Intraday Information_ Trading Volume_ and Return Volatility

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					 ACADEMIA ECONOMIC PAPERS
   32 : 1 (March 2004), 107–148




     Intraday Information, Trading Volume, and Return
          Volatility: Evidence from the Order Flows
                on the Taiwan Stock Exchange


                                  Edward H. Chow ∗
                                Department of Finance
                              National Chengchi University
                                      Yi-Tsung Lee
                               Department of Accounting
                              National Chengchi University
                                       Yu-Jane Liu
                                Department of Finance
                              National Chengchi University



Keywords: Information, Trading volume, Return volatility, Order flow
JEL classification: G14, G15, D82

 ∗
    Correspondence: Edward H. Chow, Department of Finance, National Chengchi University, Taipei
116, Taiwan. Tel: (02) 2939-3091 ext. 81206; Fax: (02) 2939-3394; E-mail: echow@nccu.edu.tw.
Chow would like to acknowledge the financial support of the National Science Council of the Republic
of China (NSC852416H004032). We are grateful to the Securities and Futures Commission, the Taiwan
Stock Exchange Corporation, and especially Nai-Kuan Hwang for their assistance in data collection.
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                                                    ABSTRACT
                 Relative to the transaction data investigated in the literature, the complete order
            flow data we have from the Taiwan Stock Exchange (TSE) is particularly appropriate for
            examining the intraday relationship between information, trading volume and volatility.
            We find that traders tend to concentrate their orders, but only the trading volume and
            volatility of the small stocks are positively associated with the concentration of orders.
            The liquidity orders do not influence volume and volatility as much as the information
            orders. The information carried by the large information orders tends to be private rather
            than public. But the trading volume at the open is not unusually high for our sample,
            notwithstanding very large order flows. We think that the TSE’s unique order-driven call
            market without specialists which imposes price limits and allows only limit orders makes
            traders very conservative at the open. In addition, the market-wide information affects
            the trading volume and volatility of the large firms through private information. The firm-
            specific information is not as clear a determinant of trading volume and volatility as the
            market-wide information. The diversity in the interpretation of information and speculative
            orders are positively related to trading volume and volatility.
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                       Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)




            1. INTRODUCTION

            A pattern of unusually large trading volume and volatility at the market open and in
            particular at the close has been found in different markets.1 The pattern is baffling be-
            cause it cannot be satisfactorily explained by equilibrium asset pricing theories such as
            the capital asset pricing model. While the contemporaneous correlation between trad-
            ing volume and return volatility has been well documented,2 little empirical research
            has been done on the determinants of intraday trading volume and return volatility. As
            Bessembinder et al. (1996) put it: “Despite the importance of the topic, surprisingly
            little empirical research has addressed the determinants of trading volume.” This pa-
            per aims to fill the deficiency by empirically evaluating the effects of information on
            intraday trading volume and volatility.
                   In particular, we examine order flows as they relate to information, trading vol-
            ume, and volatility. A distinct feature of our inquiry is the use of order flow data in
            constructing variables for the determinants of trading volume and volatility. This is
            a clear departure from extant empirical research which mainly use bid-ask spread to
            infer information contents (see, e.g., Huang and Stoll (1994)). For example, Chan,
            Christie, and Schultz (1995) compare the intraday pattern of bid-ask spreads for NAS-
            DAQ securities with that of the NYSE to conclude that tests for the importance of
            information asymmetries in determining intraday spreads, as modeled by Admati and
            Pfleiderer (1988), Foster and Viswanathan (1990), and Madhavan (1992), must first
            consider the impact of institutional factors. Chan, Chung, and Johnson (1995) study
            the intraday behavior of bid-ask spreads for actively traded CBOE options and for
            their NYSE-traded underlying stocks. They confirm previous findings of McInish and
            Wood (1992) and Brock and Kleidon (1992) that stocks have a U-shaped spread pat-
            tern. They suggest that both the degree of competition in market making and the extent


              1
                For instance, there have been findings for the New York Stock Exchange (Wood et al. (1985) and
            Lockwood and Linn (1990)), the Toronto Stock Exchange (McInish and Wood (1990)), the Tokyo Stock
            Exchange (Chang, Fukuda, Rhee, and Takano (1993)), and the Kuala Lumper Stock Exchange (Chang,
            Kang, and Rhee (1993)).
              2
                See, for example, Karpoff (1987), Gerety and Mulherin (1992) for the positive relation between
            trading volume and price volatility, Mitchell and Mulherin (1994) for the relation between volume and
            public information flows, French and Roll (1986) and Jones et al. (1994) for the effects of public and
            private information on volatility, and Anderson (1996) for an information flow interpretation of trading
            volume and return volatility.


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            of informed trading are important for understanding the intraday behavior of spreads.
                  However, contrary to the empirical evidence that the intraday bid-ask spread pat-
            tern and trading volume on the NYSE are U-shaped, many information-based models
            on spreads predict that higher volume is associated with lower spreads (Copeland and
            Galai (1983), Glosten and Milgrom (1985), Kyle (1985), Easley and O’Hara (1987,
            1992), Admati and Pfleiderer (1988), Hasbrouck (1988), Foster and Viswanathan
            (1990), and Madhavan (1992)). Thus, the feasibility of extracting information con-
            tent from the bid-ask spread is dubious for our purpose. The order flow data avoid
            this problem and allow us to design alternative information measures that were not
            explored before.
                  Examining order flow data can potentially reveal valuable information that is not
            available from transaction data. The observed transaction price and volume do not
            necessarily expose trading behavior adequately. For example, trading volume can be
            high when order is imbalance or balanced, but the information content in the two
            cases should be quite different. Order imbalance should be low when traders have
            very disparate views about market condition, when information asymmetry is severe,
            or when there is high liquidity demand and supply. Griffiths et al. (2000) find that
            aggressive buy orders are more likely to be motivated by information and tend to occur
            when bid-ask spreads are narrow. Hasbrouck and Seppi (2001) and Brown et al. (1997)
            study the interaction between order imbalance and stock prices. Empirically bid-ask
            spread is low when order imbalance is low. So when order imbalance is low, there is
            more private information. And the informed submit orders when order imbalance is
            low in order to hide their information.
                  There have been more and more studies that use order imbalance for their probe.
            For example, Chan and Fong (2000) find that after controlling for the return impact
            of order imbalance, the volatility-volume relationship becomes much weaker. In our
            paper we believe that low order imbalance can result from a low degree of conformity
            of traders’ opinions. Our intuition is that in the absence of information asymmetry,
            order is more imbalanced because traders’ opinions are more one-sided. Low order
            imbalance can be associated with high trading volume and with high or low volatility.
            When volatility is high, our finding implies that there is much information in the mar-
            ketplace. When volatility is low, there is a lack of information. In this case the trading
            volume could be due to liquidity demand and supply. Thus, by studying order flow
            one can better discern the relationship between trading volume and information.
                  The order flow data available to us are from the Taiwan Stock Exchange (TSE).


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            The TSE’s microstructure arguably provides us with a cleaner testing environment
            than the NYSE. The TSE is an order-driven computerized trading system. There are
            no specialists on the TSE. Orders are matched by computers through a call mechanism
            throughout the trading day. The time between two calls is about 45 seconds. Except
            for the intraday price limit that restricts transaction price from moving more than two
            ticks from one call to another there are no other artificial arrangements to affect the
            transaction prices. As such the intraday trading volume and volatility on the TSE
            are not affected by the complicated interaction between specialists and the informed
            and the noise traders as considered by, e.g., Stoll and Whaley (1990) and Brock and
            Kleidon (1992), or by the differences in trading mechanism at the open and the close as
            examined by Amihud and Mendelson (1987, 1991), Amihud et al. (1990), and Chow
            et al. (1996).
                  Since the TSE is a centralized, computerized, order-driven call market, it can
            readily record the entire order flow in the computer. Our order flow data are more
            comprehensive than other order flow data that have been examined in the literature in
            the sense that we have the complete order flow. In contrast, the data used by Biais et
            al. (1995) cannot be observed for order placement or cancellation outside of the best
            five bid and offer prices. In addition, the Institute for the Study of Security Markets
            (ISSM) data summarizing the NYSE transactions includes the prevailing quotations,
            but not orders away from the quote. The electronic book on the NYSE contains only
            about 30 percent of the executed trading volume. The electronic order book and order
            flow data from the NYSE (such as the Trades, Orders, Reports, and Quotes (TORQ)
            data base) do not include orders such as some market makers’ orders and most large
            orders. Given the advantage of our data we define variables based on the entire order
            flow, which avoids the bias caused by examining only a part of the whole order flow.
                  Our primary interest is in the investigation of the relationship between informa-
            tion asymmetry and trading volume and volatility. Admati and Pfleiderer (1988) sug-
            gest that the informed and noise traders tend to concentrate their transactions. The
            informed benefit more from their private information when noise traders trade, and
            the competition between the informed lowers the transaction cost for the noise traders
            if they trade along with the informed. Slezak (1994) argues that if market closure
            delays the resolution of information uncertainty which imposes excessive risk on the
            informed, the informed have the motivation to trade before the market close in order
            to shift the risk to the noise traders. As the market reopens, those who cannot trade
            overnight trade according to the information revealed during the market closure. Since


