How poor stock mkt perf affects fund f lows shrider

Journal of Business Finance & Accounting, 36(7) & (8), 987–1006, September/October 2009, 0306-686X
doi: 10.1111/j.1468-5957.2009.02149.x

Running From a Bear: How Poor Stock
Market Performance Affects the
Determinants of Mutual Fund Flows

David G. Shrider∗

Abstract: Using a proprietary data set to study how past performance affects the determinants of
mutual fund flows for a sample of load fund investors, I provide evidence that the determinants
of fund flow depend on market conditions for both redemptions and purchases. Specifically, I
show that, for redemptions, relative performance and risk adjusted performance are important
determinants during a period of record flows into mutual funds. Conversely, during a period
of poor performance, absolute performance becomes much more important and relative
performance and risk adjusted performance become less important. For purchases, absolute
performance, risk adjusted performance, and most relative performance measures become more
important during the bear market.
Keywords: fund flows, mutual funds

1. INTRODUCTION
The dollars that flow into and out of mutual funds are affected by, among other things,
past fund performance. However, the exact relation between past performance and
fund flow remains a topic of research, and numerous questions are still debated. Is the
relevant performance measure relative or absolute? Does being an extreme winner or
loser provide additional fund flow benefits or penalties? The purpose of this research
is to examine whether changes in overall market conditions affect the answers to these
questions regarding determinants of fund flow.
Early fund flow research by Ippolito (1992), Sirri and Tufano (1998) and Fant and
O’Neal (2000) finds that while past winners are rewarded with inflows, past losers are

∗ The author is from the Farmer School of Business, Miami University. He acknowledges Mary Bange, Kelly
Brunarski, Werner De Bondt, William Even, Scott Harrington, Tim Koch, Melayne McInnes, William T.
Moore, Greg Niehaus, Terry Nixon, Tom Smythe, D.H. Zhang, seminar participants at Butler University,
East Carolina University, Illinois State University, Miami University, Northeastern University, the University
of South Carolina, Xavier University, the 2003 Eastern Finance Association meeting, and the 2004 Financial
Management Association meeting for comments and suggestions. The author is especially grateful to an
anonymous referee and to Peter F. Pope (editor) for their helpful comments. (Paper received May 2008,
revised version accepted February 2009, Online publication August 2009)

Address for correspondence: David G. Shrider, Farmer School of Business, Miami University, 120 Upham
Hall, Oxford, OH 45056, USA.
e-mail: shridedg@muohio.edu
C 2009 The Author
Journal compilation C 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK
and 350 Main Street, Malden, MA 02148, USA. 987

988 SHRIDER

not symmetrically punished with the same level of outflows. 1 Some studies explain fund
flow asymmetry using rational stories like switching costs (Ippolito, 1992) or search costs
(Sirri and Tufano, 1998) while others use behavioral explanations like status-quo bias
(Patel et al., 1991) or cognitive dissonance (Goetzmann and Peles, 1997). 2
O’Neal (2004) is the first to investigate purchases and redemptions separately.
Consistent with prior aggregate fund flow research, he finds that past winners
see increased purchases; however, unlike previous studies, he reports that poor
performers are, in fact, punished with increased redemptions. Subsequently, Ivkovi´ c
and Weisbenner (2007) and Cashman et al. (2006), who focus on the determinants of
fund flows, also separate purchases and redemptions to show increased purchases to
past winners coupled with increased redemptions from poor performing funds – albeit
for different reasons. Specifically, Ivkovi´ and Weisbenner find that while inflows are
c
driven by purchases that chase relative performance, outflows are driven by absolute
performance. On the other hand, Cashman et al. find that outflows are significantly
affected by how a fund performs relative to other funds. 3
One explanation for the difference in the determinants of redemptions between
the studies by Ivkovi´ and Weisbenner (2007) and Cashman et al. (2006) is the use of
c
different sample periods. Ivkovi´ and Weisbenner’s data come from a sample of funds
c
held at a no-load brokerage firm from 1991 to 1996, and Cashman et al.’s data are taken
from Securities and Exchange Commission filings between 1997 and 2003. The former
is a bull market period of generally positive returns while the latter includes periods
of both positive and negative returns. If the determinants of fund flow change with
overall market conditions, as I hypothesize, then different performance measures will
be most relevant for samples with differing market conditions. Thus, these two samples
would likely return very different results.
I use a sample of load mutual funds provided by a full-service brokerage firm for
2001 and 2002. These two years include a period of record mutual fund inflows (2001)
and a period of increasing outflows (2002). Therefore, this data set allows me to test
specifically whether the determinants of mutual fund flows are the same in a period
when fund flow performance is good and when it is bad.
I look for differences in the determinants of fund flows between periods of good and
poor market performance for two reasons. First, as shown both by Edelen and Warner
(2001) and by Figure 1, market conditions affect fund flows. That is, the determinants
of fund flow – which are the link between market conditions and the fund flows
themselves – differ within varying market conditions. Second, investor behavior is more
likely to be influenced by behavioral biases such as loss aversion during large market
declines.

1 Asymmetric fund flow changes the incentives of mutual fund managers. Brown et al. (1996), Chevalier
and Ellison (1997), Acker and Duck (2006) and Massa and Patgiri (2007) find that managers have incentives
to adjust the level of risk the fund takes in order to compete with other funds for new purchases. Kempf and
Ruenzi (2008b) show that this competition even occurs within fund families.
2 A related stream of literature examines whether future returns are predictable based on past returns. Early
studies like Grinblatt and Titman (1992 and 1993), Hendricks et al. (1993), Brown and Goetzmann (1995),
Gruber (1996) and Carhart (1997) find that negative performance persistence is common. Otten and Bams
(2002) and Wermers (2003) find that winners persist over long periods of time, while Zheng (1999) finds
that funds with positive returns earn additional fund flows and do repeat as winners, but that the effect is
short-lived.
3 Johnson (2007) finds that purchases are related to past performance but that redemptions are not, except
through the past performance of the fund purchased in the case of an exchange.

