The Index Futures Markets: Is Screen Trading More ... - CiteSeerX

19/08/01

The Index Futures Markets: Is Screen Trading More Efficient? Laurence COPELAND Sally-Ann JONES Cardiff Business School

Kin LAM Hong Kong Baptist University

The first author would like to record his gratitude to the Julian Hodge Foundation for its support, and to Hong Kong Baptist University for its hospitality during the writing of this paper.

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ABSTRACT

The Index Futures Markets: Is Screen Trading More Efficient? Laurence COPELAND and Sally-Ann JONES Cardiff Business School

Kin LAM Hong Kong Baptist University

This paper uses a nonparametric test based on the Arc-Sine Law (see e.g. Feller (1965)), which involves comparing the theoretical distribution implied by an intraday random walk with the empirical frequency distribution of the daily high/low times, in order to address the question of whether or not the abandonment of pit trading has been associated with greater market efficiency. If market inefficiencies result from flaws in the market microstructure of pit trading, they ought to have been eliminated by the introduction of screen trading. If, on the other hand, the inefficiencies are a reflection of investor psychology, they are likely to have survived, unaffected by the changeover. We focus here on four cases. Both the FTSE-100 and CAC-40 index futures contracts were originally traded by open outcry and have moved over to electronic trading in recent years, so that we are able to compare pricing behaviour before and after the changeover. The equivalent contracts in Germany and Korea, on the other hand, have been traded electronically ever since their inception. Our results overwhelmingly reject the random walk hypothesis both for open outcry and electronic trading datasets, suggesting there has been no increase in efficiency as a result of the introduction of screen trading. One possible explanation consistent with our results would be that the index futures market is characterised by intraday overreaction.

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In recent years, a trend towards electronic trading in futures markets has become well-established, as one after another the major exchanges outside the USA replace 1 or at least supplement the traditional open outcry with automated systems. This paper uses a new test based on the frequency of maxima and minima to address the question of whether or not the abandonment of pit trading has been associated with greater market efficiency. While the motivation behind computerisation of trading may vary from market to market, greater efficiency must surely be one of the considerations. On this point, however, neither the theoretical arguments nor the evidence are unambiguous. As far as the theoretical issues are concerned, the question revolves around the relative importance of transaction costs, which are usually assumed to be lower in an electronic market, and liquidity, which is often said to be lower too (see the discussion in Kofman and Moser (1997), Martens (1998), Kappi and Siivonen (2000)). Most of the empirical papers model the spread between bid and ask (either directly observed or estimated by the well-known Roll (1984) method or some variant thereof) so as to make use of transaction data to measure the relative liquidity, depth, transparency etc of electronic markets relative to open outcry. The most heavily researched case is the competition between London and Frankfurt for dominance of the market in the Bund futures contract, which from 1990 to 1999 was traded simultaneously in the LIFFE pits and in the Frankfurt DTB electronic market (e.g. Kofman and Moser (1997), Franke and Hess (2000)). In this paper, by contrast, we take a more direct approach to measuring efficiency by addressing the question: is the ultimate outcome, the pricing process itself as observed over the typical trading day, more or less consistent with random walk behaviour after the changeover than before? If market inefficiencies (assuming they exist) result from flaws in the market microstructure of pit trading, they ought to have been eliminated by the introduction of screen trading. If, on the other hand, the inefficiencies are a reflection of investor psychology, they are likely to have survived, unaffected by the changeover. For example, if investors are subject to the type of behavioural failings that result in overreaction to news, there is no reason to expect any change when open outcry trading is abandoned. We focus here on four cases. Both the FTSE-100 and CAC-40 index futures contracts were originally traded by open outcry and have moved over to electronic trading in the last few years, so that we are able to compare pricing behaviour before and after the changeover. The equivalent contracts in Germany and Korea, on the other hand, have been traded electronically ever since their inception. In our empirical work, we implement a nonparametric test based on the Arc-Sine Law (see e.g. Feller (1965)), which involves comparing the theoretical distribution implied by an intraday random walk with the empirical frequency distribution of the intraday high/low times. Our results indicate that the relative frequency of price maxima and minima (especially in the first few minutes after the opening) is far greater than is consistent with a random walk in all cases. This statement is true for the British and French markets both before and, more surprisingly, after computerisation, and it applies equally to the markets in Germany and Korea. Taken at face value, our 1

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

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results would appear to suggest that screen trading has little to offer in terms of efficiency gains. The most likely explanation would appear to be that the market opening and, to a lesser extent, the closing are characterised by overreaction to news, a conclusion that is indirectly supported by published evidence on other futures markets (e.g. Fung, Mok and Lam (2000) on Hong Kong and USA). We give a brief overview of the literature in Section 1 and an outline of our datasets in Section 2. The details of our testing procedure are set out in Section 3, where we derive the theoretical distribution of the price maxima and minima across the trading day, first under the assumption of a constant trading volume, then relaxing this assumption to allow for the fact that volume typically fluctuates as information flows into the market. In particular, we show that the frequency distribution of highs and lows under a random walk is not uniform, as might casually have been expected, but instead is higher at the open and close of trade. In Section 4, we implement a test based on a comparison of the implied cumulative distribution with the observed distributions from our datasets.

