Intertrade Duration and Information-Based Trading on an Electronic Order-Driven Market
电子指令驱动市场上的交易持续期与知情交易的相关性研究
Jiekun HUANG*
黄 杰 鲲#
Department of Finance, Xiamen University
厦门大学 金融系
Downloadable from the SSRN website
This research is done under the guidance of Prof. Langnan Chen. Many read the manuscript and provided valuable comments including Ruey S. Tsay, Jevons Lee, Frank M. Song, Liu Feng and Paul Brockman. Special thanks are given to Ouyang Yongwei and Huang Houchuan who helped collect the data and provided computational tips. Special thanks also go to Daniel Pan who provided two references and some helpful advice. Discussions with Wu Jiangming helped improve this paper. I’m indebted to my family and my dearest Zhang Shuo whose love and encouragement is invaluable to me and this research. All remaining errors are my own responsibility.
Intertrade Duration and Information-Based Trading on an Electronic Order-Driven Market
Abstract: Microstructure literature and empirical models offered conflicting prediction regarding the relationship between trading intensity and information-based trading. In this paper, I undertake an empirical investigation that is motivated by this conflicting theory. Firstly, I develop an asymmetric specification that attempts to capture the asymmetric effect of good news and bad news on intertrade durations. One interesting point that emerges from the analysis is that good-news-based trading will generally lead to increased trading intensity, while bad-news-based trading will generally contribute to longer durations. Then I ask whether long durations are associated with bad news. It turns out that long durations will lead to declining prices and low volatility; moreover, the commonly assumed leverage effect is rejected at the transaction data level.
Keywords: ACD model, UHF-GARCH model, microstructure, trading intensity, information-based trading, volatility, asymmetric effect, Shanghai Stock Exchange (SSE)
JEL classification: G10, G15, C10, C41
Contents
11. Introduction
22. Literature Review
Theoretical Models and Some Testable Hypotheses
Empirical Studies
53. Econometric Framework
The ACD Model
The UHF-GARCH Model
84. Institutional Background and Data Description
Institutional Features of the Shanghai Stock Exchange
The Data
Preliminary Data Manipulation
145. Are Impatient Trades Information-Based?
Some Peripheral Evidence
Auxiliary Regressions
A Trading Strategy
176. Model Specifications
Duration Impact of Information-Based Trading
Information Content of Intertrade Duration
197. Estimation and Results
Estimates of the Asymmetric-WACD model
Estimates of the GJR-version UHF-GARCH model
258. Conclusion
26References
Introduction
Microstructure literature and empirical models have so far offered conflicting prediction regarding the relationship between trading intensity (the reciprocal of intertrade duration) and information-based trading. Diamond and Verrecchia (1987) consider the impact of short sale constraints on informed trader and conclude that non-trading is indicative of bad news. In Easley and O’Hara (1992), increased numbers of transactions are due to information events and the naturally increased numbers of informed traders. Thus information events will lead to short durations. However, Admati and Pfleiderer (1988) demonstrate that uninformed traders will refrain from trading when there is evidence for informed trading, which implies that informed trading lead to long durations.
The present paper has twofold purposes. Firstly, I examine the duration impact of information-based trading. So far little empirical studies have explored the causality running from information-based trading to intertrade duration. The lack is partially attributable to the difficulty of distinguishing between information-based trading and liquidity-based trading. An innovation of the paper is employing impatient buys/sells as an indictor of information-based trading. Using recently proposed ACD framework, I develop an asymmetric specification that attempts to capture the asymmetric effect of good news and bad news on intertrade durations. One interesting point that emerges from the analysis is that good-news-based trading will generally lead to increased trading intensity, while bad-news-based trading will generally contribute to longer durations.
On the other hand, does it necessarily mean long durations are associated with bad news and short durations are associated with good news? This has been examined by Engle (2000) but with conflicting results. Thus the second purpose of this paper is to investigate the information content of durations, or more specifically long durations. A GJR-version UHF-GARCH is estimated. It turns out that long durations will lead to declining prices and low volatility; moreover, the commonly assumed leverage effect is rejected at the transaction data level.
The analysis is based on transaction data for individual stocks comprising SSE 180 Index. As one of the largest emerging markets with an electronic order book, the Shanghai Stock Exchange (SSE) certainly possesses some institutional features to warrant interest.
The remainder of this paper is structured as follows. In Section 2, I review the microstructure literature and empirical literature, while some testable hypotheses are formulated. Section 3 provides the econometric framework proposed by Engle and Russell (1998) and Engle (2000). Institutional features of China’s stock market and data set used in this research is described in section 4. Section 5 presents evidence that impatient trades are information-based. Model specifications and estimation results are presented in section 6 and section 7 respectively. Section 8 concludes.
Literature Review
This section provides a review of theoretical and empirical works on the relationship between trading intensity and information-based trading. Some testable hypotheses are derived herein.
Theoretical Models and Some Testable Hypotheses
The informational role of time between trades is first developed by Diamond and Verrecchia (1987) and Easley and O’Hara (1992). These papers provide partially contradictory results regarding the relationship between trading intensity and information-based trading.
The intuition behind Diamond-Verrecchia’s model is that if traders are unable to transact in certain states of the world, then non-trading can be informative of the underlying state. At the beginning of the trading day, one of the two possible events happens, either good news or bad news. If it is good news, informed traders will always buy; while if it is bad news and short sale constraints are imposed, informed traders who do not own the stock will not trade. Uninformed traders face a similar decision problem but differ from informed traders in that their trades are liquidity based, rather than information based. This trading behavior means that an absence of trade can occur for three reasons. First, the trader selected to trade simply does not want to transact. This decision is independent of information on the asset’s value and so there is no information content to the absence of trading arising for this reason. Second, an absence of trade can occur if an uninformed trader facing positive liquidity demands is unable to short sell because of constraints. Again, this decision is not information-related and so also provides no information to the market. Finally, a trader informed of bad news may be unable to trade if short sales are prohibited. In this case, observing a non-trading outcome may signal that there is bad news about the value of the asset. Two hypotheses that emerged from Diamond-Verrecchia’s model can be summarized as,
H1. Bad news will contribute to long durations.
H2. No trade is indicative of bad news.
Easley and O’Hara (1992) introduce event uncertainty into the analysis. In the Easley-O’Hara model informed traders trade only during days with information events that influence the asset price. If the news is good (bad), informed traders buy (sell). In contrast, uninformed traders buy and sell at a constant rate regardless of circumstances. So if there is no information event, only the uninformed traders trade. Thus traders watching the market can interpret long durations as evidence that there is no news. Again, there are two hypotheses, which are
H3. Information events, either good news or bad news, will lead to increased transaction intensity. Or equivalently,
H4. No trade means no news.
Admati and Pfleiderer (1988) consider a more sophisticated behavior pattern on the part of uninformed traders. Their models include two types of liquidity traders. Nondiscretionary liquidity traders must trade a particular number of shares at a particular time. In addition, discretionary liquidity traders also have liquidity demands but can be strategic in choosing when to trade within a given period of time. A major finding is that the discretionary liquidity traders concentrate trading activity in periods where there is no indication for informed trading, while they avoid to trade when there is evidence for informed trading. In this case, the trading is not related to information event and hence volatility would be low just when the market is active. Two hypotheses can be derived. These are
H5. Information-based trading will deter uninformed traders from trading, thus leading to long durations.
H6. Slow trading is indicative of informed trading and high volatility.
These hypotheses are interrelated and some are contradictory, . H2 vs. H4, H4 vs. H6, H3 vs. H5. They can be placed in two groups, with H1, H3 and H5 placed in a group labeled “duration impact of information-based trading” and H2, H4 and H6 placed in a group labeled “information content of durations”.
Empirical Studies
With recently developed econometric techniques, these hypotheses are testable. The Autoregressive Conditional Duration (ACD) model proposed by Engle and Russell (1998) focuses on the time elapsed between the occurrences of trading events and is perfectly suited for the analysis of irregularly-spaced high frequency data. Engle (2000) proposes adapting the GARCH model for application to irregularly spaced transaction by transaction data, thus provides ultra-high-frequency measures of volatility. Following these seminal work, a large body of ACD/UHF-GARCH literature is emerging .
Like the theoretical models, empirical studies so far have not provided unambiguous results regarding the relationship between trading intensity and information-based trading.
Ghysels and Jasiak (1998) develop an ACD-GARCH model, where the intertrade durations determine the parameter dynamics. The results obtained using IBM transaction data show the existence of Granger causality between past volatility and duration and the values of cross-correlation function are positive. Conceiving volatility as an indicator for informed trading, this is consistent with H5.
Engle and Lunde (1999) make a generalization of the ACD model and formulate a bivariate point process to jointly analyze transaction and quote arrivals. Their empirical application shows that when quotes have not been revised for a long time, . volatility was low, transaction intensities increase, which is consistent with H5.
Dufour and Engle (2000) extend Hasbrouck’s (1991) VAR model for the dynamics of trades and quote revisions to allow the coefficients to vary with time. They use the ACD model for the transaction arrival time. The results obtained using transaction data of 18 NYSE traded stocks show that intertrade duration and price impact of trades are negatively related, which can be interpreted as consistent with H3.
Zhang, Russell and Tsay (2001) provide a new perspective for the issue. They develop a Threshold ACD (TACD) model to allow the expected duration to depend nonlinearly on past information variables. The model is applied to IBM transaction data. Strong evidence is provided suggesting that fast trading regime is coupled with wide spread, large volume and high volatility, all of which proxy for information-based trading. Thus, the results are consistent with H3.
In Engle (2000), several specifications of the UHF-GARCH model are proposed and applied to IBM transaction data. A major result of Engle’s empirical application is that both H2, . long durations will lead to declining prices and H4, . long durations contribute to low volatility are supported. Engle does not provide any explanations for this obvious discrepancy.
