Institutional Trading, Trading Volume, and Spread
Malay K. Dey
University of Massachusetts at Amherst
Northern Illinois University
University of Minnesota
[This Draft: March 2001]
We are grateful to NYSE for providing us with the TORQ data and to Ben Branch,
Shanta Hegde, and Robert Miller for their comments and numerous helpful suggestions.
All comments are welcome.
Please address correspondence to: Malay K. Dey, Dept. of Finance, Northern Illinois
University, Dekalb, IL 60115, Tel. (815) 753-6395, Email: firstname.lastname@example.org, OR B.
Radhakrishna, Department of Accounting, Carlson School of Management, University of
Minnesota, 321, 19th Avenue S., #3-122, Minneapolis, MN 55455, Tel. (612) 624-0302,
Fax (612) 626-1335, Email: email@example.com.
Institutional trading, Trading Volume, and Spread
Besides its academic interest, the effect of institutional trading on the bid-ask
spread is of interest to regulators and market makers. It is often (casually) argued that
greater institutional participation results in increased volatility in the market. On the
other hand, some argue that greater liquidity trading by institutions reduces spread.
There is no direct empirical evidence and little theoretical knowledge to suggest a
convincing relation between institutional trading and spread. In this paper, we present
some evidence on the nature and effect of institutional trading on spreads. We argue that
institutional trading is not completely information driven, part of it is liquidity trading in
nature. We find evidence that information induced institutional trading increases the
adverse selection component. However, large volume (liquidity) trading reduces the
order processing costs. We find the net effect of institutional trading on spread is
consistently negative. Moreover, institutional buys have differential information from
sells. Institutional trades per se reduce spreads, but only sells increase the adverse
selection component. Both effective and relative spreads impound the differential nature
of institutional buys and sells.
Keywords: Institutional Trading, Spread decomposition, information, liquidity.
JEL Classification: G19.
Institutional trading, Trading Volume, and Spread
Since Demsetz (1968) bid-ask spread is recognized as the price of liquidity
provided by the dealers in an equity market. A number of studies have investigated what
determines spread (Branch and Freed , McInish and Wood , Klock and
McCormick , Heflin and Shaw ). Some of the significant determinants of
spread found in the literature are order size, number of trades, competition in the dealers’
market, ownership structure, and the native characteristics of a stock e.g., price, and
volatility. Trading rules and mechanics of trading that proxy for information flow are
also found to affect the spread.
There is very little empirical evidence on institutional trading and spread and their
interrelationship. Keim and Madhavan (1997) find execution costs for institutional trades
are different between listed and NASDAQ stocks. Conrad, Johnson and Wahal (2001)
report an asymmetric relation between institutional buys and sells and soft-dollar
execution. However, there is some evidence of the effect of institutional trading on
securities prices. Empirical studies using order size or trading volume as a proxy for
institutional trading1 suggest an increased price effect associated with institutional
trading. Using proprietary data on institutional trading, Chan and Lakonishok (1993) find
the average price effect to be small for institutional trades, but the price effect for buys
and sells to be asymmetric; and Sias and Starks (1997) find that institutional trading
contributes to serial correlation of returns.
1 Lakonishok and Maberly (1990) use block trades as a proxy for institutional trades, and odd lot trades
plus non-margin account trades as a proxy for individual trades.
The role of institutional trading in the determination of spread is interesting since
it is often argued (casually) that increased institutional participation in the U.S. equity
market during the past decade has led to an increase in the volatility, and has widened the
bid-ask spread in the equity market. On the other hand, some argue that institutional
trades provide liquidity, and hence decrease spread. Bertisimas and Lo (1998) show how
optimal trading strategies may be devised by execution cost minimizing investment
mangers. In any case, there is very little empirical evidence or theoretical knowledge to
conclude how institutional trading affects spread. Further since spread is considered to
be a sum of two different components, adverse selection, and order processing2 it is
unclear how institutional trading affects the individual components of the spread.
