Active vs. Passive Investing and the Efficiency ofIndividual Stock Prices

Russ Wermers and Tong Yao∗

May 2010

∗Wermers is from Department of Finance, Robert H. Smith School of Business, University of Maryland.Email: wermers@umd.edu. Yao is from Department of Finance, Tippie College of Business, University ofIowa. Email: tong-yao@uiowa.edu. We gratefully thank INQUIRE-UK for financial support and MarliesvanBoven of Baring Asset Management for helpful comments on prior revisions of this work. All errors areour own.

Active vs. Passive Investing and the Efficiency ofIndividual Stock Prices

Abstract

In equilibrium, active investing must be compensated with returns from gathering costly

information about stock values (e.g., Grossman and Stiglitz, 1980). In return, active in-

vestors serve to promote price discovery in stocks. However, substantial trading is required

by active, informed investors, who may prefer to trade in the same stocks as passive, un-

informed investors to hide their intentions and better profit from their private information

(e.g., Admati and Pfleiderer, 1988). Thus, both active and passive investors must coexist in

the market for a stock to allow the efficient transfer of information into the stock price. This

paper analyzes the relation between active and passive mutual fund ownership and trading

activity in U.S. stocks during 1993 to 2006, and the resulting efficiency of stock prices. Our

study finds that active funds are drawn to the same stocks as passive funds, and that active

funds increase the price efficiency of stocks through their trades. We also find that stocks

with “excessive” levels of passive fund ownership and trading exhibit more long-term pricing

anomalies as well as a larger price reversal following trades.

I. Introduction

The efficient market paradigm, coupled with modern portfolio theory, has brought a huge

increase in index products to securities markets. For instance, by year-end 2008, assets

in ETFs and index mutual funds exceeded $1.1 trillion–growing about tenfold over the past

decade, and accounting for almost 12 percent of all mutual fund assets.1 In some sectors, pas-

sive investing has a much bigger presence. For instance, among large-blend domestic equity

funds, passively managed money accounts for 40 percent of all assets. While some studies

have documented that passive funds demand significant liquidity, little empirical research

has been conducted to directly examine the impact of this increased passive management on

the efficiency of stock prices.2

Some useful insights can be drawn from a comparison of passive funds to the “noise

traders” of microstructure models (e.g., Kyle, 1985)). Similar to noise traders, passive funds

reflect the decisions of (mainly) individual investors, who do not usually possess superior

information about the stocks in which they invest. The seminal paper by Grossman and

Stiglitz (1980) describes an equilibrium that requires trading by informed (active) investors

for the efficient transmission of costly information to stock prices. Their model would predict

that an “excess” fraction of uninformed (passive) traders would result in inefficient stock

markets, with mispricings significant enough to attract further active traders. On the other

hand, Admati and Pfleiderer (1988) and Milgrom and Stokey (1982) show how an excess

of active traders could result in a market breakdown, which could lead to inefficient price

discovery.

Passive funds and noise traders are also distinct in important ways. One such difference

is in their liquidity provision roles. Noise trades take place randomly across stocks, as well

as over time. In models of market structure, such as Kyle (1985), noise traders provide

liquidity to the market by pooling with informed traders. That is, market makers can set

1See Investment Company Institute Fact Book (2009) at www.ici.org.2For example, Madhavan (2003) and Carino and Pritamani (2007) document a significant price-pressure

effect when the Russell 2000 stock index is reconstituted, presumably from the demand for liquidity by indexfunds. Further, Goetzmann and Massa (2003) find a price impact of the daily flows of three Fidelity indexfunds. Also, using institutional transactions data, Keim and Madhavan (1997) and Jones and Lipson (1999,2001) document that, at short horizons, index funds generate a larger price impact when they trade, relativeto active funds.

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prices to offset their losses to informed traders with profits from uninformed traders, thus,

market makers are willing to trade when they do not know whether their counter-party is

informed or not. Index funds, however, tend to trade in the same direction at the same

time, as well as trading in a persistent direction over time, due to the persistent flows

from investors. In addition, index funds trade to accommodate index reconstitutions in a

correlated manner. As a consequence, index funds may generate a larger and longer-lasting

price impact than typical noise traders. This gives rise to the question of whether index funds

are much of a liquidity provider to the market, and, in particular, to active funds. Perhaps

index funds sometimes increase liquidity by pooling with active funds, and, at other times

decrease liquidity through their impatient and correlated trades. In turn, as shown by Da,

Gao, and Jagannathan (2010), actively managed funds can be liquidity-absorbing impatient

traders or liquidity suppliers, depending on the relative profits of these two competing trade

motivations.

In this paper, we empirically investigate the relation between passive and active insti-

tutional trading and stock price efficiency. Following the predictions of the aforementioned

papers, we conjecture the following “causal effect” of the presence of passive vs. active in-

stitutional investors: stocks with “too many” passive investors should have a greater level

of persistent mispricing, such as momentum- (Jegadeesh and Titman, 1993) or accrual-

based (Sloan, 1996) stock anomalies (due to the influence of flows from individuals with

predictable behavioral investing patterns) that are not adequately arbitraged by active insti-

tutional traders.3 On the other hand, stocks having too many active investors may exhibit

short-term illiquidity, as active traders must wait longer for uninformed investors with which

to pool their trades.

Complicating our analysis is the presence of a “preference effect”, which is also based

on the aforementioned papers. That is, although uninformed (passive) investors add noise

to stock prices, they are, by nature, attracted to stocks whose prices are informationally

efficient. On the other hand, when stock prices become noisy due to the presence of un-

3It is not clear why the aggregate of all passive funds does not equal the market portfolio, thus, creatingcross-sectional differences in the proportion of stocks held or traded by passive funds. However, it is likelydue to either frictions in setting up or trading passive funds or to investor preferences for certain types ofstocks.

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informed traders, active investors have a strong incentive to acquire information and trade

stocks. Therefore, passive investing is associated with higher liquidity by preference, and

active funds may have a similar preference in order to strategically pool with the uninformed,

as modeled by Admati and Pfleiderer (1988).

Our study investigates the effect of active and passive fund holdings and trades using

mutual fund holdings during the period from 1993 to 2006. Our analysis compares the

liquidity and price efficiency of stocks held and traded by passive funds and active funds

using these holdings data. To facilitate our analysis, we construct measures of stock-level

passiveness, based on total shares owned, total shares traded, and net trading (buys minus

sells) of a given stock by all passive funds. We also construct similar stock-level activeness

measures, based on holdings and trades by all active funds.

Our analysis shows that active and passive funds hold and trade similar stocks. Specif-

ically, equity holdings of passive funds exhibit a strong positive cross-sectional correlation

with equity holdings of active funds, controlling for stock characteristics known to attract

institutional investors in general (e.g., stock liquidity). Further, stocks traded more heavily

by passive funds are also traded more heavily by active funds. These findings suggest that

active funds purposely trade the same stocks as passive funds. It also suggests that the

interaction between passive and active funds is important when assessing the relation of pas-

sive funds with liquidity and efficiency. Interestingly, the correlation between net purchases

(buys minus sells) of a particular stock by passive funds and by active funds, although still

significantly positive, has a much smaller magnitude. That is, while passive and active funds

tend to trade the same stocks, their trades are often in different directions. This finding

indicates that, while active funds often strategically choose to trade in the same direction in

the same stocks as passive funds to hide their private information (Admati and Pfleiderer,

1988), they also sometimes supply liquidity to passive funds, consistent with Da, Gao, and

Jagannathan (2010).

Stock price efficiency is multi-faceted, and existing studies have analyzed this issue in

multiple dimensions. To provide a relatively comprehensive perspective, we measure price

efficiency in terms of liquidity, price impact, price informativeness, and magnitude of long-

term systematic mispricing (i.e., stock “anomalies”). That is, we measure price efficiency

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at high and low frequencies. We find that, first, there is a strong positive relation between

active-fund presence and stock liquidity. The relation between passive fund presence and

stock liquidity is also positive, but not as strong as that for active funds in terms of magnitude

in a model that includes both passive and active fund holdings of a stock. Recall that the

“preference effect” predicts a positive correlation between passiveness and liquidity, while

the “causality effect” suggests a negative relation. The results therefore suggest that the

preference effect dominates, in a way consistent with the equilibrium predictions mentioned

earlier.

We also find evidence of synchronized trading and a large price impact by passive funds.

Specifically, across stocks, trades by passive funds are much more often in the same direction

than trades made by active funds due to the highly correlated flows of passive funds and

the ensuing forced trades of all stocks within an index. Further, trading by passive funds

generates significant price reversals during subsequent quarters. For example, a higher dollar

value of shares bought by passive funds during a particular quarter results in lower returns

during subsequent quarters. By contrast, trading by active funds tend to generate return

continuations during the next quarter. This result is robust to controlling for the effect

of past stock returns and lagged stock liquidity. These findings are evidence of a causality

effect–passive fund trading has a negative impact on stock liquidity, while active fund trading

aids price discovery.

To further examine the effect of passive/active funds on the informational efficiency of

stock prices, we consider two price informativeness measures from the existing literature. The

first is the R2 that results from regressing stock returns onto market returns (e.g., Morck,

Yeung, and Yu, 2001; Durnev et al., 2003; and Durnev, Morck, and Yeung, 2004). The

second is the probability of informed trading, or PIN (e.g., Easley et al. 1996; Easley, Kiefer,

and O’Hara, 1997a and 1997b; Easley, Hvidkjaer, and O’Hara, 2002). We find that, after

controlling for stock liquidity characteristics, passive funds tend to hold stocks with a lower

R2 and lower PIN. The former suggests that passive funds prefer stocks when firm-specific

information is already substantially impounded into the stock price. The latter suggests that

trading in stocks preferred by passive funds does not contain substantial private information.

Both are consistent with the theoretical equilibrium predictions, which is that passive funds

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prefer stocks with a high degree of informational efficiency.4 We also find that active funds

tend to hold and trade stocks with a lower R2 and higher PIN; the former suggests the

causal effect that active funds improve efficiency by impounding firm-specific information

into stock prices, while the latter suggests a preference effect–active funds pursue stocks

with rich private information, and their presence might be the reason for the high PIN of

some stocks.

Finally, we quantify the informational role of passive funds by examining their impact on

the cross-sectional return predictive power of a large set of stock characteristics that have

been shown to predict returns by past research. These predictors are combined into eight

variables, including value, investment and financing activities, earnings quality, intangible

investments, price and earnings momentum, information uncertainty, profitability, and liq-

uidity. We find that the presence of active funds in stocks tends to reduce the predictive

power of these variables. By contrast, the presence of passive funds tends to increase their

predictive power. To the extent that such stock return predictability reflects market mis-

pricing rather than a risk-return trade-off, this can be interpreted as evidence that active

funds enhance, while passive funds reduce, the informational efficiency of stock prices. This

finding is consistent with the equilibrium predictions of Grossman and Stiglitz (1980).

Our study shows how the coexistence of active and passive management in stocks affects

price discovery. Active funds prefer to either trade together with passive funds–to hide the

intentions of their trades from market-makers–or to trade against active funds to supply

liquidity. In either case, active funds are mostly drawn to the same stocks as passive funds.

In turn, active funds increase the price efficiency of stocks by arbitraging mispricings. When

passive funds dominate the holdings or trades of a given stock, relative to active funds, price

discovery in that stock is hindered. Thus, a balance of active and passive funds is necessary

for the price discovery process.

Our paper is related to Boehmer and Kelley (2007), who find that stocks with greater

institutional ownership are priced more efficiently in the sense that their high-frequency

transaction prices more closely follow a random walk. In addition, Shu (2007) finds that

low-frequency pricing anomalies, such as price momentum, post-earnings announcement drift,

4There is a different interpretation on the PIN results. Some would argue that a low PIN indicates priceinefficiency; see, Chen, Goldstein, and Jiang, 2007).

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and the value premium, are mitigated in stocks with a higher fraction of institutional traders.

Our paper is the first to show the separate and joint effects of active and passive funds on

stock price efficiency.

