The Wisdom of Crowds: Mutual Fund Investors’ Aggregate Asset Allocation Decisions

John Chalmers, Aditya Kaul and Blake Phillips*

We find that the aggregate asset allocation decisions of U.S. mutual fund investors are predicted by economic conditions. An anticipated economic downturn is associated with investor flows away from relatively risky equity funds and towards lower risk money market funds. Turbulent periods see marked flows from equity to money market funds, suggesting that flight-to-safety considerations influence aggregate asset allocations. We find the same patterns in Canadian mutual fund flows. Mutual fund investors holding low fee and low turnover funds (common proxies for investor sophistication) react more sharply to changing economic conditions. These results suggest that mutual fund investors, often dismissed as noise traders, collectively pay attention to economic signals while determining allocations. Our findings are consistent with the idea that, in the aggregate, mutual fund investors’ actions, rather than disrupting price formation, likely contribute to the relation between proxies for economic conditions, such as the default and term spreads, and asset prices (e.g. Fama and French, 1989). JEL Classification: G11, G14, G23, G32 Key words: mutual funds, mutual fund flow, asset allocation

* The authors are from the Lundquist College of Business, University of Oregon, the School of Business, University of Alberta and the School of Accounting and Finance, University of Waterloo. Corresponding Author: Aditya Kaul, School of Business, University of Alberta, Edmonton, AB, Canada, T6G 2R6. E-mail: akaul@ualberta.ca. The authors are grateful for financial support from a National Research Program in Financial Services & Public Policy grant from the Schulich School of Business and for data provided by the Investment Funds Institute of Canada. We thank Dick Beason, Wolfgang Bessler, Susan Christoffersen, Ro Gutierrez, Mark Huson, Marty Luckert, Vikas Mehrotra, Matthew Spiegel, session participants at the 2008 European Financial Management Association, 2008 Financial Management Association, and 2009 Northern Finance Association meetings, and seminar participants at the Bank of Canada, and the Universities of Hitotsubashi, Kobe and Lugano for helpful comments.

The Wisdom of Crowds: Mutual Fund Investors’ Aggregate Asset Allocation Decisions

We find that the aggregate asset allocation decisions of U.S. mutual fund investors are predicted by economic conditions. An anticipated economic downturn is associated with investor flows away from relatively risky equity funds and towards lower risk money market funds. Turbulent periods see marked flows from equity to money market funds, suggesting that flight-to-safety considerations influence aggregate asset allocations. We find the same patterns in Canadian mutual fund flows. Mutual fund investors holding low fee and low turnover funds (common proxies for investor sophistication) react more sharply to changing economic conditions. These results suggest that mutual fund investors, often dismissed as noise traders, collectively pay attention to economic signals while determining allocations. Our findings are consistent with the idea that, in the aggregate, mutual fund investors’ actions, rather than disrupting price formation, likely contribute to the relation between proxies for economic conditions, such as the default and term spreads, and asset prices (e.g. Fama and French, 1989).

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1. Introduction

With worldwide assets of 23 trillion USD at year-end 2009, mutual fund investors

collectively are a major player in capital markets.1 Yet, research has shown that within a

given fund investor actions do not always have rational explanations. For example, mutual

fund investors chase returns and react to name changes, as well as non-informative

advertising campaigns.2 In this paper, we study the aggregate asset allocation decisions of

U.S. mutual fund investors. Our goal is to understand the behavior of the amalgam of

mostly small, retail investors.3 Much as diversification minimizes the effects of

idiosyncratic factors, aggregate investor allocation decisions may differ substantially from

fund-level evidence. Our analysis of the aggregate asset allocation decisions of mutual fund

investors leads to surprising new insights.

We examine four principal questions. First, when making asset allocation decisions,

do mutual fund investors react to changing economic conditions? We compute aggregate

monthly allocations to four major asset classes: domestic equities, money market, bonds,

and foreign equities.4 We then relate these allocations to proxies for economic conditions:

the Chicago Fed National Activity Index (CFNAI), the term spread (TERM), the default

spread (DEF), the change in the short-term interest rate (ΔTB), the Treasury-Eurodollar 1 From the Investment Company Institute (ICI) Fact Book, 2010.

2 See Gruber (1996), Sirri and Tufano (1998) and Lynch and Musto (2003) for evidence of return chasing. Jain and Wu (2000) and Cooper et al. (2005) report that investors direct flow towards funds that advertise more and funds that undergo name changes to reflect current market trends.

3 Individuals, rather than institutions, are the predominant holders of mutual funds. For example, using table 56 of the 2010 ICI Mutual Fund Fact Book, we calculate that in 2008 92% of net assets invested in equity mutual funds were held by individual accounts as compared to institutions. Money market funds are lone assets class where institutional holdings deviate from this pattern. In 2008, individual accounts made up 67% of net assets held in money markets or $2.55 trillion our of total money market assets of $3.08 trillion. 4 Our main allocation measure, following Frazzini and Lamont (2008), is the excess flow for the asset classes. Excess flow is computed as actual flow less flow that would have resulted on a net asset-weighted basis, and captures the extent to which investors alter asset allocation relative to an asset-weighted benchmark.

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spread (TED), and volatility in the stock and bond markets (SPV and TBV).5 We find that

fund investors alter the riskiness of their portfolios in response to shifting economic

conditions, increasing risk as the economy improves and reducing risk in anticipation of

economic downturns. Thus, when the economy is expected to perform favorably (i.e.

TERM is high, DEF is low, ΔTB is low, or TED is low), investors direct flow away from

money market funds and towards equity funds.6 Consistent with rational forecasting, the

reaction of fund flows incorporates contemporaneous real economic information within

the forward-looking financial market variables.7 To our knowledge, documenting the

relation between mutual fund flow and economic conditions is a novel contribution.8

Second, we consider if mutual fund investors flee risky investments during periods

of turmoil. The term "flight-to-safety" is ubiquitous during turbulent times, the belief being

that investors gravitate to safe investments during such periods. However, there is little

evidence to demonstrate the pervasiveness or implications of flight-to-safety reactions by

investors.9 We address this question by augmenting our flow models with a crisis variable

(CRISIS) set equal to one during major shocks occurring over our sample period.

Consistent with pervasive safe haven flows, these periods see dramatic shifts from higher

risk equity funds to lower risk money market funds. The forward-looking economic

5Among other papers in this line of research, see Fama and Schwert (1977), Fama and French (1989), Schwert (1989) and Chen (1991).

6 We cannot identify whether mutual fund investors monitor and react directly to signals provided by the proxies for economic conditions, or perhaps more plausibly, respond to media commentary which incorporates the information in these variables.

7 In particular, the composite CFNAI index is significant in univariate regressions, but loses significance in multivariate regressions that include the forward-looking financial variables.

8 In a related paper, Ben-Rephael et al. (2010) use monthly exchanges between bond and equity funds as a measure of investor sentiment.

9 Beber et al. (2009) provides evidence of flight-to-quality and flight-to-liquidity in the Euro area government bond market.

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variables retain their explanatory power in the presence of CRISIS, suggesting that the

relation between flow and economic conditions is not confined to such episodes.

Third, we study whether the relation between the predictive economic variables

and flows varies across investor profiles. Using fund expense ratios and portfolio turnover

as proxies for mutual fund investor sophistication, we find that the allocations of

sophisticated investors are more sensitive to changes in economic conditions. The

allocations of sophisticated investors account for a large part of the relation between

economy-wide flow and economic conditions. In contrast, the relation between flow for

funds held by unsophisticated investors and economic conditions is much weaker.

Finally, we explore whether investors improve portfolio performance via such time-

varying asset allocation decisions. Using DEF and TERM thresholds as trading signals, we

find that investors reduce risk in their portfolios by nearly 50% and achieve a risk-return

tradeoff that is comparable or superior to that of the average equity fund. However, these

switches have the potential to harm investors who stay with the fund across the cycle by

imposing trading costs on the entire fund and potentially disrupting the fund’s investment

strategy.

In sum, our results imply that rational motives underlie the aggregate asset allocation

decisions of mutual fund investors. Often derided as noise traders, mutual fund investors

collectively pay attention to economic signals while determining asset allocations. Further,

the relation between aggregate allocations and the proxies for economic conditions (e.g.

the positive relation between equity flow and TERM), is consistent with the well-

documented relation between expected returns and these variables (e.g. Fama and French,

1989). In other words, our results indicate that the collective actions of fund investors do

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not disrupt price formation, but rather, contribute to the relation between the proxy

variables and asset prices. They also suggest that the relation between these variables and

asset prices reflect business-cycle risks.

The rest of the paper is organized as follows: Section 2 outlines our methods, data,

and hypotheses. Section 3 describes the results, and Section 4 contains robustness checks.

Section 5 discusses the implications of our results and Section 6 concludes.