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            trading releases private information, one would expect volatility to be high at the mar-
            ket open and the close as well.3
                  For our purpose we control for the effects on trading volume and volatility of
            other information characteristics. In specific, we distinguish between market-wide
            and firm-specific information. Consistent with Subrahmanyam (1991) and Gorton and
            Pennacchi (1993), Bessembinder et al. (1996) find that traders with firm-specific in-
            formation trade primarily on the spot equity market, while those with market-wide in-
            formation trade on both the spot and index futures markets. The relationship between
            market-wide and firm-specific information and volatility and trading volume depends
            on whether the information is public or private. We are not able to separate the private
            information from the public information. But to the extent that a public information is
            not a surprise, private market-wide and firm-specific information tend to induce higher
            trading volume than the public ones and the volatility could be high for either public
            and private information or both.4
                  Another control variable is the diversity in the assessment of common informa-
            tion. In the theories of Varian (1986), Harris and Raviv (1993) and Shalen (1993)
            trading occurs because traders differ either in their prior beliefs regarding value or in
            their interpretation of common signals. Therefore, trading volume increases with the
            dispersion of traders’ private evaluation. Bessembinder et al. (1996), using S&P 500
            index futures open interest as a proxy for the divergence of traders’ opinions, find ev-
            idence in support of the theory. If wide disparity in the interpretation of information
            is caused by the arrival of public and/or private information, volatility should also be
            high in this case.
                  The last control variable is speculative liquidity trading, which should increase
            trading volume and volatility. Note that by definition this type of trading is not based
            on information.
                  We find that information and liquidity orders tend to be concentrated and affect
            the trading volume and volatility of small firms. The market-wide information is pos-
            itively related to trading volume and volatility, but more so for large firms than small

              3
                In a model in which the informed traders possess different information, Foster and Viswanathan
            (1996) suggest that the initial correlation among the informed traders’ information signals should be
            considered to explain the volume-volatility relationship using structural models of speculative trading.
              4
                 The relationship between public and private information and volatility is by no means resolved in the
            literature. French and Roll (1986) conclude that private, rather than public, information is the primary
            source of volatility. Berry and Howe (1994) find no significant correlation between intraday public in-
            formation arrival and volatility, while Jones et al. (1994) provide evidence that public information is the
            major source of short-term return volatility.


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            firms, while the speculative orders are associated with trading volume and volatility
            quite equally for large and small firms. It is also found that the trading volume is
            not unusually high at the market open despite very large order flows, which we think is
            caused by the distinct trading mechanism of the Taiwan Stock Exchange (TSE). Unlike
            the NYSE which has specialists to solicit orders to balance the orders for the opening
            call, the TSE relies exclusively on computers to match orders mechanically. The TSE’s
            call mechanism allowing only limit orders makes traders particularly conservative in
            placing orders at the market open because of the trading recess. Our evidence shows
            that the small orders are even more conservative than the large orders, since the small
            orders are likely to possess less information than large orders.
                  Our methodology considers trading volume and volatility jointly because they
            both are closely related to information. We classify our sample into four categories:
            high volume/high volatility (HH), high volume/low volatility (HL), low volume/high
            volatility (LH), and low volume/low volatility (LL). It is commonly argued (e.g.,
            French and Roll (1986), Jones et al. (1994)) that it takes trading to release private
            information, while public information can be instantly reflected in the price without
            much trading.5 Both private and public information induce volatility. Thus, HH is
            likely to be associated with private information, LH with public information, HL and
            LL with null information. By examining the effects of the variables on the probabil-
            ity that the four groups occur, one can infer whether the information content of the
            aforementioned variables is public or private.
                  For example, we proxy the information orders with the orders of greater than
            median size and the liquidity orders with the orders of smaller than median size. We
            find that for small firms the number of large orders is significantly directly related to
            the probability of HH and HL occurring. This means that large orders contain either
            private or little information but not public information. For small firms the small orders
            are directly related to HH and HL to a lesser degree than the large orders. For large
            firms the small orders are actually positively related to LL and negatively associated
            with HH. The finding means that the small orders contain either less information than
            the large orders or very little information.
                  We have some evidence that market-wide information is positively related to HH
            and negatively related to LL for large firms, implying that the bulk of market-wide in-
            formation affecting trading volume and volatility is private. However, for small firms

              5
                While public information surprise would induce trading, we assume that it occurs randomly in our
            sample because we do not have reason to believe otherwise.


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            the market-wide information is directly associated with LH and negatively associated
            with HL, meaning that public rather than private market-wide information determines
            the trading volume and volatility of small firms. In addition, for large firms the rela-
            tionship between firm-specific information and trading volume and volatility is similar
            to that of the market-wide information. However, firm-specific information is not a
            significant determinant of trading volume and volatility for small firms.
                  Speculative orders (order flows that are orthogonal to the other explanatory vari-
            ables) are positively related to HH and negatively related to LL, which is consistent
            with our expectation. Moreover, the less disparate the traders’ opinions are (mani-
            fested by the larger the order imbalance), the smaller the trading volume and volatility,
            a situation in accord with the low degree of information asymmetry where little private
            information is present.
                  This article is organized as follows. Section 2 reviews the TSE’s trading mecha-
            nism relevant to our study. Section 3 describes the data, defines variables and outlines
            our test methodology. Empirical results are presented in section 4. Section 5 discusses
            the empirical findings. Section 6 offers concluding remarks.


            2. THE TRADING MECHANISM ON THE TAIWAN STOCK
               EXCHANGE
            The two major U.S. markets, the NYSE and NASDAQ, are an agency/auction market
            and a dealer market, respectively. NASDAQ is a computerized, quote-driven system
            whose market regularity depends on the competition among market makers (dealers).
            NASDAQ matches orders continuously as orders enter into the system. NYSE is an
            order-driven floor trading system which relies on specialists to act in a passive capacity
            to stabilize the market when there is a large order imbalance. NYSE adopts a contin-
            uous auction trading mechanism for trades initiated after the market open each day.
            However, the open price is determined by specialists through a call market.
                  In contrast, the TSE is an order-driven system without designated market makers.
            Stock brokers submit orders to a computer system through the Computer Assisted
            Trading System (CATS) from their offices. CATS then matches orders without the
            physical presence of brokers. Like the opening procedure on the NYSE, the opening
            prices on the TSE are determined through a call market by the exchange. Between 8:30
            and 9:00 A.M. orders can be submitted to CATS for the opening call. However, unlike


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            the NYSE, after the open, orders on the TSE are matched about every 45 seconds
            throughout the trading day. During a 45-second call period, orders are sequentially
            accepted by the computer, then matched based on the price-time priority rule through
            the same call market mechanism as that at the open to maximize the trading volume.
            After each call the transaction price, transaction volume, the highest bid price and the
            lowest asking price among unmatched orders are released to the public. At the close
            the exchange conducts the last call for each stock. Remaining orders after the last call
            are removed from the book.
                  The exchange has a daily price limit of 7%. In addition, except for the opening
            price which is allowed to change up to 7% from the closing price of the previous trad-
            ing day, all the subsequent trades are subject to an intraday price limit which mandates
            that the stock price move within two ticks of the price determined in the previous call.
            Although officially the market orders are not allowed on the TSE, to secure match-
            ing priority, traders can submit limit orders hitting the daily price limits (limit orders
            with prices beyond the price limits are not accepted by the computer). This type of
            price-limit orders can achieve the effect of market orders.6 Thus, on the TSE traders
            can aggressively submit price-limit orders to enhance the probability of trade and yet
            have some price protection from the intraday price limit. Unless the previous transac-
            tion price is within two ticks of the daily price limit, price-limit orders would not be
            matched at the daily price limit. This type of order submitting behavior induced by the
            TSE’s microstructure, in addition to information, could affect order flows and, in turn,
            trading volume and volatility.
                  Currently the total capitalization of the TSE is the fourth largest in Asia. How-
            ever, the annual turnover rate on the TSE has consistently been the highest in the world,
            coupled with unusually high volatility. In 1990, the TSE collapsed by posing a drop
            of more than 40% in value, while in 1996 the TSE’s value rose by 34%, the highest
            jump among the equity markets in the world. The high turnover and volatility may
            have something to do with the fact that trading on the TSE is dominated by individual
            traders. Individual traders, constituting about 90% of the trading volume, are consid-
            ered to be more speculative and short-term in their investments than institutions. It is
            commonly believed that the equity market in Taiwan is excessively speculative and is
            manipulated by individuals with large endowments. As an example, in June 1990 when
            the monthly turnover rate in Taiwan was 70%, compared to 3.7% in the US, in its com-

               6
                 If market orders are allowed, according to Schwartz (1991), they are equivalent to limit orders written
            at the highest allowable call price for buy orders and the lowest allowable call price for sell orders.


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            ments on Taiwan’s market, Pacific Rim Stock Markets (Baring Securities, July 1990)
            stated: “Last quarter we warned investors that Taiwan’s monthly stock market turnover
            had reached a level that equaled its annual GNP. This represented an excessive level
            of “froth” that will probably never be seen again by any world stock market........as
            the stock market capitalization has fallen substantially over the past quarter, monthly
            turnover still represents an exceptionally frothy 70% of market capitalization. Hence,
            Taiwan remains a high risk stock market and we continue to recommend caution.”


            3. DATA, DEFINITION OF VARIABLES, AND TESTABLE
               HYPOTHESES
            3.1 Data
            The tick-by-tick transaction and order flow data of the TSE for the six months July
            through December 1994 are used for our analysis. The data consist of every original
            order for the buy and sell side as well as every transaction recorded by the computer
            by time, price and volume. Since the data volume is too large to include all stocks on
            the TSE, 60 stocks are chosen from about 300 stocks listed on the TSE, consisting of
            the 30 largest- and 30 smallest-capitalization stocks as of the beginning of our sample
            period. Stocks that had IPOs six months prior to our sample period, that distributed
            cash or stock dividends, and that had seasoned offerings during our sample period are
            excluded.7 All of the stocks in our sample are actively listed throughout the sample
            period. Preferred stocks and financially distressed stocks are not included.
                  The TSE trades from 9:00 A.M. till 12:00 P.M. on weekdays, and from 9:00 A.M.
            to 11:00 A.M. on Saturday. To avoid the potential effect of the length of trading time on
            the return-generating process, Saturday observations are not included in our sample. In
            total there are 104 trading days. To give readers some idea of the data employed in our
            analysis, we report in Table 1 summary statistics of the high- and low-capitalization
            stocks.


