Stealth Trading Tokyo

8/6/2019 Stealth Trading Tokyo

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Stealth Trading: The Case of the Tokyo Stock Exchange

Asli Ascioglu

Bryant University

1150 Douglas PikeSmithfield, RI 02917-1284

Voice: (401) 232-6305

Fax: (401) 232-6319

[email protected]

Carole Comerton-Forde

University of SydneyNSW, 2006 Australia

Voice: 61 2 9351 7650Fax: 61 2 9351 6461

[email protected]

Thomas H. McInish

University of Memphis

Memphis, TN 38111Voice: 901-678-4662

Fax: [email protected]

May 2005

Preliminary Draft

Please address correspondence to:

Thomas H. McInishProfessor and Wunderlich Chair of Finance

Department of Finance, Insurance, and Real Estate

The University of Memphis

Memphis, TN 38152


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Stealth Trading: The Case of the Tokyo Stock Exchange

Abstract

The stealth trading hypothesis asserts that cumulative price changes are due to medium size

trades. Using data for the Tokyo Stock Exchange, a pure order-driven market, we find evidence to

support this hypothesis. This result is consistent with previous research on a hybrid market, theNYSE. However, we also find that small trades contribute significantly to cumulative price

changes. Our analysis suggests that combining positive and negative price changes can obscure

the effect of trade size on cumulative price change. By separately considering positive and

negative price changes we demonstrate that small trades make the largest contribution to price

change. Therefore, in an order-driven market stealth traders appear to use smaller trades. We also

find that larger trades explain a greater portion of the cumulative price change on high volatility

days. This demonstrates that stealth trading is more difficult when volatility is high, therefore

forcing informed traders to trade more quickly using larger trades.


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I. IntroductionThe term stealth trading describes informed traders who break up their trades into smaller

lots to disguise their activities and protect their information advantage. The stealth trading

hypothesis, postulated by Barclay and Warner (1993), hypothesizes that private information is

revealed through trading and that privately informed traders concentrate their trades in medium

size trades. These authors also postulate two alternate hypotheses. The first, the public

information hypothesis, postulates that stock price volatility is due to public information. This

hypothesis predicts that stock price changes are directly proportional to the relative frequency of

trades. The second alternate hypothesis, the trading volume hypothesis postulates that price

changes are directly proportional to trading volume. Using a sample of NYSE tender-offer target

firms for the period 1981 to 1984, Barclay and Warner test these three hypotheses. They find that

99.43% of the cumulative price change occurs on medium size trades, which far exceeds medium

size trades percentage of total trades of 38.12%. Testing the trading volume hypothesis, they

find that medium size trades percentage of total volume is 58.18%, and cumulative price changes

are not proportional to trading volume. Hence, they reject both the public information hypothesis

and trading volume hypothesis, but cannot reject the stealth trading hypothesis.

Chakravarty (2001) also tests for the presence of stealth trading on the NYSE by examining

the proportion of trades, volumes, and price changes that occur in each trade size category. His

sample includes trade data from the NYSE TORQ dataset which identifies whether a trade is

initiated by an institution or by an individual. He finds that most of the cumulative stock price

change is due to medium size trades. Furthermore, he confirms that the source of the

disproportionately large cumulative price impact of medium size trades is almost entirely due to

those initiated by institutions rather than individuals. He concludes that stealth trading is mainly

due to institutions that are known as informed traders in the market.


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Campbell et al. (2004) examine institutional equity ownership and trading in the U.S. They

combine transactions data from the TAQ database and quarterly snapshots of institutional

holdings from the Spectrum database to infer institutional trading. Their sample includes all

NYSE and AMEX stocks. They report that institutions tend to trade in very large or small sizes

rather than medium size trades. These authors note that their results are contrary to those of the

Barclay and Warner (1993) and Chakravarty (2001), but do not attempt to reconcile the

difference. Campbell et al. (2004) also examine differences in trading behavior on high volume

and high volatility days. They report that institutions prefer medium and small size trades on high

volume days and large size trades on high volatility days. This is consistent with the view that

stealth trading increases when liquidity is high. But when volatility is high, traders must act

quickly, making stealth trading more difficult.

As the foregoing discussion indicates, previous work on stealth trading has focused

primarily on the NYSE, and these studies have reached different conclusions about the extent to

which stealth trading is present. The NYSE is a hybrid limit order-specialist market. To date,

there is no published research examining whether stealth trading exists in other market

structures.1

However, there is a substantial body of literature that indicates that market structure

influences trader behavior. Based on theoretical considerations, Seppi(1997) shows that under

plausible conditions a hybrid market dominates a pure limit order market for both small and large

trades. But a pure limit order market has a deeper book, which may make it preferable for

medium size trades. These differences suggest that a re-examination of stealth trading in a market

with an alternative market structure may be worthwhile.

We examine stealth trading on the worlds second largest exchange, the Tokyo Stock

Exchange (TSE), which is a pure order-driven market. A priori, it is unclear whether the order-

driven structure would encourage traders to use larger, smaller, or the medium size trades. An

1We recently discovered a working paper by Pham et al. (2004) that examines stealth trading on the

Australian Stock Exchange, a pure limit order market.