2. Data and Methods

2.1 Sample

We obtain mutual fund data from the Center for Research in Security Prices (CRSP)

Mutual Fund Database which provides monthly net asset value and returns by fund share

class as well as quarterly or annual disclosures of management fees, portfolio turnover and

fund objectives. Our dataset commences in February 1991, when monthly net asset value

data become available for all U.S. funds and concludes in March 2008.10 For each fund, we

calculate monthly net flow as the change in net assets resulting from purchases and

redemptions, after accounting for returns:

(1)

where t denotes the month, and NF, A and R are net flow, net assets, and the monthly return

for fund i. We then aggregate net flow across five broad asset categories: Domestic Equity,

Domestic Money Market, Domestic Bond, and Foreign Equity, as well as Other, which 10 Prior to 1991, CRSP reports net assets at a monthly, quarterly or annual frequency depending on data disclosures from each fund. In 1991, monthly net assets become available for all funds.

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includes all remaining funds.11 We focus on the first four categories, which expose

investors to different risks. For example, the performance of equity funds will be highly

sensitive to economic conditions, as the cash flows from the stocks they hold rise or fall

with the economy. In contrast, the performance of money market funds, which hold short-

term fixed income securities, is generally smoother, though tied directly to the level of

short-term interest rates.

Figure 1 shows the monthly proportion of overall net assets in the mutual fund

industry in the five asset classes between February 1991 and March 2008. The four asset

classes we focus on account for 82% to 90% of total mutual fund assets and the weights of

the individual classes vary significantly over time. For example, equity funds account for a

low of 17% of industry net assets in early 1991 and a high of 40% in late 2000.

2.2 Asset Allocation Measures

The objective of our analysis is to capture the extent to which fund investors alter

allocation weights across asset classes in response to economic signals. To that end, we

measure aggregate asset allocation using a measure inspired by Frazzini and Lamont

(2008). Frazzini and Lamont benchmark expected fund flow using net asset-weighted flow

(AWF) defined as:

∑

∑ (2)

11 Included in the Other category are, for example, mortgage backed security funds, international bond funds and international money market funds. In the rest of the paper, we refer to Domestic Equity, Domestic Money Market and Domestic Bond more simply as Equity, Money Market and Bond.

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Here, subscript j denotes asset category and t denotes month. AWFj,t, Aj,t-1 and NFj,t are

month t asset-weighted flow, month t-1 assets and month t net flow for asset class j. Thus,

asset-weighted flow is the flow that would have occurred if allocated according to the

lagged net asset weights for the asset classes. The excess flow for category j, EFj,t, is

constructed as actual flow for the category less net asset-weighted flow from equation (2):

(3)

Following Warther (1995), we standardize by the previous month’s NYSE, AMEX and

NASDAQ market capitalization, MCt-1, to control for flow effects resulting from price

appreciation and market growth. 12

We prefer excess flow to other flow measures for several reasons. First, excess flow

captures allocation adjustments between asset classes while imposing scale invariance. It

can be thought of as measuring changes in the size of the wedges in a pie chart through

time. Growth in overall fund industry assets that maintains the previous month’s asset

weights thus implies zero excess flow. Additionally, to an extent, the excess flow measure

captures the simultaneity of the asset allocation decision. Directing assets to one asset

class necessitates forgoing investment in other classes. This ebb and flow between asset

classes is better captured by excess flow, or even more precisely, the difference in excess

flow between asset classes.

12 To illustrate, suppose equity funds account for 40% of total assets at the end of month t-1. We then expect 40% of total flow in month t to be allocated to equity funds. Suppose total flow to all mutual funds in month t is $10b, of which $5b goes to equity funds. In this example, raw excess flow to equity funds will be $1b (= $5b - 10b*0.40).

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One potential concern is that, by definition, excess flow for the five asset categories

must sum to zero. Equity and money market funds are the largest asset categories and the

two that we focus on. If the other three categories have static weights, the weights and

thus excess flows for the equity and money market classes will show a mechanically-

induced negative correlation.13 This could lead to the erroneous conclusion that allocations

to the equity and money market asset classes have the opposite relation to the predictor

variables. For two reasons, we do not think this issue affects our conclusions. First, as

shown in Figure 1, there is significant time-series variation in all five asset class

weights. Consequently, the excess flow for any two asset classes will not mechanically sum

to zero. In particular, the sum of the equity and money market weights is far from constant,

varying in the wide range of 50% to 70% over the sample period. For instance, both

weights increase in 1995 and 1996; the money market weight is flat while the equity

weight increases between 1998 and 2001; and the two weights move in opposite directions

between 2003 and 2008. Second, a mechanical correlation between equity and money

market excess flows, even if it exists, does not mechanically induce a relation between the

excess flow for either category and the predictor variables, e.g. a positive coefficient on

TERM in the equity flow regression.

An alternative measure is percent flow, calculated as net flow from equation (1)

standardized by lagged net assets for the asset class. Sirri and Tufano (1998) use this

measure at the fund level. Percent flow effectively captures the level of flow, but does not

reflect allocations across classes. For example, general growth in the fund industry would

appear as high percent flow to all asset categories but provide little indication of investor

13 To take an extreme case, if there are only two asset categories the excess flows must be equal and opposite in every month.

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preferences across asset classes. Given our interest in allocation decisions, we focus on the

excess flow measure in our analysis. However, as a robustness check, in section 4.1 we

show that analysis using percent flow leads to similar conclusions.

2.3 Explanatory Variables

In order to understand how economic factors motivate mutual fund investor

allocations, we relate excess flow for the four major asset classes to proxies for economic

conditions drawn from the real economy and financial markets. When making allocation

decisions, we contrast the response of investors to contemporaneous economic factors,

such as income or employment levels, relative to forward-looking financial market factors.

A large number of contemporaneous real economy variables are available. Rather

than make judgments about which variables to include, we use the comprehensive Chicago

FED National Activity Index (CFNAI). CFNAI is the first principal component of 85 monthly

series that come from the broad categories of production and income, employment and

hours, consumption and housing and sales, orders and inventories. It includes most of the

series we would think of as being important indicators of current economic conditions (e.g.

industrial production and unemployment).

As financial market proxies we include the term spread (TERM), the default spread

(DEF), the change in the short-term rate (ΔTB), the Treasury-Eurodollar spread (TED), and

volatility in the stock and bond markets (SPV and TBV).14 As prior research suggests, an

14 TERM is the difference in yields on the ten-year Treasury bond and the three-month Treasury bill. DEF is the difference between the yields on portfolios of medium-term corporate bonds and medium maturity (three- to five-year) government bonds. TED is the difference between three-month LIBOR and the three-month T-bill rate. ΔTB is the change in the three-month T-bill yield. SPV is calculated as the sum of squared daily S&P 500 returns in each month. TBV is calculated as the sum of squared daily changes in the three-

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anticipated improvement in economic conditions is reflected in an increase in TERM or a

decline in DEF, ΔTB and TED. The volatility measures capture the effects of stock and bond

market uncertainty.

The forecast power of variables such as TERM and TB for returns is the subject of

some debate. Campbell and Thompson (2010) suggest that the forecast power of these

variables, while small, is economically meaningful. Henkel, Martin and Nardari (2010) find

that the predictive ability of term structure variables is largely confined to recessions. Ang

and Bekaert (2007) suggest that the short-term interest rate predicts returns at shorter

frequencies. In contrast, we investigate the ability of commonly-used return forecasting

variables (TERM, DEF, TB) to forecast mutual fund asset allocations. To the extent that

these variables predict reallocations between risky and safer assets, this suggests a channel

for how these variables come to predict equilibrium returns.

To the list of variables, we add consumer confidence (CONFID) as a measure of

consumer sentiment and the equally-weighted return to each asset class (RET) to control

for flow effects that might result from return chasing. Finally, we include CRISIS, a variable

that is equal to one (and otherwise zero) during eight major shocks observed over the

1991-2008 sample period:

1) the subprime mortgage credit crisis (Aug 2007 to the end of the sample) 2) the 9/11 terrorist attacks (Sept - Dec 2001) 3) the “Crash of 2000” when NASDAQ dropped 45.9% between Sept and Dec 2000 4) Y2K concerns at the end of 1999 (Oct - Dec 1999) 5) the failure of the Long Term Capital Management Hedge Fund (Aug - Oct 1998) 6) the Asian currency crisis (July - Sept 1997) 7) the Mexican currency crisis (Dec 1994 - Feb 1995) 8) the Pound exiting the European Exchange Rate Mechanism (Sept - Nov 1992)

month T-bill yield in each month. Data on S&P 500 returns, consumer confidence, LIBOR and U.S. bond yields come from DataStream. U.S. T-Bill data come from the St. Louis FED database.

