How poor stock mkt perf affects fund f lows shrider

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

Running From a Bear: How Poor StockMarket 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 ofmutual fund flows for a sample of load fund investors, I provide evidence that the determinantsof fund flow depend on market conditions for both redemptions and purchases. Specifically, Ishow that, for redemptions, relative performance and risk adjusted performance are importantdeterminants during a period of record flows into mutual funds. Conversely, during a periodof poor performance, absolute performance becomes much more important and relativeperformance and risk adjusted performance become less important. For purchases, absoluteperformance, risk adjusted performance, and most relative performance measures become moreimportant 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 andfund flow remains a topic of research, and numerous questions are still debated. Is therelevant performance measure relative or absolute? Does being an extreme winner orloser provide additional fund flow benefits or penalties? The purpose of this researchis to examine whether changes in overall market conditions affect the answers to thesequestions regarding determinants of fund flow.

Early fund flow research by Ippolito (1992), Sirri and Tufano (1998) and Fant andO’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, KellyBrunarski, 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 Universityof South Carolina, Xavier University, the 2003 Eastern Finance Association meeting, and the 2004 FinancialManagement Association meeting for comments and suggestions. The author is especially grateful to ananonymous 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 UphamHall, Oxford, OH 45056, USA.e-mail: [email protected]

C© 2009 The AuthorJournal compilation C© 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UKand 350 Main Street, Malden, MA 02148, USA. 987

988 SHRIDER

not symmetrically punished with the same level of outflows.1 Some studies explain fundflow 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 winnerssee increased purchases; however, unlike previous studies, he reports that poorperformers are, in fact, punished with increased redemptions. Subsequently, Ivkovicand Weisbenner (2007) and Cashman et al. (2006), who focus on the determinants offund flows, also separate purchases and redemptions to show increased purchases topast winners coupled with increased redemptions from poor performing funds – albeitfor different reasons. Specifically, Ivkovic and Weisbenner find that while inflows aredriven by purchases that chase relative performance, outflows are driven by absoluteperformance. On the other hand, Cashman et al. find that outflows are significantlyaffected by how a fund performs relative to other funds.3

One explanation for the difference in the determinants of redemptions betweenthe studies by Ivkovic and Weisbenner (2007) and Cashman et al. (2006) is the use ofdifferent sample periods. Ivkovic and Weisbenner’s data come from a sample of fundsheld at a no-load brokerage firm from 1991 to 1996, and Cashman et al.’s data are takenfrom Securities and Exchange Commission filings between 1997 and 2003. The formeris a bull market period of generally positive returns while the latter includes periodsof both positive and negative returns. If the determinants of fund flow change withoverall market conditions, as I hypothesize, then different performance measures willbe most relevant for samples with differing market conditions. Thus, these two sampleswould likely return very different results.

I use a sample of load mutual funds provided by a full-service brokerage firm for2001 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 testspecifically whether the determinants of mutual fund flows are the same in a periodwhen fund flow performance is good and when it is bad.

I look for differences in the determinants of fund flows between periods of good andpoor 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 determinantsof fund flow – which are the link between market conditions and the fund flowsthemselves – differ within varying market conditions. Second, investor behavior is morelikely to be influenced by behavioral biases such as loss aversion during large marketdeclines.

1 Asymmetric fund flow changes the incentives of mutual fund managers. Brown et al. (1996), Chevalierand Ellison (1997), Acker and Duck (2006) and Massa and Patgiri (2007) find that managers have incentivesto adjust the level of risk the fund takes in order to compete with other funds for new purchases. Kempf andRuenzi (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. Earlystudies 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) findsthat funds with positive returns earn additional fund flows and do repeat as winners, but that the effect isshort-lived.3 Johnson (2007) finds that purchases are related to past performance but that redemptions are not, exceptthrough the past performance of the fund purchased in the case of an exchange.

C© 2009 The AuthorJournal compilation C© Blackwell Publishing Ltd. 2009

RUNNING FROM A BEAR 989

Figure 1Standard & 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 2006Investment Company Fact Book from 1991–2002.

