A Study of Mutual Fund Flow and Market Return Volatility

8/6/2019 A Study of Mutual Fund Flow and Market Return Volatility

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A Study of Mutual Fund Flow and Market Return Volatility

Charles Q. CaoDepartment of Finance

The Smeal College of Business

The Pennsylvania State University

University Park, PA 16802, USA

Tel: (814) 865-7891

Fax: (814) 865-3362

Email: [email protected]

and

Eric C. Chang

School of Business

The University of Hong Kong

Pokfulam Road, Hong Kong

Tel: (852) 2857-8347

Fax: (852) 2858-5614


and

Ying WangDepartment of Finance

The Smeal College of Business

The Pennsylvania State UniversityUniversity Park, PA 16802, USA

Tel: (814) 863-0486

Fax: (814) 865-3362


This Draft: April 2003

First Draft: May 2002

Comments are welcome. Please address all comments to the corresponding author: Eric C. Chang.


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Abstract

In this paper we investigate the impact of institutional trading on the market by

examining the daily relationship between aggregate flow into U.S. equity funds and market

volatility. We examine the relationships between market volatility and fund inflow and fund

outflow, respectively. Our empirical results show that an asymmetric concurrent relationship

between fund flow and market volatility exists: fund inflow is negatively correlated with market

volatility, whereas fund outflow is positively correlated with market volatility. We discuss

potential explanations for our results and suggest that they are consistent with the notion of

information content asymmetry between buy and sell orders.


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1. Introduction

Stock market volatility has received a great deal of attention from investors, regulators,

academics, and the press. Option traders, in particular, monitor it closely, since the value of an

option depends largely on the volatility of its underlying asset. The existing literature provides

evidence that volatility is time-varying1 and calls for a better understanding of the factors that

contribute to its variability.

The popular press often quotes practitioners to suggest that increased institutional

participation may account for large market price fluctuations. One article, previously cited in Sias

(1996), states [r]eviewing this weeks events, analysts are concluding that professional investors

simply overreacted2 The quote highlights the perception that institutional traders contribute to

stock market volatility. This view is echoed by another quote: Small investors, through their

purchase of stock mutual funds, have emerged as the major driving force that has propelled the

Dow Jones Industrial Average.3

Notwithstanding these perceptions, we know little empirically about the actual

relationship between market volatility and institutional traders, especially mutual fund traders.

Recently, academics have devoted considerable attention to theprice impactof mutual fund

trading. Chan and Lakonishok (1993, 1995, 1997), Keim and Madhavan (1997), and Jones and

Lipson (1999), for example, have examined the price effect of money inflow into a mutual fund.

In general, these studies suggest that institutional trading causes bothpermanent and temporary

price impacts.

Warther (1995, 1998) examines the relationship between aggregate cash flow into all

mutual funds and market-wide returns. These studies document a strong positive relationship

between unexpected mutual fund flow and contemporaneous stock returns. He also investigates

the lead-lag relationship between flow and returns, but he rejects both sides of feedback trading,


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arguing that security returns neither lag nor lead mutual fund flow. Although he cannot determine

causality here, he suggests a plausible causal link from flow to returns. Edelen and Warner (2001)

further address this issue using higher-frequency data. They report that aggregate unexpected

mutual fund flow is positively correlated with concurrent market returns at a daily frequency.

They also find causality from flow to returns within the day and the one-day lagged response of

aggregate flow to market returns.4

While ample empirical evidence suggests that aggregate mutual fund flow is positively

related to market returns, the relationship between aggregate mutual fund flow and market

volatility is not clear. Some indirect inferences can be drawn from studies examining the impact

of institutional trading on stock price volatility, but the evidence is inconclusive. For example,

using quarterly data, Reilly and Wachowicz (1979) examine the relationship between institutional

traders (e.g., pension funds, etc.) and stock price volatility during the period 1964-1976. They

claim that institutional trading actually reduces stock price volatility. However, several studies

suggest the opposite. For example, Sias (1996) performs cross-sectional analysis for all stocks

listed on the New York Stock Exchange (NYSE) during the period 1977-1991. He documents a

positive relationship between annual change of institutional ownership and contemporaneous

security volatility after controlling for capitalization. Using a sample of 500 stocks in the S&P

500 index, Xu and Malkiel (2003) document a positive cross-sectional relationship between the

idiosyncratic volatility of the stocks in the index and institutional ownership from 1989 to 1996.

They demonstrate that most increased idiosyncratic volatility is attributable to institutional

ownership.

These studies, however, usually focus on the micro level effect. They examine the

relationship between the level of institutional ownership of a stock and the stocks price volatility.

It is still unclear how institutional trading is related to volatility on the aggregate level

empirically.


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The focus of this paper is on the daily relationship between aggregate mutual fund flow

and market price volatility.5 We use a unique data set on daily aggregate net mutual fund flow

from February 3, 1998 to December 29, 2000, for a period of 735 days. Our flow data are from

the same source used by Edelen and Warner (2001), but over a longer period. To prevent our

results from being sensitive to the particular volatility estimator used, we use three estimators of

daily market volatility: (1) the high-frequency volatility estimated from the intraday return data of

Standard & Poor (S&P) 500 index, using the method of Andersen, Bollerslev, Diebold and Ebens

(hence ABDE, 2001), (2) the high-low volatility estimator developed by Parkinson (1980), and

(3) the implied volatility index based on the option of the S&P 100 index, which is quoted by

Chicago Board Options Exchange (CBOE).

When we use aggregate net mutual fund flow across the whole flow range to examine the

relationship, the empirical results indicate a significant negative contemporaneous volatility-flow

relationship. This implies that increases in mutual fund flow are associated with a less volatile

market.

We further examine whether the negative relationship holds for both net fund inflow and

outflow. Our further investigation is mainly motivated by two facts. First, we note the marked

asymmetry between the effects on stock prices of institutional buying and selling (Kraus and Stoll,

1972; Holthausen, Leftwich and Mayers, 1987, 1990; Gemmill, 1996, Keim and Madhavan, 1996;

and Chan and Lakonishok, 1993, 1995.) Specifically, purchases of a stock made by institutional

traders are accompanied by an increase in its price, which continues to rise after the trade. But

sells of a stock are accompanied by a drop in its price, which tends to revert to its prior level. Saar

(2001) develops a theoretical model to show how the trading strategies of money managers create

a difference between the information content of buys and sells, and hence produce a permanent

price impact asymmetry between buys and sells. In general, existing evidence suggests that the

permanent price impact of institutional trading depends on whether the initiator of a transaction is

a buyer or a seller. Since unexpected inflow (outflow) should stand proxy for subsequent


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unexpected institutional buys (sells) [Keim (1999), Edelen (1999), and Edelen and Warner

(2001)], it is natural to ask whether the flow-volatility relationship holds for inflow and outflow.

Our investigation is also motivated by the documented asymmetric relationship between

price changes and trading volume. Karpoff (1987) shows that when prices go up, volume

increases, but when prices go down, volume also increases. However, previous studies that do not

consider this asymmetry usually document a positive contemporaneous relationship between

volume and price changes. Karpoff argues that tests of the volume-price changes relationship that

do not differentiate between positive and negative price changes are misspecified because they

are based on the implicit false assumption that the relationship between volume and price changes

is monotonic. Hence we take into account flow direction to ensure that a similar problem does not

occur in our study.

An interesting finding emerges after we consider the direction of aggregate net flow. The

impact of net inflow and net outflow on the market is markedly asymmetric. We show that net

fund inflow is negatively correlated with market volatility, whereas net fund outflow is positively

correlated with market volatility.

We argue that our findings are consistent with evidence in two lines of research. First,

Harris and Raviv (1993), Shalen (1993) and Delong, Shleifer, Summers and Waldman (1990a)

demonstrate that informed trades are useful in helping adjust prices to new information. However,

the presence of a substantial portion of uninformed investors, and hence their trades, may increase

market price volatility. Second, Chan and Lakonishok (1993, 1995), Keim and Madhavan (1995)

and Saar (2001) all argue that there is a difference in the information content of mutual fund

managers buying and selling orders: buys tend to convey more information than sells. They

argue that mutual fund managers devote substantial resources to gathering and analyzing

information and make decisions based on their private information. To maximize trading

performance, they search for information about stocks regardless of whether they are in their

portfolios, in order to buy stocks with favorable prospects. However, due to restrictions on the use


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of leverage and short sells, their sells are limited to the assets they already own. If individual

investors tend to redeem fund shares more in a down market, the liquidity demand would result in

an asymmetry whereby mutual fund managers trading on selling stocks contains less information

than their trading on buying stocks. The asymmetric relationship between market volatility and

fund flow documented in our paper is consistent with the asymmetric information content

argument.

The remainder of the paper is organized as follows. In Section 2 we review stabilization

and destabilization arguments pertaining to the impact of institutional trading on the stock market.