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CRISIS will allow us to examine whether turbulent episodes are characterized by safe

haven flows. While the crises are defined ex post, beyond anecdotal evidence in the

financial press, we are unaware of empirical analysis documenting the existence and

magnitude of flight-to-safety flows during these episodes. A contribution of this paper is to

document the extent to which turbulent periods are in fact associated with flight- to-safety

flows among mutual fund investors. Further, CRISIS serves as a control for turbulent

episodes, thereby allowing us to assess the general relation between flow and economic

conditions.15

2.4 Predictions

Fama and French (1989) show that TERM and DEF track economic conditions.

Specifically, TERM is wide near business cycle troughs, when conditions are expected to

improve, and narrow near peaks, when conditions are expected to worsen. DEF is wide

when business conditions are poor and narrow when conditions are favorable. Chen

(1991) shows that DEF is negatively associated with GDP growth over the following two

quarters while TERM is positively associated with GDP growth over the following five

quarters. Merton (1973) and Shanken (1990) suggest that the short-term interest rate is a

natural candidate for a state variable that captures variations in investment opportunities,

while much research (e.g. Chen, 1991) shows that high short-term rates signal future

downturns. The TED spread compares three-month LIBOR, which includes a premium for

credit risk in the interbank loan market, and the yield on the three-month T-bill, which is

15 We are not aware of a comprehensive list of events commonly accepted as crises. Our list of crises is constructed from various sources such as Allen and Gale (2007), as well as a review of the financial press. For robustness, we have repeated our analysis dropping the stock market crises. Defining crises in this way provides similar results.

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free of default risk. Higher values of TED signify tightness in credit conditions and

unsettled markets.16

Consumer confidence (CONFID) reflects consumer perceptions regarding economic

conditions and has been used by Qiu and Welch (2006) as a measure of investor

sentiment.17 Both interpretations of this variable imply a positive relation between

CONFID and allocations to riskier asset classes. To study the effects of stock and bond

market uncertainty on allocations, we include stock market and short-term interest rate

volatility, SPV and TBV.

The following table summarizes the predicted coefficients on our explanatory

variables when we estimate regressions with excess flow for the equity, bond, money

market and foreign equity asset categories as the dependent variables.

Equities Bonds Money Market

Foreign Equities

CFNAI + - - +

TERM + - - +

DEF - + + -

ΔTB + - - +

CONFID + - - +

TED - ? + -

SPV - + + ?

TBV + - - +

16 For instance, the TED spread reached an historical high in excess of 4% in October 2008, during the subprime crisis.

17 As alternative proxies for investor sentiment, we consider the Baker and Wurgler (2001) sentiment index as well as the components of this index, such as the number of IPOs, IPO underpricing and trading volume. Their inclusion does not change our conclusions regarding the other variables of interest. We use consumer confidence because, at the time of data collection, the Baker-Wurgler index ended in 2005.

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The predictions are clearest at the opposite ends of the risk spectrum, i.e. for equity and

money market funds. Investors should increase exposure to equity funds and reduce

exposure to fixed-income funds when they expect business conditions to remain strong or

improve, and do the opposite when they expect conditions to deteriorate. Thus, asset

allocations to equities should increase as TERM, CFNAI and CONFID increase and as DEF,

ΔTB and TED drop. Allocations to money market funds are predicted to react in exactly the

opposite direction. Investors might be expected to reduce their equity allocation and

increase their money market allocation at times of elevated stock market uncertainty

(higher SPV). The effect of bond market volatility (TBV) is less clear. If higher bond

volatility reflects heightened macro-economic uncertainty, investors could switch to safer

investments. On the other hand, if it reflects primarily inflation uncertainty, investors

might reduce their allocation to asset classes that produce fixed payoffs, such as money

market or bond funds.

To the extent that both foreign and domestic stocks are more sensitive to U.S.

business cycle fluctuations than are money market funds, investors will adjust allocations

to foreign funds and domestic funds in the same direction. However, since international

business cycles are imperfectly correlated, it is possible that investors will seek the relative

safety of foreign equities when U.S. economic conditions deteriorate and will invest at

home when conditions are more favorable. If the payoffs to bond funds are largely safe, we

expect the allocation to bonds to behave similarly to the money market allocation.

However, since the bond asset category includes not just government bond funds but also

corporate bond funds, which are likely to face business cycle risks, the net effect of

economic conditions should reflect a blend of these exposures.

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A possible objection is that we are estimating allocations to the asset classes

independently of each other, while the allocation decisions are in fact simultaneous. We

address this issue by also modeling the difference between the equity and money market

allocations. This should capture the joint decision to allocate to equities versus money

market funds, and is similar to a reduced form equation.

3. Results

3.1 Descriptive Statistics

Table I, Panel A presents statistics on excess flow for the four asset classes. Mean and

median excess flow are close to zero for equity and money market funds, suggesting that

net monthly flows for the equity and money market classes are roughly in line with their

asset weights over the 1991-2008 sample period. The standard deviation of excess flow is

higher for domestic than foreign equities and highest for domestic equity and money

market funds, the classes at the two extremes of the risk spectrum.

Figure 2 plots excess flow for equity and money market funds over the sample period.

There are no obvious trends in the series though they appear heteroskedastic. The greater

volatility of money market flow is apparent. Also, there is a tendency for equity and money

market flow to move in opposite directions. However, there are several periods when they

move together, e.g. 1993 to 1995.

Panel B of Table I provides the time-series correlations among the excess flows for

the four asset categories. Consistent with the risk ordering of the series, domestic equity

flow is positively correlated with foreign equity flow (0.47) and negatively correlated with

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money market flow (-0.61).18 Thus, investors appear to put money into, or pull money out

of, domestic and foreign equity funds at the same time. The strong negative correlation

between equity and money market flow confirms the visual evidence in Figure 2. Bond

flow is not significantly correlated with equity flow and is negatively correlated with

money market flow (-0.26). This is consistent with the mixed composition of the bond

category, which consists of corporate bonds (with risk profiles more akin to equities) as

well as less risky government bonds.

Table II provides summary statistics for the independent variables in our tests.

Descriptive statistics are shown in Panel A. The Chicago FED National Activity Index

(CFNAI) has a mean near zero, by construction, but shows substantial time-series variation,

as indicated by the Q1 and Q3 values of -0.35 and 0.43. TERM, the yield premium for

investing in long-term over short-term bonds, averages approximately 2% per year. DEF,

the spread between risky bond yields and safe bond yields, averages 1.7% per year. The

mean for TERM is similar to the value of 1.99% reported by Fama and French (1989) over a

longer sample period but the mean for DEF is slightly higher than the mean they report

(0.96%). The mean change in the annualized T-Bill rate is -0.025% (median zero), while

the average TED spread suggests an annualized counterparty risk premium of 0.5% over

our sample period.

The mean value of SPV, S&P 500 index volatility, translates into an annualized

standard deviation of approximately 16%. T-Bill volatility (TBV) has a mean of 1% per

month. The return to the U.S. equity fund category has a mean of 11% and a standard

deviation of 14% (annualized), while the values for the other three classes (not reported) 18 Goetzmann et al. (1999) report a correlation of -0.44 between aggregate U.S. equity and money market flow.

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are somewhat lower. By construction, CONFID has a mean close to 100 as this is the

baseline for the index.

Panel B reports the time-series correlations among these variables. TERM and

CONFID are highly negatively correlated (-0.71), consistent with the forward-looking

nature of TERM. The correlation of 0.58 between DEF and SPV is consistent with periods of

poor economic conditions also seeing high stock market volatility (e.g. Schwert, 1989).

CFNAI is correlated negatively with DEF (-0.48) and positively with ΔTB (0.42), consistent

with periods of economic prosperity seeing low corporate default potential and rising

interest rates.

3.2 Economic Conditions and Mutual Fund Flow

The first question addressed in the paper is whether economic conditions affect asset

allocation decisions. Table III presents the results of regressions of excess flow on the

proxies for economic conditions and controls. We scale the dependent and independent

variables by their standard deviations; this standardization allows us to directly assess

economic significance. To account for conditional heteroskedasticity and autocorrelation,

we compute Newey-West t-statistics.

We first study the effects of CFNAI, a composite proxy for the state of the economy, on

mutual fund allocations (Panel A). Univariate regressions show that excess flow for

domestic and foreign equities is positively and significantly related to CFNAI with t-

statistics of 2.68 and 3.79, respectively. Excess bond and money market flow is negatively

related to CFNAI, although only the coefficient for bond flow is significant (t-statistics of

2.59 and 1.15). The difference in excess flow for equity and money market funds is

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positively and significantly related to CFNAI with a t-statistic of 2.01. These univariate

results are consistent with the hypothesis that investors adjust allocations towards riskier

investments as real economic conditions improve and towards safer investments as

conditions deteriorate.