During periods when the average fund flow changes, the determinants of fund floware also more likely to change. Both redemption and purchase activity tend to belinked to past performance, and thus average fund flows are very different duringmy sample period when compared with earlier periods. Figure 1 shows similar trendlines for mutual fund inflows and the Standard & Poor’s 500-stock index from 1991 to2002, which is the time period covered by O’Neal (2004) and Ivkovic and Weisbenner(2007), as well as my data set. The Standard & Poor’s 500-stock index moves generallyupward until 2000 when it begins a series of three down years. Overall flows into mutualfunds suffer minor setbacks in 1994 and 1999, but the trend in fund flows generallyfollows stock market performance. Although flows generally follow the market, a lag isobvious: The market starts its decline in 2000, but inflows actually hit a high in 2001before dropping sharply in 2002. Therefore, my sample provides data from a timeperiod 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 pastperformance affects redemption decisions in bull and bear markets can also proveinsightful. Namely, both Odean (1998) and Grinblatt and Keloharju (2001) findevidence that individual investors are reluctant to sell poor-performing investments.Although neither study focuses on mutual fund trades, the suggestion that investorshold on to losers is consistent with the notion that poor-performing funds do notexperience large outflows. Odean (1999), who examines the purchase behavior of no-load equity investors, finds that investors purchase stocks that have performed well inthe past. Even though Odean’s sample does not include mutual fund trades, this resultis consistent with the findings of the aggregate studies, namely, that mutual funds withgood 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 fundinvestors who measure gains and losses relative to their purchase price. That is, duringbull markets, even the poorest performing funds, relative to a benchmark, see gains inabsolute performance.


990 SHRIDER

While it is clear that past performance affects fund flow, the literature examines ahost 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 fundflows 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 determinantsof fund flow. Other determinants include fund family structure (Massa, 2003), marketvolatility (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 isrepresentative of mutual funds in general by replicating findings in the prior literatureand 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). Aftercontrolling for raw performance, I find a large additional positive effect for the topperformers but no additional negative effect for the worst performers. In other words,when only accounting for net flows, the punishment provided to the worst performersis not in sync with the reward given to the best performers. However, once I separatepurchases and redemptions, my results based on the purchases of winners and theredemptions of losers are consistent with O’Neal (2004), Ivkovic and Weisbenner(2007) and Cashman et al. (2006). Specifically, when I control for raw performance, Ifind no evidence that funds in the bottom decile see fewer redemptions. In fact, thesefunds experience redemption levels as large as would normally be expected, given theirpoor track record.

While the stock market peaked in 2000, according to the Investment CompanyInstitute (ICI, 2006), overall industry-wide fund flows did not hit a high until 2001 beforeexperiencing a sharp decline in 2002. Therefore, to examine whether the determinantsof fund flows are different between periods of good and poor performance, I measureredemption and purchase flows separately for 2001 and 2002. I find systematicdifferences when I test for the determinants of fund flows. First, consistent withCashman et al. (2006), I find that during 2001, when net flows were still surging, relativereturn measures – such as the fund’s rank against other funds in the same Morningstarobjective and rank in the top or bottom performance decile – are important indetermining the percentage of redemptions. Second, consistent with Ivkovic andWeisbenner (2007), I find that when testing the determinants of redemptions in 2002when the fund performance was poor, the effect of absolute performance is nearlythree times larger while the relative measures are much less important. The resultsfor purchases show that whether past performance is measured by raw return, riskadjusted return, top-performing decile, or bottom-performing decile, investors aremore 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 sameMorningstar objective (i.e., being one of the best or worst performers among all fundsin the objective) and risk adjusted performance. In other words, under normal marketconditions, when redeeming shares investors measure fund performance relative toother 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 becomesmuch more important, trumping relative and risk adjusted performance measures.For purchases, investors become more discerning during a bear market as nearly allperformance measures become more important.



This study contributes to the literature in two ways. First, it specifically addressesthe open question in the literature of whether investors use relative or absoluteperformance when making redemption decisions. In fact, both relative and absolutemeasures of performance matter – but their importance differs depending on themarket conditions faced by investors. Specifically, investors use relative performance inbull markets and absolute performance in bear markets when making redemptiondecisions while nearly all performance measures become more important whenpurchasing during a bear market. The second and broader contribution is that generalmarket conditions affect investor behavior. While this is important in understandingmutual 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 andmethod. Section 3 presents the results. Section 4 provides robustness checks, andSection 5 concludes.

