do institutional investors have market timing skills ....doc.doc

Do Institutional Investors Have Market Timing Skills? Evidence From ETFs

Biljana NikolicUniversity of MissouriColumbia, MO 65211Phone: 573-884-7937

Email: [email protected]

Andy PuckettUniversity of Tennessee

Knoxville, TN 37922Phone: 865-974-3611


Xuemin (Sterling) Yan*University of MissouriColumbia, MO 65211Phone: 573-884-9708


July 2009

* We would like to thank Judy Maiorca and ANcerno Ltd. (formerly the Abel Noser Corporation) for providing institutional trading data.

mailto:[email protected]



Do Institutional Investors Have Market Timing Skills? Evidence From ETFs

Abstract

We employ an innovative technique to investigate the market timing skills of institutional investors. We provide a simple theoretical model that shows institutional market timers will choose to trade an ETF instead of a basket of stocks when the ETF is more liquid or exhibits lower tracking error. Building on this intuition, we test whether institutional investors exhibit superior broad-market or sector timing skills based on their trades of ETFs. We find that institutional ETF trades predict subsequent ETF excess returns, and our results suggest that up to 15% of our sample institutions have significant market-timing skills. Our findings are distinctly different from prior studies that use a non-linear regression framework to investigate market timing skill.

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I. Introduction

Market timing is a dynamic allocation of capital among broad classes of

investments, where successful market timers increase the portfolio weight on equities prior

to a rise in the market and decrease their weight in equities prior to a fall in the market. The

question of whether institutional investors have superior market-timing skill has been intensely

debated ever since Treynor and Mazuy’s (1966) seminal study. More than four decades later,

academic literature has still not reached a consensus as to whether these sophisticated investors

possess market-timing skills. In particular, studies that use non-linear regressions such as those

proposed by Treynor and Mazuy (1966) and Henriksson and Merton (1984) to investigate

market-timing, generally find little evidence of skill.1 However, recent literature is often critical

of this regression framework. Several studies suggest that non-linear relations between

portfolio returns and market returns might be attributed to factors other than market timing (e.g.

Jagannathan and Korajczyk (1986)), or that these market-timing measures are biased

downward when returns are measured at a monthly frequency and institutions engage in active

timing and trade more frequently than monthly (Goetzmann, Ingersoll, and Ivkovich (2000)).

To overcome these problems, several recent studies either use a novel dataset (e.g. daily

mutual fund returns as in Bollen and Busse (2001)) or an inventive methodology (e.g. holdings-based

measures as in Jiang, Yao, and Yu (2007)). These studies generally find more clear-cut evidence that

mutual funds possess distinct market-timing skills. Our study makes several distinct contributions to

this existing strand of literature. First, we provide a theoretical model where a market timer chooses

between an Exchange Traded Fund (ETF) and a basket of stocks in order to exploit his private

information about future market returns. We show that when the ETF is more liquid, or exhibits

lower tracking error, the market timer will choose to trade the ETF rather than a basket of stocks.

1 Studies that find insignificant (or even negative) evidence of market-timing skill include Treynor and Mazuy (1966), Heriksson and Merton (1984), Henriksson (1984), Lehman and Modest (1987), Grinblatt and Titman (1994), Daniel, Grinblatt, Titman, and Wermers (1997), and Becker, Ferson, Meyers, and Shill (1999), among others.

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Second, we use an innovative approach to directly measure the ex-post performance of institutional

investors’ market timing strategies. That is, we test whether institutional investors exhibit superior

broad-market or sector timing skills based on their holdings and trades of ETFs. Overall, we find

that institutional ETF trades predict subsequent ETF excess returns, and our results suggest that

up to 15% of our sample institutions have significant market-timing skills.

ETFs are an investment vehicle that allows investors access to a diversified portfolio of

equities. ETFs have many appealing features for investors who wish to gain exposure to broad or

sector markets that make them particularly attractive to institutional portfolio managers. Unlike

traditional index mutual funds, ETFs are exempt from short-sale constraints and are traded

continuously during market hours. The proliferation of ETFs over the last decade has also led to

excellent liquidity (compared to equities or closed-end mutual funds) (Boehmer and Boehmer

(2003)).2 According to NYSE Euronext “ ETFs offer institutional investors unique opportunities

to instantly establish, increase, or decrease exposure to broad U.S. and international equity

markets.”3 Our conversations with several institutional portfolio managers confirm that ETFs are

a preferred investment vehicle by which managers often implement directional bets on broad

market movements. If institutional investors are able to time the market, we expect to observe

positive correlation between the changes in institutional holdings of ETFs and the subsequent

performance of ETFs that track market indexes.

We collect quarterly ETF ownership for all institutional 13F filings during the 1999 to

2008 sample period. We then compute changes in quarterly ETF ownership (e.g. trades)

following the methodology of Chen, Jegadeesh, and Wermers (2000) and also calculate

subsequent quarter excess returns for all ETFs traded. In our univariate analysis, we find that

2 During the 1999 to 2008 sample period, Amihud’s illiquidity measure for ETFs is 98% smaller than that of common stocks.3 Quote from NYSE Euronext website: www.amex.com/etf/eductn/etf_edu_instit.html .

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http://www.amex.com/etf/eductn/etf_edu_instit.html

when an institution purchases an ETF, the subsequent quarter excess return is positive 50.11% of

the time; compared to when institutions sell an ETF, the subsequent quarter excess return is

positive only 45.92% of the time. We investigate this relationship in a multivariate setting that

controls for macro-economic variables that have been shown to capture variation in expected

returns. We find a significant positive relationship between institutional ETF trading and

subsequent ETF returns. We confirm our inference using a bootstrap simulation that makes no

assumption about the distribution of market returns. Our coefficient estimates suggest that ETFs

in which an institution increases its holdings outperform ETFs in which an institution decreases

(or does not change) its holdings by between 1.27% and 1.29% in the subsequent quarter. In

addition, our analysis suggests that approximately 15% of institutions in our sample have

significant market-timing skills (compared to 2.5% that could be expected to have significant

market-timing skills merely by chance).

We next investigate whether institutions’ market-timing skill is more evident for broad-

market or sector ETFs. According to Kacperczyk, Sialm, and Zheng (2005), portfolio managers

might be particularly skilled with respect to certain industries (where they concentrate their

holdings). We conduct multivariate regression analysis for broad-market and sector ETFs

separately and find that market-timing skill is generally more evident for trades in broad-market

ETFs. Specifically, institutional buys are associated with a 1.96% increase in subsequent-quarter

returns for broad-market ETFs, and coefficient estimates suggest that approximately 12% of

institutions have superior market-timing skills. Alternatively, regression coefficients are

generally insignificant for sector ETF trades; however, coefficient estimates are positive and

significant for about 10% of institutions.

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We also test whether ETF trading predicts subsequent quarter ETF volatility. Our

multivariate analysis regresses institutional ETF trading on subsequent quarter ETF volatility.

We find a negative relationship. That is, subsequent quarter ETF volatility decreases by 1.64%

following institutional purchases of ETFs. Our findings suggest that institutions increase the

Sharpe ratio of their portfolio by increasing ETF exposure prior to decreases in market volatility.

Our final analyses test the robustness of our primary findings. Portfolio managers who

receive unexpected inflows or outflows of capital might quickly gain (or decrease) equity

exposure by trading in broad-market ETFs. If ETF trading is motivated by fund flows, then the

market-timing skill that we document should be attributed to retail investors. We re-run our

multivariate regression analysis while controlling for fund flows, and continue to find evidence

of significant market timing skill.

