Does Fund Size Erode Mutual Fund Performance? The...

Does Fund Size Erode Mutual Fund Performance?The Role of Liquidity and Organization

By JOSEPH CHEN, HARRISON HONG, MING HUANG, AND JEFFREY D. KUBIK*

We investigate the effect of scale on performance in the active money managementindustry. We first document that fund returns, both before and after fees andexpenses, decline with lagged fund size, even after accounting for various perfor-mance benchmarks. We then explore a number of potential explanations for thisrelationship. This association is most pronounced among funds that have to investin small and illiquid stocks, suggesting that these adverse scale effects are relatedto liquidity. Controlling for its size, a fund’s return does not deteriorate with the sizeof the family that it belongs to, indicating that scale need not be bad for performancedepending on how the fund is organized. Finally, using data on whether funds aresolo-managed or team-managed and the composition of fund investments, weexplore the idea that scale erodes fund performance because of the interaction ofliquidity and organizational diseconomies. (JEL G2, G20, G23, L2, L22)

The mutual fund industry plays an increas-ingly important role in the U.S. economy. Overthe past two decades, mutual funds have beenamong the fastest growing institutions in thiscountry. At the end of 1980, they managed lessthan $150 billion, but this figure had grown toover $4 trillion by the end of 1997—a numberthat exceeds aggregate bank deposits (Robert C.Pozen, 1998). Indeed, almost 50 percent ofhouseholds today invest in mutual funds (In-vestment Company Institute, 2000). The mostimportant and fastest-growing part of this in-dustry is funds that invest in stocks, particularlyactively managed ones. The explosion of news-letters, magazines, and such rating services asMorningstar attest to the fact that investorsspend significant resources in identifying man-agers with stock-picking ability. More impor-tant, actively managed funds control a sizeable

stake of corporate equity and play a pivotal rolein the determination of stock prices (see, e.g.,Mark Grinblatt et al., 1995; Paul Gompers andAndrew Metrick, 2001).

In this paper, we tackle an issue that is fun-damental to understanding the role of these mu-tual funds in the economy—the economies ofscale in the active money management industry.Namely, how does performance depend on thesize or asset base of the fund? A better under-standing of this issue would naturally be usefulfor investors, especially in light of the massiveinflows that have increased the mean size offunds in the recent past. At the same time, theissue of the persistence of fund performancedepends crucially on the scale-ability of fundinvestments (see, e.g., Martin J. Gruber, 1996;Jonathan Berk and Richard C. Green, 2002).Moreover, the nature of the economies of scalein this industry may also have implications forthe agency relationship between managers andinvestors and the optimal compensation con-tract between them (see, e.g., Keith Brown etal., 1996; Stan Becker and Greg Vaughn, 2001).Therefore, understanding the effects of fundsize on fund returns is an important first steptoward addressing such critical issues.

While the effect of scale on performance is animportant question, it has received little re-search attention to date. Some practitioners

* Chen: Marshall School of Business, University ofSouthern California, Hoffman Hall 701, Los Angeles,CA 90089 (e-mail: [email protected]); Hong:Bendheim Center for Finance, Princeton University,26 Prospect Avenue, Princeton, NJ 08540 (e-mail:[email protected]); Huang: Stanford Graduate Schoolof Business and Cheong Kong Graduate School of Business,Stanford University, Stanford, CA 94305 (e-mail:[email protected]); Kubik: Department of Economics,Syracuse University, 426 Eggers Hall, Syracuse, NY 13244(e-mail: [email protected]).

1276

point out that there are advantages to scale, suchas more resources for research and lower ex-pense ratios. Others believe, however, that alarge asset base erodes fund performance be-cause of trading costs associated with liquidityor price impact (see, e.g., Andre Perold andRobert S. Salomon, 1991; Roger Lowenstein,1997). Whereas a small fund can easily put allof its money in its best ideas, a lack of liquidityforces a large fund to have to invest in itsnot-so-good ideas and take larger positions perstock than is optimal, thereby eroding perfor-mance. Using a small sample of funds from1974 to 1984, Grinblatt and Sheridan Titman(1989) find mixed evidence that fund returnsdecline with fund size. Needless to say, there isno consensus on this issue.

Using mutual fund data from 1962 to 1999,we begin our investigation by using cross-sectional variation to see whether performancedepends on lagged fund size. Since funds mayhave different styles, we adjust for such heter-ogeneity by utilizing various performancebenchmarks that account for the possibility thatthey load differently on small cap stock, valuestock, and price momentum strategies. More-over, fund size may be correlated with suchother fund characteristics as fund age or turn-over, and it may be these characteristics that aredriving performance. Hence, we regress the var-ious adjusted returns on lagged fund size (asmeasured by the log of total net assets undermanagement), and also include in the regres-sions a host of other observable fund character-istics, including turnover, age, expense ratio,total load, past-year fund inflows, and past-yearreturns. A number of studies warn that the re-ported returns of the smallest funds (those withless than $15 million in assets under manage-ment) might be upward biased. We excludethese funds from our baseline sample in esti-mating these regressions.

The regressions indicate that a fund’s perfor-mance is inversely correlated with its laggedassets under management. For instance, usingmonthly gross returns (before fees and expensesare deducted), a two-standard deviation shockin the log of a fund’s total assets under man-agement this month yields anywhere from a 5.4-to 7.7-basis-point movement in next month’sfund return, depending on the performancebenchmark (or about 65 to 96 basis points an-

nually). The corresponding figures for net fundreturns (after fees and expenses are deducted)are only slightly smaller. To put these magni-tudes into some perspective, the funds in oursample on average underperform the marketportfolio by about 96 basis points after fees andexpenses. From this perspective, a 65- to 96-basis-point annual spread in performance is notonly statistically significant but also economi-cally important.1

Even after utilizing various performancebenchmarks and controlling for other observ-able fund characteristics, there are still a num-ber of potential explanations that might beconsistent with the inverse relationship betweenscale and fund returns. To further narrow the setof explanations, we proceed to test a directimplication of the hypothesis that fund sizeerodes performance because of trading costsassociated with liquidity and price impact. If the“liquidity hypothesis” is true, then size ought toerode performance much more for funds thathave to invest in small stocks, which tend to beilliquid. Consistent with this hypothesis, we findthat fund size matters much more for the returnsamong such funds, identified as “small cap”funds in our database, than other funds.2 Indeed,for other funds, size does not significantly affectperformance. This finding strongly indicatesthat liquidity plays an important role in thedocumented diseconomies of scale.

We then delve deeper into the liquidity hy-pothesis by observing that liquidity means thatbig funds need to find more stock ideas thansmall funds do, but liquidity itself may notcompletely explain why they cannot go aboutdoing this, i.e., why they cannot scale. Presum-ably, a large fund can afford to hire additionalmanagers to cover more stocks. It can therebygenerate additional good ideas so that it can take

1 As we describe below, some theories suggest that thesmallest funds may have inferior performance to medium-sized ones because they are being run at a suboptimallysmall scale. Because it is difficult to make inferences re-garding the performance of the smallest funds, we do notattempt to measure such nonlinearities here.

2 Throughout the paper, we will sometimes refer to fundswith few assets under management as “small funds” andfunds that, by virtue of their fund style, have to invest insmall stocks as “small cap funds.” So small cap funds arenot necessarily small funds. Indeed, most are actually quitelarge in terms of assets under management.

1277VOL. 94 NO. 5 CHEN ET AL.: DOES FUND SIZE ERODE MUTUAL FUND PERFORMANCE?

small positions in lots of stocks as opposed tolarge positions in a few stocks. Indeed, the vastmajority of stocks with small market capitaliza-tion are untouched by mutual funds (see, e.g.,Hong, Lim, and Stein, 2000; Chen et al., 2002).So there is clearly scope for even very largefunds to generate new ideas. Put another way,why can’t two small funds (directed by twodifferent managers) merge into one large fundand still have the performance of the large onebe equal to the sum of the two small ones?

To see that assets under management neednot be bad for the performance of a fund orga-nization, we consider the effect that the size ofthe fund family has on fund performance. Manyfunds belong to fund families (e.g., the famousMagellan fund is part of the Fidelity family offunds), which allows us to measure separatelythe effect of fund size and the size of the rest ofthe family on fund performance. Controlling forfund size, we find that the assets under manage-ment of the other funds in the family that thefund belongs to actually increase the fund’sperformance. A two-standard deviation shock tothe size of the other funds in the family leads toabout a 4- to 6-basis-point movement in thefund’s performance the following month (orabout 48- to 72-basis-points movement annu-ally) depending on the performance measureused. The effect is smaller than that of fund sizeon performance but is nonetheless statisticallyand economically significant. As we explain indetail below, the most plausible interpretationof this finding is that there are economies asso-ciated with trading commissions and lendingfees at the family level. Bigger families likeFidelity are able to get better concessions ontrading commissions and earn higher lendingfees for the stocks held by their funds.

These two findings—that fund performancedeclines with the fund’s own size but increaseswith the size of the other funds in the family—are both interesting and intuitively appealing.First, in our cross-sectional regressions, it isimportant to control for family size in order tofind a sizeable impact of fund size on perfor-mance. The reason is that these two variablesare positively correlated, and since family sizeis good for performance, it is important to con-trol for it to identify the negative effect of fundsize. Second, the finding on family size alsorules out a number of alternative hypotheses for

our fund size finding. For instance, it is notlikely that this finding is due to large funds notcaring about returns, since large families appar-ently do make sufficient investments to main-tain performance.

More important, these two findings makeclear that liquidity and scale need not be bad forfund performance per se. In most families, ma-jor decisions are decentralized in that the fundmanagers make stock picks without substantialcoordination with each other. So a family is anorganization that credibly commits to lettingeach of its fund managers run his or her ownassets. Moreover, being part of a family mayeconomize on certain fixed costs, as explainedabove. Thus, if a large fund is organized like afund family with different managers runningsmall pots of money, then scale need not be badper se, just as family size does not appear to bebad for family performance.

Therefore, given that managers care a greatdeal about performance and that scale need notbe bad for performance per se, why does itappear that scale erodes fund performance be-cause of liquidity? Later in this paper, we ex-plore some potential answers to this question.Whereas a small fund can be run by a singlemanager, a large fund naturally needs moremanagers, and so issues of how the decision-making process is organized become important.We conjecture that liquidity and scale affectperformance because of certain organizationaldiseconomies. We pursue this perspective as ameans to motivate additional analysis involvingfund stock holdings. We want to emphasize thatour analysis is exploratory and that a number ofalternative interpretations, which we describebelow, are possible.

There are many types of organizational dis-economies that lead to different predictions onwhy small organizations outperform largeones.3 We conjecture that one type, known ashierarchy costs (see, e.g., Philippe Aghion andJean Tirole, 1997; Jeremy C. Stein, 2002), maybe especially relevant for mutual funds and mo-tivate our analysis by testing some predictionsfrom Stein (2002). The basic premise is that in

3 See Patrick Bolton and David S. Scharfstein (1998) andBengt Holmstrom and John Roberts (1998) for surveys onthe boundaries of the firm that discuss such organizationaldiseconomies.

1278 THE AMERICAN ECONOMIC REVIEW DECEMBER 2004

large organizations with hierarchies, the processof agents fighting for (and potentially not hav-ing) their ideas implemented will affect agents’ex ante decisions of what ideas they want towork on. Stein (2002) argues that in the pres-ence of such hierarchy costs, small organiza-tions ought to outperform large ones at tasksthat involve the processing of soft information,(i.e., information that cannot be directly verifiedby anyone other than the agent who producesit). If the information is soft, then agents have aharder time convincing others of their ideas andit becomes more difficult to pass this informa-tion up the organization.

In the context of mutual funds, soft informa-tion most naturally corresponds to research orinvestment ideas related to local stocks (com-panies located near a fund headquarters) sinceanecdotal evidence indicates that investing insuch companies requires that the fund processsoft information, e.g., speaking with CEOs asopposed to simply looking at hard informationlike price-earnings ratios. This means that inlarge funds with hierarchies in which managersfight to have their ideas implemented, managersmay end up expending too much research efforton quantitative measures of a company (i.e.,hard information) so as to convince others toimplement their ideas than they ideally would ifthey controlled their own smaller funds. All elseequal, large funds may perform worse thansmall ones.