Grammig and Wellner (2002) propose to extend the recursive UHF-GARCH specifications to an interdependent specification in which the transaction intensity impacts on the volatility process, and vice versa. They term it the interdependent duration-volatility (IDV) model. The model is applied to secondary market trading after the Deutsche Telekom IPO. They find that lagged volatility (which is conceived as an indicator for informed trading) has a significantly negative impact on transaction intensity, which is in support of H5.
Russell and Engle (2002) propose the Autoregressive Conditional Multinomial (ACM) model for price changes which is discrete-valued. Several results emerge from their empirical application to the Airgas (ARG) transaction data. Firstly, long durations are associated with declining prices, as predicted by H2. Secondly, long durations contribute to low volatility per unit time, which is in line with H4.
So far, no empirical work, if any, has explored the asymmetric impact of good news and bad news on intertrade duration.
Econometric Framework
The availability of high frequency financial data makes it possible for empirical investigators to take a close look at the functioning of the market. The analysis of such data, however, is complicated by the fact that they are irregularly spaced in time. This issue is first addressed by Engle and Russell (1998). They treat the event (transaction or quote) arrival times as random variables which follow a marked point process.
In this section, I start by a brief review of the ACD model introduced by Engle and Russell (1998). This model shares some features of the GARCH model and is particularly well suited to the analysis of irregularly spaced data, such as stock market data, where the time elapsed between two trades conveys information.
In the second part of this section, I discuss Engle’s (2000) UHF-GARCH model, which is a variant of the GARCH model adapted to irregularly spaced transaction by transaction data.
The ACD Model
Let be the interval of time between event arrivals which will be called the duration. Let be the expectation of the duration given the past arrival times which is given by
()
Furthermore, let
()
where with density with non-negative support, and and are variation free. A simple ACD (1, 1) parameterization for the expectation is given by
()
with the following constraints on the coefficients: , , and . The last constraint ensures the existence of the unconditional mean of the durations; the others ensure the positivity of the conditional durations.
Popular choices for the density include the exponential and the Weibull distributions. The Weibull distribution has greater flexibility than the exponential one. If , the model exhibits a decreasing hazard function: long durations will be less likely. If , long durations will be more likely.
The parameters are estimated by MLE. The log likelihood function for the Weibull ACD (WACD) is
()
for some parameterization of .
As suggested by Engle and Russell (1998), the performance of the ACD model in capturing the autocorrelation structure of the data can be evaluated by examining the standardized residuals
()
where is given by ML estimates. The ACD model successfully captures the autocorrelation of the durations if the residuals look like white noise. This can be tested with Ljung-Box Q-statistics.
The most basic application of the ACD model to financial transactions data is to model the arrival times of trades. In this case it denotes the arrival of the transaction and denotes the time between the and transactions.
The UHF-GARCH Model
Let the return from the to the transaction be denoted by . Define the conditional variance per transaction as
()
where this variance is defined conditional on the contemporaneous duration as well as the past returns and durations. The variance of interest, however, is the variance per unit time. This is related to the variance per transaction as
()
so that the relationship between the two variances is .
The volatility per unit time is then modeled as a GARCH process. Engle proposes an ARMA(1,1) model for the series . Let denote the innovation to this series. If the durations are not informative about the variance per unit time, then the GARCH(1,1) model for irregularly spaced data is simply
()
Engle terms this model the UHF-GARCH model or Ultra-High-Frequency GARCH model.
As suggested by Engle, additional variables, such as contemporaneous duration, lagged expected duration, spread and volume, might be introduced into the conditional variance equation to test various microstructure hypotheses. In particular, Engle considers the following specification
()
The inclusion of in the mean equation has two economic meaning. The longer the interval over which the return is measured, the higher the expected return since both the risky and riskless rates are measured per unit of time. However, if no trades is bad news as in Diamond and Verrecchia (H2), then long durations would imply declining prices. The reciprocal of contemporaneous duration is introduced into the conditional variance equation to test the volatility impact of duration. As suggested by Engle, is expected to have a positive sign under Easley and O’Hara hypothesis that long durations indicate no news and lower volatility (H4).
Institutional Background and Data Description
This section summarizes the key institutional features of the SSE based on the published rules of the exchange. Data and some descriptive statistics are presented herein.
Institutional Features of the Shanghai Stock Exchange
By far, the SSE is the dominant market in the trading of Chinese equities. In the year of our data sample (2002), a total of 710 stocks are listed on the SSE. All stocks are "system traded" with the assistance of a computerized matching and execution system. The system is in a fashion similar to that employed on the Paris Bourse and the Tokyo Stock Exchange .
The trading day on the SSE is divided into morning session (9:30 AM to 11:30 AM) and afternoon session (1:00 PM to 3:00 PM). The morning session on the SSE opens with a call auction, then functions as a continuous auction until the session closes. The afternoon session is a continuation of the morning one and does not open with a call auction.
To maintain price continuity, there are two types of price limit in the SSE: daily and intraday price limit. No transaction price can occur beyond the limit. The daily price limit is 10% above or below the previous day’s closing price. The intraday price limit is 10% above or below the reference price.
The tick size on the SSE is RMB . The smallest trading unit is 100 shares. Short selling is strictly prohibited.
For each transaction, the computerized trading system reports the price, execution time and the quantity exchanged, while for each order revision, it reports the time, the best three bid and ask quotes. All the information is available in real time through computerized information dissemination system. The size of the reported trade is determined by the size of the incoming order. A 1000-share buy order that executes at one price against limit sell orders of 600 and 400 shares, for example, is reported as a 1000-share trade.
The determination of the reported price is somewhat complicated and thus deserves elaboration. Consider an initial situation as presented in Table . On the buy side, the first column gives the bid quotes and the second one shows all volume available at the announced price. The volume is aggregated across all investors on the SSE, which is similar to the Paris Bourse. The sell side of the book is displayed in a similar fashion.
Table Initial Situation, Minsheng Banking (600016), October 8, 2002, 10:01:42
Bid Quote / Depth
Ask Quote / Depth
Last Transactions
Price / Size / Time
4425
600
3375
10:01:42
2100
5757
400
10:01:22
13455
2100
1400
10:00:52
Consider a buy limit order entered at 10:01:57 for 1600 shares at per share. It will consume the depth at the best ask and walk up the book to consume partially the depth at the second-best ask. Consequently, the order is executed against two sell orders, . 600 shares at per share and 1000 shares at per share. The aftermarket at 10:01:57 is shown in Table . The trading system only reports the most recent transaction price, , and the aggregated volume, 1600. In the case that a buy order consumes the depth at the best ask and walks up the book to consume partially or all the depth at the second-best ask, I term it Impatient Buy. Impatient Sell is defined in a similar way. Instead of walking up the order book to get filled, impatient sells walk down the book and are executed against second best bid or even third best bid quote. Impatient Buys (Sells) are most aggressive type of orders and I employ them as a proxy for good-news-based (bad-news-based) trading . A 10-minute bid/ask quote and transaction price process is plotted in Figure . Trade occurring at 10:01:57 is an impatient buy, while trade occurring at 10:02:12 is an impatient sell. A detailed discussion of impatient trades will be presented in next section.
Table Final Situation, Minsheng Banking (600016), October 8, 2002, 10:01:57
Bid Quote / Volume
Ask Quote / Volume
Last Transactions
Price / Volume / Time
400
4757
1600
10:01:57
4425
2100
3375
10:01:42
2100
1430
400
10:01:22
Figure Bid/Ask Quote and Price Process of Minsheng Banking (600016), October 8, 2002, 10:00-10:10
The Data
The data are abstracted from real-time high frequency transactions data recorded by China Southern Fund. The dataset contains detailed information about each transaction, including price, quantity and execution time. The order book immediately after a transaction is also reported, which includes best three bid and ask prices and the number of shares demanded or offered at each of the three bid and ask quotes.
To get a general picture of the issue at hand, I select 18 of the most frequently transacted SSE 180 Index stocks on the first day of the sample. The sample period is from Oct. 8, 2002 to Dec. 27, 2002. A description of the selected stocks is presented in Table .
Table Summary Statistics of Selected Stocks
The stocks are chosen from SSE 180 Index based on the number of transactions on Oct. 8, 2002. The most actively traded 18 stocks are selected. OBS is the number of observations after filtering. DIPO is the date of initial public offering. NEGT SHR is the number of negotiable A-shares (in millions). AVG PRC is the average daily closing price over the sample period. AVG TO is the average daily turnover ratio - defined as shares traded divided by outstanding negotiable A-shares. MED SIZE is the median of the size of an individual trade. AVG SPRD is the average spread, which is computed as the difference between the best ask and the best bid quote. AVG DUR is the average duration between consecutive trades measured in seconds. IM RT is the proportion of impatient trades.
NAME
CODE
OBS
DIPO
NEGT SHR
AVG
PRC
AVG TO
MED
SIZE
AVG
SPRD
AVG
DUR
IM RT
PUDONG DVLPMNT BANK
600000
26642
09/99
1250
HUANENG
POWER INTNL.
600011
15946
11/01
1300
MINSHENG BANKING
600016
24487
11/00
1200
ZHONGHAI DEVELOPMENT
600026
38195
05/02
3700
CHINA PETRO-CHEM
600028
31517
07/01
3000
MERCHANTS BANK
600036
34445
03/02
2700
DATANG TELECOM
600198
31325
08/98
1700
SHENGHUA BIOK BIOLOGY
600226
33938
08/99
2000
SHANDONG INFRA CONSTR
600350
32521
02/02
3000
SHANGHAI LUJIAZUI
600663
27479
06/92
1100
HU CHANG
SPECIAL STEEL
600665
23925
10/92
1600
TIANJIN PORT
600717
23993
08/92
850
JIANGSU
ZONGYI
600770
34577
11/96
1600
SP POWER
600795
15492
11/92
1200
ANSHAN TRUST
& INVESTMENT
600816
24958
05/92
1300
SHANGHAI JOIN BUY
600838
22016
10/93
1300
CHANGHONG ELECTRONIC
600839
27266
10/88
1200
CHUNLAN REFRIGERATING
600854
27506
02/94
1400
Preliminary Data Manipulation
Prior to using the data, I delete trades recorded before 9:30 am and after 15:00 pm and between 11:30 am and 13:00 pm. Overnight and lunchtime durations are also deleted.