Our second robustness test addresses potential measurement error in our trading and

performance variables. Specifically, changes in quarterly holdings do not capture intra-quarter

transactions where institutions purchase and sell or sell and re-purchase the same ETF, nor can

they identify the exact timing and execution price of trades. If institutions engage in active

timing and trade more frequently than quarterly , this measurement error will reduce the power

of our quarterly tests (Kothari and Warner (2001)) and may lead to incorrect inference

(Goetzmann, Ingersoll, and Ivkovich (2000)). We overcome the limitations of quarterly

institutional holdings data by using a large proprietary database of actual institutional trades

provided by ANcerno Ltd. (formerly the Abel Noser Corporation) for the period 1999-2008.

The database identifies the exact date and execution price of each transaction, and allows us to

track the trades of each institution through time.

We find that the excess return of ETFs in the week following actual institutional trades

is significantly positive (0.10%). We extrapolate our weekly returns to quarterly frequency and

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find abnormal returns of 1.21%. The magnitude of our result is similar to our quarterly

analysis, and confirm our previously documented market timing findings.

Overall, our analysis reveals that some institutional investors possess significant market

timing skills. Our analysis is unique since we are able to directly measure the performance of

institutional investors who change their exposure to broad-based equity markets. Our findins differ

from many prior studies that use non-linear regressions to investigate market timing skill.

The next section discusses the relevant literature in the market timing skill debate. Section III

presents our theoretical model of ETF trading. Section IV discusses our data and sample. Section V

presents our methodology and empirical results, Section VI presents robustness tests, and Section VI

concludes.

II. Related Literature

The question of whether institutional investors have superior investment skills is important

from an investor’s perspective since conclusions will affect investors’ capital allocation decisions,

and from an academic perspective, since it ultimately serves as a test of the efficient market

hypothesis. In institutional investors are skilled, there are only two ways in which they can earn

risk-adjusted excess returns: 1) through superior stock selection, and/or 2) by successfully timing

the market (Fama (1972)). At the present time, the literature is ambiguous regarding the existence of

investment skill, although many studies make meaningful contributions to the skill debate.

Market timing skill was first investigated by Treynor and Mazuy (1966, hereafter TM),

who use a quadratic regression of realized fund returns against contemporaneous market returns

and squared market returns. The intuition being that if a portfolio manager increases (decreases)

the portfolio’s market exposure prior to a market increase (decrease) then the portfolio’s return

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will be a convex function of the market’s return. TM find significant market timing ability for

only 1 fund out of the 57 in their sample.

Papers by Merton (1981) and Henriksson and Merton (1981, hereafter referred to as HM)

develop market timing tests by showing that return patterns from successful market-timing have

similar characteristics to return patterns of certain option strategies. Henriksson (1984) uses the

market timing test of HM and finds that only 3 funds out of the 116 in his sample have

significant market timing ability. Chang and Lewellen (1984), Lehman and Modest (1987),

Chen, Lee, Rahman and Chan (1992), and Grinblatt and Titman (1994) draw similar conclusions

using the same methodology.

Subsequent studies are often critical of TM and HM regression frameworks. First, several

academic studies suggest that the regression framework is misspecified. Bellow and Janjigian

(1997) use the TM measure and control for the inclusion of non-S&P 500 assets in the portfolio.

They find significant market timing ability for mutual funds during the 1984 to 1994 sample

period. Even when regressions contain these added control variables, certain dynamic trading

strategies and some stocks exhibit option like features in returns, which gives rise to artificial

market timing effects (Jagannathan and Korajczyk, 1986). In addition, Goetzmann, Ingersoll and

Ivkovich (2000) show that there is downward bias in return-based measures when returns are

measured at a monthly frequency and institutions engage in active timing and trade more

frequently than monthly.

Several recent studies overcome these problems by using more frequent fund returns or

by employing an inventive methodology. Bollen and Busse (2001) test market-timing skills

using more frequent daily returns and find positive timing ability for a sample of 230 domestic

equity funds. Alternatively, Jinag, Yao, and Yu (2007) develop a methodology that uses the

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weighted average of betas for individual stocks held in the portfolio, and tests whether the

covariance between “holdings-based” fund betas are significantly related to contemporaneous

market returns. They also find, on average, that actively managed mutual funds have positive

timing ability.

An alternative to the TM and HM regression approaches was first developed by Graham

and Harvey (1996). They directly examine market timing skill by regressing future returns on

current changes in asset allocation recommendations. This approach has several appealing

characteristics: primarily that it directly measures the subsequent performance of market timing

decisions. Their approach does not require the simultaneous estimation of stock-selection and

market timing skills, and therefore is immune to many of the criticisms of the TM and HM

models. Graham and Harvey (1996) use this methodology to investigate the market timing ability

of Investment Newsletters, and fail to find evidence of superior advice or market-timing skills.

At the current time, we are unaware of any study that applies Graham and Harvey’s

(1996) approach to institutional money managers. The most likely reason for this omission is that

institutional investors do not post asset allocation recommendations. We circumvent this

restriction and provide new evidence of market timing skill by looking at institutional trading in

broad market ETFs. In the next section, we present a simple model to explain why institutional

investors would use ETFs to exploit their market timing ability.

III. A Simple Model of Market Timing with ETFs and Stocks

We develop a simple theoretical model where a market timer chooses either an ETF or a

basket of stocks to exploit his superior information about future market returns. We show that

when the ETF is more liquid than stocks, the market timer will choose to trade the ETF rather

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than a basket of stocks. Alternatively, even when stocks are more liquid than the ETF, the market

timer might still choose to trade the ETF because it involves lower tracking error.

We consider a two-period model in which a single trader forms his portfolio today and

the assets in the portfolio pay off tomorrow. There is a risk-free bond that yields gross return R.

In addition, there are n stocks and an ETF. The payoff of the ETF is u, and one can think of the

ETF as representing the market portfolio. The payoff of stock i is , where ei is the stock-

specific return, , and . For simplicity, we assume

all stocks have a beta of 1.

The prior distributions of u and ei are and , respectively. The prior

distribution of υi is then . We do not attempt to determine the equilibrium prices

of any asset in this model. Without loss of generality, we normalize the market prices of all

securities to 1.

There is one informed trader (market timer), who receives a private signal θ about the

market return, but not about stock-specific return. The signal θ is equal to , where

and . We assume the equilibrium asset prices are unaffected by

this private information.

Since the market timer has no private information about firm-specific returns, we assume

that when he trades stocks, he will trade an equal-weighted portfolio of n stocks.

(1)

For now we assume n is fixed; in Appendix A we allow n to be determined endogenously. The

prior distribution of υ p is then:

(2)

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The posterior distribution of u is normal, with mean and variance given by:

(3)

The posterior distribution of υ p is also normal, with mean and variance given by:

(4)

Trading the ETF will incur a trading cost where . Here, Q is the

dollar amount of trading, a0 represents the fixed cost and a1 is the proportional trading cost.4 The

trading cost for each stock is where . The cost of trading the equal-

weighted stock portfolio is then . Here, the total fixed cost is n times that of trading one

stock. In practice, institutions can engage in basket trading so the fixed cost may be smaller than

na0. In this case, one can alternatively interpret na0 as capturing the inconvenience of trading n

different stocks.5 The proportional trading cost is a fraction of the dollar amount of trading and

therefore is unrelated to n.