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Literature Survey

The work reported in this paper should be seen in the context of three strands of the literature. First, our results relate to the literature on intradaily patterns in financial, and especially index futures, markets. Researchers in this area have documented a number of regularities, especially the U-shaped pattern of intradaily volume and volatility, which have proved robust across datasets (see Abhyankar et al (1999) and references therein). Second, a number of authors have addressed the question of whether screen-based trading is actually more efficient than open outcry. For the most part, researchers have tended to concentrate on efficiency in terms of transaction costs i.e. examining bid-ask spreads, whether directly observed or estimated. Blennerhassett and Bowman (1998), for example, find that spreads have narrowed since the introduction of screen trading in New Zealand equities. 2 A number of published papers focus on the Bund futures contract traded by open outcry on LIFFE and electronically in Frankfurt (e.g. Martens (1998), Kappi and Siivonen (2000), Kofman and Moser (1997)), with conclusions that are somewhat ambiguous. A third body of literature to which we relate deals with the problems involved in testing for the existence of patterns in daily financial data. A number of different approaches have been taken. The first and most direct approach would be to examine the autocorrelation patterns in high frequency (1-minute, 5-minute, 15minute etc) returns, with any evidence of significant negative autocorrelation being taken as indicative of possible overreaction. For example, Wood, McInish and Ord 2

Note that the relative efficiency of dealer versus auction markets addressed by Huang and Stoll (1996) and Roell and Pagano (1996) among others is a somewhat different question. Electronic markets can be of either kind.

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(1985), looking at NYSE data for 1971-2 and 1982, found some evidence of autocorrelation, particularly at the start and end of trading, while MacKinlay and Ramaswamy (1988) found little evidence of autocorrelation in any of the first eight lags of the 15-minute returns on the S&P 500 futures 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 daily data for two contracts on the same index. Both approaches rely on the existence and stationarity of second moments, which may be an unwarranted assumption in the context of this type of data. In recognition of this drawback, Fung, Lo and Peterson (1994) also apply the rescaled range test which, along with fractional difference methods, makes them able to confirm the absence of any long memory process in their dataset. Most recently, and most relevant to the methods adopted in this paper, Acar and Toffel (1999) and Mok, Lam and Li (2000) focus on the frequency distribution of maxima and minima across time intervals in the trading day, comparing the observed frequency in each time interval with the theoretical frequency implied by a random walk. 4 In arriving at their results, Acar and Toffel (1999) assume a constant trading volume throughout the day, an assumption we know to be unjustified (see, for example, McInish and Wood (1991) or Abhyankar, Copeland and Wong (1999)). However, in deriving the theoretical density Acar and Toffel (1999) do allow for drift, 5 although in practice the daily drift is probably too small to make any substantial difference to the outcome. Mok et al (2000) modify the test in a potentially important respect, by allowing for variation in trading volume across time slots in the day. In the present paper, we further modify their procedure, by applying the KolmogorovSmirnov test jointly to both distributions to allow us to reach an unambiguous conclusion based on the distribution across all time intervals in the day.

2.

Our Dataset

Our raw data consist of real time transaction price and volume data for 3-month futures on the FTSE-100 traded on LIFFE, the CAC-40 traded originally on the MATIF, now on MONEP, the DAX in Germany and the KOSPI-100 in Korea. Some of the main features of our dataset are summarised in Tables 1 and 2.6 The FTSE-100 is an arithmetic value-weighted index covering around 75% of the London market by value. It is updated minute-by-minute during the trading day. The futures contract began trading on the floor of LIFFE in 1984, though our dataset only begins in the second half of 1994. The changeover to screen trading occurred on 1st 3

But see also Miller, Muthuswamy and Whaley (1994) for a possible explanation of the patterns in the basis which relies on infrequent trading in index stocks. 4 The idea of analysing the frequency of highs and lows was actually pioneered by van Marrewijk and de Vries (1990) in the context of tests of Purchasing Power Parity in exchange rate data. 5 Albeit by simulation only, as they are unable to derive a closed o f rm solution for the theoretical distribution in the presence of drift. 6 For further details, refer to the four exchanges which trade the contracts (and supplied the data). in particular, note that some markets impose theoretical price fluctuation limits, though they were rarely activated during our data period. Of course, the fact that limits are not activated does not necessarily mean they have no effect whatsoever. However, the CAC-40, for example, only once or twice reached its daily limits of plus or minus 275 points, so it seems this factor can safely be ignored for present purposes.