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order-driven market may encourage informed traders to use larger orders for a number of reasons.

First, in an order driven market, liquidity is provided by limit orders. Therefore, the execution of

multiple small orders or one larger order against depth standing in the book is essentially

equivalent due to the speed of electronic order submission and execution. Campbell et al. (2004)

also suggest that in a limit order market, informed traders may use very small orders to test the

waters before using larger orders. The appeal of such a strategy in a pure limit order market

depends on the presence of a quick refresh rate. Second, the use of larger orders ensures that the

trader does not miss out on the liquidity currently available in the book. Hence, traders may prefer

to use larger orders to take all of the available liquidity. The level of transparency on the TSE is

very high and all investors are able to observe the volume of orders at each price step up to five

price steps away from the best bid and ask price,2 which may encourage informed traders to trade

in smaller parcels.

For the TSE, when we replicate the approach taken by Barclay and Warner (1993) and

Chakravarty (2001), we find that medium size trades produce a statistically significant portion of

the cumulative price change. Further, we confirm that medium size trades account for more of the

cumulative price change on the TSE than on the NYSE. Consistent with the previous research we

also reject the public information and trading volume hypotheses. However, we provide evidence

that these results are driven, at least in part, by the aggregation of positive and negative price

changes, a feature also present in previous stealth trading research. The aggregation of positive

and negative price changes can distort the relationship between the proportion of trades, volume,

and price changes. For this reason, we separately consider trades on an up-tick and on a down-

tick. These results show that while medium size trades account for a disproportionately large

fraction of the cumulative price change, small trades account for an even larger proportion. These

results support the stealth trading hypothesis, but also indicate that in a pure limit order market

informed traders also split their trades into small sizes.

2 Prior to 30 June 2003, only the volume at the best three prices could be observed by the market.


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While we continue to reject the trading volume hypothesis even taking up-tick and down-

tick trades into account, we cannot reject the public information hypothesis. That is, we find a

significant relationship between the proportion of trades and the cumulative price change in each

size categories.

Consistent with Campbell et al. (2004), we also report that larger trades contribute more to

the cumulative price change on high volatility days. This indicates that on high volatility days

stealth traders trade in larger sizes to fill their orders quickly, to reduce the chance that the market

will move against them. We also observe that the proportion of medium and large trades increases

on high volatility days. In addition, we report a U-shaped intraday pattern of cumulative price for

each category. The largest cumulative price change in all the categories occurs during the first 30

minutes of trading and the second largest occurs during the last 30 minutes of trading. On the

other hand, there is a little variation in the level of stealth trading across size categories during the

day.

II. Institutional backgroundThe TSE is the worlds second largest exchange in terms of market value of shares listed,

which totaled 2,953 billion USD in 2003 compared with 11,329 billion USD for the NYSE and

2,426 billion USD for the London Stock Exchange (LSE). In 2003, domestic companies listed on

the TSE numbered 2,174 compared with 1,842 for the NYSE and 2,311 for the LSE. Total trading

volume in billion USD in 2003 was 2,131 for the TSE, 9,692 for the NYSE, and 3,624 for the

LSE.

The TSE is comprised of a First Section trading the most active and liquid shares (1,533

issues), a Second Section of less active shares (569 firms) and a section for venture companies

(72 issues) called Mothers. A Foreign Section lists 32 issues. The TSE demutualized in

November 1999 and closed its trading floor in April 1999.


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The TSE is a pure order-driven market without market makers. The TSE typically operates

two trading sessions each day, a morning session from 0900 to 1100 and an afternoon session

from 1230 to 1500. Both limit orders and market orders are permitted. A call auction, called the

itayose method, is used to open and close trading for each session if there are offers to sell at

lower prices than bids or a mixture of market offers and bids. Orders can be placed that are to be

executed only in these opening and closing trades. There is no time priority for batch trades. All

market orders and all limit orders to sell/buy at prices lower/higher than the execution price are

executed. All limit orders on at least one side of the market are executed and at least one trading

unit from the other side of the market. After the initial call auction, trading switches to continuous

trading, referred to as the zaraba method, until the close. Cross trades are also permitted outside

regular-session hours.3

The minimum tick size depends on the price of the equity. The price (in JPY) and then the

minimum tick size (in parentheses) are:


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III.Data and MethodologyWe obtain the date, price, and time for all trades for all First and Second Section issues

traded on the TSE during the first ten months of 2003. We also obtain the bid and ask prices

prevailing at the time of each trade. These data are from Reuters Plc through the Securities

Industry Research Centre of Asia-Pacific (SIRCA).

We exclude Real Estate Investment Trusts, Exchange Traded Funds, foreign stocks, and

stocks with a minimum lot size other than 100 shares or 1,000 shares, which are the two most

common lot sizes on the TSE. Following Barclay and Warner (1993), we also include only firms

that have a positive change in value of at least 5% over the sample period. Previous researchers

have argued that it is more likely to find stealth trading in appreciating stocks so we follow the

precedent set in this research by only including these stocks. We also exclude stocks that are

listed on more than one Japanese exchange if these stocks have more than 20 trades off the TSE.