As the SSE data set does not contain information on trade direction, I use the tick test proposed by Lee and Ready (1991) to infer the trade direction. In the case of Minsheng Banking (600016), there are 11961 (%) buys, 11982 (%) sells and 544 (%) unclassifiables. Impatient buys/sells are identified by comparing the transaction price with immediate past best ask/bid quote. If the price is higher than the immediate past best ask, it is classified as an impatient buy. If the price is lower than the immediate past best bid, it is classified as an impatient sell. For Minsheng Banking (600016), there are 1168 (%) trades classified as impatient buys and 1591 (%) trades classified as impatient sells.
Table Summary Statistics of Intertrade Durations for the Minsheng Banking (600016) Case
Raw Durations
Diurnally Adjusted
Mean
Median
20
Maximum
1030
Minimum
5
Std. Dev.
Skewness
Kurtosis
ACF(15)
Ljung-Box(15)
Observations
24886
24886
Following Engle and Russell (1998) and Engle (2000), the data are first “diurnally adjusted” to remove the typical time-of-day effect. This is accomplished by regressing the durations on the time of day using a piecewise linear spline specification and then taking ratios to get “diurnally adjusted” durations that are expressed as fractions above or below normal. The spline has knots at 9:30, 10:00, 10:30, 11:00, 11:30, 1:30, 2:00 and 2:30. Summary statistics of intertrade durations for the Minsheng Banking (600016) case are tabulated in Table . The overdispersion ratio (measured by the ratio standard deviation/mean) is less than 1. The widely documented overdispersion of intertrade durations is not found for the representative stock, Minsheng Banking (600016). The daily spline for durations and volatility is shown in Figure . Similar to the finding of Engle and Russell (1998), the durations show very substantial differences over the day and have the typical pattern of high activity at the opening and end of the day. The volatility exhibits a U-shaped pattern. The volatility at lunch time is about 60% of the morning peak. Combining these two pictures together, it suggests that fast trading is coupled with high volatility, while slow trading is coupled with low volatility. This can be interpreted as an evidence against Admati and Pfleiderer’s (1988) prediction.
Figure Daily Spline for the Durations of Minsheng Banking (600016)
Figure Daily Spline for the Volatility of Minsheng Banking (600016)
Figure plots the ACF of intertrade durations for Minsheng Banking (600016). Both the raw data and the adjusted data exhibit strong autocorrelation spanning over 200 transactions, suggesting the applicability of the ACD models.
Figure ACF of Durations for Minsheng Banking (600016)
Are Impatient Trades Information-Based?
In this paper, impatient trades are employed as an indicator of information-based trading. The validity of such proxies, however, is not certain. Prior to the discussion of the relationship between intertrade duration and information-based trading, this section investigates the information content of impatient trades. Some peripheral evidence is provided. Auxiliary regressions are suggested and a trading strategy for the same purpose is built and tested.
Some Peripheral Evidence
As is commonly assumed, informed traders would choose to trade as quickly as possible and as much as possible before their private information becomes public. This suggests two features of information-based trading, . immediate execution and large size. Apparently, impatient trades possess these two features. Impatient orders are “quickest” type of orders. Impatient traders would rather execute their orders immediately though at a less favorable price than wait for more favorable quotes. Moreover, an initial handling of impatient trades shows they are generally large orders. In the Minsheng banking (600016) case, the median size of impatient trades is , about 3 times more than the median of all trades. This is consistent with the prediction of Easley and O’Hara (1987) that informed traders have the incentive to trade larger quantities to capitalize on their information before it becomes public.
As is claimed by the literature, firms with lower bid/ask spreads (. Lee, Mucklow and Ready, 1993) and larger firms (. Lo and MacKinlay, 1990) have a lower degree of information asymmetry. Hence it is interesting to look at the data from a cross-sectional perspective. As is shown in Table , the bottom 5 stocks with the smallest proportion of impatient trades rank bottom 5 in terms of average spread. Also, the top 5 stocks with the largest proportion of impatient trades rank bottom 5 in terms of liquidity A-shares.
All these suggest a possibility that impatient trades might be information-based, but not necessarily so.
Auxiliary Regressions
To test empirically whether impatient trades are information-based, or to a lesser extent, whether impatient trades are perceived by the market as information-based, two auxiliary regressions are suggested. They are
()
()
where is the return measured by price changes in time interval t; is the signed difference between the number of impatient buys and sells in time interval . is the signed difference between the number of buys and sells in time interval . Equation () is based on the intuition that if there is information event, the price will revert to its fundamental value. Thus is expected to be positive, reflecting the fact that impatient trades are indeed information-based. Equation () tests a weaker assumption, . impatient trades are perceived by the market as information-based. If it is the case, the market will follow the direction of impatient trades. A positive is consistent with the hypothesis.
Though the formulation is simple enough, the selection of the length of the time interval is delicate. It seems natural that more net impatient buys will lead to positive returns in the same time interval. The question here is whether impatient buys and sells have predictive power for future returns. However, returns measured in short time intervals often exhibit a negative first order autocorrelation. Even at intervals of a half hour or longer negative first order autocorrelation remains. So if the time interval is too short, 10 minutes or 30 minutes for example, more net impatient buys will generally lead to negative returns in the next interval, which may be misleading. But if we examine time interval longer than one day, we will miss some intraday information content of impatient trades. For this reason, the estimates of the equations are unsatisfactory. The selection of the time length remains an open question.
A Trading Strategy
An alternative approach to the question is building a trading strategy and examining its profitability. If impatient trades are indeed information-based, this suggests an apparent trading rule: buy stock after observing an impatient buy and sell stock after observing an impatient sell.
One point that should be made clear from the very beginning is that this strategy is only meant to be illustrative. In the real world, other factors have to be taken into account. These include risk analysis of the positions, short sale constraints, T+1 limitation, limitations on overnight positions, limited amount of short-selling or limit positions in trading.
For the purpose of this paper, I define the trading rules as follows:
I) If an impatient buy is observed, n shares of the stock are bought at the prevailing ask price and are added to the existing position; if the portfolio has an existing short position, it is settled at the prevailing ask price, prior to buying the n shares;
II) If an impatient sell is observed, n shares are short-sold at the prevailing bid price and are added to the existing short position; if the portfolio has an existing long position, it is settled at the prevailing bid price, prior to short-selling the n shares.
III) At the end of the three-month period, the existing long (short) position is settled at the last bid (ask) price on December 27, 2002.
I choose n = 100, with a transaction cost equal to %. The first decision is taken at the start of the second duration and the game is ended at the end of the three-month period. The portfolio is self-financed, . it borrows money (at a 5% interest rate) if shares are to be bought. When shares are short-sold or when a trading profit is realized, the amount of money received earns interests at a 5% rate. The outcome of this trading strategy after a three-month period is compared with a benchmark strategy, the buy and hold strategy in this case. The buy and hold strategy is one of the standard benchmark models in finance when actively managed portfolio are evaluated. Under the buy and hold strategy, m shares are bought at the start of the three-month period and they are sold at the end, resulting in a profit or loss. To conduct a fair comparison between the two strategies, m is chosen such that the financing cost of the buy and hold strategy is equal to the total cost of the managed portfolio, . the available amount of money needed at the start of the three-month period to buy the shares is equal to the amount that can be borrowed for an interest cost (at a 5% interest rate) equal to the total cost of the managed portfolio.
The results are striking, as for 17 out of 18 stocks, the active strategy based on impatient trades outperforms the buy and hold strategy, even when transaction costs are taken into account. For 15 out of 18 stocks, the active strategy earns a positive return, although it becomes negative when transaction costs are taken into account. The loss is due to transaction costs, because of the huge number of transactions to be made, . in the Minsheng Banking (600016) case, there are 2759 transactions to be made during the three-month period. The results are reported in Table .
Table Outcome of Buy and Hold Strategy and Active Strategy after the Three-month Period with and without Transaction Cost
CODE
Without Transaction Cost
With Transaction Cost
BUY AND HOLD
ACTIVE
BUY AND HOLD
ACTIVE
600000
600011
600016
600026
600028
600036
600198
600226
600350
600663
600665
600717
600770
600795
600816
600838
600839
600854
Model Specifications
This section extends the econometric framework of Engle and Russell (1998) and Engle (2000) to investigate the relationship between intertrade duration and information-based trading. I treat the relationship as two fold. Firstly, an Asymmetric-WACD specification based on impatient trades is developed to examine the duration impact of information-based trading. Then I propose a GJR-version UHF-GARCH model for the information content of intertrade duration.
Duration Impact of Information-Based Trading
To investigate empirically the impacts of information-based trades on transaction intensity, I assume that the impact of on conditional mean duration is different when there is good news than when there is bad news. This leads to
()
where and are lag-1 indicator variables. is equal to 1 if an impatient buy occurs at time and 0 otherwise. Similarly, is equal to 1 if an impatient sell occurs at time and 0 otherwise. A Weibull distribution for the conditional density of durations is assumed, thus I term it Asymmetric-WACD model.