The rest of the paper is organized as follows. Section II describes mutual fund sample

and empirical methodology for identifying passive funds. Section III examines the effect

of passive investing on stock liquidity and price impact. Section IV analyzes the effect of

passive investing on price informativeness and magnitude of systematic mispricing. Section

V concludes.

II. Data and Methodology

II.A. Data Sources

Data on mutual fund portfolio holdings are from Thomson Reuters. Data on fund returns and

fund characteristics such as expense ratio and turnover are from CRSP. These two datasets

are merged together via MFLINKs, which is obtained from WRDS (Wharton Research

Data Service). We additionally obtain stock pricing data from CRSP and data on financial

statements from COMPUSTAT. Analyst forecast data are from IBES.

II.B. Passive Fund Identification

Thomson Reuters and CRSP do not provide direct information on whether a mutual fund

is an index fund or active fund. The MFLINKS dataset provides an index fund indicator.

However, we find that there are still many apparent index funds not classified as such by

this indicator. Many index funds have names that contain identifiable words such as “index”

and “S&P 500”. However, some index funds do not have informative names. In addition,

there are many “closet indexers”. These are mutual funds that claim to be active funds, but

actually behave quite similarly to index funds when forming portfolios.

We take two approaches to identify passive funds. In the first approach, we take all index

funds identified via the MFLINKS index fund indicator, then add index funds manually

identified based on suggestive fund names.

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In the second approach, we attempt to include index funds with non-informative names

and “closet indexers” based on several fund characteristics. The fund characteristics we

consider include 1) annual turnover, 2) expense ratio, 3) R-square from regressing past 12-

month fund returns in excess of the riskfree rate onto the Fama-French three factors (i.e.,

MKTRF, SMB, and HML), 4) the absolute value of estimated fund alpha from the same

regression, 5) Herfindahl index of portfolio weights, and 6) Herfindahl index of portfolio

weight changes.5 In each quarter, we first estimate a Probit model, where the dependent

variable equals one for index funds identified by the first approach, onto these fund charac-

teristics. Then, we classify funds in the highest quartile in terms of the fitted probability,

together with those index funds identified by the first approach, as passive funds.6 Unlike

the permanent index fund identification in the first approach, the identity of passive funds

from the second approach may change from quarter to quarter, depending on the variations

of fund characteristics.

We select a large sample of U.S. domestic equity funds based on the Thomson data. We

start with all funds in the Thomson data whose reported investment objectives are aggressive

growth, growth, growth and income. For each fund, we calculate the average ratio of equity

value to reported total net assets across all reporting quarters. Funds with the average ratio

below 0.75 are excluded from the sample, since such funds are likely either non-equity funds

or have significant unreported holdings. The sample period is from 1993 to 2006. We start

from 1993 because the number of index funds identified via the first approach is below 20

prior to this year.

The above procedure leaves us 2,405 unique funds altogether, among which 187 are

identified as index funds via the first approach. In Table I, we provide year-by-year statistics

5The Herfindahl index for portfolio weights is a measure of portfolio concentration:

H =N∑

i=1

w2it

where w is the portfolio weight and N is the number of stocks held by the fund. The Herfindahl index forportfolio weight changes is similarly measured, by replacing wit with ∆wit, the weight change during thesix-month period ending at quarter t.

6For funds with missing characteristics, we first use cross-sectional regressions to project these character-istics onto the remaining (observed) characteristics, and then replace the missing characteristics with fittedvalues. Finally, we compute the implied probability using the parameters estimated for the Probit model.This procedure is performed during each quarter.

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on passive funds and active funds – funds in our sample that are not classified as passive

funds. In 1993, there are 35 passive funds identified via the first approach, with a median

of 427 stocks held per fund, and a median value of equity holdings of $243 million. By

2006, there are 98 passive funds; the median number of stocks held is 484 and the median

equity value held is $1,734 million. Both the number of index funds and their assets under

management have grown substantially. During the same period, the number of active funds

grows from 788 to 1179. The median number of stocks held by active funds is much lower,

at between 63 and 77. The size of active funds is also smaller, with a median equity value

of $467 million in 2006.

The number of index funds in the sample is smaller than the actual number known in

the market. According to ICI Fact Book (2007), by year-end 2006 there are 290 domestic

equity index funds. There are a few possible reasons for the lower fund number in our

sample. The first is that many index funds hold derivatives contracts (e.g., futures) instead

of holding underlying stocks. Such funds are not tracked by the Thomson data. Second,

Thomson Reuters focuses on active funds, and may have incomplete data collection for index

funds.7 Third, there are index funds with non-revealing names and thus not identified by

our first approach. Such funds are likely captured by our second approach based on fund

characteristics. The second approach will further capture “closet indexers”, whose holdings

and trades are close to index funds despite self-claimed active investment styles.

There are 144 passive fund identified via the Probit model in 1993, with a median number

of 119 stocks held per fund and median value of equity holdings of $678 million. By 2006,

the number of passive funds grows to 287, approximately the same as the number of index

funds reported by ICI. The median number of stocks held per fund is 146 and the median

equity value per fund is $1,506 million. During the same period, the number of active funds

grows from 679 to 990. The number of stocks held by active funds range between 59 and 72.

The median value of equity holdings stands at $367 million in 2006, much smaller than that

of passive funds.

Table 2 provides a description of characteristics of funds classified as passive and active,

respectively. The fund characteristics are those used in the Probit model – Rsquare, ab-

7Note that a few index funds are excluded from our sample because their average equity value to totalassets ratio is below 0.75 due to incomplete reporting of holdings.

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solute value of alpha, turnover ratio, expense ratio, concentration of portfolio holdings and

concentration of trades. The characteristics are averaged for passive funds and active funds

in each quarter, and then averaged over time. Relative to active funds, passive funds have

a higher R-square, lower absolute value of alpha, lower turnover, lower expense ratio, and a

lower concentration of fund holdings and fund trades. This holds whether passive funds are

classified by the index fund indicator or by the Probit model.

II.C. Stock Level Measures of Passiveness and Activeness

We use several measures to quantify how heavily a stock is held or traded by passive funds

and active funds. ACTIVEHOLD is the total number of shares of a stock held by all active

funds at the end of a calendar quarter, divided by total shares outstand at quarter-end.

PASSIVEHOLD is similarly calculated, for shares held by passive funds. ACTIVETRADE is

the total number of shares bought plus total number of shares sold by all active funds during

the six months ending at the current quarter-end, divided by total shares outstanding at

the current quarter-end. PASSIVETRADE is similarly calculated, for the combined number

of shares bought and sold by all passive funds. Finally, ACTIVEBUY is the net shares

purchased – number of shares bought minus the number of shares sold by all active funds

– during the past six months, divided by total shares outstanding at current quarter-end,

while PASSIVEBUY is similarly calculated for the net purchases by all passive funds.

In this study, fund trades are computed over past six months instead of quarterly, for the

reason that many funds report holdings semi-annually.8 Further, a fund that reports semi-

annually may have holdings reported for the previous quarter but does not report holdings

for the current quarter. In this case, we include its holdings and trades at the end of the

previous quarter when calculating the above statistics for the current quarter.

Funds report holdings for their fiscal quarter-ends, which may not coincide with the

calendar quarter-ends. We assume that the shares in fund holdings reported for their fiscal

quarter-ends are valid for the immediate coming calendar quarter-ends, after adjusting for

8The SEC-mandated frequency for mutual fund portfolio disclosure is quarterly before 1984, semiannuallyafterwards, and switched back to quarterly after May 2004. Many funds voluntarily report holdings quarterlyduring the period when the mandatory disclosure frequency was semiannual. However, during mid to late1990s the proportion of funds reporting semiannually is quite high.

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stock splits using the CRSP share adjustment factor. The number of shares traded are also

split-adjusted to reflect the share basis of the current calendar quarter-end.

The stock sample analyzed in this study includes all stocks held by at least one fund in

our sample, in a given quarter. For convenience we refer to this stock sample as “stocks

held by funds.” Within this sample, if there is no holding or trading by any group of funds

(active or passive) during a quarter, we set the resulting stock-level passiveness or activeness

measures to zero. Table 3 reports cross-sectional distribution of these stock level passiveness

and activeness measures. The distribution statistics include the 5th and 95th percentile, 1st

and 3rd quartile, median, and standard deviation. These statistics are first calculated in

each quarter, then averaged over time.

One clear pattern is that active funds hold and trade more shares than do passive funds.

For example, when funds are classified using the index fund indicator, for a median stock,

active funds collectively hold 6.64% of shares outstanding, while the holding by passive funds

is only 0.91%. The fraction of shares traded by active funds is 2.89%, while that by passive

funds is 0.13%. When the Probit model is used to identify passive funds, the difference

narrows, but remains quite large. For a median stock, active funds hold 3.94% of total

shares outstanding while passive funds hold 2.70%. The fraction of shares traded by active

funds is 1.85% while that by passive funds is 0.76%. The net purchases by active and passive

funds for the median stock are both slightly positive, reflecting the growth of the mutual

fund industry.

Another pattern to note is the cross-sectional standard deviation of these measures. For

passive funds identified by index fund indicator, the standard deviations of holding- and

trading-based activeness measures, ACTIVEHOLD and ACTIVETRADE, are 24.34% and

22.06%, while those for PASSIVEHOLD and PASSIVETRADE are only 1.68% and 1.10%.

Under the Probit model, the standard deviations of ACTIVEHOLD and ACTIVETRADE

are 19.46% and 17.69%, while those for PASSIVEHOLD and PASSIVETRADE are 8.44%

and 7.02%. This suggests a higher degree of homogeneity among passive funds in terms of

their holdings and trades, than among active funds.

An issue arises when interpreting the results from analysis based on these activeness

and passiveness measures. Due to possible incomplete identification of passive funds and

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incomplete reporting by some passive funds, the passiveness measures likely understate the

fraction of shares held and traded by all passive funds in the stock market. Across stocks,

the passiveness measures constructed using sample passive funds are likely to have a strong

positive correlation with the “true” passiveness measures had we observe and correctly iden-

tify all passive funds.9 Given potential incomplete reporting by active funds, the activeness

measures may also be understated. The complication this causes can be illustrated via the

following example. Suppose one wishes to measure funds’ price impact, by regressing fu-

ture stock returns onto net-purchase-based passiveness and activeness measures. Further,

suppose that for every identified passive fund, there is another identical passive fund not ob-

served in the data. As a result, the real coefficient for the passiveness measure after taking

into account the unreported funds, should be only half of the estimated coefficient in the

regression based on reported data. Therefore, while one can read sensibly from the signs

of the coefficients, one may not be able to infer much by comparing the magnitude of the

coefficients.10

Panel A of Table 4 reports the cross-sectional correlations between pairs of activeness

and passiveness measures. We compute both Pearson correlations and Spearman rank cor-

relations each quarter, and then average them over the sample period. In general, the cor-

relations are significantly positive – between ACTIVEHOLD and PASSIVEHOLD, between

ACTIVETRADE and PASSIVETRADE, and between ACTIVEBUY and PASSIVEBUY.

Some of the correlations are rather high. For example, the Spearman rank correlation be-

tween ACTIVEHOLD and PASSIVEHOLD is 0.46 under the Probit model for passive fund

identification. This suggests that it is important to control for the effect of active funds

when analyzing the effect of passive funds. On the other hand, the correlation between

ACTIVEBUY and PASSIVEBUY, although statistically significant, becomes much lower in

magnitude, compared to the other two pairs. This suggests that while passive and active

funds cluster on the stock they trade, they don’t agree very highly on the direction of their

trades.

9The idea of identifying all truly passive funds may be actually unrealistic, given the existence of “closetindexers” with a continuum of degree of passiveness and activeness.

10In empirical analysis we mainly rely on a transformation of these measures, i.e., their cross-sectional

ranks. This makes their regression coefficients somewhat more comparable.