2. DATA AND METHOD

(i) Data

The data are provided by a national full-service brokerage firm and include all mutualfund transactions during 2001 and 2002; a list of all funds in each account at year-end2000, 2001 and 2002, for all accounts with at least one mutual fund holding; and thetype 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 custodialaccounts averaging 1.6 holdings; single and joint accounts, 2.4 holdings; and retirementaccounts, 3.0 holdings.

New accounts are added to the data set throughout the sample period, and thetransactions 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 by21.2% (6.4%), but the distribution of accounts across account types is similar tothat at year-end 2000. See Panel B for full descriptive statistics for accounts as ofDecember 31, 2001.

Table 2 provides descriptive information on the transactions within single andjoint accounts, custodial accounts, retirement accounts, and other accounts betweenJanuary 1, 2001 and December 31, 2002. Nearly one-fourth of all transactionsare redemptions and more than three-fourths are purchases. The average size ofredemptions 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, Iconduct tests using past performance measures. Because Del Guercio and Tkac (2002),who compare pension fund and mutual fund investors, find that mutual fund investorsbase 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


992 SHRIDER

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994 SHRIDER

Table 2Transaction Data

% Transactions Mean ($) Median ($) Std. Dev. ($)

Panel A: RedemptionsTrade size

Full sample 23.8 8,007 3,300 19,515Joint and single 8.0 7,864 3,505 17,459Custodial 0.7 3,690 2,393 4,335IRAs 12.5 7,116 3,001 14,217Other 2.6 14,000 5,000 38,005

NAV 18.77 17.20 8.81

Panel B: PurchasesTrade size

Full sample 76.2 9,612 4,749 21,682Joint and single 21.6 10,030 4,996 22,846Custodial 1.7 3,974 2,454 5,101IRAs 44.1 8,605 4,247 14,902Other 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 includedin 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) anduse raw returns as my measure of absolute performance, I also use risk adjusted returnsas a control variable. I use alpha from a Carhart (1997) four-factor model as my measureof risk adjusted return.4

Evidence also suggests that mutual fund investors base decisions on performancerelative to other funds. For example, Capon et al. (1996) find that publishedperformance rankings are investors’ most important source of information for makinginvestment 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). Ialso identify winners (i.e., funds in the top decile) and losers (i.e., funds in the bottomdecile) among the funds that are included on the approved list of the firm that providedthe data. I use these measures to test whether placement among the very best or veryworst 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 ofthe data to the mutual fund level. By using aggregating individual investor data ratherthan overall aggregate fund flows I am able to examine purchases and redemptionsseparately. 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) asa measure of risk adjusted performance. The results using CAPM alpha are not qualitatively different fromthose using Carhart alpha, as reported in Tables 5–8.



Table 3Holdings by Asset Class

Equity (%) Fixed Income (%) Balanced (%)

Panel A: 2000Sample 83.2 6.7 10.1Aggregate load funds 85.4 9.4 5.2Aggregate no-load funds 84.1 12.6 3.2

Panel B: 2001Sample 80.7 7.1 12.3Aggregate load funds 82.5 10.7 6.7Aggregate 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 percentageof assets within the data set that is held in equity funds, fixed income funds, and balanced funds. Theaggregate load and no-load rows list the percentage of assets invested in equity, fixed income, and balancedfunds for all load and no-load funds listed in Morningstar.

asymmetric fund flow are driven by purchase decisions, redemption decisions, or acombination of the two.