Building on the work of Joshua D. Coval andTobias J. Moskowitz (1999, 2001), we find that,consistent with Stein (2002), small funds, espe-cially those investing in small stocks, aresignificantly more likely than their larger coun-terparts to invest in local stocks. Moreover, theydo much better at picking local stocks than largefunds do.4

Another implication of Stein (2002) is thatcontrolling for fund size, funds that are man-aged by one manager are better at tasks that

involve the processing of soft information thanfunds managed by many managers. Consistentwith Stein (2002), we find that solo-managedfunds are significantly more likely than co-managed funds to invest in local stocks andto do better than co-managed funds at pickinglocal stocks. Finally, we find that controlling forfund size, solo-managed funds outperform co-managed funds.

Note that such hierarchy costs are not presentat the family level precisely because the familytypically agrees not to reallocate resourcesacross funds. Indeed, different funds in a familyhave their own boards that deal with such issuesas replacement of managers. So the manager incharge of a fund generally does not have toworry about the family taking away the fund’sresources and giving them to some other fund inthe family. More generally, the idea that agents’incentives are weaker when they do not havecontrol over asset allocation or investment de-cisions is in the work of Sanford J. Grossmanand Oliver D. Hart (1986), Hart and John Moore(1990), and Hart (1995).

In sum, our paper makes a number of contri-butions. First, we carefully document that per-formance declines with fund size. Second, weestablish the importance of liquidity in mediat-ing this inverse relationship. Third, we point outthat the adverse effect of scale on performanceneed not be inevitable because we find thatfamily size actually improves fund perfor-mance. Finally, we provide some evidence thatthe reason fund size and liquidity do in facterode performance may be due to organizationaldiseconomies related to hierarchy costs. It isimportant to note, however, that our analysisinto the nature of the organizational disecono-mies is exploratory and that there are otherinterpretations, which we discuss below.

Our paper proceeds as follows. In Section Iwe describe the data and in Section II the per-formance benchmarks. In Section III we presentour empirical findings. We explore alternativeexplanations in Section IV and conclude inSection V.

I. Data

Our primary data on mutual funds come fromthe Center for Research in Security Prices(CRSP) Mutual Fund Database, which spans the

4 Stein’s analysis also suggests that large organizationsneed not underperform small ones when it comes to pro-cessing hard information. In the context of the mutual fundindustry, only passive index funds like Vanguard are likelyto rely solely on hard information. Most active mutual fundsrely to a significant degree on soft information. Interest-ingly, anecdotal evidence indicates that scale is not as big anissue for passive index funds as it is for active mutual funds.


years 1962 to 1999. Following many prior stud-ies, we restrict our analysis to diversified U.S.equity mutual funds by excluding from our sam-ple bond, international, and specialized sectorfunds.5 For a fund to be in our sample, it mustreport information on assets under managementand monthly returns. We require also that ithave at least one year of reported returns. Thisadditional restriction is imposed because we needto form benchmark portfolios based on past fundperformance.6 Finally, a mutual fund may enterthe database multiple times in the same month if ithas different share classes. We clean the data byeliminating such redundant observations.

Table 1 reports summary statistics for oursample. In panel (A), we report the means andstandard deviations for the variables of interestfor each fund size quintile, for all funds, and forfunds in fund size quintiles two (next to small-est) to five (largest). Edwin J. Elton et al. (2001)warn that one has to be careful in making in-ferences regarding the performance of fundsthat have less than $15 million in total net assetsunder management. They point out that there isa systematic upward bias in the reported returnsamong these observations. This bias is poten-tially problematic for our analysis since we areinterested in the relationship between scale andperformance. As we will see shortly, this cri-tique applies only to observations in fund sizequintile one (smallest), since the funds in theother quintiles typically have greater than $15million under management. Therefore, we focusour analysis on the subsample of funds in fundsize quintiles two through five. It turns out thatour results are robust whether or not we includethe smallest funds in our analysis.

We utilize 3,439 distinct funds and a total

27,431 fund years in our analysis.7 In eachmonth, our sample includes on average 741funds. They have average total net assets (TNA)of $282.5 million, with a standard deviation of$925.8 million. The interesting thing to notefrom the standard deviation figure is that there isa substantial spread in TNA. Indeed, this be-comes transparent when we disaggregate thesestatistics by fund size quintiles. Those in thesmallest quintile have an average TNA of onlyabout $4.7 million, whereas the ones in the topquintile have an average TNA of over $1.1 bil-lion. The funds in fund size quintiles twothrough five have a slightly higher mean of$352.3 million with a standard deviation of over$1 billion. For the usual reasons related toscaling, the proxy of fund size that we will usein our analysis is the log of a fund’s TNA(LOGTNA). The statistics for this variable arereported in the row below that of TNA. Anothervariable of interest is LOGFAMSIZE, which isthe log of one plus the cumulative TNA of theother funds in the fund’s family (i.e., the TNAof a fund’s family excluding its own TNA).

In addition, the database reports a host ofother fund characteristics that we utilize in ouranalysis. The first is fund turnover (TURN-OVER), defined as the minimum of purchasesand sales over average TNA for the calendaryear. The average fund turnover is 54.2 percentper year. The average fund age (AGE) is about15.7 years. The funds in our sample have ex-pense ratios as a fraction of year-end TNA (EX-PRATIO) that average about 97 basis points peryear. They charge a total load (TOTLOAD) ofabout 4.36 percent (as a percentage of newinvestments) on average. FLOW in month t isdefined as the fund’s TNA in month t minus theproduct of the fund’s TNA at month t � 12 withthe net fund return between months t � 12 andt, all divided by the fund’s TNA at month t � 12.The funds in the sample have an average fundflow of 24.7 percent per year. These summarystatistics are similar to those obtained for thesubsample of funds in fund size quintiles twothrough five.

5 More specifically, we select mutual funds in the CRSPMutual Fund Database that have reported one of the fol-lowing investment objectives at any point. We first selectmutual funds with the Investment Company Data, Inc.(ICDI) mutual fund objective of “aggressive growth,”“growth and income,” or “long-term growth.” We then addin mutual funds with the Strategic Insight mutual fundobjective of “aggressive growth,” “flexible,” “growth andincome,” “growth,” “income-growth,” or “small companygrowth.” Finally, we select mutual funds with the Wiesen-berger mutual fund objective code of “G,” “G-I,” “G-I-S,”“G-S,” “GCI,” “I-G,” “I-S-G,” “MCG,” or “SCG.”

6 We have also replicated our analysis without this re-striction. The only difference is that the sample includesmore small funds, but the results are unchanged.

7 At the end of 1993, we have about 1,508 distinct fundsin our sample, very close to the number reported by previ-ous studies that have used this database. Moreover, thesummary statistics below are similar to those reported inthese other studies as well.


TABLE 1—SUMMARY STATISTICS

Panel A: Time-series averages of cross-sectional averages and standard deviations

Mutual fund size quintile

All funds Quintiles 2–51 2 3 4 5

Number of funds 148.2 147.8 147.8 147.8 147.3 741.4 590.6TNA 4.7 22.2 60.6 165.4 1164.7 282.5 352.3

($ million) [3.2] [7.2] [16.6] [54.8] [1797.1] [925.8] [1022.7]LOGTNA 1.09 2.94 3.96 4.94 6.45 3.87 4.57

($ million) [1.01] [0.34] [0.27] [0.33] [0.75] [1.92] [1.38]LOGFAMSIZE 3.70 4.52 5.29 5.91 7.00 5.28 5.68

($ million) [2.98] [2.89] [2.72] [2.56] [2.16] [2.92] [2.75]TURNOVER 42.07 55.68 59.09 61.20 52.17 54.17 57.07

(% per year) [83.03] [74.00] [68.60] [64.28] [54.56] [71.84] [67.21]AGE 8.17 11.90 14.82 18.43 25.16 15.67 17.57

(years) [8.54] [10.73] [12.85] [14.38] [15.06] [13.96] [14.33]EXPRATIO 1.29 1.08 0.94 0.85 0.68 0.97 0.89

(% per year) [1.11] [0.59] [0.46] [0.37] [0.31] [0.68] [0.48]TOTLOAD 3.41 4.19 4.32 4.57 5.28 4.36 4.59

(%) [3.32] [3.32] [3.34] [3.39] [2.88] [3.36] [3.32]FLOW 30.79 30.66 26.97 21.27 13.54 24.67 23.13

(% per year) [113.55] [113.36] [101.66] [84.08] [59.04] [102.64] [96.60]

Panel B: Time-series averages of (monthly) correlations between fund characteristics (using all funds)

TNA LOGTNA LOGFAMSIZE TURNOVER AGE EXPRATIO TOTLOAD FLOW

TNA 1.00 0.56 0.24 �0.05 0.27 �0.19 0.10 �0.03LOGTNA 1.00 0.40 0.06 0.44 �0.31 0.19 �0.07LOGFAMSIZE 1.00 0.08 0.08 �0.19 0.25 �0.01TURNOVER 1.00 0.01 0.17 0.05 �0.01AGE 1.00 �0.13 0.19 �0.18EXPRATIO 1.00 �0.05 0.08TOTLOAD 1.00 �0.04FLOW 1.00

Panel C: Time-series averages of (monthly) correlations between fund characteristics (excluding smallest 20 percent of funds)

TNA LOGTNA LOGFAMSIZE TURNOVER AGE EXPRATIO TOTLOAD FLOW

TNA 1.00 0.66 0.23 �0.08 0.24 �0.24 0.09 �0.03LOGTNA 1.00 0.35 �0.05 0.37 �0.36 0.13 �0.07LOGFAMSIZE 1.00 0.07 0.03 �0.17 0.22 �0.01TURNOVER 1.00 �0.04 0.26 0.03 0.01AGE 1.00 �0.18 0.17 �0.19EXPRATIO 1.00 0.01 0.10TOTLOAD 1.00 �0.05FLOW 1.00

Panel D: Time-series averages of (monthly) cross-sectional averages of market-adjusted fund returns

Mutual fund size quintile

All funds Quintiles 2–51 2 3 4 5

FUNDRET 0.09% 0.02% 0.03% �0.06% �0.06% 0.01% �0.02%(Gross) [3.04%] [2.64%] [2.61%] [2.46%] [2.00%] [2.62%] [2.48%]

FUNDRET �0.02% �0.07% �0.05% �0.13% �0.12% �0.08% �0.09%(Net) [3.04%] [2.64%] [2.61%] [2.46%] [2.00%] [2.62%] [2.48%]

Notes: This table reports summary statistics for the funds in our sample. “Number of funds” is the number of mutual funds that meet our selectioncriteria for being an active mutual fund in each month. TNA is the total net assets under management in millions of dollars. LOGTNA is the logarithmof TNA. LOGFAMSIZE is the logarithm of one plus the assets under management of the other funds in the family that the fund belongs to (excludingthe asset base of the fund itself). TURNOVER is fund turnover, defined as the minimum of aggregate purchases and sales of securities divided by theaverage TNA over the calendar year. AGE is the number of years since the establishment of the fund. EXPRATIO is the total annual management feesand expenses divided by year-end TNA. TOTLOAD is the total front-end, deferred, and rear-end charges as a percentage of new investments. FLOWis the percentage new fund flow into the mutual fund over the past year. TNA, LOGFAMSIZE, and FLOW are reported monthly. All other fundcharacteristics are reported once a year. FUNDRET is the monthly market-adjusted fund return. These returns are calculated before (gross) and after(net) deducting fees and expenses. Panel (A) reports the time-series averages of monthly cross-sectional averages and monthly cross-sectional standarddeviations (shown in brackets) of fund characteristics. Panels (B) and (C) report the time-series averages of the cross-sectional correlations betweenfund characteristics. Panel (D) reports the time-series averages of monthly cross-sectional averages of market-adjusted fund returns. In panels (A) and(B), fund size quintile 1 (5) has the smallest (largest) funds. The sample is from January 1963 to December 1999.