11

There could be several reasons for the high correlations in the holding and trading between

passive and active funds. One apparent reason is that active funds, for the purpose of

reducing tracking errors, would hold stocks that are members of the passive benchmarks. As

a result, part of active fund portfolio holdings and their trades resemble passive funds. This

particular cause of the correlation is perhaps not a concern when we examine the impact of

active and passive funds separately on stock price efficiency. Another possible explanation

is the strategic liquidity choice by active funds, in a way similar to how informed investors

cluster their trades with large noise trades in the intraday trading pattern (Admati and

Pfleiderer, 1988). A third explanation is the efficiency preference by passive funds – stocks

with strong active fund presence may be more efficiently priced, and thus attracting passive

funds. All these reasons suggest that the interaction between passive and active funds may

be important when examining the effects of these funds on market efficiency.

To see if liquidity fully drives the correlation between the two groups of funds, we perform

a cross-sectional regression with activeness measures as dependent variables. The explana-

tory variables include the corresponding passiveness measures, and two measures of liquidity

as control variables: log market cap, and cross-sectional rank of stock trading turnover.11

The time series averages of the regression coefficients are reported in Panel B of Table 4.

For the passiveness and activeness measures, we use both their raw measures, and their

cross-sectional percentile ranks in regressions. The result suggests that the relation between

activeness and passiveness measures remain significantly positive even after controlling for

liquidity. Although not tabulated, we also include a few other liquidity measures employed

subsequently in this study and obtain similar results here. Therefore, liquidity is not the

only reason for the clustering of holdings and trades of passive and active funds.

III. Empirical Analysis: Liquidity and Price Impact

The textbook definition of market efficiency is that that security prices fully reflect all avail-

able information (Fama 1970). To make this definition operational for empirical analysis,

researchers have used various measures to quantify efficiency. In this paper, we provide a

11Turnover is ranked separately within NYSE/AMEX and within NASDAQ, to take into account thedifferent trading volume reporting practices by exchanges.

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relative comprehensive analysis on price efficiency from in the following four perspectives:

liquidity, price impact, price informativeness, and magnitude of systematic mispricing. They

capture the multi-facet nature of stock price efficiency.

III.A. The Effect on Stock Liquidity

Stock liquidity can be viewed as a measure of efficiency, in the sense that the price of a more

liquid stock is less swayed by temporarily demand-supply imbalance, thus reflecting more

information about its fundamentals. We employ the following five measures of liquidity:

1) ILLIQ, the cross-sectional percentile rank of Amihud (2002) illiquidity ratio, 2) LDV,

the latent dependent variable estimate of transaction cost, following Lesmond, Ogden, and

Trzcinka (1999), 3) SIZE, the log of market capitalization at the end of a quarter, 4) TURN,

turnover ratio, measured by the monthly trading volume divided by total shares outstanding,

averaged over a quarter, and 5) VR, the variance ratio between 5-day return and 1-day return.

We provide a detailed description on the construction of ILLIQ, LDV, and VR in Appendix

A.

To gauge the relation between active/passive fund presence and stock liquidity, we per-

form the following Fama-MacBeth regressions:

LIQi,t+1 = b0 + b1ACTIVEHOLDi,t + b2PASSIVEHOLDi,t + eit+1 (1)

LIQi,t+1 = b0 + b1ACTIVETRADEi,t + b2PASSIVETRADEi,t + eit+1 (2)

where LIQi,t+1 is one of the five liquidity variables, measured in quarter t+1 (using quarter-

t liquidity measures yields similar results). The explanatory variables, while denoted as

ACTIVEHOLD, PASSIVEHOLD, ACTIVETRADE, and PASSIVETRADE, are actually

transformed version of these variables. Two forms of transformations are considered. In the

first, we use the cross-sectional percentile rank of the variables as regressors. In the second,

we divide the original variables by their cross-sectional standard deviations, before using

them as regressors. The cross-sectional regressions are performed during each quarter, and

we obtain their time series averages and corresponding t-statistics.

The results are shown in Table 5. Across the different liquidity measures, across the

holding-based and trade-based passiveness/activeness measures, and across the two different

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approaches for defining passive funds, the coefficients obtained from regressions are generally

consistent with the following interpretation: both higher activeness and passiveness measures

are associated with higher stock liquidity. For example, when ILLIQ is the liquidity measure

(higher ILLIQ means lower liquidity), the coefficients for ACTIVEHOLD, PASSIVEHOLD,

ACTIVETRADE, PASSIVETRADE are all significantly negative.12

As discussed earlier, due to incomplete reporting, it is difficult to compare the magnitude

of the coefficients when the original passiveness and activeness measures are used as regres-

sors. After the transformations, the magnitude of the coefficients are no longer dependent

on the average magnitude of the passiveness and activeness measures. To some extent this

makes it feasible to compare the magnitude of the coefficients. That is, the difference in

the coefficients between the passiveness and activeness measures can be interpreted as the

differential effect on liquidity caused by per unit of ranking change or per standard deviation

change in the measures. For example, the coefficient for PASSIVEHOLD is always higher

(less negative) than that for ACTIVEHOLD, in both panels and under both approaches

for identifying passive funds. This suggests that passive funds’ holding and trading has a

weaker association with stock liquidity, relative to that of active funds, on the basis of per

unit ranking change and per unit standard deviation change of the measures.

Recall that the coefficients are the net of two effects: a preference effect and a causal

effect. The preference effect of active funds suggests a positive relation between active fund

holding/trading and liquidity, while the causal effect suggests a negative relation as informed

trading by active funds demands liquidity. The positive empirical relation indicates that the

preference effect dominates. Passive funds also prefer holding and trading on liquid stocks.

Further, the causal effect of noise trading is to provide liquidity to the market. These two

effects combined seem to suggest a stronger positive relation between passive fund presence

and stock liquidity than that for active funds. However, the empirical result is to the

opposite. One possible explanation is that despite a positive preference effect, passive funds

has actually a negative causal effect on liquidity – their passive holding reduces liquidity and

their trading demands liquidity.

The reason for this negative causal effect is that, as discussed earlier, passive funds are

12The only exception is in Panel A, the coefficient for PASSIVEHOLD when the liquidity measure isTURN. It is negative, but statistically insignificant.

14

different than noise traders in the conventional sense. First, since passive funds hold stocks

with low frequency of trading, a high proportion of stocks held by passive funds means

that shares available for trading in the market is low. Further, trades by noise traders are

uncoordinated and often offset each other, but passive funds often trade in the same direction

and at around the same time, because they tend to have similar response to index change,

have similar re-balancing needs, and experience similar investment flows driven by investors’

market expectations and sentiments. This means that index funds may not be much of a

liquidity provider to the market, but rather demand liquidity when they trade.

In the following, we further analyze this issue, by looking at the synchronicity of trades

and price impact of trades.

III.B. Trading Synchronicity and Price Impact

We construct two measures of trading synchronicity among passive funds and among active

funds. The first, a dollar-based measure, is the dollar value of net purchases (purchase -

sale) on a stock during the six months prior to the current quarter end, divided by the total

value of trades (purchase + sale), by all passive funds and by all active funds respectively.

The second, a trades-based measure, is the net number of funds purchasing a stock (number

of purchasing funds - number of selling funds) divided by the total number of funds trading

the stock. We calculate these two measures for each stock in each quarter, among passive

funds and among active funds separately. We then calculate their averages across stocks in

each quarter, and finally take the time series means. For both dollar-based and trades-based

measures, a higher value indicates a higher degree that funds trade on the same direction.13

The results are reported in Table 6. When passive funds are identified by the index

fund indicator, the dollar-based synchronicity measure for passive funds is 0.81, and the

trades-based synchronicity measure is 0.58. By comparison, both synchronicity measures for

active funds are significantly lower, at 0.61 and 0.42, respectively. When passive funds are

identified by the probit model, the dollar-based measure and trades-based measure are 0.71

and 0.50 for passive funds, respectively, significantly higher than those for active funds, at

13Despite differences in the scaling factor, the dollar-based synchronicity measure is similar to the herdingmeasure of Sias (2004) and the trades-based synchronicity measure is similar to the herding measure ofLakonishok, Shleifer, and Vishny (1992).

15

0.63 and 0.43. These results is consistent with the notion that passive funds trade in a much

more concerted way than active funds.

Concerted trading by passive funds has a potentially large impact on stock prices. We

investigate this using the following Fama-MacBeth regressions:

Ri,t+k = b0 + b1ACTIVEBUYi,t + b2PASSIVEBUYi,t

+b3Ri,t + b4SIZEi,t + b5TURNi,t + eit+1 (3)

where Ri,t+k is stock return during quarter t+k. We look at the four quarters of returns

after the current quarter, i.e., k=1, ...,4. Again, we transform the explanatory variables by

using their cross-sectional percentile ranks and cross-sectionally standardized values. Control

variables include stock return in current quarter(Ri,t), log market capitalization at current

quarter-end (SIZE), and cross-sectional percentile rank of trading turnover during the current

quarter (TURN), with NASDAQ stocks ranked separately from NYSE-AMEX stocks. The

regression is performed quarterly and the time series averages of the estimated coefficients

are reported in Table 7.

The coefficient of ACTIVEBUY is initially positive for the stock return during quarter

t+1, and turns negative for returns in the next three quarters, suggesting an initial continu-

ation and subsequent reversal. The initial continuation could be due to information as well

as delayed herding behavior by some investors. The subsequent reversal is consistent with

the recent evidence on the impact of herding, e.g., Brown, Wei, and Wermers (2009). By

contrast, the coefficient for PASSIVEBUY is significantly negative for quarter t+1. That is,

stocks heavily purchased by passive funds experience strong and immediate return reversals.

Note that the coefficient remains mostly negative and in many cases significantly negative

for quarter t+2 to t+4.

A few studies, such as Keim and Madhavan (1997) and Jones and Lipson (1999, 2001),

have found that index funds generate large price impact at relatively short horizons. What

is striking about our finding is that the price impact of passive funds is quite long-lasting –

the negative impact on stock return is significant for several quarters. This indicates that

excess trading by passive funds reduces price efficiency.

16

IV. Empirical Analysis: Price Informativeness and Re-

turn Predictability

We now turn to two other aspects of price efficiency. The first is based on measures of

price informativeness that we adopt from the existing literature. In the second set of analy-

sis, we examine whether passive fund presence has an impact on cross-sectional stock return

predictability. Many forms of stock return predictability are considered anomalies or system-

atic patterns of mispricing, and stronger predictability is indicative of lower price efficiency.

Therefore, by examining the effect of passive/active funds on these anomalies, we can infer

the relation between passive/active fund presence and stock price efficiency.

IV.A. Analysis based on Price Informativeness Measures

We consider two measures of price informativeness. The first is R2, the R-square obtained

from regressing weekly stock returns onto weekly market returns. Morck, Yeung, and Yu

(200l) argue that a low R2 means that a large dose of firm-specific information is impounded

into stock prices, hence an indication of price efficiency.The second is PIN, or probability of

informed trading. The PIN is estimated from a model of informed trading (Easley, Hvidkjaer,

and O’Hara, 2002), and a higher PIN implies that a stronger proportion of trades arrived are

informed trades. Both measures have been used in the existing studies (e.g., Chen, Goldstein,

and Jiang, 2007). It is interesting to point out a nuance between the two measures. R2 reflect

the degree to which firm-specific information is impounded into stock prices; on the other

hand, PIN measures the intensity of informed trading, and such information may or may

not be immediately impounded into stock prices.

We estimate R2 in each quarter, using data starting from 12 months before the current

quarter-end and ending 12 months after the current quarter-end. The PIN data are directly

obtained from Soren Hvidkjaer’s website. Because Hvidkjaer’s data are for the period from

1983 to 2001, correspondingly our analysis involving PIN is for the period from 1993 to 2001.

The PIN is an annual measure – i.e., one observation per year for each stock. We therefore

assign the same annual PIN value to the four quarters with the year.

To examine the link between passive fund presence and price informativeness, we per-

17

form Fama-MacBeth regressions by regressing R2 or PIN onto activeness and passiveness

measures, as well as a set of control variables that are related to stock liquidity. In Table

8, we report the results when we use market capitalization and stock turnover as control

variables.14

When we use R2 as the price information measure, the coefficients for ACTIVEHOLD

and ACTIVETRADE are always significantly negative. The coefficients for PASSIVEHOLD

and PASSIVETRADE are also significantly negative. This suggests that holding and trading

by both active funds and passive funds are positively related to information efficiency. It is

not clear the association for passive funds is stronger or weaker than that for active funds,

as the difference between the coefficients take both positive and negative signs, depending

on model specifications.