              7
                 We have to exclude stocks that paid cash or stock dividends because of a peculiar phenomenon
            in Taiwan. Before a stock goes ex-dividend, there is usually expectation on the part of the investors,
            founded or unfounded, that the stock price would soon rise back to its pre-dividend level. Investors’
            trading behavior is affected by the expectation. In order to avoid the effect of this kind of phenomenon
            on stock price, we exclude stocks that pay cash or stock dividend from our sample. It is possible that
            this kind of sample selection criterion could introduce bias in our result. But we do not feel that there is
            reason to believe that the bias would outweigh the disadvantage from the ex-dividend phenomenon.


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                      Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)



                          Table 1        Summary Statistics of Large and Small Stocks
            The variables are denominated in New Taiwanese Dollars (NT). Capitalization is in thousands
            of NT. Daily number of shares (orders) submitted is scaled by the total number of shares
            outstanding. The unscaled numbers are in parentheses, for which the daily number of shares
            outstanding is in thousands of shares. T -tests are conducted for the test of equal means between
            large and small stocks. Significance levels of 10%, 5%, and 1% are indicated by *, **, and
            ***.


                                                Whole
                                                                  Large            Small         T -test
                                                sample
                    No. of stocks                 60                30               30
                    No. of trading days          104               104              104
                    Avg. price                  36.24             42.56            29.91           *
                    Avg. daily return           0.0025           0.0030           0.0020
                    Std.-dev. of daily          0.0232           0.0222           0.0241
                    return
                    Daily trading volume       3,636.1           5,286.1          1,986.2          **
                    in 1,000 shares.
                    Daily trading volume        603.4             824.7            382.0           **
                    in no. of orders
                    Daily turnover              0.0147           0.0085           0.0209          ***
                    Capitalization            14,156,532       25,513,596        2,799,478        ***
                    Daily no. of shares         0.0660            0.0369          0. 0952         ***
                    submitted                  (13,827)          (18,861)         (8,743)        (***)
                    Daily no. of orders         0.0071           0.0037           0.0106          ***
                    submitted                   (1,237)          (1,560)           (915)         (***)


                 The large stocks’ average capitalization is NT25.5 billion, vs. the NT2.8 bil-
            lion of the small stocks. The large stocks’ share price and daily trading volume in
            terms of shares traded and number of orders traded are greater than those of the small
            stocks. However, the daily turnover of the large stocks (0.0085) is lower than that of
            the small stocks (0.0209), and the volatility is somewhat lower as well. The overall
            daily turnover is 0.0147, amounting to about 400% per year, i.e., on average a share
            changes hands four times a year. The numbers of shares and orders submitted for trad-
            ing are much larger than the trading volume. From Table 1 one can calculate that about
            28% of orders submitted for trading for the large stocks result in trades, compared to
            only 23% for the small stocks.


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            3.2 Definition of variables

            3.2.1 Information vs. Liquidity orders
            Following what is generally presumed in the literature, we assume that large-size or-
            ders contain more information and are more likely to reflect traders’ private informa-
            tion than small-size ones. We classify orders into the two categories based on whether
            or not an order size (in number of shares) is greater than the intraday median size of the
            orders.8 Throughout this paper, unless otherwise noted, our variables are calculated
            for each 6-minute interval. This is because there are about eight 45-second call-back
            periods in the interval, which in most cases are sufficient to accommodate large intra-
            day price moves so that the intraday price limit is not binding. Including the opening
            call as a separate interval, there are in total 31 intraday intervals. Note that only new
            order flows that occur in a particular 6-minute interval are used to calculate the values
            of different variables. Orders whose sizes are greater (smaller) than the median size
            are called information (liquidity) orders, denoted as MOIijt (MOLijt ) for stock i, in-
            traday interval j, and day t. MOI and MOL are then divided by their intraday 6-minute
            averages to obtain OIijt and OLijt , which are the basis of our analysis of the intraday
            pattern of information and liquidity orders.
                  If information and liquidity traders concentrate their trading at the close and the
            open, we would expect the ratio of information and liquidity orders to be greater at the
            two points of time. Trading volume and volatility will be positively correlated with
            OI and OL if order concentration induces trading and volatility. Of course, the set of
            information orders according to our classification might include some liquidity traders,
            and likewise the set of liquidity orders might include some information orders. But as
            long as the crossovers occur randomly, our measures should be quite indicative of what
            we intend to capture.

              8
                Whether or not large orders contain more information than other orders is not yet conclusive in
            the literature. For example, Barclay et al. (1993) suggest that medium-sized rather than large orders
            contain more information. However, many studies find that large orders contain more information than
            small orders. Lee et al. (1999) find that on the Taiwan Stock Exchange large orders have much more
            information contents than small orders. Easley and O’Hara (1987) find that informed traders prefer to
            trade larger amounts at any given price. Hasbrouck (1988) provides strong evidence that large trades
            convey more information than samll trades. Since our paper does not aim to analyze the information
            content of different order sizes, we somewhat arbitrarily adopt the view that large orders have more
            information content. In light of our finding, to be shown later in our paper, that large orders have more
            impacts on the high volume/high volatility group, it seems that our classification achieves what it is meant
            to.


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            3.2.2 The degree of divergence in opinions
            The degree of divergence in opinions measures the diverse views on the direction of
            market movement. Our proxy is order imbalance in every six-minute interval, which is
            calculated as the absolute value of the difference between the total shares of buy orders
            and those of sell orders, scaled by the total number of shares outstanding. The variable
            is denoted as OBijt , meaning the order imbalance of stock i in interval j on day t. The
            greater the order imbalance, the smaller the opinion difference. If trading is caused by
            diverse interpretation of market conditions, we expect trading volume to be high when
            OB is low. And if the diverse interpretation is induced by new information, then the
            volatility should be high as well.

            3.2.3 The scope of information
            The scope of information refers to the classification of information as market-wide or
            firm-specific. Orders driven by market-wide information should be quite evenly dis-
            tributed across different stocks, while those caused by firm-specific information should
            be concentrated in specific stocks. It is natural to assume that market-wide informa-
            tion would generate orders in proportion to a firm’s market capitalization. Hasbrouck
            (1996) uses the orders from program trading to proxy for the effect of public informa-
            tion on order flow, because program orders cover multiple stocks and therefore should
            reflect market-wide information. In the case of index arbitrage, the broad-market index
            is usually weighted by market capitalization.
                  We do not have measures similar to that used by Hasbrouck (1996) because there
            is neither program trading on the TSE nor trading based on equity indexes such as
            index futures. But there is no obvious reason to believe that the market-wide infor-
            mation would have consistent asymmetric impacts on the order flow of stocks, dispro-
            portionate to the stocks’ capitalizations. Note that all stocks are traded actively on the
            TSE. In our sample the average turnover rate per six-minute interval is about 0.07%
            of outstanding shares. Table 1 shows that the daily turnover of small stocks actually is
            significantly higher than that of large stocks. There is thus very little non-synchronous
            trading problem or cross-autocorrelation between stock returns. We do not have the
            means to ascertain how market-wide information would generate orders differently
            in large and small stocks. But given that small stocks are also very active in terms of
            their trading volume, it seems that our assumption that market-wide information would
            generate orders in proportion to a firm’s market capitalization is innocuous. We define
            MNijt as the capitalization-weighted number of shares in interval j for stock i on day
            t, calculated by multiplying the total amount of orders (in shares) of all stocks in the

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                                            Academia Economic Papers 32:1 (2004 )



            sample in the interval j on day t by stock i’s proportion of the total capitalization of
            the stocks in the sample.
                  Letting stock i’s orders in interval j on day t be Nijt , the firm-specific information
            variable (ACNijt ) is proxied by the absolute value of CNijt = (Nijt − MNijt )/MNijt ,
            which is the absolute deviation of stock i’s orders in interval j on day t from the
            weighted number of shares. Both MNijt and ACNijt can be public and private.9
            Whether or not MNijt and ACNijt are larger at the open and the close is not clear
            a priori. But if trading volume and volatility are caused by market-wide and firm spe-
            cific information, we would expect the two variables to be positively correlated with
            trading volume and volatility.

            3.2.4 Speculative orders
            Orders that are not related to the above variables are called speculative orders, which
            are proxied by the residuals of the regression of the total order flows, TODijt , on
            the variables defined above. The regression residuals are denoted as RTODijt . In
            general, orders are generated out of information, liquidity, speculation and market
            making. There are no designated market makers on the TSE and to the best of our
            knowledge there are no dealers who act like liquidity providers. Hence the portion of
            order flows arising from inventory management should be quite insignificant. Since
            we have defined variables that are proxies for orders generated out of information
            and liquidity, we define the residual order flow that is not related to information and
            liquidity as speculative orders. This, of course, is not the perfect proxy for speculative
            orders. Basically, it is just something that is left in the order flow that is not captured by
            liquidity and information variables. Since speculation tends to induce trading volume
            and volatility, if RTOD captures the effect of speculative trading well, RTOD ought to
            be positively related to volume and volatility.

            3.3 Testing methodology
            We now suggest a classification scheme to test the relationship between information,
            trading volume, and return volatility. Our design will consider volume and volatility


               9
                 It is less likely for market-wide information to be private than for firm-specific information. But one
            cannot completely rule out the possibility that some market-wide information might be private. If we
            define private information strictly as insider information, then it is indeed very unlikely that market-wide
            information is private. However, if we allow private information to be acquired through superb research
            that could predict policy moves or market trends, then the domain of private market-wide information is
            expanded to include such information.