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Journal compilation C Blackwell Publishing Ltd. 2009

RUNNING FROM A BEAR 989

Figure 1
Standard & Poor’s 500-Stock Index versus Mutual Fund Flows

Note:
This figure graphs the level of the S&P 500 and total mutual fund inflows according to the 2006
Investment Company Fact Book from 1991–2002.

During periods when the average fund flow changes, the determinants of fund flow
are also more likely to change. Both redemption and purchase activity tend to be
linked to past performance, and thus average fund flows are very different during
my sample period when compared with earlier periods. Figure 1 shows similar trend
lines for mutual fund inflows and the Standard & Poor’s 500-stock index from 1991 to
2002, which is the time period covered by O’Neal (2004) and Ivkovi´ and Weisbenner
c
(2007), as well as my data set. The Standard & Poor’s 500-stock index moves generally
upward until 2000 when it begins a series of three down years. Overall flows into mutual
funds suffer minor setbacks in 1994 and 1999, but the trend in fund flows generally
follows stock market performance. Although flows generally follow the market, a lag is
obvious: The market starts its decline in 2000, but inflows actually hit a high in 2001
before dropping sharply in 2002. Therefore, my sample provides data from a time
period in which fund flows are very different in the two years.
In addition to differences in the level of fund flows, differences in the way past
performance affects redemption decisions in bull and bear markets can also prove
insightful. Namely, both Odean (1998) and Grinblatt and Keloharju (2001) find
evidence that individual investors are reluctant to sell poor-performing investments.
Although neither study focuses on mutual fund trades, the suggestion that investors
hold on to losers is consistent with the notion that poor-performing funds do not
experience large outflows. Odean (1999), who examines the purchase behavior of no-
load equity investors, finds that investors purchase stocks that have performed well in
the past. Even though Odean’s sample does not include mutual fund trades, this result
is consistent with the findings of the aggregate studies, namely, that mutual funds with
good track records attract the bulk of mutual fund inflows. Although O’Neal (2004)
finds that poor performers are punished, loss aversion is not an issue for mutual fund
investors who measure gains and losses relative to their purchase price. That is, during
bull markets, even the poorest performing funds, relative to a benchmark, see gains in
absolute performance.
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990 SHRIDER

While it is clear that past performance affects fund flow, the literature examines a
host of other fund flow determinants. Del Guercio and Tkac (2001) and Faff et al.
(2007) show that fund ratings affect flow. Kempf and Ruenzi (2008a) show that fund
flows are related to the fund’s relative position within the fund family. Sirri and Tufano
(1998) and Barber et al. (2002) show that fees and expenses are important determinants
of fund flow. Other determinants include fund family structure (Massa, 2003), market
volatility (Cao et al., 2008) and investor sentiment (Massa et al., 1999; and Indro,
2004).
Before measuring differences by time period, I first show that my sample is
representative of mutual funds in general by replicating findings in the prior literature
and find that my results on overall fund flow are consistent with previous literature,
including Ippolito (1992), Sirri and Tufano (1998) and Fant and O’Neal (2000). After
controlling for raw performance, I find a large additional positive effect for the top
performers but no additional negative effect for the worst performers. In other words,
when only accounting for net flows, the punishment provided to the worst performers
is not in sync with the reward given to the best performers. However, once I separate
purchases and redemptions, my results based on the purchases of winners and the
redemptions of losers are consistent with O’Neal (2004), Ivkovi´ and Weisbenner
c
(2007) and Cashman et al. (2006). Specifically, when I control for raw performance, I
find no evidence that funds in the bottom decile see fewer redemptions. In fact, these
funds experience redemption levels as large as would normally be expected, given their
poor track record.
While the stock market peaked in 2000, according to the Investment Company
Institute (ICI, 2006), overall industry-wide fund flows did not hit a high until 2001 before
experiencing a sharp decline in 2002. Therefore, to examine whether the determinants
of fund flows are different between periods of good and poor performance, I measure
redemption and purchase flows separately for 2001 and 2002. I find systematic
differences when I test for the determinants of fund flows. First, consistent with
Cashman et al. (2006), I find that during 2001, when net flows were still surging, relative
return measures – such as the fund’s rank against other funds in the same Morningstar
objective and rank in the top or bottom performance decile – are important in
determining the percentage of redemptions. Second, consistent with Ivkovi´ and c
Weisbenner (2007), I find that when testing the determinants of redemptions in 2002
when the fund performance was poor, the effect of absolute performance is nearly
three times larger while the relative measures are much less important. The results
for purchases show that whether past performance is measured by raw return, risk
adjusted return, top-performing decile, or bottom-performing decile, investors are
more affected by performance during the 2002 bear market.
In sum, during a period in which new money is pouring into mutual funds,
redemptions are sensitive to the fund’s rank against other funds in the same
Morningstar objective (i.e., being one of the best or worst performers among all funds
in the objective) and risk adjusted performance. In other words, under normal market
conditions, when redeeming shares investors measure fund performance relative to
other funds in the objective and relative to the level of risk the fund takes. However,
when the market turns and investors begin to panic, absolute performance becomes
much more important, trumping relative and risk adjusted performance measures.
For purchases, investors become more discerning during a bear market as nearly all
performance measures become more important.

C 2009 The Author


This study contributes to the literature in two ways. First, it specifically addresses
the open question in the literature of whether investors use relative or absolute
performance when making redemption decisions. In fact, both relative and absolute
measures of performance matter – but their importance differs depending on the
market conditions faced by investors. Specifically, investors use relative performance in
bull markets and absolute performance in bear markets when making redemption
decisions while nearly all performance measures become more important when
purchasing during a bear market. The second and broader contribution is that general
market conditions affect investor behavior. While this is important in understanding
mutual fund flows it is also important in any research involving individual investors.
The remainder of the paper proceeds as follows. Section 2 discusses the data and
method. Section 3 presents the results. Section 4 provides robustness checks, and
Section 5 concludes.