Our final sample comprises 269 stocks with a minimum lot size of 100 shares (Minvol_100) and

675 stocks with a minimum lot size of 1,000 shares (Minvol_1000). Over the period there were

29,921,368 trades in these stocks.

We classify trades into categories. First, we identify the first and last trade for the morning

and afternoon sessions. These trades may be batch trades. The first trade of the morning session is

an overnight return, which Wood, McInish and Ord (1985) demonstrate has a different

distribution from intraday trades for US data. All of these trades potentially occur in a batch

rather than a continuous trading environment. The first trade of the morning session and the last

trade of the morning session are categorized as S1F an S1L, respectively. The first trade of

the afternoon session and the last trade of afternoon session are S2F and S2L, respectively.

Next, from the remaining trades, we identify trades with a trade-to-trade price change more than

the daily trade-to-trade price-limit allows. We label these trades as LIMIT. We do not believe

that LIMIT trades reflect stealth trading for two reasons. First, anyone placing an order that


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moves the market more than the limit can not be trying to hide what they are doing. Also, special

rules are activated that place special requirements on trades that result in a large trade-to-trade

price change. One such rule is that there is a delay in executing the trade while counterparty

orders are solicited. We focus on typical trades rather than on trades involving special handling.

The remaining trades are classified based on their size. The small size category includes

trades that have fewer than 5 lots (i.e.; 400 shares when the lot size is 100, and 4,000 shares when

the lot is 1,000). In some cases, we further divide the small size category into 4 subcategories

from 1 to 4 lot sizes. Medium size trades have lot sizes of 5 through 99. In some cases we

subdivide medium sizes trades into categories of 5 lots, 6 to 9 lots, 10 to 19 lots, 20 to 49 lots, and

50 to 99 lots because these smaller categories are reported by Barclay and Warner (1993). Large

trades are 100 lots are more.

A. Percentage cumulative price changeFollowing Barclay and Warner (1993) and Chakravarty (2001), we define a price change

(CHG) for a given trade (t) as the difference between the trade price at time (t) and the price of

the previous transaction at time (t-1). Therefore, trade-to-trade price change is calculated as

CHG=Pt -Pt-1, where Pt and Pt-1 are the trade prices at time t and t-1, respectively. Next, we

calculate the change in price from the first to last trade in the sample (FULLCHG) for each firm.

Chakravarty (2001) suggests that stocks with a significant price increase are more likely to have

informed trading. Following this suggestion, we also restrict our sample to firms with FULLCHG

of at least a 5%.

We use the approach of Barclay and Warner (1993), which is also used by Chakravarty

(2001), to calculate the cumulative price change. For each firm, we sum the trade-by-trade price

changes for each category and divide by FULLCHG to obtain a percentage cumulative price

change (PCHG) and confirm that these sum to 1.0 for each firm.


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B. Percentage of trades and percentage of volumeFollowing Barclay and Warner (1993), we calculate the percentage of trades (PTRDS) as

the sum of all transactions for a stock in a given category, divided by the total number of

transactions for that stock. The percentage of trading volume (PVOL) is the sum of all trading

volume for a stock in a given category divided by the total trading volume for that stock. If a firm

has no trade in any given category, we count that as a zero percentage for that size category.

IV.ResultsTable 1 reports the percentage of trades, volumes, and price changes for each category for

our sample and for the Barclay and Warner (1993) and Chakravarty (2001) samples. For our

sample, we separately present results for Minvol_100 and Minvol_1000 stocks. Consistent with

prior research, for this analysis, we exclude the first and last trade of each session and LIMIT

trades. For both Minvol_100 and Minvol_1000, there are more 1 lot trades than any other lot size.

In fact, trades of 1 lot represent more than 40% of all trades. Generally, there is an inverse

relation between lot size and the number of trades at that size. However, because of the small

number of shares, lot sizes of 4 and less account for a smaller percentage of volume than of

trades. For example, 1 lot trades represent over 40% of trades but only about 13% of volume. The

same pattern is also reflected in the results presented by Barclay and Warner (1993) and

Chakravarty (2001). However, for our sample, small trades represent a higher percentage of the

overall trades and volume than for Barclay and Warner. Our sample includes approximately 70%

of trades in the small category compared to only 60% in Barclay and Warner (1993). Both our

sample and Barclay and Warners have a substantially larger percentage of small trades and of

small volume than Chakravarty (2001). Small trades represent only 40% of all trades in


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Chakravarty (2001). The relatively high proportion of trades in our sample is likely due to the fact

that we are examining an order-driven market.

The distribution of volume across the trade size categories is also skewed toward small

trades in our sample. Between 32% and 43% of volume is executed using small trades compared

to only 16% and 3% in the Barclay and Warner and Chakravarty studies, respectively. In

addition, large trades represent a smaller percentage of volume in our sample. Only three to four

percent of our sample volume is in the large trade category. This compares to 26% and 51% in

the Barclay and Warner and Chakravarty studies, respectively.