Information Content of Intertrade Duration
The Asymmetric-WACD model answers the question of duration impact of information-based trading. Reversely, what is the information content of durations? If bad news contributes to long duration, does it necessarily mean long durations are associated with bad news? This seems not be the case. As discussed in Diamond and Verrecchia (1987), long durations may not be information-related. Two non-information-related factors may contribute to long durations.
Engle (2000) proposes UHF-GARCH model to address the question empirically and he infers contradictory results in his UHF-GARCH estimation. The mean equation indicates long durations lead to declining prices, which is consistent with Diamond and Verrecchia’s prediction that no trade means bad news (H2), however, the conditional variance equation shows that long durations lead to low volatility, which is interpreted by Engle as being consistent with Easley and O’Hara’s prediction that no trade means no news (H4). Engle (2000) does not provide any explanations for this obvious discrepancy.
An open question here is whether low volatility is associated with no news. If low volatility does mean no news, then there’s a discrepancy. But if low volatility is connected with bad news, there’s actually no inconsistency as both equations support Diamond and Verrecchia’s prediction.
The GARCH literature abounds with asymmetric specifications capturing the asymmetric effect of good news and bad news on volatility, namely leverage effect. EGARCH of Nelson (1989) and GJR GARCH of Glosten et al. (1995) are two excellent examples. Many empirical studies have focused on examining the leverage effect using monthly data, weekly data and daily data. Though it becomes general knowledge that bad news is associated with high volatility in relatively low frequency data, no investigation has been made for high frequency data at transaction-by-transaction level.
To incorporate the asymmetric effect of good news and bad news on durations, I propose a specification that is similar in spirit to GJR GARCH, which I term GJR-version UHF-GARCH. This can be written as,
()
where is equal to 1 if and 0 otherwise. A positive is consistent with the well documented leverage effect, which indicates that bad news introduces more volatility than good news.
Estimation and Results
This section presents estimation results. The results are obtained using EVIEWS4 programming. LogL object is employed for Asymmetric-WACD model estimation. ARCH object is used for GJR-version UHF-GARCH model estimation.
Estimates of the Asymmetric-WACD model
Prior to estimation, the raw durations are filtered and diurnally adjusted as described in Section . Maximum likelihood estimation of the Asymmetric-WACD model is performed using the BHHH algorithm. Table reports the estimated coefficients for the Minsheng Banking (600016) case. The Ljung-Box (15) for autocorrelation in standardized residuals for the Minsheng Banking (600016) case is which has a p-value of % but is quite reasonable considering the huge sample size. The estimated coefficients for all 18 stocks are tabulated in Table . I format in bold the values of coefficients that are significantly different from zero at the 5% level of confidence. Although not presented, the Ljung-Box statistics for autocorrelation in standardized residuals for other stocks are very similar to that obtained in the Minsheng Banking (600016) case.
Table Estimates of Asymmetric-WACD(1,1) Model for Minsheng Banking (600016)
Coefficient
Std. Error
z-Statistic
Weibull
Notice that 12 out of 18 stocks have a significantly negative , indicating that good-news-based trading will generally lead to increased trading intensity. 13 out of 18 stocks have a significantly positive , indicating that bad-news-based trading will generally contribute to longer durations (H1). Interestingly, 3 stocks have both a significantly negative and a significantly negative , which implies information events, no matter it is good news or bad news, will always contribute to increased trading intensity (H3). These stocks are Datang Telecom (600198), Shenghua Biok Biology (600226) and Shanghai Lujiazui (600663). Further investigation shows that all three stocks can be classified as manipulation-prone stocks because they rank among the highest in terms of price but rank among the lowest in terms of negotiable A-shares (see Table ). For manipulation-prone stocks, information events (good news and bad news) will generally lead to increased trading intensity with good news having a larger impact on durations ().
Another interesting finding is obtained by summing up , and . In all stocks, the sums are larger than , suggesting high degree of duration persistence when hit by bad news. Three stocks have a sum larger than 1. They are Zhonghai Development (600026), Shandong Infrastructure Construction (600350) and Shanghai Join Buy (600838) and the sums are , and , respectively. means once the process enters the bad news regime, there is a force to magnify the duration in an exponential speed. Further examination reveals that all 3 stocks rank among the lowest in terms of price (see Table ). It suggests that longer durations combined with bad news are more likely for low-priced stocks. Note that 2 of the stocks, . Zhonghai Development (600026) and Shandong Infrastructure Construction (600350) are both newly listed stocks. Another newly listed stock, Merchant Bank (600036) has a sum very close to 1, . . As claimed by the corporate finance literature, asymmetrically informed trading is more likely for firms with a recent IPO. A possible explanation for the finding here is uninformed traders assume the presence of informed traders and thus refrain from trading with the latter. Thus the speed of price adjustment to bad news is slower for IPO stocks. This is consistent with the above H5 of Admati and Plfeiderer (1988).
As the results here are confounded with order size , I distinguish large impatient trades from small impatient trades (now there're 4 dummy variables: large buy, large sell, small buy and small sell), and reestimate the Asymmetric-WACD model. The results are quite consistent with previous findings. Impatient buys (large and small) will generally lead to shortened duration, while impatient sells (large and small) will generally lead to long duration. The results are not uniform, though. In some cases, large impatient sells tend to induce shortened duration.
Table Estimates of the Asymmetric-WACD (1,1) Model for all 18 stocks
Estimates are obtained by maximizing the likelihood function equation () using BHHH algorithm. For each parameter, the first row reports the estimated coefficient and the second row reports the z-statistics. I format in bold the values of coefficients that are significantly different from zero at the 5% level of confidence.
600000
600011
600016
600026
600028
600036
600198
600226
600350
600663
600665
600717
600770
600795
600816
600838
600839
600854
Estimates of the GJR-version UHF-GARCH model
Estimates of the GJR-version UHF-GARCH model are reported in Table for the Mingsheng Banking (600016) stock and Table for all 18 stocks. The estimation has been performed by ML using the BHHH algorithm. Standard errors are computed as discussed in Bolleslev and Wooldridge (1992). I format in bold the values of coefficients that are significantly different from zero at the 5% level of confidence.
Table Estimates of GJR-version UHF-GARCH Volatility Model for Minsheng Banking (600016)
Coefficient
Std. Error
z-Statistic
Similar to the findings of Engle (2000), the model shows strong autocorrelation in the mean through the highly significant AR and MA coefficients. Another similar result is a negative for all 18 stocks, though only 4 are significant. It implies long durations will lead to declining prices as predicted by Diamond-Verrecchia model (H2), though marginally.
Further interesting results are found in the conditional variance equation. For all 18 stocks, is highly significant and 17 out of 18 stocks have a positive sign, which means long durations will lead to low volatility. There’s a pitfall here. As low volatility is commonly recognized as associated with no news and high volatility associated with bad news, Engle concludes that long duration means no news (H4). But, is it really the case?
To answer the question, we need only look at the values of . For 15 out of 18 stocks, the coefficient has a negative sign, though only 4 are significant at the 5% level of confidence. The results are striking, as they are opposed to the well documented leverage effect. I call it counter-leverage effect, which means bad news will contribute to low volatility. Thus all the findings here supports Diamond and Verrecchia’s prediction that long durations means bad news (H2).
Table Estimates of the GJR-version UHF-GARCH (1,1) Model for all 18 stocks
Estimates are obtained by maximizing the likelihood function using BHHH algorithm. Standard errors are computed as discussed in Bolleslev and Wooldridge (1992). For each parameter, the first row reports the estimated coefficient and the second row reports the z-statistics. I format in bold the values of coefficients that are significantly different from zero at the 5% level of confidence.
600000
600011
600016
600026
600028
600036
600198
600226
600350
600663
600665
600717
600770
600795
600816
600838
600839
600854
X100
Conclusion
This research is an empirical contribution to the fast growing literature on high frequency data. New specifications within the ACD/UHF-GARCH framework are developed to characterize the relationship between trading intensity and information-based trading on an electronic order-driven market. The research is of interest to policy makers in terms of market design and supervision, to economics in terms of model building, and to market participants as it sheds some light on how information affects trader behavior and how to read the market.
Employing an asymmetric specification of the ACD model, I find strong evidence indicating that good-news-based trading will generally lead to increased trading intensity, while bad-news-based trading will generally contribute to longer durations. This suggests a good-news-pursuing behavior pattern of traders on the SSE. For manipulation-prone stocks, information events (good news and bad news) will generally lead to increased trading intensity with good news having a larger impact on durations. Another interesting finding is for low-priced stocks and IPO stocks. It will take longer for the price of these stocks to adjust to bad news.
A GJR-version UHF-GARCH model is developed and estimated to address the issue of the information content of durations. Consistent with the ACD results, long duration is indeed indicative of bad news. Empirical evidence supporting a counter-leverage effect is found, though marginally. Whether it is related specifically to institutional features of China’s stock market or it is always the case for transaction data remains an open question.
References
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Engle, Robert F., 2000, The Econometrics of Ultra High Frequency Data, Econometrica 68, 1-22.
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Engle, Robert F. and Jeffery R. Russell, 1998, Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data, Econometrica 66, 1127-1162.
Engle, Robert F. and Jeffery R. Russell, 2002, Analysis of High Freqeuncy Data, Working Paper, New York University and University of Chicago.
Glosten, Laurence R., Ravi Jagannathan, and David E. Runkle, 1993, On the Relation between the Expected Value and the Volatility of the Normal Excess Return on Stocks, Journal of Finance 48, 1779-1801
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Hasbrouck, Joel, 1991, Measuring the information content of stock trades, Journal of Finance 46, 179-207
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* Corresponding author: P. O. Box 817, Xiamen University, Xiamen, Fujian 361005, P. R. China; Home Phone: +86-592-5911218; Mobile: +86-13015914677; Email: jkhuang77@
# 通讯地址：福建省厦门大学817号信箱（361005），电话：0592-5911218，移动电话：(0)13015914677，电子邮件：easist@
Engle and Russell (2002) provide a comprehensive review of recent econometric techniques for high frequency financial data.
