the index futures markets: is screen trading more efficient?

THE INDEX FUTURES

MARKETS: IS SCREEN

TRADING MORE EFFICIENT?

LAURENCE COPELAND*KIN LAMSALLY-ANN JONES

This article uses a nonparametric test based on the arc-sine law (see, e.g.,Feller, 1965), which involves comparing the theoretical distributionimplied by an intraday random walk with the empirical frequency distribu-tion of the daily high/low times, in order to address the question ofwhether the abandonment of pit trading has been associated with greatermarket efficiency. If market inefficiencies result from flaws in the marketmicrostructure of pit trading, they ought to have been eliminated by theintroduction of screen trading. If, on the other hand, the inefficiencies area reflection of investor psychology, they are likely to have survived,unaffected by the changeover. We focus here on four cases. Both the

We are grateful for helpful comments to a referee of this journal, and to participants in the 2001CBOT Asia Conference in Bangkok, the 2002 APFA/PACAP/FMA Conference in Tokyo, and depart-mental seminars in the universities of Cardiff, Strathclyde, Durham, and National University ofIreland, Maynooth. The first author would like to record his gratitude to the Julian HodgeFoundation for its support, and to Hong Kong Baptist University for its hospitality during the writ-ing of this article.*Correspondence author, Cardiff Business School, Cardiff University, Aberconway Building, ColumDrive, Cardiff CF10 3EU, Wales, U.K.; e-mail: [email protected]

Received October 2002; Accepted July 2003

� Laurence Copeland is a professor of finance at the Cardiff Business School in Cardiff,Wales, U.K.

� Kin Lam is a professor of finance at Hong Kong Baptist University in Kowloon Tong,Hong Kong.

� Sally-Ann Jones is at the Cardiff Business School in Cardiff, Wales, U.K.

The Journal of Futures Markets, Vol. 24, No. 4, 337–357 (2004) © 2004 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/fut.10119

338 Copeland, Lam, and Jones

1See Tsang (1999) for information on futures exchanges around the world.

FTSE-100 and CAC-40 index futures contracts were originally traded byopen outcry and have moved over to electronic trading in recent years, sothat we are able to compare pricing behavior before and after thechangeover. The equivalent contracts in Germany and Korea, on the otherhand, have been traded electronically ever since their inception. Ourresults overwhelmingly reject the random-walk hypothesis both for open-outcry and electronic-trading data sets, suggesting there has been noincrease in efficiency as a result of the introduction of screen trading. Onepossible explanation consistent with our results would be that the indexfutures market is characterized by intraday overreaction. © 2004 WileyPeriodicals, Inc. Jrl Fut Mark 24:337–357, 2004

INTRODUCTION

In recent years, a trend toward electronic trading in futures markets hasbecome well established, as one after another the major exchanges out-side the U.S. replace or at least supplement the traditional open outcrywith automated systems.1 This article uses a new test based on the fre-quency of maxima and minima to address the question of whether or notthe abandonment of pit trading has been associated with greater marketefficiency.

Although the motivation behind computerization of trading mayvary from market to market, greater efficiency must surely be one of theconsiderations. On this point, however, neither the theoretical argu-ments nor the evidence are unambiguous. As far as the theoretical issuesare concerned, the question revolves around the relative importance oftransaction costs, which are usually assumed to be lower in an electronicmarket, and liquidity, which is often said to be lower too (see the discus-sions in Kappi & Siivonen, 2000; Kofman & Moser, 1997; Martens,1998). Most of the empirical articles model the spread between bid andask (either directly observed or estimated by the well-known Roll (1984)method or some variant thereof) so as to make use of transaction data tomeasure the relative liquidity, depth, transparency, etc., of electronicmarkets relative to open outcry. The most heavily researched case is thecompetition between London and Frankfurt for dominance of the mar-ket in the Bund futures contract, which from 1990 to 1999 was tradedsimultaneously in the LIFFE pits and in the Frankfurt DTB electronicmarket (e.g., Franke & Hess, 2000; Kofman & Moser, 1997).

In this article, by contrast, we take a more direct approach to meas-uring efficiency by addressing the question: Is the ultimate outcome, thepricing process itself as observed over the typical trading day, more orless consistent with random-walk behavior after the changeover than

Efficiency of Screen Trading 339

before? If market inefficiencies (assuming they exist) result from flaws inthe market microstructure of pit trading, they ought to have been elimi-nated by the introduction of screen trading. If, on the other hand, theinefficiencies are a reflection of investor psychology, they are likely tohave survived, unaffected by the changeover. For example, if investorsare subject to the type of behavioral failings that result in overreaction tonews, there is no reason to expect any change when open-outcry tradingis abandoned.