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May 1999, so that by extending our data period up to the end of 2000, we are able to include about 19 months of results from electronic trading. The CAC-40 is a value-weighted index representing around 80% of the value of the Paris stock market in 1998 and is published at 30-second intervals throughout the trading day. The associated futures contract has undergone a number of changes since it started trading by open outcry on the MATIF in 1988. Electronic trading was introduced initially in 1993, but only for after-hours sessions. Pit trading survived during normal business hours until the introduction of the NSC electronic trading mechanism on 1st June 1998. Our dataset starts in January 1996, giving five years observations, approximately half before and half after the changeover. The DAX is an arithmetic value-weighted index of the prices of the top 30 German companies, accounting for some 66% of turnover on the Frankfurt stock exchange. Since 1988, it has been computed every mi nute during market opening hours. The futures contract was introduced towards the end of 1990, although our data only start from April 1997. From its inception, it has always traded electronically. The KOSPI-200 is a market capitalization-weighted real-time index of the prices of 200 major stocks listed on the Korean Stock Exchange, covering nearly 80% of total market value, with the list of constituent stocks updated annually. It is computed and published every 30 seconds during the trading hours of 09.30 to 11.30 for the morning session, and 13.00 to 15.00, for the afternoon. The fact that the day is divided into two separate sessions is potentially a challenge to the methodology adopted in this paper, a point to which we shall return later. The futures contract has been traded at the KSE since its inception in May 1996. Trading hours are the same as for the cash, except that the afternoon session for the futures does not end until 15.15. As is clear from Table 2, all the contracts trade in substantial volume on a typical day. It should also be noted that the daily pattern is characterised by considerable variation in trade intensity, with extremely low levels at some points (mostly around the middle of the day) and maximal levels many times greater.

3.

Testing Methodology

We focus here on the pattern of returns over the typical day’s trading. Since trading futures incurs no opportunity cost of money, any expected return could only be compensation for risk, which is likely to be negligible over a holding period measured in hours. Given that transaction costs are small, we would expect returns to show no discernible pattern i.e. we expect prices to follow a random walk. This is the hypothesis we test in this paper. 3.1

Daily High/Low

Our basic approach is an extension of the procedure in Mok, Lam and Li (2000) to test for time series dependence in the intraday pattern of high and low prices. Essentially, we make no assumptions about the nature of any dependence over the trading day. In particular, we do not assume stationarity in the underlying process. Instead, we concentrate on observing the time at which the day’s maximum or 6

minimum occurs. This approach has two advantages. First, the timing of the day’s maximum or minimum is interesting in its own right, since technical analysts believe it can be used as a basis for designing profitable trading strategies (e.g. Kaufman (1987)). Second, by examining the theory of random walk processes, we are able to develop a procedure which can not only provide a test for deviations from randomness, but can also tell us at what times of the day these effects are most likely to occur. It should be emphasised that we are not concerned here with the actual level of the maximum or minimum values of the index, though this statistic has received considerable attention from researchers, especially as an input into intraday volatility estimates (e.g. Garman and Klass (1980)). Instead, we are concerned with the frequency distribution across our daily data set of the time at which the market reaches what turns out, with hindsight, to have been its daily high or low. It will be shown in the next section that, if a price follows a random walk, this distribution is Ushaped rather than uniform, as might have been expected by a casual consideration of the theory. It follows that traders who claim that highs and lows tend to occur most frequently soon after the open or not long before the close may in fact be observing a phenomenon which is perfectly consistent with randomness. 3.2

Theoretical Density of the Daily Maximum 7

We start with the observation that the structure of any dependence in high frequency financial data is potentially complicated. In particular, we can see no grounds for assuming stationarity in index futures market data, given the well-documented patterns in the underlying indices (e.g. Wood, McInish and Ord (1985)). At the very least, the pattern is likely to vary over the day, as Froot and Perold (1995) found in the case of the S&P 500. With these considerations in mind, we choose here to implement a modified version of a nonparametic test for which the theoretical basis is set out in Mok, Lam and Li (1999) (though see also van Marrewijk and de Vries (1990), and Acar and Toffel (1999)). For simplicity, suppose the trading day consists of only one hour, during which time the sequence of observed prices corresponding to the n trades executed during the hour is p1 , p 2 ,...., p n . The random walk assumption amounts to postulating that, conditional on the value o f n, the series of price changes ∆p i = ln pi − ln pi −1 i = 2,..., n is an independent identically distributed sequence symmetrical about zero, which we denote F (⋅) . If we make the temporary assumption that the volume of trade in the futures contract is uniform throughout the day, and denote the time at which the price i reaches its maximum 8 by T = where i is the order of the maximum in the sequence n 7

All statements should be taken to apply equally to the distribution of the daily minimum. To avoid tedious repetition, we restrict attention to the distribution of the maximum. 8

Note that when there are tied maxima, T indexes the first time the futures price reaches its maximum.

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of n price observations, then as n → ∞ , the probability density of T approaches the limit: f (t ) =

1 π t (1 − t )

0 < t