For a detailed description of the electronic trading mechanism, see Biais et al. (1994) and Hamao and Hasbrouck (1995)
In search of proxy variables for information-based trading, I experimented with several variables related to the trading process. These include large trades (large buys and large sells), wide spreads (buyer-initiated wide spreads and seller-initiated wide spreads), volatility and impatient trades. Among them, impatient trades seem to have the best explanatory power.
See, for example, Engle and Ng (1993) and Glosten et al. (1993).
Note that the LB(15) for the raw and adjusted durations are and , respectively.
It is interesting to note a small firm effect on China’s stock market, . small firms have higher prices than large firms and are generally traded more actively than large firms. Table shows 4 stocks which rank among top 5 in terms of prices rank among bottom 5 in terms of negotiable A-shares. Similarly, 4 stocks which rank among top 5 in terms of daily turnover ratio rank among bottom 5 in terms of negotiable A-shares.
This point is suggested by Ruey S. Tsay.
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tm code price vol amount bid1 bidvol1 bid2 bidvol2 bid3 bidvol3 ask1 askvol1 ask2 askvol2 ask3 askvol3
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2002/10/08 10:03:47 600016 154706 1794817 1843 5525 2300 1100 1430 5678
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2002/10/08 10:08:12 600016 161306 1871915 10700 3843 10825 2260 1230 5378
2002/10/08 10:08:42 600016 162106 1881259 10700 3843 10825 11160 1230 5378
2002/10/08 10:10:02 600016 175506 2037504 2843 10425 2500 200 16480 1230
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2002/10/08 10:13:32 600016 187249 2174196 4725 2900 13455 129 1657 3100
2002/10/08 10:13:47 600016 188249 2185826 3725 4900 13455 129 1657 3100
2002/10/08 10:15:07 600016 194174 2254754 4900 13455 69275 2204 1657 3100
2002/10/08 10:15:37 600016 196274 2279176 4800 13455 69375 204 1657 3100
2002/10/08 10:16:32 600016 198274 2302416 3800 13155 69375 1304 1657 3100
2002/10/08 10:18:02 600016 200274 2325666 2800 13455 70075 1604 857 3100
2002/10/08 10:18:52 600016 200474 2327992 3100 13455 71275 1404 857 3100
2002/10/08 10:19:37 600016 200879 2332700 3095 13455 71775 1204 857 3100
2002/10/08 10:19:52 600016 206040 2392833 44839 3095 13455 500 16480 1230
2002/10/08 10:20:27 600016 208085 2416678 42794 30000 4595 500 16480 1230
2002/10/08 10:21:12 600016 208585 2422508 42294 30000 4595 500 16480 1230
2002/10/08 10:21:42 600016 209482 2432967 41397 30000 4595 500 16480 1230
2002/10/08 10:22:07 600016 210781 2448113 40898 30000 4595 16480 1230 5778
2002/10/08 10:22:27 600016 211881 2460939 40298 30000 4595 16480 1230 5778
2002/10/08 10:22:42 600016 211981 2462105 40198 30000 4595 16480 1230 5778
2002/10/08 10:23:27 600016 222181 2581043 31298 30000 4595 16180 1230 5778
2002/10/08 10:22:57 600016 216981 2520405 36198 30000 4595 16480 1230 5778
2002/10/08 10:24:47 600016 224181 2604393 31298 30000 4895 1000 12400 1230
2002/10/08 10:24:12 600016 223181 2592723 31298 30000 4895 9680 1230 5778
2002/10/08 10:25:42 600016 227181 2639403 33298 30000 4895 4400 13470 1730
2002/10/08 10:25:27 600016 225181 2616063 33298 30000 4895 5500 13470 1730
2002/10/08 10:26:32 600016 229381 2665057 31298 30000 4895 5200 13470 1730
2002/10/08 10:25:57 600016 228881 2659225 31598 30000 4895 4400 13470 1730
2002/10/08 10:27:02 600016 230603 2679305 30076 700 30000 5200 13470 1730
2002/10/08 10:26:42 600016 229903 2671143 30776 30000 4895 5200 13470 1730
2002/10/08 10:27:27 600016 231603 2690965 29376 700 30000 5300 13470 1730
2002/10/08 10:27:47 600016 232203 2697961 28776 700 30000 5300 13470 1730
2002/10/08 10:28:17 600016 242203 2814561 26776 700 30000 5300 13470 1730
2002/10/08 10:28:47 600016 245003 2847216 17176 700 30000 4500 13470 1730
2002/10/08 10:29:02 600016 246003 2858876 17276 700 30000 4500 13470 1730
2002/10/08 10:29:47 600016 246503 2864706 15776 700 30000 4500 13470 1730
2002/10/08 10:30:17 600016 250803 2914844 11476 700 30000 4500 13470 1730
2002/10/08 10:31:07 600016 251203 2919508 11076 300 700 6000 13470 1730
2002/10/08 10:31:17 600016 251703 2925338 10576 300 700 6000 13470 1730
2002/10/08 10:31:47 600016 259503 3016364 700 10576 300 13470 1730 5778
2002/10/08 10:31:32 600016 257703 2995358 2500 10576 300 13470 1730 5778
2002/10/08 10:32:17 600016 260203 3024533 700 10576 300 13470 1730 5778
2002/10/08 10:33:02 600016 260503 3028034 11576 300 700 3150 13470 1730
2002/10/08 10:33:47 600016 261203 3036203 11576 400 700 5250 13470 1730
2002/10/08 10:34:22 600016 262403 3050207 11576 400 700 3650 13470 1730
2002/10/08 10:36:07 600016 263403 3061867 10576 1900 700 4250 13470 1730
2002/10/08 10:37:12 600016 271903 3160977 3076 1900 700 4250 13470 1730
2002/10/08 10:36:22 600016 264903 3079357 10576 1900 700 4250 13470 1730
2002/10/08 10:36:37 600016 266903 3102677 7076 1900 700 4250 13470 1730
2002/10/08 10:37:27 600016 272903 3172647 3076 1900 700 3250 13470 1730
2002/10/08 10:38:12 600016 284303 3305762 16200 15276 1900 5820 1730 5878
2002/10/08 10:37:42 600016 283803 3299927 16700 5076 1900 5820 1730 5878
2002/10/08 10:38:42 600016 291853 3393963 8570 13680 16400 5878 1000 1000
2002/10/08 10:38:57 600016 302231 3515341 14022 4070 13680 1000 1000 11306
2002/10/08 10:39:12 600016 303731 3532891 12522 14170 13680 1000 1000 11306
2002/10/08 10:39:57 600016 304731 3544591 12922 14170 13680 1500 1500 15306
2002/10/08 10:40:12 600016 305031 3548101 12622 19170 13680 1500 1500 15306
2002/10/08 10:40:42 600016 305531 3553951 11922 19170 13680 1700 1500 15306
2002/10/08 10:41:12 600016 308231 3585595 1100 12922 19170 12000 9306 600
2002/10/08 10:41:32 600016 308831 3592627 1000 12922 19170 12000 9306 600
2002/10/08 10:42:17 600016 321331 3739252 7400 11500 11200 9306 600 570
2002/10/08 10:42:32 600016 321831 3745117 6900 11500 11200 9306 600 570
2002/10/08 10:42:47 600016 322046 3747639 6685 11500 11200 9306 600 570
2002/10/08 10:43:17 600016 322246 3749987 6685 11500 11200 9106 600 1570
2002/10/08 10:44:02 600016 325746 3791042 3685 11500 11200 9106 2600 1570
2002/10/08 10:43:52 600016 325246 3785177 4185 11500 11200 9106 2600 1570
2002/10/08 10:44:22 600016 326746 3802782 3685 11500 11200 8106 2600 1570
2002/10/08 10:45:22 600016 335052 3900292 3794 13485 11500 600 500 1570
2002/10/08 10:44:52 600016 326946 3805128 3485 11500 11200 8106 600 500
2002/10/08 10:45:37 600016 337152 3924967 16000 3794 13485 500 1570 500
2002/10/08 10:46:07 600016 338452 3940255 8600 16000 3794 500 1570 500
2002/10/08 10:46:22 600016 339837 3956543 7215 16000 3794 500 1570 500
2002/10/08 10:46:53 600016 340837 3968303 7515 16000 3794 1570 500 1340
2002/10/08 10:47:22 600016 341837 3980073 7715 16000 3794 1570 500 1640
2002/10/08 10:47:52 600016 349752 4073153 16000 3794 13485 1598 4000 1870
2002/10/08 10:47:37 600016 349552 4070801 16000 3794 13485 1199 4000 1870
2002/10/08 10:48:07 600016 350352 4080204 15500 3794 13485 1498 4000 1570
2002/10/08 10:48:22 600016 351852 4097829 14500 3794 13485 1199 4000 1570
2002/10/08 10:48:42 600016 364352 4244750 4400 3794 13485 800 1570 1000
2002/10/08 10:49:12 600016 368752 4296450 3794 13485 11500 1099 1300 1570
2002/10/08 10:49:42 600016 371652 4330496 894 13485 11500 4399 3300 1570
2002/10/08 10:50:12 600016 378646 4412623 13185 11500 11200 5126 7799 1800