We focus here on four cases. Both the FTSE-100 and CAC-40 indexfutures contracts were originally traded by open outcry and have movedover to electronic trading in the last few years, so we are able to comparepricing behavior before and after the changeover. The equivalent con-tracts in Germany and Korea, on the other hand, have been traded elec-tronically ever since their inception.

In our empirical work, we implement a nonparametric test based onthe arc-sine law (see, e.g., Feller, 1965), which involves comparing thetheoretical distribution implied by an intraday random walk with theempirical frequency distribution of the intraday high/low times. For thispurpose, the theoretical distribution is derived both under the defaultassumption of constant trading volume and the more general assumptionthat volume follows its observed intradaily pattern. Because it has beennoted elsewhere that volume and volatility follow broadly the sameintradaily pattern in the index futures market (see, e.g., Abhyankar,Copeland, & Wong, 1999, on LIFFE), this generalization serves also todeal with the observed nonconstancy of volatility.

Compared to other methodologies for testing randomness, theadvantages of our approach are, first, that it enables us to proceed with-out the (demonstrably false) assumption that the price process is covari-ance stationary, and second, that it allows us to identify the times of theday when the departures from randomness are greatest. Usually, varianceratio tests and/or autocorrelation tests are performed to detect nonran-dom behavior in index futures prices. The time series of 5-min(or 15-min) returns are analysed, and the first-order autocorrelation orvariance ratio statistics are used to test for a random walk. To performthese classical tests, the tick-by-tick data are first collapsed into a timeseries of 5-min returns. As a result, the tick-by-tick data are not fullyexploited. Acar and Toffel (1999) and Mok, Lam, and Li (2000) used adifferent approach, but they relied on a goodness-of-fit test in which thefrequency counts are also collapsed into 5-min intervals. Although theirresults are capable of pinpointing any nonrandomness in the first 5-mininterval, they are unable to tell whether or not the nonrandomness isheterogeneous within the interval. The Kolmogorov-Smirnov test we


employ in this article has the advantage that we do not need to collapsethe tick-by-tick data, because we are able to deal with the data in contin-uous time and observe departures from randomness continuously throughthe day.

Our results indicate that the relative frequency of price maxima andminima (especially in the first few minutes after the opening) is fargreater than is consistent with a random walk in all cases. This statementis true for the British and French markets both before and, more surpris-ingly, after computerization, and it applies equally to the markets inGermany and Korea. Taken at face value, our results would appear tosuggest that screen trading has little to offer in terms of efficiency gains.The most likely explanation would appear to be that the market openingand, to a lesser extent, the closing are characterized by overreaction tonews, a conclusion that is indirectly supported by published evidence onother futures markets (e.g., Fung, Mok, & Lam, 2000, on Hong Kongand the U.S.).

We first give a brief overview of the literature, and then an outline ofour data sets. The details of our testing procedure are set out, and wederive the theoretical distribution of the price maxima and minimaacross the trading day, first under the assumption of a constant tradingvolume, then relaxing this assumption to allow for the fact that volumetypically fluctuates as information flows into the market. In particular,we show that the frequency distribution of highs and lows under a ran-dom walk is not uniform, as might casually have been expected, butinstead is higher at the open and close of trade. A test based on a com-parison of the implied cumulative distribution with the observed distri-butions from our data sets is implemented.

LITERATURE SURVEY

The work reported in this article should be seen in the context of threestrands of the literature.

First, our results relate to the literature on intradaily patterns infinancial, and especially index futures, markets. Researchers in this areahave documented a number of regularities, especially the U-shaped pat-tern of intradaily volume and volatility, which have proved robust acrossdata sets (see McInish & Wood, 1992; and on futures Eckman, 1992;Abhyankar et al., 1999, and references therein).