Turning to cumulative price change, we calculate FULLCHG as the difference in JPY

between the last and first trade for a firm during our sample period. For each firm, we sum the

trade-by-trade price changes for each category and divide by FULLCHG. This produces the

PCHG variable. In Table 1 we report the weighted means across firms. The reason for using

weighted cross-sectional analysis is to reduce heteroskedasticity in the PCHG variable.4

Consistent with Barclay and Warner (1993), the weights are calculated using the FULLCHG for

each firm relative to FULLCHG aggregated across all firms. This analysis excludes the first and

last trade of each session and LIMIT trades so that we can compare our results to previous

research.

The PCHG results indicate that small trades cause a large negative price change. While

the direction of this result is consistent with the previous research, the magnitude of the change is

much larger. As a result, the PCHG for medium and large trades in our sample is considerably

larger. Consistent with the previous research, the medium size trades contribute the most to price

4 Barclay and Warner (1993) provide an example to discuss the heteroskasdicity problem in the PCHG

variable: Suppose a firm has only a 1/8 point cumulative price change (FULLCHG), which is the sum of a

1/8 point price change in each of the small- and medium-size categories, and a -1/8 point price change in

the large-size category. This leads to a percentage of the cumulative price change in each category to be +

100% or 100%. Therefore, weighting the cross-sectional observations by the FULLCHG assigns less

weight to the PCHG of firms with small FULLCHG, since those firms PCHG can be much larger due to

the fact that FULLCHG is small. This significantly reduces the heteroskedasticity in these percentages.


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change. For Minvol_100 (Minvol_1000) these trades represent 190% (241%) of the total price

change or [190.22/(190.22 + 41.73) =] 82% (93%) of the positive price change.

Despite the fact that prices obviously move in different directions during the sample

period, previous researchers have consistently aggregated the positive and negative price changes

for each category. This aggregation may obscure the impact of stealth trading. For example,

suppose that early in the sample period stealth traders move the price up, but later stealth traders

move the price down. These two sets of trades would offset in the traditional analysis so that it

would appear that stealth trading did not move the prices. To overcome this problem, we partition

our sample to separately examine trades with positive and negative price changes. However, in

doing this, we are likely to overestimate the total price change due to the fact that some price

changes merely represent a movement between the best bid and ask price (ie. Bid-ask bounce).

These movements do not represent genuine price changes, and, therefore, these trades are

eliminated from the sample.

If a trade first occurs at a bid (ask) price and then a trade occurs at an ask (bid) price

without a quote change, the price change is solely due to bid-ask bounce. We eliminate the

second trade from our analysis. If there are more than two such sequential trades, we only keep

the first trade in our dataset. This approach reduces the sample size from 29,921,368 trades to

22,683,171 trades.

The results reported in Table 2 reflect a bid-ask bounce adjusted sample of trades. The

results generated for the Minvol_100 and Minvol_1000 samples are qualitatively similar;

therefore, the remainder of our analysis combines these samples. Table 2 also expands the results

presented in Table 1 by including the first and last trades of each session and the LIMIT trades.

Columns 2 to 4 report the results for all trades in the sample. This illustrates that the first trade of

the day represents only 1.69% of the trades, but more than 13% of the volume and more than 60%

of the cumulative price change. The finding that a large percentage of the days price change is

earned on the first trade is consistent with Wood, McInish, and Ord (1985). This result is


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attributed to the fact that company news is often released overnight and the opening trade is the

first opportunity for this information to be reflected in prices. The first trade of the second session

also represents a large fraction of total volume, but has a negative impact on cumulative price

change. Surprisingly the price change on the last trade of the day is negative and represents

approximately -13% of the cumulative price change. This result is inconsistent with prior research

which suggests that prices rise at the end of the trading day.

The distribution of trades and volumes across the size categories is consistent with the

results reported in Table 1. Small trades remain the largest category by number of trades,

representing approximately 70% of the sample, and medium trades are the largest category by

volume, representing approximately 44% of volume. It is noteworthy that the PCHG for the small

trades is no longer negative. This shows that the negative PCHG result is driven by bid-ask

bounce induced price changes for small trades. Consistent with prior research, these results

indicate that medium size trades contribute most to price changes, representing approximately

44% of cumulative price changes. However, unlike the prior research, small trades also contribute

significantly to price change, representing approximately 15%. However, this may be due to the

fact that small trades comprise 70% of all trades in the sample. Consistent with the prior research,

large trades contribute relatively little to cumulative price changes.

Next, we disaggregate trades with positive and negative price changes to separately

consider stealth traders that move prices up and stealth traders that move prices down. We label

the trades with positive price changes as up-tick trades and trades with negative price changes as

down-tick trades. Trades that are associated with no price change are labeled zero-tick.

Approximately 63% of trades, representing 52% of volume, are zero-tick trades. Zero-tick trades

are not reported as they do not contribute to the cumulative price change. Columns 5 to 7 of Table

2 report the results for up-tick trades and columns 8 to 10 report the results for down-tick trades.

The number of up-tick trades is approximately equal to the number of down-tick trades, although

the down-tick trades represent a smaller proportion of the volume traded.


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These results indicate that the volume traded at the open is higher for up-tick trades. The

results also indicate that the lowest price change occurs in the LIMIT category. This finding is

consistent with our conjecture that traders placing orders that move the market sufficiently to

trigger the trade-to-trade price limit rules cannot be attempting to hide their intentions and are not

stealth traders.

