing. Research has generallymodeled expected returns as functions of such vari-ables as proxies for size and default risk. Amihudand Mendelson (1986) showed that expectedreturns are a decreasing function of liquiditybecause investors must be compensated for thehigher transaction costs that they bear in the lessliquid markets. The presence of trading costs(asymmetric-information, inventory, and othertransaction costs) reduces the equilibrium value ofthe asset. Indeed, Amihud, Mendelson, and Lauter-bach documented large changes in asset values forstocks that moved to more liquid trading systemson the Tel-Aviv Stock Exchange. These and otherstudies confirm the importance of liquidity as afactor to be considered by portfolio managers inbuilding an alpha-generating model.

From a cross-sectional viewpoint, variation inexpected returns among securities arises because ofdifferences in trading costs. In this context, measur-ing trading costs accurately is critical. As anextreme example, consider a market with risk-neu-tral investors and two assets—a security that issubject to trading costs and a security that is not.Trading costs in the less liquid security comprisethe bid–ask spread, s, and the price impact of thetrade (measured by the responsiveness of prices tothe average order size), denoted by λ. Keim andMadhavan (1998) showed that the price-impactcosts can be substantially larger than the observedspread, but these costs are typically difficult tomeasure accurately.

In equilibrium, the returns of both assets rela-tive to the price net of trading costs should equalthe risk-free rate, rf . Computation of the return onthe security (ignoring transaction costs; that is,using the midquote as the basis for value) producesreturn premium r – rf > 0, which represents (afterother factors affecting returns have been controlledfor) the compensation for trading costs λ + s acrossa sample of stocks. This phenomenon may alsoexplain, in part, the observed size effect, becausetransaction costs are higher in less liquid assets,where the omission of λ in the computation ofreturns has the strongest effects. Brennan and Sub-rahmanyam (1996) estimated such a cross-sectionalmodel of returns with some success. Exhibit 7 pro-vides an example of when portfolio managers

should consider liquidity as a factor in their stockselection models.

Commonality in liquidity and returns is a log-ical area for future research. Consider a model inwhich the price change in each of N stocks is lin-early related to order flows in one’s own andrelated stocks, in addition to other factors. In matrixnotation,

∆p = X� + U,

where∆p = an N × 1 vector of price changesX = an N × k matrix of order flows, current

and lagged, and other predeterminedvariables affecting price movements

� = a k × 1 vector of coefficientsU = an N × 1 vector of error terms

Returns to stock i may depend on current andlagged flows in stock j. Commonality in order flowsis manifested in that, although X has full rank, onlya few sources of independent variation explainmost of the variation in the data.

Hasbrouck and Seppi (2001) used principalcomponents analysis to characterize the extent towhich common factors are present in returns andorder flows. Principal components analysis can beviewed as a regression that tries to find a linearcombination of the columns of the data matrix Xthat best describes the data, subject to normaliza-tion restrictions imposed to remove indeterminacy.Hasbrouck and Seppi found that common factorsare present in both returns and order flows and thatcommon factors in order flows account for 50 per-cent of the commonality in returns. Whether suchfactors can help predict short-run returns, variationin intraday risk premiums, or the observed rela-tionship between price variability and volume isstill an open question.

Another interesting application of marketmicrostructure in the asset-pricing area concernstechnical analysis, in which past price movements

Exhibit 7. Liquidity and Portfolio Risk

If liquidity is a factor in expected returns, its omission froma risk model can result in substantial understatement of truerisk. Suppose, for the sake of simplicity, that the expectedreturn on asset i is determined by a two-factor risk model,Ri = rf + β1iF1+ β2iF2, where the first factor is the market factorand the second is a proxy for illiquidity (i.e., a factor posi-tively related to the implicit costs of trading). Consider atrader who follows a market-neutral strategy but incorrectlyignores the liquidity factor. The trader goes long in securitiesthat have positive alphas relative to the incorrect model(where β2iF2 > 0) and short those securities with negativealphas (where β2iF2 < 0). If liquidity decreases, the portfoliois exposed to considerable risk, even though it is “marketneutral.” This liquidity risk can be significant; the failure ofthe hedge fund Long-Term Capital Management is a goodexample.


40 ©2002, AIMR®

are used to predict future returns. Financial econ-omists are traditionally skeptical of technical anal-ysis; in an efficient market, current prices shouldimpound all available information, so past pricepatterns should not have predictive power. Yet,microstructure theory suggests several avenuesthrough which technical analysis might havevalue, at least over short horizons, as described inExhibit 8.