Panel (B) of Table 1 reports the time-seriesaverages of the cross-sectional correlations be-tween the various fund characteristics. A num-ber of patterns emerge. First, LOGTNA isstrongly correlated with LOGFAMSIZE (0.40).Second, EXPRATIO varies inversely with LOG-TNA (�0.31), while TOTLOAD and AGE varypositively with LOGTNA (0.19 and 0.44, re-spectively). Panel (C) reports the analogousnumbers for the funds in fund size quintiles twothrough five. The results are similar to those inpanel (B). It is apparent from panels (B) and (C)that we need to control for these fund charac-teristics in estimating the cross-sectional rela-tionship between fund size and performance.

Finally, we report in panel (D) the means andstandard deviations for the monthly fund re-turns, FUNDRET, where we measure these re-turns in a couple of different ways. We firstreport summary statistics for gross fund returnsadjusted by the return of the market portfolio(simple market-adjusted returns). Monthlygross fund returns are calculated by adding backthe expenses to net fund returns by taking theyear-end expense ratio, dividing it by 12, andadding it to the monthly returns during the year.For the whole sample, the average monthly per-formance is 1 basis point with a standard devi-ation of 2.62 percent. The funds in fund sizequintiles two through five do slightly worse,with a mean of �2 basis points and a standarddeviation of 2.48 percent. We also report thesesummary statistics using net fund returns. Thefunds in our sample underperform the market by8 basis points per month, or 96 basis points peryear, after fees and expenses are deducted.

These figures are almost identical to thosedocumented in other studies. These studies findthat fund managers do have the ability to beat orstay even with the market before managementfees (see, e.g., Grinblatt and Titman, 1989;Grinblatt et al., 1995; Kent Daniel et al., 1997).However, mutual fund investors are apparentlywilling to pay a lot in fees for limited stock-picking ability, which results in their risk-adjusted fund returns being significantlynegative (see, e.g., Michael C. Jensen, 1968;Burton G. Malkiel, 1995; Gruber, 1996).

Moreover, notice that smaller funds appear tooutperform their larger counterparts. For in-stance, funds in quintile two have an averagemonthly gross return of 2 basis points, while

funds in quintile five underperform the marketby 6 basis points. The difference of 8 basispoints per month, or 96 basis points a year, is aneconomically interesting number. Net fund re-turns also appear to be negatively correlatedwith fund size, though the spread is somewhatsmaller than using gross returns. We do notwant to overinterpret these results since we havenot controlled for heterogeneity in fund stylesnor calculated any type of statistical signifi-cance in this table.

In addition to the CRSP Mutual Fund Data-base, we also utilize the CDA Spectrum Data-base to analyze the effect of fund size on thecomposition of fund stock holdings and theperformance of these holdings. The reason weneed to augment our analysis with this databaseis that the CRSP Mutual Fund Database doesnot contain information on fund positions inindividual stocks. The CDA Spectrum Databasereports a fund’s stock positions on a quarterlybasis but it is not available until the early 1980sand does not report a fund’s cash positions.Russ Wermers (2000) compared the funds inthese two databases and found that the activefunds represented in the two databases are com-parable. So while the CDA Spectrum Databaseis less effective than the CRSP Mutual FundDatabase in measuring performance, it is ade-quate for analyzing the effects of fund size onstock positions. We will provide a more detaileddiscussion of this database in Section III.

II. Methodology

Our empirical strategy utilizes cross-sectionalvariation to see how fund performance varieswith lagged fund size. We could have adopted afixed-effects approach by looking at whetherchanges in a fund’s performance are related tochanges in its size. Such an approach is subject,however, to a regression-to-the-mean bias. Afund with a year or two of lucky performancewill experience an increase in fund size. Butperformance will regress to the mean, leading toa spurious conclusion that an increase in fundsize is associated with a decrease in fund re-turns. Measuring the effect of fund size onperformance using cross-sectional regressionsis less subject to such bias. Indeed, it may evenbe conservative given our goal, since largerfunds are likely to be better funds or they would


not have become large in the first place. We arelikely to be biased toward finding any disecono-mies of scale using cross-sectional variation.

There are two major worries that arise, how-ever, when using cross-sectional variation. Thefirst is that funds of different sizes may be indifferent styles. For instance, small funds mightbe more likely than large funds to pursue smallstock, value stock, and price momentum strate-gies, which have been documented to generateabnormal returns. While it is not clear that onenecessarily wants to adjust for such heterogene-ity, it would be more interesting if we found thatpast fund size influences future performanceeven after accounting for variations in fundstyles. The second worry is that fund size mightbe correlated with other fund characteristicssuch as fund age or turnover, and it may bethese characteristics that are driving perfor-mance. For instance, fund size may be measur-ing whether a fund is active or passive (whichmay be captured by fund turnover). While wehave tried our best to rule out passive funds inour sample construction, it is possible that somefunds may just be indexers. And if it turns outthat indexers happen to be large funds becausemore investors put their money in such funds,then size may be picking up differences in thedegree of activity among funds.

A. Fund Performance Benchmarks

A very conservative way to deal with the firstworry about heterogeneity in fund styles is toadjust for fund performance by various bench-marks. In this paper, we consider, in addition tosimple market-adjusted returns, returns adjustedby the Capital Asset Pricing Model (CAPM) ofWilliam F. Sharpe (1964). We also considerreturns adjusted using the Eugene F. Famaand Kenneth R. French (1993) three-factormodel, and this model augmented with themomentum factor of Narasimhan Jegadeesh andTitman (1993), which has been shown invarious contexts to provide explanatory powerfor the observed cross-sectional variation infund performance (see, e.g., Mark M. Carhart,1997).

Panel (A) of Table 2 reports the summarystatistics for the various portfolios that make upour performance benchmarks. Among theseare the returns on the CRSP value-weighted

stock index net of the one-month Treasuryrate (VWRF), the returns to the Fama andFrench (1993) SMB (small stocks minus largestocks) and HML (high book-to-market stocksminus low book-to-market stocks) portfolios,and the returns-to-price momentum portfolioMOM12 (a portfolio that is long stocks that arepast-12-month winners and short stocks that arepast-12-month losers and hold for one month).The summary statistics for these portfolio re-turns are similar to those reported in other mu-tual fund studies.

Since we are interested in the relationshipbetween fund size and performance, we sortmutual funds at the beginning of each monthbased on the quintile rankings of their previous-month TNA.8 We then track these five portfoliosfor one month and use the entire time series oftheir monthly net returns to calculate the load-ings to the various factors (VWRF, SMB, HML,MOM12) for each of these five portfolios. Foreach month, each mutual fund inherits the load-ings of the one of these five portfolios that itbelongs to. In other words, if a mutual fundstays in the same-size quintile throughout itslife, its loadings remain the same. But if itmoves from one size quintile to another duringa certain month, it inherits a new set of load-ings with which we adjust its next month’sperformance.

Panel (B) reports the loadings of the fivefund-size (TNA) sorted mutual fund portfoliosusing the CAPM

(1)

Ri,t � �i � �i VWRFt � �i,t t � 1, ... , T

where Ri,t is the (net fund) return on one of ourfive fund-size-sorted mutual fund portfolios inmonth t in excess of the one-month T-bill re-turn, �i is the excess return of that portfolio, �iis the loading on the market portfolio, and �i,tstands for a generic error term that is uncorre-lated with all other independent variables. As

8 We also sort mutual funds by their past-12-month re-turns to form benchmark portfolios. Our results are un-changed when using these benchmark portfolios. We omitthese results for brevity.


other papers have found, the average mutualfund has a beta of around 0.91, reflecting thefact that mutual funds hold some cash or bondsin their portfolios. Notice that there is only aslight variation in the market beta (�i’s) fromthe smallest to the largest fund size portfolio:the smallest portfolio has a somewhat smallerbeta, but not by much.

Panel (C) reports the loadings for two addi-tional performance models, the Fama-Frenchthree-factor model and this three-factor modelaugmented by a momentum factor

(2) Ri,t � �i � �i,1 VWRFt � �i,2 SMBt

� �i,3 HMLt � �i,t t � 1, ... , T

(3) Ri,t � �i � �i,1 VWRFt � �i,2 SMBt

� �i,3 HMLt � �i,4 MOM12t � �i,t

t � 1, ... , T

where Ri,t is the (net fund) return on one of ourfive size-sorted mutual fund portfolios in montht in excess of the one-month T-bill return, �i isthe excess return, �i’s are loadings on the var-ious portfolios, and �i,t stands for a generic errorterm that is uncorrelated with all other indepen-dent variables. We see that small funds tend tohave higher loadings on SMB and HML, butlarge funds tend to load a bit more on momen-tum. For instance, the loading on SMB for the

TABLE 2—SUMMARY STATISTICS FOR PERFORMANCE BENCHMARKS

Panel A: Summary statistics of the factors

FactorMeanreturn

SD ofreturn

Cross-correlations

VWRF SMB HML MOM12

VWRF 0.58% 4.37% 1.00 0.32 �0.39 �0.02SMB 0.17% 2.90% 1.00 �0.16 �0.30HML 0.34% 2.63% 1.00 �0.15MOM12 0.96% 3.88% 1.00

Panel B: Loadings calculated using the CAPM

Portfolio

CAPM

Alpha VWRF

1 (small) 0.04% 0.872 �0.02% 0.913 �0.01% 0.934 �0.09% 0.925 (large) �0.07% 0.91

Panel C: Loadings calculated using the 3-Factor model and the 4-Factor model

Portfolio

3-Factor model

HML

4-Factor model

MOM12Alpha VWRF SMB Alpha VWRF SMB HML

1 (small) 0.01% 0.82 0.29 0.03 �0.02% 0.82 0.30 0.04 0.032 �0.03% 0.84 0.27 �0.01 �0.09% 0.85 0.30 0.00 0.053 0.01% 0.87 0.22 �0.05 �0.06% 0.87 0.25 �0.03 0.064 �0.06% 0.87 0.18 �0.05 �0.13% 0.87 0.20 �0.04 0.065 (large) �0.05% 0.88 0.08 �0.06 �0.10% 0.88 0.10 �0.05 0.05

Notes: This table reports the loadings of the five (equal-weighted) TNA-sorted fund portfolios on various factors. Panel (A)reports the summary statistics for the factors. VWRF is the return on the CRSP value-weighted stock index in excess of theone-month Treasury rate. SMB is the return on a portfolio of small stocks minus large stocks. HML is the return on a portfoliolong high book-to-market stocks and short low book-to-market stocks. MOM12 is the return on a portfolio long stocks thatare past-12-month winners and short those that are past-12-month losers. Panel (B) reports the loadings calculated using theCAPM. Panel (C) reports the loadings calculated using the Fama-French (1993) 3-Factor model and this model augmentedwith the momentum factor (4-Factor model). The sample period is from January 1963 to December 1999.


three-factor model for funds in quintile one is0.29 while the corresponding loading for fundsin quintile five is 0.08. And whereas large fundsload negatively on HML (�0.06 for the largestfunds), the smallest funds load positively onHML (0.03). (Eric G. Falkenstein [1996] alsofinds some evidence that larger funds tend toplay large and glamour stocks by looking atfund holdings.)

We have also redone all of our analysis bycalculating these loadings using gross fund re-turns instead of net fund returns. As the resultsare very similar to using net fund returns, forbrevity we will use the loadings summarized inTable 2 to adjust fund performance below(whether it be gross or net returns). Using theentire time series of a particular fund (we re-quire at least 36 months of data), we also cal-culate the loadings separately for each mutualfund using equations (1) to (3). This techniqueis not as good in the sense that we have a muchmore selective requirement on selection and theestimated loadings tend to be very noisy. In anycase, our results are unchanged, so we omitthese results for brevity.