The market timer has an initial wealth of W0, which is entirely invested in the risk-free

bond. The market timer has a mean-variance utility over his terminal wealth with a constant

coefficient of absolute risk aversion A. Let x denote the amount invested in the bond; y denote

the amount invested in the ETF; and z denote the amount invested in the equal-weighted stock

portfolio. The market timer maximizes his utility:

(5)

4 The model is still tractable with a quadratic trading cost function. The intuitions of the model are similar if one makes suitable assumptions about the convexity of the trading cost function.5 Most of our results require only the fixed cost of trading the stock portfolio be larger than that of the ETF, not necessarily n times as large.

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Since trading cost is a function of the dollar amount of trading, (5) is not differentiable at y=0 or

z=0. Therefore, the problem is best solved separately for positive and negative regions of y and z.

Specifically, when E (u | θ) > R, y and z will take non-negative values; when E (u | θ) < R, y and z

will take non-positive values. In addition, because of the presence of fixed trading cost, the

problem may admit corner solutions.

Let’s first consider the special case where z is constrained to be 0. It is easily checked that

the solutions are:

when E (u | θ) > R (6)

when E (u | θ) < R (7)

Proposition 1: If the market timer can only trade the ETF but not stocks, his optimal investment in the ETF (y*) is as follows: (1) If E (u | θ)-R >a1 and U(y1+, 0)>W0R, then y*= y1+; (2) If E (u | θ)-R <- a1 and U(y1-, 0) >W0R, then y*= y1-; and (3) Otherwise, y*=0.

Note that for y* to be nonzero, the following two conditions have to be satisfied: (1) the

magnitude of the expected excess return has to be greater than the proportional transactions cost;

and (2) the maximized expected utility has to be greater than that of the corner solution, W0R. (6)

and (7) are standard demand functions for a mean-variance investor adjusted for transactions

costs. Based on (6), we can see that demand for the risky asset increases with the expected

excess return and decreases with risk aversion, volatility, and the proportional trading cost.

Similarly, the solutions for the special case where y is constrained to be 0 are:

when E (u | θ) > R (8)

when E (u | θ) < R (9)

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Proposition 2: If the market timer can only trade stocks but not the ETF, his optimal investment in the equal-weight stock portfolio z* is as follows: (1) If E (u | θ)-R >a2 and U(z1+, 0)>W0R, then z*= z1+; (2) If E (u | θ)-R <- a2 and U(z1-, 0)>W0R, then z*= z1-; (3) Otherwise, z*=0.

Next we solve the unconstrained problem where the market timer can trade both the ETF and

stocks. We first consider the case E (u | θ)-R>0. In this case, it is not optimal to short either the

ETF or stocks. The market timer maximizes:

(10)

The first-order-conditions are:

(11)

Solving (11) we obtain:

(12)

(13)

Proposition 3: If the market timer can trade both the ETF and stocks and assuming E (u | θ)>R , his optimal investment in the ETF (y**) and equal-weight stock portfolio (z**) are as follows: (1) If E (u | θ)-R < min(a1, a2), then y**=z**=0; (2) If a1< E (u | θ)- R <a2, then y**=y* and z**=0, where y* is given in Proposition 1; (3) If a2< E (u | θ)- R <a1, then y**=0 and z**=z*, where z* is given in Proposition 2;(4) If E (u | θ)-R> a2≥a1, then y**=y* and z**=0; (5) If E (u | θ)-R> a1>a2 and y2+<0, then y**= 0 and z**= z*; (6) Otherwise if U(y2+, z2+)>W0R, then y**= y2+ and z**= z2+ .

Basically, when the ETF is more liquid than the stocks (a2>a1), the market timer will not trade

the stocks. On the other hand, when the stocks are more liquid than the ETF, the market timer

may still trade the ETF because the ETF offers a lower tracking error. In the last scenario when

optimal investments are given by (12) and (13), we note that the optimal investment in the ETF

increases with a2 and the expected excess return, and decreases with a1 and A. The optimal

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investment in the equal-weight stock portfolio increases in the difference between a1 and a2, and

decreases in A and κ. It is interesting to note that in the last scenario the sum of total investments

in risky assets takes a simple form and is equal to y1+ in (6):

(14)

Similarly, we have the following results for the case E (u | θ)<R:

Proposition 4: If the market timer can trade both the ETF and stocks and assuming E (u | θ)<R , his optimal investment in the ETF (y**) and equal-weight stock portfolio (z**) are as follows: (1) If R -E (u | θ) < min(a1, a2), then y**=z**=0; (2) If a1< R -E (u | θ) <a2, then y**=y* and z**=0, where y* is given in Proposition 1; (3) If a2< R -E (u | θ) <a1, then y**=0 and z**=z*, where z* is given in Proposition 2; (4) If R -E (u | θ) > a2≥a1, then y**=y* and z**=0; (5) If R -E (u | θ)> a1>a2 and y2->0, then y**= 0 and z**= z*; (6) Otherwise if U(y2-, z2-)>W0R., then y**= y2- and z**= z2-,where y2- and z2- are given below.

(15)

(16)

In this case, the market timer possesses negative information about the market return. When the

ETF is more liquid than the stocks (a2>a1), the market timer will not short the stocks. On the

other hand, when the stocks are more liquid than the ETF, the market timer may still short the

ETF because it offers a lower tracking error. We show in Appendix A that the basic intuitions of

the model carry over when we allow n to be endogenous.

IV. Data and Sample

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We construct our initial sample by first identifying ETFs in the CRSP database.6 The first

ETF, the SPDR S&P 500 index, was listed on AMEX in 1993. Since then, the number (and

market value) of ETFs has increased dramatically. The total net assets of ETFs have gone from

464 million dollars in 1993, to 304 billion dollars in 2008. During the 1993 to 2008 time period,

we find 820 ETFs that have been traded in U.S. markets. We collect returns, volume, and shares

outstanding data for all of the 820 ETFs from the CRSP database. We also obtain a Lipper

objective description for each ETF, and based on that objective description, we classify each

ETF into one of six mutually exclusive categories.7 These categories include U.S. broad market,

U.S. sector, International equity, Fixed income, Commodities, and Bear Market.

The first two categories encompass ETFs that exclusively invest in U.S. equities. U.S

broad market ETFs invest in a broad and diversified portfolio of stocks (e.g., S&P500 or

S&P1500), whereas U.S sector ETFs invest in stocks in a particular industry (e.g., financials) or

style (e.g., small stocks or growth stocks). The final four categories hold stocks outside of the

U.S. (International equity), hold fixed income investments (Fixed income), invest in commodity

futures (Commodities), or include short positions (Bear market). Since we are primarily

concerned with investigating market timing skill in U.S. markets, we restrict our sample to ETFs

in the first two categories: U.S. broad market and U.S. sector. This restriction leaves us with a

final sample of 359 ETFs.

We obtain quarterly institutional ownership data for all sample ETFs from 13F filings

obtained through Thompson Financial.8 Descriptive statistics for both the ETF sample and

6 We identify ETFs in two ways: first, we require that the stock has a CRSP sharecode equal to 73. Second, we require that the stock has an ETF/ETN flag value equal to one in the CRSP mutual fund database.7 Lipper objective descriptions are available in the CRSP mutual fund database.8 The Securities Act Amendment of 1975 requires that institutional investors managing more than $100 million report their portfolio holdings to the Securities and Exchange Commission (SEC) on a quarterly basis (13F filings). Institutions are required to disclose holdings in U.S finrms if their stock position is greater than $200,000 or 10,000 shares.