However, data aggregated at the individual investor level may not be representativeof all load fund investors. Therefore, to determine whether the investors in my sampleare similar to investors in general, I compare the asset classes of the funds they ownto the overall averages of all funds in Morningstar as reported in Table 3, Panel A. In2000, of the total holdings in my sample, 83.2% are in equity funds, 6.7% are in fixedincome funds, and 10.1% are invested in balanced funds. This distribution of assetsis similar to the allocation of assets by investors in general. Of the funds included inMorningstar as of December 31, 2000, load fund investors have 85.4% of their assets inequity funds, 9.4% in fixed income funds, and 5.2% in balanced funds, whereas no-loadfund investors have 84.1% in equity funds, 12.6% in fixed income, and 3.2% in balancedfunds. Results for year-end 2001, as reported in Panel B, are similar. Differences betweenthis sample and mutual fund investors at large in the percentage of assets invested inretirement 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 numbersfor 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 betweenJanuary 1, 2001 and December 31, 2002 at national full-service broker-dealer6 with

5 Another way to examine these data is to study the decision-making of individual investors, which is a topicof ongoing research. In that analysis, controlling for a fund being held in a retirement account does notqualitatively affect the other results.6 I only omit trades of less than $1,000 and trades that could have been exchanges, but instead the sameaccount made both a purchase and a redemption on the same day and paid a sales charge. These potential


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prior fund flow studies, I employ an OLS regression to examine the combined fundflow. The dependent variable used in the analysis is the proportion of fund flows todollars in the fund. This fund flow proportion (FFP) for fund f during month t isdefined as:

FFP f,t = DP ft − DR f

t

DH ft

, (1)

where:

DP ft = dollar value of shares purchased of fund f during month t;

DR ft = dollar value of shares redeemed of fund f during month t; and

DH ft = 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 + β1Return f,t + β2Rank Obj f,t + β3Winner f,t + β4Loser f,t

+ β5Alpha f,t + β6B Share f,t + β7C Share f,t + β7Fixed Income f,t

+ β7Balanced f,t + β7Expense Ratio f,t + β8Log Total Value f,t

+ β9LogTNA f,t + β10Age f,t + 23 Month Dummies + ε f,t , f = 1, . . . , n,

(2)

where Returnf ,t is the one year total return for the year prior to time t; Rank Objf ,t

is the rank of the fund within its Morningstar objective over the year prior to time t;Winnerf ,t is a binary variable, which equals 1 if fund f is in the top-performing decileranked against other funds in the Morningstar objective for the year prior to timet, and zero otherwise; Loserf ,t is a binary variable, which equals 1 if fund f is in thebottom-performing decile ranked against other funds in the Morningstar objective forthe year prior to time t, and zero otherwise; B Sharef ,t is a dichotomous variable, whichequals 1 if the fund t is a class B share, and zero otherwise; C Sharef ,t is a dichotomousvariable, which equals 1 if the fund t is a class C share, and zero otherwise; Alphaf ,t is ameasure of risk adjusted performance that is the intercept from a regression of excessmutual fund returns on the four factors described in Carhart (1997); Expense Ratiof ,t

is the expense ratio for fund f at time t; Log Total Valuef ,t is the natural log of the totalassets invested in the fund at the firm studied; Log TNAf ,t is the natural log of the totalnet assets for fund f at time t; and Agef ,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 arecorrelated. Correlation coefficients of the return variables are shown in Table 4.Because the largest (in absolute value) correlation coefficient is −0.60, I run collinearitydiagnostics. Condition indices show that collinearity is not a large problem as thelargest condition indices are 24.18 and 12.97. In addition, I also run all modelsomitting each return variable individually. These results are qualitatively similar to thosereported.

The expected sign on Return, Rank Obj, Winner , and Alpha is positive because higherreturns 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.



Table 4Correlation Coefficients

Return Rank Obj. Winner Loser Alpha

Return 1.00Rank Obj 0.36 1.00Winner 0.41 0.25 1.00Loser −0.60 −0.30 −0.12 1.00Alpha −0.15 0.21 0.06 0.12 1.00

Notes:This table presents correlation coefficients for the return variables. The return variables are theaverage 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 thesign for Loser to be negative. However, Ippolito (1992), Sirri and Tufano (1998) andFant and O’Neal (2000) all provide evidence that poor performers are not punishedto the extent that winners are rewarded. The signs on B Share and C Share shouldbe positive because investors avoid upfront sales charges. I expect the sign on ExpenseRatio to be negative as investors try to avoid funds with higher fees. The signs for LogTotal Value, Log TNA, and Age should all be positive as investors are more likely to buyfunds that are popular at this particular firm as well as funds that are better known ingeneral.