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            together rather than separately. Trading volume for an interval is taken as the number
            of shares traded over the interval.10 Volatility is measured as the absolute price change
            from the end of the previous interval to the end of the current interval divided by the
            average of the two prices. Each interval i on a particular trading day t of stock j is clas-
            sified as one of the four volume/volatility groups: high volume or high volatility (HH),
            high volume/low volatility (HL), low volume/high volatility (LH), or low volume/low
            volatility (LL). An interval is in the HH group if its trading volume is higher than the
            intraday median volume and its volatility is greater than 80% of intraday volatilities,
            in the HL group if its trading volume is higher than the intraday median volume and its
            volatility is smaller than (including) 60% of intraday volatilities, and so forth for the
            LH and LL groups. For any 6-minute interval j of stock i the percentage (denoted as
            Pijk ) of the number of trading days belonging to one of the four groups, k (k = HH,
            HL, LH, LL), is calculated. For example, suppose that there are 52 days of stock i for
            which the last six-minute interval is in the HH group, then the calculated percentage,
            Pi31HH is 50% (52/104) for the stock.
                  We are not able to screen the volatility based on the median because it is likely
            that the median takes the value of zero. Table 2 shows, for the high-capitalization and
            low-capitalization groups, respectively, the percentage of the 30 stocks’ trading days
            that the volatility value is zero at a certain percentile of intraday volatility observations
            ranked in ascending order. One can see that at the 60th percentile, on average 25%
            of trading days have volatility values of zero. But the percentage drops to about 10%
            at the 70th percentile, and 3% at the 80th percentile. We therefore choose to classify
            volatility according to the rule explained above.11
                  The summary statistics of our classification are shown in Table 3. Except for
            share price, the values of other variables such as volatility and trading volume are
            statistically different across groups and the order of magnitude is as expected for each
            group. Except for volatility and turnover, the values of the variables of the large stocks
            are in general greater than those of the small stocks. Note that in Table 1 we already
            observed that the small stocks’ turnover is higher, which is accompanied by higher
            volatility. The P ’s in Table 3 show that ninety percent of the intraday intervals are
            classified into one of the four groups. The total percentage in groups LL and HH
            (49%) are greater than that in groups HL and LH (41%), meaning that more often

              10
                 Our results are invariant to different definitions of trading volume: number of shares traded or
            turnover.
             11
                  Our results are robust to different cut-off values: 60%, 70% or 80%.


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                                          Academia Economic Papers 32:1 (2004 )



            Table 2 Percentage of Trading Days of the Large and Small Stocks Whose 6-
                    Minute Volatility Is Zero for a Percentile of Intraday Volatilities Ranked
                    by Their Absolute Values
            The intraday 6-minute interval volatilities are ranked for each trading day in ascending order.
            The volatility is measured as the absolute value of the 6-minute return. There are in total 3,120
            (104 × 30) trading days for the 30 stocks in each capitalization group. The percentage reported
            in the table is that of the 3,120 observations that are zero for a particular percentile of ranked
            intraday volatilities.


                                                        Percentage of trading days
                                    Percentile of
                                                      Large stocks      Small stocks
                                    zero returns
                                         10               99.5               99.4
                                         20               96.2               95.2
                                         30               88.6               83.0
                                         40               76.3               62.1
                                         50               56.0               37.0
                                         60               32.8               17.8
                                         70               14.1                6.4
                                         80                3.9                1.8
                                         90                0.5                0.5



            than not, trading volume is positively related to volatility. The LH group occurs least
            frequently, indicating that low volume does not often happen with high volatility. But
            since the percentage for the HL group is quite high (32%), it is quite often that high
            volume is accompanied by low volatility.
                 We are now ready to state the various hypotheses regarding the relationship be-
            tween information, trading volume, and volatility in terms of the regression equation
            below. For each volume/volatility group, Pijk is regressed on OB, OI or OL, MN,
            ACN, and RTOD.

                Pijk = b0ik + b1ik (OIij or OLij ) + b2ik OBij + b3ik MNij

                                                         +b4ik ACNij + b5ik RTODij + eij ,                (1)

                 where bik ’s are the estimated slopes for the group k of stock i.




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                         Table 3         Summary Statistics of Volume/Volatility Groups
            High volume/high volatility, high volume/low volatility, low volume/high volatility, and low
            volume/low volatility groups are denoted as HH, HL, LH, and LL. T -tests are for the tests of
            equal means between large and small stocks. F -tests are for the tests of equal means across
            the four groups. Significance levels of 10%, 5%, and 1% are indicated by *, **, and ***.

                                             Group      Large stocks      Small stocks       T -test
                   Average per               HH            42.45             29.83            2.47**
                   share price               HL            42.40             29.99            2.42**
                                             LH            42.64             29.87            2.44**
                                             LL            42.60             30.00            2.45**
                                             F -value       0.00059           0.00163
                   Average                   HH             0.0480            0.0469         0.09
                   six-minute                HL             0.0031          −0.002           2.38***
                   return                    LH             0.0322            0.0591        −1.76*
                                             LL           −0.0058           −0.0089          1.38
                                             F -value      11.80***          24.95***
                   Average                   HH             0.7990            0.8787        −2.10*
                   six-minute                HL             0.1015            0.1237        −3.30***
                   volatility (%)            LH             0.6996            0.7695        −2.38**
                                             LL             0.0846            0.1056        −3.22***
                                             F -value     516.74***         535.37***
                   Average trading           HH           280.85            129.39            2.27**
                   volume in 1,000           HL           195.93             93.83            2.44**
                   shares                    LH            55.19             25.02            2.57**
                                             LL            64.31             25.18            2.34**
                                             F -value       7.46***          42.83***
                   Average trading           HH            45.04             26.83            2.44**
                   volume in                 HL            31.55             19.44            2.54**
                   number of                 LH            13.15              8.24            2.77**
                   orders                    LL            13.75              7.69            2.54**
                                             F -value      11.81***          47.40***
                   Pij (%)                   HH            15.57             15.3             0.68
                                             HL            32.76             31.49            3.64**
                                             LH             9.69              9.29            1.61
                                             LL            33.56             33.38            0.36
                                             F -value   1,951.58***       1,875.57***
                   Average                   HH             0.00053           0.00137       −4.19***
                   turnover                  HL             0.00038           0.00101       −4.08***
                                             LH             0.00010           0.00027       −5.29***
                                             LL             0.00010           0.00026       −4.79***
                                             F -value       5.82**           36.14***


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                                              Academia Economic Papers 32:1 (2004 )



                  Note that one regression is run by one of the four classifications of Pijk for each
            of the 60 stocks in our sample.12 The explanatory variables are the six-minute inter-
            val values averaged across trading days. Each regression has 31 intraday observations
            including the open and its subsequent 30 six-minute intervals. Since the correlation
            coefficient of OI or OL is usually above 0.9, either OI or OL is included in the re-
            gressions.13 The estimation results for the other explanatory variables are very similar
            whether OI or OL is included. In the following we report the results based on the
            regressions inclusive of OI. For the regressions with OL we only report the estima-
            tion results for the variable OL. The explanatory variables do not change with the
            volume/volatility category.
                  Since our variables are measured over a six-minute interval, if the error terms
            in the regression are autocorrelated, there could be bias in the estimated variance-
            covariance matrix of the error terms. We thus employ the method of White (1980)
            and Hansen (1982) to adjust the variance-covariance matrix for general conditional
            heteroskedasticity and autocorrelations in the error terms. Further to ensure that the
            Hansen-White procedures yield a positive semi-definite variance-covariance matrix in
            finite samples, the Newey and West (1987) weight is applied wherever necessary. We
            ran seperate regressions under the assumptions that the error terms are not autocorre-
            lated and that there is first-order autocorrelation. Both yield results similar to those
            of OLS. We thus only report the results assuming that the autocorrelation is of the
            first-order.
                  By examining the slope coefficients of each group, we can determine the rela-
            tionship between the independent variables in (1) and different combinations of vol-
            ume and volatility. Moreover, the regressions allow us to discern whether or not the
            relationship is related to public or private information. Since it is generally believed
            that private information affects volatility through trading, while public information is
            reflected in the price instantly, without much trading, the HH group is likely to be
            caused by private information, the LH group by public information, and the HL and
            LL groups by little information. Thus, one can induce whether the effects of the ex-
            planatory variables in (1) on the trading volume and volatility are caused by public or
            private information.

              12
                 Since our paper is primarily interested in jointly explaining the volume-volatility relationship, we
            do not regress volatility and volume separately on explanatory variables. It is possible that the four
            regressions are correlated in some way. But for any particular stock during any six-minute segment,
            the volume/volatility pair could fall in any one of the four groups, determined by the order flow of the
            six-minute period. Thus, it seems that even if there is some correlation between groups, we do not have
            reason to believe that our methodology results in systematic bias in our findings. We thank the referee for
            pointing out this potential problem to us.
             13
                  The results are similar if both OI and OL are included.