In this paper, we investigate if institutional trading has any information content
beyond what has been documented as a size or volume effect. In a multivariate, panel
regression framework, we determine if there exists a relation between bid-ask spread and
institutional trading after adjusting for size and price effects. Most studies on the
determinants of spread focus on the supply side of dealership market i.e., competition in
the dealer market, and use a cross sectional regression approach. Our approach is
different from previous studies in two important ways. First, we focus on the demand
side (investors’ characteristics) to determine the relation between spread and institutional
trading3; and second, we use a panel data approach accounting specifically for both serial
and contemporaneous correlation in the error terms4. We report regression results using
2 Arguably there is also inventory effect in spread. In this paper, we use a decomposition technique that
ignores inventory effect.
3 Recently, some other papers have studied the demand side, particularly the ownership structure. For
example, Heflin and Shaw (2000) document a relation between block holding and spread, and Chung and
Charoenwong (1998) look at insider holding as a determinant of spread.
4 Dey (2000) introduces a similar but reduced form panel data regression model for effective spread.
both effective ($) and relative spread as the dependent variable in a set of regression
Further we decompose the spread into order processing and adverse selection
components and investigate how those components vary with changes in trading volume,
net order flow (buy vs. sell), and institutional trading. We assume contemporaneous
correlation between the disturbances and use an SUR (Seemingly Unrelated Regression)
analysis to find the significant determinants of the adverse selection and the order
processing components of the spread for our sample firms. We use a unique data set
(TORQ) that identifies institutional trading. Prior studies proxy institutional participation
by using measures based on trade size that are subject to measurement error.
Our results show that institutional trading proportion is inversely related to both
effective and relative spreads. We also find that the negative slope (suggestive of the
inverse relation) is not constant and flattens out at higher concentrations of institutional
trading. We find that this negative slope is provided by both institutional buys and sells
alike. Results from a SUR analysis show that the adverse selection costs tend to increase
and the order processing costs tend to decrease with increases in institutional trades.
The rest of this paper is organized as follows. In section II, we present the
motivation for this study. In section III, we describe an empirical model for spread using
panel regressions, provide data description, and explain the results. Further, we describe
the decomposition of the spread into order processing and adverse selection costs
components and report results from a SUR analysis of the determinants of those
components. Section IV concludes the paper.
II.A. Relation between Spread and Institutional trading
Schwartz (1988) identifies four classes of variables, namely, activity, risk,
information, and competition as determinants of spread. Existing literature find trade
size, number of trades, ownership structure, and extent of market power in the dealership
market to be the key determinants of bid-ask spread (McInish and Wood , Laux
[1993, 1995], Klock and McCormick , Heflin and Shaw ). Dealer market
competition represents the supply side of the market for liquidity services5. On the other
hand, trade size, ownership structure, and frequency of trading measure the activity in
securities markets and represent the demand side of the market for liquidity services.
Prior research suggests an inverse relationship between spread and trading activity
measured by order size, and number of trades (McInish and Wood ). Institutions
trade large sizes, and also trade frequently.6 Thus institutional trading will induce low
spread. However, trading activity also contributes to both information and risk associated
with a security7. Hasbrouck (1991) provides evidence that large trades contain more
information than small trades and cause spreads to widen. Lin, Sanger and Booth –
hereafter LSB (1995) find evidence of an increasing (although not continuously), non-
linear relation between spread and trade size. Jones, Kaul, and Lipson (1994) report that
5 In a securities market, dealers provide liquidity (both buying and selling of securities) services, while
investors demand those services.
6 Lee and Radhakrishna (2000) confirm earlier evidence that institutions tend to trade in larger volumes.
They present a technique of classifying trades into institutional and non-institutional based on size in a way
that reduces the error in classification to statistically manageable levels.
7 See, for example, the summary of the empirical evidence on the price effect of block trades in Dey (2000).
the number of trades captures the essence of volatility in financial markets even in the
presence of volume and trade size.8
Seppi (1990) argues that institutions use trade size strategically; they trade large
orders when they can signal to the market that their trades are not information motivated
and hence large institutional trades may not have information content. Dey and Kazemi
(2000) distinguish between large, pure information trades and large institutional trades
and argue that institutional trades are driven by both “pure information” and liquidity
needs. Dey and Kazemi (2000) predict the “pure information” component of equilibrium
spread to be an increasing, while the liquidity component of the spread to be a decreasing
function of institutional trading.