2. DATA AND METHOD

(i) Data
The data are provided by a national full-service brokerage firm and include all mutual
fund transactions during 2001 and 2002; a list of all funds in each account at year-end
2000, 2001 and 2002, for all accounts with at least one mutual fund holding; and the
type of account.
Panel A of Table 1 provides descriptive statistics for all accounts as of December 31,
2000. Of the total accounts, 39.6% are single or joint accounts; 13.3%, custodial; 38.9%,
retirement; and 8.2%, other non-individual accounts. Based on value of holdings, 36.5%
are single or joint accounts; 2.1%, custodial; 42.0%, retirement; and 19.1%, other non-
individual accounts. On average, an account has 2.5 holdings, with the smaller custodial
accounts averaging 1.6 holdings; single and joint accounts, 2.4 holdings; and retirement
accounts, 3.0 holdings.
New accounts are added to the data set throughout the sample period, and the
transactions from the new accounts are included in the analysis. As shown in Table 1,
Panel B, by year-end 2001, the number of accounts (dollars invested) increased by
21.2% (6.4%), but the distribution of accounts across account types is similar to
that at year-end 2000. See Panel B for full descriptive statistics for accounts as of
December 31, 2001.
Table 2 provides descriptive information on the transactions within single and
joint accounts, custodial accounts, retirement accounts, and other accounts between
January 1, 2001 and December 31, 2002. Nearly one-fourth of all transactions
are redemptions and more than three-fourths are purchases. The average size of
redemptions and purchases are similar. The mean (median) redemption is $8,007
($3,300) and the mean (median) purchase size is $9,612 ($4,749).

(ii) Performance Measures
To examine how investor transaction decisions are related to past performance, I
conduct tests using past performance measures. Because Del Guercio and Tkac (2002),
who compare pension fund and mutual fund investors, find that mutual fund investors
base their decisions on raw return numbers rather than risk adjusted performance,
I measure performance with raw one year total returns. Jain and Wu’s (2000) results
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992

Table 1
Account Characteristics
Panel A: Account Characteristics as of December 31, 2000
% of Mean Median Std. Dev. Avg. No. Mean Median Std. Dev. of % of Total
Total Acct. Acct. of Acct. MF of % of Holding Holding Holding Value of MF
Type of Account Accounts Size ($) Size ($) Size ($) Holdings Value Size ($) Size ($) Size Holdings

Single 20.0 35,947 11,558 82,458 2.4 19.4 15,041 7,580 26,621 19.4
Joint 19.6 32,285 12,095 73,925 2.3 17.1 13,821 7,242 25,061 17.2
Custodian 13.3 5,832 1,899 12,864 1.6 2.1 3,753 1,615 6,715 2.2
Trust 6.6 88,243 38,066 1,368,144 3.1 15.6 28,291 14,935 209,572 15.6
Partnership 0.1 157,372 42,279 388,435 3.4 0.4 46,295 20,331 86,491 0.4
Investment club 0.0 6,398 1,700 19,738 1.6 0.0 4,091 1,456 7,406 0.0
SHRIDER

Corporation 0.5 98,011 27,978 343,091 2.7 1.3 36,324 15,029 97,866 1.3
Church 0.1 55,893 19,506 141,006 2.2 0.2 24,918 12,505 44,425 0.2
Bank 0.0 1,082,058 50,733 6,964,942 7.7 0.1 141,242 41,450 330,442 0.1
Estate 0.1 84,602 39,419 126,058 2.7 0.2 31,556 17,688 44,139 0.2
Regular IRA 27.5 51,610 22,771 89,796 3.3 38.4 15,716 8,791 22,925 38.4
SEP IRA 2.0 43,099 15,536 80,579 3.3 2.3 13,168 6,574 21,571 2.3

Journal compilation
Roth IRA 7.1 5,448 2,049 17,842 1.9 1.0 2,857 1,303 6,906 1.1

C
Simple IRA 2.3 5,492 2,805 7,146 2.0 0.3 2,729 1,416 3,822 0.3
Other 0.8 3.0 1.3 1.3
Total 100.0 2.5 100.0 100.0

Blackwell Publishing Ltd. 2009
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Table 1 (Continued)

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Journal compilation
C
Panel B: Account Characteristics as of December 31, 2001
% of Mean Median Std. Dev. Avg. No. Mean Median Std. Dev. of % of Total
Total Acct. Acct. of Acct. MF of % of Holding Holding Holding Value of MF
Type of Account Accounts Size ($) Size ($) Size ($) Holdings Value Size ($) Size ($) Size Holdings

Single 18.9 32,563 10,049 75,625 2.5 19.0 13,149 6,166 24,449 19.0
Joint 18.1 29,297 10,561 67,796 2.4 16.3 12,064 5,876 22,698 16.3
Custodian 12.6 4,903 1,540 11,971 1.6 1.9 3,062 1,247 6,170 1.9
Trust 6.2 81,560 35,203 1,228,868 3.2 15.7 25,339 13,033 183,791 15.7
Partnership 0.1 149,133 36,347 368,935 3.5 0.4 42,467 17,849 87,004 0.4

Blackwell Publishing Ltd. 2009
Investment club 0.0 5,411 1,522 18,920 1.6 0.0 3,448 1,371 7,272 0.0
Corporation 0.5 93,014 24,864 349,886 2.7 1.3 33,900 12,854 107,946 1.3
Church 0.1 53,970 19,105 132,513 2.3 0.2 23,368 11,328 41,039 0.2
Bank 0.0 1,584,172 45,273 10,204,552 8.7 0.1 181,625 40,005 439,827 0.1
Estate 0.1 76,621 33,611 122,817 2.7 0.2 28,600 15,425 43,671 0.2
Regular IRA 28.6 44,974 19,179 80,139 3.4 39.6 13,195 6,965 20,257 39.6
RUNNING FROM A BEAR