B. Regression Specifications

To deal with the second concern related to thecorrelation of fund size with other fund charac-teristics, we analyze the effect of past fund sizeon performance in the regression frameworkproposed by Fama and James D. MacBeth(1973), where we can control for the effects ofother fund characteristics on performance. Spe-cifically, the regression specification that weutilize is

(4) FUNDRETi,t � � � �LOGTNAi,t � 1

� �Xi,t � 1 � �i,t i � 1, ... , N

where FUNDRETi,t is the return (either gross ornet) of fund i in month t adjusted by variousperformance benchmarks, � is a constant, LOG-TNAi,t�1 is the measure of fund size, and Xi,t�1is a set of control variables (in month t � 1) thatincludes LOGFAMSIZEi,t�1, TURNOVERi,t�1,AGEi,t�1, EXPRATIOi,t�1, TOTLOADi,t�1, andFLOWi,t�1. In addition, we include in the right-hand side LAGFUNDRETi,t�1, which is thepast-year return of the fund. Here, �i,t again

stands for a generic error term that is uncorre-lated with all other independent variables. Thecoefficient of interest is �, which captures therelationship between fund size and fund perfor-mance, controlling for other fund characteris-tics. � is the vector of loadings on the controlvariables. We then take the estimates from thesemonthly regressions and follow Fama and Mac-Beth (1973) in taking their time series meansand standard deviations to form our overall es-timates of the effects of fund characteristics onperformance.

We will also utilize an additional regressionspecification given by the following:

(5) FUNDRETi,t � � � �1 LOGTNAi,t � 1

� �2 Ind�Style� � �3LOGTNAi,t � 1 Ind�Style�

� �Xi,t � 1 � �i,t i � 1, ... , N

where the dummy indicator Ind{Style} (thatequals one if a fund belongs to a certain stylecategory and zero otherwise) and the remainingvariables are the same as in equation (3). Thecoefficient of interest is �3 , which measures thedifferential effect of fund size on returns acrossdifferent fund styles. It is important to note thatwe do not attempt to measure whether the rela-tionship between fund performance and fundsize may be nonlinear. While some theoriesmight suggest that very small funds may haveinferior performance to medium-sized ones be-cause they are being operated at a suboptimallysmall scale, we are unable to get at this issuebecause inference regarding the performance ofthe smallest funds is problematic for the reasonsarticulated in Section II.

III. Results

A. Relationship between Fund Size andPerformance

In Table 3, we report the estimation resultsfor the baseline regression specification given inequation (4). We begin by reporting the resultsfor gross fund returns. The sample consists offunds from fund size quintiles two through five.Notice that the coefficient in front of LOGTNAis negative and statistically significant acrossthe four performance measures. The coefficients


obtained using either market-adjusted orCAPM-adjusted returns are around �0.028with t-statistics of around three. Since one stan-dard deviation of LOGTNA is 1.38, a two-stan-dard deviation shock to fund size means thatperformance changes by �0.028 times 2.8, or 8basis points per month (96 basis points peryear). For the other two performance bench-marks, the 3-factor and 4-factor adjusted re-turns, the coefficients are slightly smaller at�0.02, but both are still statistically significantwith t-statistics of between 2.1 and 2.5. Forthese coefficients, a two-standard deviationshock to fund size means that performancechanges by around 70 basis points annually.

To put these magnitudes into some perspec-tive, observe that a standard deviation of mutualfund returns is around 10 percent annually, withslight variations around this figure dependingon the performance measure. Hence, a two-

standard deviation shock in fund size yields amovement in next year’s fund return that isapproximately 10 percent of the annual volatil-ity of mutual funds (96 basis points divided by10 percent). Another way to think about thesemagnitudes is that the typical fund has a grossfund performance net of the market return thatis basically near zero. As a result, a spread infund performance of anywhere from 70 to 96basis points a year is quite economicallysignificant.

Table 3 also reveals a number of other inter-esting findings. The only other variables besidefund size that are statistically significant areLOGFAMSIZE and LAGFUNDRET. Interest-ingly, LOGFAMSIZE predicts better fund per-formance. We will have much more to sayabout the coefficient in front of LOGFAMSIZElater. It should be emphasized at this point thatit is important to control for family size in order

TABLE 3—REGRESSION OF FUND PERFORMANCE ON LAGGED FUND SIZE

Gross fund returns Net fund returns

Market-Adj Beta-Adj 3-Factor 4-Factor Market-Adj Beta-Adj 3-Factor 4-Factor

INTERCEPT 0.056 0.087 0.080 0.019 0.026 0.056 0.049 �0.011(0.96) (1.63) (1.28) (0.30) (0.44) (1.05) (0.79) (0.18)

LOGTNAi,t�1 �0.028 �0.028 �0.023 �0.020 �0.025 �0.025 �0.020 �0.018(3.02) (3.04) (2.42) (2.15) (2.75) (2.76) (2.16) (1.89)

LOGFAMSIZEi,t�1 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007(2.26) (2.27) (2.22) (2.21) (2.33) (2.34) (2.29) (2.28)

TURNOVERi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(0.85) (0.86) (0.85) (0.83) (0.77) (0.78) (0.77) (0.75)

AGEi,t�1 �0.001 �0.001 �0.001 �0.001 0.000 0.000 �0.001 �0.001(0.62) (0.62) (0.64) (0.63) (0.52) (0.53) (0.55) (0.54)

EXPRATIOi,t�1 �0.004 �0.004 �0.007 �0.007 �0.039 �0.038 �0.041 �0.041(0.11) (0.09) (0.18) (0.18) (0.97) (0.95) (1.04) (1.05)

TOTLOADi,t�1 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003(1.26) (1.25) (1.26) (1.29) (1.21) (1.20) (1.21) (1.25)

FLOWi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(0.50) (0.50) (0.51) (0.49) (0.47) (0.47) (0.48) (0.46)

LAGFUNDRETi,t�1 0.029 0.028 0.028 0.029 0.029 0.029 0.029 0.029(6.00) (5.98) (6.00) (5.99) (6.03) (6.01) (6.03) (6.02)

No. of months 444 444 444 444 444 444 444 444

Notes: This table shows the Fama-MacBeth (1973) estimates of monthly fund returns regressed on fund characteristics laggedone month. The sample includes only funds that fall within fund size quintiles two to five. Fund returns are calculated before(gross) and after (net) deducting fees and expenses. These returns are adjusted using the market model, the CAPM, theFama-French (1993) 3-Factor model, and the 4-Factor model. The dependent variable is FUNDRET. LOGTNA is the naturallogarithm of TNA. LOGFAMSIZE is the natural logarithm of one plus the size of the family that the fund belongs to.TURNOVER is fund turnover and AGE is the number of years since the organization of the mutual fund. EXPRATIO is thetotal annual management fees and expenses divided by TNA. TOTLOAD is the total front-end, deferred, and rear-end chargesas a percentage of new investments. FLOW is the percentage new fund flow into the mutual fund over the past one year.LAGFUNDRET is the cumulative (buy-hold) fund return over the past 12 months. The sample is from January 1963 toDecember 1999. The t-statistics are adjusted for serial correlation using Newey-West (1987) lags of order three and are shownin parentheses.


to find a sizeable impact of fund size on perfor-mance. This is because fund and family size arepositively correlated, and since family size isgood for performance, it is important to controlfor it to identify the negative effect of fund size.This is a major reason why other studies thathave had fund size as a control variable inexplaining fund returns do not find any signifi-cant effect. As a result, these studies mentionfund size only in passing and do not provide anyanalysis whatsoever.

The fact that the coefficient in front ofLAGFUNDRET is significant suggests thatthere is some persistence in fund returns. As forthe rest of the variables, some come in withexpected signs, though none is statistically sig-nificant. The coefficient in front of EXPRATIOis negative, consistent with industry observa-tions that larger funds have lower expense ra-tios. The coefficients in front of TOTLOAD andTURNOVER are positive, as these two variablesare thought to be proxies for whether a fund isactive or passive. Fund flow has a negligibleability to predict fund returns, and the age of thefund comes in with a negative sign but is sta-tistically insignificant.

We next report the results of the baselineregression using net fund returns. The coeffi-cient in front of LOGTNA is still negative andstatistically significant across all performancebenchmarks. Indeed, the coefficient in front ofLOGTNA is only slightly smaller using net fundreturns than using gross fund returns. The ob-servations regarding the economic significanceof fund size made earlier continue to hold. Ifanything, they are even more relevant in thiscontext since the typical fund tends to under-perform the market by about 96 basis pointsannually. The coefficients in front of the othervariables have similar signs to those obtainedusing gross fund returns. Importantly, keep inmind that the coefficient in front of LOGFAM-SIZE is just as statistically and economicallysignificant using net fund returns as gross fundreturns.

In Table 4, we present various permutationsinvolving the regression specification in equa-tion (4) to see if the results in Table 3 are robust.In panel (A), we present the results using all thefunds in our sample, including those in thesmallest fund size quintile. As we mentionedearlier, the performance of the funds in the

bottom fund size quintile is biased upward, sowe should not draw too much from this analysisother than that our results are unchanged byincluding them in the sample. For brevity, wereport only the coefficients in front of LOGTNAand LOGFAMSIZE. Using gross fund returns,the coefficient in front of LOGTNA ranges from�0.019 to �0.026 depending on the perfor-mance measure. For net fund returns, it rangesfrom �0.015 to �0.022. All the coefficients arestatistically significant at the 5-percent level,with the exception of the coefficient obtainedusing 3-factor adjusted net fund returns. Thecoefficient in this instance is significant only atthe 10-percent level of significance. The mag-nitudes are somewhat smaller using the fullsample than the sample that excludes the small-est quintile. This difference, however, is notlarge. Moreover, the coefficients in front ofLOGFAMSIZE are similar in magnitude tothose obtained in Table 3. As such, we concludethat our key findings in Table 3 are robust toincluding all funds in the sample.

In panel (B), we attempt to predict a fund’scumulative return next year rather than its returnnext month. Not surprisingly, we find similarresults to those in Table 3. The coefficient infront of LOGTNA is negative and statisticallysignificant across all performance benchmarks.Indeed, the economic magnitudes implied bythese estimates are similar to those obtained inTable 3. These statements apply equally toLOGFAMSIZE.

In panels (C) and (D), we split our bench-mark sample in half to see whether our esti-mates on LOGTNA and LOGFAMSIZE dependon particular subperiods 1963 to 1980 and 1981to 1999. It appears that LOGTNA has a strongnegative effect on performance regardless of thesubperiods, since the economic magnitudes arevery similar to those obtained in Table 3. Wewould not be surprised if the coefficients werenot statistically significant since we havesmaller sample sizes in panels (C) and (D). Buteven with only half the sample size, LOGTNAcomes in significantly for a number of the per-formance measures. In contrast, it appears thatthe effect of LOGFAMSIZE on performance ismuch more pronounced in the latter half of thesample.

The analyses in Tables 3 and 4 strongly in-dicate that fund size is negatively related to


future fund performance. Moreover, we are ableto rule out that this relationship is driven bydifferences in fund styles or mechanical corre-lations of fund size with other observable fundcharacteristics. However, there still remain a

number of potential explanations for thisrelationship.