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institutional ETF holdings are presented in Panel A of Table 1. Although the first ETF (SPY)

was traded in U.S. markets beginning in 1993, investors were slow to adapt to the new trading

vehicles and trading was generally light for several years. By 1999 there were 10 ETFs listed in

U.S. markets and trading volume had increased significantly. From 1999 until 2008 the number

of total ETFs increases from 12 to 333, and the total ETF market capitalization rises from $10.9

billion to $304.2 billion. This increasing trend is quite apparent when viewed in Figure 1. The

trend is evident both for U.S. broad market ETFs and for U.S. sector ETFs. For the same time

period, the number of broad market (sector) ETFs increases from 2 to 18 (10 to 315) and market

capitalization increases from $5.1 billion to $145 billion ($5.8 billion to $159.2 billion).

During our sample period, the bredth of ETF ownership for institutional investors

increased dramatically. In 1999 only 300 different institutional investors report ETF ownership,

which represents 15.94% of 13F reporting institutions. By 2008, 48.74% of institutions (1,533

institutions) held ETFs in their portfolios, with a total market value of $198.32 billion.

In Panel B of Table 1 we present a comparison of our ETF sample to common stocks in

the CRSP database during the 1999 to 2008 sample period. Not surprisingly, ETFs are younger

and smaller than the average common stock. However, ETFs experience significant

improvements in liquidity when compared to common stocks. Average monthly turnover is

99.79% for ETFs compared to 14.77% for common stocks. In addition, Amihud’s illiquidity

measure for ETFs is only 2% of that for common stocks, and quoted spreads for ETFs (0.044)

are about half as large as those for common stocks (0.078).

V. Methodology and Empirical results

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Successful market timers increase their portfolio weight in equities prior to a rise in the

market and decrease their weight in equities prior to a fall in the market. Following this logic, if

institutional investors have market-timing skills we expect the ETFs that they purchase to

perform well and the ETFs that they sell to perform poorly during the subsequent period. We test

this relationship in both univariate and multivariate settings.

A. Univariate

We measure ETF trades for each institution in a manner similar to Chen, Jegadeesh and

Wermers (2000). Specifically, in each quarter we calculate the fractional ETF holding (w) as:

(17)

Where Number of Shares Heldi,j,t is the number of shares held in ETF i, by institution j, at the end

of quarter t.

We then measure trades of an ETF by each institution as the quarterly change in w for

that ETF. Specifically, the trades in ETF i, for institution j, in quarter t are:

Δwi,j,t = wi,j,t - wi,j,t-1 (18)

We take all ETF trades and separate them into buys (∆w>0) and sells (∆w<0), and track

the excess return for ETFs traded in each group during the quarter following portfolio formation.

Excess returns are calculated as the ETF return minus the return on the 90-day treasury bill.

Our results presented in Panel A of Table II show a positive relationship between ETF

trades and subsequent quarter excess returns. In the quarter following institutional buy trades, we

find 50.11% of next quarter’s excess returns are greater than zero, compared to sell trades where

only 45.92% of next quarter’s excess returns are greater than zero. The difference of 4.19% is

significant (t-statistic=6.13) and suggests that institutions have at least some market timing

ability. Evidence of market timing skill is stronger for broad market ETFs than for Sector ETFs.

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Specifically, 58.60% (47.81%) of next quarter’s excess returns are greater than zero for buy

trades in broad market (sector) ETFs, whereas 47.82% (45.91%) of next quarter’s excess returns

are greater than zero for sell trades in broad market (sector) ETFs. The difference is 10.79% (t-

statistic=13.39) for the broad market ETF sample, and 1.90% (t-statistic=2.49) for the sector ETF

sample.

We also present average quarterly returns for our sample in Panel B of Table II. In the

full sample, we find that buy trades outperform sell trades by 1.24% (t-statistic=6.83) in the

quarter following portfolio formation. Again, we find the magnitude of our result is larger for

broad market ETFs (2.10%, t-statistic=12.04) than it is for sector ETFs (1.08%, t-statistic=4.92).

Overall, univariate results support the hypothesis that institutional investors have market

timing skill. We next test whether our findings hold in a multivariate setting.

B. Multivariate

Our multivariate tests of market timing closely follow the methodology of Graham and

Harvey (1996). We use the following regression model:

Ri,t+1 = δi,1 + δi,2Δwi,j,t +δi,3Ri,t + δ’iZt + εi,t+1, (19)

Where, Ri,t+1 is the one-quarter ahead excess return on ETF i; Δwi,j,t is (as previously described)

the quarterly change in ETF i holdings for institution j; Ri,t is the lagged return on ETF i; and Zt

is a vector of control variables. The Zt includes the treasury bill rate (TB) to control for expected

inflation, the difference between the long term yield and treasury bill rate (Term Spread), the

Standard and Poor BBB-AAA yield spread (Default Spread), and the market dividend yield

(DV). These variables have been shown to capture variation in expected returns (Fama and

French (1989); Harvey (1989)) and are obtained from Amit Goyal’s website.

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We pool observations by institution and run our regression separately for each institution.

We require a minimum of 24 observations for an institution to be included in our regression

analysis. We then calculate the average regression coefficient across all institutions and use the

cross-sectional variation in coefficient estimates to compute significance. Our methodology is

similar to that used by Fama and MacBeth (1973) and helps alleviate any concerns that our

results might be driven by variation in the relative size of some institutions rather than market

timing skill.

We have several potential concerns with our regression specification. The first is that our

standard error calculations assume a normally distributed dependent variable. We use Jaque-Bera

tests to test for the normality of excess ETF returns and reject the null of a normal distribution (t-

statistic=19.94). In order to overcome this challenge, we bootstrap the posterior distribution of

excess returns in a manner similar to Bollen and Busse (2001) and Kosowski, Timmerman,

Wermers, and White (2006). Details of our bootstrap analysis are available in Appendix B.

Second, we are also concerned that our inference on the independent variable of interest (Δw)

might be driven by outliers. To address this concern, we construct two alternate independent

variables to substitute for Δw. The first, Δw+, is a dummy variable that is equal to 1 if Δw is

positive and is equal to zero otherwise. The second, ΔwRANK, is a decile rank value that is

assigned to each ETF-institution observation. Specifically, each quarter we rank all Δwi,j into ten

groups and assign a decile rank value of 10 to the highest-value group and a value of 1 to the

lowest-value group. The alternate measures have several appealing properties, primarily they

reduce the influence of extreme outliers in our coefficient estimates.

We present all regression results in Table III. Columns 1-3 present regressions for each of

our three measures of institutional trading, excluding the vector of control variables, Zt.

18

Coefficient estimates for Δw, Δw+, and ΔwRANK are 0.57, 1.29, and 0.21, and all are statistically

significant at the 1% level using bootstrapped p-values. Columns 4-6 of Table III include the

control vector Zt. Both the magnitude and significance of institutional trading variables are

consistent with earlier results. Coefficient estimates for Δw, Δw+, and ΔwRANK are 0.66, 1.27, and

0.20 and all are statistically significant at the 1% level using bootstrapped p-values. All results

support the hypothesis that institutional investors have market timing skill.