Panel B presents two models that use financial market proxies for economic

conditions, excluding and including the crisis variable. Crisis periods typically overlap with

periods of high counterparty risk, captured by TED, and the inclusion of CRISIS

substantially reduces the significance of TED. In order to consider the effects of TED, we

also report a specification for each asset class that excludes CRISIS.19

When CRISIS is excluded from the model, we find that equity flow is positively and

significantly associated with TERM and negatively and significantly associated with both

DEF and TED. Thus, an expected improvement in economic conditions (high values of

TERM) causes investors to increase their allocation to equity funds. A deterioration in

current economic conditions (high DEF) or tightness in financial markets (high TED) leads

to reduced equity allocations. Higher values of CONFID lead to higher equity allocations,

which is consistent with sentiment-driven models although the significance of the

coefficient is marginal. Interestingly, equity excess flow is unrelated to equity market

volatility (SPV) but positively associated with bond market volatility (TBV). If bond

volatility is high when inflation uncertainty is high, this result is consistent with investors

increasing their allocations to equity funds as a hedge against inflation risk. Money market

excess flow is negatively associated with TERM and TBV and positively associated with

TED. Thus, money market allocation increases during periods of financial tightness, and

19 When we exclude TED from the second specification, our results are stronger.

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declines when economic conditions are expected to improve or when interest rate

uncertainty rises.

When CRISIS is included in the equity flow regression, the coefficient is negative and

significant, confirming anecdotal evidence that equity flow declines during the major

shocks occurring over our sample period. In the money market regression, the coefficient

on CRISIS is positive and significant, indicating that these disruptions see increased

allocations to money market funds. The final column in Table III reports the difference

between the allocations to the equity and money market asset classes and shows that the

coefficients on TERM, DEF, TED, TBV, as well as CRISIS are significant. Thus, as

expectations regarding economic conditions change, investors re-allocate between the two

asset classes at the extremes in the spectrum of business cycle exposures.

Taken together, these results provide compelling evidence that mutual fund investors

actively shift their portfolios to less risky assets during crisis periods. Furthermore, after

controlling for the safe haven effects of crises, the proxies for economic conditions remain

significant predictors of allocations between the risky equity and relatively safe money

market asset classes.

The coefficient on lagged returns (a control) is not statistically significant, which is

puzzling given the robust evidence of return chasing in the literature (e.g. Brown, Harlow,

Starks (1996), Sirri and Tufano, (1998)). However, as discussed in section 2, the excess

flow variable that we study is not the same as the flow measure used in the return chasing

literature. When we separately consider the two components of excess flow, net flow and

asset-weighted flow (NF and AWF in equation (2), each standardized by total market

capitalization), we find significant return chasing in both components. We conclude that

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high aggregate equity fund returns lead to greater flows to equity funds, but does not

appear to cause reallocation across asset classes.20

Consistent with the blended risks of the bond funds, the allocation to bond funds is

less strongly related to the predictive variables. The coefficients on DEF, TBV and SPV are

positive and significant, while that on CRISIS is insignificant. The positive coefficient on

TBV suggests that investors treat bond funds in the same way as equity funds. However,

the positive coefficients on DEF and SPV indicate that investors increase bond fund

allocation in the face of weak conditions and heightened stock market uncertainty. These

opposite conclusions likely stem from the fact that this asset class includes bonds with

differing exposures to business cycle risks.

For the foreign equity allocation, the coefficients are similar to those for domestic

equities. The coefficients on DEF, TED and CRISIS are negative and significant, and the

coefficient on TBV is positive and significant. Thus, deteriorating U.S. conditions cause

investors to reduce their allocations to foreign equities, not just domestic equities. It does

not appear that U.S. investors opt for increased international diversification at such times.

This result could also reflect the strong influence of the U.S. economy on global economic

conditions. 21

In unreported results, we jointly consider the effects of CFNAI and the financial

market variables and find that CFNAI loses significance. This is consistent with forward-

20 This result is not entirely unexpected. For instance, if the sizes of all asset classes other than equity funds are held constant, a positive equity return in period t-1 results in an increase in the equity class weight at the end of period t-1 and thus in expected equity flow in period t. The return chasing in raw flow will only appear in excess flow if this return chasing is strong enough to exceed the increase in expected flow.

21 To deal with possible collinearity (e.g. between TERM and CONFID), we also estimate subsets of the full model for each asset class and find similar coefficients on the included variables.

19

looking financial market variables subsuming the information in current measures of real

economic conditions. Given these results, we focus on the financial market variables in the

remainder of our tests.

We conclude from the results in Table 3 that flight-to-safety considerations affect

mutual fund investors’ collective asset allocation decisions. Furthermore, above and

beyond such crisis-driven reallocations, fund investors’ asset allocations react to forward-

looking financial variables in a manner one would expect of sophisticated investors.

3.3 Investor Sophistication and Asset Allocation

The third question addressed in the paper is the extent to which the sensitivity of

asset allocations to economic conditions varies with investor sophistication. We use fund

fees and fund turnover as proxies for investor sophistication, expecting more sophisticated

investors to hold funds with low fees and lower turnover given the evidence that both

factors are a drag on performance.22

We sort domestic equity funds by fees and turnover as follows. At the start of each

month, we examine the most recent cross-sectional distribution of fund fees and classify a

fund as belonging to: i) the low fee group, if fee ≤ Quartile 1; ii) the intermediate fee group,

if Quartile 1 < fee < Quartile 3; or iii) the high fee group, if fee ≥ Quartile 3. Using equations

(1) to (3), we then compute aggregate excess flow for the three fee groups in the month.

The turnover sort proceeds identically. The spread in fees and turnover is large: On

average, the third quartile values for fees and turnover are twice and thrice the first 22 Support for these proxies is provided by Gil-Bazo and Ruiz-Verdu (2009), who show that funds with worse before-fee performance charge higher fees, and Carhart (1997) and Gompers and Metrick (2001), who find that net fund return is negatively related to fund turnover. Houge and Wellman (2006) show that mutual funds segment customers by their level of sophistication and then charge less-knowledgeable investors higher fees.

20

quartile values (1.8% vs. 0.9% for fees, and 1.15 vs. 0.34 for turnover). Further, the

average cross-sectional correlation between fund fees and fund turnover is insignificant,

suggesting that fees and turnover provide independent evidence on allocations.

Table IV, Panel A reports the results when we divide the sample based on fees.

Allocation to the low fee funds is highly responsive to variations in economic conditions.

Excess flow for these funds increases with increases in TERM, CONFID and TBV and drops

with increases in DEF and TED. Furthermore, the low fee funds see significant outflows

during crises. In contrast, excess flow for the high fee funds responds only to DEF (with a

negative sign) and to CONFID with a positive sign and marginal significance. Excess flow

for intermediate fee funds displays intermediate sensitivity to the economic variables, with

the coefficients on DEF, TED, TBV and CRISIS being significant or marginally significant. As

previously noted for the full sample models (Table III), when CRISIS is excluded from the

model specifications the significance of TED increases while the effect on the other

explanatory variables is minimal.

A comparison of the coefficients shows that economic significance for the variables

tends to be largest for the low fee funds and smallest for the high fee funds. Further, the

difference in allocations for low versus high fee funds (reported in the final column) is

significantly related to TERM, DEF, TED, TBV and CRISIS. Finally, the regression R2 drops

dramatically for high fee (0.14) relative to lower fee funds (0.32), based on the full

specification. We interpret these results to imply that more sophisticated mutual fund

investors are highly sensitive to changing economic conditions while naïve mutual fund

investors tend to be relatively insensitive. In fact, the more sophisticated fund investors

21

appear to be driving the relations for aggregate flow we have documented in the previous

sub-section,

Panel B provides the results of the turnover sort. The results are stronger than those

for fees. For low turnover funds, the coefficients on TERM, CONFID, and TBV are positive

and significant, while those on DEF, TED, and CRISIS are negative and significant. In the

case of high turnover funds, only ΔTB is significant at conventional levels. Excess flow for

funds with intermediate turnover is significantly related to the same variables that drive

flow for the low turnover funds, though with smaller coefficients (an exception is the

coefficient on DEF). As with fees, the R2 is sharply higher for low turnover funds than

higher turnover funds. To the extent that sophisticated investors hold funds with low

turnover, these results buttress the conclusion from Panel A that sophisticated investors

reallocate funds more aggressively than more naïve investors do as economic conditions

change.

A concern with using fees and turnover as proxies for investor sophistication is that

high fee funds could also have high back-end loads, which could lead to sluggish investor

flow. As a result of low investor flow, high fee funds may also have low portfolio turnover.

However, Nanda et al. (2009) document that back-end load funds tend to have flows that

are more sensitive to performance, and Christoffersen and Sarkissian (2009) report that

high fee funds have significantly higher portfolio turnover. Bergstresser, Chalmers and

Tufano (2009) find little difference in performance-chasing between higher fee (broker-

sold) and lower fee (directly sold) funds. These studies suggest that loads are unlikely to

deter investor movement between funds and, if anything, general trends in investor

behaviour in relation to loads bias against our results.