Corporate Finance. Close economic tiesbetween corporations and their sources of financ-ing characterize many financial markets. Sucharrangements are common in countries where cor-porations rely primarily on bank financing. Simi-larly, equity markets for small-cap stocks arecharacterized by close relationships between newissuers and the underwriters who bring the stockpublic. In particular, underwriters sponsor newissues by arranging analyst coverage, promote thestock through marketing efforts, and provideliquidity by acting as broker/dealers in subsequentsecondary-market trading. Financial economistshave only recently recognized the importance ofsuch “relationship markets.” Underwriters of smallstocks, for example, often dominate trading in thepost-IPO market, which gives them considerableability to affect security prices. The provision ofsecondary-market liquidity is valuable to the com-panies, and in a relationship market, it is natural forthe broker/dealer that best knows the company(i.e., the underwriter) to also be the primary marketmaker. Indeed, Ellis, Michaely, and O’Hara (2000)found that for Nasdaq stocks, the lead underwriteris almost always the primary market maker in theaftermarket. The withdrawal of secondary-marketliquidity (e.g., by the failure of a broker/dealer) is

often associated with significant price drops of thestocks in which that entity made markets.

Another interesting application concerns stocksplits. Early analyses of motivation for stock splitsfocused on traditional corporate finance explana-tions, such as signaling. From a microstructureviewpoint, however, splits might be a device for acorporation to adjust its stock price relative to ticksize to affect its own trading costs and liquidity. Fora given tick size, a higher price implies a lower costof capital and, therefore, higher share values; at thesame time, higher prices may discourage liquidity-based trading by small, retail investors. Thus, anoptimal price level that maximizes share value mayexist. Indeed, average stock prices are relativelyconstant over long periods of time within countries,despite variations among countries, and the aver-age stock price relative to the minimum tick is evenmore constant.

International Finance. Microstructure andinternational finance intersect in several areas,including the microstructure of foreign exchangemarkets, cross-border competition for order flow,arbitrage in cross-listed securities, capital marketsegmentation, and international transmission ofvolatility.

Foreign exchange markets are by far the largestasset markets in terms of volume. Consequently,researchers have shown considerable interest inhow they operate and how prices are determined.An unusual feature of the foreign exchange marketis the extremely large trading volumes, far largerthan one would expect given the level of importsand exports. Lyons provided an elegant explana-tion for this phenomenon. The intuition behind themodel can be explained simply. Suppose an inves-tor initiates a large-block trade with a particulardealer. The trade causes this dealer’s inventory tomove from the desired level. This change is costlybecause of the risk of an adverse price movement.In a dealer market, the dealer can offset this addedinventory risk by passing a portion of the blocktrade on to other dealers by hitting their quotes. Theblock is passed around to successive dealers in a“hot potato” way, so the ultimate trading volumegreatly exceeds the size of the initial trade.

What is interesting about this explanation forthe volume phenomenon is its reliance on two keyassumptions about market microstructure: (1) thedealer structure of the foreign exchange market and(2) a lack of transparency in trade reporting. Whendealers trade bilaterally over the telephone, still themost important method of dealing, the trade isinformative to them. The advent of electronictrading (e.g., Reuters D2000-2) is changing the

Exhibit 8. The Value of Technical Analysis

Can technical analysis be of value even if markets are effi-cient? Microstructure theory suggests several possibilities.First, dealer inventories must be mean reverting, as shown inthe section “The Crucial Role of Market Makers.” So, if inven-tory affects prices, cyclicality could occur in prices. Second,specialist incentives to smooth prices will induce short-runautocorrelation. Third, if large traders break up their blocktrades (and if this information leaks slowly to the market),short-run trends will occur. To the extent that order flowcontains commonalities, such factors might also aggregate tothe overall market level. Finally, technical analysis mighthelp traders discover hidden liquidity. Kavajecz and Odders-White (2002) found that technical support and resistancelevels coincide with peaks in depth on the limit-order book.Furthermore, moving-average forecasts reveal informationabout the relative position of depth on the book. So, technicalanalysis is valuable, even if markets are efficient, because itreveals patterns in liquidity, which helps traders avoid largetrading costs.



structure and availability of information to someextent, however, and could alter the nature of theequilibrium, dramatically reducing volumes.