The coefficient estimates suggest that ETFs in which an institution increases its holdings

outperform ETFs in which an institution decreases (or does not change) its holdings by between

1.27% and 1.29% in the subsequent quarter. These estimates are economically significant, and

suggest an annualized excess performance difference of more than 5%. The coefficient estimates

for ΔwRANK further suggest that coefficient estimates are significant at the 1% level. Our

estimates suggest that a one decile increase (move to higher buying decile) is associated with a

0.20% increase in excess returns during the subsequent quarter. By comparing extreme decile

ranks, our estimate suggest a 2% subsequent return difference between the extreme buy and

extreme sell ETF portfolio.

In Table III we also include statistics for the percentage of coefficient estimates that are

positive (negative) and the percentage that are positive (negative) and significant. We compare

the coefficients to the bootstrapped posterior distribution to determine whether our results are

statistically different than those that would have been attained by chance. The percentage of

positive coefficients for Δw, Δw+, and ΔwRANK are 58.64%, 74.12%, and 72.32% respectively,

and the percentage that are positive and significant are 9.94%, 14.46%, and 13.90% respectively.

This proportion is significantly different from 2.5%, which we would expect under 5%

significance level. Furthermore, the bootstrap analysis suggests that the percentage of negative

19

coefficients does not significantly differ from that expected by chance. Our results suggest that

between 10 and 15 percent of institutions are skilled market timers.

C. Multivariate by ETF Type

To ensure the robustness of our results and to gain insights into whether market-timing

skills are evident for both the broader market and sectors, we investigate trading in broad market

and sector ETFs separately. Kacperczyk, Sialm, and Zheng (2005) find that portfolio manager

skills are more pronounced when portfolio managers concentrate their portfolio holdings within a

particular industry. Thus market timing skill might be restricted to industries in which a portfolio

manager specializes, rather than skill in timing broad-market movements. Our results are

presented in Table IV.

Panel A presents our results for the sample of broad market ETFs. Our results are

significant and generally stronger than in full sample tests. Columns 1-3 present regressions for

each of our three measures of institutional trading, excluding the vector of control variables, Zt.

Coefficient estimates for Δw, Δw+, and ΔwRANK are 4.67, 1.74, and 0.29. Columns 4-6 of Panel A

include the control vector Zt. Both the magnitude and significance of institutional trading

variables are consistent with earlier results. All independent variables of interest, in general, are

statistically significant at the 5% level using bootstrapped p-values. Our estimates suggest that

institutional buying is associated with subsequent quarter excess returns of 1.74%.

Similar to results presented in Table III, we also include statistics for the percentage of

coefficient estimates that are positive (negative) and significant. The percentage of positive and

significant coefficients for Δw, Δw+, and ΔwRANK are 11.29%, 11.98%, and 12.67% respectively.

These statistics are almost four times as large as what would be suggested by chance, and again

suggest that up to 10% of institutions in our sample are skilled market timers.

20

Panel B of Table IV presents our results for Sector ETFs. We generally find lower

regression coefficients for our independent variables of interest. Coefficient estimates for Δw,

Δw+and ΔwRANK (in columns 1-3) are 1.15, 1.24, and 0.l9 respectively. However, when we

include the vector (Zt) of macro-economic control variables, our regression coefficients of

interest generally become insignificant. Only ΔwRANK is marginally significant. Although our

results suggest little support, on average, for sector timing skills, we continue to find evidence of

market timing in the percentage of institutions with positive and significant regression

coefficients. The percentage of positive and significant coefficients for Δw, Δw+, and ΔwRANK are

9.24%, 9.24%, and 10.05% respectively.

Overall our results suggest that institutions use ETFs to time broad (macro) market

movements, on average, and that these timing skills are evident for about 10% of institutions in

our sample when examining Sector ETFs.

D. Volatility

We also test whether institutional trading is related to future volatility. Portfolio

managers who are able to predict future stock market volatility will increase ETF holdings prior

to declines in volatility and will decrease ETF holdings prior to increases in stock market

volatility. Institutional investors who are successful in implementing these trading strategies will

increase the Sharpe ratio of their portfolio. We run the following regressing to see if institutions

are effective in predicting future volatility:

Voli,t+1 = δi,1 + δi,2Δwi,j,t +δi,3Voli,t + δi,3Negi,t+1 + εi,t+1, (20)

Where, Voli,t+1 is the logarithm of one-quarter ahead realized volatility for ETF i estimated using

daily return data; Δwi,j,t is (as previously described) the quarterly change in ETF i holdings for

institution j; Voli,t is the logarithm of lagged return volatility for ETF i; and Negi,t+1 is a dummy

21

variable that equals 1 if the one-quarter ahead excess return for ETF i is negative and zero

otherwise.

Table V presents our results for ETF trading and volatiltiy. Our results are insignificant

for the independent variable Δw; however, coefficient estimates for alternate variables Δw+ and

ΔwRANK are negative and significant in some specifications. We also include statistics for the

percentage of coefficient estimates that are positive (negative) and significant. The percentage of

negative and significant coefficients for Δw, Δw+, and ΔwRANK are 5.42%, 6.55%, and 5.76%

respectively. Our results suggest that approximately 6% of institutions in our sample are

successful in timing future market volatility.

V. Robustness

A. Fund Flows

It is possible that our results are driven by exogenous liquidity shocks to fund managers

from retail investors. In order to test this proposition, we explicitly control for fund flows in our

multivariate analysis. We augment regression equation (19) as follows:

Ri,t+1 = δi,1 + δi,2Δwi,j,t +δi,3Ri,t + δi,4Flowj,t + εi,t+1, (21)

Where Flow is the net growth (or decline) in stock holdings for institution j during quarter t. All

other variables are as defined previously.

Table VI presents regression results for all independent trading variables of interest: Δw,

Δw+, and ΔwRANK. We find that both the magnitude and statistical significance of results are

similar to those presented in Table III. Overall, we conclude that fund flows do not change our

finding of significant market timing skill for institutional investors.

B. Actual Institutional Trades

22

We next address potential measurement error in our trading and performance variables.

Specifically, changes in quarterly holdings do not capture intra-quarter transactions where

institutions purchase and sell or sell and re-purchase the same ETF, nor can they identify the exact

timing and execution price of trades. If institutions engage in active timing and trade more frequently

than quarterly , this measurement error will reduce the power of our quarterly tests (Kothari and

Warner (2001)) and may lead to incorrect inference (Goetzmann, Ingersoll, and Ivkovich (2000)).

We overcome the limitations of quarterly institutional holdings data by using a large proprietary

database of actual institutional trades provided by ANcerno Ltd. (formerly the Abel Noser

Corporation).

We obtain data on institutional trades for the period from January 1, 1999 to September 30,

2008 from ANcerno Ltd. ANcerno is a widely recognized consulting firm that works with

institutional investors to monitor their equity trading costs. ANcerno clients include pension plan

sponsors such as CalPERS, the Commonwealth of Virginia, and the YMCA retirement fund, as well

as money managers such as MFS (Massachusetts Financial Services), Putman Investments, and

Lazard Asset Management. Previous academic studies that have used ANcerno data include

Goldstein, Irvine, Kandel and Wiener (2008), Chemmanur, He, and Hu (2008), and Puckett and Yan

(2009). The ANcerno database contains a total of ?? different institutions that are responsible for

approximately ?? million trades (reported executions) over our sample period. For each execution,

the database provides an identity code for the institution, date of execution, stock traded, number of

shares executed, execution price, commissions paid, and whether the execution is a buy or sell.