2002/10/08 10:49:27 600016 369252 4302320 3294 13485 11500 2199 3300 1570
2002/10/08 10:49:57 600016 374546 4364492 13485 11500 11200 5106 6799 3800
2002/10/08 10:50:42 600016 379646 4424353 12185 11500 11200 5626 8299 1800
2002/10/08 10:51:12 600016 380446 4433737 11385 11000 11200 6426 8299 1800
2002/10/08 10:51:27 600016 383746 4472446 8085 11000 11200 6626 8299 1800
2002/10/08 10:51:57 600016 387946 4521717 4385 11000 11200 8126 4699 2000
2002/10/08 10:52:12 600016 391046 4558080 6285 10500 11200 8126 4699 2200
2002/10/08 10:53:02 600016 392320 4573024 5011 10500 11200 8126 1599 2200
2002/10/08 10:53:17 600016 396883 4626548 448 10500 11200 8126 1599 2200
2002/10/08 10:52:42 600016 391120 4558948 6211 10500 11200 8126 1599 2200
2002/10/08 10:54:02 600016 397331 4631803 10500 11200 14422 1802 8126 1898
2002/10/08 10:54:47 600016 397431 4632976 11500 11200 14422 2402 8126 1898
2002/10/08 10:55:02 600016 397631 4635320 11300 11200 14422 1802 8126 1898
2002/10/08 10:55:17 600016 398731 4648212 10200 11200 14422 2802 8126 1898
2002/10/08 10:55:47 600016 399431 4656416 9900 11200 14422 2802 8126 898
2002/10/08 10:56:32 600016 400131 4664620 9500 11200 16422 2802 8126 898
2002/10/08 10:56:47 600016 400631 4670480 9000 11200 16422 2802 8126 898
2002/10/08 10:57:02 600016 402131 4688060 7500 11200 16422 2802 8126 898
2002/10/08 10:57:22 600016 404031 4710328 5600 11200 16422 2802 8126 898
2002/10/08 10:58:22 600016 411833 4801806 1698 11200 15222 8126 898 1800
2002/10/08 10:57:37 600016 404131 4711501 5600 11200 15222 2702 8126 898
2002/10/08 10:57:52 600016 409633 4776013 898 3000 11200 8126 898 1800
2002/10/08 10:58:07 600016 411633 4799462 1898 11200 15222 8126 898 1800
2002/10/08 10:58:42 600016 412833 4813546 1698 11200 15222 6626 898 1800
2002/10/08 10:59:12 600016 414531 4833447 1698 11200 15222 6626 1398 1800
2002/10/08 11:00:12 600016 418531 4880287 7200 15222 19170 302 200 6626
2002/10/08 11:01:17 600016 418831 4883800 6900 15722 19370 302 200 6626
2002/10/08 11:01:27 600016 419831 4895510 5900 15722 19370 302 200 6626
2002/10/08 11:03:02 600016 422831 4930640 4900 25722 19370 502 200 6925
2002/10/08 11:03:17 600016 428231 4993869 25222 19370 13980 5100 502 200
2002/10/08 11:03:47 600016 431431 5031311 22222 19370 13980 6400 502 200
2002/10/08 11:04:52 600016 436631 5092151 17222 19370 13980 6400 302 1200
2002/10/08 11:05:07 600016 438031 5108531 15622 19370 13980 7400 302 1200
2002/10/08 11:05:52 600016 442331 5158841 13722 19370 13980 5900 302 1200
2002/10/08 11:05:37 600016 439931 5130761 13722 19370 13980 5900 302 1200
2002/10/08 11:06:22 600016 444831 5188091 8822 19370 13980 4900 302 1200
2002/10/08 11:07:22 600016 446831 5211496 7822 19370 13980 4400 1199 1200
2002/10/08 11:07:07 600016 446631 5209156 8022 19370 13980 4400 1199 1200
2002/10/08 11:07:37 600016 452231 5274676 7822 19370 14980 4400 1199 1200
2002/10/08 11:08:07 600016 467631 5454726 6392 14980 16400 4400 1199 1200
2002/10/08 11:07:57 600016 452831 5281696 1822 19370 14980 4400 1199 1200
2002/10/08 11:08:57 600016 468131 5460576 6392 14980 16400 2800 4400 1199
2002/10/08 11:10:12 600016 472131 5507336 2392 14980 16400 4000 4400 1199
2002/10/08 11:09:42 600016 470131 5483956 4392 14980 16400 4000 4400 1199
2002/10/08 11:10:57 600016 475931 5551796 2392 14980 16400 200 4800 1199
2002/10/08 11:11:32 600016 476031 5552965 2292 14980 16400 200 4800 1199
2002/10/08 11:12:32 600016 476231 5555303 2092 14980 16400 700 4800 1199
2002/10/08 11:13:47 600016 477875 5574521 448 15280 16400 2700 4800 1199
2002/10/08 11:15:02 600016 477975 5575690 348 15280 16400 2700 4800 1199
2002/10/08 11:16:37 600016 478323 5579758 15780 16400 15676 652 2200 4800
2002/10/08 11:16:52 600016 478523 5582096 15780 16400 15676 452 2200 4800
2002/10/08 11:17:22 600016 478975 5587380 9748 15480 16400 2200 4800 1199
2002/10/08 11:17:07 600016 478723 5584434 15780 16400 15676 252 2200 4800
2002/10/08 11:18:37 600016 481275 5614290 47700 10148 15480 4800 1199 1200
2002/10/08 11:19:37 600016 481575 5617803 47700 9748 65480 4500 1199 1200
2002/10/08 11:20:37 600016 482075 5623653 51600 9748 65480 4500 1199 1200
2002/10/08 11:21:07 600016 482875 5633021 52000 9748 65480 4000 1199 1200
2002/10/08 11:21:37 600016 483475 5640047 57000 9748 65480 3100 1199 1200
2002/10/08 11:22:22 600016 483675 5642387 57100 9748 65480 2700 1199 1200
2002/10/08 11:23:07 600016 485075 5658771 56100 9748 65480 2300 1199 1200
2002/10/08 11:23:22 600016 485675 5665791 55500 9748 65480 2300 1199 1200
2002/10/08 11:23:37 600016 485875 5668133 55500 9748 65480 2100 1199 1200
2002/10/08 11:23:52 600016 485975 5669304 55500 9748 65480 2000 1199 1200
2002/10/08 11:25:27 600016 487775 5690364 53700 10048 65480 3500 1199 1200
2002/10/08 11:25:42 600016 488275 5696219 53700 10048 65480 3000 1199 1200
2002/10/08 11:26:27 600016 491275 5731319 50700 10048 65480 3000 1199 1200
2002/10/08 11:27:12 600016 491775 5737174 50700 10048 65480 2500 1199 1500
2002/10/08 11:27:27 600016 492175 5741854 50300 10048 65480 2500 1199 1500
2002/10/08 11:29:12 600016 492275 5743025 50300 10048 65480 2400 1199 500
2002/10/08 13:00:01 600016 498474 5815629 6001 52100 10048 500 4925 2398
2002/10/08 13:00:21 600016 499374 5826168 5101 52100 10048 1000 500 4925
2002/10/08 13:01:36 600016 500374 5837878 4101 52900 10048 1000 500 4925
2002/10/08 13:03:01 600016 502874 5867153 1601 52900 10048 1000 500 4925
2002/10/08 13:03:16 600016 504674 5888229 52701 10048 66380 1000 500 4925
2002/10/08 13:02:46 600016 500874 5843733 3601 52900 10048 1000 500 4925
2002/10/08 13:04:46 600016 507594 5922393 49781 10048 66380 500 1000 500
2002/10/08 13:04:16 600016 505594 5898993 51781 10048 66380 500 1000 500
2002/10/08 13:06:01 600016 507891 5925869 49584 10048 66380 500 1000 500
2002/10/08 13:06:31 600016 509391 5943419 48084 10048 66380 400 1000 500
2002/10/08 13:07:01 600016 509791 5948103 400 48584 10048 1000 500 4925
2002/10/08 13:08:01 600016 511291 5965657 48484 10048 66380 1000 1500 4925
2002/10/08 13:11:56 600016 511891 5972688 1200 51984 10048 1000 1500 4925
2002/10/08 13:13:01 600016 512091 5975032 1700 52484 10048 2800 1500 4925
2002/10/08 13:16:47 600016 512191 5976203 4700 49484 10048 4845 1500 4925
2002/10/08 13:17:32 600016 514891 6007834 3400 49484 10048 3445 1500 4925
2002/10/08 13:17:02 600016 513591 5992611 4700 49484 10048 3445 1500 4925
2002/10/08 13:18:02 600016 517891 6042994 3400 49484 10048 445 1500 4925
2002/10/08 13:19:02 600016 520191 6069927 1100 49484 10048 45 1500 4425
2002/10/08 13:19:32 600016 521391 6083984 400 49484 10048 45 1500 4425
2002/10/08 13:19:17 600016 520891 6078124 400 49484 10048 545 1500 4425
2002/10/08 13:20:22 600016 521791 6088668 49484 10048 66880 600 1500 4425
2002/10/08 13:20:52 600016 521891 6089839 49484 10048 66880 500 1500 4425
2002/10/08 13:21:52 600016 522391 6095694 49484 10048 66880 1045 1500 4425
2002/10/08 13:22:22 600016 523436 6107931 455 49484 10048 1500 4425 3098
2002/10/08 13:22:37 600016 524436 6119635 48939 10048 66880 1500 4425 3098
2002/10/08 13:24:07 600016 524836 6124327 1800 49439 10048 1100 4425 3098
2002/10/08 13:25:52 600016 525736 6134878 2500 49839 10048 1100 4425 3198
2002/10/08 13:26:22 600016 526236 6140743 2500 49839 10048 4425 3198 1847
2002/10/08 13:27:37 600016 528236 6164221 500 2500 49839 2620 3698 1847
2002/10/08 13:28:37 600016 530436 6190049 500 3100 50339 720 2998 1847
2002/10/08 13:31:42 600016 530536 6191222 600 3100 50339 500 720 2998