Chan and Lakonishok (1993), and Keim and Madhavan (1994) find that the price
effect and cost implications of institutional buys and sells are not symmetric. Koski and
Michaely (2000) find that buys and sells provide different information for different trade
sizes. Saar (2000) provides a theoretical framework based on a dynamic portfolio
rebalancing process of institutions to explain the documented asymmetry in the price
effect of institutional buys and sells.
In this paper, in a multivariate regression framework, we determine whether
institutional trading has information content beyond that provided by trade size denoted
by trading volume, and number of trades. Specifically, we hypothesize that the variation
in bid-ask spread can be explained by trading volume, number of trades, price, and
institutional trading. Further, we hypothesize that institutional trading per se and not the
8 Recently, Chan and Fong (1999) find that after adjusting for order imbalance, trade size is important for
the volume-volatility relation.
direction of institutional trading - buy or sell affects the bid-ask spread. Stated in
alternate form, we hypothesize:
H1: The bid-ask spread should vary significantly with institutional trading
after controlling for number of trades, price, and trading volume. Further,
institutional trades per se affect bid ask spread, and thus institutional buys
and sells do not have any differential effect on the bid-ask spread after
controlling for number of trades, price, and trading volume.
II.B. Components of the spread
We extend our analysis of the relation between institutional trading and spread by
decomposing the spread into its order processing and adverse selection components and
investigating the effect of institutional trading on the individual components. We use a
technique from LSB (1995) to decompose the spread into order processing and adverse
selection components and hypothesize a relation between the individual components and
Sias (1996) reports positive correlation between institutional activity and market
volatility; however, Cohen et al (1987) suggest that institutions trade frequently because
of their low order processing costs. Bertisimas and Lo (1998) derive optimal trading
strategies of institutions based on minimum execution cost. LSB (1995) find the order
processing cost to be decreasing and adverse selection cost to be increasing in trade size.
We conjecture that if institutional trading is a mix of information and liquidity trading
then the information effect should increase the adverse selection component and the
liquidity effect should decrease the order processing costs. We also recognize that while
gross volume is important for the determination of order processing cost, trade direction
or net volume (buy volume - sell volume), is important in the determination of adverse
selection costs. We therefore include log (buy/sell) as a variable in the regression model
for the adverse selection component.
We determine through a set of simultaneous equations how institutional trading
affects the order processing and the adverse selection components of the spread after
controlling for number of trades, volume, and trade direction. The simultaneous
equations approach uses the cross correlation between the two regression equations to
improve the estimates. Further we determine how the asymmetric information content
and the liquidity motive in institutional buys and institutional sells affects the adverse
selection (information) component of the spread. Stated in alternate form, our hypotheses
H2a. The adverse-selection component should increase with institutional
trading, and the order-processing component should decrease with
H2b. Institutional buys should have a differential effect on the adverse
selection component of the spread from institutional sells.
III. Regression Models, Data Description, and Results
III.A.1. Panel Data Regression Model
We use a multivariate, panel (time series – cross sectional) regression framework
to investigate the effect of institutional trading on the bid-ask spread. We use a unique
data set (TORQ) that allows us to identify the order origination for a trade as institutional
or otherwise. Most studies on the determinants of spread use pooled OLS estimates of
the parameters of a regression model. OLS estimates ignore the covariance structure of
the error term both across firms and over time.
We assume disturbances are both serially and contemporaneously correlated.
Specifically, we assume an AR(1) process with contemporaneous correlation for the
disturbance term. In our model for the spread, the serial correlation may be due to lagged
spread or lagged values of the independent variables or their interactions. Kim and
Ogden (1996) find higher order serial correlation for the spread, and Peles (1992) report
contemporaneous correlation among equity trading of institutional investors. Parks
(1967) provide consistent and efficient estimates of the parameters when disturbances
follow a first order auto regressive process - AR(1) with contemporaneous correlation.
We run the following regression model for our panel data:
nt ntnt nt nt
NtradePctDly Avgice Instprop
n = 1…N; number of firms in sample, t = 1…T; number of trading days
Spreadnt = Effective or Relative spread for the nth firm on day t
NtradePctnt = Number of trades on day t for firm n expressed as a proportion of
average number of daily trades over the entire sample period
Dly_Avgnt = Volume of trade on day t for firm n expressed as a proportion of
average daily trading volume over the entire sample period
Pricent = Closing price on day t for firm n
Instpropnt = Institutional trading (number of trades) as a proportion of total
trading for the nth firm on day t.