SEP IRA 2.0 36,163 12,422 71,072 3.4 2.3 10,657 4,944 18,916 2.3
Roth IRA 9.3 4,323 1,978 13,377 2.1 1.2 2,061 974 4,997 1.2
Simple IRA 2.7 5,665 3,003 7,186 2.3 0.5 2,511 1,328 3,522 0.5
Other 0.7 3.1 1.2 1.2
Total 100.0 2.7 100.0 100.0
Notes:
This table provides the characteristics of accounts with mutual fund holdings from a national full-service brokerage firm. Panel A provides information from
accounts at the beginning of the sample period, December 31, 2000 and Panel B provides the same data as of December 31, 2001. MF = mutual fund.
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994 SHRIDER

Table 2
Transaction Data
% Transactions Mean ($) Median ($) Std. Dev. ($)

Panel A: Redemptions
Trade size
Full sample 23.8 8,007 3,300 19,515
Joint and single 8.0 7,864 3,505 17,459
Custodial 0.7 3,690 2,393 4,335
IRAs 12.5 7,116 3,001 14,217
Other 2.6 14,000 5,000 38,005
NAV 18.77 17.20 8.81
Panel B: Purchases
Trade size
Full sample 76.2 9,612 4,749 21,682
Joint and single 21.6 10,030 4,996 22,846
Custodial 1.7 3,974 2,454 5,101
IRAs 44.1 8,605 4,247 14,902
Other 8.8 15,331 6,887 40,913
NAV 20.38 18.40 9.13
Notes:
This table provides data on transactions from all accounts for 2001 and 2002. Panel A (Panel B)
provides data for redemptions (purchases). % Transactions is the percentage of total transactions included
in the study. NAV is the net asset value at which trades took place.

support the use of one-year returns. While I follow Del Guercio and Tkac (2002) and
use raw returns as my measure of absolute performance, I also use risk adjusted returns
as a control variable. I use alpha from a Carhart (1997) four-factor model as my measure
of risk adjusted return. 4
Evidence also suggests that mutual fund investors base decisions on performance
relative to other funds. For example, Capon et al. (1996) find that published
performance rankings are investors’ most important source of information for making
investment decisions. Therefore, I use two different measures of relative performance.
First, I use performance rank relative to all funds in the same Morningstar objective.
I rank funds into percentiles from zero (worst performer) to 100 (best performer). I
also identify winners (i.e., funds in the top decile) and losers (i.e., funds in the bottom
decile) among the funds that are included on the approved list of the firm that provided
the data. I use these measures to test whether placement among the very best or very
worst performing funds has an additional effect.

(iii) Aggregating Individual Account Data
Because the focus of this research is on the impact of fund flows, I aggregate all of
the data to the mutual fund level. By using aggregating individual investor data rather
than overall aggregate fund flows I am able to examine purchases and redemptions
separately. This approach provides insight into whether the incentives that arise from

4 In tests not reported in the paper, I also use Jensen’s alpha from the capital asset pricing model (CAPM) as
a measure of risk adjusted performance. The results using CAPM alpha are not qualitatively different from
those using Carhart alpha, as reported in Tables 5–8.

C 2009 The Author


Table 3
Holdings by Asset Class
Equity (%) Fixed Income (%) Balanced (%)

Panel A: 2000
Sample 83.2 6.7 10.1
Aggregate load funds 85.4 9.4 5.2
Aggregate no-load funds 84.1 12.6 3.2
Panel B: 2001
Sample 80.7 7.1 12.3
Aggregate load funds 82.5 10.7 6.7
Aggregate no-load funds 79.7 17.6 2.6
Notes:
Panel A (Panel B) provide data for 2000 (2001). The first row of each panel gives the percentage
of assets within the data set that is held in equity funds, fixed income funds, and balanced funds. The
aggregate load and no-load rows list the percentage of assets invested in equity, fixed income, and balanced
funds for all load and no-load funds listed in Morningstar.

asymmetric fund flow are driven by purchase decisions, redemption decisions, or a
combination of the two.
However, data aggregated at the individual investor level may not be representative
of all load fund investors. Therefore, to determine whether the investors in my sample
are similar to investors in general, I compare the asset classes of the funds they own
to the overall averages of all funds in Morningstar as reported in Table 3, Panel A. In
2000, of the total holdings in my sample, 83.2% are in equity funds, 6.7% are in fixed
income funds, and 10.1% are invested in balanced funds. This distribution of assets
is similar to the allocation of assets by investors in general. Of the funds included in
Morningstar as of December 31, 2000, load fund investors have 85.4% of their assets in
equity funds, 9.4% in fixed income funds, and 5.2% in balanced funds, whereas no-load
fund investors have 84.1% in equity funds, 12.6% in fixed income, and 3.2% in balanced
funds. Results for year-end 2001, as reported in Panel B, are similar. Differences between
this sample and mutual fund investors at large in the percentage of assets invested in
retirement accounts could also affect whether or not these results are representative.
However, my sample is similar to mutual fund investors in general. At year-end 2000,
42% of the assets in the sample are invested in retirement accounts compared with 36%
for all mutual funds according to the ICI Mutual Fund Fact Book. The same numbers
for year-end 2001 are 44% and 34%. 5

3. RESULTS

(i) Net Fund Flow
To compare the compatibility of my sample of mutual fund transactions between
January 1, 2001 and December 31, 2002 at national full-service broker-dealer 6 with

5 Another way to examine these data is to study the decision-making of individual investors, which is a topic
of ongoing research. In that analysis, controlling for a fund being held in a retirement account does not
qualitatively affect the other results.
6 I only omit trades of less than $1,000 and trades that could have been exchanges, but instead the same
account made both a purchase and a redemption on the same day and paid a sales charge. These potential