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            4. EMPIRICAL RESULTS

            Figures 1 and 2 show, for the large- and small-capitalization stock groups separately,
            the intraday six-minute average trading volume, average number of shares submitted,
            and average return volatility. For the two figures and other figures that follow we
            scale the variables to fit into the figures. Thus, the figures are only meaningful to
            the extent of their intraday pattern, not their magnitudes. The variables are averaged
            across trading days and stocks. The first observations in the figures are those for the
            opening call, and the rest are for the remaining six-minute intervals. The volatility for
            the opening call is measured from the close of the previous day to the open of the next
            day. Notwithstanding the volatility at the open, similar to the findings in the existing
            literature, volatility exhibits a clear U-shape. Although the trading volume clearly
            increases toward the end of the trading day, it is not distinctly high at the beginning of
            the trading day. However, the total order flow in terms of number of shares submitted
            has a clear U-shape as well. Thus, we have observed that large order flows do not
            necessarily result in large trading volume.
                  Table 4 depicts the descriptive statistics of the variables employed in this paper
            for four time sections of the trading day: open, 9:00 to 9:06, 9:06 to 11:54, and 11:54 to
            12:00. Like in Figures 1 and 2, these variables are measured over six-minute intervals.
            Large and small stocks have very similar patterns. Except for the trading volume
            (VOL), ACN, and RTOD, all the other variables have values higher near the open and
            the close than during the middle of the trading day. As we have seen from Figures
            1 and 2, the trading volume is only distinctly higher near the close, but not the open.
            ACN is higher during the middle of the day than near the open and close. RTOD is
            low near the open but very high at the close. Figures 3 and 4 show for the large and
            small stocks the intraday pattern of OB, OI, OL, MN, ACN, and RTOD. The pattern is
            consistent with the observations made from Table 4.
                  Figures 5 and 6 present for the large and small stocks the intraday pattern of the
            four volume/volatility groups in terms of the average percentage (Pij ) across stocks of
            the number of six-minute intervals falling into one of the four groups. The patterns
            for the large stocks and the small stocks are very much alike. The HH group has a
            U-shaped pattern, while the pattern of the LL group is almost the mirror image of that
            of the HH group. The pattern of HL is monotonically increasing, but that of LH is just


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                                             Academia Economic Papers 32:1 (2004 )



                   9
                   8
                   7
                   6
                                                                                     TOD
                   5
                                                                                     VOL
                   4
                                                                                     ARET
                   3
                   2
                   1
                   0
                       1     3    5    7    9 11 13 15 17 19 21 23 25 27 29 31

            Figure 1 Intraday Patterns of Total Order Flow (TOD), Volume (VOL), and
                     Volatility (ARET) for Large Firms



                   25


                   20


                   15                                                                TOD
                                                                                     VOL
                   10                                                                ARET


                    5


                    0
                        1    3     5   7    9 11 13 15 17 19 21 23 25 27 29 31


            Figure 2 Intraday Patterns of Total Order Flow (TOD), Volume (VOL), and
                     Volatility (ARET) for Small Firms




                                                             126
                                      Table 4    Summary Statistics for Different Intraday Time Sections

      All the variables except for MN, ACN and volatility are scaled by the number of shares outstanding. The number “1,000” in the parentheses
      indicates that the variables are multiplied by 1,000.

      Panel A. Large Stocks
                                       Open                        9:00–9:06                       9:06–11:54                     11:54–12:00
                              Mean    Median Std. dev.     Mean     Median Std. dev.      Mean      Median Std. dev.      Mean     Median Std. dev.
      No. of shares traded 0.20607 0.04679      0.6888    0.22714 0.04147      0.7503    0.2661 0.06242         0.9025   0.76851 0.28953 1.4915
      (×1, 000)
      No. of orders sub-   0.50506 0.29538 0.69728        0.10207 0.03955 0.23412        0.10273 0.03832 0.20497         0.21546 0.09042 0.38736
      mitted (×1, 000)




127
      No. of shares sub-    4.6994    2.444     7.3873    1.0905    0.3462     2.5266    1.0376     0.2958      2.9183   2.0787 0.7515      4.2912
      mitted (×1, 000)
      Volatility (%)       0.77161    0.4662    1.2125    0.42454 0.32733      0.5257    0.26795       0        0.3638   0.49545 0.44053 0.5419
      OB (×1, 000)          1.5847    0.5619    3.725     0.3786   0.10592      1.083    0.3281 0.08221         1.2217   0.4729 0.1465      1.2173
      OI                      4.483   3.769     3.179     −0.046 −0.269         0.958    −0.187 −0.368          0.728     0.79      0.594       1.126
      OL                      4.225   4.014      2.19     −0.191 −0.254         0.543    −0.169 −0.222          0.463     0.698     0.588       0.785
      MN                   3434.78 2026.19 4472.29        839.85    468.84     1225.75   688.62     351.33   1097.81     1481.53 823.14 2102.27
      ACN                  0.71804 0.62795 0.68745        0.98108 0.77153 1.42121        0.98817 0.76555 1.52452         0.9089 0.75178 1.02878
      RTOD (×1, 000)       −0.0424 −0.0125 0.1142        −0.0529 −0.0449 0.0854          0.0025 −0.0005 0.1209           0.0654 0.0162      0.1678
                               Table 4     Summary Statistics for Different Intraday Time Sections (continued)

      All the variables except for MN, ACN and volatility are scaled by the number of shares outstanding. The number “1,000” in the parentheses
      indicates that the variables are multiplied by 1,000.

      Panel B. Small Stocks
                                       Open                      9:00–9:06                     9:06–11:54                     11:54–12:00
                              Mean Median Std. dev.      Mean    Median Std. dev.      Mean     Median Std. dev.      Mean     Median Std. dev.
      No. of shares traded   0.7184      0.2   2.4421   0.7139 0.1821        1.8602   0.6672    0.2816      1.3104   2.2862     1.4182   2.8307
      (×1, 000)
      No. of orders sub-     1.3456 0.9596     1.4503   0.3081 0.1558        0.5475   0.2942    0.1817      0.3813   0.6702     0.4838   0.6533
      mitted (×1, 000)




128
      No. of shares sub-     13.596 7.8653     28.152    3.271    1.2369     6.871     2.579     1.2521     4.428     6.094     3.7062      7.334
      mitted (×1, 000)
      OB (×1, 000)            5.709   2.1342   21.51    1.2642 0.4227        3.863    0.8481     0.3451     2.026    1.5136     0.6638      2.849
      Volatility (%)         0.9256 0.5420     1.3285   0.5001 0.3968        0.6203   0.2975     0.2265     0.3874   0.5213 0.44346 0.5567
      OI                      3.694   3.237    2.385    −0.059 −0.296        0.913    −0.17      −0.35      0.723     1.117     0.904       1.227
      OL                      3.289   3.101    1.567    −0.218 −0.314        0.555    −0.145 −0.223         0.512     1.002     0.891       0.917
      MN                     376.958 309.039 234.407    92.305 69.370        72.572   75.275     51.818     67.294   161.740 124.961 118.571
      ACN                    2.51213 1.53104 4.13957    3.10185 0.92774 6.31277       3.08045 1.14053 5.66102        3.20584 1.66378 4.52277
      RTOD (×1, 000)         −0.069 −0.054     0.084     0.024   −0.042      0.276    −0.0008 −0.0026       0.179     0.117     0.142       0.18
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                        4
                   3.5
                        3
                   2.5                                                                             OB
                        2                                                                          OI
                                                                                                   OL
                   1.5
                                                                                                   MN
                        1                                                                          ACN
                   0.5                                                                             RTOD

                        0
                  -0.5
                    -1
                            0       2       4       6   8 10 12 14 16 18 20 22 24 26 28 30

                                     Intraday Six-Minute Intervals
            Figure 3 Intraday Patterns of the Information, Liquidity, and Speculation
                     Variables for Large Stocks


                    7

                    6

                    5

                    4                                                                              OB
                    3                                                                              OI/2
                                                                                                   OL/2
                    2                                                                              MN
                    1                                                                              ACN
                                                                                                   RTOD
                    0

                  -1

                  -2
                        0       2       4       6       8 10 12 14 16 18 20 22 24 26 28 30

                                     Intraday Six-Minute Intervals
            Figure 4 Intraday Patterns of the Information, Liquidity, and Speculation
                     Variables for Small Stocks


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                                                   Academia Economic Papers 32:1 (2004 )




                        0.6

                        0.5

                        0.4
                 Pijk                                                                      HH
                                                                                           HL
                        0.3
                                                                                           LH
                                                                                           LL
                        0.2

                        0.1

                            0
                                0    2   4     6   8 10 12 14 16 18 20 22 24 26 28 30

                                   Intraday Six-Minute Intervals
            Figure 5 Intraday Volume/Volatility Classification Patterns for Large Stocks



                      0.5
                      0.45
                      0.4
                      0.35
                                                                                            HH
                      0.3
                                                                                            HL
                      0.25                                                                  LH
               Pijk
                      0.2                                                                   LL
                      0.15
                      0.1
                      0.05

                         0
                             0      2    4     6   8 10 12 14 16 18 20 22 24 26 28 30

                                   Intraday Six-Minute Intervals
            Figure 6 Intraday Volume/Volatility Classification Patterns for Small Stocks




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                     Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)



            the opposite. The patterns of the four groups start off from the open of the day quite
            near to each other, then diverge to different directions throughout the trading day. That
            is, near the open there is a similar chance for a six-minute interval to fall in one of the
            four groups, but near the close the HH and HL cases have a much higher probability of
            occurring than the LH and LL cases. Thus, volume is more likely than volatility to be
            extremely high at the close, while the chance of the four groups occurring at the open
            is even.
                  Table 5 reports the regression results of equation (1). The summary statistics
            of the regression coefficients and their t-statistics (in parentheses) are included. The
            numbers of significant positive and negative coefficients are counted for significance
            levels of 10% and 5% for two-sided tests. Since the regression is run for each stock
            and each volume/volatility group, there are altogether 240 regressions. To conserve
            space, we report the summary statistics of the estimated coefficients only. Table 5
            shows the number of significant coefficients for the two size-groups and the four vol-
            ume/volatility groups. From the number of significant coefficients and their signs we
            are able to judge if our testable hypotheses are supported by the regression results.
                  The results for OI indicate that the information orders are positively related to
            the trading volume and volatility, although OI also seems to be related to high trading
            volume and low volatility. For the small stocks, according to the significance level
            of 10% for two sided tests, there are 15(3) and 21(1) significantly positive (negative)
            coefficients for HH and HL groups, while 0(16) and 2(15) for the LH and LL groups.
            In addition, 25 and 26 out of 30 stocks are positive for the HH and HL groups and the
            same number of stocks are negative for LH and LL groups. The means and medians
            of t-statistics of the four groups are also significant. This means that some of the large
            trading volume happens when large information orders are present. But sometimes
            OI carries information (HH is positively associated with OI), while other times little
            information (HL is directly related to OI as well). In addition, the relationship between
            OI and the trading volume and volatility is not clear for the large stocks. For small
            stocks since there is positive association between OI, OL and the HL group, some of
            the trading volume is a result of liquidity trading,
                  Regarding the small stocks, OL’s relationship to the trading volume and volatility
            is very similar to but somewhat less significant than that of OI. But for the large stocks
            it is more likely that OL is negatively related to trading volume and volatility, because
            there are more stocks (25) that are negatively related to the probability of HH group
            than are positively related (5). Fifteen (5) out of 30 stocks are negatively (positively)