We identify all ETF trades in the ANcerno database and aggregate trading on each day for

each institution and ETF. We then regress daily (or weekly) subsequent excess ETF returns on daily

ETF imbalances in a manner identical to regression equation (19). Our results are presented in Table

VII. Panel A presents regression estimates where the dependent variable is the one-day-ahead excess

23

return. We find insignificant coefficient estimates for Δw, and positive and significant coefficient

estimates for Δw+ and ΔwRANK. Specifically, after controlling for lagged returns, the coefficient

estimate on Δw+ is 0.04 (t-statistic=3.12) and for ΔwRANK is 0.01 (t-statistic=3.33).

We also present regression results in Panel B of Table VII where the dependent variable is

one-week-ahead excess returns. Again, we find insignificant coefficient estimates for Δw, and

positive and significant coefficient estimates for Δw+. However, we now find that regression

coefficients for ΔwRANK are insignificant. The coefficient estimates for Δw+ (0.10, t-

statistic=3.15) suggests that excess returns are 0.l0% larger in the week following institutional

trades for ETFs that institutions buy when compared to ETFs that institutions sell. If we

extrapolate this return to a quarterly frequency, we estimate that the magnitude of excess returns

is approximately 1.20%, which is similar to our estimate of 1.27% in Table III.

VI. Conclusion

In this paper we make two distinct contributions to the existing market timing literature.

First, we provide a theoretical model where a market timer chooses between an Exchange Traded

Fund (ETF) and a basket of stocks in order to exploit his private information about future market

returns. We show that when the ETF is more liquid, or exhibits lower tracking error, the market timer

will choose to trade the ETF rather than a basket of stocks. Second, we use an innovative approach to

directly measure the ex-post performance of institutional investors’ market timing strategies. That is,

we test whether institutional investors exhibit superior broad-market or sector timing skills based

on their trades of ETFs.

In univariate tests, we find that excess returns are more likely to be positive after an

institution purchases an ETF when compared to excess returns after an institution sells an ETF.

For the broad market ETF sample, the excess return is positive 58.60% of the time after an

24

institution purchases an ETF, compared to only 47.82% of the time after an institution sells an

ETF. The difference in average excess return between buys and sells is 2.10% (t-statistic=12.05)

over the next quarter.

We continue to find evidence of superior market timing when investigating institutional

ETF trading in a multivariate framework. Specifically, we find a significant positive relationship

between institutional ETF trading and subsequent ETF returns. Our coefficient estimates suggest

that ETFs in which an institution increases its holdings outperform ETFs in which an institution

decreases (or does not change) its holdings by between 1.27% and 1.29% in the subsequent

quarter.

Our results are somewhat stronger for broad market ETFs when compared to sector

ETFs, however, we find evidence of significant market timing skill in both samples.

Furthermore, we find that these results are robust even after controlling for fund flows,

suggesting that our results cannot be attributed to individual investor demand.

Overall, we find that institutional ETF trades predict subsequent ETF excess returns, and

our results suggest that up to 15% of our sample institutions have significant market-timing

skills. The results of our innovative study are distinctly different than many prior market timing

studies that use non-linear regressions to investigate market timing skill.

25

References

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Henriksson, Roy D., and Robert C.Merton, 1981, On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills, Journal of Business 54, 513-533.

Jagannathan, Ravi, and Robert A. Korajczyk, 1986, Assessing the market timing performance of managed portfolios, Journal of Business 59, 217-235.

Jiang, George J., Tong Yao, and Tong Yu, 2007, Do mutual funds time the market? Evidence from portfolio holdings, Journal of Financial Economics 86, 724-758.

26

Treynor and Mazuy, 1966 Can mutual funds outguess the market? Harvard Business Review 44, 131-136.

27

Appendix A

In Section 2, we assume n to be fixed. In practice the market timer may choose n

optimally. We show next that the basic intuitions of the model are similar when we allow n to be

endogenous. The first-order condition with respect to n is:

(A1)

Solving (A1) gives:

(A2)

Using (A2) we can resolve z1+ and z1- while y is constrained to be zero:

(A3)

(A4)

Solutions to the unconstrained problem when E (u | θ)>R are:

(A5)

(A6)

Proposition A1: If the market timer can invest in both the ETF and stocks and assuming E (u | θ)>R and n is endogenous , his optimal investment in the ETF (y**) and equal-weight stock portfolio (z**) are as follows: (1) If E (u | θ)-R < min(a1, a2), then y**=z**=0; (2) If a1< E (u | θ)- R <a2, then y**=y* and z**=0, where y* is given in Proposition 1; (3) If a2< E (u | θ)- R <a1, then y**=0 and z**=z*, where z* is given in Proposition 2 with z1+ and z1- replaced by those in (A3) and (A4); (4) If E (u | θ)-R> a2≥a1, then y**=y* and z**=0; (5) If E (u | θ)-R> a1>a2 and y2+<0, then y**= 0 and z**= z*; (6) Otherwise if y2+>0, z2+>0, and U(y2+, z2+)>W0R, then y**= y2+ and z**= z2+. Furthermore, when z** is positive, optimal n is given by:

(A7)

28

In addition, n** increases in the difference between a1 and a2, η, and decreases in A and a0.

Overall, the basic intuitions of the model continue to hold even when n is endogenous.9

When the ETF is more liquid than the stocks, the market timer will not trade stocks. On the other

hand, even if the stocks are more liquid than the ETF, the market timer may still trade the ETF

because the ETF contains zero tracking error.

9 The case when E (u | θ)<R has similar solutions and is omitted for brevity.

29

Appendix B: Bootstrap analysis

To improve statistical inferences we employ bootstrap technique. We draw with

replacement from the empirical distribution of returns for each ETF. The number of draws is

equal to the sample size for particular ETF. In other words, the simulated return series for each

ETF is completely random and by construction should not be predictable by changes in

institutional holdings. Once we obtain these return series, we regress them on our changes in

fractional holdings variables and controls as we did in original specifications. In addition, we

count the number of statistically significant positive and negative coefficients for each

replication.

The reported p-values are one sided probabilities that we observe coefficient that is equal

or larger in magnitude than the one we obtain in the original data sample. Alternatively, it is the

probability that percentage of positive (negative) significant coefficients is larger or equal to the

one we observe in original data. Probabilities are based on 1000 replications.

30

Table IDescriptive Statistics on U.S. Equity ETFs and Institutional Holdings

This table presents descriptive statistics on 359 U.S. equity ETFs over the period 1999-2008. We obtain our sample of ETFs using the CRSP Mutual Fund database and the CRSP stock database. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stocks. We obtain institutional holdings from the Thomson 13F database. Number of institutions holding ETFs is the number of distinct manager numbers (mgrno) from Thomson database that have at least one ETF in their holdings in a given year. Average Number of ETFs held per institution is the average among the institutions that hold at least one ETF. Panel B compares characteristics of ETFs with common stocks. For each characteristic, we first take a monthly cross-sectional average within each of ETF and Common Stock category. We then report the time-series average of these monthly cross-sectional averages. Age is number of months since the first month the security appears in the CRSP stock database.