22

4. Robustness Checks

4.1 An Alternative Asset Allocation Measure

As argued in section 2, we believe that excess flow is ideal for the purposes of

studying asset allocation because it has a natural and clear interpretation, reflecting the

extent to which flow deviates from a value-weighted benchmark. However, to address

potential concerns with excess flow, we examine an alternative asset allocation measure. A

measure suggested by Sirri and Tufano (1998) is percent flow, defined as flow scaled by

total assets.23 Percent flow avoids the adding-up problem with excess flow, but does not

capture asset allocation as cleanly. For instance, if economic conditions improve, investors

are likely to increase flow to both equity and money-market funds. In the absence of a

benchmark for expected flow, the precise shift towards or away from equities is more

difficult to identify.

Percent flow is calculated as net flow (defined in equation (1)) for each asset class

scaled by lagged total net assets. We relate percent flow to the lagged economic indicators

(TERM, DEF, ΔTB, CONFID, TED, SPV, and TBV), CRISIS, lagged return (RET), as well as the

lagged asset class weight (WEIGHT).24

Table V reports the results. The percent flow measure of asset allocation yields

conclusions similar to those drawn from Table III. Paralleling our earlier results, aggregate

percent flow for domestic equity funds is related negatively to DEF and TED, and positively

to CONFID. For percent flow, unlike excess flow, the coefficients on TERM and TBV are not

significant, but the coefficient on ΔTB now is negative and significant. Since increases in T-

23 Jayaramann et al. (2002) and Khorana et al. (2007), among others, also use the percent flow measure.

24 Sirri and Tufano (1998) include lagged total assets in their fund-level study. Since our focus is asset allocation, the relative size of each class is more relevant. We find similar results using lagged assets.

23

Bill rates tend to foreshadow downturns, it is reasonable that investors move away from

risky assets as short rates increase. As with excess flow, the negative coefficient on CRISIS

indicates that percent flow to equity funds drops during crises. The coefficients on the

control variables RET and WEIGHT are significant. The positive coefficient on lagged

returns indicates return-chasing at the asset class level. The negative coefficient on

WEIGHT is consistent with Sirri and Tufano’s evidence at the fund level.

Aggregate percent flow to money market funds is associated positively with CRISIS

and lagged DEF, and negatively with CONFID, TBV and ΔTB (the last coefficient is

significant at the 10% level in one specification). Therefore, periods of crises, deteriorating

economic conditions (high values of DEF) or low interest rate uncertainty see increased

allocations to money market funds. The coefficients on the control variables are significant

and positive for lagged returns and negative for the asset class weight. Thus, money

market funds see inflows following high money market returns.25

For bonds, percent flow is associated negatively with ΔTB, TED and CONFID and

positively with SPV. The first two coefficients suggest that bond flow behaves like equity

flow, while the coefficients on confidence and equity volatility indicate that bond flow

behaves like money market flow. As mentioned earlier, these conflicting conclusions are

consistent with the blended risks of bond funds. Foreign equity flow is negatively related

to DEF and CONFID. High values of DEF indicate that when the U.S. economy is not

performing well, U.S. investors tend to avoid foreign stocks. When confidence is high and

U.S. investors feel good about the home economy, they tend to invest less overseas. The

positive coefficients on RET for both categories are again consistent with return chasing. 25 Comparing the coefficients for equity and money market flow, we see that the coefficients on DEF and TED are significantly smaller for equity flow, while those on CONFID and TBV are significantly larger.

24

The analysis of percent flow shows that investors increase their allocations to riskier

asset classes when the U.S. economy is expected to perform well and move to safer assets

in the face of deteriorating conditions. Thus, these results are consistent with the results in

Section 3.2 showing that asset allocation is sensitive to economic conditions.

4.2 An Alternative Setting: Asset Allocation in Canada

We use a sample of Canadian mutual funds to evaluate the consistency of our results

across countries. The Investment Funds Institute of Canada (IFIC) provides Canadian

mutual fund data including monthly sales, asset values and redemptions by fund, as well as

broad fund objectives from January 1991 through October 2005. When we study Canadian

allocations, we use Canadian TERM, DEF, ΔTB, TED, TSX index volatility (TSV) and TBV.

TSX index returns come from DataStream, data for Canadian government bonds come from

the Statistics Canada database, and the yields on medium-term Canadian corporate bonds

(investment-grade corporate bonds with maturity below 10 years) are obtained from the

Economist intelligence unit.26

Table VI presents results for excess flow to Canadian asset classes, calculated using

equations (1) to (3).27 As in the U.S., the Canadian domestic equity allocation is positively

associated with TERM and negatively associated with DEF and TED. Canadian money 26 Note some differences between the analysis of Canadian and U.S. funds. First, we are forced to proceed without CONFID, for which we do not have a Canadian counterpart. Second, the number of Canadian funds investing in U.S. equities is relatively large (and accounts for approximately 6% of total assets). Thus, we add a fifth asset category, U.S. equity, and separately examine the flows to U.S. equity funds and to equity funds investing outside the U.S. (we refer to the latter category as Foreign equity). The summary statistics and correlation matrices for the Canadian data are similar to those for the U.S. and are available upon request. Relative to U.S. mutual fund investors, Canadian investors allocate slightly larger fractions to balanced, U.S. and foreign funds, and less to domestic equity and money market funds.

27 Canadian flow is standardized by lagged TSX market capitalization. We report only the full model specification since the correlation between CRISIS and TED is much lower in the Canadian dataset (0.13). If CRISIS is excluded we obtain the same results.

25

market excess flow is related positively to DEF and CRISIS and negatively to TERM. These

results point to allocations to (from) equity funds relative to money market funds when

Canadian economic conditions are expected to improve (deteriorate), and to increased

money market allocations during periods of global stress. Unlike the U.S., money market

flow is positively associated with TBV, and the coefficient is not significant for equity flow.

This suggests that Canadian fund investors seek shorter-term investments when bond

market uncertainty is high.

Paralleling the U.S. results, the results for Canadian bond excess flow show limited

significance, although the ΔTB coefficient is positive and the TBV and CRISIS coefficients

are negative and significant. When we consider the determinants of foreign equity flow, we

see that the coefficients on DEF and TED are negative and marginally significant while the

coefficient on ΔTB is positive and significant. The first two coefficients are similar to those

for foreign funds in the U.S., and indicate that unfavorable conditions at home lead to

reduced allocations to funds investing overseas. More intriguing are the determinants of

allocations to the U.S. fund category. Here, the coefficient on TED is positive and marginally

significant while that on TERM is negative and marginally significant, suggesting that

Canadian investors increase allocations to U.S. funds rather than international funds when

economic prospects at home are dim. As seen in the negative coefficients on TBV, Canadian

investors avoid foreign and U.S. equity investments during periods of high bond market

uncertainty.

The results in Sections 4.1 and 4.2 reinforce the results in Section 3.2. Collectively,

the analysis suggests that mutual fund investors increase allocations to relatively safe

money market funds when conditions are expected to worsen. When conditions are

26

expected to improve, allocations to riskier equity funds rise. These results are consistent

with mutual fund investor allocations being driven by economic conditions and crises.

5. Implications of Time Varying Asset Allocation

In this section we address our final question, exploring the implications of economic

conditions-based reallocation strategies for portfolio performance. We consider two sets

of investors. One group implements a buy-and-hold strategy and maintains fund holdings

over the business cycle, while the other implements a reallocation strategy based on

economic conditions. The activity of the second group necessitates liquidity trading by the

fund manager, and the associated trading costs impair fund performance. Moreover, large

outflows or inflows can disrupt the fund’s investment strategy and also hurt

performance.28 Thus, reallocation strategies are likely to hurt investors who remain with

the fund.

From the perspective of the reallocating investor, whether this behavior is beneficial

or not is an open question. There are interesting parallels between our asset allocation

analysis and the mutual fund market-timing literature, which generally concludes that fund

managers have limited market-timing ability.29 The reallocations associated with economic

conditions that we document can be thought of as fund investors attempting to time the

market. In general, fund managers are precluded from reallocating across asset classes,

and our results suggest that investors recognize this limitation and undertake the

reallocations themselves.

28 See, for example, Edelen (1999), Coval and Stafford (2007) and Rakowski (2010).

29 Among others, see Daniel et al. (1997), Ingersoll et al. (2000), Chance and Hemler (2001).

27

To evaluate the efficacy of the reallocation strategy, we examine the raw and risk

standardized returns for two portfolios. The first is a buy-and-hold equity fund portfolio,

which we assume receives the mean monthly return across all U.S. domestic equity funds.