Further, the error structure is assumed as follows:
e r em
ntn n tnt
and ,(due to contemporaneous correlation),
(disturbance term is AR(1)),
, 0 b
0 where (cross correlation is zero).
To estimate the parameters of our regression model, we use data from the TORQ
data set. The TORQ files released by NYSE were prepared under the supervision of
Professor Joel Hasbrouck during his tenure as a Visiting Economist to the NYSE. This
dataset contains trades, quotes, order processing, and audit trail data for a sample of 144
NYSE stocks for the three months (63 trading days) from November 1990 through
January 1991.9 These firms represent a size stratified random sample of firms in the
NYSE and thus cover the broad spectrum of NYSE firms.
As noted by Lee and Radhakrishna (2000), the marginal contribution of TORQ
data over ISSM or TAQ data is in providing identification for traders’ classes, as
institutions, individuals, and dealers. Most studies using other trades/quotes databases
use size as a proxy for institutional trades.
We impose a restriction that is common among studies that study effective
spreads or the components of the spread to ensure adequacy of data in estimation. We
select all firms in the data set that have on average 20 trades per day or more during the
sample period. Further, in classifying the trades, whenever there are executions of
multiple orders on the active side of a trade, we take the trader class of the largest order
9For more details on the TORQ database and on trading procedures at the NYSE, see Hasbrouck (1992).
on the active side as the initiator of that trade. The active side of the trade is determined
using the Lee-Ready (1991) algorithm.10 This reduces our sample to 65 firms.
Of the 65 firms that survived our initial cutoff, 14 firms have one or more days of
missing observations or days with trading halts. We leave out the firms with trading halt
days from our study since the effect of trading halts on spreads and the price discovery
process is unique and beyond the realm of this paper. We chose to omit the firms with
missing observations, since there are questions about the reliability of estimates using an
unbalanced panel11. Thus we have a balanced panel of 51 firms with 63 days data that we
use in our panel regression.
The panel data set includes daily data for the firms in the data set. For each firm,
we calculate the mean daily effective bid-ask spread, and the proportion of buy and sell
orders initiated by institutions for each day. We compute the effective spread for each
trade defined as twice the absolute value of the difference between trade price and the
prevailing mid-quote. The mean effective spread is the average of effective spreads
across all trades in a day. To determine the mean proportion of trades by institutions in
that firm, we calculate, for each day, the proportion of trades by institutions on the buy
and sell side.
Besides proportion of institutional trading, there are three other independent
variables in the panel regression. The “number of trades” variable is computed as the
number of trades each day divided by the average number of trades for the firm over the
10 Lee and Radhakrishna (2000) show the effectiveness of Lee-Ready algorithm for classifying trades into
buys and sells.
11 We explored another alternative i.e., to consider all the firms (65) but for fewer days (61). However,
there was a problem in deciding which 2 days to delete for firms that have full 63 days data. Since any
choice on this was likely to be arbitrary, we chose to use a balanced panel of 51 firms over 63 days.
sample period. Thus this is a measure of abnormal trading in each day. Trading above
(below) mean would give this variable a value higher (lower) than one. The “trading
volume” variable is computed by dividing the daily share volume for the firm by the
average daily share volume over the sample period. Therefore, this variable also has
values above (below) one when trading volume is higher (lower) than the average daily
share volume. The price variable is the closing price of the stock.12
In Table 1, we present means of the computed statistics of the variables used in
the panel regression. We first compute the relevant statistics for the sample firms over
the sample period and then compute the means of those statistics. Thus we report the
means of the cross sections of firm means, medians, and standard deviations. The inter-
firm mean (median) spread is .126 (.12) that is about an eighth. The inter-firm mean
standard deviation is quite low at .02. The largest spread in our sample is .514,
approximately one-half, and the lowest .019 or approximately one-sixty-fourth. The
inter-firm mean (and median) institutional trading in our sample is around 30%.
Although institutions generally trade on a regular basis, in some trading days there is no
institutional trading. Of the 3,213 (51×63) firm trading days covered in our sample there
were 10 firm-days when there was no institutional trading, and one firm-day when all the
trading was initiated by institutions. There is also sufficient variability in the number of
trades and volume. The low trading (volume) in our sample was 9% (3%) of average
daily trades (volume). The high trading (volume) was 392% (1235%) of average daily
volume. In Table 2, we present Pearson correlations for variables in the regression.