When PIN is used as dependent variable, the coefficients for ACTIVEHOLD and AC-

TIVETRADE are always significantly positive, suggesting that stocks pursued by active

funds are more likely to have informed trading (perhaps by active funds themselves). The

coefficients for PASSIVEHOLD and PASSIVETRADE, on the other hand, are almost always

significantly negative, suggesting that there are less informed trades on these stocks.

How do we interpret the PIN-based results in terms of price informativeness? Some

would argue that a higher PIN means a higher degree of private information incorporated

into the stock price, hence higher price efficiency. Others would say that informed trades

are attracted by less informed stock price, hence a negative relation between PIN and price

informativeness. Their difference appears to be the difference between the preference effect

and the causal effect.

IV.B. Analysis based on Stock Return Predictability

The literature has documented many market anomalies, or cross-sectional stock return pre-

dictability by firm specific characteristics. To the extent that these anomalies reflect mis-

pricing with respect to publicly available information, stronger anomalies means lower price

efficiency. Therefore, by examining whether passive/active fund presence alleviates or exac-

14We have also performed the analysis using other liquidity measures as control variables, such as theAmhihud illiquidity ratio, variance ratio, LDV trading cost. We obtained similar results with these alternatespecifications.

18

erbates anomalies, we can infer the role of these funds in price efficiency.

We consider an extensive set of market anomalies – 25 in total. In Appendix B, we

provide detailed descriptions of each firm-specific variable associated with the anomalies.

While large in number, many variables are related to each other. Based on their nature, we

further group them into eight categories: 1) value (VALUE), 2) investment and financing

activities (INVFIN), 3) earnings quality (EQAL), 4) intangible investments (INTANG), 5)

momentum (MOM), 6) information uncertainty (UNCERT), 7) profitability (PROF), and

8) liquidity (LIQ). We combine variables in each group by a simple average of their cross-

sectional percentile ranks, into 8 summary variables. The variables are signed so that they

should be positively related to stock returns, according to existing literature. These eight

variables are the focus of our analysis. The details for constructing these measures are also

explained in Appendix B.

In Table 9, we report the univariate Fama-MacBeth regression of stock returns during

the next four quarters (Q1 to Q4) onto each of the eight predictive variables. Most variables

exhibit predictive power on stock returns as indicated by the existing literature.

It is also noted that the predictive power of each variable varies across the four quarterly

holding periods (Q1 to Q4). To obtain a summary measure of the return predictive power

across all four quarters, we take an approach that is similar to the “overlapping portfolio”

approach of Jegadeesh and Titman (1993) for the analysis of momentum portfolios. In the

context of Fama-MacBeth regressions, the specific procedure is as follows. First, in each

quarter, we perform the following four cross-sectional regressions:

RETi,t = a + bkXi,t−k + ei,t,k (4)

for k=1, 2, 3, and 4. Xi,t−k is the predictive variable in quarter t-k. That is, we predict stock

returns during quarter t by the k-quarter-lagged predictive variable X. Second, we take the

average of the coefficients bk (k=1, ..., 4), and compute its time series mean. The result is

reported in the last column of Table 9, referred to as the “JT-Average”.

Most “JT-Average” coefficients are significant except two, for UNCERT and LIQ. The

main reason for the insignificant result is the relative short sample period (1993-2006).

Nonetheless, we include them as return predictor, as we are interested in whether pas-

sive/active fund presence makes a difference in the predictive power of these variables.

19

To see the impact of passive and active fund presence on the return predictive power of

these variables, we perform the following Fama-MacBeth regressions:

RETi,t = b0,k + b1,kXi,t−k + b2,kXi,t−k ∗ ACTIVEi,t−k + b3,kXi,t−k ∗ PASSIVEi,t−k

+b4,kXi,t−k ∗ SIZEi,t−k + b5,kXi,t−k ∗ TURNi,t−k + ei,t,k (5)

where k=1, ..., 4. Xi,t is one of the eight firm-specific predictive variables. ACTIVEi,t is the

one of the activeness measures and PASSIVEi,t is the corresponding passiveness measure.

SIZE and TURN are log market cap and stock turnover ranks, respectively. When LIQ is

the dependent variable, we do not include SIZE or TURN as explanatory variable.

For the purpose of reporting brevity, we only compute the JT-average of the coefficients,

that is, the average of bj,k (k=1, ...., 4). Further, the results on the “JT-Average” coefficients

are by and large similar when the passiveness and activeness measures are based on holdings

(e.g., PASSIVEHOLD) or based on trades (e.g., PASSIVETRADE). To save space we only

tabulate the results for the holding-bases passiveness and activeness measures and when they

are rank-transformed.

The results are in Table 10. The patterns are as follows. First, the coefficients for the

predictive variables are significant except for two (INTANG and LIQ). Second, a majority

coefficients for the product term X*ACTIVE are negative, and a few are significantly neg-

ative. Third, most coefficients for X*PASSIVE are positive, with quite a few significantly

positive. It is also worth-noting that although UNCERT and LIQ per se are not significant in

predicting returns in univariate regressions (Table 9), their interaction terms with activeness

and passiveness are significant predictors.

Therefore, once we control for stock liquidity and for the respective effect on each other

by active vs. passive funds, there is some evidence that the presence of active funds reduces

stock return predictability, and even stronger evidence that the presence of passive funds

exacerbates stock return predictability. This is consistent with the causal effect for both the

passive funds and active funds.

20

V. Conclusions

This paper investigates the interaction in ownership and trading activity on individual stocks

between active and passive mutual fund, and analyzes the resulting impact on the efficiency

of stock prices. Our study finds that active funds are drawn to the same stocks as passive

funds, and that active funds increase the price efficiency of stocks through their trades. We

also find that stocks with high levels of passive fund ownership and trading exhibit more

long-term pricing anomalies as well as a larger price reversal following trades. Our study is

the first to analyze the separate as well as joint roles of active and passive fund ownership

and trades of U.S. stocks. Our results suggest that further research should account for the

mix of these two institutional types in studying the price discovery process as well as the

tendency of stocks to exhibit pricing anomalies.

21

APPENDIX A: Stock Liquidity Measures

We investigate five measures of stock liquidity. Among the five, SIZE and TURN are readilyexplained in the main text. Below we provide details of the remaining three: ILLIQ, LDV, andVR.

A.1 ILLIQ

The Amihud illiquidity ratio (IR) is based on Amihud (2002) and is further used in Acharya and

Pedersen (2005). IR is computed as

IRit =dt∑k=1

|rik|dvolik

,

where rik is the return on stock i during day k of quarter t; dvolik is the dollar volume traded instock i during that day, and dt is the number of trading days in quarter t. We require stock i tobe traded during at least 44 days during quarter t to compute Ait. Note that a more illiquid stockwill have a larger (absolutely value of) return for the same level of dollar volume traded, since theprice impact will be larger.

Since the structure of the Nasdaq market is different from that of the NYSE and AMEX, werank stocks, at the end of each quarter, on their IR measure relative to all same-market stocks. Thatis, Nasdaq-listed stocks are ranked against all other Nasdaq stocks, and NYSE/AMEX stocks areranked against all other NYSE/AMEX stocks. Then, we express the ranking, ILLIQ, in percentileterms, so that the most illiquid stock receives a ranking of 100 and the most liquid receives aranking of 1.

A.2 LDV

Lesmond, Ogden, and Trzcinka (1999) develop a model that exploits the idea that less-liquid stocks

are more likely to have zero return days. Specifically, using a single-index market model for the

true day t return on stock j, Rjt, the measured stock return is nonzero only if the true return R∗jt

exceeds the trading cost (in absolute value). That is,

R∗jt = βjRmt + εjt

Rjt = R∗jt − α1j if R∗

jt < α1j

Rjt = 0 if α1j < R∗jt < α2j

Rjt = R∗jt − α2j if R∗

jt > α2j

,

where α1j and α2j are the trade costs of selling and buying a stock, respectively. Note that largertrade costs, α1j and α2j , result in a larger set of true return values over which measured returns arezero. Then, α1j and α2j are estimated using maximum liklihood estimation methods that assumethat εjt is normally distributed. The LDV measure of trading costs (or illiquidity) for stock j isthen computed as (α2j − α1j) /2. Since this model assumes a latent dependent variable (R∗

jt), werefer to trading costs estimated using this model as LDV estimates of trading costs.

22

A.3 VR

Another measure we use is the variance ratios as applied in early market efficiency research. Ifprices are a random walk, then this implies that the ratio of long-term to short-term variancesshould be one. If prices are strongly mean-reverting, then long-term variance should be much lowerthan short-term variance.

The m- to n-day (m > n) variance ratio is defined as

V Rmn =σ2

mmσ2

nn

,

where σ2m and σ2

n are the volatility of daily log returns over m- and n-days, respectively. Stockprices following a random walk have an expected variance ratio of one over all values of m and n;stock prices that are mean-reverting have an expected variance ratio between zero and one. Higherlevels of mean reversion in stock returns (less efficient stock prices) give lower expected values ofV Rmn.

23

APPENDIX B: Return Predictive Variables

We construct the following 24 stock characteristic variables based on data from CRSP, COMPU-STAT, and IBES. The variables are measured at the end of each quarter t. When COMPUSTATdata is involved, a variable of quarter t means a variable for the fiscal quarter reported in calendarquarter t. The reporting date is from the COMPSTAT quarterly file. If the COMPUSTAT re-porting date is missing, we assume a two month time lag between fiscal quarter end and reportingdate.1. Value (VALUE)

1) Book-to-Market ratio (BM): book value of equity of quarter t divided by the market capi-talization of common shares at end of quarter t

2) Earnings to price ratio (E/P): net income of quarter t divided by market capitalization ofcommon shares at the end of quarter t.

3) Long term growth forecast (LTG): analyst consensus forecast for long term growth rateduring last month of quarter t.

4) Sales growth (SG): Sales revenue of quarter t divided by sales revenue of quarter t-3.2. Investment and Financing Activities (INVFIN)

5) Capital expenditure (CAPEX): capital expenditure during quarter t-3 to quarter t, dividedby the total assets of quarter t.

6) Asset growth (AG): total assets of quarter t divided by total assets of quarter t-3.7) Net share issues (NS): total shares outstanding at the end of quarter t divided by total shares

outstanding 4 quarters ago, adjusting for stock splits.3. Earnings Quality (EQAL)

8) Accruals (ACC): balance-sheet measure of accruals from quarter t-3 to quarter t, dividedby the average total assets of quarter t-3 and quarter t. The balance-sheet measure of accrualsis change in current assets, minus change in cash and short-term investments, minus change incurrent liabilities, plus change in debt in current liabilities, plus change in deferred taxes, minusdepreciation.

9) Net operating assets (NOA): operating assets of quarter t minus operating liabilities ofquarter t, divided by total assets of quarter t. Operating assets is total assets minus cash andshort-term investments. Operating liabilities is total assets minus debt in current liabilities, longterm debt, minority interests, preferred shares, and common equity.4. Intangible Investments (INTANG)

10) R&D expenditure (RD): R$D expenditure of most recently reported fiscal year, divided bymarket cap at the end of the reported fiscal year. Annual data is used because R&D data reportedin COMPUSTAT quarterly file tends to be sporadic.

11) Selling, general, and administrative expenditure (SGA): SGA expenditure of quarter t,divided by market cap at the end of quarter t. We use SGA to proxy for advertising expenditure,which is not available in the COMPUSTAT quarterly file.5. Momentum (MOM)

12) Price momentum (PRRET): stock returns during the 12 months prior to the last month ofquarter t.

13) Analyst forecast revision (FREV): analyst consensus EPS forecast for the currently unre-ported fiscal year during last month of quarter t, in excess of the consensus EPS forecast for thesame fiscal year made three months ago, divided by stock price at the time the current quarterconsensus forecast is measured.

14) Standardized unexpected earnings (SUE): EPS change from 4-quarter ago (i.e., EPS forquarter t minus EPS for quarter t-3), divided by the standard deviation of EPS changes from4-quarter ago. The standard deviation is measured using EPS change of past 8 quarters, with aminimum of 4 quarters of observations required.

