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Trading mechanisms and value-discovery: Cross-national evidence and policy implications
Carnegie-Rochester North-Holland
Conference Series on Public Policy 34 (1991) 131-134
TRADINGMECHANISMSAND VALUE-DISCOVERY: CROSS-NATIONALEVIDENCE AND POLICYIMPLICATIONS A Comment KENNETH University
R.
FRENCH
of Chicago and
National
Bureau
of Economic
Research
In their ingenious paper, Amihud and Mendelson exploit differences in the organization of the New York, Tokyo and Milan Stock Exchanges to examine the effect of different trading mechanisms on the price-discovery process. Each of these exchanges uses some combination of a periodic and a continuous trading system. In a periodic system, buy and sell orders accumulate over a period of time and are then executed in one clearing transaction. In a continuous system, market orders or marketable limit orders are executed as they arrive. From a trader’s perspective, there are several differences between a continuous trading system and clearing transactions. The most obvious is liquidity.
A continuous
system allows traders
to transact
is open; with a system involving only clearing zero between transactions.
whenever the market
transactions,
liquidity
falls to
Amihud and Mendelson point out another important difference. An investor submitting a typical limit order in a continuous system knows that, if he makes a trade, it will occur at his limit price. The price he submits determines the price of his trade. In contrast, all trades in a clearing transaction occur at the single price that clears the market, not at the individual prices submitted by each trader. If there are many other orders in the clearing transaction, each trader knows his quote will affect the probability that his order is executed, but it is unlikely to determine the clearing price. Thus, the continuous trading system is like a first-price auction, in which the winner’s
0167-2231/91/$03.50
0 1991 - Elsevier Science Publishers B.V.
All rights reserved
bid determines the price he pays. Clearing transactions are like second-price auctions, in which the second highest bid determines the winner’s price. With this insight, researchers (and traders) can draw on the extensive auction literature
to better
understand
the optimal
trading
strategies
of a continuous
trading system and a clearing transaction system. Amihud and Mendelson examine the effect of these different market mechanisms by comparing daily returns from the New York, Tokyo and Milan Stock Exchanges. Specialists on the New York Stock Exchange (NYSE) open trading each day with a clearing transaction and then trade continuously for the rest of the day. There are two trading sessions each day on the Tokyo Stock Exchange (TSE), one in the morning (9-11 AM) and one in the afternoon (l-3 PM). Each session is like a short trading day on the NYSE, with a clearing transaction at the opening transaction, followed by continuous trading until the close. The organization of the Milan Stock Exchange (MSE) is not as simple. Typically, the clearing transaction for major stocks occurs in the middle of the day, with a continuous trading period both before and after the clearing transaction. On some days, however, the clearing transaction is the first trade. These differences across exchanges allow Amihud and Mendelson to compare the impact of clearing transactions and continuous trading on the value-discovery process. The paper’s simplest and most powerful tests compare the behavior of close-to-close and open-to-open returns on each exchange. For the 30 NYSE stocks in the Dow Jones Industrial Index, open-to-open returns are on average about 20% more volatile than close-to-close returns. Similarly, open-to-open NYSE returns are negatively autocorrelated, with an average autocorrelation of -S%, and close-to-close returns are positively correlated, with an average autocorrelation of 5%. If we assume that “efficient” returns are uncorrelated, the higher volatility of open-to-open returns seems to reAect both random noise in opening prices and some sort of smoothing process in closing prices. Conceptually, the noise in the opening prices on the NYSE could arise either because of the different trading mechanisms used at the open and the close, or because the opening prices are set after a long period in which the market is closed. The authors turn to the TSE returns to separate these two effects. They find that, like the NYSE results, the opening prices for the morning session are more volatile than the closing prices. There is little difference, however, between the opening and closing volatilities for the afternoon session. Since the opening prices for both sessions are set in clearing transactions, Amihud and Mendelson argue that it is the long period after the previous day’s close, rather than the trading mechanism, that causes more volatile opening prices. These results are corroborated by the returns from the Milan Stock Exchange. Amihud and Mendelson are clever in their use of returns from various
132
However, one might be able to go even farther by using stock exchanges. the prices for other financial assets. For example, futures contracts on most exchanges in the United States are traded continuously, without any clearing transactions
at all.