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                                           Academia Economic Papers 32:1 (2004 )



            Table 5 Summary of the Relationship between Information, Volatility and Trad-
                    ing Volume Based on the Following Regression
                              Pijk = b0ik + b1ik (OIij or OLij ) + b2ik OBij + b3ik MNij
                                                           +b4ik ACNij + b5ik RTODij + εijk .
            In the above regression Pijk is stock i’s percentage of the j th intraday 6-minute volume and
            volatility falling in one of the four volume/volatility groups (k = HH, HL, LH, LL for the
            high volume/high volatility, high volume/low volatility, low volume/high volatility and low
            volume/low volatility group). The subscript j, j = 0, 1, 2, ..., 30, denotes the 31 intervals from
            open to close. Variables OIjk , OLjk , OBjk , MNjk , ACNjk , RTODjk are as defined in the
            paper and do not change with k. We include either OI or OL in the regression because the
            correlation coefficient of OI and OL is usually over 0.9. The regression is run for each stock in
            our sample. In the following we summarize the estimated coefficients of the 120 regressions
            for the large and small stocks respectively for the case of including OI only. Since, the results
            for the case of including OL are similar, for this case we only report the estimation results for
            OL. Numbers in parentheses are the summary statistics of t-statistics, and NP and NN indicate
            the number of significant positive and negative coefficients for the indicated significance level
            of two-sided tests. Numbers in parentheses below the numbers of significant coefficients are
            the numbers of positive and negative coefficents.

             Variable Size Group                   Estimated coefficients                   NP           NN
                                         Mean     Median      σ     Max.     Min.     10% 5% 10% 5%
                                         0.048 −0.047 0.346 0.783 −0.625               7     6      6      5
                                  HH     (0.14) (−0.48) (2.49) (6.95) (−3.96)               (14)         (16)
                                         0.058    0.037 0.288 0.782 −0.537             6     6      7      5
                                  HL     (0.41)   (0.29) (2.53) (7.96) (−2.97)              (16)         (14)
                       Large
                                         −0.033 −0.001 0.156 0.272 −0.363              4     3      6      6
                                  LH     (−0.15) (−0.02) (2.04) (4.29) (−5.38)              (15)         (15)
                                         −0.025 0.051 0.398 0.886 −0.803               6     6      7      6
                                  LL     (−0.09) (0.38) (2.41) (3.41) (−7.89)               (17)         (13)
                OI
                                         0.252    0.268 0.284 0.810 −0.258             15    15     3         1
                                  HH     (2.85)   (1.78) (3.66) (10.87) (−4.03)             (25)             (5)
                                         0.263    0.241 0.203 0.731 −0.093             21    19     0         0
                                  HL     (2.64)   (2.66) (2.06) (7.95) (−0.99)              (26)             (4)
                       Small
                                         −0.150 −0.130 0.131 0.057 −0.478              0      0    16     15
                                  LH     (−1.93) (−1.92) (1.72) (1.03) (−5.62)               (5)         (25)
                                         −0.356 −0.397 0.336 0.352 −0.932              2      2    15     13
                                  LL     (−1.94) (−1.67) (2.52) (4.86) (−8.40)               (4)         (26)




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            Table 5 Summary of the Relationship between Information, Volatility and Trad-
                    ing Volume Based on the Following Regression (continued)
            Variable Size Group                    Estimated coefficients                      NP            NN
                                         Mean     Median     σ      Max.       Min.     10% 5% 10% 5%
                                         −0.128 −0.115 0.274        0.556    −0.879       5     4       15    13
                                 HH      (−1.32) (−1.64) (2.92)     (9.44)   (−4.86)           (5)           (25)
                                         0.053 −0.001 0.222         0.673    −0.431       9      7      6      5
                                 HL      (0.29) (−0.03) (2.29)      (6.25)   (−3.76)           (15)          (15)
                     Large
                                         −0.046 −0.023 0.124        0.205    −0.393       4      2      8      8
                                 LH      (−0.52) (−0.59) (1.94)     (4.27)   (−4.45)           (12)          (18)
                                         0.118    0.109    0.291    0.798    −0.632      13     12      3         2
                                 LL      (1.06)   (1.19)   (2.50)   (4.84)   (−6.91)           (21)              (9)
               OL
                                         0.136    0.130    0.220    0.634    −0.315      11     10      3     2
                                 HH      (1.03)   (0.96)   (2.33)   (5.48)   (−6.27)           (20)          (10)
                                         0.237    0.231    0.163    0.682    −0.072      23     21      0         0
                                 HL      (2.85)   (2.54)   (1.88)   (6.44)   (−0.54)           (28)              (2)
                     Small
                                         −0.146 −0.118 0.099        0.026    −0.367       0     0       21    17
                                 LH      (−2.23) (−2.22) (1.42)     (0.42)   (−5.02)           (2)           (28)
                                         −0.209 −0.255 0.260        0.409    −0.731       2     2       13    13
                                 LL      (−1.35) (−1.43) (1.56)     (2.92)   (−3.58)           (5)           (25)
                                         −381.3 −423.2 1025.3 4138.1 −1500.4              1     1       13    10
                                 HH      (−1.48) (−1.53) (1.84) (2.89) (−6.99)                 (5)           (25)
                                         −243.3 −178.2 651.5 1323.3 −1173.5               2     1       11    10
                                 HL      (−1.07) (−1.07) (1.63) (2.20) (−4.96)                 (7)           (23)
                     Large
                                         112.4     85.5    355.4    885.1    −866.3       5      5      2         1
                                 LH      (0.65)   (0.80)   (1.47)   (4.79)   (−3.16)           (21)              (9)
                                         403.5    514.2 1087.1 3233.4 −2577.3            10      8      2         1
                                 LL      (1.03)   (1.22) (1.53) (3.69) (−1.96)                 (22)              (8)
               OB
                                         −221.7 −162.3 256.5        223.2 −979.3          2     2       17    17
                                 HH      (−3.26) (−2.49) (4.61)     (7.65) (−18.40)            (3)           (27)
                                         −116.0 −76.0 200.5         245.9    −602.7       1     1       16    12
                                 HL      (−1.51) (−1.75) (2.31)     (2.77)   (−7.58)           (9)           (21)
                     Small
                                          42.3     34.4     71.6    193.0    −116.3       8      4      0         0
                                 LH      (0.80)   (0.77)   (1.21)   (3.98)   (−1.42)           (21)              (9)
                                         202.5    173.4    331.0 1049.1 −474.1           18     13      4         3
                                 LL      (2.03)   (1.90)   (3.51) (13.32) (−5.69)              (22)              (8)




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                                             Academia Economic Papers 32:1 (2004 )



            Table 5 Summary of the Relationship between Information, Volatility and Trad-
                    ing Volume Based on the Following Regression (continued)
            Variable Size Group                     Estimated coefficients                     NP        NN
                                         Mean     Median       σ      Max.      Min.     10% 5% 10% 5%
                                     0.00010 0.00026 0.00079 0.00131 −0.00209 14 13 4                     4
                               HH     (1.24)  (1.55) (2.76) (7.73) (−5.97)       (21)                    (9)
                                    −0.00012 0.00008 0.00065 0.00100 −0.00231 9 7 5                       4
                               HL    (0.25)   (0.40) (2.75) (7.91) (−6.97)      (17)                     (13)
                    Large
                                     0.00013 0.00003 0.00033 0.00125 −0.00032 5 5 3                       3
                               LH     (0.21)  (0.20) (2.13) (5.58) (−5.68)      (17)                     (13)
                                    −0.00009 −0.00033 0.00082 0.00193 −0.00130 4                2 14      10
                               LL    (−1.12) (−1.20) (2.50) (4.05) (−7.66)                     (9)       (21)
               MN
                                     0.00024 0.00089 0.00392 0.00818 −0.00756 8 6 9                       9
                               HH    (−0.01) (0.50) (2.83) (7.37) (−6.66)       (17)                     (13)
                                    −0.00272 −0.00257 0.00308 0.00461 −0.01090 2                2 16      15
                               HL    (−1.73) (−1.89) (1.93) (2.55) (−5.58)                     (6)       (24)
                    Small
                                     0.00230 0.00202 0.00218 0.00759 −0.00089 19 16 0                     0
                               LH     (2.09)  (2.44) (1.73) (5.92) (−1.37)       (25)                    (5)
                                     0.00104 −0.00003 0.00516 0.01070 −0.01213 10 10 3                    3
                               LL     (0.46) (−0.01) (3.11) (7.92) (−8.08)        (15)                   (15)
                                     −0.420       −0.128     0.928    0.610    −3.579     1      0 10     7
                               HH    (−0.93)      (−1.24)    (1.58)   (1.66)   (−5.49)         (10)      (20)
                                     −0.484       −0.240     0.753    0.874    −2.500     2     2 11       8
                               HL    (−1.17)      (−1.35)    (1.98)   (3.58)   (−6.52)         (8)       (22)
                    Large
                                         0.153     0.058     0.324    1.125    −0.265     7      5 3      2
                               LH        (0.55)    (0.48)    (1.67)   (4.06)   (−2.94)         (20)      (10)
                                         0.751     0.391     1.191    4.342    −0.589    14 12 1          1
                               LL        (1.35)    (1.29)    (1.81)   (4.10)   (−3.65)      (23)         (7)
              ACM
                                     −0.007        0.011     0.105    0.226    −0.438     4      3 5      4
                               HH    (−0.12)       (0.23)    (2.09)   (3.88)   (−7.01)         (17)      (13)
                                     −0.025       −0.013     0.070    0.109    −0.188     0      0 8      8
                               HL    (−0.73)      (−0.60)    (1.39)   (1.35)   (−3.81)         (11)      (19)
                    Small
                                         0.012     0.015     0.043    0.133    −0.078     9      8 2      1
                               LH        (0.77)    (0.61)    (2.20)   (7.89)   (−2.92)         (19)      (11)
                                         0.030     0.018     0.127    0.519    −0.137     6      4 4      3
                               LL        (0.22)    (0.53)    (2.53)   (5.67)   (−7.81)         (16)      (14)