24

15) Earnings surprise (SUR): reported EPS for quarter t minus the last consensus EPS forecastprior to earnings announcement, divided by stock price when the forecasts are measured.6. Information uncertainty (UNCERT)

16) Return standard deviation (STDR): standard deviation of daily returns during quarter t.17) Idiosyncratic volatility (IVOL): standard deviation of residuals from regressing daily stock

returns during quarter t onto daily market returns and 3 lags of market returns. CRSP value-weighted index is used as proxy for the market.

18) Analyst forecast dispersion (DISP): the cross-sectional standard deviation of EPS forecastfor the currently unreported fiscal year, made during month m, divided by the stock price measuredat the time of forecast.7. Profitability (PROF)

19) Return on assets (ROA): net income of quarter t divided by the total assets at beginningof quarter t.

20) Change in return on assets (DROA): ROA of quarter t minus ROA of quarter t-3.8. Liquidity (LIQ)

21) Size (SIZE): log market capitalization at end of quarter t.22) Trading turnover (TURN): average monthly trading volume during quarter t divided by

total shares outstanding at end of quarter t.23) Dollar turnover: (DTURN): average monthly dollar trading volume (shares traded multipled

by month-end stock price) during quarter t divided by total shares outstanding at end of quartert.

24) Amihud illiquidity ratio (AMIHUD): the absolute daily return divided by the dollar amountof trading (number of shares traded multiplied by end-of-day stock price), averaged over quarter t.A minimum of 44 daily observations are required.

After constructing the 24 characteristic variables, we take the following steps to convert theminto 8 predictors.

First, we adjust the sign of each variable so that variables of similar nature are in the samedirection. For example, a high value of TURN is an indication of liquidity, while a high value ofAMIHUD is an indication of illiquidity. So is the relationship between EP and SG. To make thesevariables consistent with each other, we add a negative sign in front of the following variables:LTG, SG, CAPEX, AG, NS, ACC, NOA, STDR, IVOL, DISP, TURN, DTURN. After adjustingthe signs, all the variables are expected to be positively correlated with stock returns during thesubsequent quarter, based on evidence from existing literature.

Second, in each quarter we cross-sectionally rank all 18 signed variables into percentiles tomake them comparable. For the two variables involving trading volume – TURN, DTURN, andAMIHUD, since NYSE/AMEX and NASDAQ report trading volume differently, we rank stocksmainly traded on NYSE/AMEX separately from those traded on NASDAQ.

Third, we combine 18 variables into 8 characteristic measures by taking the average of thepercentile ranks. Specifically, VALUE is the average of percentile ranks of BM, EP, -LTG, -SG.INVFIN is the average percentile ranks of -CAPEX, -AG, and -NS. EQAL is the average percentileranks of -ACC and -NOA. INTANG is the average percentile ranks of RND and SGA. MOM is theaverage of percentile ranks of PRRET, FREV, SUE, and SUR. UNCTN is the average percentileranks of -STDR, -IVOL and -DISP. PROF is the percentile rank of ROA and DROA. Finally, LIQis the average percentile ranks of -TURN, -DTURN, and AMIHUD. The negative signs in front ofthe variables indicate that we have changed the signs of these variables in the first step.

If any of the 24 variables is missing, it is not used to compute the corresponding characteristicmeasure. We require a minimum of 12 non-missing characteristic variables for a stock to be includedin our sample. If any of the resulting 8 predictors is still missing, we replace it with the cross-sectional mean (across all valid stocks during the quarter t). However, if more than four resultingcharacteristic measures are missing the stock is excluded from the sample.

25

REFERENCES

1. Adams, John C., Mansi, Sattar A. and Nishikawa, Takeshi, 2009, Internal governance mecha-nisms and operational performance: Evidence from index mutual funds, Review of FinancialStudies, Forthcoming.

2. Admati, Anat R. and Paul Pfleiderer, 1988, A theory of intraday patterns: Volume and pricevariability, Review of Financial Studies, 1, 3-40.

3. Boehmer, Ekkehart, and Eric Kelley, 2007, Institutional investors and the informationalefficiency of prices, Review of Financial Studies, forthcoming.

4. Brown, N., K. Wei, and R. Wermers, 2009, Analyst Recommendations, Mutual Fund Herding,and Overreaction in Stock Prices, em Working Paper, University of Maryland.

5. Carino, David, and Mahesh Pritamani, 2007, Price pressure at Russell index reconstitution,www.russell.com.

6. Chen, Qi, Itay Goldstein, and Wei Jiang, 2007, Price informativeness and investment sensi-tivity to stock prices, Review of Financial Studies, vol 20, 619-650.

7. Choi, James, David Laibson, and Brigitte Madrian, 2008, Why does the law of one price fail?An experiment of index mutual funds, Yale ICF Working Paper.

8. Cici, Gjergji and Boldin, Michael D., 2009, The index fund rationality paradox, WorkingPaper, Temple University.

9. Chen, Hsiu-Lang, Narasimhan Jegadeesh and Russ Wermers, 2000, The value of active mutualfund management: An examination of the stockholdings and trades of fund managers, Journalof Financial and Quantitative Analysis 35, 343-368.

10. Durnev, A., R. Morck, B. Yeung, and P. ZarowinDoes, 2003, Greater Firm-specific ReturnVariation Mean More or Less Informed Stock Pricing? Journal of Accounting Research 41,797-836.

11. Durnev, A., R. Morck, and B. Yeung, 2004, Value Enhancing Capital Budgeting and Firm-specific Stock Return Variation, Journal of Finance 59, 65-105.

12. Easley David, Soeren Hvidkjaer and Maureen O’Hara, 2002, Is information risk a determinantof asset returns? Journal of Finance 47, 2185-2221.

13. Easley, D., N. Kiefer, andM. O’Hara, 1997a, The Information Content of the Trading Process,Journal of Empirical Finance 4, 159–186.

14. Easley, D., N. Kiefer, and M. O’Hara, 1997b, One Day in the Life of a Very Common Stock,Review of Financial Studies 10, 805–835.

15. Easley, D., N. M. Kiefer, M. O’HARA, and J. B. Paperman, 1996, Liquidity, Information,and Infrequently Traded Stocks, Journal of Finance 51, 1405-1436.

16. Elton, Edwin, Martin Gruber, and Jeffrey Busse, 2004, Are investors rational? Choicesamong index funds, Journal of Finance 59, 261-288.

26

17. Fama, E., 1970, Efficient capital markets: A review of theory and empirical work, Journal ofFinance 25, 383-417.

18. Goetzmann, W., and M. Massa, 2003, Index Funds and Stock Market Growth, Journal ofBusiness 76, 1-27.

19. Grossman, Sanford J. and Joseph E. Stiglitz, 1980, On the impossibility of informationallyefficient markets, American Economic Review 70, 393-408.

20. Hasbrouck, Joel, 1993, Assessing the quality of a security market: A new approach totransaction-cost measurement, Review of Financial Studies, 6, 191-212.

21. Hortacsu, Ali, and Chad Syverson, 2004, Product differentiation, search costs, and competi-tion in the mutual fund industry: A case study of S&P 500 index funds, Quarterly Journalof Economics 119, 403-456.

22. Jegadeesh, N. and S. Titman, 1993, Returns to buying winners and selling losers: Implicationsfor stock market efficiency, Journal of Finance 48, 65-91.

23. Lakonishok, Josef, Andrei Shleifer, and Robert Vishny, 1992, The impact of institutionaltrading on stock prices, Journal of Financial Economics 32, 23-43.

24. Jones, C., and M. Lipson, 1999, Execution Costs of Institutional Equity Orders, Journal ofFinancial Intermediation 8, 123-140.

25. Jones, C., and M. Lipson, 2001, Sixteens: Direct Evidence on Institutional Execution Costs,Journal of Financial Economics 59, 253-278.

26. Kasperczyk, Marcin, Clemens Sialm, and Lu Zheng, 2005, On the industry concentration ofactively managed equity mutual funds, Journal of Finance 60, 1983-2011.

27. Keim, D., and A. Madhaven, 1997, Transactions Costs and Investment Style: An Inter-exchange Analysis of Institutional Equity Trades, Journal of Financial Economics 46, 265-292.

28. Kyle, Albert, 1985, Continuous auctions and insider trading, Econometrica 53, 1315-1336.

29. Lesmond, David, Joseph Ogden, and Charles Trzcinka, 1999, A new estimate of transactioncosts, Review of Financial Studies, 12, 1113-1141

30. Madhavan, Ananth, 2003, The Russell reconstitution effect, Financial Analysts Journal 59,51–64.

31. Milgrom, Paul and Nancy Stokey, 1982, Information, trade and common knowledge, Journalof Economic Theory 26, 17–27.

32. Morck, Randall, Bernard Yeung, and Wayne Yu, 2000, The Information Content of StockMarkets: Why Do Emerging Markets Have Synchronous Stock Price Movements? Journalof Financial Economics 59, 215-260.

33. Shu, Tao, 2007, Trader composition, price efficiency, and the cross-section of stock returns,Working Paper, University of Georgia.

34. Sloan, R., 1996, Do stock prices fully reflect information in accruals and cash flows aboutfuture earnings? Accounting Review 71, 280-315.

27

35. Sias, R., 2004, Institutional Herding, Review of Financial Studies 17, 165-206.

28

Tab

le1.

Sum

mar

ySta

tist

ics:

Annual

Snap

shot

sof

Pas

sive

and

Act

ive

Funds

Thi

sta

ble

repo

rts

sum

mar

yst

atis

tics

ofpa

ssiv

ean

dac

tive

mut

ual

fund

sin

each

year

from

1993

to20

06.

Pas

sive

fund

sar

eid

enti

fied

byei

ther

the

inde

xfu

ndin

dica

tor

inth

eM

FL

INK

data

com

bine

dw

ith

thos

em

anua

llyid

enti

fied,

orby

apr

obit

mod

elth

atar

eba

sed

onfu

ndch

arac

teri

stic

s.In

each

year

,fo

rpa

ssiv

efu

nds

and

acti

vefu

nds

sepa

rate

ly,

we

repo

rtth

eto

tal

num

ber

offu

nds,

med

ian

num

ber

ofst

ocks

held

byfu

nds,

and

the

med

ian

valu

eof

equi

tyhe

ldby

fund

s.N

umbe

rof

stoc

ksan

dva

lue

ofeq

uity

hold

ings

are

base

don

end-

of-y

ear

obse

rva-

tion

s.If

afu

nddo

not

repo

rtho

ldin

gsfo

rth

ela

stqu

arte

rof

aye

ar,w

eus

eth

eir

late

stre

port

edho

ldin

gsof

the

year

toco

mpu

teth

ese

stat

isti

cs.

Iden

tifi

cati

on

ind

icato

rp

rob

it

Pass

ive

fun

ds

Act

ive

fun

ds

Pass

ive

fun

ds

Act

ive

fun

ds

Nu

mb

erM

edia

nM

edia

nN

um

ber

Med

ian

Med

ian

Nu

mb

erM

edia

nM

edia

nN

um

ber

Med

ian

Med

ian

Yea

rof

Sto

ckE

qu

ity

of

Sto

ckE

qu

ity

of

Sto

ckE

qu

ity

of

Sto

ckE

qu

ity

Fu

nd

sN

um

ber

Valu

e($

m)

Fu

nd

sN

um

ber

Valu

e($

m)

Fu

nd

sN

um

ber

Valu

e($

m)

Fu

nd

sN

um

ber

Valu

e($

m)

1993

35

427

243

788

63

193

144

119

678

679

59

159

1994

53

493

286

967

64

210

192

123

618

828

60

176

1995

69

488

252

1152

67

176

226

134

395

995

62

160

1996

97

487

374

1301

69

249

287

119

580

1111

64

222

1997

142

383

363

1476

69

284

371

111

644

1247

65

232

1998

148

396

334

1538

66

307

369

116

824

1317

60

257

1999

140

393

436

1492

65

348

350

113

1010

1282

61

287

2000

137

390

755

1433

68

513

379

118

1303

1191

62

415

2001

123

397

445

1317

72

388

329

121

752

1111

66

326

2002

109

363

357

1258

71

338

331

114

622

1036

67

287

2003

106

409

639

1245

77

271

325

123

676

1026

72

235

2004

96

438

1121

1212

72

401

281

128

1002

1027

66

354

2005

90

489

1572

1157

72

481

286

147

1418

961

65

386

2006

98

484

1734

1179

70

467

287

146

1506

990

64

376

29

Tab

le2.