Thus,
a comparison
of open-to-open
and close-to-close
volatilities for these contracts would be a clear test of the hypothesis that higher volatility on the open is caused by the prior period of no trading. Table 1 reports the results of just such a test. The standard deviations, variance ratios and autocorrelations in Table 1 compare the daily open-to-open and close-to-close returns for corn, soybeans and wheat futures contracts from 1969 through 1987. These estimates corroborate Amihud and Mendelson’s conclusion. The volatility of open-to-open returns is roughly 20% higher than the volatility of close-to-close returns for all three commodities. Since none of these contracts opens with a clearing transaction, it appears that the higher volatility of futures - and stock prices at the open is caused by the prior period of no trading rather than by the trading mechanism. The results in this paper are important because differences in trading mechanisms play an important role in many of the arguments debated by financial economists and practitioners. For example, the arguments against open outcry in futures markets and against the specialist system on stock exchanges seem to be based on the view that these mechanisms increase the cost of trading or that they hamper the price-discovery process. On the other hand, many claim that the stock-market crash of 1987 was at least exacerbated by portfolio insurance and program trading, and that these activities were fostered by the low cost of trading. The results in this paper should help resolve these and other related questions.
133
Standard
Table 1: Deviations and Autocorrelations for Daily Percent Changes Corn, Soybean, and Wheat Futures Prices, 1969-1987 Corn Standard
Soybeans
Deviations 1.28 1.15
Open-to-Open Close-to-Close Variance Open-to-Open/Close-to-Close
Wheat
(in Percent) 1.41 1.30
1.56 1.39
1.18
1.26
-0.10 -0.05
-0.06 0.00
Ratios 1.24
Autocorrelations -0.11 -0.03
Open-to-Open Close-to-Close
Note: Each statistic
is estimated
with roughly 4000 observations.
134
in
Conference Series on Public Policy 34 (1991) 131-134
TRADINGMECHANISMSAND VALUE-DISCOVERY: CROSS-NATIONALEVIDENCE AND POLICYIMPLICATIONS A Comment KENNETH University
R.
FRENCH
of Chicago and
National
Bureau
of Economic
Research
In their ingenious paper, Amihud and Mendelson exploit differences in the organization of the New York, Tokyo and Milan Stock Exchanges to examine the effect of different trading mechanisms on the price-discovery process. Each of these exchanges uses some combination of a periodic and a continuous trading system. In a periodic system, buy and sell orders accumulate over a period of time and are then executed in one clearing transaction. In a continuous system, market orders or marketable limit orders are executed as they arrive. From a trader’s perspective, there are several differences between a continuous trading system and clearing transactions. The most obvious is liquidity.
A continuous
system allows traders
to transact
is open; with a system involving only clearing zero between transactions.
whenever the market
transactions,
liquidity
falls to
Amihud and Mendelson point out another important difference. An investor submitting a typical limit order in a continuous system knows that, if he makes a trade, it will occur at his limit price. The price he submits determines the price of his trade. In contrast, all trades in a clearing transaction occur at the single price that clears the market, not at the individual prices submitted by each trader. If there are many other orders in the clearing transaction, each trader knows his quote will affect the probability that his order is executed, but it is unlikely to determine the clearing price. Thus, the continuous trading system is like a first-price auction, in which the winner’s
0167-2231/91/$03.50
0 1991 - Elsevier Science Publishers B.V.