The second, reallocation portfolio holds either equity funds or money market funds

depending on the values of the predictive variables. We assume that the investor allocates

100% of his investible wealth to equity funds, but switches his wealth entirely to money

market funds in one of four cases: when TERM drops into its bottom quartile or tercile, or

when DEF climbs into its top quartile or tercile. These cutoffs are computed each month

using the five most recent years of monthly data.30 The portfolio is assumed to receive the

mean return for the equity or money market asset class in months where wealth is

allocated to that class. By necessity, we focus our analysis on raw average returns and the

risk-return trade off reflected by the Sharpe and Treynor-Black ratios. The buy-and-hold

portfolio replicates the market portfolio which by definition has an alpha of 0 and the

reallocation portfolios toggle between the market and the risk-free portfolio. Thus, factor

model performance analysis is non-informative in this context.

Table VII reports performance statistics for the two portfolio strategies. These

statistics are computed using the time-series of monthly asset class returns over the

February 1991 through March 2008 sample period. The mean annualized returns to the

four reallocation portfolios, which range between 6.4% and 9.2%, are significantly lower

than the mean equity buy-and-hold portfolio return (10.7%). So, too, however, are the

standard deviations, which vary between 9.6% and 10.9%, compared to 14.3% for the buy-

and-hold portfolio. The Sharpe ratio for the buy-and-hold portfolio (0.75) is similar to the 30 We select TERM and DEF as switching indicator variables because they are widely used business conditions proxies (e.g. Fama and French, 1989).

28

values for the reallocation portfolios using TERM as the trading signal, but the ratios using

DEF as a trading signal suggest the reallocation strategy outperforms a buy-and-hold

strategy (ratios of 0.913 and 0.884), on a risk-adjusted basis

In order to get a sense of the effects on market exposure, the remaining rows of Table

VII present the market betas and resultant Treynor-Black ratios for each portfolio. The

reduction in market beta occurs because the switching strategy invests either in equities,

with a market beta of 1, or in the money market portfolio, with a beta of essentially 0.

Thus, a combination of the two portfolios will have a beta below 1 and the actual beta will

reflect the time the portfolio is in equities versus money market funds. While the market-

timing portfolio beta must be lower than the buy-and-hold portfolio beta, the main

question of interest is how the switching strategy affects risk-adjusted returns.

The single-factor market betas for the four reallocation portfolios, which range

between 0.44 and 0.58, are approximately half as large as the beta for the buy-and-hold

portfolio, which is 1.01. As a result of these appreciably lower betas, the Treynor-Black

ratio of mean excess return to beta is larger for all four reallocation portfolios and

materially so (30%-70%) for three portfolios. The final row of the table shows the market

beta from a four-factor model that includes the excess market return, the Fama and French

(1993) SMB and HML factors and momentum. The market risk of these portfolios does not

differ materially from that in the previous row.31 Collectively, the ratio analysis suggests

that, on a risk-adjusted basis, the performance of the reallocation strategies is on par with,

or even exceeds, the performance of the buy-and-hold strategy.

31 Note that this analysis has ignored transaction costs. We incorporate costs using the fees and holding periods reported by Khorana et al. (2009). Due to more frequent trading, the reallocation portfolios face extra trading costs of 0.20% per annum relative to the buy-and-hold portfolio. The small size of these costs means that the preceding conclusions will not change.

29

6. Concluding Comments

This paper examines the aggregate asset allocation decisions of U.S. mutual fund

investors, focusing on the effects of economic conditions. Our results suggest that an

expected deterioration in economic conditions leads mutual fund investors to allocate less

to equity funds and more to money market funds, while an anticipated improvement in

conditions induces rebalancing in the opposite direction. In addition, we document

significant shifts from risky to less risky assets during crises. These patterns are stronger

in funds with relatively low fees and turnover, consistent with sophisticated investors

being more sensitive to changing conditions. The results are robust, holding for alternative

measures of asset allocation and for the universe of Canadian mutual funds.

Our research indicates that anticipated changes in economic conditions cause

investors to adjust the riskiness of their asset holdings in sensible ways. Further, investors

who follow such strategies do not face a poorer risk-return tradeoff. Thus, our results

imply that, in the aggregate, fund flows have at least partly rational motivations. In

contrast, fund-level flow research documents a set of possibly irrational factors that

motivate fund flows.

These finds suggest that small investors collectively play a significant role in price

formation. Among others, Fama and French (1989) and Ferson and Harvey (1991) show

that variables such as DEF and TERM have predictive power for asset prices. We show that

fund investors use these predictive variables as cues for reallocations across asset classes.

The relation between aggregate allocations and the proxies for economic conditions (e.g.

the positive relation between equity flow and TERM) suggests that fund investors

30

contribute to the relation between the predictive variables and asset prices, and that this

relation reflects business-cycle risks rather than irrationality.

Our evidence suggests that mutual fund investors collectively respond to the

information in forward-looking financial variables. A reasonable question to ask is

whether the financial market proxies for economic conditions, while well-established in the

finance literature, are the types of signals that mutual fund investors are likely to use in

their asset allocation decisions. Exactly how investors make these decisions reaches

beyond the granularity of our data. However, plausible information transmission channels

may include financial analysts, journalists, or advisors who rely on these variables in

writing their reports with asset allocation recommendations.

Although our analysis indicates that time-varying asset allocation decisions might

benefit investors, through reduced portfolio risk or an improved risk-return tradeoff, it is

worth noting that these benefits come at a cost. The implementation of such market-timing

strategies imposes transaction costs on the affected funds, borne predominantly by buy-

and-hold investors who remain with the fund over the cycle. Thus, our results point to

wealth transfers from buy-and-hold investors to more transient investors seeking to time

the business cycle. Our research also suggests a set of aggregate-level variables that fund

managers can use to anticipate flow variations and reduce their effects on performance.

31

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34

Table I

Summary Statistics for Excess Flow by Asset Category Panel A of Table I reports descriptive statistics for monthly excess flow, where excess flow is calculated as aggregate net flow for each asset category in excess of the net flow that would have resulted on an asset-weighted basis, standardized by total market capitalization of the NYSE, AMEX and NASDAQ. Excess flow is reported separately for four mutual fund types: equity, bond, money market and foreign equity. Panel B reports the correlation matrix of excess flow across fund types, values significant at the 5% level of significance appear in bold face. The sample period is February 1991 to March 2008.

Panel A: Descriptive Statistics for Excess Flow (x103)

Category Mean Median Q1 Q3 STD

Equity -0.250 0.051 -5.641 6.542 10.161

Bond -1.184 -1.139 -4.470 1.710 7.095

Money Market 0.075 0.006 -14.191 13.140 19.432

Foreign Equity 1.498 1.250 -1.099 4.191 4.376

Panel B: Correlation Matrix for Excess Flow

Equity Bond

Money Market

Foreign Equity

Equity 1

Bond -0.057 1

Money Market -0.607 -0.255 1

Foreign Equity 0.474 -0.178 -0.451 1

35

Table II

Summary Statistics for Independent Variables Panel A of Table II reports descriptive statistics for the independent variables used as proxies for economic conditions in the U.S. The correlation matrix for these variables appears in Panel B, values significant at the 5% level appear in bold face. The variables are available monthly between January 1991 and March 2008. CFNAI is the Chicago Fed National Activity Index. TERM, the term spread, is the difference in yield between the ten year Government Bond and the three month Treasury Bill. DEF, the default spread, is the difference in yield between medium term corporate bonds and three to five year Government Bonds. ΔTB is the change in the yield on the three month Treasury Bill. CONFID is the consumer confidence index value derived from consumer survey responses related to current and planned spending. TED, the Treasury Eurodollar spread, is the difference between three month LIBOR and the three month T-BILL rate. RET is the equally-weighted monthly return to the U.S. equity fund asset category. SPV, S&P volatility, is the monthly sum of squared daily returns for the S&P 500 index and TBV, T-Bill volatility, is the monthly sum of squared daily yield changes for the three month T-Bill. All variables are presented in percentage terms, with the exception of CONFID and CFNAI which are reported as index levels relative to neutral values of 100 and 0.