In Section 3 we discuss our mutual flow data and volatility measures. In Section 4 we present our

main results using daily data. In section 5 we discuss possible explanations for the asymmetric

relationship. Section 6 concludes the paper.

2. Potential effects of institutional trading on market volatility

Existing literature offers mixed views on whether the increasing presence of institutional

trading in the financial market stabilizes or destabilizes financial asset prices. One of the

difficulties in addressing this issue is the absence of a benchmark for normal price volatility. Most

existing financial theories deal with asset price determination rather than volatility. In particular,

the aim of valuation theories is to offer paradigms on how to rationally determine the fundamental

value of financial assets, but taking asset price volatilities and/or co-variabilities as exogenously

given.

Since a normal level of volatility is neither empirically observable nor theoretically

determinable, whether or not a particular market force has impacted price volatility can only be

indirectly inferred. In general, any market forces that tend to drive a financial assets price

temporarily away from its fundamental value have been viewed as destabilizing factors, and vice

versa. The rationale is straightforward. In a competitive market, any disequilibrium cannot be

sustainable in the long run. Therefore, in the subsequent market correction process, a relatively

large price change in absolute value is bound to occur and results in a higher volatility than it


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would be the case without such a market force. In this section, we review several views that imply

that institutional trading may either affect or correlate with stock price volatility.

2.1. Herding and positive-feedback trading

Lakonishok, Shleifer, and Vishny (1992) argue that institutions may destabilize stock

prices and increase market volatility through herding or positive-feedback trading. Herding

occurs when money managers trade in the same direction at the same time withoutnecessarily

focusing on fundamental values. Positive-feedback trading occurs when money managers chase

winners and sell losers without regard to fundamental values.6 Scharfstein and Stein (1990) favor

the first view, arguing that money managers are typically evaluated against each other, not some

absolute standard. To minimize the chance of becoming an outlier, money managers have

incentives to follow the trading patterns of others rather than responding to their private

information. Herding, motivated by this agency problem, may therefore amplify exogenous stock

price shocks.7

However, Lakonishok, Shleifer, and Vishny (1992), Bikhchandani, Shilfer, and Welch

(1992), and Hirshleifer, Subrahmanyam, and Titman (1994) argue that if, for example,

institutional investors are better informed than individual investors, they will probably herd to

undervalued stocks but away from overvalued stocks. Their trading actually moves prices toward

rather than away from equilibrium values8. Chopra, Lakonishok, and Ritter (1992), Lakonishok,

Shleifer, and Vishney (1994), and Brennan (1995) all suggest that institutional investors are more

likely to behave rationally than individual investors.

The empirical evidence in regard to this phenomenon is mixed. Sias and Starks (1997)

demonstrate that the returns on portfolios dominated by institutional investors lead returns on

portfolios dominated by individual investors. Wermers (1999) investigates the impact of mutual

fund herding on stock prices, and shows that stocks bought by herds outperform stocks sold by

herds. Both findings are consistent with the conjecture that managers herd on new information on

fundamentals and help to speed up the information incorporation process. Nofsinger and Sias


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(1999) show that analyses of post-herding returns provide no evidence that institutional herding is

irrational. This is consistent with the hypothesis that institutional investors, at the margin, are

better informed than other investors. However, Dennis and Strickland (2002) argue that earlier

studies using quarterly or annual ownership data may not reveal herding that occurs over a shorter

time interval. To circumvent this problem, they perform cross-sectional tests to investigate the

relationship between ownership structure and returns of firms on event days when the absolute

value of market return exceeds 2%. They find that a firms abnormal return on event days is

related to the percentage of institutional ownership. They conclude that the evidence is consistent

with the positive feedback behavior on the part of some institutions, particularly mutual and

pension funds, which may drive asset prices away from their true values.

2.2. Investors preference

Some scholars, although without asserting a causal relation, have inferred either a

positive or a negative contemporaneous relationship between volatility and institutional trading

based on institutional traders preferences. Kothare and Laux (1995), for example, predict a

positive relation on the ground that institutional investors are attracted to more volatile stocks.

Badrinath, Gay, and Kale (1989) and Arbel, Carvell, and Strebel (1983), however, suggest that

institutional traders are prudent and more likely to avoid riskier (and typically smaller) stocks;

they are thus associated with decreased volatility. Gompers and Metrick (2001) also suggest that

large institutions, compared with other investors, prefer to invest in large, more liquid stocks.

It is fair to say that existing theories offer no definitive conclusion about whether

institutional trading is associated with either increased or reduced volatility. Therefore, we must

appeal to empiricism to determine the relationship between institutional trading and market

volatility.

3. Data and volatility measurement

3.1. Mutual fund flow data


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We use data on daily net mutual fund flow from Trim Tabs (TT) financial services of

Santa Rosa, CA. TT furnished us with the daily data on net asset values (NAVs) and total net

assets (TNAs) for a sample of over 800 mutual funds9. The sampling period was from February 2,

1998, to December 29, 2000. The data include equity funds and bond funds, which represent

approximately 15% and 12% respectively of the total funds covered by the Investment Company

Institute(ICI). TT also provided us with daily net fund flow (new subscription less redemptions)

based on the following formula:

1

1

=t

ttttNAV

TNANAVTNAFlow . (1)

3.1.1. Fund classification and aggregation

Since we want to explore the relationship between aggregate mutual fund flow and U.S.

stock market volatility, our focus in this paper is on domestic equity mutual funds. Therefore, we

isolate domestic equity funds from other funds. We then match the whole sample with those in

the Center for Research in Security Prices (CRSP) survivor-bias free U.S. mutual fund database

and classify the funds by the investment objectives. Like Warther (1995), we classify mutual

funds by ICI category10 and include the funds with the following investment objectives in our

sample: aggressive growth (AG), growth and income (GI), long-term growth (LG), sector funds

(SF), total return (TR), utility funds (UT), income (IN) and precious metals (PM).11 The final

sample contains 411 domestic equity mutual funds.

3.1.2. Data filter

TT advised us that its data are prone to errors such as interchanged digits and digit

transposition, because the data are collected by hand.12 Thus, we need to filter NAVs and TNAs

before aggregating the daily U.S. equity mutual fund flow data. We use the same two filters as

employed in Chalmers, Edelen, and Kadlec (hence CEK, 2001) to eliminate potential data error in


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NAV and TNA series13. After the filtering, we calculate the net flow on the basis of these two

series.

Some funds may send one-day-old data or partially updated data (with updated NAVs but

not reflecting the days fund-share transactions) to TT. However, Edelen and Warner (2001)

suggest that TT include the funds in their sample only if the funds can reliably provide up-to-date

daily NAVs. They employ various tests to reject the hypothesis that TT reports one-day-old

data14. They caution that merely adjusting the fund's data by one day can result in severe

classification errors. For this reason, Edelen and Warner (2001) make no adjustments to their data

based on concern of data timeliness and argue that their decision would only strengthen their

papers main conclusion: that flow-motivated trade has an aggregate price impact.

Goetzmann, Ivkovic, and Rouwenhorst (hence GIR, 2001) also examine the reporting

practices of each international fund in their sample. They compare TTs fund data with that in the

CRSP mutual fund database. They identify 88 of 116 funds in their sample that report appropriate

TNAs and 3 that report one-day-lagged TNAs. Data for the remaining 25 funds were either too

noisy to allow for a determination or were not available for 1998. GIR conclude that the

overwhelming majority of the funds in their sample follow the proper practice of reporting post-

flow TNAs. Moreover, they point out that the results obtained under the assumption that all 116

funds report timely data and those obtained from the 91 funds with clearly identifiable reporting

practice are very similar. As a result, they only report results based on the whole sample.

Taken together, previous studies suggest that in general TT reports timely data on mutual

fund flow. Therefore, like Edelen and Warner (2001) and GIR (2001), we make no adjustments to

fund flow data in this research.

3.1.3. Properties of daily aggregate mutual fund flow

The dollar value of the TT asset base varied dramatically from 340 to 810 billion during

our three-year sample period. Therefore, we normalize flow by expressing it as a percentage of


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5-minute returns.16 They argue that the estimators thus constructed are, in theory, both free of

measurement error and model free.

Following ABDE (2001), We construct the five-minute return series of Standard and

Poor (S&P) 500 index from the logarithmic difference between the prices recorded at or

immediately before the corresponding five-minute interval. We obtain the intraday transaction-

level data from Tick Data, Inc.17 As pointed out by ABDE (2001), the use of fixed discrete time

interval and the inherent bidask spread could induce the negative serial correlation in the

return series and thus systematically bias the volatility measure. Therefore, much as ABDE

(2001) did, we estimate an MA (1) model for the five-minute return series for the whole sample

to remove the potential negative serial correlation. Consistent with ABDE (2001), we find that the

estimated moving-average coefficient is 0.09818 with a t-value of 23.8. Then we construct the

high-frequency volatility estimator

=

+=

/1

1

2

, )(i

ittHigh r (2)

so that tHigh, denotes daily market volatility based on high-frequency data on day t, is the

observation interval length (e.g., five minutes), 1/ is the number of observation intervals in one

trading day (e.g., 79 five-minute intervals per day), and+itr is the intraday de-meaned MA(1)-

filtered five-minute S&P 500 index returns in interval on day t.