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 tothe 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 thanredemptions. Past performance has a positive effect as the coefficient on Return ispositive and statistically significant. The sign on the coefficients for the relative returnmeasure, Rank Obj is also positive but not statistically significant. After controllingfor general performance, being top decile of funds has an additional effect, whichholds true across both time periods as the coefficient on Winner is positive and highlysignificant. The coefficient on Loser is negative as predicted, but it is not statisticallysignificant. The coefficient on Alpha is positive and highly statistically significant. Thisfinding that funds with positive risk adjusted performance attract additional flows isconsistent 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 significantcoefficients on the B Share and C Share variables. Both fixed income and balanced fundshave larger fund flow proportions when compared to equity funds as the coefficientson Fixed Income and Balanced are both positive and statistically significant. Fewer dollarsflow into funds with higher expense ratios as evidenced by the negative and statisticallysignificant coefficient on the Exp Ratio variable. The positive and statistically significantsign on the coefficients of Log Total Value suggests that funds more widely held at thefirm from which I obtained the data have larger fund flow proportions. However, aftercontrolling for assets held at the firm, large funds and older funds have smaller fund


998 SHRIDER

Table 5Fund Flows

1-Year

FFP p-values

Intercept 0.0477∗∗ 0.000Return 0.0216∗∗ 0.000Rank Obj 0.0013 0.407Winner 0.0175∗∗ 0.000Loser −0.0011 0.517Alpha 0.0126∗∗ 0.000B Share 0.0130∗∗ 0.000C Share 0.0057∗∗ 0.000Fixed Income 0.0055∗∗ 0.000Balanced 0.0165∗∗ 0.000Expense Ratio −0.0099∗∗ 0.000Log Total Value 0.0034∗∗ 0.000Log TNA −0.0050∗∗ 0.000Age −0.0001∗ 0.011Adj. R2 0.0737N 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 pastyear (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 Morningstarobjective, and zero otherwise; the risk adjusted performance (Alpha) from a Carhart (1997) four-factormodel; a dichotomous variable, (B Share (C Share)), which equals 1 if the fund is a B share (C share), andzero 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 ina given fund at the broker-dealer that provided the data (Log Total Value); and the natural log of the totalnet assets of the fund (Log TNA); and the age of the fund in years (Age). Fixed time effects are includedusing 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 LogTNA and Age.

(ii) How Performance Affects Total Redemptions and Total Purchases

I use the proportion of fund holdings redeemed or purchased measured in dollarvalue of shares as the dependent variable in a separate analysis of the determinants ofpurchases and redemptions. The proportion of the value of shares redeemed in fundf during month t is defined as:

$R f,t = DR ft

DH ft

, (3)

where:

DR ft = dollar value of shares redeemed of fund f during month t, and

DH ft = total dollar value of shares of fund f at the beginning of month t.



The proportion of the value of shares purchased ($Pf ,t) is defined similarly. Themean value for $Rf ,t ($Pf ,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 + β1Return f,t + β2Rank Obj f,t + β3Winner f,t + β4Loser f,t

+ β5Alpha f,t + β6B Share f,t + β7C Share f,t + β7Fixed Income f,t

+ β7Balanced f,t + β7Expense Ratio f,t + β8Log Total Value f,t

+ β9LogTNA f,t + β10Age f,t + 23 Month Dummies + ε f,t , f = 1, . . . , n.

(4)

The coefficients reported in Table 6 represent the marginal effect of a one-unitchange in the explanatory variable on the expected value of the proportion of thefund that is purchased or redeemed in a given month. For the dichotomous variablessuch as Winner and Loser , the marginal effects are calculated by changing the variablefrom zero to 1. Marginal effects for all other variables are evaluated at the samplemean.

