GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.93

Does Pre-trade Transparency Affect Market Quality in
the Tokyo Stock Exchange?

Hideaki Sakawa and Masato Ubukata

October 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Does Pre-trade Transparency Affect Market Quality in
the Tokyo Stock Exchange? *Hideaki Sakawa † and Masato Ubukata‡

Abstract
This paper presents an examination of the relation between pre-trade transparency and market
quality in the Tokyo Stock Exchange (TSE). Mixed evidence related to this relation has beenreported worldwide. We analyzed this relation using a discrete change of disclosure policy inthe 2000s. A positive relation pertains between pre-trade transparency and market quality. This
result implies that the change of disclosure policy on the TSE might be effective for market
quality improvement to some extent.
JEL Classifications: G14; G15; G18
Keywords: Pre-trade transparency; Market quality; Quote Disclosure*This research is financially aided by Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young

Scientists (B), 21730252 and (Start-up), 20830047, 2009. The authors are grateful to Yoshiro Tsutsui, Kosuke Oya,
Hitoshi Takehara, Feng Wu, and Alastair Marsden for their helpful comments and suggestions. This paper ispresented at Asian Finance Association 2009 International Conference.We also thanks to all the participants of oursession at the conference.
Graduate School of Economics, Nagoya City University, 1 Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya
467-8501, Aichi, Japan. Email:sakawa@econ.nagoya-cu.ac.jp‡
Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, Osaka 560-0043, Japan.
Email:ubukata@econ.osaka-u.ac.jp†

1. Introduction
Rule changes enhancing pre-trade transparency have been undertaken for several stock
exchanges worldwide. These disclosure reforms are expected to increase market quality, but noconsensus exists in relation to their effectiveness for market quality improvement. This paper isintended to reveal whether or not the increase of pre-trade transparency improves market
quality. We analyze a disclosure event in an order driven market, the Tokyo Stock Exchange(TSE), and attempt to provide new evidence to clarify the relation between pre-tradetransparency and market quality.
Trade transparency in a stock market is defined by O’Hara (1995) as “the ability of market
participants to observe the information in the trading system”. Pre-trade transparency is
generally defined as a public release of buy and sell orders before the orders are executed.
The increase of pre-trade transparency affects market quality in the following ways. First,
the higher the pre-trade transparency becomes, the lower the precision of each trader’sinference of the true stock value. Second, each trader must change their optimal strategy inaccordance with changes of other traders’ optimal strategies. Consequently, a new equilibrium
is realized after the change of disclosure rules.
Mixed evidence pertains to the effect of change for disclosure policy. In 1990, both thefloor and the Computer Aided Trading System (CATS) at the Toronto Stock Exchange (TSX)
began to disclose the limit order book publicly. Madhavan et al. (2005) empirically analyze this1

TSX policy change and show that the increase of pre-trade transparency engenders decreased
market quality. This effect is especially apparent for so-called floor stocks, for which pre-tradetransparency is lower than that of CATS. In contrast, Boehmer et al. (2005) found that greater
pre-trade transparency of the limit order book improves market quality. Hendershott and Jones(2005) also found that reduction in the pre-trade transparency of the order book of the Island
ECN decreased market quality. Eom et al. (2007) support effectiveness of disclosure changes
with evidence from their analyses of two disclosure changes of the Korean Exchange (KRX) in
2000 and 2002. They show that market quality is increased and concave in pre-tradetransparency. Eom et al. (2007) examine two discrete disclosure changes in pre-tradetransparency; the effect of improving market quality in the latter disclosure change is weaker
than that in the former one.
Previous theoretical studies have examined pre-trade and post-trade transparency.
Madhavan (1996) shows that the pre-trade transparency improves market quality in a
sufficiently large market. Baurch (2005) shows an increase of market quality occurringconcomitantly with increased pre-trade transparency. Theoretical studies of Naik et al. (1999)
and Madhavan et al. (1995) analyze post-transparency, which is defined as the public release of
buy orders and sell orders after the orders are executed. Gemmill (1996) empirically show thatless post-transparency does not affect the spread on the London Stock Exchange.
Experimental studies of Bloomfield and O’ Hara (1999) and Flood et al. (1999) also2confirm the relation between transparency and market quality. Bloomfield and O’Hara (1999)
confirm that the informational efficiency and spreads increases with increased transparency.
Flood et al. (1999) find that market depth and trading volume increase with increasedtransparency.
In this paper, we focus on the former event on 2000 and analyze whether or not pre-tradetransparency improve the market quality in TSE. On December 25, 2000, the publicly
disclosed number of prices and quotes was increased from one to three ((the bid and the next
two buy prices, the ask and next two sell prices) 1. Until then, the TSE only disclosed the best
buy and sell prices and quotes.
There are few studies of these disclosure reforms in TSE, a large order-driven market.
Comerton-Forde et al. (2005) show that the removal of broker identities effectively improved
the market quality. On June 30, 2003, TSE abandon disclosure of broker identities from a
trading screen that information had been provided only to its trading participants. Their paper
also shows that the removal of broker identities improved the market quality in KRX at 1999.
In other words, the degree of pre-trade anonymity of TSE on the former event on 2000 is lessthan that of KRX because broker IDs are displayed only to brokers.
This paper examines the relation between pre-trade transparency and market quality. We