Two additional measures of daily market volatility are used to check the robustness of the

results. The first is the extreme value estimator developed by Parkinson (1980), which is defined

as

)/ln(601.0, tttHL LH= (3)


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whereHtandLt, respectively, are the highest and lowest index prices on day t. The daily high and

low S&P 500 prices are obtained from the Reuters Database.

The second measure is the implied volatility of an option on a market index. The Chicago

Board Options Exchange (CBOE) began to quote a daily implied volatility index ( VIX ) based on

the option of the S&P 100 index in 1986. Our sample begins on February 2, 1998, and ends on

December 29, 2000.

3.2.2. Properties and correlations of volatility estimators

Summary statistics for alternative daily volatility estimators are shown in Table 2. Two

observations merit discussion here. First, Panel A shows that all three volatility estimators exhibit

substantial positive autocorrelation. Thus, we should control for this autocorrelation in our later

tests. Second, implied volatility ( VIX ) is on average higher than volatility estimated from high-

frequency intraday data ( High ) and high-low volatility ( HL ). But, from the correlations shown

in Panel B, we can see that these three volatility estimators are highly correlated. The time series

of three volatility estimators are depicted in Figure 2.19

4. Empirical Results

4.1. Daily flow-return relationships

Edelen and Warner (2001) document a daily relationship between aggregate equity

mutual fund flow and NYSE composite index returns during the period February 1998 to June

1999. Our data on flow come from the same source as theirs, but differ from theirs in that we do

not have aggregate equity mutual fund flow. Instead, TT furnished us with daily data on NAVs,

TNAs, and flow for a sample of about 800 funds. We then aggregated the domestic equity fund

flow on our own and normalized the flow by dividing it by the previous days TNAs. Thus, in this

section, we use data over a longer period to replicate their flow-return-relationship test. The

purpose of this test is twofold: (1) to validate our self-constructed flow data and (2) to extend

their tests to a more recent period.


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We employ the same expected-unexpected flow decomposition that Edelen and Warner

(2001) use to accomplish our replication flow-return-relationship tests. Edelen and Warner argue

that, given the substantial persistence in the flow series20 and the strong dependence of flow on

lagged returns21, it is necessary to explicitly separate expected and unexpected flow. Also, many

previous studies [e.g., Warther (1995, 1998) and Edelen and Warner (2001)] have pointed out that

returns correlate only with the unexpected component of flow but not with expected flow.

We use three sample periods: (1) a whole period from February 1998 to December 2000,

(2) the identical sample period (i.e., February 1998 to June 1999) used in Edelen and Warner

(2001), and (3) a second sub-period from July 1999 to December 2000. All three sample periods

produce qualitatively the same results, although the result based on the more recent sub-period is

slightly weaker than that based on the first sub-period. For brevity, we report only the results in

Table 3, based on the whole period.

Table 3 is divided into two panels. Panel A reports the results of examining the

dependence of fund flows on returns and past flows, and Panel B reports the dependence of

returns on fund flows.

In Panel A, daily flow is regressed on lagged flow and concurrent and lagged returns. As

in Edelen and Warner (2001), usually several different lag values of independent variables are

used in the regressions. Columns 1 and 2 of Panel A indicate that flow is closely related to lagged

flow and lagged return, which is consistent with Edelen and Warner (2001). Their main focus is

the concurrent flow-return relationship, which is shown in Column 3. We confirm that their

results hold in an extended period and document a significantly positive concurrent flow-return

relationship (coefficient 0.009 with a t-value of 2.01) after controlling for lagged flow and lagged

returns.

In Panel B, returns are regressed on concurrent and lagged flow using both the raw series

and the expected-unexpected flow series. Column 4 presents the regression of returns on

concurrent and lagged raw flow. Columns 5 and 6 show the regression of returns on expected and


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unexpected flow. Expected daily flow is taken from the fitted values of model 2 in Panel A, and

unexpected flow is actual minus expected. Again, we confirm the main finding of Edelen and

Warner (2001) in this extended sample period. In particular, Column 6 shows that returns are

positively related to contemporaneous unexpected flow (coefficient 0.629 with a t-value of 2.01)

but not to expected flow (coefficient 0.459 with a t-value of 1.01).

4.2. Regression of volatility on aggregate flow

The main focus of this paper is to investigate the concurrent daily volatility-flow

relationship. In this subsection we first present our regression specifications, discuss the

dependent and independent variables used, and then report the main empirical results. To examine

the relationship between market volatility and aggregate mutual fund flows, our regression

specifications take the following forms:

t

i

itHighittHigh LnFlowLn +++= =

3

1

,10, )()( (4a)

t

i

itHighittHigh LndownUpFlowLn ++++= =

3

1

,210, )(_)( (4b)

t

i

itHighitttHigh LnTVdownUpFlowLn +++++= =

3

1

,3210, )(_)( (4c)

wheretHigh, is the daily volatility estimator based on high-frequency data on day t, tFlow is the

aggregate net mutual fund flow on day t, downUp_ is a dummy variable equal to one when the

market return is positive and equal to zero otherwise, and tTV is the market turnover rate on day t.

The subscripts indicate the days lagged.

The dependent variable in these regressions is the natural logarithm of the daily volatility

estimator based on high-frequency data on day t ( )( ,tHighLn ). As mentioned, we have

constructed three estimators of volatility. For brevity, we use the high-frequency volatility


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estimator to present our main results and use the high-low-volatility and implied-volatility index

as a robustness check.

A discussion of the independent variables and their expected relationships with the

dependent variable are as follows:

Aggregate mutual fund flow ( tFlow ). The coefficient on tFlow is the main focus of

this subsection, since this coefficient reflects the concurrent volatility-flow

relationship.

Lagged values of the natural logarithm of market volatility ( )( , itHighLn ). We

include these values in our tests to account for persistence in market volatility (see,

e.g., Bollerslev, Chou, and Kroner, 1992). Without including them as controlling

regressors, there will be a positive bias on any included regressor that covaries with

lagged volatilities. Thus, the estimation process will be biased and will incorrectly

reflect the concurrent relationship between volatility and flow. Since existing

evidence suggests that high volatility is often followed by high volatility and vice

versa (see, e.g., Bollerslev, Chou, and Kroner, 1992), we expect the coefficients on

these lag values to be significantlypositive. The number of lagged differences to be

included (i.e., the value of maximum i) is determined by the standard t-test of

significance on the last lagged difference term.

Up_down. This is a dummy variable equal to one when the market return is positive

and equal to zero otherwise. Existing literature (see, e.g., Campbell, Koedijk, and

Kofman, 2002) suggests that bear markets are associated with higher volatility than

bull markets. Cox and Ross (1976) and Christie (1982), among others, document a

negative correlation between market returns and volatility. Therefore, it is possible

that the flow-volatility relationship thus gained in regression (4a) is only a spurious

result of the positive flow-return relationship documented by Edelen and Warner


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(2001). We include this dummy variable in regressions (4b and 4c) to determine

whether meaningful changes occur after we control for changes in the volatility when

the market is up that are not related to volatility to flow sensitivity. If the flow-

volatility relationship still holds after we control for this dummy variable, we then

have reason to believe that the concurrent daily flow-volatility relationship does not

simply derive from the positive correlation between daily flow and returns. We

expect the coefficient on this dummy variable to be negative.

Market turnover rate ( tTV ). This rate is defined as the daily trading volume divided

by shares outstanding at the end of the previous day. It is well known that trading

volume is positively related to volatility [See, e.g., Karpoff (1987)]. We thus include

TVt as a control variable in some versions of the tests22. Accordingly, we expect the

estimated coefficient to be positively significant.

Table 4 presents the main results of our volatility-to-flow-sensitivity tests. Results based

on regressions (4a), (4b) and (4c) are reported in Columns 1, 2 and 3, respectively. Our discussion

focuses on Model (4c). As indicated, the sign of estimated coefficients on the controlling

variables are all consistent with the existing finance literature. First, the results confirm that

market volatility persists over time [see, e.g., Bollerslev, Chou, and Kroner (1992)], with all

coefficients on the lagged volatilities positively significant. Second, the coefficient of 0.11 on

the Up_down dummy (with t-statistics of 6.1) suggests that bear markets are associated with

higher volatility than bull markets. This result is consistent with the finding in Campbell, Koedijk,

and Kofman (2002). Third, the coefficient on the turnover rate is significantly positive (with t-

statistics of10.32).