Panel A: Descriptive Statistics for Independent Variables

Variable Mean Median Q1 Q3 STD

CFNAI 0.007 0.060 -0.35 0.43 0.58

TERM 2.047 1.846 0.918 3.469 1.434

DEF 1.653 1.524 1.279 1.91 0.557

ΔTB -0.025 0 -0.11 0.11 0.244

CONFID 100.3 101.4 83.6 116.1 23.835

TED 0.468 0.386 0.270 0.566 0.310

RET 0.904 1.352 -1.824 3.780 4.105

SPV 0.214 0.141 0.075 0.252 0.221

TBV 1.045 0.168 0.086 0.395 8.212

36

Panel B: Correlation Matrix for Independent Variables

TERM DEF ΔTB CONFID TED RET SPV TBV CFNAI

TERM 1

DEF -0.154 1

ΔTB -0.072 -0.240 1

CONFID -0.712 0.211 0.139 1

TED -0.400 0.109 -0.335 0.246 1

RET -0.035 -0.105 0.063 -0.026 -0.008 1

SPV -0.069 0.578 -0.317 0.226 0.189 -0.266 1

TBV 0.064 0.140 -0.182 -0.112 0.272 -0.045 0.157 1

CFNAI -0.017 -0.478 0.419 0.201 -0.050 -0.034 -0.212 -0.145 1

37

Table III

Mutual Fund Excess Flow and Economic Conditions Table III reports coefficients and Newey-West t-statistics from time-series regressions of monthly excess flow for four asset categories (equities, bonds, money market, and foreign equities) on lagged asset category return and proxies for economic conditions. The dependent variable in the final column is the difference between equity and money market excess flow. Excess flow is calculated as aggregate net flow for each asset category in excess of the net flow that would have resulted on an asset weighted basis, standardized by total market capitalization of the NYSE, AMEX and NASDAQ. The independent variables are lagged by one period and are as described in Table II, except for CRISIS, which is a dummy variable equal to one for the following shocks: the subprime mortgage credit crisis (Aug 2007 to the end of the sample), the 9/11 terrorist attacks (Sept - Dec 2001), the “Crash of 2000” when the NASDAQ dropped 45.9% between Sept and Dec 2000, Y2K concerns at the end of 1999 (Oct - Dec 1999), the failure of the Long Term Capital Management Hedge Fund (Aug - Oct 1998), the Asian currency crisis (July - Sept 1997), the Mexican currency crisis (Dec 1994 - Feb 1995) and the Pound exiting the European Exchange Rate Mechanism (Sept - Nov 1992). Panel A presents results related to real economy variables and Panel B focuses on financial market variables. Due to the high correlation between CRISIS and TED, two models are reported in Panel B, one with CRISIS (shaded) and the other without CRISIS. The dependent and independent variables are standardized and intercepts are not reported for brevity. Coefficients significant at the 10% level appear in bold face. The sample period is February 1991 to March 2008.

Panel A: Real Economy Variables

Equity Money Market

Bond Foreign Equity

Equity - Money Market

CFNAI t-1 0.21 -0.09 -0.19 0.31 0.29 (2.68) (1.15) (-2.59) (3.79) (2.01)

R2 0.04 0.00 0.01 0.09 0.02

38

Panel B: Financial Market Variables

Equity Money Market Bond Foreign Equity Equity –

Money Market

TERM t-1 0.27 0.33 -0.20 -0.25 0.09 0.09 -0.02 0.03 0.47 0.58 (2.37) (2.88) (1.93) (2.48) (0.78) (0.79) (0.15) (0.31) (3.03) (3.67)

DEF t-1 -0.18 -0.17 0.06 0.06 0.16 0.16 -0.21 -0.20 -0.24 -0.23 (2.35) (2.24) (1.07) (1.03) (2.37) (2.36) (2.57) (2.46) (1.87) (1.75)

ΔTB t-1 0.10 0.12 -0.10 -0.11 0.10 0.10 0.13 0.14 0.20 0.23 (0.93) (1.23) (0.85) (1.12) (1.01) (1.01) (1.24) (1.56) (0.97) (1.28)

CONFID t-1 0.17 0.19 0.05 0.03 -0.16 -0.16 -0.18 -0.16 0.12 0.16 (1.43) (1.62) (0.48) (0.35) (1.41) (1.42) (1.60) (1.43) (0.61) (0.79)

TED t-1 -0.24 -0.11 0.28 0.17 -0.13 -0.13 -0.27 -0.16 -0.52 -0.28 (2.52) (1.05) (3.42) (1.69) (1.51) (1.36) (3.40) (1.75) (3.08) (1.35)

RET t-1 0.05 -0.10 0.04 0.03 0.14 0.14 0.09 0.04 0.01 -0.13 (0.52) (0.13) (0.40) (0.31) (1.80) (1.79) (1.56) (0.76) (0.07) (0.65)

SPV t-1 -0.01 0.04 0.00 -0.05 0.15 0.15 -0.11 -0.08 -0.01 0.09 (0.09) (0.31) (0.00) (0.48) (1.80) (1.67) (1.21) (0.76) (0.09) (0.30)

TBV t-1 0.11 0.12 -0.15 -0.16 0.07 0.07 0.19 0.20 0.26 0.28 (4.12) (4.79) (6.92) (8.16) (3.37) (3.37) (10.26) (10.65) (5.54) (6.43)

CRISIS -0.27 0.23 0.00 -0.22 -0.50 (2.36) (2.08) (0.01) (2.83) (2.43)

R2 0.21 0.25 0.24 0.28 0.15 0.15 0.32 0.35 0.23 0.28

39

Table IV

Mutual Fund Excess Flow and Economic Conditions: Fee and Turnover Sub-samples Table IV reports coefficients and Newey-West t-statistics from time-series regressions of monthly excess flow for U.S. equity fund sub-samples on the lagged sub-sample return and proxies for U.S. economic conditions. The dependent variable in the final column is the difference between excess flow for the low and high portfolios. The variables are as defined in Table III, and are standardized with intercepts not reported for brevity. Due to the high correlation between CRISIS and TED, two models are reported, one with CRISIS (shaded) and the other without CRISIS. Coefficients significant at the 10% level appear in bold face. Panel A reports the results for a sort based on fund fees and Panel B for a sort based on fund portfolio turnover. Funds are sorted into terciles based on reported fee or turnover values at the end of the prior month (details in Section 3.3). The sample period is February 1991 to March 2008.

Panel A: U.S. Equity Funds Sorted by Fund Fees

Low Fee Intermediate Fee High Fee Low - High

TERMt-1 0.30 0.37 0.16 0.19 0.16 0.19 0.14 0.18 (2.93) (3.86) (1.39) (1.65) (0.87) (0.97) (2.93) (4.01)

DEF t-1 -0.16 -0.15 -0.17 -0.17 -0.28 -0.28 0.12 0.13 (2.14) (2.16) (2.16) (2.06) (2.75) (2.69) (1.63) (1.63)

ΔTB t-1 0.07 0.09 0.13 0.14 0.03 0.04 0.04 0.05 (0.67) (1.03) (1.19) (1.34) (0.32) (0.39) (0.68) (1.05)

CONFID t-1 0.18 0.20 0.04 0.05 0.23 0.23 -0.05 -0.03 (1.43) (1.75) (0.31) (0.38) (1.60) (1.65) (1.22) (1.53)

TED t-1 -0.29 -0.11 -0.19 -0.12 -0.14 -0.08 -0.15 -0.03 (2.53) (0.97) (2.01) (1.11) (1.35) (0.77) (2.53) (0.92)

RET t-1 0.06 -0.01 0.00 -0.03 0.07 0.04 0.01 0.05 (0.72) (0.15) (0.03) (0.30) (0.94) (0.60) (0.64) (0.26)

SPV t-1 -0.02 0.04 0.01 0.04 0.02 0.04 0.00 0.00 (0.17) (0.31) (0.11) (0.33) (0.24) (0.43) (0.20) (0.29)

TBV t-1 0.17 0.18 0.06 0.06 -0.00 0.00 0.17 0.18 (5.91) (7.09) (2.20) (2.43) (0.06) (0.01) (6.80) (8.16)

CRISIS -0.35 -0.15 -0.11 -0.24 (3.02) (1.46) (1.19) (3.16)

R2 0.24 0.32 0.15 0.17 0.14 0.14 0.10 0.18

40

Panel B: U.S. Equity Funds Sorted by Portfolio Turnover

Low Turnover Intermediate

Turnover High Turnover Low - High

TERM t-1 0.36 0.43 0.22 0.28 0.07 0.09 0.29 0.34 (3.49) (4.38) (1.74) (2.15) (0.55) (0.74) (3.33) (4.09)

DEF t-1 -0.18 -0.17 -0.20 -0.19 -0.01 -0.01 -0.17 -0.16 (2.39) (2.49) (2.24) (2.13) (0.10) (0.07) (2.10) (2.19)

ΔTB t-1 -0.02 0.00 0.07 0.09 0.26 0.27 -0.28 -0.27 (0.22) (0.03) (0.65) (0.88) (2.39) (2.52) (1.40) (1.39)

CONFID t-1 0.28 0.31 0.19 0.21 -0.04 -0.03 0.32 0.34 (2.22) (2.53) (1.54) (1.73) (0.25) (0.19) (2.14) (2.33)

TED t-1 -0.37 -0.21 -0.25 -0.12 0.00 0.06 -0.37 -0.15 (3.70) (2.17) (2.33) (1.00) (0.04) (0.60) (4.10) (2.86)

RET t-1 0.07 0.00 0.08 0.03 0.08 0.05 -0.01 -0.05 (0.86) (0.03) (0.97) (0.40) (0.96) (0.70) (0.53) (0.31)