Three explanations come to mind. First, thelagged fund size and performance relationshipis due to transaction costs associated with li-

TABLE 4—REGRESSION OF FUND RETURNS ON LAGGED FUND SIZE, ROBUSTNESS CHECKS

Panel A: Sample includes all funds



LOGTNAi,t�1 �0.023 �0.026 �0.019 �0.021 �0.020 �0.022 �0.015 �0.017(2.59) (2.94) (2.22) (2.48) (2.16) (2.49) (1.76) (2.02)

LOGFAMSIZEi,t�1 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006(1.99) (2.03) (1.99) (2.05) (2.10) (2.15) (2.11) (2.17)

No. of months 444 444 444 444 444 444 444 444

Panel B: Dependent variable is 12-month (non-overlapping) fund returns



LOGTNAi,t�1 �0.436 �0.440 �0.345 �0.312 �0.402 �0.406 �0.312 �0.280(3.26) (3.27) (2.40) (2.19) (3.07) (3.07) (2.20) (1.99)

LOGFAMSIZEi,t�1 0.088 0.089 0.088 0.088 0.090 0.091 0.090 0.090(2.05) (2.06) (2.05) (2.07) (2.10) (2.12) (2.11) (2.12)

No. of years 37 37 37 37 37 37 37 37

Panel C: Sample period is from 1963 to 1980



LOGTNAi,t�1 �0.035 �0.034 �0.021 �0.019 �0.032 �0.032 �0.019 �0.016(2.41) (2.42) (1.51) (1.33) (2.22) (2.23) (1.32) (1.15)

LOGFAMSIZEi,t�1 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002(0.49) (0.50) (0.45) (0.44) (0.56) (0.58) (0.53) (0.52)

No. of months 216 216 216 216 216 216 216 216

Panel D: Sample period is from 1981 to 1999



LOGTNAi,t�1 �0.021 �0.022 �0.024 �0.022 �0.019 �0.019 �0.022 �0.019(1.84) (1.86) (1.93) (1.71) (1.64) (1.65) (1.74) (1.53)

LOGFAMSIZEi,t�1 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012(2.53) (2.53) (2.50) (2.50) (2.55) (2.55) (2.52) (2.52)

No. of months 228 228 228 228 228 228 228 228

Notes: This table presents robustness checks of the regression specification in Table 3. In panel (A), the sample includes allfunds. In panel (B), the dependent variable is 12-month fund returns and the regressions are non-overlapping. In panel (C),the sample consists only of observations from 1963 to 1980. In panel (D), the sample consists only of observations from 1981to 1999. LOGTNA is the natural logarithm of TNA. LOGFAMSIZE is the natural logarithm of one plus the size of the familythat the fund belongs to. Estimates of the intercept and other independent variables are omitted for brevity. The otherindependent variables include TURNOVER, AGE, EXPRATIO, TOTLOAD, FLOW, and LAGFUNDRET. The t-statistics areadjusted for serial correlation using Newey-West (1987) lags of order three and are shown in parentheses.


quidity or price impact. We call this the “liquid-ity hypothesis.” Second, perhaps investors inlarge funds are less discriminating about returnsthan investors in small funds. One reason whythis might be the case is that such large funds asMagellan are better at marketing and are ableto attract investors through advertising. Incontrast, small funds without such marketingoperations may need to rely more on perfor-mance to attract and maintain investors. Wecall this the “clientele hypothesis.” Third,fund size is inversely related to performancebecause of fund incentives to lock in assetsunder management after a long string of goodpast performances.9 When a fund is small andhas a limited reputation, the manager goesabout the business of stock picking. But as thefund gets large because of good past perfor-mance, the manager may for various reasonslock in his fund size by being passive (orbeing a “closet indexer” as practitioners putit). We call this the “agency-risk-taking hy-pothesis.” The general theme behind the sec-ond and third hypotheses is that after fundsreach a certain size, they no longer care aboutmaximizing returns.

B. The Role of Liquidity: Fund Size, FundStyles, and the Number of Stocks in a Fund’s

Portfolio

In order to narrow the list of potential expla-nations, we design a test of the liquidity hypoth-esis. To the extent that liquidity is driving ourfindings above, we would expect to see thatfund size matters much more for performanceamong funds that have to invest in small stocks(i.e., stocks with small market capitalization)than funds that get to invest in large stocks. The

reason is that small stocks are notoriously illiq-uid. As a result, funds that have to invest insmall stocks are more likely to need new stockideas with asset base growth, whereas largefunds can simply increase their existing posi-tions without being hurt too much by priceimpact.

Importantly, this test of the liquidity hypoth-esis also allows us to discriminate between theother two hypotheses. First, existing researchfinds that there is little variation in incentivesbetween “small cap” funds (i.e., funds that haveto invest in small stocks) and other funds (see,e.g., Andres Almazan et al., 2001). Hence, thisprediction ought to help us discriminate be-tween our hypothesis and the alternative“agency-risk-taking” story involving fund in-centives. Moreover, since funds that have toinvest in small stocks tend to do better thanother funds, it is not likely that our results aredue to the clientele of these funds being moreirrational than those investing in other funds.This allows us to distinguish the liquidity storyfrom the clientele story.

In the CRSP Mutual Fund Database, we arefortunate that each fund self-reports its style,and so we looked for style descriptions contain-ing the words “small cap.” It turns out that onestyle, “Small Cap Growth,” fits this criterion.Funds in this category are likely to have toinvest in small stocks by virtue of their styledesignation. Thus, we identify a fund in oursample as either Small Cap Growth if it has everreported itself as such or “Not Small CapGrowth.” (Funds rarely change their self-reported style.) Unfortunately, funds with thisdesignation are not prevalent until the early1980s. Therefore, throughout the analysis inthis section, we limit our sample to the 1981–1999 period. During this period, there are onaverage 165 such funds each year. The corre-sponding number for the overall sample duringthis period is about 1,000. Therefore, Small CapGrowth represents a small but healthy slice ofthe overall population. Also, the average TNA ofthese funds is $212.9 million with a standarddeviation of $566.7 million. The average TNAof a fund in the overall sample during this latterperiod is $431.5 million with a standard devia-tion of $1.58 billion. Thus, Small Cap Growthfunds are smaller than the typical fund. But theyare still quite big and there is a healthy fund size

9 More generally, it may be that after many years of goodperformance, bad performance follows for whatever reason.We are offering here a plausible economic mechanism forwhy this might come about. The ex ante plausibility of thisalternative story is, however, somewhat mixed. On the onehand, the burgeoning empirical literature on career concernssuggests that fund managers ought to be bolder with pastsuccess (see, e.g., Judith A. Chevalier and Glenn D. Ellison,1999 and Hong, Kubik, and Solomon, 2000). On the otherhand, the fee structure means that funds may want to lock inassets under management because investors are typicallyslow to pull their money out of funds (Brown et al., 1996;Chevalier and Ellison, 1997).


distribution among them, so that we can mea-sure the effect of fund size on performance.

Table 5, panel (A), reports what happens tothe results in Table 3 when we augment theregression specifications by including adummy indicator Ind{not SCG} (which equalsone if a fund is not Small Cap Growth andzero otherwise) and an additional interactionterm involving LOGTNA and Ind{not SCG} asin equation (5). We first report the results forgross fund returns. The coefficient in front ofLOGTNA is about �0.06 (across the fourperformance benchmarks). Importantly, thecoefficient in front of the interaction term ispositive and statistically significant (about0.04 across the four performance bench-marks). This is the sign predicted by the li-quidity hypothesis since it says that for NotSmall Cap Growth funds, there is a smallereffect of fund size on performance. The effectis economically interesting as well. Since thetwo coefficients, �0.06 and 0.04, are similarin magnitude, a sizeable fraction of the effectof fund size on performance comes fromsmall cap funds. The results using net fundreturns reported are similar.

In panel (A) of Table 5, we compared SmallCap Growth funds to all other funds. In panel(B) of Table 5, we delve a bit deeper into the“liquidity hypothesis” by looking at how fundperformance varies with fund size dependingon whether a fund is a Large Cap fund, onethat is supposed to invest in large-cap stocks.10

We augment the regression specification ofTable 3 by introducing a dummy variableInd{LC} (which equals one if a fund is a Large

Cap fund and zero otherwise) and an addi-tional interaction term involving LOGTNA andInd{LC} as in equation (5). We first report theresults for gross fund returns. The coefficient infront of LOGTNA is about �0.03 (across thefour performance benchmarks). Importantly,the coefficient in front of the interaction term ispositive and statistically significant (about 0.03across the four performance benchmarks). Thisis the sign predicted by the liquidity hypothesissince it says that for Large Cap funds, there isno effect of fund size on performance. Theresults using net fund returns reported aresimilar.

These findings suggest that liquidity plays animportant role in eroding performance. More-over, as many practitioners have pointed out,since managers of funds get compensated onassets under management, they are not likelyvoluntarily to keep their funds small just be-cause it hurts the returns of their investors, whomay not be aware of the downside of scale (seeBecker and Vaughn, 2001, and Section IV forfurther discussion).11

As we pointed out in the beginning of thepaper, however, liquidity means that largefunds need to find more stock ideas than smallfunds, but it does not therefore follow thatthey cannot. Indeed, large funds can hiremore managers to follow more stocks. To seethat this is possible, we calculate some basicsummary statistics on fund holdings by fundsize quintiles. Since the CRSP Mutual FundDatabase does not have this information, weturn to the CDA Spectrum Database. We takedata from the end of September 1997 andcalculate the number of stocks held by eachfund. The median fund in the smallest fundsize quintile has about 16 stocks in its port-folio, while the median fund in the largestfund size quintile has about 66 stocks in itsportfolio, even though the large funds aremany times bigger than their smaller counter-parts. These numbers make clear that largefunds do not significantly scale up the numberof stocks that they hold or cover relative totheir smaller counterparts. Yet, there is plenty

10 We merged the CRSP Mutual Fund Database and theCDA Spectrum Database so that we have information on thestocks held by the funds in our sample. We have verifiedthat mutual funds with the self-reported fund style of SmallCap Growth do indeed invest in stocks with a much lowermarket capitalization than held by funds of other styles. Incontrast, mutual funds whose self-reported fund style is“Growth and Income” invest in much larger stocks whencompared to funds with other styles. As a result, we char-acterize these funds as Large Cap funds in our analysisabove. Moreover, we have also verified that Small Capfunds hold stocks that are more illiquid in the sense that thestocks in their portfolios tend to have much larger Kylelambdas and bid-ask spreads than the stocks held byfunds of other styles. And Large Cap funds hold stocksthat are much more liquid than those held by funds ofother styles.

11 Related literature finds that mutual fund investors aresusceptible to marketing (see, e.g., Gruber, 1996; Eric R.Sirri and Peter Tufano, 1998; Lu Zheng, 1999).


TABLE 5—EFFECT OF FUND SIZE ON PERFORMANCE BY FUND STYLE

Panel A: Small Cap Growth funds



INTERCEPT 0.205 0.257 0.320 0.263 0.175 0.226 0.289 0.231(1.27) (1.53) (2.44) (1.98) (1.08) (1.34) (2.20) (1.75)

IND{not SCG} �0.250 �0.252 �0.260 �0.262 �0.249 �0.251 �0.259 �0.261(1.54) (1.55) (1.62) (1.63) (1.53) (1.54) (1.61) (1.62)

LOGTNAi,t�1 �0.058 �0.058 �0.063 �0.061 �0.055 �0.056 �0.060 �0.058(3.15) (3.17) (3.40) (3.29) (3.03) (3.05) (3.28) (3.17)

LOGTNAi,t�1 � 0.040 0.041 0.043 0.044 0.040 0.041 0.043 0.044IND{not SCG} (2.36) (2.39) (2.53) (2.56) (2.35) (2.37) (2.52) (2.55)

LOGFAMSIZEi,t�1 0.012 0.012 0.011 0.011 0.012 0.012 0.012 0.012(2.57) (2.57) (2.54) (2.54) (2.59) (2.59) (2.57) (2.56)

TURNOVERi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(1.62) (1.61) (1.62) (1.61) (1.57) (1.56) (1.57) (1.57)

AGEi,t�1 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001(0.98) (0.97) (0.98) (0.97) (0.87) (0.87) (0.88) (0.87)

EXPRATIOi,t�1 �0.018 �0.018 �0.019 �0.019 �0.062 �0.062 �0.064 �0.063(0.44) (0.43) (0.46) (0.44) (1.49) (1.48) (1.52) (1.51)

TOTLOADi,t�1 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001(0.54) (0.54) (0.52) (0.52) (0.48) (0.47) (0.46) (0.46)

FLOWi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(0.96) (0.95) (0.90) (0.92) (1.02) (1.02) (0.96) (0.99)

FUNDRETi,t�1 0.022 0.022 0.021 0.021 0.022 0.022 0.022 0.022(3.45) (3.45) (3.43) (3.42) (3.48) (3.48) (3.45) (3.45)

No. of months 228 228 228 228 228 228 228 228

Panel B: Large Cap funds



INTERCEPT 0.016 0.068 0.128 0.068 �0.017 0.035 0.095 0.034(0.19) (0.84) (1.72) (0.92) (0.20) (0.43) (1.27) (0.47)

IND{LC} �0.119 �0.121 �0.119 �0.120 �0.112 �0.114 �0.112 �0.113(1.31) (1.33) (1.33) (1.34) (1.23) (1.25) (1.24) (1.26)

LOGTNAi,t�1 �0.029 �0.029 �0.032 �0.029 �0.027 �0.027 �0.029 �0.027(2.22) (2.24) (2.45) (2.25) (2.02) (2.05) (2.26) (2.05)