The results reported in Table 2 also illustrate that the distribution of trades and volumes

across the size categories is approximately equal for up-tick and down-tick trades. The total

PCHG is extremely large (2,218.40% for up-tick trades and -2,028.4% for down-tick trades)

because it aggregates all individual price changes over the sample period. When these PCHG

values are aggregated they sum to 100%. These results indicate that small trades represent the

largest percentage of the cumulative price changes for both up-tick and down-tick trades. For up-

tick trades, small trades represent approximately 1,245% of the price change compared to only

545% for medium size trades. Consistent with the results for all trades, large trades make little

contribution to the price changes. These results are broadly consistent with the stealth trading

hypothesis, as they indicate that informed traders use small and medium size trades.

We test for both the public information hypothesis and the trading volume hypothesis for

all trades, and for both up-tick and down-tick trades, separately. The public information

hypothesis postulates that stock price changes are due to the release of public information.

Therefore, if the public information hypothesis holds, we expect that the percentage cumulative

price change will equal the percentage of trades in each category. The trading volume hypothesis

proposes that trading volume derives the prices, and each lot traded has the same cumulative price

effect regardless of which category the trade is in. Therefore, if the trading volume hypothesis

holds, we expect that the percentage cumulative price change will equal the percentage of volume

in each category.

To test for both hypotheses, we first scale the percentage of cumulative price change

represented in Table 2 so that the price changes add up to one for up-tick trades. We repeat this


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scaling for down-tick trades. We also scale the percentage of trades and percentage of volume in

the same way. These scaled percentages are reported in Table 3. We use a t-test to test whether

the mean of percentage cumulative price changes equals the mean of percentage of trades

(volume) over the 944 firms in each category. The hypothesis that the percentage cumulative

price change equals the percentage of volume is rejected at the 1% level for each size category for

up-tick and down-tick trades. Thus, we conclude that there is no direct relationship between the

percentage of volume and the percentage of cumulative price change. Therefore, we reject the

trading volume hypothesis for up-tick trades and down-tick trades. On the other hand, we cannot

reject the trading volume hypothesis for the medium- and large-trade-size categories when we

include all trades.

We do not find any support for the public information hypothesis when we analyze the

percentage cumulative price change for all trades. This result is consistent with Barclay and

Warner (1993) and Chakravarty (2001). We cannot reject the public information hypothesis for

up-tick and down-tick trades for the large category. This shows that the cumulative price change

occurring in trades in a large category is proportional to the fraction of transactions in that

category.

The preceding analysis demonstrates that aggregating trades with positive and negative

price changes may present a distorted view of the contribution of different trade sizes to

cumulative price changes. Aggregation is likely to underestimate the contribution of small trades.

For this reason, the remaining analysis presented in the paper considers up-tick and down-tick

trades separately.


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V. Sensitivity AnalysisA. Volatility

Campbell et al. (2004) argue that institutional trading strategies and the use of different

sized trades varies with volume and volatility. Therefore, we also investigate stealth trading for

groups based on median volatility. For a given firm, we calculate the standard deviation of trade-

to-trade returns for each trading day. We classify days with a standard deviation above the

median as high volatility days and those below the median as low volatility days. We repeat the

procedure for each firm. Consequently, a high volatility day for one firm may be a low volatility

day for another firm.

Table 4, Panel A, reports the trade, volume, and price changes results for high and low

volatility days. Because the results are qualitatively similar for both up-tick and down-tick trades,

we report results only for up-tick trades. These price change results are striking and readily

apparent. For each trade size category, far more of the return is earned on high volatility days

than on low volatility days. Overall, the total PCHG for high volatility days (1,474.79%) is more

than twice that associated with low volatility days (653.61%). There is also a larger number of

trades and higher volume on high volatility days. This is consistent with the prior literature which

finds a positive relationship between volume and volatility (see for example Karpoff, 1987).

The difference in the number of trades on high and low volatility days makes it difficult to

compare the contribution that each trade size category makes to PCHG. Therefore, we consider

the relative amount of PTRDS, PVOL, and PCHG in each category on high and low volatility

days in Table 4, Panel B. For PTRDS for small trades, the percentages are about the same

(68.60% versus 68.25%) on low and high volatility days. In contrast, the percentages for medium

and large trades are higher on high volatility days (medium, 22.78% versus 16.16%; large, 0.25%

versus 0.11%). For PVOL for small trades, the percentages are lower on high volatility days than

on low volatility days (22.71% versus 25.34%); the comparable percentages are 2.51% versus


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1.23% for medium trades and 2.51% versus 1.23% for large trades. Investors are more likely to

use medium and large trades on high volatility days. On these days traders need to trade in larger

sizes to get their orders filled quickly.

The PCHG results also indicate that small trades explain a marginally larger fraction of the

price change on low volatility days than high volatility days. On these days stealth traders are

able to use smaller trades to disguise their trading without risking adverse price movements. In

contrast, when volatility is high, informed traders are more likely to use medium and large trades.