The expected sign on Return, Rank Obj, Winner and Alpha is negative for redemptionsand positive for purchases because higher returns should lead to fewer redemptionsand more purchases in subsequent periods. The expected sign on Loser is thereverse – that is, positive for the redemption specifications and negative for the purchasespecifications. Because buy-and-hold investors self-select into A shares and because Ashares have higher initial costs in the form of the upfront sales charge, I expect thesign on the alternatives B Share and C Share to be positive for both purchases andredemptions. Expense Ratio should have a positive sign for redemptions and a negativesign for purchases as investors try to avoid funds with higher fees. Finally, Log TotalValue, Log TNA and Age should all have negative signs for redemptions and positivesigns for purchases as investors are more likely to buy funds that are popular at the firmunder 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 resultsshow that funds see more dollars redeemed when their performance is worse. This resultis consistent with the expected result. The coefficient on the absolute performancevariable, Return, is negative and statistically significant at the 1% level. The sign onthe coefficients of the relative performance variable, Rank Obj, is also negative but notstatistically significant.

I include the Winner and Loser dummy variables to test whether an effect is associatedwith being an extreme performer. After controlling for total return, the results suggestthat these investors sell larger proportions of the top-performing funds. The sign ofthe coefficient on Winner is positive and statistically significant at the 1% level. In otherwords, after controlling for performance, the best performers experience larger totalredemptions than other funds. Although this result is counterintuitive, it is consistentwith the results of Cashman et al. (2006) before they directly control for the persistenceof fund flows.7 The sign of the coefficient on Loser is positive, but not statisticallysignificant. 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 forthe 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 usethis control in Table 6 in order to match the existing literature. However, I do control for persistent fundflows, in the same way as Cashman et al., in robustness checks by using the lagged dependent variable.


1000 SHRIDER

Table 6Tobit 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 purchasedin terms of dollars as defined in equation (3). The independent variables are the average annual totalreturn over the past year (Return); the rank of the fund within its Morningstar objective (Rank Obj); adichotomous variable, Winner (Loser), which equals 1 if the fund is in the top (bottom) performing decileof 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 amountinvested in a given fund at the broker-dealer that provided the data (Log Total Value); and the natural logof the total net assets of the fund (Log TNA); and the age of the fund in years (Age). Fixed time effects areincluded using month dummy variables. p-values are in parentheses.∗∗indicates statistical significance at the 0.01 level.∗indicates statistical significance at the 0.05 level.

statistically significant at the 1% significance level. The sign on the Rank Obj variable isnegative but not statistically significant. The sign on the coefficient on Alpha is positiveand highly significant. These results indicate that more total purchases are made forfunds that have high absolute performance and high risk adjusted performance. Evenafter controlling for performance, the sign of the coefficient on the Winner variable ispositive and statistically significant at the 1% significance level. This finding suggeststhat while funds with better performance are rewarded with greater purchases, top



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

(iii) Test of Time Period

Having established that the results of my sample are consistent with the existingliterature’s understanding of aggregate fund flows and of the determinants of fundflow when purchases and redemptions are separated, I now focus on whether thedeterminants of fund flow change based on market conditions. To this end, I runseparate tobit models on equation (4) for 2001 and 2002.

As shown in Figure 1, mutual funds in general saw record inflows in 2001 before fundflows 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 theredemptions and purchases in Table 6. Like Table 6, the coefficients in Tables 7 and 8represent marginal effects.

The results for redemptions by year are reported in Table 7. The first column reportsthe results for 2001 and the second column shows the results for 2002. The coefficient onReturn, which is simply the absolute performance, is negative and statistically significantin both the 2001 and 2002 specifications. However, the size of the coefficient is nearlythree times larger in the 2002 bear market specification. A pattern can be seen in theresults for the three relative performance measures, Rank Obj, Winner and Loser : Thatis, relative performance is more important in 2001 than in 2002. All of the coefficientsare statistically significant at nearly the 1% significance level in 2001. In the bear marketspecification neither Rank Obj nor Loser are statistically significant and the size of thecoefficient on Winner is smaller than in the 2001 specification. The same pattern holdsfor Alpha, the measure of risk adjusted fund performance. Alpha is significant at the1% significance level in 2001, but it is not statistically significant in 2002. These resultsare consistent with the idea that investors spend more time combing through numberswhen markets are normal, but in a bear market, they are focused on exiting funds andprimarily concerned with absolute performance.