More important for our analysis, however, is the coefficient on tFlow for all three

columns. The coefficients remain negatively significant in all three cases (t-values = - 4.75, -4.50

and 5.60, respectively). The negative and significant coefficient on flow implies that increased


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aggregate fund flow is associated with decreased market volatility. The results also indicate that

adding the dummy term and the turnover rate does not materially affect the flow coefficient.

4.3. Regression of volatility on aggregate net inflow and outflow

The finding that the concurrent relationship between volatility and flow is negative

suggests that the larger the fund cash flow, the less volatile the market. However, as Table 1

shows, of the 735 observations of aggregate net flow data, 416 are positive and 319 are negative.

In other words, 43% of the trading days over Feb. 3, 1998 to Dec. 29, 2000, collectively, involve

cash flowing out of rather than into the equity mutual funds. In this section, we further investigate

the question of whether the above result holds for aggregate net inflow and outflow, respectively.

Our further investigation is motivated by two facts. First, existing studies document that

block purchases have a larger permanent price impact than block sales. Specifically, trades

induced by block transactions and institutional traders impact prices in an asymmetric manner.

Whereas prices go up on buys and down on sells of block trading, they tend to revert after sells

but remain high after buys. In other words, the market seems to react differently to buy and sell

orders23. Saar (2001) develops a theoretical model showing that trading strategies of mutual fund

managers could make buy orders convey more information than sell orders. Therefore,

equilibrium prices should adjust more for buys than for sells. Since unexpected inflow (outflow)

could stand proxy for subsequent unexpected institutional buys (sells) [Keim (1999), Edelen

(1999), and Edelen and Warner (2001)], it is natural to ask whether inflow and outflow will have

different effects on market volatility.

Second, Karpoff (1987) offers a review of the relationship between trading volume and

price changes and presents a model that suggests an asymmetric concurrent relationship between

volume and price changes in financial markets. He points out that, as the V shape in Figure 3

illustrates, there exists a positive relationship between volume and positive price changes and a

negative relationship between volume and negative price changes. Moreover, he argues that tests

on linear relationships between volume and price changes per se will yield positive correlations,


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as indicated by the slope of the dotted line that connects the midpoints of the two sides of the Vin

Figure 3. Morgan (1976), Rogalski (1978), Harris (1986, 1987), and Richardson, Sefcik, and

Thompson (1987) all document such a positive concurrent relationship between volume and price

changes. However, Karpoff (1987) asserts that tests of the volume-price change relationship that

do not differentiate between positive and negative price changes could be misspecified, since they

may be based on the implicit false assumption that the relationship between volume and price

changes is monotonic.

Our investigation of the volatility-fund flow relationship bears some similarities with that

of the volatility-flow relationship. While price changes can be either positive or negative, volume

can never be negative. Similarly, while flow can be either positive (net inflow) or negative (net

outflow), market volatility is always positive. Moreover, our previous models are also based on

the implicit assumption that the volatility-flow relationship is functional and/or monotonic. Thus,

it seems reasonable to take into account the direction of flow to see if any asymmetric volatility-

flow relationship exists.

To examine the impact of inflow and outflow respectively on market volatility, we

introduce two dummy variables ( 1D and 2D ) to differentiate between inflow and outflow. 1D is

defined as 1 when aggregate net flow is positive and as 0 otherwise, and2D is defined as 1

when aggregate net flow is negative and as 0 otherwise. We then construct two new variable

series, namely tInflow and tOutflow , with these two dummy variables. Specifically, we multiply

the tFlow series in the previous models (Regressions (4a)-(4c)) by 1D to derive the new

t

Inflow series. Similarly, we multiply thet

Flow series by2

D to derive the newt

Outflow

series. Note, however, that we actually obtain the absolute value of outflow whentFlow is

multiplied by2D , but, for brevity, we refer to it as outflow henceforth.


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Next, we replace the tFlow series in regressions (4a)-(4c) with these two new series,

tInflow and tOutflow , on the right-hand sides of the regressions. In addition, we include the

lagged values of market volatility, the downUp_ dummy variable defined before, and market

turnover rate ( tTV ) as controlling variables in our tests. Our regression specifications take the

following forms:

t

i

itHighitttHigh LnOutflowInflowLn ++++= =

3

1

,210, )()( (5a)

t

i

itHighitttHigh LndownUpOutflowInflowLn +++++= =

3

1

,3210, )(_)( (5b)

t

i

itHighi

ttttHigh

Ln

TVdownUpOutflowInflowLn

++

++++=

=

3

1

,

43210,

)(

_)(

(5c)

Table 5 presents the results of estimating the revised models. In Column 1, the natural

logarithm of daily volatility is regressed on inflow and outflow respectively after controlling for

the persistence of volatility. In Column 2, we further control for the downUp_ dummy variable,

as previously discussed. We also include the market turnover rate in Column 3 as an explanatory

variable.

The results in Table 5 confirm that relationships between volatility and control variables

remain unchanged, as documented in Table 4. Specifically, significant and positive coefficients

are found on lagged volatilities and market turnover rate while significant and negative

coefficients on the downUp_ dummy variable are documented.

The most important results, however, are the coefficients on tInflow and tOutflow . The

three columns in which these coefficients are shown display a consistent pattern of significantly

negative coefficients on tInflow and significantly positive coefficients on tOutflow . For


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22

example, Column 1 shows that market volatility is negatively correlated with aggregate net cash

flow into mutual funds with t-statistics of 3.32 and positively correlated with aggregate outflow

(actually the absolute value of outflow, as mentioned) with t-statistics of 2.39. These results do

not materially change even after controlling for the impact of market returns and trading volume

on market volatility.

Table 5 reveals an interesting finding: an asymmetric concurrent correlation between

volatility and flow, depending on the direction of flow. We find that fund inflow is negatively

related to market volatility, and that fund outflow is positively related to market volatility. That is,

the larger the aggregate cash flow into the mutual funds, the less volatile the market. On the other

hand, the larger the aggregate cash flow out of the mutual funds, the more volatile the market. We

illustrate this asymmetry in Figure 4.

The slopes of solid lines in Figure 4 roughly illustrate the asymmetric relationship

between volatility and fund flows. Moreover, the slope of the dotted line that connects the

midpoints of the two solid lines ofVin the figure suggests that a negative correlation is likely to

be detected when the volatility-flow relationship is examined across a whole spectrum of flow

range. This is in fact the case; as indicated in Table 4, we detect significantly negative concurrent

relationships between volatility and flow.

4.4. Robustness tests

4.4.1. Tests of outliers

Our sample covers 411 U.S. equity mutual funds, including funds with various

investment objectives such as aggressive growth and precious metals. The fund flow patterns are

diversified. For example, whereas average flows of funds with the investment objectives of AG,

GI, LG, and SF are positive, average flows of funds including TR, UT, IN, and PM are negative

(see Panel A of Table 1). We perform robust tests to ensure that the results based on aggregate

fund flows are not driven by a few outliers.


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23

To test this possibility, we construct a new variable, dispersion rate ( tDR ), as follows:

t

ttt

T

NMDR

= (6)

where Mtrefers to the number of funds whose flow on day tis positive,Ntis the number of funds

whose flow on day tis negative, and Ttis the total number of funds on day t.

Table 6 shows the summary statistics ofDR. Among 735 observations of DR, 250

observations are positive with the mean of 0.119, and 485 are negative with the mean of 0.177.

In addition, the correlation between flow and the DR variable across the whole time period is 0.63.

We perform similar tests onDR to examine whether any outliers induce this asymmetric

volatility-flow relationship. First, we introduce two new dummy variables:3D is defined as 1

when tDR is positive and as 0 otherwise; 4D is defined as 1 when tDR is negative and as 0

otherwise. Next, we construct two new series, pDRt_ and nDRt_ , with these two dummy

variables. Specifically, pDRt_ is tDR multiplied by 3D , and nDRt_ is tDR multiplied by

4D . The models are specified as follows:

t

i

itHighitttHigh LnnDRpDRLn ++++= =

3

1

,210, )(__)( (7a)

t

i

itHighitttHigh LndownUpnDRpDRLn +++++= =

3

1

,3210, )(___)( (7b)

t

i

itHighi

ttttHigh

Ln

TVdownUpnDRpDRLn

++

++++=

=

3

1

,

43210,

)(

___)(

. (7c)

The results are shown in Table 7. In Column 1, we regress natural logarithm of volatility

on positive tDR and absolute value of negative tDR respectively, after controlling for the


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24

persistence of volatility. In Column 2, we also control for the downUp_ dummy variable.

Turnover rate is included in Column 3 as a controlling variable.