Fund

Char

acte

rist

ics:

Pas

sive

vs.

Act

ive

Funds

Thi

sta

ble

repo

rts

char

acte

rist

ics

ofpa

ssiv

ean

dac

tive

mut

ual

fund

s.P

assi

vefu

nds

are

iden

tifie

dby

eith

erth

ein

dex

fund

indi

cato

ror

bya

prob

itm

odel

.Fu

ndch

arac

teri

stic

sin

clud

eth

eR

-squ

are

(R2)

and

the

abso

lute

valu

eof

fund

alph

a(|α|),

both

obta

ined

from

regr

essi

ngpr

e-ex

pens

efu

ndre

turn

son

toth

eFa

ma-

Fren

ch3-

fact

orm

odel

usin

gro

lling

past

12m

onth

retu

rns,

fund

annu

altu

rnov

erra

tio

(Tur

nove

r),

annu

alex

pens

era

tio

(Exp

ense

),H

erfin

dahl

inde

xof

fund

hold

ings

(HH

OL

D),

and

Her

finda

hlin

dex

offu

ndtr

adin

g(H

TR

AD

E).

We

first

calc

ulat

eth

em

ean,

med

ian,

and

stan

dard

devi

atio

nof

thes

ech

arac

teri

stic

mea

sure

sin

each

quar

ter

for

pass

ive

and

acti

vefu

nds

sepa

rate

ly,

and

then

aver

age

them

over

the

sam

ple

year

sfr

om19

93to

2006

.

Iden

tific

atio

nin

dica

tor

prob

it

Pas

sive

fund

sA

ctiv

efu

nds

Pas

sive

fund

sA

ctiv

efu

nds

mea

nm

edia

nst

dev

mea

nm

edia

nst

dev

mea

nm

edia

nst

dev

mea

nm

edia

nst

dev

R2

0.93

0.97

0.12

0.87

0.91

0.13

0.93

0.95

0.09

0.85

0.89

0.13

|α|(

%)

0.36

0.18

0.46

0.59

0.43

0.58

0.35

0.24

0.39

0.65

0.49

0.61

Tur

nove

r0.

520.

221.

120.

870.

660.

790.

470.

340.

710.

960.

760.

83

Exp

ense

(%)

0.71

0.60

0.50

1.28

1.22

0.42

0.87

0.89

0.37

1.35

1.29

0.41

HH

OL

D(%

)1.

360.

881.

182.

401.

982.

221.

471.

320.

972.

572.

132.

35

HT

RA

DE

(%)

0.03

0.01

0.09

2.00

0.05

56.2

50.

030.

020.

052.

490.

0562

.87

30

Tab

le3.

Cro

ss-s

ecti

onal

Dis

trib

uti

onof

Sto

ckL

evel

Act

iven

ess

and

Pas

sive

nes

sM

easu

res

Thi

sta

ble

repo

rts

cros

s-se

ctio

nal

dist

ribu

tion

ofst

ock

leve

lpa

ssiv

enes

san

dac

tive

ness

mea

sure

s.P

assi

vefu

nds

are

iden

tifie

dby

eith

erth

ein

dex

fund

indi

cato

ror

bya

prob

itm

odel

.PA

SSIV

EH

OL

D(A

CT

IVE

HO

LD

)is

the

tota

lnu

mbe

rof

shar

esof

ast

ock

held

byal

lpa

ssiv

e(a

ctiv

e)fu

nds

incu

rren

tqu

arte

rdi

vide

dby

shar

esou

tsta

ndin

gat

curr

ent

quar

ter-

end.

PASS

IVE

TR

AD

E(A

CT

IVE

TR

AD

E)

isth

eto

tal

purc

hase

plus

tota

lsal

eof

ast

ock

byal

lpas

sive

(act

ive)

fund

sdu

ring

the

curr

ent

and

prev

ious

quar

ter,

divi

ded

byto

tals

hare

sou

tsta

ndin

g.PA

SSIV

EN

ET

BU

Y(A

CT

IVE

NE

TB

UY

)is

the

net

purc

hase

–to

talp

urch

ase

min

usto

tals

ale

–by

allp

assi

ve(a

ctiv

e)fu

nds

ona

stoc

kdu

ring

the

curr

ent

and

prev

ious

quar

ter,

divi

ded

byth

eto

tal

shar

esou

tsta

nd.

The

cros

s-se

ctio

nal

stat

isti

csin

clud

eth

e5t

hpe

rcen

tile

,1s

tqu

arti

le,

med

ian,

3rd

quar

tile

,95

thpe

rcen

tile

,an

dst

anda

rdde

viat

ion.

We

first

calc

ulat

eth

ese

stat

isti

csfo

rea

chqu

arte

r,an

dth

enta

keth

eav

erag

eov

erth

esa

mpl

epe

riod

from

1993

to20

06.

Iden

tific

atoi

nin

dica

tor

prob

it

P5

Q1

Med

ian

Q3

P95

Mea

nSt

dP

5Q

1M

edia

nQ

3P

95M

ean

Std

AC

TIV

EH

OL

D0.

162.

156.

6412

.86

23.9

38.

9424

.34

0.02

1.12

3.94

8.31

16.9

35.

8719

.46

AC

TIV

ET

RA

DE

0.00

0.68

2.89

6.77

15.2

65.

0222

.06

0.00

0.34

1.85

4.71

11.5

83.

5817

.69

AC

TIV

EB

UY

-4.4

9-0

.63

0.11

1.53

6.59

0.80

20.9

5-3

.57

-0.4

50.

041.

065.

100.

5816

.93

PASS

IVE

HO

LD

0.00

0.30

0.91

1.65

3.05

1.13

1.62

0.00

0.89

2.70

6.04

12.6

74.

208.

44

PASS

IVE

TR

AD

E0.

000.

020.

130.

320.

950.

271.

100.

000.

140.

762.

185.

871.

707.

02

PASS

IVE

BU

Y-0

.27

-0.0

10.

030.

160.

630.

101.

05-2

.10

-0.1

00.

050.

603.

140.

356.

62

31

Table 4. Correlations between Stock-level Passiveness and Activeness Measures

This table reports relations between the stock-level measures of passiveness and activeness. Passive fundsare identified by either the index fund indicator or by a probit model. The three activeness measuresare ACTIVEHOLD, ACTIVETRADE, ACTIVENETBUY, and the corresponding passiveness measures arePASSIVEHOLD, PASSIVETRADE, and PASSIVENETBUY. Panel A report the pairwise correlations. Wefirst calculate the pairwise correlation between a passiveness measure and the corresponding activenessmeasure across all stocks in each quarter. We then report the time series averages and the correspondingt-statistics (in parenthesis) over the sample years from 1993 to 2006. In Panel B, we perform quarterlycross-sectional regressions and report the time-series averages of the estimated coefficients. The dependentvariable is one of the activeness measure and the explanatory variables include the corresponding passivenessmeasure, log market cap (SIZE), and cross-sectional rank of trading turnover (TURN). Regression interceptis not reported.

Panel A: CorrelationsIdentification indicator probit

Pearson Spearman Pearson Spearman

(PASSIVEHOLD, ACTIVEHOLD) 0.16 (4.85) 0.25 (10.95) 0.29 (11.54) 0.46 (48.51)

(PASSIVETRADE, ACTIVETRADE) 0.16 (4.94) 0.33 (10.21) 0.28 (11.52) 0.48 (37.89)

(PASSIVENETBUY, ACTIVENETBUY) 0.11 (3.31) 0.04 (1.83) 0.17 (5.56) 0.12 (18.10)

Panel B: Cross-sectional Regressions with Control for LiquidityDependent variables: activeness measures

Identification indicator probit

HOLD TRADE NETBUY HOLD TRADE NETBUY

Raw activeness and passiveness measures

PASSIVE 1.04 0.95 0.62 0.36 0.35 0.27

(4.62) (2.75) (2.08) (12.03) (8.42) (4.55)

SIZE 3.98 -4.81 -3.51 -2.07 -5.72 -3.04

(1.43) (-1.53) (-1.23) (-0.73) (-1.84) (-1.10)

TURN 1.12 1.26 0.24 0.86 0.91 0.18

(12.65) (11.84) (2.81) (10.55) (9.14) (2.22)

Rank-transformed activeness and passiveness measures

PASSIVE 0.21 0.14 0.02 0.33 0.30 0.11

(14.27) (15.59) (1.99) (47.56) (40.87) (17.12)

SIZE 4.70 3.94 0.30 2.52 2.24 0.02

(50.71) (39.04) (2.05) (21.41) (28.00) (0.12)

TURN 0.31 0.51 0.09 0.33 0.45 0.07

(44.75) (69.89) (11.36) (38.39) (57.51) (10.51)

32

Table 5. Passiveness, Activeness, and Stock Liquidity

This table reports the results of Fama-MacBeth regressions that examine the effect of activeness and pas-siveness on stock liquidity. The dependent variables include five stock liquidity measures: cross-sectionalpercentile rank of Amihud illiquidity ratio (ILLIQ), the latent dependent variable estimate of trading cost(LDV), log market capitalization (SIZE), cross-sectional percentile rank of stock trading turnover (TURN),and five-day vs. one-day variance ratio (VR). In Panel A, the explanatory variables are the cross-sectionalpercentile rank of activeness and passiveness measures PASSIVEHOLD, ACTIVEHOLD, PASSIVETRADEand ACTIVETRADE. In Panel B, the explanatory variables are the cross-sectional standardized measuresof activeness and passiveness. DIF is the difference in estimated coefficients between the passiveness measureand the corresponding activeness measure. Passive funds are identified by either the index fund indicator orby a probit model. Stock liquidity measures are for the quarter subsequent to the passiveness and activenessmeasures. The cross-sectional regressions are performed in each quarter. Reported are the time series meansand the corresponding t-statistics (in parenthesis) of the estimated coefficients. Regression intercept is notreported. Reported coefficients are multiplied by 100 when LDV is the dependent variable.