2002/10/08 13:32:42 600016 531036 6197087 700 600 3600 4020 2998 2347
2002/10/08 13:32:27 600016 530736 6193568 600 3600 50339 300 4020 2998
2002/10/08 13:33:27 600016 541056 6314594 180 50339 18648 2998 2347 300
2002/10/08 13:34:17 600016 541756 6322805 180 50339 18648 960 2998 2347
2002/10/08 13:35:02 600016 542716 6334066 40 180 50339 2900 2998 2347
2002/10/08 13:35:17 600016 549676 6415501 43599 18648 67980 2900 2998 2347
2002/10/08 13:37:18 600016 550321 6423064 400 2255 46499 1500 4900 2998
2002/10/08 13:36:47 600016 549821 6417199 1555 45299 18648 2000 4900 2998
2002/10/08 13:38:07 600016 551821 6440659 4500 400 2255 5100 2998 2347
2002/10/08 13:38:22 600016 553221 6457081 3100 400 2255 5100 2798 2347
2002/10/08 13:38:37 600016 553521 6460600 2800 400 2255 4900 2798 2347
2002/10/08 13:38:52 600016 554021 6466465 2300 900 2255 4900 2798 2347
2002/10/08 13:39:52 600016 557021 6501685 2300 900 3255 5100 2798 2347
2002/10/08 13:40:07 600016 558521 6519295 2300 900 3255 600 2798 2347
2002/10/08 13:42:22 600016 558721 6521643 2900 900 3255 300 3798 2347
2002/10/08 13:43:12 600016 559021 6525165 200 3900 900 3798 2347 300
2002/10/08 13:43:27 600016 560121 6538074 3200 900 3255 3598 2347 300
2002/10/08 13:44:27 600016 560821 6546292 3700 900 2555 300 4598 3347
2002/10/08 13:44:57 600016 561521 6554503 3000 900 2555 300 4598 3347
2002/10/08 13:46:02 600016 561821 6558025 600 3000 900 4598 2347 300
2002/10/08 13:47:02 600016 562821 6569765 1000 3000 900 4598 2347 300
2002/10/08 13:48:02 600016 563821 6581505 400 3000 900 5098 2347 300
2002/10/08 13:48:47 600016 564421 6588549 3000 900 2555 800 5098 2347
2002/10/08 13:49:32 600016 565521 6601463 9200 3000 900 5098 2347 300
2002/10/08 13:51:17 600016 574421 6705949 3200 900 2555 9200 5398 2347
2002/10/08 13:51:07 600016 571721 6674251 2700 3200 900 5398 2347 300
2002/10/08 13:51:32 600016 575721 6721211 3200 900 2555 10900 5398 2347
2002/10/08 13:52:07 600016 578721 6756431 3200 900 2555 7900 5398 2347
2002/10/08 13:52:32 600016 579721 6768161 2200 900 2555 7900 5398 2347
2002/10/08 13:54:52 600016 584021 6818605 900 2755 47199 3600 6800 5598
2002/10/08 13:53:37 600016 581121 6784583 800 900 2755 11200 5398 2347
2002/10/08 13:54:07 600016 581621 6790453 1100 900 2755 6800 5398 2547
2002/10/08 13:54:22 600016 582721 6803356 1100 900 2755 6800 5398 2547
2002/10/08 13:54:37 600016 583021 6806875 900 2755 47699 4600 6800 5598
2002/10/08 13:56:47 600016 584321 6822121 600 2255 47199 2600 6800 5598
2002/10/08 13:57:07 600016 584421 6823294 600 2255 47199 3248 6800 5598
2002/10/08 13:58:52 600016 585421 6835014 2255 48199 19648 7848 8000 6698
2002/10/08 14:00:37 600016 585521 6836186 2255 48199 19648 900 9648 8000
2002/10/08 14:00:52 600016 588576 6871961 48199 19648 74080 9648 8000 6698
2002/10/08 14:01:52 600016 592421 6916977 45554 19648 75080 9148 9000 6698
2002/10/08 14:01:37 600016 591576 6907091 46199 19648 75080 9148 9000 6698
2002/10/08 14:01:22 600016 590576 6895361 46199 19648 75080 10148 8000 6698
2002/10/08 14:02:07 600016 593421 6928677 44554 9900 75080 9148 9000 6698
2002/10/08 14:02:22 600016 599421 6998877 38754 9900 75080 9148 9000 6698
2002/10/08 14:03:22 600016 599521 7000048 39254 9900 75080 1900 9148 9500
2002/10/08 14:03:37 600016 600021 7005903 40254 9900 75080 1400 9148 9500
2002/10/08 14:04:37 600016 603791 7050012 36284 9900 75080 1400 9148 9500
2002/10/08 14:04:22 600016 603291 7044162 36784 9900 75080 1400 9148 9500
2002/10/08 14:06:37 600016 603941 7051767 37534 9900 75580 2000 9148 9500
2002/10/08 14:07:07 600016 604241 7055277 38234 9900 76280 3500 9148 9500
2002/10/08 14:07:37 600016 605741 7072842 38934 9900 76280 2000 9148 9500
2002/10/08 14:08:22 600016 648741 7575902 5834 76280 16400 2000 9148 9500
2002/10/08 14:09:37 600016 650241 7593457 6034 76680 16400 1000 1500 9148
2002/10/08 14:09:07 600016 649041 7579415 1000 6034 76580 1700 9148 9500
2002/10/08 14:10:07 600016 651241 7605157 6034 76680 16400 1800 9148 9500
2002/10/08 14:11:27 600016 651441 7607497 7800 11234 72580 1800 3000 9148
2002/10/08 14:12:17 600016 661441 7724497 8500 28234 72580 2200 3000 9148
2002/10/08 14:12:02 600016 659241 7698757 28234 72580 16400 2200 2200 3000
2002/10/08 14:12:32 600016 661941 7730347 8000 28234 72580 2200 3000 9148
2002/10/08 14:13:17 600016 662941 7742047 7000 28334 72580 2200 3000 9148
2002/10/08 14:14:02 600016 668265 7804350 3876 28334 72580 2700 3000 9148
2002/10/08 14:13:32 600016 663965 7754027 6876 28334 72580 2200 3000 9148
2002/10/08 14:13:47 600016 667465 7794982 3876 28334 72580 1700 3000 9148
2002/10/08 14:14:47 600016 669765 7821915 3876 28334 73580 900 3000 9148
2002/10/08 14:14:32 600016 669265 7816060 3876 28334 73580 1400 3000 9148
2002/10/08 14:15:02 600016 670665 7832445 2976 28334 73580 900 3000 9148
2002/10/08 14:15:17 600016 671565 7842984 7800 4176 28134 3000 9148 9500
2002/10/08 14:15:37 600016 671865 7846497 7700 4376 28134 3000 9148 9500
2002/10/08 14:16:07 600016 672365 7852357 8700 5176 28134 3000 9148 9500
2002/10/08 14:16:37 600016 673860 7869864 7705 5176 28134 2500 9148 9500
2002/10/08 14:16:47 600016 681461 7958871 104 5176 28134 2500 9148 9500
2002/10/08 14:18:22 600016 682161 7967074 4 5176 28034 1900 9148 9500
2002/10/08 14:18:02 600016 682061 7965903 104 5176 28034 2500 9148 9500
2002/10/08 14:19:07 600016 712161 8317826 12100 3214 73580 1900 9148 9500
2002/10/08 14:19:22 600016 724161 8458346 8800 12100 3214 1900 9148 9500
2002/10/08 14:19:52 600016 746061 8714664 26214 73580 16400 8100 2000 9148
2002/10/08 14:20:07 600016 757861 8852606 24414 73580 16400 8100 2000 9148
2002/10/08 14:20:22 600016 758861 8864296 23414 73580 16400 8100 2000 9148
2002/10/08 14:19:37 600016 745061 8702964 25814 73580 16400 9100 1900 9148
2002/10/08 14:20:37 600016 783361 9150726 23814 73580 16400 5600 1000 2000
2002/10/08 14:20:57 600016 820718 9587074 38037 16400 66876 5600 1000 2000
2002/10/08 14:21:27 600016 827718 9668904 38037 16400 66876 3000 500 1000
2002/10/08 14:22:12 600016 833718 9738989 38037 16400 66876 2500 1905 1000
2002/10/08 14:23:12 600016 834918 9753005 31637 16400 66876 4000 1905 1000
2002/10/08 14:23:47 600016 850718 9937549 16337 16400 66876 4000 1905 1000
2002/10/08 14:24:02 600016 851718 9949229 15337 16400 66876 4000 1905 1000
2002/10/08 14:23:27 600016 835768 9762933 31537 16400 66876 4000 1905 1000
2002/10/08 14:24:17 600016 852718 9960909 14337 16400 66876 5000 1905 1000
2002/10/08 14:24:37 600016 853918 9974925 13137 16400 66876 5000 1905 1000
2002/10/08 14:25:07 600016 862218 10071869 5037 16400 66876 5000 1905 1000
2002/10/08 14:24:52 600016 860918 10056685 6137 16400 66876 5000 1905 1000
2002/10/08 14:25:22 600016 862618 10076541 4637 16400 66876 5000 1905 1000
2002/10/08 14:25:37 600016 863118 10082381 4637 16400 66876 5000 2405 1000
2002/10/08 14:25:52 600016 867255 10130701 16400 66876 22200 1863 5000 2405
2002/10/08 14:26:12 600016 877255 10247512 16400 66876 22200 8963 5000 2405
2002/10/08 14:27:02 600016 878255 10259202 16400 66876 22200 2963 6405 1000
2002/10/08 14:27:17 600016 879255 10270892 16400 66876 22200 1963 6405 1000
2002/10/08 14:27:47 600016 880655 10287238 15400 66876 23700 2263 6405 1000
2002/10/08 14:27:28 600016 880255 10282562 15400 66876 22700 1963 6405 1000
2002/10/08 14:28:02 600016 880955 10290742 200 15400 66876 2263 6405 1000
2002/10/08 14:28:37 600016 884555 10332762 400 12600 66876 2263 8405 1000