Another important aspect of the interactionbetween microstructure and international financeconcerns segmentation in internal capital markets.Such barriers to investment are important becausethey may give rise to various documented “anom-alies,” such as discounts on international closed-end funds. They also may give rise to arbitragetrading or other cross-border order flows and thusaffect market efficiency. Finally, an analysis of seg-mentation may shed light on the positive abnormalstock returns observed following economic liberal-izations. Exhibit 9 illustrates how microstructurevariables can be used to predict exchange ratechanges.

A puzzling aspect of international segmenta-tion arises when domestic companies issue differ-ent equity tranches aimed at different investors. Forexample, countries as diverse as Mexico, China,and Thailand have foreign ownership restrictionsthat mandate different shares for foreign anddomestic investors. The objective of such a parti-tion of otherwise identical shares is to ensure thatownership of corporations rests in the hands ofdomestic nationals. Interestingly, the prices ofthese two equity tranches vary widely among com-panies and over time. The share price premiums ordiscounts can be explained in terms of relativetrading costs: If both shares are otherwise equal butone share has higher transaction costs, that sharewould have to have a lower price if holding-periodreturns are to be equal. Elimination of market seg-mentation should reduce costs, thus lowering the

cost of capital and boosting share prices in seg-mented markets.

Similar logic suggests that a stock traded indifferent markets might trade at different prices(adjusted for exchange rates), even though it essen-tially represents claims to the same underlying cashflows, because of differences in relative liquidity.In particular, a cross-listed stock that is containedin different stock market indexes in the local andforeign markets need not always trade in the samedirection.

ConclusionsSeveral conclusions from this survey of the litera-ture are especially relevant for practitioners.

First, markets are a great deal more complexthan commonly believed. One of the major achieve-ments of the microstructure literature is success inilluminating the black box by which prices andquantities are determined in financial markets. Therecognition that order flows can have long-lastingeffects on prices has many practical implications.For example, large price impacts may drive institu-tional traders to lower-cost venues, creating apotential for alternative trading systems. It may alsoexplain the anomalous return behavior associatedwith periodic index reconstitutions like those of theFrank Russell Company indexes in June each year.

Second, microstructure matters. Under certainprotocols, markets may fail and large deviationsbetween “fundamental value” and price mayoccur. These issues are especially relevant forexchange officials, operators of trading systems,regulators, and traders.

Third, “one size fits all” approaches to regula-tion and policy making should be avoided. Forexample, greater transparency does not alwaysenhance liquidity.

Finally, the interface of microstructure withother areas of finance is an exciting new area. Amore complete understanding of the time-varyingnature of liquidity and its relationship to expectedreturns is needed; evidence is growing that liquid-ity is a “factor” in explaining stock returns. Differ-ences in liquidity over time may explain variationsin the risk premium and may, therefore, influencestock-price levels.

Exhibit 9. Exchange Rate Models

Economic theory suggests that exchange rate movements aredetermined by macroeconomic factors. Yet, macroeconomicexchange rate models, with R2s below 0.10, do not fit the datawell. Evans and Lyons (1999) proposed a microstructuremodel of exchange rate dynamics based on portfolio shiftsthat augments the standard macroeconomic variables withsigned order flow. The model has the form ∆pt = β1∆(it – )+ β2xt + εt , where ∆pt is the daily change in the (log) spot rate,∆(it – ) is the change in the overnight interest rate differen-tial between the two countries, and xt is the signed order flow.They estimated their model for the mark/dollar and yen/dollar exchange rates. As predicted, both β1 and β2 werepositive and significant. The estimated R2 improved substan-tially when signed order flow was included. More than 50percent of the daily changes in the mark/dollar rate and 30percent in the yen/dollar rate were explained by the model.Applications include short-run exchange rate forecasting,targeting of central bank intervention, and prediction of trad-ing costs for large transactions.

it∗

it∗

I thank Ian Domowitz, Margaret Forster, Larry Harris,Don Keim, and Seymour Smidt for many helpful discus-sions over the years that influenced my thoughts. Ofcourse, any errors are entirely my own. The opinionscontained in this article reflect solely the personal viewsof the author and do not necessarily reflect the views ofITG Inc., its employees, officers, or affiliates.


42 ©2002, AIMR®

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market microstructure a practitioner’s guide.pdf

Documents

passive index fund

world market structures

signed order flow

foreign exchange market

london stock exchange

foreign exchange markets

stock market indexes

primary market maker