1

There are two recent events of two discrete disclosure events on the TSE at 2000 and 2003. On June 30, 2003, thedisclosed number was increased from three to five. This latter event also includes another disclosure change. The
TSE abandoned this policy of disclosure to prevent confusion and misunderstanding among investors, especiallyamong individual investors. However, this latter event becomes a weaker natural experiment because the increaseof pre-trade transparency is occurred in same time.
3

measure market quality as the quoted spread, effective spread, high-low volatility, and thedecomposed component of bid-ask spread: adverse selection cost following the model ofMadhavan et al. (1997) (hereinafter, MRR).
Our event study makes use of four market quality variables. We find that the event
increased market quality. Three market quality measures––bid-ask spread, effective spread andthe MRR adverse selection cost ––improved after the event, although the high-low volatility isinsignificantly improved.
In market micro-structure event studies, variables such as volume and price affect themarket quality and are endogenously determined 2. Eom et al. (2007) adopt panel-data analysis
controlling for volume and price. In this paper, we also reran our event using panel-data setting,controlling for volume and price. We find that market quality improved after the event.
Therefore, we can conclude that market quality is increasing function of pre-tradetransparency.
The empirical results are summarized as a following point. The market quality is weakly
increasing with the expanding disclosed quantities of quotes. This effect is lower than the same
type of disclosure events of KRX.
The remainder of this paper is organized into the following four sections. In section 2, we

2In event studies, the changes of other relevant variables need to be adequately controlled. If they are notadequately controlled, they may contaminate the event study. If the other relevant variables are exogenous,
controlling is straightforward.
4

introduce data, methodology, and event features. Section 3 describes hypotheses of this paper.
In addition, section 4 summarizes the empirical results. Finally, we conclude this paper insection 5.

2. Data and Methodology
2.1. Data Sources and Sample periods
The sample period includes the 50 trading days before and after the two events describedabove. The sample periods for our event are October 12 – December 22, 2000 and December
25, 2000 – March 9, 2001.
Our dataset includes all transactions and quotes for the 225 stocks that constitute the Nikkei
Price Average Index. The component stocks of the Nikkei 225, which are selected from theTSE 1st section, are commonly used as an index of the Japanese stock market.
We use real-time TSE trade and quote data from the Nikkei Economic Electronic Database
System (NEEDS) historical tick data. The database is time-stamped to the nearest minute; each
datum includes information related to all quotes and trades in both price and quantity. Flags ofthe data include buy/sell indicators, opening/closing indicators, and special and warning quotes
indicators.
The data filtering processes are explained as follows:5

· We selected a sample firms from among Nikkei 225 component stocks whose minimum
tick sizes were ¥1, ¥5, and ¥10. The stocks priced less than or equal to ¥2000 have a ticksize of ¥1; those between ¥2000 and ¥3000 have ¥5; and those between ¥3000 and ¥30000
have ¥10. In our sample periods, the number of stocks whose tick size is greater than ¥10
are eight firms. We removed them to avoid possible effects attributable to the considerablylarger tick size, as pointed out by Ahn et al. (2002) who analyzed the component stocks of
Nikkei 225’s intraday pattern.

· We required the sample firms to be traded as those with more than six trades per half-hour
during the sample periods following the data filtering method used by Lin et al. (1995).

· We removed firms whose minimum tick size changed during the sample period to prevent
its effect on the quote spread and other market statistics measures.
The final samples consist of 149 for our event.