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996 SHRIDER

prior fund flow studies, I employ an OLS regression to examine the combined fund
flow. The dependent variable used in the analysis is the proportion of fund flows to
dollars in the fund. This fund flow proportion (FFP) for fund f during month t is
defined as:
f f
DPt − DRt
FFP f,t = f
, (1)
DHt

where:
f
DPt = dollar value of shares purchased of fund f during month t;
f
DRt = dollar value of shares redeemed of fund f during month t; and
f
DHt = total dollar value of shares held of fund f at the beginning of month t.
I use OLS regression on the following model:

FFP f,t = α0 + β1 Return f,t + β2 Rank Obj f,t + β3 Winner f,t + β4 Loser f,t
+ β5 Alpha f,t + β6 B Share f,t + β7 C Share f,t + β7 Fixed Income f,t
(2)
+ β7 Balanced f,t + β7 Expense Ratio f,t + β8 Log Total Value f,t
+ β9 Log TNA f,t + β10 Age f,t + 23 Month Dummies + ε f,t , f = 1, . . . , n,

where Return f ,t is the one year total return for the year prior to time t; Rank Obj f ,t
is the rank of the fund within its Morningstar objective over the year prior to time t;
Winner f ,t is a binary variable, which equals 1 if fund f is in the top-performing decile
ranked against other funds in the Morningstar objective for the year prior to time
t, and zero otherwise; Loser f ,t is a binary variable, which equals 1 if fund f is in the
bottom-performing decile ranked against other funds in the Morningstar objective for
the year prior to time t, and zero otherwise; B Share f ,t is a dichotomous variable, which
equals 1 if the fund t is a class B share, and zero otherwise; C Share f ,t is a dichotomous
variable, which equals 1 if the fund t is a class C share, and zero otherwise; Alpha f ,t is a
measure of risk adjusted performance that is the intercept from a regression of excess
mutual fund returns on the four factors described in Carhart (1997); Expense Ratio f ,t
is the expense ratio for fund f at time t; Log Total Value f ,t is the natural log of the total
assets invested in the fund at the firm studied; Log TNA f ,t is the natural log of the total
net assets for fund f at time t; and Age f ,t is the age in years of fund f at time t.
There is some concern that collinearity is a problem as the return variables are
correlated. Correlation coefficients of the return variables are shown in Table 4.
Because the largest (in absolute value) correlation coefficient is −0.60, I run collinearity
diagnostics. Condition indices show that collinearity is not a large problem as the
largest condition indices are 24.18 and 12.97. In addition, I also run all models
omitting each return variable individually. These results are qualitatively similar to those
reported.
The expected sign on Return, Rank Obj, Winner , and Alpha is positive because higher
returns lead to more dollars invested in subsequent periods. Because prior research on

exchanges might be the result of a conflict of interest between the client and the investment representative;
however, as they only total 0.4% of all transactions, they do not skew the results.

C 2009 The Author


Table 4
Correlation Coefficients
Return Rank Obj. Winner Loser Alpha

Return 1.00
Rank Obj 0.36 1.00
Winner 0.41 0.25 1.00
Loser −0.60 −0.30 −0.12 1.00
Alpha −0.15 0.21 0.06 0.12 1.00
Notes:
This table presents correlation coefficients for the return variables. The return variables are the
average annual total return over the past year (Return); the rank of the fund within its Morningstar objective
(Rank Obj); a dichotomous variable, Winner (Loser ), which equals 1if the fund is in the top (bottom)
performing decile of its Morningstar objective, and zero otherwise; the risk adjusted performance (Alpha)
from a Carhart (1997) four-factor model.

performance persistence (e.g., Brown and Goetzmann, 1995) suggests that the worst-
performing funds are more likely to repeat as poor performers, one could expect the
sign for Loser to be negative. However, Ippolito (1992), Sirri and Tufano (1998) and
Fant and O’Neal (2000) all provide evidence that poor performers are not punished
to the extent that winners are rewarded. The signs on B Share and C Share should
be positive because investors avoid upfront sales charges. I expect the sign on Expense
Ratio to be negative as investors try to avoid funds with higher fees. The signs for Log
Total Value, Log TNA, and Age should all be positive as investors are more likely to buy
funds that are popular at this particular firm as well as funds that are better known in
general.
The results, as reported in Table 5, are consistent with aggregate fund flow studies
(Fant and O’Neal, 2000; Ippolito, 1992; and Sirri and Tufano, 1998) with respect to
the sign and statistical significance of the coefficients for Return, Rank Obj, Winner ,
Loser and Alpha. I find that, on average, these funds experience larger purchases than
redemptions. Past performance has a positive effect as the coefficient on Return is
positive and statistically significant. The sign on the coefficients for the relative return
measure, Rank Obj is also positive but not statistically significant. After controlling
for general performance, being top decile of funds has an additional effect, which
holds true across both time periods as the coefficient on Winner is positive and highly
significant. The coefficient on Loser is negative as predicted, but it is not statistically
significant. The coefficient on Alpha is positive and highly statistically significant. This
finding that funds with positive risk adjusted performance attract additional flows is
consistent with Jain and Wu (2000).
The results for the control variables are generally consistent with the expected signs.
More dollars flow into B and C shares as shown by the positive and statistically significant
coefficients on the B Share and C Share variables. Both fixed income and balanced funds
have larger fund flow proportions when compared to equity funds as the coefficients
on Fixed Income and Balanced are both positive and statistically significant. Fewer dollars
flow into funds with higher expense ratios as evidenced by the negative and statistically
significant coefficient on the Exp Ratio variable. The positive and statistically significant
sign on the coefficients of Log Total Value suggests that funds more widely held at the
firm from which I obtained the data have larger fund flow proportions. However, after
controlling for assets held at the firm, large funds and older funds have smaller fund
C 2009 The Author