Panel A: Descriptive Statistics All ETFs U.S. Broad Mkt ETFs U.S. Sector ETFs Institutional ETF ownership

Year NumberMkt Cap($ billion) Number

Mkt Cap($ billion) Number

Mkt Cap($ billion)

# ofInstitutions

Avg # ETFs/institution

$ of inst.ownership

1999 12 $10.90 2 $5.10 10 $5.80 300 1.84 $9.33

2000 63 $30.99 8 $21.83 55 $9.16 470 2.76 $15.61

2001 78 $46.00 9 $26.59 69 $19.41 613 3.36 $27.08

2002 78 $75.95 9 $50.16 69 $25.79 710 4.15 $27.58

2003 83 $109.01 11 $64.81 72 $44.19 774 4.99 $51.43

2004 111 $160.95 12 $84.55 99 $76.40 910 5.81 $80.52

2005 148 $192.80 13 $92.09 135 $100.72 979 6.84 $107.91

2006 243 $241.50 16 $103.79 227 $137.71 1136 7.83 $110.69

2007 325 $340.04 18 $150.07 307 $189.97 1332 9.45 $197.02

2008 333 $304.26 18 $145.00 315 $159.25 1533 9.90 $198.32

Panel B: Characteristics of ETFs versus Common StocksMarket

Capitalization(Billion)

Age(months)

Dollar Trading Volume (Billion

per month)

Turnover(% per month)

Amihud’sIlliquidity

QuotedSpread (%)

StockVolatility

(% per day)

ETFs $0.99 31.63 $2.10 99.79 1.02×10-7 0.044 1.53

Common Stocks $2.68 174.71 $0.42 14.77 0.51×10-5 0.076 3.71

31

Table IIChanges in ETF Institutional Holdings and Subsequent Market Directions

This table presents the frequency relation between changes in ETF institutional holdings and subsequent market directions. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stocks. We obtain institutional holdings from the Thomson 13F database. Δw is the change in shares held for an ETF scaled by the total shares outstanding. ETF excess return is defined as ETF return minus the risk-free return. Numbers in parentheses are t-statistics based on paired t-tests. Numbers in brackets are bootstrap p-values based on 1000 replications.

Panel A: Percentage of Excess Returns >0Fraction of next quarter’s ETF

excess return is >0

All ETFs Broad-Market ETFs Sector ETFs

Δw > 0 50.11% 58.60% 47.81%

Δw ≤ 0 45.92% 47.82% 45.91%

Difference 4.19% 10.79% 1.90%(6.13) (13.39) (2.49)[0.024]

Panel B: Average Excess ReturnsAverage ETF excess returns

All ETFs Broad-Market ETFs Sector ETFs

Δw > 0 -1.60% -0.27% -2.25%

Δw ≤ 0 -2.84% -2.37% -3.33%

Difference 1.24% 2.10% 1.08%(6.83) (12.05) (4.92)

32

Table IIIChanges in ETF Institutional Holdings and Future ETF Excess Returns

This table presents the regression of one-quarter-ahead ETF excess returns on changes in institutional holdings controlling for macroeconomic variables. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stocks. We obtain institutional holdings from the Thomson 13F database. Δw is the change in shares held for an ETF scaled by the total shares outstanding. Δw + is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. TB is the t-bill rate. TERM is the term spread, the difference between the long term yield and t-bill rate. DEF is the default spread, or the difference in yields on BBB and AAA rated bonds. DP is dividend yield. The data necessary for computation of TB, TERM, DEF, and DP comes from Amit Goyal’s website. Dependent variable is the one-quarter-ahead excess return of ETF over the risk-free rate. We estimate the regression manager by manager and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each manager have at least 24 (12) valid observations when estimating with full (subsample) of ETFs. Numbers in brackets are bootstrap p-values based on 1000 replications.

(1) (2) (3) (4) (5) (6)Intercept -2.10 -2.75 -3.06 -0.36 -0.31 -0.34

(-17.56) (-20.06) (-19.65) (-3.20) (-3.22) (-3.04)Δw 0.57 0.66

(1.62) (1.65)[0.010]

Δw+ 1.29 1.27(13.79) (14.31)[0.002] [0.003]

ΔwRANK 0.21 0.20(11.91) (12.39)[0.001] [0.002]

Lagged Return 0.14 0.14 0.13 -0.13 -0.13 -0.13(15.12) (14.78) (14.03) (-15.10) (-15.13) (-15.08)

TB 5.93 5.95 5.87(3.01) (3.05) (3.02)

TERM 3.57 3.59 3.55(8.14) (8.81) (7.74)

DEF -1.62 -2.22 -2.38(-1.53) (-2.21) (-2.20)

DP 0.38 0.26 0.32(1.54) (1.18) (1.26)

Coefficients on Δw+or ΔwRANK

% negative 41.69 27.80 30.85 41.36 25.88 27.68% positive 58.31 72.20 69.15 58.64 74.12 72.32

% negative significant 6.21 1.81 3.05 5.08 1.58 2.26[0.003] [0.705] [0.270] [0.830] [0.619]

% positive significant 10.06 15.14 15.48 9.94 14.46 13.90[0.001] [0.001] [0.001] [0.001] [0.001]

33

Table IVChanges in ETF Institutional Holdings and One-Quarter-Ahead ETF Excess Returns:

Broad Market and Sector ETFs

This table presents the regression of one-quarter-ahead ETF excess returns on changes in institutional holdings. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stocks. We obtain institutional holdings from the Thomson 13F database. Δw is the change in shares held for an ETF scaled by the total shares outstanding. Δw+ is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. Dependent variable is the one-quarter-ahead excess return of ETF over the risk-free rate. We estimate the regression manager by manager and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each manager have at least 12 valid observations. Numbers in brackets are bootstrap p-values based on 1000 replications.

Panel A: Borad-Market ETFs(1) (2) (3) (4) (3) (6)

Intercept -0.94% -1.87% -2.34% -0.88% -0.43% -0.42%(-11.30) (-17.07) (-16.68) (-1.97) (-5.02) (-4.77)

Δw 4.67 4.41(4.26) (3.54)[0.054]

Δw+ 1.74% 1.96%(14.14) (10.56)[0.026] [0.041]

ΔwRANK 0.29% 0.33%(12.00) (8.10)[0.029] [0.044]

Lagged Return 0.29 0.28 0.28 -0.08 -0.10 -0.10(17.61) (17.04) (17.08) (-3.60) (-6.09) (-6.21)

TB 6.36 4.17 4.06(2.78) (6.27) (7.14)

TERM 4.18 2.78 2.71(3.05) (9.05) (8.98)

DEF -1.07 -0.79 -1.21(-0.82) (-0.55) (-0.85)

DP 1.58 0.63 0.62(1.72) (3.01) (2.89)

Coefficients on Δw, Δw+,or ΔwRANK




34

Panel B: Sector ETFs(1) (2) (3) (4) (5) (6)

Intercept -2.76% -3.37% -3.58% -0.27% 0.16% -0.25%(-19.26) (-20.23) (-18.66) (-1.77) (0.36) (-1.35)

Δw 1.15 1.80(1.01) (1.45)[0.131]

Δw+ 1.24% -0.41%(9.69) (-0.24)[0.004] [0.870]

ΔwRANK 0.19% 0.12%(7.68) (1.17)[0.005] [0.087]

Lagged Return 0.10 0.09 0.09 -0.20 -0.13 -0.17(8.97) (8.74) (8.81) (-15.43) (-2.36) (-7.25)

TB 5.73 5.31 5.13(3.21) (3.24) (3.10)

TERM 3.42 3.17 3.11(6.55) (6.01) (4.80)

DEF -5.44 -1.70 -4.87(-3.00) (-0.28) (-2.13)

DP 0.28 -0.87 0.25(0.76) (-0.72) (0.61)

Coefficients on Δw, Δw+,or ΔwRANK




35

Table VChanges in Institutional ETF Holdings and One-Quarter-Ahead ETF Volatility

This table presents the regression of ETF realized volatility on changes in institutional holdings. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. We obtain institutional holdings from the Thomson 13F database. Δw is the change in shares held for an ETF scaled by the total shares outstanding. Δw+ is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. Neg. return dummy takes the value of one if the next quarter ETF excess return is negative and zero otherwise. Dependent variable is the logarithm of one-quarter-ahead realized volatility of ETF estimated from daily data. We estimate the regression manager by manager and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each manager have at least valid 24 observations. Numbers in brackets are bootstrap p-values based on 1000 replications.