Panel A: Rank-transformed passiveness and activeness measures

Identification indicator probit

ILLIQ LDV SIZE TURN VR ILLIQ LDV SIZE TURN VR

ACTIVEHOLD -34.05 -0.32 1.84 34.83 0.19 -30.57 -0.22 1.24 35.77 0.16

(-41.14) (-9.77) (55.46) (56.55) (19.25) (-26.86) (-10.92) (15.42) (46.48) (20.17)

PASSIVEHOLD -1.90 -0.04 0.20 -0.35 0.03 -12.39 -0.21 1.18 4.75 0.06

(-1.74) (-2.66) (2.48) (-0.43) (3.33) (-10.96) (-7.77) (12.75) (6.68) (5.13)

ACTIVETRADE -40.23 -0.27 1.77 48.21 0.22 -34.29 -0.17 1.14 44.81 0.17

(-45.73) (-10.25) (35.16) (79.76) (16.83) (-32.68) (-10.49) (14.35) (69.12) (19.25)

PASSIVETRADE -14.79 -0.12 0.83 7.09 0.02 -21.62 -0.24 1.58 12.56 0.10

(-12.36) (-8.72) (9.26) (7.32) (2.64) (-23.38) (-9.83) (20.56) (16.14) (6.78)

Panel B: Cross-sectionally standardized passiveness and activeness measures

Identification indicator probit

ILLIQ LDV SIZE TURN VR ILLIQ LDV SIZE TURN VR

ACTIVEHOLD -27.36 -0.27 1.47 30.06 0.15 -25.92 -0.20 0.93 33.31 0.13

(-4.67) (-3.92) (4.57) (4.73) (4.39) (-4.19) (-3.70) (4.42) (4.14) (4.37)

PASSIVEHOLD -0.53 -0.00 0.03 1.82 0.01 -6.20 -0.09 0.60 4.06 0.02

(-0.44) (-0.35) (0.48) (1.75) (1.36) (-5.15) (-4.63) (5.18) (4.69) (3.19)

ACTIVETRADE -50.28 -0.33 2.05 65.48 0.24 -42.45 -0.20 1.07 61.37 0.19

(-4.82) (-4.03) (4.66) (4.72) (4.41) (-3.90) (-3.45) (3.73) (3.84) (3.61)

PASSIVETRADE -9.33 -0.03 0.36 11.40 0.03 -18.72 -0.16 1.32 18.79 0.11

(-2.14) (-1.26) (1.65) (2.35) (1.71) (-4.53) (-4.27) (4.10) (4.08) (2.85)

33

Table 6. Trading Synchronicity: Passive Funds vs. Active Funds

This table reports synchronicity of trades among passive funds and among active funds. Passive funds areidentified by either the index fund indicator in the MFLINK data or by a probit model. Two synchronicitymeasures are used. |net $trades|/total $trades is the absolute value of net purchase by all passive or activefunds on a stock during the current quarter and the previous quarter, divided by the total purchase plustotal sale, over the current quarter and the prior quarter. |net #trades|/total #trades is the absolute value ofnet number of funds purchasing a stock during the current quarter and the previous quarter, divided by thetotal number of funds trading on the stock, over the current quarter and the prior quarter. In each quarter,we calculate these measures on each individual stock, for passive funds and for active funds separately, andthen average them across stocks. Their time series means over the sample years from 1993 to 2006, and thecorresponding t-statistics (in parenthesis) are reported. Passive-Active is the difference of the synchronicitymeasures between the passive funds and active funds.

Identification indicator probit

Passive Active Passive-Active Passive Active Passive-Active

|net $trades|/total $trades 0.81 0.61 0.20 (29.37) 0.71 0.63 0.09 (17.09)

|net #trades|/total #trades 0.58 0.42 0.16 (9.88) 0.50 0.43 0.08 (7.48)

34

Table 7. Price Impact of Passive Funds and Active Funds

This table reports the results of Fama-MacBeth regressions that examine the price impact of active and pas-sive funds. The dependent variables are stock returns in subsequent four quarters (RETQ1 to RETQ4). Themain explanatory variables are PASSIVENETBUY and ACTIVENETBUY. These variables are transformedinto cross-sectional percentile ranks (Panel A) and cross-sectionally standardized (Panel B), before used inregressions. DIF is the coefficient for PASSIVENETBUY minus the coefficient for ACTIVENETBUY. Con-trol variables include stock return during the current quarter (RETQ0), log market capitalization (SIZE),and exchange-specific cross-sectional percentile rank of stock trading turnover (TURN). Identification ofpassive funds is based on either the index fund indicator or the probit model. The cross-sectional regressionsare performed in each quarter from 1993 to 2006. Reported are the time series means and the correspondingt-statistics (in parenthesis) of the estimated coefficients. Regression intercept is not reported.

Panel A: Rank-transformed passiveness and activeness measures

Identification indicator probit

RETQ1 RETQ2 RETQ3 RETQ4 RETQ1 RETQ2 RETQ3 RETQ4

ACTIVEBUY 0.56 -0.54 -1.23 -0.83 0.89 -0.34 -0.97 -0.64

(0.95) (-0.87) (-2.18) (-1.87) (1.54) (-0.60) (-1.85) (-1.49)

PASSIVEBUY -0.91 -0.59 -0.41 -0.38 -0.38 -0.62 -0.73 -0.60

(-2.56) (-1.82) (-1.17) (-1.16) (-0.98) (-1.65) (-2.27) (-2.01)

RETQ0 2.18 4.59 2.36 0.03 2.16 4.60 2.36 0.03

(1.52) (4.65) (1.88) (0.02) (1.51) (4.66) (1.88) (0.03)

SIZE -0.11 -0.08 -0.08 -0.05 -0.12 -0.07 -0.07 -0.04

(-0.66) (-0.46) (-0.48) (-0.28) (-0.70) (-0.43) (-0.42) (-0.22)

TURN -0.95 -1.29 -1.49 -1.41 -1.01 -1.32 -1.49 -1.42

(-0.49) (-0.67) (-0.80) (-0.81) (-0.53) (-0.68) (-0.81) (-0.82)

Panel B: Cross-sectionally standardized passiveness and activeness measures

Identification indicator probit

RETQ1 RETQ2 RETQ3 RETQ4 RETQ1 RETQ2 RETQ3 RETQ4

ACTIVENETBUY 0.23 -0.02 -0.18 -0.02 0.13 -0.04 -0.10 -0.03

(0.96) (-0.15) (-1.60) (-0.28) (0.81) (-0.28) (-1.15) (-0.31)

PASSIVENETBUY -0.37 -0.22 0.01 -0.02 -0.23 -0.18 -0.08 -0.09

(-1.66) (-2.25) (0.09) (-0.39) (-2.01) (-2.24) (-0.96) (-1.61)

RETQ0 2.17 4.54 2.27 -0.03 2.18 4.54 2.28 -0.01

(1.51) (4.63) (1.80) (-0.02) (1.51) (4.62) (1.81) (-0.01)

SIZE -0.12 -0.07 -0.08 -0.05 -0.12 -0.07 -0.08 -0.05

(-0.70) (-0.44) (-0.46) (-0.25) (-0.68) (-0.44) (-0.48) (-0.25)

TURN -1.01 -1.40 -1.64 -1.55 -1.01 -1.42 -1.65 -1.54

(-0.52) (-0.71) (-0.87) (-0.88) (-0.52) (-0.72) (-0.87) (-0.87)

35

Tab

le8.

Pas

sive

nes

s,A

ctiv

enes

s,an

dP

rice

Info

rmat

iven

ess

Thi

sta

ble

repo

rts

the

resu

lts

ofFa

ma-

Mac

Bet

hre

gres

sion

sth

atex

amin

eth

eeff

ect

ofst

ock

leve

lac

tive

ness

and

pass

iven

ess

onst

ock

pric

ein

form

ativ

enes

s.T

hede

pend

ent

vari

able

sin

clud

eR

2,th

eR

squa

reof

regr

essi

ngw

eekl

yin

divi

dual

stoc

kre

turn

son

tow

eekl

ym

arke

tre

turn

s,an

dP

IN,

the

mea

sure

ofpr

obab

ility

ofin

form

edtr

adin

gas

per

Eas

ley

etal

.(2

002)

.T

hem

ain

expl

anat

ory

vari

able

s,A

CT

IVE

and

PAS-

SIV

E,

refe

rto

stoc

kle

vel

acti

vene

ssan

dpa

ssiv

enes

sth

atar

eei

ther

hold

ing-

base

d(A

CT

IVE

HO

LD

and

PASS

IVE

HO

LD

),or

trad

ing-

base

d(A

CT

IVE

TR

AD

Ean

dPA

SSIV

ET

RA

DE

).PA

SSIV

EH

OL

D(A

CT

IVE

HO

LD

)is

the

tota

lnu

mbe

rof

shar

esof

ast

ock

held

byal

lpa

ssiv

e(a

ctiv

e)fu

nds

incu

rren

tqu

arte

rdi

vide

dsh

ares

outs

tand

ing

end

ofcu

rren

tqu

arte

r.PA

SSIV

ET

RA

DE

(AC

TIV

ET

RA

DE

)is

the

sum

ofto

tal

purc

hase

and

tota

lsa

leof

ast

ock

byal

lpa

ssiv

e(a

ctiv

e)fu

nds

duri

ngth

ecu

rren

tqu

arte

ran

dth

epr

evio

usqu

arte

rdi

vide

dby

tota

lsh

ares

outs

tand

ing.

The

seva

riab

les

are

eith

ercr

oss-

sect

iona

llyra

nk-t

rans

form

ed(P

anel

A)

orcr

oss-

sect

iona

llyst

anda

rdiz

ed(P

anel

B),

befo

reus

edin

regr

essi

ons.

Pas

sive

fund

sar

eid

enti

fied

byei

ther

the

inde

xfu

ndin

dica

tor

orby

apr

obit

mod

el.

The

two

cont

rolv

aria

bles

are

log

mar

ket

cap

(SIZ

E)

and

cros

s-se

ctio

nal

rank

oftr

adin

gtu

rnov

er(T

UR

N).

The

cros

s-se

ctio

nal

regr

essi

ons

are

perf

orm

edin

each

quar

ter

from

1993

to20

06.

Rep

orte

dar

eth

eti

me

seri

esm

eans

and

the

corr

espo

ndin

gt-

stat

isti

cs(i

npa

rent

hesi

s)of

the

esti

mat

edco

effici

ents

.R

egre

ssio

nin

terc

ept

isno

tre

port

ed.

DIF

isth

edi

ffere

nce

ines

tim

ated

coeffi

cien

tsbe

twee

nPA

SSIV

Ean

dA

CT

IVE

.R

epor

ted

coeffi

cien

tfo

rT

UR

Nis

pre-

mul

tipl

ied

by1,

000.

Pan

elA

:R

an

k-t

ran

sform

edp

ass

iven

ess

an

dact

iven

ess

mea

sure

s

Iden

tifi

cati

on

ind

icato

rp

rob

it

hold

ing-b

ase

dtr

ad

ing-b

ase

dh

old

ing-b

ase

dtr

ad

ing-b

ase

d

R2

PIN

R2

PIN

R2

PIN

R2

PIN

AC

TIV

E-0

.28

(-9.2

3)

0.0

7(3

.13)

-0.1

6(-

5.1

4)

0.0

4(2

.68)

-0.2

6(-

7.9

5)

0.0

9(4

.12)

-0.1

4(-

4.5

0)

0.0

6(3

.35)

PA

SS

IVE

-0.3

7(-

10.9

1)

-0.2

4(-

12.5

9)

-0.3

9(-

8.0

3)

-0.2

4(-

11.3

0)

-0.2

9(-

6.9

5)

-0.1

0(-

4.2

4)

-0.2

9(-

6.4

3)

-0.0

8(-

3.6

5)

SIZ

E0.0

2(1

5.8

2)

-0.0

3(-

56.5

5)

0.0

2(1

6.3

4)

-0.0

3(-

57.9

0)

0.0

2(1

6.5

8)

-0.0

2(-

64.8

7)

0.0

2(1

6.8

0)

-0.0

2(-

62.1

0)

TU

RN

0.1

0(1

0.8

9)

-0.0

5(-

24.5

2)

0.0

8(9

.95)

-0.0

5(-

18.1

0)

0.1

0(1

1.0

9)

-0.0

5(-

25.9

5)

0.0

8(9

.84)

-0.0

5(-

19.3

3)

Pan

elB

:C

ross

-sec

tion

ally

stan

dard

ized

pass

iven

ess

an

dact

iven

ess

mea

sure

s

Iden

tifica

tion

ind

icato

rp

rob

it

hold

ing-b

ase

dtr

ad

ing-b

ase

dh

old

ing-b

ase

dtr

ad

ing-b

ase

d

R2

PIN

R2

PIN

R2

PIN

R2

PIN

AC

TIV

E-3

5.2

2(-

3.6

8)

31.9

1(4

.51)

-8.2

6(-

3.8

4)

6.5

2(4

.25)

-30.2

3(-

3.1

6)

39.1

2(3

.16)

-4.3

2(-

1.6

4)

10.2

4(3

.78)

PA

SS

IVE

-29.1

6(-

2.2

9)

-6.7

8(-

3.4

2)

-17.6

6(-

3.5

7)

-7.1

7(-

2.4

7)

-11.0

5(-

2.4

0)

-1.5

0(-

0.4

1)

-10.6

3(-

3.9

8)

-1.7

9(-

1.1

9)

SIZ

E0.0

2(1

4.0

2)

-0.0

3(-

55.6

4)

0.0

2(1

5.4

3)

-0.0

3(-

54.7

5)

0.0

2(1

4.4

8)

-0.0

3(-

56.6

4)

0.0

2(1

5.7

6)

-0.0

3(-

58.8

6)

TU

RN

0.0

9(1

0.9

7)

-0.0

6(-

20.6

8)

0.0

8(9

.96)

-0.0

6(-

18.6

2)

0.0

9(1

1.1

9)

-0.0

6(-

21.3

2)

0.0

8(9

.94)

-0.0

5(-

18.8

9)

36

Table 9. Fama-MacBeth Regressions of Stock Returns onto Return Predictors

This table reports the results of Fama-MacBeth regressions of stock returns onto each return-predictive vari-able. In each regression, the dependent variable is stock return during one of the subsequent four quarters(RETQ1 to RETQ4); the explanatory variable is one of the following eight stock return predictors: value(VALUE), investment and financing activities (INVFIN), earnings quality (EQAL), intangible investments(INTANG), momentum (MOM), uncertainty (UNCERT), profitability (PROF), and liquidity (LIQ). In thecolumn “JT-Average”, we report the average coefficients of regressions with predictors lagged by one to fourquarters. The explanatory variables are signed so that their correlations with next-quarter stock returns,according to existing literature, are positive. The cross-sectional regressions are performed in each quarterfrom 1993 to 2006. Reported are the time series means and the corresponding t-statistics (in parenthesis)of the estimated coefficients. Regression intercept is not reported. Reported coefficients are multiplied by 100.