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                      Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)



            Table 5 Summary of the Relationship between Information, Volatility and Trad-
                    ing Volume Based on the Following Regression (continued)
            Variable Size Group                    Estimated coefficients                        NP       NN
                                         Mean   Median       σ       Max.        Min.      10% 5% 10% 5%
                                     468.503 414.988 674.439 3044.440 −627.150 16 12 0                     0
                               HH     (1.66)  (1.71) (1.57) (6.36)     (−1.02)    (26)                    (4)
                                     136.746 131.719 506.846 1217.210 −840.160              9     8 2      2
                               HL     (0.69)  (0.54) (1.84) (4.93)     (−2.64)                   (18)     (12)
                     Large
                                    −133.537 −108.193 242.132 372.920 −727.740              0     0 11 10
                               LH    (−0.98) (−1.02) (1.26) (1.34)     (−4.04)                   (7)   (23)
                                    −357.910 −377.366 537.060 695.420 −1384.600 2                 1 13 11
                               LL    (−1.22) (−1.07) (2.08) (4.49)     (−5.48)                   (8)   (22)
              RTOD
                                     136.288 109.606 211.100 720.260 −369.320 16 15 3                      3
                               HH     (1.92)  (1.97) (2.30) (6.42)    (−2.53)    (25)                     (5)
                                     119.598    92.433 170.023 509.280 −145.590 14 11 2                    2
                               HL     (1.31)    (1.40) (1.84) (5.06)    (−2.66)    (23)                   (7)
                     Small
                                    −69.300 −63.291 85.417 60.180 −283.470                  2     1 10 8
                               LH   (−1.07) (−1.01) (1.92) (2.76)  (−6.08)                       (7)   (23)
                                    −173.765 −144.034 271.594 456.400 −859.340              4     4 16 11
                               LL    (−1.86) (−1.77) (2.56) (3.12)     (−7.66)                   (6)   (24)


            significant at the 10% significance level for the two-sided tests for the HH group,
            while 13 (3) are positively (negatively) significant for the LL group. The means of the
            t-statistics for the large stocks for the HH and LL groups are −1.32 and 1.06. Thus,
            trading volume and volatility tend to be lower when there are more small orders for
            the large stocks. Taking the findings for OI and OL together, it seems that the large
            information traders and small liquidity traders drive trading volume and volatility for
            the small stocks but not for the large stocks. In addition, the small orders contain
            less private information than the large orders because OL is less directly related to the
            trading volume and volatility than OI.
                  In terms of the impact of market-wide relative to firm-specific information, our
            evidence indicates that the former has greater impacts on trading volume and volatility
            than the latter. Contrary to the findings for OI and OL, the market-wide information
            proxied by MN is positively related to trading volume and volatility more so for the
            large stocks than the small stocks. The HH and LL groups have 14(14) significantly
            positive (negative) coefficients for the large stocks at the 10% significance level of two-
            sided tests. The number of positive (negative) coefficients for the HH group is 21(21)

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                                         Academia Economic Papers 32:1 (2004 )



            for the large stocks. The means (medians) of the t-statistics for the HH and LL groups
            are 1.24(1.55) and −1.12(−1.2). We interpret this as weak evidence of the presence
            of private market-wide information. There could be many reasons for this result. We
            offer one plausible explanation as follows. When some traders acquire private market-
            wide information, they tend to trade large stocks on the information. This may be
            because they are institutional traders who are required to trade large stocks more than
            small stocks because of their institutions’ asset allocation guidelines. Take foreign
            institutional investors as an example. Their global asset allocation strategy dictates
            that they only trade large stocks for liquidity reason and benchmarking. As a result,
            when they have private market-wide information, they trade on large stocks more than
            small stocks.
                  In the case of small stocks, MN is negatively related to HL and positively related
            to LH. There are 16 and 19 significant coefficients for the two groups respectively.
            Since high volatility occurs when the trading volume is low, the public information
            affects the trading volume and volatility of small stocks.
                  The relationship between ACN and the trading volume and the volatility is not
            clear for the small stocks. Less than one third of the coefficients of the four groups are
            significant. For the large firms, there is some indication that ACN is negatively related
            to the trading volume and volatility. And the information is more likely to be public
            than private because ACN is negatively associated with HL and positively associated
            with LH.
                  The degree of consensus proxied by the magnitude of order imbalance, OB, tends
            to be negatively related to trading volume and volatility. There are 13(17) signifi-
            cantly negative and 10(18) significantly positive coefficients for the large (small) stocks
            for the HH and LL groups respectively at the 10% significance level of two-sided
            tests. The means of the t-statistics for the HH and LL groups are −1.48(−3.26) and
            1.03(2.03) for the large stocks (small stocks). There are 25(27) negative coefficients
            for the HH group of the large (small) stocks, 22(22) positive coefficients for the LL
            group. The finding suggests that a higher degree of consensus in the traders’ interpre-
            tation of information is associated with low trading volume and volatility. It is also
            consistent with the situation in which information asymmetry is mild due to lack of
            private information. Otherwise, the volatility would be high if there was information
            asymmetry. Since this is evidence of low information asymmetry, our finding also
            suggests that the bulk of trading volume is due to liquidity demand and supply.
                  Speculative order flows, RTOD, unrelated to the information and liquidity vari-


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                     Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)



            ables are positively associated with trading volume and volatility. Fourteen (15) and
            11(13) out of 30 large (small) stocks for the HH and LL groups have significantly pos-
            itive and negative coefficients. The means of the t-statistics of the HH and LL groups
            for the large (small) stocks are 1.51(1.72) and −1.19(−1.51). For the HH group there
            are 26(25) positive coefficients for the large (small) stocks, and 22(24) negative co-
            efficients for the small stocks. Thus, order flows that are not accounted for by the
            information and liquidity variables also drive trading volume and volatility. As argued
            earlier, these orders are likely speculation oriented and constitute a significant part of
            TSE’s order flows.
                  Another observation born out of our analysis is that the explanatory variables
            do not explain the HL and LH groups as much as the HH and LL groups. This is
            consistent with the findings in the literature that high trading volume and volatility
            happen together and are driven by information, liquidity and speculation. In sum,
            we have found that information orders and liquidity orders drive the trading volume
            and volatility of small firms, while market-wide information affect the volume and
            volatility of large firms. Low degree of consensus in the traders’ interpretation of
            information and speculative orders drive trading volume and volatility for both large
            and small stocks. There is no clear sign that the firm-specific information produces
            volume and volatility.
                  Since we have some evidence that large information orders are more related to
            trading volume and volatility than small liquidity orders for the small stocks, and that
            the liquidity orders could actually be negatively associated with volume and volatility
            for the large stocks, it ought be the case that OI is more aggressive in placing orders
            than OL. Our data confirm this conjecture. Table 6 depicts the order-submitting pat-
            terns of the large and small orders. It shows, for a particular intraday interval, the
            average percentage (across the trading days and stocks in the sample) of buy (sell) or-
            ders in number of shares (or orders, in parentheses) placed for different price ranges:
            at the upper price-limit (lower price limit), within one percent away from the upper
            price-limit (lower price limit), between one percent away from the upper price limit
            (lower price limit) and two percent away from the upper price limit (lower price limit),
            between two percent away from the upper price limit (lower price limit) and three per-
            cent away from the upper price limit (lower price limit), and over three percents away
            from the upper price limit (lower price limit).




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                                            Academia Economic Papers 32:1 (2004 )



                         Table 6     Intraday Order Submission Patterns of OI and OL
            The orders are classified for different price intervals which include, for the buy orders, orders
            placed at the upper price limit, the interval between the upper price limit and 1% from the upper
            price limit, the interval between 1% from the upper price limit and 2% from the upper price
            limit, etc. The sell orders are classified similarly except that price intervals are measured from
            the lower price limit. For each intraday time interval, the percentage of orders falling in a price
            interval is calculated. The percentage numbers reported are the averages across 104 trading
            days and the 60 stocks in our sample based on the number of shares submitted. Numbers in
            parentheses are based on the number of orders submitted.
                                                        Price     1% from     1–2%      2–3%     3% and
                                                        limit      price       from      from     over
                                                                   limit       price     price
                                                                               limit     limit
                                                         7.03      3.04        3.3       4.53     82.1
                                         Open           (7.97)    (2.19)      (2.56)    (3.58)   (83.7)
                                                        13.96      6.02        5.82      7.16     67.04
                                         9–9:06        (17.71)    (4.14)      (4.68)    (5.09)   (68.38)
                                OI
                                                        10.14      6.99        8.35      8.73     65.79
                                         9:06–11:54    (11.89)    (5.21)      (6.41)    (6.92)   (69.57)
                                                        15.7       6.19        7.38      7.15     63.63
                                         11:54–12      (15.54)    (4.3)       (6.02)    (5.75)   (68.39)
               Buy orders
                                                         7.88      3.15        3.26      3.34     82.37
                                         Open           (7.41)    (3.62)      (3.48)    (3.2)    (82.29)
                                                        12.78      3.75        4.9       4.6      73.97
                                         9–9:06        (11.97)    (3.85)      (4.34)    (4.36)   (75.48)
                                OL
                                                         9.94      5.32        6.42      6.76     71.56
                                         9:06–11:54     (9.58)    (5.2)       (6.15)    (6.45)   (72.62)
                                                        12.82      4.28        5.54      5.68     71.68
                                         11:54–12      (12.44)    (4.08)      (5.24)    (5.34)   (72.9)
                                                         7.15      2.11        1.53      1.52     87.69
                                         Open           (3.97)    (1.67)      (1.54)    (1.63)   (91.19)
                                                        11.55      3.82        3.76      3.75     77.12
                                         9–9:06        (14.16)    (3.41)      (3.46)    (3.11)   (75.86)
                                OI
                                                         6.34      2.83        3.78      4.45     82.6
                                         9:06–11:54     (9.2)     (3.22)      (3.51)    (3.91)   (80.16)
                                                        17.04      2.76        2.82      3.24     74.14
                                         11:54–12      (20.07)    (3.04)      (2.64)    (3.27)   (70.98)
               Sell orders
                                                         3.6       2.64        1.65      1.7      90.41
                                         Open           (3.85)    (3.42)      (1.65)    (1.77)   (89.31)
                                                        11.98      4.26        3.37      3.19     77.2
                                         9–9:06        (11.76)    (3.94)      (3.61)    (3.54)   (77.15)
                                OL
                                                         8.22      3.27        3.17      3.5      81.84
                                         9:06–11:54     (8.03)    (3.48)      (3.2)     (3.58)   (81.71)
                                                        14.22      2.88        3.03      3.1      76.77
                                         11:54–12      (13.87)    (2.82)      (2.94)    (3.21)   (77.16)