12 Using average price over a day instead of closing price does not change the results.
Panel A presents pooled correlations computed from 3,213 observations. In Panel B, we
present the means of correlations computed in time series for each firm.
III.A.3 Regression Results of Panel Model
Table 3 reports the regression results for the panel data regressions with effective
spread (in dollars) and relative spread as dependent variables. For our first model (1-ES),
the independent variables are number of trades, trading volume, price, and the proportion
of institutional trades. All four variables are significant in determining effective spread.
The significant coefficients show that effective spread increases as trading (number of
trade) and price increase and decreases as trading volume and institutional trading
increase. The coefficient for institutional proportion is negative (-.0111) and significant at
less than 1% level. Thus an increase in institutional proportion reduces the spread. The
R2 for this model is 22%. However, for a similar model with relative spread (1-RS) as
the dependent variable, number of trades fails to be a significant determinant of spread.
All other variables, namely average trading volume (-.0137), price (-.028), and
institutional proportion (-.024) remain significant at less than 1% level, and the R2 for the
model is 90%. The change in the effect of price on relative spread (decreasing in price)
from that on effective spread (increasing in price) is expected since relative spread is
computed as effective spread over mid quote. This change in the effect of price on
effective and relative spreads is consistent across all the four models.
For our second model, we introduce two dummy variables for high and medium
institutional proportions. The high (medium) institutional proportion variable has a value
of 1 when the level of institutional trading is in the top (middle) 33% percentile, and zero
otherwise. The coefficients of the dummy variables are both positive and significant in
the regression. The high dummy has a larger coefficient (.0016) than the medium
dummy variable (.0007). Taken in conjunction with the significant negative (-0.0153)
coefficient of the institutional proportion variable, this suggests that the negative slope of
the institutional proportion variable flattens out at higher levels of institutional trading.
This conjecture is confirmed with model 3.
In model 3, the institutional proportion variable is replaced by three variables –
high, medium and low proportion. The high (medium, low) proportion variable has the
same value as institutional proportion if institutional trading proportion is in the top
(middle, bottom) 33 percentile, and zero otherwise. The coefficients of all three variables
are negative and significant in the regression, but while the coefficient for low proportion
is -.0144, that for the high proportion is significantly (14%) less at -.0122. We interpret
these results as follows. On average, there may be a mean positive effect on institutional
trading embodied in the positive intercept. However, when there is an increase in
institutional trading within a level, the spread declines, but the rate of decrease is lower at
higher levels of institutional proportion. Between low and medium levels, the rate of
decrease is similar indicating these are perhaps two discrete levels of institutional
Finally, for our fourth model, we break up the institutional proportion into
institutional buy and institutional sell to test for a difference in their effect on the spread.
Results from the fourth model show that spread reduces as institutional trading increases,
be it buy or sell. The coefficients for buy and sell are significantly negative for both
effective spread and relative spread.
These results support our hypotheses. We show (H1) that institutional trading is a
significant variable in the determination of both effective and relative spreads. We also
show that this relation has a downward slope that gradually flattens out. Finally, we
show that institutional trading per se drives the relation between spread and institutional
trading, and not the direction of institutional trade - buy or sell.
III.B.1 Decomposition of Spread
In order to test our third hypothesis, we decompose the quoted spread for the
firms in our sample to determine the adverse selection and order processing
components13. We follow a decomposition technique originally proposed by Stoll
(1989)14 and used recently by LSB (1995). The parameters ˆ
lg of the following
regression models are estimates of the adverse selection and the order processing
components of the spread.
∆ = −+
where, Pt = log trade price at time t
Qt = log quote mid point at time t
∆= Change in the relative variable from t to t+1
∆ zt = Pt – Qt and
et+1, ut+1 = Random error terms with zero mean and constant variance
13 We don’t do any analysis with inventory holding cost component.
14There are several decomposition methodologies other than Stoll (1989) that are available in the literature.
Huang and Stoll (1997), Glosten and Harris (1988), and George, Kaul and Nirmalendran (1991) provide
estimates of the different components of the spread including adverse selection, order processing and
inventory holding cost. Studies using different methodologies find the components of the spread to be
different. There is no consensus about any one of the methodologies being more robust than the others.