LOGTNAi,t�1 � 0.031 0.031 0.031 0.031 0.030 0.030 0.030 0.030IND{LC} (2.29) (2.32) (2.34) (2.36) (2.23) (2.26) (2.28) (2.30)

LOGFAMSIZEi,t�1 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011(2.54) (2.54) (2.51) (2.51) (2.58) (2.58) (2.55) (2.55)

TURNOVERi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(1.80) (1.79) (1.80) (1.79) (1.75) (1.75) (1.76) (1.75)

AGEi,t�1 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001(1.26) (1.25) (1.26) (1.25) (1.17) (1.17) (1.17) (1.16)

EXPRATIOi,t�1 �0.020 �0.019 �0.021 �0.020 �0.065 �0.065 �0.066 �0.065(0.49) (0.48) (0.52) (0.50) (1.63) (1.62) (1.66) (1.64)

TOTLOADi,t�1 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001(0.35) (0.35) (0.34) (0.35) (0.30) (0.30) (0.29) (0.29)

FLOWi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(0.84) (0.82) (0.77) (0.79) (0.89) (0.88) (0.83) (0.85)

FUNDRETi,t�1 0.020 0.020 0.020 0.020 0.021 0.020 0.020 0.020(3.10) (3.09) (3.07) (3.07) (3.13) (3.12) (3.10) (3.09)

No. of months 228 228 228 228 228 228 228 228

Notes: This table reports the Fama-MacBeth (1973) estimates of monthly fund returns regressed on fund characteristics lagged one month. Thesample includes only funds that fall within fund size quintiles two to five. Fund returns are calculated before (gross) and after (net) deductingfees and expenses. These fund returns are adjusted using the market model, the CAPM, the Fama-French (1993) 3-Factor model, and the4-Factor model. In panel (A), the regression specification is the one in Table 3 but augmented with IND{not SCG}, which is a dummy variablethat equals one if the self-reported fund style is not Small Cap Growth and zero otherwise and this indicator variable interacted with LOGTNA.In panel (B), instead of IND{not SCG}, we augment the regression with IND{LC}, which is a dummy variable that equals one if the fund styleis identified as a Large Cap fund. The sample is from January 1981 to December 1999. The t-statistics are adjusted for serial correlation usingNewey-West (1987) lags of order three and are shown in parentheses.


of scope for them to do so given the thousandsof stocks available.

C. The Role of Organization: The Effect ofFamily Size on Performance

To see that assets under management neednot be bad for the performance of a fund orga-nization, recall from Table 3 that controlling forfund size, assets under management of the otherfunds in the family that the fund belongs toactually increase the fund’s performance. Thecoefficient in front of LOGFAMSIZE is roughly0.007 regardless of the performance benchmarkused. One standard deviation of this variable is2.75, so a two-standard deviation shock in thesize of the family that the fund belongs to leads

to about a 3.85-basis-point movement in thefund’s performance the following month (orabout a 46-basis-point movement annually) de-pending on the performance measure used. Theeffect is smaller than that of fund size on returnsbut is nonetheless statistically and economicallysignificant. In other words, assets under man-agement are not bad for a fund organization’sperformance per se.

In Table 6, we extend our analysis of theeffect of family size on fund returns by seeingwhether this effect varies across fund styles.Our hope is that family size is just as importantfor Small Cap Growth funds as for other funds.After all, it is these funds that are most affectedby scale. For us to claim that scale is not bad perse, even accounting for liquidity, we would like

TABLE 6—EFFECT OF FAMILY SIZE ON PERFORMANCE BY FUND STYLE

Panel A: Fund size quintiles two to five



LOGTNAi,t�1 �0.051 �0.052 �0.056 �0.054 �0.048 �0.049 �0.053 �0.051(2.83) (2.86) (3.13) (3.02) (2.70) (2.72) (3.01) (2.89)


LOGFAMSIZEi,t�1 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007(0.70) (0.70) (0.69) (0.70) (0.70) (0.70) (0.69) (0.70)

LOGFAMSIZEi,t�1 � 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004IND{not SCG} (0.41) (0.41) (0.41) (0.40) (0.42) (0.42) (0.42) (0.41)

No. of months 228 228 228 228 228 228 228 228

Panel B: All fund size quintiles one to five



LOGTNAi,t�1 �0.067 �0.070 �0.071 �0.072 �0.064 �0.067 �0.068 �0.069(3.67) (3.83) (3.87) (3.91) (3.51) (3.67) (3.71) (3.76)


LOGFAMSIZEi,t�1 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012(1.13) (1.13) (1.11) (1.11) (1.15) (1.15) (1.13) (1.13)

LOGFAMSIZEi,t�1 � �0.003 �0.002 �0.002 �0.002 �0.002 �0.002 �0.002 �0.002IND{not SCG} (0.23) (0.21) (0.23) (0.21) (0.23) (0.21) (0.22) (0.21)

No. of months 228 228 228 228 228 228 228 228

Notes: This table reports the Fama-MacBeth (1973) estimates of monthly fund returns regressed on fund characteristics laggedone month. Fund returns are calculated before (gross) and after (net) deducting fees and expenses. These returns are adjustedusing the market model, the CAPM, the Fama-French (1993) 3-Factor model, and the 4-Factor model. The regressionspecification is the one in Table 5 but augmented with Ind{not SCG}, which is a dummy variable which equals one if theself-reported fund style is Not Small Cap Growth and zero otherwise, interacted with LOGFAMSIZE. Estimates of theintercept and the other independent variables are omitted for brevity. The other independent variables include TURNOVER,AGE, EXPRATIO, TOTLOAD, FLOW, and LAGFUNDRET. The sample is from January 1981 to December 1999. Thet-statistics are adjusted for serial correlations using Newey-West (1987) and are shown in parentheses.


to find that the benefits of family size are de-rived even by funds that are most affected byliquidity. To see whether this is the case, weaugment the regression specification in Table5 by adding an interaction term involving LOG-FAMSIZE and the dummy indicator Ind{not SCG}(which equals one if a fund is not Small CapGrowth and zero otherwise). The variable ofinterest is the coefficient in front of this inter-action term.

Panel (A) presents the regression results forfunds in fund size quintiles two through five.The coefficient in front of this interaction termis positive but is not statistically significant. Inother words, it does not appear that there aremajor differences between the effect of familysize on performance among Small Cap Growthfunds and other funds. This finding is consistentwith our story. To make sure that this findingholds more generally, we reestimate this regres-sion specification using all funds. The coeffi-cient in front of the interaction term is negative,but is again not statistically significant. Usingeither sample, it does not appear that the effectof family size is limited to Not Small CapGrowth funds. So we can be assured that fundsdo benefit from being part of a large family.

This finding—fund performance declineswith own fund size but increases with the size ofthe other funds in the family—fits nicely withanecdotal evidence from industry practitioners(see, e.g., Pozen, 1998; Albert J. Fredman andRuss Wiles, 1998). One plausible interpretationof the family size finding is that it is capturingeconomies of scale associated with marketing.This begs the question, however, of why afund’s expense ratio is not capturing this scaleeffect since a fund’s expense ratio is on theright-hand side of the cross-sectional fund re-turn regressions.

It turns out that the expense ratio reported bymutual funds accounts for only management,administrative, and marketing fees.12 Tradingcommissions charged by brokers and lendingfees for (shorting) stocks are not treated as partof expenses but are simply deducted or added toincome and hence net returns. It is well knownthat there are tremendous economies of scale

associated with trading commissions and lend-ing fees at the family level. Bigger families likeFidelity are able to get better concessions ontrading commissions and earn higher lendingfees for the stocks held by their funds.

It is difficult to figure out these trading com-missions because families bury them in theirfund prospectuses. Nonetheless, practitionersbelieve these costs are substantial and can be asmuch as a few percentage points a year. Hence,the spread in performance between funds be-longing to large versus those in small familiescan easily be accounted for by the economies ofscale in trading commissions and lending fees atthe family level.13

Importantly, according to industry anecdotes,in most families, major decisions are decentral-ized in that the fund managers make stock pickswithout substantial coordination with othermanagers. Managers in charge of differentfunds choose stocks as they see fit without wor-rying about resources being taken away fromthem by the family. So a family is an organiza-tion that credibly commits to letting each of itsfund managers run his own assets.

As such, the family size finding makes clearthat liquidity and scale need not be bad for fundperformance, depending on how the fund isorganized. After all, if a large fund is organizedlike a fund family with a number of small fundsrun by different managers, then scale need notbe bad per se, just as family size does not appearto be bad for family performance.

More importantly, the finding regarding fam-ily size also helps us further to rule out alterna-tive hypotheses related to managers of largefunds not caring about maximizing net returns.This line of argument does not likely supportour findings since managers in large familiesapparently do care enough about net fund re-turns to make sufficient investments to maintainperformance at the family level. Moreover,most of the extant evidence on fund flows indi-cates that managers care about net fund returns,

12 See John Hechinger (2004) for a detailed discussion ofmutual fund expense accounting.

13 Zoran IvKovic (2002) argues that being part of a largefamily may improve performance because of other spill-overs. In his analysis of family size, he happened to controlfor fund size and found that fund size indeed erodes per-formance. Though the goal of his paper was not to examinefund size, it is comforting to know that some of our baselineresults have been independently verified.


since better past returns lead to larger assetsunder management (see, e.g., Sirri and Tufano,1998; Anthony W. Lynch and David Musto,2003).

D. Organizational Diseconomies and FundPerformance

If managers care about net fund returns andscale need not be bad for performance per se,why then does it appear that scale erodes fundperformance because of liquidity? We hypoth-esize that in addition to liquidity, fund sizeerodes performance because of organizationaldiseconomies. To make things concrete, imag-ine that there is a small fund company X withone fund operated by one manager who picksthe stocks. Since the fund is small, the managercan easily invest the assets under managementby generating a few stock ideas. Now imaginethat there is a large fund Y in which the managerno longer has the capacity to invest all themoney. So the manager needs co-managers tohelp him run the fund. For fund Y, the stockpicks need to be coordinated among many moreagents and therefore organizational form (e.g.,flat versus hierarchical) becomes important. Soorganizational diseconomies may arise.

There are many types of organizational dis-economies that lead to different predictions onwhy small organizations outperform large ones.One set of diseconomies, from the work ofOliver E. Williamson (1975, 1988), includesbureaucracy and related coordination costs.Another set of diseconomies comes from theinfluence-cost literature (see, e.g., Paul R. Mil-grom and Roberts, 1988). Yet another set ofdiseconomies centers on the adverse effects ofhierarchies (or authority) for the incentives ofagents who do not have any control over asset-allocation decisions (see, e.g., Aghion and Ti-role, 1997; Stein, 2002).

Interestingly, the findings in Tables 3 and 6allow us to discriminate among different typesof organizational diseconomies. For instance, ifWilliamsonian diseconomies are behind the re-lationship between size and performance, thenone expects that funds that belong to large fam-ilies do worse, since bureaucracy ought to bemore important in huge fund complexes. Thefact that we do not find this indicates that bu-reaucracy is not likely an important reason

behind why performance declines with fundsize.

We conjecture that hierarchy costs may beespecially relevant for mutual funds. We take acloser look at the effect of organizational dis-economies due to hierarchy costs on fund per-formance by testing some predictions fromStein (2002). Stein argues that in the presenceof such hierarchy costs, small organizationsought to outperform large ones at tasks thatinvolve the processing of soft information, (i.e.,information that cannot be directly verified byanyone other than the agent who produces it).The basic premise is that in larger organizationswith hierarchies, the process of agents fightingto have their ideas implemented will affect out-comes. If the information is soft, then agentshave a hard time convincing others of theirideas and it is more difficult to pass this infor-mation up the organization.14

In the context of mutual funds, this meansthat in funds in which managers fight to havetheir ideas implemented, efforts to uncover cer-tain investment ideas in this setting are dimin-ished relative to a situation in which themanagers control their own smaller funds. Forinstance, managers may end up expending toomuch research effort on quantitative measuresof a company (i.e., hard information) so as toconvince others to implement their ideas eventhough more time and effort might ideally beinvested in talking to CEOs of companies andgetting an impression of them (i.e., soft infor-mation). All else equal, large funds may per-form worse than small ones.