The results confirm the asymmetric pattern reported in Table 5. Specifically, there is a

negative relationship between concurrent volatility and positive dispersion rate, and there is a

positive relationship between concurrent volatility and absolute value of negative dispersion rate.

The results imply that a day where a higher number of mutual funds experience net inflow is

more likely to be one of a lower market volatility.

4.4.2. Alternative volatility estimators

In this section, we repeat our tests using two previously discussed alternative volatility

estimators: high-low volatility and implied volatility index. We report the results in Table 8.24

Table 8 illustrates that the results using two alternative volatility estimators are similar to

the previously presented results.25 In both cases, we find that fund inflow is negatively related to

market volatility and fund outflow is positively related to market volatility. We conclude that our

finding of an asymmetric relationship between volatility and flow is not driven by the particular

volatility estimator used.

4.4.3. Sub-time periods tests

We also divide our sample period into two sub-time periods: one from February 1998 to

June 199926 and the other from July 1999 to December 2000. We then replicate all the tests for

the two periods.27 Table 9 presents the results.

The results are largely consistent with our previous results. For example, we obtain

inflow estimates of 33.4 with the tstatistic of 2.94 and 38.1 with the tstatistic of 2.83 for

two sub-time periods. At the same time, the outflow estimates during two sub-time periods are

25.5 with the tstatistic of 1.93 and 63.1 with the tstatistic of 2.33, respectively.

4.4.4. Other tests


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25

We perform two additional tests. First, we include global equity (GE) and balanced funds

(BL) in our sample to check whether the results are sensitive to these specific types of fund.

Second, we repeat the analysis, excluding the December data from our sample, to check the

dividend effect. Our data providers advised us that determining flow in the presence of dividends

is pure guesswork because mutual funds do not handle distributions in a uniform manner. Since

most distributions happen in December, for the sake of a robustness check, we re-estimate our

models by discarding December data from the sample to see whether a nontrivial change occurs.

In both cases, we find that the results remain qualitatively the same28.5. Discussion

In this section we attempt to offer an explanation for the documented asymmetric

relationship between volatility and mutual fund flows. We first establish a link between market

volatility and the information content of a trade. We then discuss a possible asymmetry in the

information content between the trades that induce an average aggregate fund inflow and fund

outflow.

Harris and Raviv (1993) and Shalen (1993) demonstrate that the presence of a substantial

portion of uninformed investors in the markets, and hence their trades, may increase market price

volatility. They show that a wider dispersion of beliefs (that is, a wider dispersion of expectations

in the current and preceding rounds of trade) creates excess price variability relative to

equilibrium value. In particular, Shalen (1993) points out that uninformed (or less-informed)

investors have difficulty interpreting the noisy signals of price change. They cannot differentiate

between short-term random liquidity demand and overall fundamental changes in supply and

demand. This could result in a general discrepancy in the true price embodied in revealed

information. Moreover, uninformed investors tend to revise their beliefs more frequently than

their informed counterparts after new information becomes publicly available, resulting in the

slower disappearance of price fluctuations from their trading. In these ways, uninformed investors

(and hence their trades) are more likely to overreact to fundamental price movements, which lead


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26

to increased price volatility. Informed trades, however, are arguably useful in helping adjust

prices to new fundamentals, and thus are immune from these problems.

Next, we offer three arguments that suggest that, on average, mutual fund inflow may

contain better information than outflow. The first argument comes from the joint nature of fund

flow decisions by individual investors and fund managers. To buy or redeem mutual fund shares

is basically an exclusive decision made by individual investors. However, mutual fund money

managers proactive trading strategies (e.g., market timing, stock selection) may also play a

crucial role in determining the timing of fund flow. Therefore, it is fair to say that the timing and

the magnitude of fund flow into and out of the market are generally determined jointly by

individual investors and fund managers. However, mutual funds are required by law to entertain

share redemption requests by individual investors on a daily basis. The obligation to

accommodate the liquidity, as pointed out by Edelen (1999), forces mutual fund managers to

engage at times in a substantial amount of uninformed trading. A large fund outflow day could be

a day characterized by substantial redemption on the part of individual investors. It is expected

that the relative decision role fund managers play on such a day is less significant than that on a

normal day. We hence argue that, on average, mutual fund inflow contains better information

than outflow does.

Chan and Lakonishok (1993) offer the second argument on the difference in institutional

trades information content. They argue that since an institutional investor typically does not

hold the market portfolio, the choice of a particular issue to sell, out of the limited alternatives in

a portfolio, does not necessarily convey negative information. Rather, the stocks that are sold may

already have met the portfolios objectives, or there may be other mechanical rules, unrelated to

expectations about future performance, for reducing a position. As a result, there are many

liquidity-motivated reasons to dispose of a stock. In contrast, the choice of one specific issue to

buy, out of the numerous possibilities on the market, is likely to convey favorable firm-specific


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27

news. Implied in this argument also is the suggestion that institutions buy orders convey more

information than their sell orders

Saar (2001) develops a model which provides the third argument that the trading strategy

of mutual fund managers creates a difference between the information content of buys and sells.

The asymmetry is mainly driven by two factors: (1) portfolio managers ability to gather, analyze,

and optimally use private information and (2) a set of trading constraints (e.g., restriction on the

use of leverage and short sells) that portfolio managers face. In particular, fund managers profit-

maximizing trading strategies propel them to search for information about stocks not in their

portfolios in order to buy stocks with favorable information, as well as about stocks already in

their portfolios, in order to sell stocks whose price is expected to drop or on which there is no

special information. Saar (2001) argues that such a dynamic strategy creates a difference between

the information content of buys and sells, if the market knows that institutional investors are

informed investors about the prospects of stocks29.

Our findings of an asymmetric concurrent volatility-flow relationship are consistent with

the differences of information content of mutual fund managers buying and selling behavior

discussed above. When individual investors buy mutual fund shares, large sums of cash flow into

mutual funds and induce mutual fund managers to invest money in a diversified portfolio. As

pointed out by Saar (2001), mutual fund managers devote substantial resources to gathering and

analyzing private information and to makingselection and timingdecisions based on their

research departments predictions and recommendations. These trades on average contribute to

adjusting prices to new fundamentals. We thus expect to see a negative relationship between

market volatility and aggregate mutual fund flow. That is consistent with our finding that higher

fund inflow is associated with a less-volatile market.

In contrast, when individual investors redeem mutual fund shares, large sums of money

exit the mutual funds. As mentioned previously, mutual funds are subject to some operational

constraints (Saar, 2001). For example, use of leverage is restricted, and short sells are forbidden.


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28

Hence fund managers are forced to sell stocks from their portfolios to satisfy the liquidity

requirement of individual investors more often than they are forced to buy stocks. Such less-

informed trades may drive the prices away from the assets fundamental values. This is also

consistent with our findings: the greater the aggregate mutual fund outflow, the more volatile the

market. In a word, we link the impact of individual investors and mutual fund managers in a

unified framework and provide a possible explanation for our findings.

6. Conclusion

In this study, we use daily data to directly investigate the concurrent relationship between

aggregate mutual fund flow and market volatility. Our high-frequency data enable us to conduct

rigorous tests that offer new evidence. The study sheds light on the impactof institutional trading

on the market and should be important to investors, practitioners, academics, and regulators.

Our initial evidence suggests a significantly negative contemporaneous relationship

between volatility and aggregate net mutual fund flow across the whole flow range. However, we

gain additional insight into this issue by separating net inflow and outflow data. An important

finding emerges after we take into account the direction of aggregate net flow: there is a marked

asymmetry between the impact of inflow and the impact of outflow on the market. Increases in

aggregate net inflow are associated with a less-volatile market, while increases in aggregate net

outflow are associated with more-volatile market.

We discuss the possible explanations for our findings of an asymmetric concurrent

volatility-flow relationship and suggest the joint roles played by individual investors and mutual

fund managers in the market. Our results are consistent with the differences of information

content of mutual funds buys and sells.


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29

Endnotes

1For example, based on monthly observations, Schwert (1989) reports that stock volatility varied

substantially during the period 18571987. Using daily return data, Haugen, Talmor, and Torous (1991)document a large variation in volatility during the period 18971988. Wood, McInish, and Ord (1985),

among others, examine intraday market returns and show that market volatility is high at the beginning and

the end of the trading day.

2 See Wall Street Journal (WSJ), July 21, 1995, p. A1.

3 See WSJ, February 26, 1993, p. C1.

4 However, unlike Warther (1995), who finds a high correlation (R2=55%) between monthly flow and

returns, they show that variation in aggregate flow accounts for only 3% of the variation of daily market

index returns, thus providing limited evidence for the common view that mutual fund flow causes

movement of security prices.

5 Warther (1998) does question whether increased mutual fund flow leads to increased market instability,

but he provides no empirical evidence that directly addresses the flow-volatility-relationship question.