Second, a number of authors have addressed the question ofwhether screen-based trading is actually more efficient than open outcry.For the most part, researchers have tended to concentrate on efficiency interms of transaction costs, that is, examining bid–ask spreads, whether


2Note that the relative efficiency of dealer versus auction markets addressed by Huang and Stoll(1996) and Pagano and Roell (1996), among others, is a somewhat different question. Electronicmarkets can be of either kind.3But see also Miller, Muthuswamy, and Whaley (1994) for a possible explanation of the patterns inthe basis, which relies on infrequent trading in index stocks.4The idea of analyzing the frequency of highs and lows was actually pioneered by van Marrewijk andde Vries (1990) in the context of tests of purchasing power parity in exchange-rate data.

directly observed or estimated. Blennerhassett and Bowman (1998), forexample, find that spreads have narrowed since the introduction of screentrading in New Zealand equities.2 A number of published articles focus onthe Bund futures contract traded by open outcry on LIFFE and electron-ically in Frankfurt (e.g., Breedon & Holland, 1997; Kappi & Siivonen,2000; Kofman & Moser, 1997; Martens, 1998), with conclusions that aresomewhat ambiguous.

A third body of literature to which we relate deals with the problemsinvolved in testing for the existence of patterns in daily financial data. Anumber of different approaches have been taken. The first and mostdirect approach would be to examine the autocorrelation patterns inhigh frequency (1-min, 5-min, 15-min, etc.) returns, with any evidenceof significant negative autocorrelation being taken as indicative of possi-ble overreaction. For example, Wood, McInish, and Ord (1985), lookingat NYSE data for 1971–1972 and 1982, found some evidence of auto-correlation, particularly at the start and end of trading, whereasMacKinlay and Ramaswamy (1988) found little evidence of autocorrela-tion in any of the first eight lags of the 15-min returns on the S&P 500futures in 1983 and 1984.3 On the other hand, Fung, Lo, and Peterson(1994) reject a random walk on the basis of variance ratio tests on dailydata for two contracts on the same index. Both approaches rely on theexistence and stationarity of second moments, which may be an unwar-ranted assumption in the context of this type of data. In recognition ofthis drawback, Fung et al. (1994) also apply the rescaled range testwhich, along with fractional difference methods, makes them able toconfirm the absence of any long memory process in their data set.

Most recently, and most relevant to the methods adopted in thisarticle, Acar and Toffel (1999) and Mok et al. (2000) focus on the fre-quency distribution of maxima and minima across time intervals in thetrading day, comparing the observed frequency in each time interval withthe theoretical frequency implied by a random walk.4 This approach hasthe advantage that it does not rely on the assumption that the momentsof the return distribution are stationary, which is clearly not the case inthis data set. In arriving at their results, Acar and Toffel (1999) assume aconstant trading volume throughout the day, an assumption we know tobe unjustified (see, for example, Abhyankar et al., 1999; or McInish &


TABLE I

Data Period and Trading Hours (Local Times)

Pre-Screen Trading Post-Screen Trading Contract Data Set Trading Hours Data Set Trading Hours

FTSE-100 28 June 1994–30 April 1999 1 May 1999–28 December 200008:35–16:30a 08:00–18:00

CAC-40 2 January 1996–29 May 1998 1 June 1998–28 February 200110:00–17:00 09:00–17:30

DAX 1 April 1997–1 June 2000b

09:00–20:00

KOSPI-200 3 May 1996–31 May 199909:30–11:30 and 13:00–15:15

aWith call market from 16:10–16:30, and computerized after-hours trading from 16:32 to 17:59.b09:00 to 17:00 from 1 January 1997 to 31 March 1997, with the opening hour moved to 08:30 from 1 April 1997to 19 July 1999. The hours changed again to 09:00 to 17:30 from 20 July 1999 to 1 June 2000.

5Albeit by simulation only, as they are unable to derive a closed-form solution for the theoretical dis-tribution in the presence of drift. 6For further details, refer to the four exchanges that trade the contracts (and supplied the data). Inparticular, note that some markets impose theoretical price-fluctuation limits, though they wererarely activated during our data period. Of course, the fact that limits are not activated does not nec-essarily mean they have no effect whatsoever. However, the CAC-40, for example, only once or twicereached its daily limits of plus or minus 275 points, so it seems this factor can safely be ignored forpresent purposes.

Wood, 1991). However, in deriving the theoretical density Acar andToffel (1999) do allow for drift,5 although in practice the daily drift isprobably too small to make any substantial difference to the outcome.Mok et al. (2000) modify the test in a potentially important respect, byallowing for variation in trading volume across time slots in the day. Inthe present article, we further modify their procedure, by applying theKolmogorov-Smirnov test jointly to both distributions to allow us toreach an unambiguous conclusion based on the distribution across alltime intervals in the day.