2.2. Methodology
The objective of these analyses is to discover whether or not pre-trade transparency affects
market quality improvement. Using a market-micro structure event-study method, we test
whether or not the market quality of TSE is improved by the disclosure event. Our test is to6

compare differences of market quality before and after the events.
We measure market quality using the following variables: quoted spread, effective spread,high-low volatility, and MRR adverse selection cost. These market quality measures were also
adopted for the order driven market analysis undertaken by Eom et al. (2007).
We use a disclosure reform event on the TSE, a large order-driven market. The TSE
publicly discloses a specified number of best buy and sell prices and their number of shares at
each price. On December 25, 2000, the number of disclosed prices and shares was increased
from one to three (the bid and the next two buy prices, the ask and the next two sell prices). This
event related to the change of the disclosure policy are helpful to reveal the effects of pre-tradetransparency on market quality, which is not analyzed by the previous study of
Camerton-Forde et al. (2005).
In the market micro-structure event study method, trading volume and prices of stocks are
determined endogenously and are known to affect the market quality. We control for thechange of market quality using a panel-data analysis same as Eom et al. (2007). We obtain thecoefficients and p-values using fixed effect estimation, which is robust to endogeneity
problems 3. Because there are insufficiently many trades per day to accurately estimate MRR
model on a daily basis within the small- and medium-size firms, we aggregate each five-day
period into a single period; for the other variables, we use daily data. Thus, the number ofobservations for each measure of market quality is about 20 per firm per event for MRR, and7

100 per firm per event for the other measures of market quality. For each of market quality
measures y, our panel-data specification is following equation (1):y it = β 0 + β 1 PostDummy it + β 2 log(volumeit ) + β 3 log( priceit ) + ci + ε it

(1)

Where the subscript i means each of firms, t indexes the period, PostDummy is a dummy
variable which becomes 1 after the event. The Volume denotes average of daily volume andPrice denotes average daily price. ci denotes individual firm-specific effects, andε it ~ i.i.d (0, σ 2 ) .

3.

Hypotheses

The TSE is an order-driven market in which there is no market maker. Lacking market
makers, the limit order is the only source of liquidity. The limit order’s free option properties
are identified and analyzed by Copeland and Galai (1983), Easley and O’ Hara (1991), Seppi(1997), and Foucault (1999) 4. Foucault (1999) describes that the limit order trader’s risk is‘picked off’ by market orders when stock values change with the arrival of new information.
In other words, the efficiency of market orders becomes higher and the monitoring cost oflimit order traders are also raised by the increased pre-trade transparency. After all, limit orders

3
4

The Hausman test rejects the random effect specification.
A limit order trade at a specified price becomes a free option at a specified strike price after the trade.
8are less likely to be provided as free options in the market, and market liquidity decreases, withwidening spreads, and increased price volatility.
The change of bid-ask spread components are explained as follows. The adverse selection
costs related to pre-trade transparency are similarly discussed by Madhavan et al. (2005). The
expected profits of informed traders are larger in a more transparent market because they more
efficiently use liquidity available through limit orders. On the other hand, uninformed traders
are less likely to choose limit orders in a more transparent market. Therefore, the adverse
selection components of the spread are widened.
In the following subsection 3.1, we produce hypotheses of changes in market quality after
events enhancing pre-trade transparency.

3.1. Changes in Market Statistics
3.1.1.

Liquidity Change: Bid-Ask spread

No consensus pertains of whether a spread is decreasing in pre-trade transparency. Brauch(2005) shows theoretically that the improvement of pre-trade transparency increases
information efficiency, and engenders the tightening of spreads. An empirical study by Boemer
et al. (2005) confirms this effect. On the other hand, a theoretical study by Madhavan (1996)
and empirical work of Madhavan et al. (2005) reveal that the spread increases withimprovement of pre-trade transparency. They conclude that quote disclosure increases the cost9

of limit order traders and spread increases because of their reduced limit orders.
Our null hypothesis related to the change of the bid-ask spread is the following.

H1: Pre-trade transparency increases do not affect the spread width.