6See also De Long, Shleifer, Summers, and Waldmann (1990a).

7 Investors who follow either of these trading strategies can be included in the class of noise traders

proposed by Delong, Shleifer, Summers and Waldmann (1990b). They show that the unpredictability ofnoise traders beliefs creates excessive risk, resulting, in turn, in a significant divergence of stock prices

from fundamental values

8 Lakonishok, Shleifer, and Vishny (1992) also caution that herding and positive-feedback trading

phenomena per se do not necessarily destabilize the market. Institutions might appear to irrationally herd if

they all react to the same fundamental information in time or if they all counter the same irrational moves inindividual investor sentiment.

9 TNA is the current market value of all the funds assets minus its liabilities. NAV is the price per share atwhich shares are redeemed; it is defined as TNA divided by the total number of shares outstanding.

10 Refer toICI Mutual Fund Factbook, 2001, pp. 36.

11 Unlike Warther, we exclude international equities funds from our sample, given that we are concerned

with U.S. domestic equity market volatility. This is also consistent with Edelen and Warner (2001).

However, it is debatable whether global equity (GE) and balanced (BL) funds should be included in our

sample, since GE funds invest in both U.S. and international equities, and BL funds invest in a mix ofequity securities and bonds. Hence we will perform a robustness check by including GE and BL funds to

see if the results are, in particular, sensitive to them.

12 Greene and Hodges (2002), Goetzmann, Ivkovic, and Rouwenhorst (2001), and Chalmers, Edelen, and

Kadlec (2001) also address this issue.

13The first is a five-standard-deviation filter intending to eliminate outliers due to typos. The second is a

filter designed to catch potential false reversals. For details, please see CEK (2001).

14 Although they cannot reject the partially updated data hypothesis, they also note that the tests power is

limited by the availability of semiannual Security Exchange Commission (SEC) reports. Furthermore, even


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if a fund shows a one-day reporting lag based on comparisons of SEC and TT data, it is unclear whether

there is also a one-day lag for all other days.

15 Statistics on flow data are consistent with Edelen and Warner (2001).

16

ABDL (2001) justify using a sampling frequency of five minutes, stating that this amount of time is longenough to avoid most measurement errors and short enough to avoidmicrostructure biases.

17 Tick Data, Inc. provided tick-by-tick data on 1500 U.S. equities, 60 futures, and 7 most popular cash

indices. We refer the interested reader to www.tickdata.com for a more complete description of the actualdata and the method of data capture.

18 The negative serial correlation may suggest the spurious dependence induced by non-synchronous

trading and bid-ask bounce effects.

19 To save space, we present our results mainly by using high-frequency volatility. Also, to prevent our

inferences from being sensitive to the particular estimators used, we repeat all the analyses with the high-

low-volatility and implied-volatility index time series.

20 Table 1, Panel A shows that flow is highly predictable.

21 This is shown in Table 3, Panel A.

22 Edelen and Warner (2001) suggest that unexpected flow should stand proxy for subsequent unexpected

institutional trading volume. Edelen (1999) documents a positive relationship between gross flow (half of

the sum of inflow and outflow) and trading volume; however, he argues that a positive relationship betweennet flow (inflow minus outflow) and trading volume does not necessarily follow.

23 See, for example, Kraus and Stoll (1972), Holthausen, Leftwich and Mayers (1987, 1990), Chan and

Lakonishok (1993), Gemmill(1996), and Keim and Madhavan (1996)

24 For brevity, we only show the results for the most comprehensive models.

25 Nevertheless, the results are slightly weaker than those using high-frequency volatility.

26 The time period is consistent with that used in Edelen and Warner (2001).

27 For brevity, we only show the most comprehensive models using high-frequency volatility.

28 To save space, these results are not reported.

29 Keim and Madhavan (1995) also suggest that the information content of buys is greater than that of sells:Following a buy decision, institutional traders can choose among various potential assets; however, their

sells are limited to the assets they already own, owing to the short sells restriction. Keim and Madhavan

(1997) also show that trading costs for buyer-initiated trades exceed seller-initiated trades, and their

findings are consistent with the differences between the information content of buys and sells.


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References

Andersen, T.G., Bollerslev, T., Diebold, F.X., Ebens, H., 2001. The distribution of realized stock

return volatility. Journal of Financial Economics 61, 43-76.

Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2001. The distribution of realized

exchange rate volatility. Journal of the American Statistical Association 96, 42-55.

Arbel, A., Carvell, S., Strebel, P., 1983. Giraffes, institutions and neglected firms. Financial

Analysts Journal 39, 55-63.

Badrinath, S.G., Gay, G.D., Kale, J.R., 1989. Patterns of institutional investment, prudence, and

the managerial safety-net hypothesis. Journal of risk and insurance 55, 605-629.

Bikhchandani, S., Hirshleifer, D., Welch, I., 1992. A theory of fads, fashion, custom, and cultural

change as informational cascades. Journal of Political Economy 100, 992-1026.

Bollerslev, T., Chou, R.Y., Kroner, K.F., 1992. ARCH modeling in finance: a review of the

theory and empirical evidence. Journal of Econometrics 52, 5-59.

Brennan, M.J., 1995. The individual investor. Journal of Financial Research 18, 59-74.

Brown, K.C., Brooke, B.A., 1993. Institutional demand and security price pressure: the case of

corporate spinoffs. Financial Analysts Journal 49, 53-62.

Busse, J.A., 1999. Volatility timing in mutual funds: evidence from daily returns. Review ofFinancial Studies 12, 1009-1041.

Campbell, J.Y., 1987. Stock returns and the term structure. Journal of Financial Economics 18,

373-399.

Campbell, R., Koedijk, K., Kofman, P., 2002. Increased correlation in bear markets: a downside

risk perspective. Financial Analysts Journal 58, 87-94.

Chalmers, J.M.R., Edelen, R.M., Kadlec G.B., 2001. On the perils of financial intermediaries

setting security prices: the mutual fund wild card option. Journal of finance 56, 2209-2236.

Chan, L., Lakonishok, J., 1993. Institutional trades and intraday stock price behavior. Journal of

Financial Economics 33, 173-200.

Chan, L., Lakonishok, J., 1995. The behavior of stock prices around institutional trades. Journal

of Finance 50, 1147-1174.

Chan, L., Lakonishok, J., 1997. Institutional equity trading costs: NYSE versus NASDAQ.

Journal of Finance 52, 713-735.

Chopra, N., Lakonishok, J., Ritter, J., 1992. Measuring abnormal performance: do stocks

overreact? Journal of Financial Economics 49, 235-268.

Christie, A.A., 1982. The stochastic behavior of common stock variances: value, leverage and

interest rate effects. Journal of Financial Economics 10, 407-432.


32/46

32

Cox, J.C., Ross, S.A., 1976. The valuation of options for alternative stochastic processes. Journal

of Financial Economics 10, 407-432.

DeLong, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990a. Noise trader risk in financial

markets. Journal of Political Economy 98, 703-739.

DeLong, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990b. Positive feedback investment

strategies and destabilizing rational speculation. Journal of Finance 45, 379-395.

Dennis, P.J., Strickland, D., 2002. Who blinks in volatile markets, individuals or institutions?


Edelen, R.M., 1999. Investor flows and the assessed performance of open-end mutual funds.Journal of Financial Economics 53, 439-466.

Edelen, R.M., Warner, J.B., 2001. Aggregate price effects of institutional trading: a study of

mutual fund flow and market returns. Journal of Financial Economics 59, 195-220.

Ferson, W.E., Warther, V.A., 1996. Evaluating fund performance in a dynamic market. Financial

Analysts Journal 52, 20-28.

French, K.R., Schwert, G.W., Stambaugh, R.F., 1987. Expected stock returns and volatility.

Journal of Financial Economics 19, 3-30.

Gemmill, G., 1996. Transparency and liquidity: a study of block trades on the London Stock

Exchange under different publication rules. Journal of Finance 51, 1765-1790.

Glosten, L.R., Jagannathan, R., Runkle, D.E., 1993. On the relation between the expected value

and the volatility of the nominal excess return on stocks. Journal of Finance 48, 1779-1801.

Goetzmann, W.N., Ivkovic, Z., Rouwenhorst, K.G., 2001. Day trading international mutual funds:

evidence and policy solutions. Journal of Financial and Quantitative Analysis 36,287-309.

Gompers, P., Metrick, A., 2001. Institutional investors and equity prices. Quarterly Journal of

Economics 116, 229-259.

Greene, J.T., Hodges, C.W., 2002. The dilution impact of daily fund flows on open-end mutual

funds. Journal of Financial Economics 65, 131-158.

Harris, L., 1986. Cross-security tests of the mixture of distributions hypothesis. Journal of

Financial and Quantitative Analysis 21, 39-46.