RETQ1 RETQ2 RETQ3 RETQ4 JT-Average

VALUE 4.41 (1.28) 4.72 (1.79) 4.98 (1.77) 4.19 (1.26) 4.42 (2.88)

INVFIN 3.04 (2.15) 3.61 (2.50) 3.19 (2.08) 2.45 (1.63) 2.89 (3.52)

EQAL 2.22 (2.71) 2.13 (2.37) 1.93 (2.18) 2.10 (2.53) 1.92 (3.58)

INTANG 3.09 (2.94) 3.23 (3.19) 3.70 (3.60) 3.05 (3.08) 3.18 (5.70)

MOM 6.39 (5.43) 4.90 (4.59) 0.99 (0.77) -0.21 -(0.22) 3.10 (7.06)

UNCERT 2.48 (1.87) 1.63 (0.56) 1.60 (0.67) 1.41 (0.51) 1.78 (1.38)

PROF 5.23 (3.74) 4.29 (2.95) 3.18 (2.17) 1.77 (1.29) 3.61 (5.33)

LIQ 1.42 (0.76) 1.98 (1.69) 1.94 (1.45) 1.41 (0.78) 1.56 (1.50)

37

Tab

le10

.P

assi

venes

s,A

ctiv

enes

s,an

dC

ross

-sec

tion

alSto

ckR

eturn

Pre

dic

tabilit

y

Thi

sta

ble

repo

rts

the

resu

lts

ofFa

ma-

Mac

Bet

hre

gres

sion

sth

atex

amin

eth

eeff

ect

ofpa

ssiv

ean

dac

tive

fund

son

retu

rn-p

redi

ctiv

epo

wer

offir

mch

arac

teri

stic

s.In

each

regr

essi

on,

the

depe

nden

tva

riab

leis

stoc

kre

turn

duri

ngon

eof

the

subs

eque

ntfo

urqu

arte

rs(R

ET

Q1

toR

ET

Q4)

.T

hem

ain

expl

anat

ory

vari

able

sin

clud

eX

,on

eof

the

eigh

tst

ock

retu

rnpr

edic

tors

,an

dtw

opr

oduc

tte

rms

X*A

CT

IVE

and

X*P

ASS

IVE

,w

here

,A

CT

IVE

isth

ecr

oss-

sect

iona

lra

nkof

the

acti

vene

ssm

easu

reA

CT

IVE

HO

LD

,an

dPA

SSIV

Eis

the

cros

s-se

ctio

nal

rank

ofth

eco

rres

pond

ing

pass

iven

ess

mea

sure

PASS

IVE

HO

LD

.The

retu

rn-p

redi

ctor

s(X

)ar

e,re

spec

tive

ly,v

alue

(VA

LU

E),

inve

stm

ent

and

finan

cing

acti

viti

es(I

NV

FIN

),ea

rnin

gsqu

alit

y(E

QA

L),

inta

ngib

lein

vest

men

ts(I

NT

AN

G),

mom

entu

m(M

OM

),un

cert

aint

y(U

NC

ER

T),

profi

tabi

lity

(PR

OF

),an

dliq

uidi

ty(L

IQ).

The

sepr

edic

tive

vari

able

sar

esi

gned

soth

atth

eir

corr

elat

ions

wit

hne

xt-q

uart

erst

ock

retu

rns,

acco

rdin

gto

exis

ting

liter

atur

e,ar

epo

siti

ve.

Inad

diti

on,w

ein

clud

etw

oco

ntro

lvar

iabl

esX

*SIZ

Ean

dX

*TU

RN

,whe

reSI

ZE

isth

elo

gm

arke

tca

pan

dT

UR

Nis

the

exch

ange

-spe

cific

cros

s-se

ctio

nal

perc

enti

lera

nkof

trad

ing

turn

over

.T

hecr

oss-

sect

iona

lre

gres

sion

sar

epe

rfor

med

inea

chqu

arte

rfr

om19

93to

2006

.T

heex

plan

ator

yva

riab

les

are

lagg

edby

one

tofo

urqu

arte

rsan

din

each

quar

ter

we

take

the

aver

ages

ofth

eco

effici

ents

acro

ssth

efo

urre

gres

sion

s(i

.e.,

the

“JT

-Ave

rage

”co

effici

ent)

.R

epor

ted

are

the

tim

ese

ries

mea

nsan

dth

eco

rres

pond

ing

t-st

atis

tics

(in

pare

nthe

sis)

ofth

ees

tim

ated

quar

terl

y-av

erag

eco

effici

ents

.R

egre

ssio

nin

terc

ept

isno

tre

port

ed.

Coe

ffici

ents

inth

eta

ble

are

all

mul

tipl

ied

by10

0.

Pan

elA

:R

an

k-t

ran

sform

edp

ass

iven

ess

an

dact

iven

ess

mea

sure

s

VA

LU

EIN

VF

INE

QA

LIN

TA

NG

MO

MU

NC

ER

TP

RO

FL

IQ

ind

icato

r

X6.1

3(3

.09)

5.6

2(2

.88)

6.2

3(3

.33)

2.5

7(1

.43)

9.5

0(5

.11)

5.8

7(2

.39)

8.9

1(4

.34)

0.8

0(0

.93)

X*A

CT

IVE

-1.5

6(-

0.2

8)

0.5

1(0

.74)

1.7

6(2

.64)

-0.5

3(-

0.9

4)

0.7

3(1

.05)

-0.1

3(-

0.2

9)

-0.2

4(-

0.4

4)

-0.1

4(-

0.2

1)

X*P

AS

SIV

E1.4

3(0

.43)

1.4

6(2

.74)

2.4

4(2

.93)

0.4

8(0

.92)

2.4

2(3

.25)

0.8

7(2

.40)

2.0

4(3

.08)

2.1

1(4

.22)

X*S

IZE

-0.2

3(-

1.4

8)

-0.3

0(-

1.8

3)

-0.4

2(-

2.4

4)

0.0

6(0

.40)

-0.5

3(-

2.8

8)

-0.3

7(-

3.0

6)

-0.4

1(-

2.6

8)

X*T

UR

N1.2

8(1

.22)

-0.3

8(-

0.2

4)

-1.4

6(-

0.7

2)

-0.8

2(-

0.5

1)

-1.9

6(-

1.0

5)

0.5

1(1

.09)

-1.4

3(-

0.8

2)

pro

bit

X5.8

1(2

.94)

5.2

0(2

.62)

6.0

4(3

.22)

2.1

9(1

.27)

9.1

7(4

.90)

6.1

0(2

.35)

8.2

3(3

.85)

1.3

2(1

.46)

X*A

CT

IVE

-2.0

7(-

1.7

9)

-1.4

8(-

1.2

7)

-1.0

5(-

0.8

7)

-1.4

3(-

1.6

7)

-2.7

8(-

1.9

0)

-1.9

5(-

1.6

1)

-1.8

2(-

1.8

7)

-4.2

0(-

2.5

3)

X*P

AS

SIV

E0.1

3(0

.52)

1.3

2(2

.02)

2.2

2(2

.93)

0.7

7(1

.23)

1.9

2(2

.53)

0.8

3(2

.19)

1.0

4(2

.19)

1.1

9(1

.92)

X*S

IZE

-0.2

1(-

1.3

5)

-0.2

2(-

1.3

9)

-0.3

2(-

1.9

3)

0.0

9(0

.62)

-0.4

3(-

2.4

6)

-0.3

6(-

2.9

4)

-0.3

2(-

2.1

5)

X*T

UR

N1.2

4(1

.12)

-0.1

6(-

0.1

0)

-1.1

9(-

0.6

1)

-0.8

1-(

0.5

1)

-1.7

0(-

0.9

3)

0.8

5(1

.92)

-1.3

1(-

0.7

6)

38

Pan

elB

:C

ross

-sec

tion

ally

stan

dard

ized

pass

iven

ess

an

dact

iven

ess

mea

sure

s

VA

LU

EIN

VF

INE

QA

LIN

TA

NG

MO

MU

NC

ER

TP

RO

FL

IQ

ind

icato

r

X6.0

3(3

.05)

5.7

0(2

.89)

6.4

1(3

.33)

2.3

6(1

.32)

9.5

9(5

.02)

5.6

9(2

.29)

8.8

2(4

.20)

1.1

8(1

.32)

X*A

CT

IVE

-1.4

2(-

1.6

3)

-1.1

5(-

1.1

4)

0.0

7(0

.08)

-1.3

9(-

1.7

9)

-1.2

7(-

1.1

4)

-1.0

9(-

1.2

5)

-1.1

7(-

1.4

8)

-2.7

2(-

2.0

6)

X*P

AS

SIV

E-0

.17

(-0.6

5)

0.5

7(1

.36)

0.9

5(2

.20)

0.1

9(0

.46)

0.9

9(2

.11)

0.2

2(0

.72)

0.6

6(1

.73)

0.6

1(1

.74)

X*S

IZE

-0.2

1(-

1.4

2)

-0.2

7(-

1.6

9)

-0.3

7(-

2.1

7)

0.0

8(0

.50)

-0.4

8(-

2.6

6)

-0.3

4(-

2.9

0)

-0.3

7(-

2.4

5)

X*T

UR

N1.2

8(1

.21)

-0.4

3(-

0.2

7)

-1.4

7(-

0.7

3)

-0.8

7(-

0.5

4)

-2.0

3(-

1.0

9)

0.5

0(1

.10)

-1.5

6(-

0.8

9)

pro

bit

X5.8

1(2

.94)

5.2

0(2

.62)

6.0

4(3

.22)

2.1

9(1

.27)

9.1

7(4

.90)

6.1

0(2

.35)

8.2

3(3

.85)

1.3

2(1

.46)

X*A

CT

IVE

-2.0

7(-

1.7

9)

-1.4

8(-

1.2

7)

-1.0

5(-

0.8

7)

-1.4

3(-

1.6

7)

-2.7

8(-

1.9

0)

-1.9

5(-

1.6

1)

-1.8

2(-

1.8

7)

-4.2

0(-

2.5

3)

X*P

AS

SIV

E0.1

3(0

.52)

1.3

2(2

.02)

2.2

2(2

.93)

0.7

7(1

.23)

1.9

2(2

.53)

0.8

3(2

.19)

1.0

4(2

.19)

1.1

9(1

.92)

X*S

IZE

-0.2

1(-

1.3

5)

-0.2

2(-

1.3

9)

-0.3

2(-

1.9

3)

0.0

9(0

.62)

-0.4

3(-

2.4

6)

-0.3

6(-

2.9

4)

-0.3

2(-

2.1

5)

X*T

UR

N1.2

4(1

.12)

-0.1

6(-

0.1

0)

-1.1

9(-

0.6

1)

-0.8

1-(

0.5

1)

-1.7

0(-

0.9

3)

0.8

5(1

.92)

-1.3

1(-

0.7

6)

39