SPV t-1 -0.04 0.02 0.00 0.05 0.02 0.04 -0.06 -0.02 (0.35) (0.18) (0.03) (0.40) (0.20) (0.35) (0.54) (0.04)

TBV t-1 0.19 0.19 0.09 0.10 0.04 0.04 0.15 0.14 (7.22) (8.53) (3.02) (3.50) (1.62) (1.77) (7.16) (8.17)

CRISIS -0.33 -0.26 -0.12 -0.21 (3.26) (2.21) (1.21) (3.54)

R2 0.30 0.37 0.18 0.22 0.08 0.09 0.22 0.28

41

Table V

Mutual Fund Percent Flow and Economic Conditions Table V reports coefficients and Newey-West t-statistics from time-series regressions of monthly percent flow for four U.S. asset categories (equities, bonds, money market, and foreign equities) on the lagged asset category return and proxies for economic conditions. The dependent variable in the final column is the difference between equity and money market percent flow. Percent flow is calculated as aggregate net flow for each asset category, standardized by lagged category total net assets. The independent variables are lagged by one period and are as described in Table III, except for WEIGHT, which is the lagged value of the category’s total net assets divided by total net assets for all mutual funds. Due to the high correlation between CRISIS and TED, two models are reported, one with CRISIS (shaded) and the other without CRISIS. The dependent and independent variables are standardized and the intercepts not reported for brevity. Coefficients significant at the 10% level appear in bold face. The sample period is February 1991 to March 2008.

Equity Money Market Bond Foreign Equity Equity –

Money Market

TERM t-1 0.03 0.07 0.12 0.09 -0.09 -0.10 -0.07 -0.04 -0.09 -0.02 (0.22) (0.54) (0.66) (0.47) (0.58) (0.60) (0.66) (0.36) (1.77) (2.35)

DEF t-1 -0.33 -0.32 0.14 0.14 -0.15 -0.15 -0.36 -0.36 -0.47 -0.46 (3.24) (3.07) (1.57) (1.63) (1.39) (1.38) (3.78) (3.66) (2.56) (2.34)

ΔTB t-1 -0.15 -0.14 -0.17 -0.19 -0.19 -0.19 -0.03 -0.02 0.02 0.05 (2.00) (2.01) (1.70) (2.00) (2.39) (2.42) (0.39) (0.31) (0.10) (0.31)

CONFID t-1 0.50 0.53 -0.15 -0.16 -0.48 -0.48 -0.38 -0.37 0.65 0.69 (3.20) (3.47) (1.57) (1.73) (3.11) (3.11) (2.86) (2.76) (2.08) (2.43)

TED t-1 -0.21 -0.12 0.11 0.03 -0.16 -0.16 -0.11 -0.05 -0.32 -0.15 (2.70) (1.45) (1.39) (0.27) (2.10) (2.09) (1.42) (0.68) (3.61) (1.75)

RET t-1 0.21 0.17 0.34 0.33 0.29 0.29 0.15 0.12 -0.13 -0.16 (4.21) (3.95) (2.05) (2.11) (4.05) (4.05) (2.54) (2.08) (1.11) (0.38)

SPV t-1 0.05 0.08 0.06 0.02 0.20 0.19 -0.01 0.00 -0.01 0.06 (0.73) (1.21) (0.70) (0.18) (2.84) (2.62) (0.19) (0.03) (0.10) (0.59)

TBV t-1 0.02 0.03 -0.15 -0.15 -0.02 -0.02 0.01 0.01 0.17 0.18 (0.97) (1.23) (7.22) (7.63) (0.80) (0.81) (0.65) (0.77) (4.73) (5.28)

CRISIS -0.19 0.18 0.01 -0.11 -0.37 (2.50) (1.95) (0.17) (1.88) (2.90)

WEIGHT t-1 -0.73 -0.74 -0.39 -0.40 -0.17 -0.17 -0.03 -0.02 -0.34 -0.34 (6.36) (6.57) (2.52) (2.70) (0.96) (0.95) (0.32) (0.23) (4.05) (4.85)

R2 0.46 0.48 0.22 0.24 0.31 0.31 0.39 0.40 0.34 0.39

42

Table VI

Mutual Fund Excess Flow and Economic Conditions: Canadian Evidence Table VI reports coefficients and Newey-West t-statistics from time-series regressions of monthly excess flow for five asset categories (equities, bonds, money market, U.S. equities and foreign equities) on the lagged asset category return and proxies for economic conditions. The dependent variable in the final column is the difference between equity and money market excess flow. Excess flow is calculated as aggregate net flow for each asset category in excess of the net flow that would have resulted on an asset weighted basis, standardized by total market capitalization of the TSX. The independent variables are described in Table II but are calculated using the Canadian equivalent of each variable (e.g. TSV is TSX volatility). The dependent and independent variables are standardized and intercepts not reported for brevity. Coefficients significant at the 10% level appear in bold face. The sample period is February 1991 to September 2005.

Equity Money Market

Bond U.S.

Equity Foreign Equity

Equity – Money Market

TERM t-1 0.55 -0.33 -0.01 -0.17 0.07 0.88

(3.16) (2.93) (0.10) (1.62) (0.66) (3.22)

DEF t-1 -0.50 0.30 0.21 -0.08 -0.43 -0.80

(2.64) (2.20) (1.47) (0.76) (3.09) (1.41)

ΔTB t-1 0.00 -0.28 0.25 0.08 0.19 0.28

(0.01) (3.04) (2.42) (1.41) (2.67) (3.51)

TED t-1 -0.44 0.03 0.06 0.20 -0.24 -0.47

(1.99) (0.37) (0.45) (1.82) (1.65) (0.43)

RET t-1 0.08 0.16 0.27 0.24 0.30 -0.08

(0.82) (2.15) (2.54) (2.98) (1.96) (0.38)

TSV t-1 0.29 -0.11 0.09 0.20 0.09 0.40

(3.09) (1.32) (0.84) (1.77) (0.64) (1.68)

TBV t-1 -0.09 0.36 -0.34 -0.11 -0.12 -0.45

(0.61) (5.10) (3.87) (2.14) (2.76) (3.42)

CRISIS 0.05 0.19 -0.17 0.07 0.07 -0.14

(0.55) (2.76) (1.98) (0.70) (0.61) (1.94)

R2 0.15 0.45 0.25 0.30 0.21 0.40

43

Table VII

Performance Analysis Table VII reports performance analysis for five portfolios using the U.S. fund dataset. The buy-and-hold portfolio (first column) allocates 100% of wealth to equity funds and realizes the mean monthly equity fund return. The reallocation portfolios (the remaining four columns) allocate 100% of wealth to equity funds but transfer 100% of wealth to money market funds when the term spread is in its lowest quartile or tercile (the second and third columns) or when the default spread is in its top quartile or tercile (the fourth and fifth columns). These cutoff values, which are designed to capture predictions of poor economic conditions, are rolling and based on the previous five years of monthly data. The term spread (TERM) is the difference in yield between the 30 year Government Bond and the three month Treasury Bill at the end of the previous month. The default spread (DEF) is the difference in yield between medium term corporate bonds and three to five year Government Bonds. Mean Return is the annualized mean monthly return and STD Return is the annualized standard deviation of monthly returns for each portfolio over the sample period of February 1991 to March 2008. The Sharpe Ratio is Mean Return less the mean risk free rate divided by STD Return, where the mean risk free rate over the sample period is 0.125%. The final rows present the market betas from asset pricing models. The 1-Factor Model includes MKT, the Fama-French excess market return, as the only independent variable, while the 4-Factor Model adds the SMB, HML and MOM factors to MKT. The Treynor-Black Ratio is calculated as the Mean Return less the mean risk free rate divided by the Market Beta from the 1-Factor Model.

Buy-and-Hold

Portfolio Reallocation Portfolios

TERM Quartile

TERM Tercile

DEF Quartile

DEF Tercile

Mean Return 10.74 8.14 6.43 9.24 8.50

STD Return 14.30 10.92 10.66 10.12 9.61

Sharpe Ratio 0.751 0.745 0.603 0.913 0.884

1 Factor Market Beta 1.01 0.58 0.55 0.49 0.44

4 Factor Market Beta 1.00 0.61 0.57 0.54 0.51

Treynor-Black Ratio 9.14 11.44 9.86 15.80 15.90

44

Figure 1

U.S. Asset Class Weights Figure 1 displays the proportion of total net assets under management in five U.S. mutual fund asset categories between February 1991 and March 2008. The categories are Equity, Money Market, Bond, Foreign Equity and Other.

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Figure 2

Excess Flow for the U.S. Equity and U.S. Money Market Asset Categories Figure 2 displays monthly excess flow for the U.S. Equity and U.S. Money Market asset categories between February 1991 and March 2008. Excess flow is calculated as aggregate net flow for the asset category in excess of the net flow that would have resulted on an asset weighted basis, standardized by total market capitalization of the NYSE, AMEX and NASDAQ.

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