All rights reserved
bid determines the price he pays. Clearing transactions are like second-price auctions, in which the second highest bid determines the winner’s price. With this insight, researchers (and traders) can draw on the extensive auction literature
to better
understand
the optimal
trading
strategies
of a continuous
trading system and a clearing transaction system. Amihud and Mendelson examine the effect of these different market mechanisms by comparing daily returns from the New York, Tokyo and Milan Stock Exchanges. Specialists on the New York Stock Exchange (NYSE) open trading each day with a clearing transaction and then trade continuously for the rest of the day. There are two trading sessions each day on the Tokyo Stock Exchange (TSE), one in the morning (9-11 AM) and one in the afternoon (l-3 PM). Each session is like a short trading day on the NYSE, with a clearing transaction at the opening transaction, followed by continuous trading until the close. The organization of the Milan Stock Exchange (MSE) is not as simple. Typically, the clearing transaction for major stocks occurs in the middle of the day, with a continuous trading period both before and after the clearing transaction. On some days, however, the clearing transaction is the first trade. These differences across exchanges allow Amihud and Mendelson to compare the impact of clearing transactions and continuous trading on the value-discovery process. The paper’s simplest and most powerful tests compare the behavior of close-to-close and open-to-open returns on each exchange. For the 30 NYSE stocks in the Dow Jones Industrial Index, open-to-open returns are on average about 20% more volatile than close-to-close returns. Similarly, open-to-open NYSE returns are negatively autocorrelated, with an average autocorrelation of -S%, and close-to-close returns are positively correlated, with an average autocorrelation of 5%. If we assume that “efficient” returns are uncorrelated, the higher volatility of open-to-open returns seems to reAect both random noise in opening prices and some sort of smoothing process in closing prices. Conceptually, the noise in the opening prices on the NYSE could arise either because of the different trading mechanisms used at the open and the close, or because the opening prices are set after a long period in which the market is closed. The authors turn to the TSE returns to separate these two effects. They find that, like the NYSE results, the opening prices for the morning session are more volatile than the closing prices. There is little difference, however, between the opening and closing volatilities for the afternoon session. Since the opening prices for both sessions are set in clearing transactions, Amihud and Mendelson argue that it is the long period after the previous day’s close, rather than the trading mechanism, that causes more volatile opening prices. These results are corroborated by the returns from the Milan Stock Exchange. Amihud and Mendelson are clever in their use of returns from various
132
However, one might be able to go even farther by using stock exchanges. the prices for other financial assets. For example, futures contracts on most exchanges in the United States are traded continuously, without any clearing transactions
at all.
Thus,
a comparison
of open-to-open
and close-to-close
volatilities for these contracts would be a clear test of the hypothesis that higher volatility on the open is caused by the prior period of no trading. Table 1 reports the results of just such a test. The standard deviations, variance ratios and autocorrelations in Table 1 compare the daily open-to-open and close-to-close returns for corn, soybeans and wheat futures contracts from 1969 through 1987. These estimates corroborate Amihud and Mendelson’s conclusion. The volatility of open-to-open returns is roughly 20% higher than the volatility of close-to-close returns for all three commodities. Since none of these contracts opens with a clearing transaction, it appears that the higher volatility of futures - and stock prices at the open is caused by the prior period of no trading rather than by the trading mechanism. The results in this paper are important because differences in trading mechanisms play an important role in many of the arguments debated by financial economists and practitioners. For example, the arguments against open outcry in futures markets and against the specialist system on stock exchanges seem to be based on the view that these mechanisms increase the cost of trading or that they hamper the price-discovery process. On the other hand, many claim that the stock-market crash of 1987 was at least exacerbated by portfolio insurance and program trading, and that these activities were fostered by the low cost of trading. The results in this paper should help resolve these and other related questions.
133
Standard
Table 1: Deviations and Autocorrelations for Daily Percent Changes Corn, Soybean, and Wheat Futures Prices, 1969-1987 Corn Standard
Soybeans
Deviations 1.28 1.15
Open-to-Open Close-to-Close Variance Open-to-Open/Close-to-Close
Wheat
(in Percent) 1.41 1.30
1.56 1.39
1.18
1.26
-0.10 -0.05
-0.06 0.00
Ratios 1.24
Autocorrelations -0.11 -0.03
Open-to-Open Close-to-Close
Note: Each statistic
is estimated
with roughly 4000 observations.
134
in