Our Data Set

Our raw data consist of real-time transaction price and volume data for3-month futures on the FTSE-100 traded on LIFFE, the CAC-40 tradedoriginally on the MATIF, now on MONEP, the DAX in Germany and theKOSPI-100 in Korea. Some of the main features of our data set are sum-marized in Tables I and II.6

The FTSE-100 is an arithmetic value-weighted index covering around75% of the London market by value. It is updated minute-by-minute


TABLE II

Futures Market Daily Volume

Number of Contracts Traded

UK France

Floor Trade Electronic Floor Trade Electronic Germany Korea

Avg daily volume 18,254 39,186 39,802 55,627 37,809 40,101Number of minutes 455 599 418 422 505 257Median minute 38 57 70 132 64 162Maximum minute 223 352 812 339 487 783Minimum minute 18 4 22 80 30 2

during the trading day. The futures contract began trading on the floor ofLIFFE in 1984, though our data set only begins in the second half of1994. The changeover to screen trading occurred on 1 May 1999, so thatby extending our data period up to the end of 2000, we are able to includeabout 19 months of results from electronic trading.

The CAC-40 is a value-weighted index representing around 80% ofthe value of the Paris stock market in 1998 and is published at 30-sintervals throughout the trading day. The associated futures contract hasundergone a number of changes since it started trading by open outcryon the MATIF in 1988. Electronic trading was introduced initially in1993, but only for after-hours sessions. Pit trading survived during nor-mal business hours until the introduction of the NSC electronic tradingmechanism on 1 June 1998. Our data set starts in January 1996, giving5 years observations, approximately half before and half after thechangeover.

The DAX is an arithmetic value-weighted index of the prices of thetop 30 German companies, accounting for some 66% of turnover on theFrankfurt stock exchange. Since 1988, it has been computed everyminute during market opening hours. The futures contract was intro-duced toward the end of 1990, although our data only start from April1997. From its inception, it has always traded electronically.

The KOSPI-200 is a market capitalization-weighted real-time indexof the prices of 200 major stocks listed on the Korean Stock Exchange,covering nearly 80% of total market value, with the list of constituentstocks updated annually. It is computed and published every 30 s duringthe trading hours of 09:30 to 11:30 for the morning session, and 13:00to 15:00, for the afternoon. The fact that the day is divided into two sep-arate sessions is potentially a challenge to the methodology adopted in


this article, a point to which we shall return later. The futures contracthas been traded at the KSE since its inception in May 1996. Tradinghours are the same as for the cash, except that the afternoon session forthe futures does not end until 15:15.

As is clear from Table II, all the contracts trade in substantial vol-ume on a typical day. It should also be noted that the daily pattern ischaracterized by considerable variation in trade intensity, with extremelylow levels at some points (mostly around the middle of the day) and max-imum levels many times greater.

TESTING METHODOLOGY

We focus here on the pattern of returns over the typical day’s trading.Because trading futures incurs no opportunity cost of money, any expectedreturn could only be compensation for risk, which is likely to be negligibleover a holding period measured in hours. Given that transaction costsare small, we would expect returns to show no discernible pattern; that is,we expect prices to follow a random walk. This is the hypothesis we test inthis article.

Daily High/Low

Our basic approach is an extension of the procedure in Mok et al. (2000)to test for time-series dependence in the intraday pattern of high and lowprices. Essentially, we make no assumptions about the nature of anydependence over the trading day. In particular, we do not assume sta-tionarity in the underlying process. Instead, we concentrate on observingthe time at which the day’s maximum or minimum occurs. This approachhas two advantages. First, the timing of the day’s maximum or minimumis interesting in its own right, because technical analysts believe it can beused as a basis for designing profitable trading strategies (e.g., Kaufman,1987). Second, by examining the theory of random-walk processes, weare able to develop a procedure that can not only provide a test for devi-ations from randomness, but can also tell us at what times of the daythese effects are most likely to occur.

It should be emphasized that we are not concerned here with theactual level of the maximum or minimum values of the index, thoughthis statistic has received considerable attention from researchers, espe-cially as an input into intraday volatility estimates (e.g., Garman & Klass,1980). Instead, we are concerned with the frequency distribution across


7All statements should be taken to apply equally to the distribution of the daily minimum. To avoidtedious repetition, we restrict attention to the distribution of the maximum.8Note that when there are tied maxima, T indexes the first time the futures price reaches itsmaximum.

our daily data set of the time at which the market reaches what turnsout, with hindsight, to have been its daily high or low. It will be shown inthe next section that, if a price follows a random walk, this distribution isU-shaped rather than uniform, as might have been expected by a casualconsideration of the theory. It follows that traders who claim that highsand lows tend to occur most frequently soon after the open or not longbefore the close may in fact be observing a phenomenon that is perfectlyconsistent with randomness.