Note that one of our previous findings—thatfund performance does not decline with familysize—is consistent with Stein (2002) since hi-

14 Stein’s analysis also indicates that large organizationsmay actually be very efficient at processing hard informa-tion. In the context of the mutual fund industry, one canthink of passive funds that mimic indices as primarilyprocessing hard information. As such, one would expectthese types of funds not to be very much affected by scale.Two pieces of evidence are consistent with this prediction.First, Vanguard seems to dominate the business for indexfunds. Indeed, for indexers, being large is an asset since onecan then acquire better tracking technologies and bettercomputer programmers. Second, our evidence in Table5 that fund size affects only small cap funds is consistentwith this observation since most index funds, which tend tomimic the S&P 500 index, trade predominantly large stocks.


erarchy costs are not present at the family levelprecisely because the family typically agreesnot to reallocate resources across funds. Differ-ent funds in a family have their own boards thatdeal with such issues as managerial replace-ment. So the manager in charge of a fund gen-erally does not have to worry about the familytaking away his resources and giving those re-sources to some other fund in the family.

Below, we test several predictions of Stein(2002) to see if the inverse relationship betweenfund size and performance is indeed influencedby organizational diseconomies.

Fund Size and Fund Investments.—One pre-diction of Stein (2002) is that we expect thatsmall funds are better than large ones, which aremore likely to have hierarchies, at the process-ing of soft information. To test this prediction,we compare the investments of large and smallfunds in local stocks (companies located nearwhere a fund is headquartered). Investing insuch companies requires that the organizationprocess soft information as opposed to a strictlyquantitative investing approach, which wouldtypically process hard information like price-to-earnings ratios.

Our work in testing this prediction builds onthe very interesting work of Coval and Mos-kowitz (1999, 2001) whose central thesis is thatmutual fund managers do have ability when itcomes to local stocks. Anecdotal evidence indi-cates that this ability comes in the form ofprocessing soft information, e.g., talking to andevaluating CEOs of local companies. They findthat funds can earn superior returns on theirlocal investments. We focus on the effect of sizeon investment among small cap funds. We arealso interested in the effect of family size on thecomposition of fund investments.

The CDA Spectrum Database does not havethe same information on fund characteristics asthe CRSP Mutual Fund Database. Luckily, weare able to construct from the fund stock hold-ings data a proxy for fund size, which is simplythe value of the fund’s stock portfolio at the endof a quarter. While this is not exactly the sameas asset base since funds hold cash and bonds,we believe that the proxy is a reasonable onebecause we can look at the tails of the sizedistribution to draw inferences, i.e., comparevery small funds to other funds. Moreover, the

noise in our fund size measure does not obvi-ously bias our estimates.

In addition, we can construct better style con-trols for each fund by looking at their stockholdings. We use a style measure constructedby Daniel et al. (1997), which we call theDGTW style adjustment. For each month, eachstock in our sample is characterized by its lo-cation in the (across-stocks) size quintiles,book-to-market quintiles, and price-momentumquintiles. So a stock in the bottom of the size,book-to-market, and momentum quintiles in aparticular month would be characterized by atriplet (1, 1, 1). For each fund, we can charac-terize its style along the size, book-to-market,and momentum dimension by taking the aver-age of the DGTW characteristics of the stocksin their portfolio weighted by the percentage ofthe value of their portfolio that they devote toeach stock. We can then define a small cap fundas one whose DGTW size measure falls in thebottom 10 percent when compared to otherfunds.

Table 7 reports the effect of fund size on thepercentage of the value of a fund’s portfolio

TABLE 7—EFFECT OF FUND AND FAMILY SIZE ON

INVESTMENTS IN LOCAL STOCKS

(1) (2) (3)

SMALLFUND 0.0759 0.0580 0.0584(9.49) (6.74) (6.79)

SMALLCAPFUND 0.0515 0.0340 0.0341(6.28) (3.86) (3.88)

SMALLFUND � 0.1244 0.1229SMALLCAPFUND (5.58) (5.80)

FAMILYSIZE 0.0001(1.01)

Momentum effects Yes Yes YesBook-to-market effects Yes Yes YesFund city effects Yes Yes Yes

Notes: This table shows the effect of fund and family sizeon mutual fund investments in local stocks. The dependentvariable is the percentage of the value of a fund’s portfolioinvested in local stocks. SMALLFUND equals one if afund’s size is in the bottom 10 percent of the fund sizedistribution and zero otherwise. SMALLCAPFUND equalsone if a fund’s Daniel, Grinblatt, Titman, and Wermers(1997) small cap style score is in the bottom 10 percentacross funds. FAMILYSIZE is the number of funds in thefamily that the fund belongs to. Momentum effects andbook-to-market effects control for other differences in fundstyles. Data are from the end of September 1997. Robustt-statistics are reported in parentheses.


devoted to local stocks, where locality is de-fined relative to the headquarters of the funds,as in Coval and Moskowitz (1999, 2001). Spe-cifically, a stock is considered a local fundinvestment if the headquarters of the companyis in the same census region as the mutualfund’s headquarters.15 The dependent variableis the total value of the local stocks in the fundportfolio divided by the total value of the stocksin the fund portfolio. The first independent vari-able is SMALLFUND, which is a dummy vari-able that equals one if the fund is in the bottom10 percent of the fund size distribution. SMALL-CAPFUND equals one if a fund has an averageDGTW size score for its stocks that is in thebottom 10 percent across funds. The regressionsalways have Fund City Effects (i.e., the citywhere the fund is headquartered) as controls.

From column (1), small funds are more likelyto invest a larger percentage of their portfolio inlocal stocks than do small cap funds. The aver-age percentage of stocks that are local in afunds’ portfolio is 16.57 percent. The coeffi-cient on SMALLFUND is 0.0759. That meansthat being a small fund increases the amount oflocal stocks a fund owns by 0.0759 divided by0.1657, or 46 percent. In column (2), the coef-ficient of interest is the interaction term involv-ing small and small cap funds. It is positive andstatistically significant, which is consistent withour conjecture. The coefficient of 0.1244 meansthat being a small fund and a small cap fundraises the percentage of local stock investmentson average by 0.1244 divided by 0.1657, or 75percent.

One interesting question is the degree towhich family size affects a fund’s investmentpolicy. In column (3), we add a family-sizeproxy as measured by the number of funds inthe family. The coefficient in front of this vari-able is positive but the economic magnitude issmall and it attracts a t-statistic of only one. Inother words, family size does not have an effect

on whether a fund invests in local stocks. Ifwe define family size as the log of the assets ofthe other funds in the fund’s family, then weget similarly small economic magnitudes andt-statistics near zero. For brevity, we omit theseadditional results. Again, one can interpret thisresult as consistent with Stein’s model to theextent that fund size has an effect on fund actionbut not family size.

These findings, however, may also be consis-tent with larger funds that have more resourcesto visit companies not located nearby. To dis-tinguish between this explanation and our pre-ferred explanation that small funds and fundsbelonging to larger families are better at theprocessing of soft information, we see in Table8 whether the local stocks picked by small fundsdo better than those picked by large funds. Thedependent variable is the return to the fund’sinvestments in local stocks. In column (1), wefind that small funds and small cap funds notonly invest more of their assets in local stocksbut also do better at investing in them. Thestandard deviation of local stock investmentreturns in our sample is 0.2077. Being a small

15 There are nine census regions: New England (CT,MA, ME, NH, RI, VT); Middle Atlantic (NJ, NY, PA); EastNorth Central (IL, IN, MI, OH, WI); West North Central(IA, KS, MN, MO, NE, ND, SD); South Atlantic (DE, FL,GA, MD, NC, SC, VA, WV); East South Central (AL, KY,MS, TN); West South Central (AR, LA, OK, TX); Moun-tain (AZ, CO, ID, MT, NE, NM, UT, WY); and Pacific(AK, CA, HI, OR, WA).

TABLE 8—EFFECT OF FUND AND FAMILY SIZE ON THE

PERFORMANCE OF INVESTMENTS IN LOCAL STOCKS

(1) (2) (3)

SMALLFUND 0.0258 0.0143 0.0150(1.83) (0.94) (0.99)

SMALLCAPFUND 0.1190 0.1078 0.1079(8.21) (6.95) (6.96)

SMALLFUND � 0.0795 0.0776SMALLCAPFUND (2.02) (1.96)

FAMILYSIZE 0.0002(1.02)

Momentum effects Yes Yes YesBook to market effects Yes Yes YesFund city effects Yes Yes Yes

Notes: This table shows the effect of fund and family sizeon the performance of mutual fund investments in localstocks. The dependent variable is the 4-quarter return tofund investments in local stocks. SMALLFUND equals oneif a fund’s size is in the bottom 10 percent of the fund sizedistribution and zero otherwise. SMALLCAPFUND equalsone if a fund’s Daniel, Grinblatt, Titman, and Wermers(1997) small cap style score is in the bottom 10 percentacross funds. FAMILYSIZE is the number of funds in thefamily that the fund belongs to. Momentum effects andbook-to-market effects control for other differences in fundstyles. Data are from the end of September 1997. Robustt-statistics are reported in parentheses.


fund raises local return by 0.0258 divided by0.2077, or 12 percent of a standard deviation. Incolumn (2), we add an interaction term involv-ing these two variables and find that the perfor-mance difference between small funds and otherfunds is especially big in small cap funds. Incolumn (3), we add a family size proxy asmeasured by the number of funds in the family.The coefficient is positive but is statisticallyinsignificant. So a fund that belongs to a largefamily does not do better in local stock invest-ments. Similar results obtain using total netassets for the family.

The findings in Table 8 suggest that the per-formance differences between small and largefunds have something to do with their differingability to invest in local stocks. Note that Tables7 and 8 report the results using only the cross-section in September 1997. We have replicatedour analysis using the other quarters in the pe-riod of 1997 to 1998. The results are very robustacross quarters.

Management Structure and the Compositionof Fund Investments.—Perhaps a more directimplication of Stein’s model is to look at therelationship between the management structureof a fund and the fund’s investments. When afund is co-managed and there is more fighting toimplement ideas, then we should find that,controlling for fund size, funds run by commit-tee should invest less in local stocks (i.e., man-agers should pitch hard-information stockideas) and do worse in these soft-informationstocks.

To test this prediction, we obtain data on thenumber of managers running a fund for thecross-section of funds described in Tables 7 and8. In the CRSP database, there is a field that liststhe names of managers in charge of pickingstocks. We create a new variable MT, whichcaptures how many managers are running thefund. If there is only one name, then MT equalsone. If there are two names, then MT equalstwo, and so forth. The maximum number ofnames in the database is four. Moreover, a smallfraction of the funds are listed as “Team Man-aged.” For these funds, we set MT equal to fouras well. Thus, MT can take on values of one,two, three, or four. From reading the prospec-tuses of various funds, it appears that funds thathave co-managers do make decisions within a

committee. We lose a fraction of the cross-section of funds in Tables 7 and 8 because somefunds do not have entries in the manager field.

In Table 9, we consider the effect of mana-gerial structure on the composition of fund in-vestments in local stocks and the performanceof these local investments. The first column ofTable 9 is similar to those in Table 7 exceptthat we have added a dummy variable MULTI-MANAGER, which equals one when MT isgreater than one and is zero otherwise, asan additional explanatory variable. Moreover,since we are concerned with isolating the effectof managerial structure, we have introducedinto this regression very conservative fund size,family size, and style controls. Even with theseconservative controls, the coefficient in front ofMULTI-MANAGER is �0.012 with a t-statisticof 1.75. In other words, a solo-managed fund issignificantly more likely to invest in localstocks than team-managed funds. The economicmagnitude is smaller than that of being a smallfund but is still economically interesting. In thissample, the average percentage of stocks thatare local in a fund’s portfolio is 15.2 percent.Since the coefficient on MULTI-MANAGER is

TABLE 9—EFFECT OF MANAGEMENT STRUCTURE ON THE

AMOUNT AND PERFORMANCE OF INVESTMENTS IN LOCAL

STOCKS

Holdings Returns(1) (2)

MULTI-MANAGER �0.0102 �0.0190(�1.75) (�1.73)

Fundsize effects Yes YesFamily size effects Yes YesMomentum effects Yes YesBook-to-market effects Yes YesStyle effects Yes YesFund city effects Yes Yes

Notes: This table shows the effect of fund managementstructure on the holdings and performance of mutual fundinvestments in local stocks. The dependent variable in col-umn (1) is the percentage of the value of a fund’s portfolioinvested in local stocks. The dependent variable in column(2) is the 4-quarter return-to-fund investments in localstocks. MULTI-MANAGER is an indicator that the fund ismanaged by more than one person. Fundsize effects, familysize effects, momentum effects, book-to-market effects andstyle effects control for other differences in fund styles.Fund city effects are indicators for the city where the fundis located. Data are from the end of September 1997. Robustt-statistics are reported in parentheses.