2002/10/08 14:29:03 600016 885155 10339770 12600 66876 23700 100 2263 8405
2002/10/08 14:29:22 600016 945155 11039496 19476 23700 11800 11700 100 2263
2002/10/08 14:29:37 600016 965931 11281754 23700 11800 1400 8224 20700 1580
2002/10/08 14:30:07 600016 966931 11293404 22700 11800 1400 8224 20700 1580
2002/10/08 14:30:37 600016 967531 11300394 22100 11800 1400 8224 20700 3580
2002/10/08 14:30:52 600016 967631 11301559 22000 11800 1400 9224 20700 3580
2002/10/08 14:31:07 600016 970101 11330334 19530 11800 1400 9224 700 3580
2002/10/08 14:31:22 600016 980101 11446834 10530 11800 1400 9224 700 3580
2002/10/08 14:31:37 600016 990101 11563334 530 11800 1400 9224 700 3580
2002/10/08 14:31:57 600016 990631 11569509 11800 1400 4995 670 9224 700
2002/10/08 14:32:42 600016 991831 11583494 11800 1400 4995 7894 700 3580
2002/10/08 14:33:27 600016 993231 11599810 800 12000 1400 7294 700 3580
2002/10/08 14:33:42 600016 994131 11610295 12000 1400 5495 1100 7294 700
2002/10/08 14:34:12 600016 994331 11612625 12000 1400 5495 600 7294 700
2002/10/08 14:34:27 600016 995231 11623110 100 12000 1400 7294 700 3580
2002/10/08 14:34:57 600016 998231 11658070 3200 12000 1400 6294 700 3580
2002/10/08 14:35:47 600016 1002431 11707042 3200 12100 1400 2094 700 3580
2002/10/08 14:37:07 600016 1003431 11718702 10600 17200 1400 1094 700 3580
2002/10/08 14:37:37 600016 1004525 11731458 2306 40900 17200 700 3580 4263
2002/10/08 14:38:22 600016 1008805 11781442 720 2306 44400 4263 8605 1000
2002/10/08 14:38:37 600016 1009805 11793132 720 2306 46400 3263 8605 1000
2002/10/08 14:39:07 600016 1011905 11817638 200 3226 46700 3163 8605 1000
2002/10/08 14:39:52 600016 1012905 11829328 200 4326 47100 2163 8605 1000
2002/10/08 14:40:37 600016 1024505 11964579 16100 41926 17200 763 8605 1000
2002/10/08 14:40:52 600016 1025268 11973499 437 12300 16100 8605 1000 2000
2002/10/08 14:40:22 600016 1022905 11945875 200 4326 47100 2363 8605 1000
2002/10/08 14:41:08 600016 1026268 11985178 11737 16100 42726 8605 1000 2000
2002/10/08 14:41:42 600016 1035873 12097546 11400 10500 26637 8605 1000 2000
2002/10/08 14:41:57 600016 1037773 12119776 3995 21800 10500 1000 2000 8383
2002/10/08 14:42:42 600016 1042773 12178276 9595 33400 10500 1000 2000 8383
2002/10/08 14:43:47 600016 1055856 12331653 3217 9695 33400 6800 11198 2347
2002/10/08 14:44:02 600016 1056556 12339864 2517 200 500 6800 11198 2347
2002/10/08 14:44:32 600016 1057556 12351594 1517 200 1000 6800 11198 2347
2002/10/08 14:45:02 600016 1058206 12359218 2967 200 1000 6800 17098 2347
2002/10/08 14:44:47 600016 1058056 12357459 1717 200 1000 6800 11198 2347
2002/10/08 14:45:37 600016 1060756 12389130 417 200 1000 6800 17098 2347
2002/10/08 14:45:22 600016 1058706 12365083 2467 200 1000 6800 17098 2347
2002/10/08 14:46:07 600016 1061373 12396367 200 1000 30395 883 6800 20298
2002/10/08 14:45:47 600016 1060856 12390303 317 200 1000 6800 18098 2347
2002/10/08 14:47:07 600016 1063273 12418636 600 30395 33400 50 183 7200
2002/10/08 14:46:37 600016 1062073 12404576 200 1000 30395 183 6800 20298
2002/10/08 14:46:52 600016 1063173 12417465 700 30395 33400 50 183 7200
2002/10/08 14:47:22 600016 1063506 12421369 767 600 30395 7200 20298 2347
2002/10/08 14:48:27 600016 1064273 12430366 300 600 34495 258 7700 20298
2002/10/08 14:50:33 600016 1073573 12539212 28595 33400 10500 1958 7700 20298
2002/10/08 14:50:17 600016 1064573 12433885 1300 100 36195 1958 7700 20298
2002/10/08 14:50:47 600016 1074073 12545077 29395 33400 10500 1458 7700 20598
2002/10/08 14:51:37 600016 1074873 12554460 1000 29695 33400 758 8200 20598
2002/10/08 14:51:52 600016 1075873 12566170 29695 33400 10500 958 8200 20598
2002/10/08 14:53:12 600016 1076773 12576714 29795 33700 10500 3900 300 1958
2002/10/08 14:53:32 600016 1077073 12580227 29795 33700 10500 4200 300 1958
2002/10/08 14:54:17 600016 1083273 12652779 25095 33700 10500 2700 300 2958
2002/10/08 14:54:37 600016 1084773 12670329 23595 33700 10500 2700 300 3458
2002/10/08 14:54:47 600016 1087773 12705432 20895 33700 10500 2700 400 3458
2002/10/08 14:55:22 600016 1090173 12733522 21195 33700 10500 400 3458 8200
2002/10/08 14:55:08 600016 1088573 12714800 200 20895 33700 400 3458 8200
2002/10/08 14:55:42 600016 1093173 12768622 18295 33700 10500 400 3458 8200
2002/10/08 14:56:32 600016 1095273 12793192 19795 33700 10500 400 3458 8200
2002/10/08 14:57:02 600016 1095773 12799042 19295 33700 10500 400 3458 8200
2002/10/08 14:57:22 600016 1096173 12803722 19895 33700 11000 400 3458 8200
2002/10/08 14:58:12 600016 1096973 12813082 20895 34000 11000 400 3458 8200
2002/10/08 14:58:27 600016 1097973 12824802 7500 200 22195 3458 8200 16698
2002/10/08 14:59:02 600016 1106473 12924422 200 22195 34000 1700 3458 8200
2002/10/08 14:59:22 600016 1106573 12925594 200 22195 34000 1600 3458 8200
2002/10/08 14:59:42 600016 1107573 12937296 22595 34000 11000 1600 3458 8200
Sheet2
tm code price vol Volume amount bid1 bidvol1 bid2 bidvol2 bid3 bidvol3 ask1 askvol1 ask2 askvol2 ask3 askvol3
100022 600016 140274 1626837 12155 56675 6500 900 1000 5957
100037 600016 140674 400 1631489 7600 800 12155 1000 5957 2100
100052 600016 142074 1,400 1647771 6200 1100 13155 1000 5957 2100
100122 600016 142474 400 1652427 6400 2100 13455 600 5957 2100
100142 600016 145849 3,375 1691678 4425 2100 13455 600 5757 2100
100157 600016 147449 1,600 1710312 400 4425 2100 4757 2100 1430
100212 600016 148049 600 1717294 4225 2100 13455 4757 2100 1430
100232 600016 149449 1,400 1733604 200 4725 2100 4157 2100 1430
100247 600016 149549 100 1734768 1100 6625 2100 3057 2100 1430
100302 600016 152149 2,600 1765028 5725 2100 13455 2557 1100 1430
100317 600016 153749 1,600 1783668 5725 2600 13455 1257 1100 1430
100347 600016 154706 957 1794817 1843 5525 2300 1100 1430 5678
100507 600016 154906 200 1797153 2043 5525 2300 900 1730 5978
100552 600016 155706 800 1806497 6500 4843 10825 100 1730 5978
100607 600016 155806 100 1807665 200 6500 3843 1730 5678 1000
100652 600016 156306 500 1813508 2800 6800 3843 2230 5678 1000
100722 600016 159006 2,700 1845046 700 10200 3843 1730 5378 1000
100757 600016 160306 1,300 1860233 500 10200 3843 1430 5378 1000
100812 600016 161306 1,000 1871915 10700 3843 10825 2260 1230 5378
100842 600016 162106 800 1881259 10700 3843 10825 11160 1230 5378
101002 600016 175506 13,400 2037504 2843 10425 2500 200 16480 1230
Sheet2
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price
bid1
ask1
Sheet3
100142 600016 145849 0 1691678 4425 2100 13455 600 5757 2100
Bid Quote / Depth Ask Quote / Depth Last Transaction Price / Size / Time
4425 600 3375 10:01:42
2100 5757 400 10:01:22
13455 2100 1400 10:00:52
142074 142,074
142474 400
145849 3,375
100157 600016 147449 147,449 1710312 400 400 2100 4757 2100 1430
Bid Quote / Depth Ask Quote / Depth Last Transaction Price / Size / Time
400 4757 1600 10:01:57
4425 2100 3375 10:01:42
2100 1430 400 10:01:22
Chart2
0
0
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Adjusted
Lag
ACF
Chart1
0
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0
Raw
Adjusted
Lag
ACF
ACF of Durations for Minsheng Bank (600016)
Sheet1
Raw Adjusted
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163 0
164
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184 0
185 0
186
187 0
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Sheet1
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0 0
Raw
Adjusted
Lag
ACF
Figure ACF of Durations for Minsheng Bank (600016)
Sheet2
Sheet3