Theoretical Density of the Daily Maximum7

We start with the observation that the structure of any dependence inhigh-frequency financial data is potentially complicated. In particular,we can see no grounds for assuming stationarity in index futures marketdata, given the well-documented patterns in the underlying indices (e.g.,Wood et al., 1985). At the very least, the pattern is likely to vary over theday, as Froot and Perold (1995) found in the case of the S&P 500.

With these considerations in mind, we choose here to implement amodified version of a nonparametric test for which the theoretical basisis set out in Mok, Lam, and Li (2000) (though see also Acar & Toffel,1999; van Marrewijk & de Vries, 1990).

For simplicity, suppose the trading day consists of only 1 hour, dur-ing which time the sequence of observed prices corresponding to then trades executed during the hour is The random-walkassumption amounts to postulating that, conditional on the value of n,the series of price changes is an inde-pendent identically distributed sequence symmetrical about zero, whichwe denote F(�). If we make the temporary assumption that the volume oftrade in the futures contract is uniform throughout the day, and denotethe time at which the price reaches its maximum8 by where i isthe order of the maximum in the sequence of n price observations, thenas the probability density of T approaches the limit

(1)f(t) �1

p1t(1 � t) 0 � t � 1

nS �,

T � i�n,

¢pi � ln pi � ln pi�1, i � 2, . . . , n

p1, p2, . . ., pn.


and the cumulative distribution given by the arc-sine law:

(2)

as proved in Feller (1965, p. 398). For present purposes, the salient fea-ture of the density function is that it peaks at t � 0 and t � 1. In otherwords, under these assumptions, we are more likely to observe a maxi-mum at or near the market open or close than in the middle of the trad-ing day. This fact is familiar to traders in the market, who may think thatthe phenomenon represents an anomaly running counter to the random-walk model. Equation (2) shows that this is not the case, and the preva-lence of market highs at the open and close could be entirely consistentwith a random walk.

Having derived the distribution of T under the assumption of a ran-dom walk with uniform trading intensity, we now generalize the analysisto allow for the fact that volume varies over the day (see Table II). LetS(t) denote the cumulative percentage of trades transacted from timeslot 0 to time slot t, so that as t varies from 0 to 1, S(t) likewise variesover the same range. Then S�(t) is the trading intensity or speed, in thesense that if the number of trades in the interval 0 to t is i, thenand S�(t) is the number of trades in the small interval around t, what wecould call the instantaneous volume. Now consider the time, T, at whichthe maximum occurs. Up to this point, n � S(t) transactions will havebeen completed, with the final trade being the maximum for the day.This time corresponds under a uniform clock to which by Equation (1) has the limiting density:

(3)

Because has the limiting density:

(4)

and the corresponding cumulative distribution:

(5)

Because there are more trades near market open and market close, wewould expect that the high/low time T, would be more likely to be near amarket open/close than is predicted by a model based on the assumptionof uniform trading speed throughout the day. In order to deal with this

F(t) �2p

sin�11S(t)

f(t) �S�(t)

p1S(t)(1 � S(t)) 0 � t � 1

T � S�1(w), T

f(w) �1

p1w(1 � w) 0 � w � 1

w � [nS(T)�n] � S(T),

S(t) � i�n

F(t) �2p

sin�1 1t


9Note that Mok, Lam, and Li (2000) dealt with this problem by reporting results for 5-, 10- and15-minute slots. However, this leaves open the possibility of getting conflicting results for differenttime intervals.

potential problem, we have derived the theoretical distribution not onlyunder the default assumption of constant trading volume [Equation (2)],but also under the more general assumption that volume follows itsobserved intradaily pattern [Equation (5)]. It has been noted elsewherethat, in the index futures market, volume and volatility exhibit broadlythe same intradaily pattern (see e.g., Abhyankar et al., 1999, on LIFFE),presumably attributable, at least to some extent, to the daily inflow ofinformation. Generalizing our test to allow for variation in daily volumetherefore has the added advantage of dealing with the problem of time-varying volatility.