(1) (2) (3) (4) (5) (6)Intercept -0.17% 0.56% -0.15% -0.55% -0.16% -0.56%

(-5.50) (-20.99) (-4.91) (-20.63) (-5.08) (-20.75)Δw -0.78 -0.45

(-1.37) (-1.12)[0.142] [0.262]

Δw+ -2.98% -1.64%(-9.24) (-5.89)[0.007] [0.104]

ΔwRANK -0.35% -0.17%(-6.44) (-3.61)[0.040] [0.225]

Lagged Volatility 0.94 0.82 0.94 0.82 0.94 0.82(144.76) (146.15) (146.19) (147.85) (146.29) (147.97)

Negative 0.31 0.31 0.31Return Dummy (81.00) (81.55) (82.07)

Coefficients on Δw, Δw+, or ΔwRANK


% negative significant 5.42 5.42 11.75 6.55 7.34 5.76[0.113] [0.110] [0.001] [0.014] [0.005] [0.029]

% positive significant 1.92 2.60 2.60 3.16 2.60 3.39[0.912] [0.602] [0.503] [0.324] [0.464] [0.222]

36

Table VIChanges in Institutional ETF Holdings and One-Quarter-Ahead ETF Excess Returns:

Controlling for Fund Flows

This table presents the regression of ETF excess returns on changes in institutional holdings controlling for Fund Flows. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. We obtain institutional holdings from the Thomson 13F database. Δw is the change in shares held for an ETF scaled by the total shares outstanding. Δw+ is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. Fund Flow is the net growth (decline) of total stock holdings over the quarter. Dependent variable is the one-quarter-ahead excess return of ETF over the risk-free rate. We estimate the regression manager by manager and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each manager have at least 12 valid observations. Numbers in brackets are bootstrap p-values based on 1000 replications.

(1) (2) (3) (4) (5) (6)Intercept -2.81% -2.69% -3.36% -3.26% -3.61% -3.51%

(-13.50) (-12.56) (-15.65) (-14.73) (-15.96) (-15.15)Δw 0.62 0.67

(1.75) (1.84)

Δw+ 1.10% 1.14%(12.04) (12.77)

ΔwRANK 0.18% 0.18%(10.29) (10.81)

Lagged Return 0.12 0.12 0.12(13.07) (12.80) (12.89)

Lagged Flows -0.15 -0.15 -0.15 -0.14 -0.15 -0.14(-0.49) (-0.50) (-0.50) (-0.49) (-0.49) (-0.49)



% negative significant 7.01 6.21 2.03 2.15 2.37 2.94

% positive significant 9.60 9.04 11.98 13.56 13.22 12.94

37

Table VIIDaily Institutional Trades in ETF and Future ETF Excess Returns

This table presents the regression of ETF excess returns on daily institutional investor trading. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stock. We obtain institutional trading data from the proprietary database provided by ANcerno Corporation. Δw is the net purchase of ETF by an institution on a given day scaled by the total shares outstanding of the ETF. Δw+ is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. Dependent variable is the one-day-ahead (or one-week-ahead) excess return of ETF over the risk-free rate. We estimate the regression for each manager – ETF combination, and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each regression have at least 24 valid observations.

Panel A:Predicting One-Day-Ahead Excess Return(1) (2) (3) (4) (5) (6)

Intercept -3.01% 7.27% -0.01% -0.02% -0.02% -0.03%(-0.05) (0.13) (-1.30) (-1.67) (-2.40) (-2.85)

Δw -0.78 -1.14(-0.32) (-0.45)

Δw+ 0.03% 0.04%(2.50) (3.12)

ΔwRANK 0.01% 0.01%(2.83) (3.33)

Lagged Return -0.04 -0.04 -0.04(-7.61) (-7.80) (-8.03)



% negative significant 3.31 3.20 2.43 2.21 2.10 1.99% positive significant 2.32 2.43 1.99 2.76 2.43 3.20

38

Panel B:Predicting One-Week-Ahead Excess Return(1) (2) (3) (4) (5) (6)

Intercept -0.01% -0.01% -0.04% -0.07% -0.02% -0.05%(-0.79) (-0.62) (-1.87) (-2.37) (-0.93) (-1.59)

Δw -1.73 -1.36(-0.19) (-0.14)

Δw+ 0.06% 0.10%(2.05) (3.15)

ΔwRANK 0.00% 0.01%(0.30) (1.36)

Lagged Return -0.13 -0.13 -0.13(-23.51) (-23.83) (-23.66)




39

Table VIIIDaily Institutional Trades in ETF and Future ETF Excess Returns – Pension Plan

Sponsors and Money Managers

This table presents the regression of ETF excess returns on daily institutional investor trading. We obtain our sample of ETFs from the CRSP Mutual Fund database and the CRSP stock database for the period 1999-2008. We classify ETFs based on Lipper Objective Code and include in our sample only U.S. equity ETFs. An ETF belongs to the “broad market” category if it covers large portion of the securities in the market (e.g. NYSE Composite, S&P500, and S&P1500). A sector ETF is an ETF that covers a particular industry or a particular style such as small cap or growth stock. We obtain institutional trading data from the proprietary database provided by ANcerno Corporation. Δw is the net purchase of ETF by an institution on a given day scaled by the total shares outstanding of the ETF. Δw+ is a dummy variable that takes a value of one if Δw is positive and zero otherwise. ΔwRANK is the decile rank of Δw. Dependent variable is the one-day-ahead (or one-week-ahead) excess return of ETF over the risk-free rate. We estimate the regression for each manager – ETF combination, and report average coefficients and Fama-MacBeth t-statistics (in parentheses). We require that each regression have at least 24 valid observations.

Panel A:Pension Plan SponsorsOne-Day-Ahead Return One-Week-Ahead Return

(1) (2) (3) (4) (5) (6)Intercept

Δw -0.81 1.655(1.86) (1.63)

Δw+ 0.07% 0.20%(3.18) (2.31)

ΔwRANK 0.01% 0.03%(2.84) (2.14)

Lagged Return -0.04 -0.04 -0.04 -0.14 -0.13 -0.14(-3.41) (-3.85) (-3.89) (-12.46) (-11.05) (-11.26)




40

Panel B:Money ManagersOne-Day-Ahead Return One-Week-Ahead Return

(1) (2) (3) (4) (5) (6)Intercept

Δw -0.45 -0.79(-1.51) (-0.63)

Δw+ 0.03% 0.08%(1.98) (2.33)

ΔwRANK 0.01% 0.00%(2.20) (0.31)

Lagged Return -0.04 -0.04 46.54 -0.13 -0.14 -0.13(-6.82) (-6.74) 53.46 (-19.02) (-17.26) (-16.94)




41

Figure INumber and Total Asset Value of All ETFs, 1993-2008

This figure presents the number and size of 820 U.S.ETFs over the period 1993-2008. We obtain the sample of all ETFs using the CRSP Mutual Fund database and the CRSP stock database.

0

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1993 1995 1997 1999 2001 2003 2005 2007

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