C Share and Expense Ratio exhibit a similar pattern and support the idea that investorsfocus on absolute performance in bear markets. The coefficient on C Share is positiveand 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 forwhich they paid an upfront sales charge, but in a bear market, the loss of this sunkcost is no longer a high priority and the difference between C shares and A sharesbecomes insignificant. The coefficient on Expense Ratio is statistically significant in bothspecifications, but the sign changes from negative in the 2001 specification to positivein the 2002 specification. These results indicate that funds with higher expense ratiosactually see fewer redemptions in normal markets, but in bear market conditions, fundsthat take more expenses out of their returns see more redemptions. The results on BShare are virtually identical between 2001 and 2002. Fixed income funds are more likelyto see redemptions in 2002 while balanced funds are less likely. The results on Log TotalValue, Log TNA and Age are inconsistent across specifications and often counter totheir expected signs. Taken together, these results suggest that these variables do notsubstantially control fund reputation among investors.


1002 SHRIDER

Table 7Tobit 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


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



Table 8Tobit 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


when making purchases in 2002. Specifically, the coefficients on Return, Winner andAlpha, are all statistically significant at the 1% significance level, while Loser is significantat the 5% level. The positive sign on Loser indicates that the worst performers see largerpurchases than would be predicted by their return.

The signs on the control variables are somewhat consistent with expectations in thepurchase specifications found in Table 8. B Share, C Share, Expense Ratio and Log Total


1004 SHRIDER

Value are consistent with the expected sign in both specifications. Fixed Income is notsignificant 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 consistentwith the expected sign across both specifications.

While the results between redemptions and purchases are not the same, they areboth consistent with the idea that investors are reacting to a bear market. Whenmaking redemptions under normal conditions, relative performance measures andrisk 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 flowsas investors rapidly exit funds. For purchases, investors become very selective as rawreturn, risk adjusted return, and most relative performance measures become moreimportant 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 differin good and bad markets, the definitions of a good market and bad market are veryimportant. The beginning of the market correction could be marked by, among otherthings, the Dow Jones Industrial Average’s high on May 22, 2001, the unexpected shockof September 11, 2001, or the peak in fund flows late in 2001; therefore, I conducttests in which I divide my time period at different points before and after year-end2001. The results (not tabulated) are not significantly different from those reported inTables 7 and 8. Robustness results show the same general pattern when the split is closeto the calendar-year split reported here and becomes progressively weaker the furtherthe split moves (in either direction) from year-end 2001.

Cashman et al. (2006) show that fund flows are very persistent and that controllingfor this persistence with the lagged dependent variable causes the results to better matchexpected signs. When the lagged dependent variable is included, the sign on the laggeddependent 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-stepprocess 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 arequalitatively similar to the tobit model results. None of the inverse mills ratios from thefirst 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 LogTNA are positively correlated (correlation coefficient = 0.69) and because they havethe largest variance inflation factors, I run all tests without Log Total Value. I find thatdropping Log Total Value does not cause the results to be qualitatively different fromthose previously reported.

5. CONCLUSION

This paper investigates whether the determinants of fund flow are affected by theshifting market conditions by examining the performance of a sample of mutualfunds during 2000 (strong fund flow market) and 2001 (weak fund flow market).To determine whether my sample and, thus, my findings are comparable to previousliterature, I first replicate the results of the prior studies. Specifically, my results for



aggregate fund flows show that winners are rewarded to a greater degree than losersare punished, which is consistent with Ippolito (1992), Sirri and Tufano (1998) and Fantand O’Neal (2000). When I separate purchases and redemptions, I find evidence thatlosers see large redemptions but that these redemptions are masked by new purchases,which is consistent with O’Neal (2004), Ivkovic and Weisbenner (2007) and Cashmanet 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 marketconditions. I find that redemptions are strongly affected by relative performanceand risk adjusted performance under normal market conditions. However, in bearmarket conditions, redemptions are more strongly affected by absolute performance,and measures of relative performance and risk adjusted performance become lessimportant. For purchases, absolute performance, risk adjusted performance, and mostrelative 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 andabsolute performance measures. However, which performance measures are the mostrelevant depends on overall market conditions. In a more general way this study showsthe importance of including controls for market conditions in future individual investorresearch.

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Economy & Finance

How poor stock mkt perf affects fund f lows shrider