E e u
The assumption about zero correlation between the error terms is based on the
reported findings in LSB (1995).
Table 4 reports descriptive statistics about the spread decomposition for the 65
firms in the sample. We find that the order processing costs for our sample firms are
higher, and adverse selection costs lower than that reported in LSB 1995. The means and
medians of the components are similar indicating a symmetric (low skewness)
distribution for the component costs. Not surprisingly the descriptive statistics from our
sample correspond closely with those from the largest volume sub-sample in LSB (1995)
confirming the notion that institutions generally trade in large sizes and in high volume,
III.B.2. SUR model
We use a cross sectional SUR (Seemingly Unrelated Regression) model to
estimate the parameters of the following system of equations for the adverse selection
and the order processing components of the daily spread for our sample firms.
OPC NumTrd InstitutionTradingAvgVole
2 2122 23
ADVNumTrd InstitutionTrading AvNetVole
OPC = Order Processing cost
ADV = Adverse Selection cost
NumTrd = Mean daily number of trades for firm
InstitutionTrading = Mean institutional trading proportion for firm
AvgVol = Log(Average daily trade volume for firm)
AvNetVol = Log(Average buy volume per trade/average sell volume per trade)
The SUR model assumes that
oi aj oa
E e e
if i = j and 0 otherwise for i,jÎ (1..65).
III.B.3. Results of the SUR model
Table 5 reports the results of the SUR model. The number of observations for
each system of equations estimated is 65 – one observation for each firm in the sample.
Our results show that institutional trading significantly increases the adverse selection
component of the spread but reduces the order processing cost component. This suggests
that the market views institutional trading to have a dual character, both information and
liquidity trading and prices both in the determination of the spread. The order flow
variable (AvNetVol) is significantly negative, thus an increase in buys has the effect of
reducing the spread. The “number of trades” variable is significantly negative in the
adverse selection equation and positive in the order processing equation – indicating its
dichotomous effect on the spread components. The volume variable is negative in the
order processing equation as expected. As volume increases, order processing costs
decrease. We interpret this result as supporting Bertisimas and Lo (1998) that higher
trading by institutions are motivated by lower order processing costs.
For our second model, we estimate the system of equations with institutional buys
and sells in place of total institutional proportion. We find that institutional sells are
positive and significant (at 8% level) in determining the adverse component. Buys are
insignificant in the adverse selection equation.
The role of institutional trading in the determination of spread is of interest to
regulators and market makers. It is often argued that institutional investors have superior
information, better processing power to assimilate the information, and greater access to
markets.15 Institutions have low transaction costs and thus trade frequently. The
increased institutional participation is often considered an attribution for the increased
volatility in the U.S. equity market. However there is no empirical evidence suggesting a
relation between institutional trading and spread. We present empirical evidence to
suggest a non-linear inverse relation between the bid ask spread and institutional trading
in the equity market. Our analysis shows that institutional trading is not just information
driven – a part of their trading is liquidity trading in nature. Institutional trading affects
both the adverse selection and order processing components of the spread. Increased
institutional trading increases the adverse selection component, while it reduces the order
processing costs through large volume trading. We find the net effect of increased
institutional trading to be a reduction spread. Increased institutional buys seem to reduce
spreads, but sells seem to increase the adverse selection component. The market makers
recognize this and accordingly adjust the effective spreads.
15 Radhakrishna (1995) shows the swiftness with which institutional traders enter the market after a news
event like an earnings announcement.
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Descriptive Statistics of Variables Used in Panel Regressions
1Effective spread is defined as Abs[(Price – Mid Quote)*2]. The variable used in the
panel regressions is average spread per day for each firm. Relative Spread Percent is
computed as effective spread/mid quote *100.
2Institutional trading proportion is computed as trades by institutions as a proportion of
total trades for a day.
3Number of trades is computed as the number of trades for firm each day divided by
average trades per day over the entire trading period. Thus, the mean of this variable on a
firm-by-firm basis is by design equal to 1.
4Trade Volume is computed as share volume of firm for each day divided by average
share volume per day over the entire trading period. Thus, the mean of this variable on a
firm-by-firm basis is by design equal to 1. The standard deviation presented here is the
mean of the within firm standard deviations.