Testing Procedure

From this point onward, we could follow Acar and Toffel (1999) and Moket al. (2000) in dividing the trading day into 5-, 10- or 15-min time slotsand proceeding to compare the observed frequency in each slot with theprediction from the limiting distributions (1) and/or (4), depending onwhether or not we allow for varying volume. One disadvantage of thisapproach is that the results may well be sensitive to the (arbitrarily cho-sen) width of the time slots on which the test is conducted.9

To avoid this difficulty, we propose instead to test across the whole ofthe frequency distribution. Specifically, we examine the cumulative fre-quency, treating it as the smooth distribution of a continuous variablecomparable with the theoretical distributions given in (2) and (5), andapplying the Kolmogorov-Smirnov test to see whether we can reject thehypothesis that the distributions taken as a whole are identical. Not only isthis approach more in the spirit of the continuous-time analysis on whichit is based, it also has the practical advantages that it avoids the problemsthat arise in comparing results across days with different trading hours, aparticularly important point for our data set, which covers a number ofextensions to the trading day.

RESULTS

Our testing procedure involves in the first instance comparing each of ourempirical frequency distributions with the two theoretical distributionsderived in the previous section under the assumptions of constant andvariable trading volume, respectively. For the U.K. and France, we splitthe data period so as to examine the relative efficiency of the pre- and


FIGURE 1FTSE-100 daily highs with floor trading 6/28/94–7/28/99.

FIGURE 2FTSE-100 daily lows with floor trading 6/28/94–7/28/99.

10In order to make the graphs easier to read, we have included only the empirical and the volume-adjusted theoretical distributions, as given by Equation (5).

post-electronic markets. Figures 1–12 graph the cumulative distributionsof highs and lows, respectively, for each of the six data sets: U.K. andFrance before and after their respective changeovers to screen trading,Germany and Korea.10 Formal test statistics are given for each of the fourcountries in Tables III–VI.

Consider first the results for the U.K. in Table III and Figures 1–4.On the one hand, although the volume-unadjusted results suggest rejec-tion (at least at the 5% level) of a random walk for the open-outcry


FIGURE 3FTSE-100 daily highs with screen trading 5/1/99–12/28/00.

FIGURE 4FTSE-100 daily lows with screen trading 5/1/99–12/28/00.

period, this conclusion is reversed when we allow for variations in trad-ing volume. On the other hand, our conclusion regarding screen tradingis unambiguous. We overwhelmingly reject a random walk at higher thanthe 99% level. As is clear from the graphs, the rejection is due to the factthat both highs and lows are far more common early in the day than isconsistent with random behaviour. Not surprisingly, we can also rejectthe hypothesis that the pre- and post-changeover empirical distributionsare identical.

In summary, we can conclude that moving the FTSE-100 contractover to screen trading certainly had a significant impact on the priceprocess, but that after the changeover, the intraday pattern was less ran-dom than before, not more random, as many people would have expectedbefore the fact.


FIGURE 6CAC-40 daily lows with floor trading 1/2/96–5/29/98.

FIGURE 5CAC-40 daily highs with floor trading 1/2/96–5/29/98.

As far as the CAC-40 contract is concerned, the situation is onlyslightly less clear cut. By contrast with the U.K. results, on the basis ofthe Kolmogorov-Smirnov tests in Table IV, we reject randomness at the10% level for the open-outcry period, whether we assume constant vol-ume or allow for its fluctuations over the day. However, rejection for thescreen trading period is much more decisive. We can also reject thehypothesis that the observed distributions are the same before and afterthe changeover. In fact, the data appear to suggest that the alteration inthe microstructure did cause a change in the nature of the deviations


FIGURE 7CAC-40 daily highs with screen trading 6/1/98–2/28/01.

FIGURE 8CAC-40 daily lows with screen trading 6/1/98–2/28/01.

from an intradaily random walk, but with the effect of magnifying thedeviations from randomness.

Taking the UK and France together, a number of points are clear.First, in both countries the price process appears to have changed aroundthe time pit trading was terminated. Because automation occurred nearly


FIGURE 9DAX daily highs 4/1/97–6/1/00.

FIGURE 10DAX daily lows 4/1/97–6/1/00.

FIGURE 11KOSPI daily highs 5/1/96–5/31/99.