Harris, L., 1987. Transactions data tests of the mixture of distributions hypothesis. Journal of


Harris, M., Artur, R., 1993. Differences of opinion make a horse race. Review of FinancialStudies 6, 473-506.

Haugen, R.A., Talmor, E., Torous, W.N., 1991. The effect of volatility changes on the level of

stock prices and subsequent expected returns. Journal of Finance 46, 985-1007.


33/46

33

Hirshleifer, D., Subrahmanyam, A., Titman, S., 1994. Security analysis and trading patterns when

some investors receive information before others. Journal of Finance 49, 1665-1698.

Holthausen, R.W., Leftwich, R.W., Mayers, D., 1987. The effect of large block transactions on

security prices. Journal of Financial Economics 19, 237-267.

Holthausen, R.W., Leftwich, R.W., Mayers, D., 1990. Large-block transactions, the speed of

response, and temporary and permanent stock-price effects. Journal of Financial Economics 26,

71-95.

Jones, C., Lipson, M., 1999. Execution costs of institutional equity orders. Journal of Financial

Intermediation 8, 123-140.

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


Keim, D.B., Madhavan, A., 1995. Anatomy of the trading process: empirical evidence on the

behavior of institutional trades. Journal of Financial Economics 37, 371-398.

Keim, D.B., Madhavan, A., 1996. The upstairs market for large-block transactions: analysis and

measurement of price effects. Review of Financial Studies 9, 1-36.

Keim, D.B., Madhavan, A., 1997. Transactions costs and investment style: an inter-exchange

analysis of institutional equity trades. Journal of Financial Economics 46, 103-131.

Kothare, M., Laux, P.A., 1995. Trading costs and the trading systems for Nasdaq stocks.

Financial Analysts Journal 51, 42-53.

Kraus, A., Stoll, H.R., 1972. Price impacts of block trading on the New York Stock Exchange.


Lakonishok, J., Shleifer, A., Vishny, R.W., 1992. The impact of institutional trading on stock

prices. Journal of Financial Economics 32, 23-44.

Lakonishok, J., Shleifer, A., Vishny, R.W., 1994. Contrarian investment, extrapolation, and risk.


Morgan, I.G., 1976. Stock prices and heteroskedasticity. Journal of Business 49, 496-508.

Nofsinger, J.R., Sias, R.W., 1999. Herding and feedback trading by institutional and individual

investors. Journal of Finance 54, 2263-2295.

Parkinson, M., 1980. The extreme value method for estimating the variance of the rate return.

Journal of Business 53, 61-66.

Reilly, F.K., Wachowicz, J.M., Jr., 1979. How institutional trading reduces market volatility.

Journal of Portfolio Management 5, 11-17.

Richardson, G., Sefcik, S.E., Thompson, R., 1987. A test of dividend irrelevance using volume

reaction to a change in dividend policy. Journal of Financial Economics 17, 313-333.


34/46

34

Rogalski, R.J., 1978. The dependence of prices and volume. Review of Economics and Statistics

36, 268-274.

Saar, G., 2001. Price impact asymmetry of block trades: an institutional trading explanation.

Review of Financial Studies 14, 1153-1181.

Scharfstein, D.S., Stein, J.C., 1990. Herd behavior and investment. American Economic Review

80, 465-479.

Schwert, G.W., 1989. Why does stock market volatility change over time? Journal of Finance 44,

1115-1153.

Shalen, C.T., 1993. Volume, volatility, and the dispersion of beliefs. Review of Financial Studies6, 405-434.

Sias, R.W., Starks, L.T., 1997. Return autocorrelation and institutional investors. Journal of

Financial Economics 46, 103-131.

Sias, R.W., 1996. Volatility and the institutional investor. Financial Analysts Journal 52, 13-20.

Warther, V., 1998. Has the rise of mutual funds increased market instability? Brookings-Wharton

Papers on Financial Services, 239-262.

Warther, V., 1995. Aggregate mutual fund flows and security returns. Journal of Financial

Economics 39, 209-235.

Wermers, R., 1999. Mutual fund herding and the impact on stock prices. Journal of Finance 54,

581-622.

Whitelaw, R.F., 1994. Time variations and covariations in the expectation and volatility of stockmarket returns. Journal of Finance 49, 515-541.

Wood, R.A., McInish, T.H., Ord, J.K., 1985. An investigation of transactions data for NYSE

stocks. Journal of Finance 40, 723-741.

Xu, Y., Malkiel, B.G., 2003. Investigating the behavior of idiosyncratic volatility. Journal of

Business, forthcoming.

Zweig, M., 1973. An investor expectations stock price predictive model using closed-end fund

premiums. Journal of Finance 28, 67-78.


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35

Table 1

Summary statistics for daily mutual fund flow

Our data on daily flow (new subscriptions less redemptions), NAVs, and TNAs come from TT financial

services of Santa Rosa, CA. We match the whole sample of about 850 mutual funds in TT with those in theCRSP survivor-bias-free U.S. mutual fund database and classify the mutual funds by the investment

objectives defined by ICI. Included in our sample of all U.S. equity funds are funds from aggressive growth

to precious metals. We apply two filters described in detail in the main text, the absolute-value filter and thereversal filter, to the TNA and NAV series, and we aggregate the two series. Flow is defined as the one-day

percentage change in aggregate TNAs less the one-day percentage change in aggregate NAVs.

Distributions are not accounted for in these data.

Time period: 2/3/9812/29/00 (735 observations)Sample: 411 U.S. equity funds

Panel A. Univariate statistics and autocorrelations of fund flow

AutocorrelationsFund

investment

objective

Mean

(b.p.)

Median

(b.p.)

Std. dev.

(b.p.)

Std.err

of mean

(b.p.) Lag1 Lag2 Lag5

Aggressivegrowth

4.54 3.54 25.6 0.95 -0.023* -0.144* 0.094

Growth and

income1.33 1.11 8.9 0.33 -0.193* 0.051 0.129*

Long-term

growth3.63 2.57 23.3 0.86 -0.065* -0.310* 0.003

Sector funds 3.44 1.33 40.0 1.48 -0.223* -0.075* -0.012

Total return -4.44 -4.35 18.2 0.67 -0.076* 0.031 0.122*

Utility funds -1.91 -2.97 38.0 1.41 -0.361* 0.009* 0.041

Income -1.68 -2.18 13.0 0.48 -0.076* 0.071 0.140*

Precious

metals-4.77 -21.3 198.0 7.33 -0.147* -0.237* -0.034

All U.S. equity

funds2.94 1.63 15.6 0.58 -0.091* -0.227* 0.060

Panel B. Univariate statistics of aggregate net inflow and outflow

Obs. Mean (b.p.) Median (b.p.)Std. dev.

(b.p.)

Std. err of

mean (b.p.)

Aggregate net

inflow416 11.83 9.00 12.8 0.63

Aggregate net

outflow319 -8.62 -6.69 10.5 0.59

* Significant at 0.05 level, two-tailed test.


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36

Table 2

Summary statistics for alternative volatility estimators

Panel A shows the univariate statistics and autocorrelations of market high-frequency volatility ( High ),

high-low volatility ( HL ), and implied volatility ( VIX ). High is calculated from S&P 500 index five-

minute intraday returns using the estimator of ABDL (2001) and ABDE (2001). HL is calculated usingthe methods of Parkinson (1980). VIX is the implied volatility index based on the option of the S&P 100

index quoted by the CBOE. Panel B shows the correlations of these three volatility estimators. t-statisticsare in parentheses.

Time period: 2/3/9812/29/00 (735 observations)

Panel A. Univariate statistics and autocorrelations of volatility estimators

Autocorrelations

Mean (%)Median

(%)Std.dev.