5The standard deviation presented here is the mean of the within firm standard deviations.
PANEL A: Pooled sample Pearson Correlations (N=3213) a
Variables Spread Relative
Spread % (<.0001)
PANEL B: Mean of firm Correlations a
Variables Spread Relative
InstProp Dly_Avg Price5
NtradePct InstProp Dly_Avg Price5
aPanel A table values are 1) Parameter estimates, and 2) P-Values in parentheses. Panel
B table values are 1) Parameter estimates based on mean of firm estimates, 2) the number
of firms for which coefficients are significant at p-values of less than 10%. The mean
coefficient is computed from the 51 firms in the sample.
1Spread = Effective spread is defined as Abs[(Price – Mid Quote)*2]. The variable used in the panel
regressions is average spread per day for each firm.
2NTradePct = Number of trades - This is computed as the number of trades for firm each day divided by
average trades per day over the entire trading period.
3InstProp = Institutional trading proportion – This is computed as trades by institutions as a proportion of
total trades for a day.
4Dly_Avg = Daily Trade Volume - This is computed as share volume of firm for each day divided by
average share volume per day over the entire trading period.
5Price = Closing Price.
Panel Regression Results
Dependent Variable: Spread and Relative Spread (for all models)
Model 1-ES 2-ES
(<.0001) (<.0001) (<.0001)
.223 .220 .219 .220 .899 .892
All regression models in Table 3 above are run with a panel data set that has 51 firms each with
63 trading days. The Parks (1967) method with AR(1) and contemporaneously correlated error
term is used for estimation. Table values are parameter estimates with p-values in parentheses.
Spread = Effective spread is defined as Abs[(Price – Mid Quote)*2]. The variable used in the panel regressions is
average spread per day for each firm.
NTradePct = Number of trades - This is computed as the number of trades for firm each day divided by average
trades per day over the entire trading period.
Dly_Avg = Daily Trade Volume - This is computed as share volume of firm for each day divided by average share
volume per day over the entire trading period
InstProp = Institutional trading proportion – This is computed as trades by institutions as a proportion of total trades
for a day. HighInstProp has the value of InstProp if the institutional proportion is in the top third percentile of
institutional proportions, and 0 otherwise. MedInstprop has the value of InstProp if the institutional proportion is
in the middle third percentile of institutional proportions, and 0 otherwise. LowInstprop has the value of InstProp
if the institutional proportion is in the bottom third percentile of institutional proportions, and 0 otherwise.
HighDum has the value 1 if the institutional proportion is in the top third percentile of institutional proportions, and
0 otherwise. MedDum has the value 1 if the institutional proportion is in the middle third percentile of institutional
proportions, and 0 otherwise.
InstBuy = Institutional Buy trading proportion – This is computed as #buys by institutions as a proportion of total
trades for a day.
InstSell = Institutional Sell trading proportion – This is computed as #sells by institutions as a proportion of total
trades for a day.
Descriptive Statistics: Components of the spread expressed as percentage of total spread
1The effective spread is decomposed into its components using methodology originally proposed by Stoll (1989)
and used recently by Lin, Sanger and Booth (1995). The descriptive statistics presented above is for the cross-
section of 65 firms in the sample and is calculated from the mean proportion for each firm in our sample, of the
adverse selection component and order processing component of the spread.
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Regression Results of System of Equations with Adverse Selection and Order Processing Costs on Trades,
Volume and Institutional Trading
Intercept NumTrd Institution
All regressions are run as seemingly unrelated regressions (SUR). The data set has observations for 65 firms for which data is
available. Table values are parameter estimates with p-values in parentheses.
(All averages are computed over the entire 3-month trading period in the TORQ data set on a firm-by-firm basis.)
OPV = Order Processing component of the average effective spread for firm.
ADV = Adverse Selection component of the average effective spread for firm.
NumTrd = Average trades per day.
Institutional Trading =Average institutional proportion of trades.
Institution Buy = Average institutional buy proportion of trades.
institution Sell = Average institutional sell proportion of trades.
AvNetVol = Natural Log of [Average Buy Volume per trade/Average Sell Volume per trade]
AvgVol = Natural Log of Average Daily Trade Volume.