100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Time interval

empirical

theoretical

Cum

ulat

ive

dist

ribut

ion

0

0.03

0.07 0.1

0.13

0.17 0.2

0.24

0.27 0.3

0.34

0.37 0.4

0.44

0.47 0.5

0.54

0.57

0.61

0.64

0.67

0.71

0.74

0.77

0.81

0.84

0.87

0.91

0.94

0.97

FIGURE 12KOSPI daily lows 5/1/96–5/31/99.

TABLE III

FTSE-100 Empirical versus Theoretical Distributions

KS Test Statistic

Maxima (p Value) Minima (p Value)

Open outcryEmpirical vs. theoretical 1.56 1.52

(volume unadjusted) (0.02) (0.02)

Empirical vs. theoretical 0.60 0.83(volume adjusted) (0.87) (0.50)

Screen tradingEmpirical vs. theoretical 4.85 3.98



Open outcry versus screen tradingEmpirical vs. empirical 4.29 2.98

(0.00) (0.00)

a year earlier in France than in the U.K., any change in the price processcannot have been the result of a change in external circumstances com-mon to both countries (e.g., a shock originating in the U.S.).


TABLE IV

CAC-40 Empirical versus Theoretical Distributions

KS Test Statistic

Maxima (p Value) Minima (p Value)

Open outcryEmpirical vs. theoretical 1.99 1.28



Screen tradingEmpirical vs. theoretical 2.07 3.51



Open outcry versus screen tradingEmpirical vs. empirical 3.27 1.73

(0.00) (0.01)

TABLE V

DAX Empirical versus Theoretical Distribution

KS Test Statistic

Maxima Minima

Empirical vs. theoretical 1.78 2.60(volume unadjusted) (0.00) (0.00)


TABLE VI

KOSPI-200 Empirical versus Theoretical Distribution

KS Test Statistic

Maxima Minima

Empirical vs. theoretical 6.26 8.62(volume unadjusted) (0.00) (0.00)



The results for Germany and Korea again suggest nonrandomness,overwhelmingly in the case of Korea, on balance for Germany. The evi-dence suggests the DAX contract rarely trades at its maximum or mini-mum level for the day immediately after the opening, but is dispropor-tionately likely to reach a peak or, more particularly, a trough a fewminutes after the opening. Overall, looking at the volume-adjusted sta-tistic for the lows, we reject randomness.

The tests for Korea allow for the fact that the market closes for anhour and a half in the middle of the day, hence the flat section in themiddle of the graphs in Figures 11 and 12. Nonetheless, we again rejectrandomness in all cases over the day as a whole. These results reflect thefact that the KOSPI futures contract is disproportionately likely to regis-ter a maximum or minimum at the start and end of the day’s trading ses-sion, but also immediately before and after the midday break. This sug-gests that our findings are not purely the consequence of information ortime-zone effects, but are inherently part of the price-formation process.

How can we explain a consistent pattern of departures from ran-domness which appears to be robust in the face of changes as significantas a shift to screen trading? The explanation cannot be microstructural,given that the phenomenon is apparent in France and the U.K. both pre-and post-changeover, and in Germany and Korea, which have alwaystraded electronically. The explanation must therefore be behavioral. Itseems that screen trading more directly reflects investor behavior, espe-cially at the start of a trading session. In an electronic market, thereappears to be exchange of information before trade opens, so that marketresponses are observable at the start of the trading day. The fact thatwe subsequently observe a moderation of the initial price maxima andminima suggests that overreaction is intrinsic to the behavior of tradersin the market.

CONCLUSIONS

In this article, we have proposed a new test for intradaily randomnessbased on the frequency distribution of highs and lows, and demonstratedits usefulness in examining real-time data on four liquid futures markets.Our results indicate that the relative frequency of price maxima andminima (especially in the first few minutes after the opening) is fargreater than is consistent with a random walk in all cases. This statementis true for the British and French markets both before and, more surpris-ingly, after computerization, and it applies equally to the German andKorean markets, which were computerized throughout our data period.


Taken at face value, our results would appear to suggest that screen trad-ing has little to offer in terms of efficiency gains. The most likely expla-nation would appear to be that the market opening and, to a lesserextent, the closing are characterized by overreaction to news, a conclu-sion that is indirectly supported by published evidence on other futuresmarkets (e.g., Fung et al., 2000, on Hong Kong and the U.S.). We findconvincing evidence of intradaily inefficiencies consistent with traders’overreaction, though possibly also with other anomalies. As such, ourresults add to the large literature on this phenomenon in financial mar-kets. Because there is a violation of the random-walk hypothesis, a nec-essary (though not sufficient) condition exists for the implementation oftrading strategies similar to those suggested by Fung et al. (2000).