998 SHRIDER

Table 5
Fund Flows
1-Year
FFP p-values

Intercept 0.0477∗∗ 0.000
Return 0.0216∗∗ 0.000
Rank Obj 0.0013 0.407
Winner 0.0175∗∗ 0.000
Loser −0.0011 0.517
Alpha 0.0126∗∗ 0.000
B Share 0.0130∗∗ 0.000
C Share 0.0057∗∗ 0.000
Fixed Income 0.0055∗∗ 0.000
Balanced 0.0165∗∗ 0.000
Expense Ratio −0.0099∗∗ 0.000
Log Total Value 0.0034∗∗ 0.000
Log TNA −0.0050∗∗ 0.000
Age −0.0001∗ 0.011
Adj. R 2 0.0737
N 21,093
Notes:
This table presents results from an ordinary least squares model on the fund flow proportion (FFP)
as defined in equation (1). The independent variables are the average annual total return over the past
year (Return); the rank of the fund within its Morningstar objective (Rank Obj); a dichotomous variable,
Winner (Loser ), which equals 1 if the fund is in the top (bottom) performing decile of its Morningstar
objective, and zero otherwise; the risk adjusted performance (Alpha) from a Carhart (1997) four-factor
model; a dichotomous variable, (B Share (C Share)), which equals 1 if the fund is a B share (C share), and
zero otherwise; a dichotomous variable (Fixed Income (Balanced)) if the fund is a fixed income (balanced)
mutual fund; the expense ratio for the fund (Expense Ratio); the natural log of the total amount invested in
a given fund at the broker-dealer that provided the data (Log Total Value); and the natural log of the total
net assets of the fund (Log TNA); and the age of the fund in years (Age). Fixed time effects are included
using month dummy variables.
∗∗ indicates statistical significance at the 0.01 level.
∗ indicates statistical significance at the 0.05 level.

flow proportions, shown by the negative and statistically significant coefficients on Log
TNA and Age.

(ii) How Performance Affects Total Redemptions and Total Purchases
I use the proportion of fund holdings redeemed or purchased measured in dollar
value of shares as the dependent variable in a separate analysis of the determinants of
purchases and redemptions. The proportion of the value of shares redeemed in fund
f during month t is defined as:

f
DRt
$R f,t = f
, (3)
DHt

where:
f
DRt = dollar value of shares redeemed of fund f during month t, and
f
DHt = total dollar value of shares of fund f at the beginning of month t.
C 2009 The Author


The proportion of the value of shares purchased ($P f ,t ) is defined similarly. The
mean value for $R f ,t ($P f ,t ) is 0.01 (0.02), and the standard deviation is 0.03 (0.05).
The tobit model is:
$R f,t ($P f,t ) = α0 + β1 Return f,t + β2 Rank Obj f,t + β3 Winner f,t + β4 Loser f,t
+ β5 Alpha f,t + β6 B Share f,t + β7 C Share f,t + β7 Fixed Income f,t
(4)
+ β7 Balanced f,t + β7 Expense Ratio f,t + β8 Log Total Value f,t
+ β9 Log TNA f,t + β10 Age f,t + 23 Month Dummies + ε f,t , f = 1, . . . , n.

The coefficients reported in Table 6 represent the marginal effect of a one-unit
change in the explanatory variable on the expected value of the proportion of the
fund that is purchased or redeemed in a given month. For the dichotomous variables
such as Winner and Loser , the marginal effects are calculated by changing the variable
from zero to 1. Marginal effects for all other variables are evaluated at the sample
mean.
The expected sign on Return, Rank Obj, Winner and Alpha is negative for redemptions
and positive for purchases because higher returns should lead to fewer redemptions
and more purchases in subsequent periods. The expected sign on Loser is the
reverse – that is, positive for the redemption specifications and negative for the purchase
specifications. Because buy-and-hold investors self-select into A shares and because A
shares have higher initial costs in the form of the upfront sales charge, I expect the
sign on the alternatives B Share and C Share to be positive for both purchases and
redemptions. Expense Ratio should have a positive sign for redemptions and a negative
sign for purchases as investors try to avoid funds with higher fees. Finally, Log Total
Value, Log TNA and Age should all have negative signs for redemptions and positive
signs for purchases as investors are more likely to buy funds that are popular at the firm
under study as well as funds that are better known in general.
Tobit results for redemptions are found in the first column of Table 6. The results
show that funds see more dollars redeemed when their performance is worse. This result
is consistent with the expected result. The coefficient on the absolute performance
variable, Return, is negative and statistically significant at the 1% level. The sign on
the coefficients of the relative performance variable, Rank Obj, is also negative but not
statistically significant.
I include the Winner and Loser dummy variables to test whether an effect is associated
with being an extreme performer. After controlling for total return, the results suggest
that these investors sell larger proportions of the top-performing funds. The sign of
the coefficient on Winner is positive and statistically significant at the 1% level. In other
words, after controlling for performance, the best performers experience larger total
redemptions than other funds. Although this result is counterintuitive, it is consistent
with the results of Cashman et al. (2006) before they directly control for the persistence
of fund flows. 7 The sign of the coefficient on Loser is positive, but not statistically
significant. The sign on the coefficient on Alpha is positive and statistically significant.
The last column of Table 6 shows the tobit results for purchases. The results for
the Return variable are consistent with expected sign as the coefficient is positive and

7 Cashman et al. (2006) control for persistent fund flows by including a lagged fund flow term. I do not use
this control in Table 6 in order to match the existing literature. However, I do control for persistent fund
flows, in the same way as Cashman et al., in robustness checks by using the lagged dependent variable.