Medium trades explain 27% of price changes on high volatility days compared to 23% on low

volatility days. Large trades explain 0.75% on high volatility days compared to only 0.3% on low

volatility days. Our results are consistent with Campbell et al. (2004) who argue that on high

volatility days informed traders are particularly urgent about their trades forcing them to trade in

larger sizes.

B. Intraday patternsWe also examine the intraday pattern of PTRDS, PVOL, and PCHG for the nine half-

hours of the trading day. Once again, the results are similar for the up-tick and down-tick trades

so to conserve space we report only the up-tick results. Consistent with prior research, volume

and the number of trades exhibit a U-shaped pattern across the day, although the U is somewhat

flatter than that documented in prior research. This result is driven by small and medium trades,

which represent a larger percentage of trading during first and last hour of trading. For the large

size trades, there is little variation in volume and number of trades across the day.

Figure 1 presents the intraday pattern in PCHG for each of the trade size categories for

up-tick trades. We repeat our analysis for down-tick trades. We obtain very similar results. For

brevity, we only show the results for up-tick trades. This figure illustrates that PCHG is largest in

the first and last half-hour of the trading day. We find that the mean of the percentage cumulative


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price change in the first half-hour of trading is significantly greater at the 0.01 level than in the

second half-hour for small and medium trade sizes. The t-test values are 10.08 and 12.24 for

small and medium sizes, respectively. We find similar results when we compare the first half-

hour trading with other half-hour time periods. We also find that the mean of the percentage

cumulative price change in the last half-hour of trading is significantly greater than the one in the

previous half-hour for small size category and for medium size category. The t-test values are

16.10 and 13.02 for small and medium size categories, respectively. This is consistent with the

strategic trading literature, which suggests that informed traders concentrate their trading at the

beginning and end of the trading day (Foster and Viswanathan (1994, 1996) and Wang (1998)).

This literature hypothesizes that informed traders enter the market early in the trading day to

exploit their information advantage. Their information is revealed through the trading process.

However, toward the end of the day, divergent beliefs about the underlying value of the stock

give rise to an increase in trading by informed traders. The increased proportion of PCHG at these

times suggests that informed traders are active in the market at these times.

VI.Summary and ConclusionsPrevious literature, using NYSE data, provides strong support for the stealth trading

hypothesis, which asserts that cumulative price changes are caused by medium size trades.

Informed traders break up their trades to disguise their trading activity. The existing evidence also

rejects the public information hypothesis and the volume hypothesis. The public information

hypothesis states that stock price volatility is due to public information so that stock price

changes are directly proportional to the relative frequency of trades. The trading volume

hypothesis predicts that price changes are directly proportional to trading volume.

We extend previous literature using data for the worlds second largest exchange, the

Tokyo Stock Exchange (TSE), which is a pure order-driven exchange. We confirm that medium


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References

Barclay, Michael J. and Jerold B. Warner, 1993, Stealth trading and volatility, Journal of

Financial Economics 34, 281-305.

Campbell, John Y., Tarun Ramadorai, and Tuomo O. Vuolteenaho, 2004, Caught on tape:

Predicting institutional ownership with order flow data, Working paper, Harvard University.

Chakravarty, Sugato, 2001, Stealth-trading: Which traders trades move stock prices? Journal of

Financial Economics 61, 289-307.

Comerton-Forde, Carole and James Rydge, 2005, The Current State of Asia-Pacific Stock

Exchanges: A Critical Review of Market Design, Pacific Basin Finance Journal, forthcoming.

Foster, F.D. and Viswanathan, S., 1994, Strategic Trading with Asymmetrically Informed Traders

and Long-Lived Information, Journal of Financial and Quantitative Analysis 29, 499-518.

Foster, F.Douglas. and S. Viswanathan, 1996, Strategic Trading when Agents Forecast the

Forecasts of Others, Journal of Finance 51, 1437-1478.

Karpoff, J., 1987, The relation between price changes and trading volume: a survey, Journal of

Financial and Quantitative Analysis 22, 109-126.

Pham, Linh, Sugato Chakravarty, and Petko Kalev, 2004, Stealth trading in volatile markets,

working paper, Monash University, Australia.

Seppi, J. Duane, 1997, Liquidity provisions with limit orders and a strategic specialist, Review of

Financial Studies 10, 103-150.

Wang, F.A., 1998, Strategic trading, Asymmetric information and heterogeneous prior beliefs,

Journal of Financial Markets 1, 321-352.

Wood, Robert A., Thomas H. McInich and J. Keith Ord, 1985 An investigation of transactions

data for NYSE stocks, Journal of Finance 40, 723-739.


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Appendix

Price limits on the Tokyo Stock Exchange

Table A1. Daily price limits (in JPY)

We report the daily price limits in Japanese Yen. These price limits are based on the changein price from each stocks previous closing price. If trading cannot take place for several days

because the equilibrium price is outside the daily price limits, the Exchange may broaden the

limits so that trading can resume.