The following pairs of null hypotheses for changes of spread (Sp) and effective spread(Esp) are tested respectively. We respectively compare these two measures before and after the
events. We first calculate the quoted bid-ask spread defined as the difference between the best
bid and ask price. When calculating the quoted spread, we exclude the opening/closing quotes
and special quotes; quotes outside the exchange opening hours are excluded.
The daily spread is the transaction time-average of the spread between the best bid and ask
quotes. The effective spread is defined as 2|pt – qt |, where pt is the transaction price and qt is the
midpoint of the quote at the time of the trade. We average the daily spread and daily effective
spread over the sample for the before and after periods.

3.1.2.

Volatility

No consensus pertains on the relation between pre-trade transparency and volatility ofstock returns. Madhavan (1996) show theoretically that the volatility of stock returns might
increase with pre-trade transparency in a market that is insufficiently large. This theoretical10

implication is confirmed empirically by Madhavan et al. (2005) by a similar event at the TSX.
On the other hand, Eom et al. (2007) show empirically that transient volatility of stock returns
decreases with pre-trade transparency in the order driven market of KRX.
Therefore, our null hypothesis of the relation between the volatility of stock returns andpre-trade transparency becomes the following.

H2: Pre-trade transparency increases do not affect volatility.

We test the null hypothesis for changes of the volatility of stock returns ( σ ). We
respectively compare the volatility before and after the events. We adopt “high-low volatility”
to test the null hypothesis of H2 following the earlier study of Comerton-Forde et al. (2005).
Volatility is measured as following equation (2).

 Highit Volatility it = ln
 Lowit (2)where High is the highest price during each 30-min interval of the trading day and Low is thelowest price during each 30-min interval of the trading day. Wiggins (1992) show that thisvolatility is an extreme value estimator, and is used because it is more efficient than estimators
based on closing prices. Volatility is averaged across each day in order to produce one
11observation per stock per day. The high-low volatility is that for all trading days in each period.

3.1.3.

Adverse Selection Cost

Mixed evidence pertains for relations between pre-trade transparency and the adverse
selection components of the spread. Madhavan et al. (2005) find that the adverse selection
components increased after improvement of pre-trade transparency. On the other hand, Eom etal. (2007) show that the decrease of adverse selection components is concave in pre-tradetransparency.
We derive the implied spread and its two components––adverse selection and transitorycost––using the structural model described by Madhavan et al. (1997) 5 (Hereinafter, we call
MRR model).

∆Pt = θ ( xt − ρxt −1 ) + φ ( xt − xt −1 ) + ut ,(3)

In that equation, P is the stock’s trading price, x is the buy-sell trade indicator variable for thetransaction price (if buyer (seller) initiated order x=1(-1)). Also, θ denotes the effects ofrevisions in beliefs, where positive θ measures adverse selection cost arising asymmetric
information among traders, and φ reflects the effects of bid-ask bounce, where positive5

Ahn et al. (2002), for example, point out that MRR model can be readily applied to the study of bid-ask
components in an order driven market such as the TSE.
12

φ measures the transitory cost of supplying liquidity attributed to the limit-order trader.
Actually, ρ is the serial autocorrelation of the indicator x.
The three parameters ( θ , φ , ρ ) in equation (3) can be estimated using the generalized
method of moments (GMM).
We test the null hypothesis H3 as follows:

H3: Pre-trade transparency increases do not affect the asymmetric component of the
spread.

The following null hypothesis for changes of adverse selection cost ( θ ) is tested. We
compare the adverse selection cost before and after the events.

4.

Empirical Results

4.1. Descriptive Statistics
Table 1 shows descriptive statistics of our sample firms classified into three firm groups:
small, medium, and large. The table lists the number of firms, the firms’ average market
capitalizations (in billions of yen), average daily volume (in thousands of shares), and average
daily closing prices in each group.
The market capitalization of sample firms is 340.8 billion yen. The respective averages of13the three groups’ market capitalization range from 50.8 billion yen to 763 billion yen. Table 1
also presents the increase in trading volume from before to after the event. Table 1 portrays thatthe daily average number of trades is almost identical for the sample period.

(Insert Table 1)

4.2. Empirical Results
Our empirical results show the change of market quality using the spread, effective spread,transient volatility, and MRR adverse cost component. Differences of market quality are tested
using Wilcoxon’s (nonparametric) signed-rank test. Table 2 presents results of liquidity,
volatility, and MRR adverse selection cost component from the event study for all samples.