BIBLIOGRAPHY

Abhyankar, A., Copeland, L. S.. & Wong, W. (1999). LIFFE Cycles: Intradayevidence from the FTSE 100 stock index futures market. The EuropeanJournal of Finance, 5, 123–139.

Acar, E., & Toffel, R. (1999). Highs and lows: Times of the day in the currencyCME market (Chap. 5). In P. Lequeux (Ed.), Financial markets tick by tick.New York: Wiley.

Blennerhassett, M., & Bowman, R. G. (1998). A change in market microstruc-ture: The switch to electronic screen trading on the New Zealand StockExchange. Journal of International Financial Markets, Institutions andMoney, 8, 261–276.

Breedon, F., & Holland, A. (1997). Electronic versus open outcry markets: Thecase of the Bund futures contract. Bank of England Discussion Paper.

Eckman, P. D. (1992). Intraday patterns in the S&P 500 futures market. Journalof Futures Markets,12, 365–381.

Feller, W. (1965). An introduction to probability theory and its applications(Vol. 2). New York: Wiley.

Franke, G., & Hess, D. (2000). Information diffusion in electronic and floortrading. Journal of Empirical Finance, 7, 455–478.

Froot, K. A., & Perold, A. (1995). New trading practices and short run marketefficiency. Journal of Futures Markets, 15, 731–765.

Fung, A. K. W., Mok, D. M. Y., & Lam, K. (2000). Intraday price reversals forindex futures in the US and Hong Kong. Journal of Banking and Finance,24, 1179–1201.

Fung, H. G., Lo, W. C., & Peterson, J. E. (1994). Examining the dependency inintra-day stock index futures. Journal of Futures Markets, 14, 405–419.

Garman, M. B., & Klass, M. J. (1980). On the estimation of security pricevolatilities from historical data. Journal of Business, 53, 67–78.

Huang, R. D., & Stoll, H. R. (1996). Dealer versus auction markets: A pairedcomparison of execution costs on NASDAQ and the NYSE. Journal ofFinancial Economics, 41, 313–358.


Kappi, J., & Siivonen, R. (2000). Market liquidity and depth on two differentelectronic trading systems: A comparison of Bund futures trading on theAPT and DTB. Journal of Financial Markets, 3, 389–402.

Kaufman, P. J. (1987). The new commodity trading systems and methods.New York: Wiley.

Kofman, P., & Moser, J. T. (1997). Spreads, information flows and transparencyacross trading systems. Applied Financial Economics, 7, 281–294.

MacKinlay, A. C., & Ramaswamy, K. (1988). Index futures arbitrage and thebehaviour of stock index futures prices. Review of Financial Studies, 1,137–158.

Martens, M. (1998). Price discovery in high and low volatility periods: Openoutcry versus electronic trading, 8, 243–260.

McInish, T. H., & Wood, R. A. (1991). Daily returns, volume, trade size, andnumber of trades. Journal of Financial Research, 14, 303–312.

McInish, T. H., & Wood, R. A. (1992). An analysis of intraday patterns inbid/ask spreads for NYSE stocks. Journal of Finance, 47, 345–374.

Miller, M. H., Muthuswamy, J., & Whaley, R. E. (1994). Mean reversion ofStandard and Poor’s 500 Index basis changes: Arbitrage-induced or statisticalillusion? Journal of Finance, 49, 479–513.

Mok, D. M. Y., Lam, K., and Li, W. (2000). Using daily high/low time to test forintraday random walk in two index futures markets. Review of QuantitativeFinance and Accounting, 14, 381–397.

Pagano, M., & Roell, A. (1996). Transparency and liquidity: A comparison ofauction and dealer markets with informed trading. Journal of Finance, 51,599–612.

Tsang, R. (1999, Spring). Open outcry and electronic trading in futuresexchanges. Bank of Canada Review, 21–38.

van Marrewijk, C., & de Vries, C. (1990). The customs union argument for amonetary union. Journal of Banking and Finance, 14, 877–887.

Wood, R. A., McInish, T. H., & Ord, J. K. (1985). An investigation of transac-tions data for NYSE stocks. Journal of Finance, 40, 723–742.

the index futures markets: is screen trading more efficient?

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