Previous day's closing price Daily price limits

Less than 100 30

Less than 200 50

Less than 500 80

Less than 1,000 100

Less than 1,500 200

Less than 2,000 300

Less than 3,000 400

Less than 5,000 500

Less than 10,000 1,000

Less than 20,000 2,000

Less than 30,000 3,000

Less than 50,000 4,000

Less than 70,000 5,000

Less than 100,000 10,000

Less than 150,000 20,000

Less than 200,000 30,000

Less than 300,000 40,000

Less than 500,000 50,000

Less than 1 million 100,000

Less than 1.5 million 200,000Less than 2 million 300,000



Less than 10 million 1 million





50 million or more 10 million


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Table A2. Trade-to-trade price change limits (in JPY)

We report the limits on the trade-to-trade changes in Japanese Yen. Limits

are based on the current trade price. Market and limit orders are not

permitted to execute in violation of these limits. Instead, special quotes are

disseminated in an effort to attract additional orders to offset the orderimbalance. These special quotes can be renewed upward or downward,

within the daily price limits, until sufficient countervailing orders arrive.

Trade price range Price change limit

Less than 500 5

500


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Table 2. Number of trades, volume and price change by trade category

We classify trades into fifteen categories. The first trade of the morning session and the last trade of the morning

session are categorized as S1F an S1L, respectively. The first trade of the afternoon session and the last trade of

afternoon session are S2F and S2L, respectively. Trades with a trade-to-trade price change of more than the daily

trade-to-trade price limit are categorized as LIMIT. We label these collectively as Special trades. The remainingtrades are classified based on their size. Trades of less than 5 lots are classified as SMALL, trades of 5 to 99 lots

are classified as MEDIUM, and trades greater than or equal to 100 lots are classified as LARGE. Columns 2 to 4

present data for all trades, columns 5 to 7 represent trades that occur on an up-tick trades, and columns 8 to 10

represent trades which occur on a down-tick. Trades due to bid-ask bounce are excluded. For each stock, the

percentage of trades (PTRDS) is the sum of all transactions for a stock in a given category, divided by the total

number of transactions for that stock. The percentage of trading volume (PVOL) is the sum of all transactions

volume for a stock in a given category divided by the total trading volume for that stock. We report the equally-

weighted average across firms for both PTRDS and PVOL. For each stock, the percentage of cumulative price

change (PCHG) for a given size category is the sum of all stock price changes in that size category divided by

FULLCHG, the total cumulative price change for that stock over the sample period. The weighted cross-sectional

mean of the cumulative stock price changes are reported. The weight used for each stock is the ratio of

FULLCHG divided by the cumulative price change of all stocks over the sample period.

All Trades Up-tick Trades* Down-tick Trades*

Category PTRDS PVOL PCHG PTRDS PVOL PCHG PTRDS PVOL PCHG

S1F 1.69% 13.19% 60.49% 0.80% 7.57% 207.16% 0.54% 3.87% -146.67%

S1L 1.39% 1.00% -2.14% 0.30% 0.25% 18.78% 0.31% 0.25% -20.92%

S2F 1.71% 5.81% -9.08% 0.53% 2.05% 49.28% 0.55% 1.93% -58.36%

S2L 1.35% 1.71% -13.04% 0.33% 0.54% 31.14% 0.42% 0.55% -44.19%

LIMIT 0.12% 0.20% 0.80% 0.06% 0.10% 19.01% 0.06% 0.10% -18.21%

Total Special 6.26% 21.92% 37.02% 2.02% 10.52% 325.37% 1.87% 6.70% -288.35%

1 lot 42.66% 12.43% 14.61% 7.85% 2.56% 738.93% 7.36% 2.39% -724.32%

2 lots 15.20% 8.03% 1.33% 2.65% 1.59% 266.67% 2.64% 1.57% -265.34%

3 lots 8.29% 6.14% -0.80% 1.41% 1.20% 147.20% 1.45% 1.21% -148.00%

4 lots 4.65% 4.40% 0.22% 0.83% 0.89% 92.64% 0.85% 0.90% -92.41%

Total Small 70.80% 31.00% 15.36% 12.75% 6.24% 1245.44% 12.29% 6.07% -1230.07%

5 lots 5.82% 6.22% 3.85% 0.92% 1.14% 105.63% 0.90% 1.10% -101.78%

6 - 9 lots 6.15% 9.20% 2.98% 1.13% 1.96% 140.15% 1.16% 1.95% -137.18%

10 - 19 lots 6.97% 14.05% 15.90% 1.15% 2.86% 174.27% 1.09% 2.60% -158.38%

20 - 49 lots 3.17% 11.31% 16.22% 0.54% 2.44% 100.33% 0.45% 1.88% -84.11%

50 - 99 lots 0.60% 3.70% 5.11% 0.10% 0.83% 24.20% 0.08% 0.55% -19.09%

Total Medium 22.71% 44.48% 44.06% 3.85% 9.23% 544.59% 3.68% 8.09% -500.53%

Large (>= 100 lots) 0.23% 2.60% 3.55% 0.04% 0.57% 13.00% 0.02% 0.31% -9.45%

TOTAL 100.00% 100.00% 100.00% 18.65% 26.56% 2128.40% 17.87% 21.18% -2028.40%

*The PTRDS and PVOL columns for up-tick and down-tick trades do not sum to 100% because there are a large

number of zero-tick trades.