(Insert Table 2)

As for bid-ask spread, the null hypothesis of the daily spread: H1 is negatively rejected forany sample and three groups, suggesting an increase of market quality. The decrease in bid-ask
spread is by about 5.5% after the event. For effective spread, the null hypothesis: H1 isnegatively rejected at 1% level. These two results imply that market liquidity measured asbid-ask and effective spread are enhanced by the improvement of pre-trade transparency.
14

The change of high-low volatility was averaged over all transactions during each of the two
sample periods. The change is not significant for all groups. In other words, the null hypothesisof H2 is not significantly negative. Therefore, we cannot infer any relation between volatility
and market quality.
The null hypothesis of H3 is negatively rejected for all samples. The decrease of adverse
selection cost ( θ ) is about 3 % at event, which indicates that the degree of asymmetric
information among traders is reduced by disclosure event.
In comparing the empirical results of our four market quality measures, changes of bid-ask
spread, effective spread and adverse selection cost indicates the positive relation between
pre-trade transparency and market quality. However, changes of high-low volatility are
insignificant. We imply that market quality is weakly increasing in pre-trade transparency in
TSE after the quote disclosure on 2000. However, there is a possibility that the endogenous
relations market quality and volume or price. We adopt panel data analysis controlling for priceand volume in next section.

4.3. Panel Data Analysis Controlling for price and volume
Using event-study methodology without controlling for volume or price, only one of market
quality variables: high-low volatility does not show a significant result and other threevariables show the improvements of market quality. Thus, we cannot strongly conclude that15

market quality is improved after the event 1 by the standard event-study methodology.
However we use the panel-data analysis to control for volume and price.

(Insert Table 3)

Table 3 reports the results of our panel data analysis. In panel A, we use daily observation
for three market quality variables: bid-ask spread, effective spread, and high-low volatility.
Since MRR model cannot be estimated on a daily basis for firms with insufficiently trading
observations, it is estimated using weekly observations and reported in panel B. We focus on
the coefficient β1 which indicates the post event dummy variable. This table shows that post
the event, controlling by a panel-data setting for volume and price, bid-ask spread, effective
spread, transient volatility, and MRR adverse selection cost are all significantly negative,
indicating an improvement in market quality.
We conclude that the event improved market quality using this panel-data analysis.

5.

Conclusions

This paper presents results of an examination of the relation between pre-tradetransparency and market quality in the TSE, a large order driven market. For this examination,
we analyzed TSE event: increased disclosure of the number of quotes from one to three on
16

December 25, 2000. We measure the market quality by spread, effective spread, transient
volatility, MRR adverse selection cost.
We compare the changes of market quality measures before and after the event using an
event study method. The same type of disclosure reform was established in the order driven
market of KRX. We find only weak positive relations for the event of TSE between market
quality and pre-trade transparency.
The different interpretation can be made of the differences between our results and those of
Eom et al. (2007). We might interpret that these differences depend on the degrees of pre-trade
anonymity before the disclosure reforms. Comparing the degrees of pre-trade anonymity, thatof TSE is less than that of KRX for the event because broker IDs, which is displayed only to
brokers, affects the lower market quality in TSE than KRX in which broker IDs are publicly
disclosed.
In conclusion, we provide evidence of a positive relation between pre-trade transparency
and market quality in TSE: an order driven market. This positive relation might depend on the
degree of pre-trade anonymity. For examination of this possibility, more evidence for stock
exchanges worldwide would be helpful. This remains as a valuable task for future study.

17

References
Ahn, H.J., Cai, J., Hamao, Y., Ho, Y.K., 2002. The components of the bid-ask spread in a limit
order market: evidence from the Tokyo Stock Exchange. Journal of Empirical finance 9,399-430.
Baurch, S., 2005. Who benefits from an open limit-order book? Journal of Business 78,
1267-1306.
Bloomfield, R., O’Hara, M., 1999. Market transparency: who wins and who loses? Review of
Financial Studies 12, 5-35.
Boehmer, E., Saar, G., Yu, L., 2005. Lifting the veil: an analysis of pre-trade transparency at the
NYSE. Journal of Finance 60, 783-815.
Comerton-Forde, C., Frino, A., Mollica, V., 2005. The impact of limit order anonymity on
liquidity: evidence from Paris, Tokyo and Korea. Journal of Economics and Business 57,
528-540.
Eom, K.S., Ok, J., Park, J-H., 2007. Pre-trade transparency and market quality. Journal of
Financial Markets 10, 319-341.
Flood, M.D., Huisman, R., Koedijk, K.G., Mahieu, R.J., 1999. Quote disclosure and pricediscovery in multiple-dealer financial markets. Review of Financial Studies 12, 37-59.
Foucault, T., 1999. Order flow composition and trading costs in a dynamic limit order market.
Journal of Financial Markets 2, 99-134.
18