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                 One can make a few observations from Table 6. For every one of the four intraday
            intervals included in the table, open, 9–9:06, 9:06–11:54, and 11:54–12, and especially
            for the buy orders, there are more large information orders that are placed close to
            price limits than small liquidity orders. For example, for the first six-minute interval
            9–9:06 there are 33% (100% minus the 67% for the range over 3% away from the
            upper price limit) of the large buy orders that are placed at the price limits or not more
            than 3% away from the price limits, compared to 26% of the small buy orders. In
            addition, among the four intraday intervals, the aggressiveness of large orders relative
            to small orders is most obvious for the last six-minute interval during the day. The
            buy (sell) orders have 15.7%(17.04%) price-limit orders in the closing interval for
            the large orders, whereas only 12.82%(14.22%) for the small orders. This evidence
            indicates that OI orders are more aggressive than OL orders.
                 The orders at the market open are the least aggressive among the four intraday
            intervals reported, and are the most aggressive at the close. But at the open, OI of
            sell orders are still much more aggressive than OL. Thus, large information orders
            have greater effects on trading volume and volatility because they are more aggressive,
            while small liquidity orders are somewhat correlated with low trading volume and
            volatility because they are less aggressive.


            5. DISCUSSION

            This paper finds that the total order flow is the largest and the volatility is high at the
            open, but trading volume is not unusually high and traders are least aggressive at the
            open. In addition, the order flow, trading volume, and volatility are all very high and
            traders are the most aggressive at the close.
                  Also, we find that large information orders and small liquidity orders do concen-
            trate their trading, but the two types of orders in combination only induce high trading
            volume and volatility for small stocks. For large stocks we have some evidence that
            small liquidity traders are actually negatively related to volume and volatility. Linking
            this finding to the meager trading volume at the open, one can infer that the concen-
            tration of information and liquidity orders do not necessarily result in high trading
            volume, at least not on the TSE.
                  The finding of mild trading volume at the open is uncharacteristic of the equity
            markets in the US and Canada, where volume at the open is very high. Many theories


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                                         Academia Economic Papers 32:1 (2004 )



            predict high trading volume and volatility both at the open and close. For example,
            Slezak (1994) argues that since market closure delays the resolution of information
            uncertainty, which imposes excessive risk on the informed, the informed have the mo-
            tivation to trade before the market close in order to sift the risk to the noise traders. As
            the market reopens, those who cannot trade overnight transact according to the infor-
            mation revealed during the market closure. Brock and Kleidon (1992) and Gerety and
            Mulherin (1992) suggest that the investors with less ability to bear overnight risk will
            exchange their positions with investors having a greater ability for overnight risk bear-
            ing, creating above-average trading volume at the close. In addition, if the investors
            that transfer overnight risk reacquire their positions at the next day’s open, trading
            volume at the open would be large as well.
                  Kyle (1985) and Admati and Pfleiderer (1988) suggest that the interactions be-
            tween the market maker, the informed, and the uninformed liquidity traders result in
            concentrated trading. The concentration of trading mitigates the problem of asym-
            metry because the competition between the informed reveals information to the un-
            informed, and the informed can better take advantage of their information when the
            uninformed trade. Admati and Pfleiderer (1988) take their theory to imply that high
            trading volume and volatility at the open and the close on the NYSE are caused by
            trade concentration.
                  Our findings indicate that traders indeed have a strong desire to trade at the open
            because TOD and MN are very high, but traders are not as aggressive as at other points
            of time during the trading day and there is a high degree of agreement in opinion
            (OB). We believe that three market microstructure features of the TSE – periodical
            call trading mechanism without market makers, differential price limits imposed on
            the opening call and all the other calls throughout the trading day, and the fact that
            only limit orders can be submitted, all result in the finding that although the total order
            flow is large and information and liquidity orders are concentrated, the trading volume
            is low at the open. Note that the theoretical and empirical findings in the US are
            developed on the backdrop of the presence of market makers and continuous trading
            (except at the open in which a call mechanism is adopted) in which both limit and
            market orders can be placed. But the TSE has no market makers and is an exclusively
            order-driven call market where only limit orders can be placed.
                  At the open on the NYSE, specialists solicit orders from other traders to balance
            the orders if orders are unbalanced. This practice would make transactions more likely
            than otherwise. But the TSE does not have this feature. In addition, the price limit


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                     Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)



            on the TSE for the opening call is 7% from the previous close, much wider than the
            two-tick limit imposed on all the other calls throughout the trading day. The difference
            in the price limit feature between the opening call and the other calls as well as the call
            mechanism are the culprits for the opening volume being disproportionate to the order
            flow.
                  Madhavan (1992) shows that relative to continuous mechanisms (defined as the
            quote-driven system and the continuous auction), the periodic auction (call) pooling
            orders for simultaneous execution aggregates information efficiently and overcomes
            the problems of information asymmetry better. However, a periodic system cannot
            provide immediate order execution and imposes higher costs for traders who must
            collect market information instead of observing price quotations. Thus, traders on
            the TSE who do not observe trading as frequently under a call mechanism as under
            a continuous market could tend to be more conservative in placing the orders than
            traders under a continuous market. The traders on the TSE would tend to be even more
            conservative because they can gain the highest transaction priority only by placing
            price-limit orders. Furthermore, since there is a long recess before the open, it is
            natural for traders to be more conservative at the open than at other intraday points of
            time. And if there is not great disparity in the traders’ opinions, the trading volume
            would tend to be low.
                  The call market allowing only limit orders also would make the uninformed more
            cautious than the informed. Although price-limit orders are in effect market orders,
            market order traders are by no means certain about the price at which their orders will
            transact. Advanced indications of market condition are not posted on the TSE, and
            bid/ask prices are not revealed until the market is called. By being more conservative
            than the informed, the uninformed protect themselves from buying (selling) at prices
            above (below) their reservation prices in periodic auctions. Local fund managers told
            us that they often would submit small orders at very conservative prices to “test” the
            market so as to acquire the information about market condition. This more passive
            attitude than that of the informed makes the uninformed exert less impacts on trading
            volume and liquidity.
                  In contrast, the information traders have reasons to be more aggressive. Even the
            price-limit orders are not assured a transaction, let alone any other orders. Since on
            TSE the price established at a call cannot differ by more than two ticks from the price
            established at the previous call, the informed can aggressively place orders to secure
            priority, while at the same time buying (selling) at a price very likely substantially


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                                         Academia Economic Papers 32:1 (2004 )



            lower (higher) than the submitted price. The call market also allows the informed to
            place price-limit orders without giving others an option to trade against them to their
            detriment in a fast-moving market. But since at the open the price could move by as
            much as 7%, the informed would be more conservative than at other intraday time
            points.


            6. CONCLUSION

            This paper is an original attempt to empirically examine an important issue: the intra-
            day relationship between information, trading volume and volatility. Our novelty lies
            in the use of order flow data to estimate variables and the sample classification scheme
            to relate the intraday relationship under investigation to public and private information.
            We find support for extant theories regarding our research objective.
                  In sum, we find that traders tend to concentrate their orders, but only the trading
            volume and volatility of the small stocks are positively associated with the concentra-
            tion of orders. The liquidity orders do not influence volume and volatility as much as
            the information orders. The information carried by the large information orders tends
            to be private rather than public. Since the concentration of orders releases private in-
            formation, it is reasonable that the information content of the large orders tends to be
            private for the small firms. The small firms are affected more by the order concen-
            tration through private information maybe because the small firms are not as much
            followed by analysts as large firms.
                  The market-wide information affects the volume and the volatility of large stocks
            in the nature of private information. In contrast, the market-wide information affects
            the volume and the volatility of small stocks in the nature of public information. The
            firm-specific information is not as clear a determinant of trading volume and volatility
            as the market-wide information, but speculative orders are.
                  Finally, we think that the call market without specialists on the TSE, which only
            allows price-limit orders, makes the informed more aggressive than the uninformed in
            placing the orders and the traders more conservative at the open than at other intraday
            time points. This is the reason why we observe mediocre trading volume in the pres-
            ence of very large order flows at the open. Our study is a good example of how studies
            of markets with diverse trading mechanisms can help researchers distinguish theories
            that are applicable to different microstructures from those that are not.


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                      Intraday Information, Trading Volume, and Return Volatility (Chow, Lee, and Liu)




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