C 2009 The Author

1000 SHRIDER

Table 6
Tobit Results
Redemptions Purchases
1-Year 1-Year

Return −0.0155∗∗ 0.0148∗∗
(0.000) (0.000)
Rank Obj −0.0006 −0.0015
(0.394) (0.056)
Winner 0.0043∗∗ 0.0134∗∗
(0.000) (0.000)
Loser 0.0010 −0.0008
(0.123) (0.463)
Alpha 0.0025∗∗ 0.0104∗∗
(0.000) (0.000)
B Share 0.0036∗∗ 0.0130∗∗
(0.000) (0.000)
C Share 0.0026∗∗ 0.0103∗∗
(0.000) (0.000)
Fixed Income −0.0001 0.0016∗
(0.874) (0.017)
Balanced 0.0001 0.0069∗∗
(0.881) (0.000)
Expense Ratio 0.0007 −0.0091∗∗
(0.089) (0.000)
Log Total Value 0.0018∗∗ 0.0061∗∗
(0.000) (0.000)
Log TNA −0.0004∗∗ −0.0051∗∗
(0.002) (0.000)
Fund Age −0.0001∗∗ −0.0001∗∗
(0.002) (0.048)
N 20,985 20,976
Notes:
This table presents results from a tobit model on the proportion of the fund redeemed or purchased
in terms of dollars as defined in equation (3). The independent variables are the average annual total
return over the past year (Return); the rank of the fund within its Morningstar objective (Rank Obj); a
dichotomous variable, Winner (Loser ), which equals 1 if the fund is in the top (bottom) performing decile
of its Morningstar objective, and zero otherwise; the risk adjusted performance (Alpha) from a Carhart
(1997) four-factor model; a dichotomous variable (B Share (C Share)), which equals 1 if the fund is a B share
(C share), and zero otherwise; a dichotomous variable (Fixed Income (Balanced)) if the fund is a fixed income
(balanced) mutual fund; the expense ratio for the fund (Expense Ratio); the natural log of the total amount
invested in a given fund at the broker-dealer that provided the data (Log Total Value); and the natural log
of the total net assets of the fund (Log TNA); and the age of the fund in years (Age). Fixed time effects are
included using month dummy variables. p-values are in parentheses.

statistically significant at the 1% significance level. The sign on the Rank Obj variable is
negative but not statistically significant. The sign on the coefficient on Alpha is positive
and highly significant. These results indicate that more total purchases are made for
funds that have high absolute performance and high risk adjusted performance. Even
after controlling for performance, the sign of the coefficient on the Winner variable is
positive and statistically significant at the 1% significance level. This finding suggests
that while funds with better performance are rewarded with greater purchases, top
C 2009 The Author


performers are rewarded at an even greater rate than other funds. The coefficient on
Loser is negative but not statistically significant.

(iii) Test of Time Period
Having established that the results of my sample are consistent with the existing
literature’s understanding of aggregate fund flows and of the determinants of fund
flow when purchases and redemptions are separated, I now focus on whether the
determinants of fund flow change based on market conditions. To this end, I run
separate tobit models on equation (4) for 2001 and 2002.
As shown in Figure 1, mutual funds in general saw record inflows in 2001 before fund
flows declined precipitously in 2002. The expected signs for the results of redemptions
(purchases) in Table 7 (Table 8) are the same as those discussed previously for the
redemptions and purchases in Table 6. Like Table 6, the coefficients in Tables 7 and 8
represent marginal effects.
The results for redemptions by year are reported in Table 7. The first column reports
the results for 2001 and the second column shows the results for 2002. The coefficient on
Return, which is simply the absolute performance, is negative and statistically significant
in both the 2001 and 2002 specifications. However, the size of the coefficient is nearly
three times larger in the 2002 bear market specification. A pattern can be seen in the
results for the three relative performance measures, Rank Obj, Winner and Loser : That
is, relative performance is more important in 2001 than in 2002. All of the coefficients
are statistically significant at nearly the 1% significance level in 2001. In the bear market
specification neither Rank Obj nor Loser are statistically significant and the size of the
coefficient on Winner is smaller than in the 2001 specification. The same pattern holds
for Alpha, the measure of risk adjusted fund performance. Alpha is significant at the
1% significance level in 2001, but it is not statistically significant in 2002. These results
are consistent with the idea that investors spend more time combing through numbers
when markets are normal, but in a bear market, they are focused on exiting funds and
primarily concerned with absolute performance.
C Share and Expense Ratio exhibit a similar pattern and support the idea that investors
focus on absolute performance in bear markets. The coefficient on C Share is positive
and statistically significant at better than the 1% significance level in the normal market
(2001) specification but it is not statistically significant in the bear market (2002)
specification. This finding is consistent with the notion that in a normal market,
investors are more willing to redeem C shares when compared with A shares for
which they paid an upfront sales charge, but in a bear market, the loss of this sunk
cost is no longer a high priority and the difference between C shares and A shares
becomes insignificant. The coefficient on Expense Ratio is statistically significant in both
specifications, but the sign changes from negative in the 2001 specification to positive
in the 2002 specification. These results indicate that funds with higher expense ratios
actually see fewer redemptions in normal markets, but in bear market conditions, funds
that take more expenses out of their returns see more redemptions. The results on B
Share are virtually identical between 2001 and 2002. Fixed income funds are more likely
to see redemptions in 2002 while balanced funds are less likely. The results on Log Total
Value, Log TNA and Age are inconsistent across specifications and often counter to
their expected signs. Taken together, these results suggest that these variables do not
substantially control fund reputation among investors.
C 2009 The Author

1002 SHRIDER

Table 7
Tobit Results by Year: Redemptions
1-Year
2001 2002

Return −0.0125∗∗ −0.0322∗∗
(0.000) (0.000)
Rank Obj −0.0023∗∗ 0.0004
(0.006) (0.695)
Winner 0.0056∗∗ 0.0051∗∗
(0.000) (0.000)
Loser −0.0019∗ 0.0001
(0.019) (0.902)
Alpha 0.0039∗∗ 0.0010
(0.000) (0.082)
B Share 0.0035∗∗ 0.0038∗∗
(0.000) (0.000)
C Share 0.0058∗∗ −0.0007
(0.000) (0.356)
Fixed Income 0.0007 0.0021∗∗
(0.224) (0.005)
Balanced 0.0030∗∗ 0.0001
(0.002) (0.943)
Expense Ratio −0.0025∗∗ 0.0042∗∗
(0.000) (0.000)
Log Total Value 0.0025∗∗ 0.0011∗∗
(0.000) (0.000)
Log TNA −0.0024∗∗ 0.0017∗∗
(0.000) (0.000)
Age 0.0000 −0.0001∗∗
(0.488) (0.000)
N 9,787 11,198
Notes:

The purchase results, reported in Table 8, also show a difference between 2001
and 2002. However, the difference is not between absolute and relative performance
as is the case with the redemptions. In the 2001 period, investors purchased past
winners and funds with positive risk adjusted performance as shown by the positive and
significant coefficients on Winner and Alpha while the coefficients on Return and Loser
are statistically insignificant and the coefficient on Rank Obj is negative. However, the
bear market specification shows that investors became very concerned about all returns
C 2009 The Author


Table 8
Tobit Results by Year: Purchases
1-Year
2001 2002

Return 0.0032 0.0620∗∗
(0.213) (0.000)
Rank Obj −0.0037∗∗ 0.0003
(0.003) (0.832)
Winner 0.0088∗∗ 0.0141∗∗
(0.000) (0.000)
Loser −0.0004 0.0042∗
(0.775) (0.016)
Alpha 0.0077∗∗ 0.0106∗∗
(0.000) (0.000)
B Share 0.0088∗∗ 0.0158∗∗
(0.000) (0.000)
C Share 0.0124∗∗ 0.0078∗∗
(0.000) (0.000)
Fixed Income −0.0006 0.0021
(0.484) (0.051)
Balanced 0.0060∗∗ 0.0069∗∗
(0.000) (0.000)
Expense Ratio −0.0065∗∗ −0.0093∗∗
(0.000) (0.000)
Log Total Value 0.0048∗∗ 0.0074∗∗
(0.000) (0.000)
Log TNA −0.0048∗∗ −0.0048∗∗
(0.000) (0.000)
Age 0.0000 −0.0003∗∗
(0.851) (0.000)
N 9,786 11,190
Notes:

when making purchases in 2002. Specifically, the coefficients on Return, Winner and
Alpha, are all statistically significant at the 1% significance level, while Loser is significant
at the 5% level. The positive sign on Loser indicates that the worst performers see larger
purchases than would be predicted by their return.
The signs on the control variables are somewhat consistent with expectations in the
purchase specifications found in Table 8. B Share, C Share, Expense Ratio and Log Total
C 2009 The Author

1004 SHRIDER

Value are consistent with the expected sign in both specifications. Fixed Income is not
significant in either year while Balanced is positive and statistically significant at the 1%
level in both years. As in the previous findings, Log TNA and Age are not consistent
with the expected sign across both specifications.
While the results between redemptions and purchases are not the same, they are
both consistent with the idea that investors are reacting to a bear market. When
making redemptions under normal conditions, relative performance measures and
risk adjusted performance are the most important factors in terms of fund flows. But,
in the bear market of 2002 raw returns become the most important factor of fund flows
as investors rapidly exit funds. For purchases, investors become very selective as raw
return, risk adjusted return, and most relative performance measures become more
important when selecting funds to purchase during the 2002 bear market period.

4. ROBUSTNESS
Because the main focus of this study is whether the determinants of fund flow differ
in good and bad markets, the definitions of a good market and bad market are very
important. The beginning of the market correction could be marked by, among other
things, the Dow Jones Industrial Average’s high on May 22, 2001, the unexpected shock
of September 11, 2001, or the peak in fund flows late in 2001; therefore, I conduct
tests in which I divide my time period at different points before and after year-end
2001. The results (not tabulated) are not significantly different from those reported in
Tables 7 and 8. Robustness results show the same general pattern when the split is close
to the calendar-year split reported here and becomes progressively weaker the further
the split moves (in either direction) from year-end 2001.
Cashman et al. (2006) show that fund flows are very persistent and that controlling
for this persistence with the lagged dependent variable causes the results to better match
expected signs. When the lagged dependent variable is included, the sign on the lagged
dependent variable is positive and highly statistically significant in all specifications,
however, none of the other results are qualitatively changed.
To test further for robustness, I model all of the tobit specifications using a two-step
process based on (a) whether to make a transaction and (b) the size of the transaction,
using a Heckman (1979) procedure. The results (not tabulated) using this process are
qualitatively similar to the tobit model results. None of the inverse mills ratios from the
first step (probit) are statistically significant at the 5% level in the second step (OLS).
I also run robustness checks on the size variables. Because Log Total Value and Log
TNA are positively correlated (correlation coefficient = 0.69) and because they have
the largest variance inflation factors, I run all tests without Log Total Value. I find that
dropping Log Total Value does not cause the results to be qualitatively different from
those previously reported.

5. CONCLUSION
This paper investigates whether the determinants of fund flow are affected by the
shifting market conditions by examining the performance of a sample of mutual
funds during 2000 (strong fund flow market) and 2001 (weak fund flow market).
To determine whether my sample and, thus, my findings are comparable to previous
literature, I first replicate the results of the prior studies. Specifically, my results for
C 2009 The Author


aggregate fund flows show that winners are rewarded to a greater degree than losers
are punished, which is consistent with Ippolito (1992), Sirri and Tufano (1998) and Fant
and O’Neal (2000). When I separate purchases and redemptions, I find evidence that
losers see large redemptions but that these redemptions are masked by new purchases,
which is consistent with O’Neal (2004), Ivkovi´ and Weisbenner (2007) and Cashman
c
et al. (2006).
After I establish that my study sample is, in fact, in line with previous research,
I address whether the determinants of mutual fund flows are affected by market
conditions. I find that redemptions are strongly affected by relative performance
and risk adjusted performance under normal market conditions. However, in bear
market conditions, redemptions are more strongly affected by absolute performance,
and measures of relative performance and risk adjusted performance become less
important. For purchases, absolute performance, risk adjusted performance, and most
relative performance measures become more important in 2002 than in 2001.
This study contributes to the literature in both a specific and a general way. First,
it provides evidence that mutual fund flows are affected by relative, risk adjusted and
absolute performance measures. However, which performance measures are the most
relevant depends on overall market conditions. In a more general way this study shows
the importance of including controls for market conditions in future individual investor
research.

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