Gemmill, G., 1994, Transparency and liquidity: a study of block trades on the London Stock
Exchange under different publication rules. Journal of Finance 51, 1765-1790.
Hendershott, T., Jones, C.M., 2005. Island goes dark: transparency, fragmentation, andregulation. Review of Financial Studies 18, 743-793.
Lin, J.C., Sanger, G., Booth, G., 1995. Trade size and components of the bid–ask spread.
Review of Financial Studies 8, 1153–1183.
Madhavan, A., 1995. Consolidation, fragmentation, and the disclosure of trading information.
Review of Financial Studies 8, 579-603.
Madhavan, A., 1996. Security price and market transparency. Journal of Financial
Intermediation 5, 255-283.
Madhavan, A., Richardson, M., Roomans, M., 1997. Why do security prices change? A
transaction level analysis of NYSE stocks. Review of Financial Studies 10, 1035-1064.
Madhavan, A., Porter, D.C., Weaver, D.G., 2005. Should securities markets be transparent?
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19

Wiggins, J., 1992. Estimating the volatility of S&P 500 futures prices using the extreme-value
method. Journal of Futures Markets 12, 265-273.

20

Table 1
Descriptive statistics
Group (basedon volume)

MarketAverage

Averagecap.

VolumeTrading(billion ¥)(1,000 shares)

Number (/day)Price (Yen)

Mean

Mean

Mean

Mean

No. of

Firms

Average Closing

Pre

Post

Pre

Post

Pre

Post

ALL

149

340.8 1752.7 1839.1

265.4

264.7

765.3

740.9

Small-firm

50

50.8

678.8

141.4

132.5

377.4

363.9

Medium-firm

50

185.3 1615.4 1692.8

239.3

244.4

666.8

639.3

Large-firm

49

763.0 2932.2 3172.2

418.7

420.3 1261.5

1229.4734.1

21

Table 2
Spread, effective spread, volatility, and adverse selection cost before and after the event
Event
Pre

Post

Wilcoxon’s
Signed-rank

Panel : Estimated Results of all firms
All Firms
sp

2.137

2.022

0.000

esp

2.140

2.026

0.000

σ

1.089

1.096

0.306

θ

0.098

0.095

0.001

Note: The cross-sectional means of the spread (sp), effective spread (esp), high-low volatility (Σ),
and the adverse selection cost components of the implied spread ( θ ) are estimated for the 50
trading days before and after event day. The structures of each null hypothesis mean that (sp, esp,

σ , θ ) are statistically same before and after the events. Reported in the 4th columns are thep-values for Wilcoxon’s signed-rank test for nonparametric testing of the null hypotheses.

22

Table 3
Panel Data Analysis controlling for endogenous variables: price and volume
Event 1
β0

β1

β2

β3

Panel A
No. of firms (observations per
149(100)
firm)
Sp (Spread)-1.99468*** -0.08130*** -0.11037***(0.000)Esp (Effective Spread)(0.000)(0.000)-2.01278*** -0.07945*** -0.10897*** 0.90136 ***(0.000)Volatility (Hi-Lo Volatility)(0.000)0.90118***(0.000)(0.000)(0.000)-0.00016** -0.00001***

0.00007*** -0.00010***(0.049)(0.000)(0.001)(0.000)Panel BNo. of firms (observations per
149(20)firm)

θ （MRR adverse selection cost）

0.97246*** -0.00678*** -0.01418*** -0.10775***(0.000)(0.000)(0.000)(0.000)Note: The hausman test rejects the random effects specification which indicates that endogeneity is an
issue. This Table reports the coefficients and p-values from a panel data analysis, using fixed effects
estimation, which is robust to endogeneity problems. For each measure of market quality, we estimate
the equation (2). We use daily observations in Panel A; since MRR cannot be readily computed on adaily basis for infrequently-traded firms, it is calculated using weekly observations and reported in Panel
B. ** and ***denote statistical significance at the 5% and 1% level, respectively.
23