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Table 3. Hypothesis Testing

We classify trades into fifteen categories. The first trade of the morning session and

the last trade of the morning session are categorized as S1F an S1L, respectively. The

first trade of the afternoon session and the last trade of afternoon session are S2F and

S2L, respectively. Trades with a trade-to-trade price change of more than the daily

trade-to-trade price limit are categorized as LIMIT. These first five categories are

consolidated and reported as Special trades. However, the Special category is not

included in out analysis. The remaining trades are classified based on their size. Trades

of less than 5 lots are classified as SMALL, trades of 5 to 99 lots are classified as

MEDIUM, and trades greater than or equal to 100 lots are classified as LARGE. . For

each stock, the percentage of trades (PTRDS) is the sum of all transactions for a stock

in a given category, divided by the total number of transactions for that stock. The

percentage of trading volume (PVOL) is the sum of all transactions volume for a stock

in a given category divided by the total trading volume for that stock. We report the

equally-weighted average across firms for both PTRDS and PVOL. For each stock, the

percentage of cumulative price change (PCHG) for a given size category is the sum of

all stock price changes in that size category divided by FULLCHG, the total

cumulative price change for that stock over the sample period. The weighted cross-

sectional mean of the cumulative stock price changes are reported. The weight used foreach stock is the ratio of FULLCHG divided by the cumulative price change of all

stocks over the sample period. We use a t-test to test for equality of means for PCHG

and PTRDS and then for PCHG and PVOL.

Category PTRDS t-statistic PVOL t-statistic PCHG

Panel A: All Trades

Special 6.26% 21.92% 37.02%

Small 70.80% -16.10 *** 31.00% -4.01 *** 15.36%

Medium 22.71% 7.19 *** 44.48% -1.41 44.06%

Large 0.23% 8.43 *** 2.60% 1.90 * 3.55%

Total 100.00% 100.00% 100.00%

Panel B: Up-tick TradesSpecial 10.80% 39.61% 15.29%

Small 68.36% -9.20 *** 23.49% 59.13 *** 58.52%

Medium 20.63% 4.92 *** 34.77% -23.57 *** 25.59%

Large 0.20% 0.23 2.13% -13.47 *** 0.61%

Total 100.00% 100.00% 100.00%

Panel C: Down-tick Trades

Special 10.48% 31.66% 14.22%

Small 68.79% -8.24 *** 28.65% 51.95 *** 60.64%

Medium 20.60% 4.10 *** 38.21% -26.68 *** 24.68%

Large 0.14% 1.81 1.47% -10.50 *** 0.47%

Total 100.00% 100.00% 100.00%*significant at the 0.10 level.

***significant at the 0.01 level.


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Table 4. Effects of volatility on number of trades, volume, and price change by trade category

We begin with columns 5-7 from Table 2, which comprise up-tick trades. These columns report PTRDS,

PVOL, and PCHG, respectively. We analyze the following rows: Total, Total Small, Total Medium,

Large, and TOTAL. In Panel A, we divide the PTRDS for the Total Special PTRDS of 2.02% from Table

2 into those resulting from low volatility days (0.92%) and high volatility days (1.10%). We proceedsimilarly for each variable and category combination. In Panel B we report each of the Panel A column

values divided by the TOTAL value for that column.

Low Volatility Days High Volatility Days

Category PTRDS PVOL PCHG PTRDS PVOL PCHG

Panel A: Unscaled results

Special 0.92% 3.60% 118.76% 1.10% 6.92% 206.61%

Small 4.16% 1.99% 385.46% 8.60% 4.25% 859.98%

Medium 0.98% 2.17% 147.45% 2.87% 7.07% 397.15%

Large 0.01% 0.10% 1.95% 0.03% 0.47% 11.05%

TOTAL 6.06% 7.86% 653.61% 12.59% 18.70% 1474.79%

Panel B: Relative results

Special 15.12% 45.85% 18.17% 8.72% 36.99% 14.01%

Small 68.60% 25.34% 58.97% 68.25% 22.71% 58.31%

Medium 16.16% 27.58% 22.56% 22.78% 37.79% 26.93%

Large 0.11% 1.23% 0.30% 0.25% 2.51% 0.75%

TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%


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Figure 1. Intraday pattern in price change by trade category

We classify trades into fifteen categories. The first trade of the morning session and the last trade of the

morning session are categorized as S1F an S1L, respectively. The first trade of the afternoon session and

the last trade of afternoon session are S2F and S2L, respectively. Trades with a trade-to-trade price change

of more than the daily trade-to-trade price-limit allows are categorized as LIMIT. The remaining trades are

classified based on their size. Trades of less than 5 lots are classified as SMALL, trades of 5 to 99 lots are

classified as MEDIUM and trades greater than or equal to 100 lots are classified as LARGE. For this

analysis we include only the SMALL, MEDIUM, and LARGE trades. We omit trades that are due to bid-

ask bounce. For each stock, the percentage of cumulative price change (PCHG) for a trade of a given size is

the sum of all stock price changes in that size category divided by FULLCHG, the total cumulative price

change for that stock over the sample period. Figure 1 reports PCHG for each trade size category for each

half-hour interval across the trading day.

0.00%

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Documents

Stealth Trading Tokyo