(%)Std.err. ofmean (%)

Lag 1 Lag 2 Lag 3

High 16.3 15.2 6.8 0.25 0.624* 0.525* 0.480*

HL 15.6 14.0 8.2 0.30 0.368* 0.349* 0.286*

VIX 25.7 24.7 5.1 0.19 0.931* 0.877 0.832

Panel B. Correlations of volatility estimators

High HL VIX

High 1.0000

HL 0.8059*

(


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37

Table 3

Contemporaneous relationships between returns and flow

In Panel A, daily flow (Flowt) is regressed on current and past observations of market returns of the NYSEindex (Returnt-i) and past observations of flow (Flowt-i). In Panel B, daily returns of the NYSE index are

regressed on concurrent and lagged daily flow in Column 4 and on concurrent and lagged unexpected daily

flow (Uflowt-i) and concurrent expected daily flow (Eflowt) in Columns 5 and 6. Expected daily flow istaken from Panel A, Column 2. Unexpected flow is actual minus expected. The subscripts indicate the days

lagged. t-statistics are in parentheses.Time Period: 2/3/9812/29/00(735 observations)

Sample: 411 U.S. Equity funds

Panel A. Flow dependence on returns and past flow

1 2 3

Coefficient on:

Intercept0.00031*

(6.2)

0.00031*

(8.2)

0.00030*

(8.1)

Returnt-

-

-

-

0.009*

(2.01)

Returnt-1 0.068*(15.1)0.069*(15.6)

0.066*(15.1)

Returnt-2-0.036*

(-5.9)

-0.035*

(-8.0)

-0.038*

(-8.5)

Returnt-3-0.010

(-1.87)

-0.009

(-1.67)

-

-

Flowt-1-

-

-0.077*

(-2.11)

-0.077*

(-2.12)

Flowt-2--

-0.220*(-6.03)

-0.225*(-6.18)

R2 28.5% 32.3% 33.3%Panel B. Returns dependence on flow

Raw flow 4 Exp.-Unexp. flow 5 6

Coefficient on: Coefficient on:

Intercept0.00012

(0.25)Intercept

0.00043

(0.01)

0.00013

(1.07)

Flowt0. 317*(1.97)

Uflowt0. 663*(2.48)

0.629*(2.01)

Flowt-10.021

(0.08)Uflowt-1

0.006

(0.02)

-

-

Flowt-2-0.038

(-0.14)Uflowt-2

0.273

(0.81)

-

-

Flowt-3-0.00002

(-0.00)

Uflowt-3-0.325

(-1.03)

-

-Flowt-4

-0.119(-0.44)

Uflowt-4-0.089(-0.28)

--

Flowt-50.115

(0.43)Uflowt-5

0.166

(0.52)

-

-

Eflowt0.483

(0.98)

0.459

(1.01)

R2 1.0% 2.3% 1.9%



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38

Table 4

Regressions of high-frequency volatility on flow

Daily high-frequency volatility (ln(tHigh ,

)) is regressed on concurrent aggregate domestic equity fund

flow in Column 1, after controlling for the persistence of volatility. Column 2 controls for a dummy

variable ( downUp_ ). Column 3 also controls for market turnover rate ( tTV ) in addition to downUp_ .

tHigh , is calculated from S&P 500 index five-minute intraday returns using the estimator of ABDL (2001)

and ABDE (2001). downUp_ is defined as 1 when the S&P 500 daily return is positive and as 0

otherwise. tTV is the daily trading volume scaled by shares outstanding at the end of the previous day. The

subscripts indicate the days lagged. t-statistics are in parentheses.

Time period: 2/3/9812/29/00 (735 observations)


1 2 3

Coefficient on:

Intercept -0.389*(-6.56)

-0.318*(-5.38)

-1.049*(-11.68)

Flowt-31.530*(-4.75)

-29.228*(-4.50)

-34.099*(-5.60)

downUp_ --

-0.120*(-6.00)

-0.114*(-6.08)

TVt-

-

-

-

88.165*

(10.32)

Ln(1, tHigh

) 0.395*(10.94)

0.404*

(11.44)

0.339*

(10.09)

Ln(2, tHigh

) 0.236*(6.10)

0.240*

(6.33)

0.036*

(5.88)

Ln(3, tHigh

) 0.157*(4.36)

0.150*

(4.27)

0.128*

(3.89)

Adj R2 48.14% 50.52% 56.80%



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39

Table 5

Regressions of high-frequency volatility on aggregate net inflow and outflow


)) is regressed on concurrent aggregate net inflow ( tInflow )

and outflow ( tOutflow ) in Column 1, after controlling for the persistence of volatility. Column 2 controls

for a dummy variable ( downUp_ ). Column 3 also controls for market turnover rate ( tTV ) in addition to

downUp_ .tHigh ,

is calculated from S&P 500 index five-minute intraday returns using the estimator of

ABDL (2001) and ABDE (2001). tInflow and tOutflow are two series representing net inflow and net

outflow, respectively. downUp_ is defined as 1 when the S&P 500 daily return is positive and as 0

otherwise. tTV is the daily trading volume scaled by shares outstanding at the end of the previous day. The

subscripts indicate the days lagged. t-statistics are in parentheses.Time period: 2/3/9812/29/00 (735 observations)


1 2 3

Coefficient on:

Intercept-0.389*

(-6.45)

-0.315*

(-5.23)

-1.296*

(-13.83)

tInflow -31.647*

(-3.32)

-31.215*

(-3.35)

-36.819*

(-4.37)

tOutflow 31.335*

(2.39)

25.934*

(2.02)

28.035*

(2.42)

downUp_ -

-

-0.121*

(-6.01)

-0.115*

(-6.32)

TVt--

--

110.740*(12.85)

Ln(1, tHigh

) 0.395*(10.93)

0.404*

(11.43)

0.317*

(9.70)

Ln(2, tHigh

) 0.236*(6.09)

0.240*

(6.33)

0.200*

(5.82)

Ln(3, tHigh

) 0.157*(4.36)

0.150*(4.27)

0.127*(3.99)

Adj R2 48.07% 50.46% 59.61%



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40

Table 6

Dispersion rate statistics

Dispersion rate ( tDR ) is calculated by dividing the difference between the number of funds with positive

flow and the number of funds with negative flow on day tby the total number of funds on the same day.

Statistics of PDRt_ ( 0>tDR ) and NDRt_ ( 0


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41

Table 7

Regression of high-frequency volatility on dispersion rate


)) is regressed on positive ( PDRt_ ) and negative dispersion

rate ( NDRt_ ) in Column 1, after controlling for the persistence of volatility. Column 2 controls for a

dummy variable ( downUp_ ). Column 3 also controls for market turnover rate ( tTV ) in addition to

downUp_ .tHigh ,

is calculated from S&P 500 index five-minute intraday returns using the estimator of

ABDL (2001) and ABDE (2001). PDRt_ and NDRt_ are positive and negative tDR , respectively,

where tDR is calculated by dividing the difference between the number of funds with positive flow and

the number of funds with negative flow on day tby the total number of funds on the same day.

downUp_ is defined as 1 when the daily S&P 500 return is positive and as 0 otherwise. tTV is the daily

trading volume scaled by shares outstanding at the end of the previous day. The subscripts indicate the days

lagged. t-statistics are in parentheses.Time period: 2/3/9812/29/00 (735 observations)

1 2 3

Coefficient on:

Intercept-0.517*

(-8.24)

-0.443*

(-7.13)

-1.306*

(-13.72)

PDRt_ -0.380*

(-2.36)

-0.362*

(-2.31)

-0.235#

(-1.63)

NDRt_ 0.493*

(4.99)

0.500*

(5.20)

0.496*

(5.44)

downUp_ --

-0.125*

(-6.40)

-0.121*

(-6.72)

TVt-

-

-

-

98.612*

(11.35)

Ln(1, tHigh

) 0.357*(9.90)

0.365*

(10.39)

0.298*

(9.04)

Ln(2, tHigh

) 0.233*(6.20)

0.238*(6.51)

0.195*(5.73)

Ln(3, tHigh

) 0.159*(4.52)

0.152*(4.43)

0.134*(4.24)

Adj R2 50.42% 53.01% 60.08%

* Significant at 0.05 level, two-tailed test

# Significant at 0.10 level, two-tailed test


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42

Table 8

Dependence of high-low volatility and implied volatility on aggregate net inflow and outflow

Controlling for the persistence of volatility, a dummy variable ( downUp_ ), and market turnover rate

( tTV ), daily high-low volatility (Ln( tHL , )) and implied volatility (Ln( tVIX, )) are regressed on

concurrent aggregate net inflow ( tInflow ) and outflow ( tOutflow ) in Column 1 and Column 2,respectively.

tVIX, is calculated using the methods of Parkinson (1980).

tVIX, is the implied volatility

index based on the option of the S&P 100 index quoted by the CBOE. tInflow and tOutflow are two

series representing net inflow and outflow, respectively. downUp_ is defined as 1 when the S&P 500

daily return is positive and as 0 otherwise. tTV is the daily trading volume scaled by shares outstanding at

the end of the previous day. The subscripts indicate the days lagged.Time period: 2/3/9812/29/00 (735 observations)


1 2

Ln(tHL ,) Ln(

tVIX,)

Estimate t-value Estimate t-value

Intercept -1.535* -11.97 -0.034# -1.67

tInflow -39.671* -3.03 -4.835* -2.69

tOutflow 48.481* 2.71 4.066# 1.82

downUp_ -0.160* -5.68 -0.085* -22.96

TVt 114.305* 9.20 3.885* 2.34

Ln( 1t ) 0.151* 4.43 0.950* 90.32

Ln( 2t ) 0.237* 7.00 - -

Ln( 3t ) 0.133* 3.94 - -

Adj R2 34.70% 94.02%

* Significant at 0.05 level, two-tailed test.# Significant at 0.10 level, two-tailed test.

Documents

A Study of Mutual Fund Flow and Market Return Volatility