�0.012, being a committee-managed fund de-creases the amount of local stocks a fund ownsby 0.012 divided by 0.152, or about 8 percent.

The second column of Table 9 is similar tothose in Table 8 except that we have added thevariable MULTI-MANAGER as an additionalexplanatory variable for the performance offund local investments. Again, we have intro-duced into this regression very conservativefund size, family size, and style controls. Thecoefficient in front of MULTI-MANAGER is�0.0190 with a t-statistic of 1.73, suggestingthat co-managed funds also do worse at pickinglocal companies. The standard deviation of lo-cal stock investment returns in this sample is0.186. So being a committee-managed fund de-creases local return by 0.0190 divided by 0.186,or 10 percent of a standard deviation. Bothfindings are consistent with Stein’s model.

Management Structure and Fund Perfor-mance.—Finally, we look to see whether solo-managed funds outperform team-managed onesafter controlling for fund size. Our prior is thatorganization form might be a less powerful pre-dictor of performance than size. The reason isthat few managers would turn away moremoney even if it hurt net fund returns for inves-tors. But conditional on a certain fund size, onemight think that the fund would choose thebetter organizational form to maximize net fundreturns. In other words, conditional on size,form may be a less powerful predictor of per-formance. Moreover, we are much more limitedin terms of data when looking at the effect ofmanagerial structure on fund performance.Whereas we are able to obtain data on fund sizeback to the 1960s, we are able to obtain data onmanagerial structure only back to 1992 becausethe data on how many managers are running afund are unavailable from the CRSP before thatdate. As before, we create the MT variablefor as many funds as possible back to 1992.Despite these caveats, it is interesting to lookat the effect of managerial structure on fundperformance.

The result for the effect of management struc-ture on fund returns is given in Table 10. Theupshot is that all the previous results regardingfund size continue to hold. For instance, thecoefficient in front of fund size is the same asin Table 3. We find that funds run by com-

mittee underperform by about 4 basis points amonth or 48 basis points a year. This effect iseconomically smaller than the effect of fundsize but is statistically significant. Again, thisfinding on performance is consistent withStein’s model.

IV. Alternative Explanations and FurtherDiscussion

In Section III, we ruled out a number ofalternative explanations for our findings in es-tablishing the role of liquidity and organizationin influencing the relationship between fundsize and performance. In this section, we dis-cuss additional explanations. One institutionalreason for our finding may be that it is easier forfund families to manipulate the performance ofsmall funds in small cap stocks. While thisalternative explanation is possible, we do notthink that it is driving our results, for severalreasons. First, we exclude the very smallestfunds for which this is likely and our results areunchanged. Moreover, in analysis that is notreported for reasons of brevity, we have alsodropped out very young funds, for which suchmanipulation is most likely, and our results areagain unchanged. Finally, such manipulationcannot account for the relationship betweenfund size and the composition of fund invest-ments, or for the relationship between manage-rial structure and fund investments andperformance documented in Section III D.

Besides such institutional reasons, it may bethe case that our findings are due to other orga-nization-related issues as well as the hierarchy-cost hypothesis presented in Section III D. Forinstance, many managers talk about how diffi-cult it is to train new hires when their fundgrows. We view such comments as broadlysupportive of our contention that organizationmatters for fund performance.

Ideally, we would like to have informationabout the incentives inside fund organizations.For instance, our hierarchy-cost hypothesis alsosuggests that a crucial unobservable determi-nant of fund performance is the nature of theincentives inside the fund. More concretely,suppose that the organizational diseconomiesare due to the adverse effects of a hierarchy onmanagerial effort. Then an optimal organiza-tional structure is to limit managers to a small


pot of money and let them manage it as theychoose. With such an organizational structure,scale may not affect performance. This is ex-actly what happens at the family level and prob-ably why performance does not decline withfamily size.

Our analysis here is similar to the work ofAllen N. Berger et al. (2002). They test the ideain Stein (2002) that small organizations are bet-ter than large ones in activities that require theprocessing of soft information in the context ofbank lending to small firms. They find that largebanks are less willing than small ones to lend tofirms that do not keep formal financial records.They also find that large banks lend at a greaterdistance, interact more impersonally with theirborrowers, have shorter and less exclusive rela-tionships, and do not alleviate credit constraintsas effectively. Interestingly, they find that thesize of the bank-holding company that a bankbelongs to does not affect the lending policies ofthe bank. In other words, size itself is not nec-

essarily bad for banks. This finding is related toour finding regarding the neutral-to-beneficialeffects of family size on fund performance.They argue that their findings are most con-sistent with the property-rights approach ofGrossman-Hart-Moore regarding the disadvan-tages of integration.

Our paper is also related to other papers thatattempt to test the basic Grossman-Hart-Mooreinsight in particular settings. Notable examplesinclude George P. Baker and Thomas Hubbard(2000), whose work centers on the truckingindustry and the question of whether driversshould own the trucks they operate, and DuncanSimester and Binger Wernerfelt (2000), wholook at the ownership of tools in the carpentryindustry.

V. Conclusion

To the best of our knowledge, we are the firstto find strong evidence that fund size erodes

TABLE 10—EFFECT OF FUND MANAGEMENT STRUCTURE ON PERFORMANCE



INTERCEPT �0.069 �0.018 0.039 �0.008 �0.092 �0.042 0.014 �0.032(0.66) (0.19) (0.25) (0.05) (0.88) (0.44) (0.09) (0.21)

MULTI-MANAGERi �0.040 �0.040 �0.041 �0.041 �0.040 �0.041 �0.042 �0.042(2.22) (2.23) (2.30) (2.30) (2.26) (2.27) (2.34) (2.34)

LOGTNAi,t�1 �0.024 �0.023 �0.026 �0.025 �0.022 �0.022 �0.025 �0.023(2.34) (2.30) (1.98) (1.83) (2.19) (2.15) (1.86) (1.70)

LOGFAMSIZEi,t�1 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016(2.38) (2.38) (2.38) (2.38) (2.40) (2.39) (2.40) (2.39)

TURNOVERi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(1.28) (1.29) (1.28) (1.29) (1.26) (1.26) (1.26) (1.26)

AGEi,t�1 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001 �0.001(0.64) (0.65) (0.64) (0.64) (0.60) (0.61) (0.59) (0.59)

EXPRATIOi,t�1 �0.002 �0.002 �0.003 �0.003 �0.043 �0.043 �0.045 �0.045(0.03) (0.03) (0.04) (0.04) (0.54) (0.54) (0.55) (0.55)

TOTLOADi,t�1 �0.002 �0.002 �0.002 �0.002 �0.002 �0.002 �0.002 �0.002(0.28) (0.29) (0.30) (0.29) (0.36) (0.36) (0.37) (0.37)

FLOWi,t�1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000(1.64) (1.68) (1.68) (1.69) (1.64) (1.68) (1.68) (1.69)

FUNDRETi,t�1 0.030 0.030 0.029 0.029 0.031 0.030 0.030 0.030(3.38) (3.38) (3.34) (3.34) (3.41) (3.41) (3.37) (3.37)

No. of months 96 96 96 96 96 96 96 96

Notes: This table reports the Fama-MacBeth (1973) estimates of monthly fund returns regressed on fund characteristics laggedone month. The sample includes only funds that fall within fund size quintiles two to five. Fund returns are calculated before(gross) and after (net) deducting fees and expenses. These returns are adjusted using the market model, the CAPM, theFama-French (1993) 3-Factor model, and the 4-Factor model. The regression specification is the one in Table 3 but augmentedwith MULTI-MANAGERi, which is a dummy variable that equals one if the fund is managed by two or more individuals orby a team. The sample is from January 1992 to December 1999. The t-statistics are adjusted for serial correlation usingNewey-West (1987) lags of order three and are shown in parentheses.


performance. We then consider various expla-nations for why this might be the case. We findthat this relationship is not driven by heteroge-neity in fund styles, fund size being correlatedwith other observable fund characteristics, orany type of survivorship bias. Instead, we findthat the effect of fund size on fund returns ismost pronounced for funds that play small capstocks. This suggests that liquidity is an impor-tant reason why size erodes performance. More-over, we argue that organizational diseconomiesrelated to hierarchy costs may also play a role inaddition to liquidity in the documented disec-onomies of scale. Consistent with this view, wefind that the size of a fund’s family does notsignificantly erode fund performance. Finally,using data on whether funds are solo-managedor co-managed and the composition of fundinvestments, we find that organizational disec-onomies affect the relationship between fundsize and performance along the lines predictedby Stein (2002).

Importantly, our findings have relevancefor the ongoing research into the question ofRonald H. Coase (1937) regarding the determi-nants of the boundaries of the firm. While anenormous amount of theoretical research hasbeen done on this question, there has been farless empirical work. Our findings suggest thatmutual funds may be an invaluable laboratorywith which to study related issues in organiza-tion. The amount of data on mutual funds, un-like that on corporations, is plentiful because ofdisclosure regulations and the fact that the tasksand performance of mutual funds are measur-able. We plan to refine our understanding of thenature of organizations in the future using thislaboratory.

REFERENCES

Aghion, Philippe and Tirole, Jean. “Formal andReal Authority in Organizations.” Journalof Political Economy, 1997, 105(1), pp. 1–29.

Almazan, Andres; Brown, Keith C.; Murray,Carlson and Chapman, David A. “Why Con-strain Your Mutual Fund Manager?” Unpub-lished Paper, 2001.

Baker, George P. and Hubbard, Thomas N. “Con-tractibility and Asset Ownership: OnboardComputers and Governance in US Trucking.”

National Bureau of Economic Research, Inc.,NBER Working Papers: 7634, 2000.

Becker, Stan and Vaughan, Greg. “Small IsBeautiful.” Journal of Portfolio Manage-ment, 2001, 27(4), pp. 9–18.

Berger, Allen N.; Miller, Nathan H.; Petersen,Mitchell A.; Rajan, Raghuram G. and Stein,Jeremy C. “Does Function Follow Organiza-tional Form? Evidence from the LendingPractices of Large and Small Banks.” Na-tional Bureau of Economic Research, Inc.,NBER Working Papers: 8752, 2002.

Berk, Jonathan B. and Green, Richard C. “Mu-tual Fund Flows and Performance in RationalMarkets.” National Bureau of Economic Re-search, Inc., NBER Working Papers: 9275,2002.

Bolton, Patrick and Scharfstein, David S. “Cor-porate Finance, the Theory of the Firm, andOrganizations.” Journal of Economic Per-spectives, 1998, 12(4), pp. 95–114.

Brown, Keith C.; Harlow, W. V. and Starks,Laura T. “Of Tournaments and Temptations:An Analysis of Managerial Incentives in theMutual Fund Industry.” Journal of Finance,1996, 51(1), pp. 85–110.

Carhart, Mark M. “On Persistence in MutualFund Performance.” Journal of Finance,1997, 52(1), pp. 57–82.

Chen, Joseph; Hong, Harrison and Stein, JeremyC. “Breadth of Ownership and Stock Re-turns.” Journal of Financial Economics,2002, 66(2–3), pp. 171–205.

Chevalier, Judith and Ellison, Glenn. “Risk Tak-ing by Mutual Funds as a Response to Incen-tives.” Journal of Political Economy, 1997,105(6), pp. 1167–200.

Chevalier, Judith and Ellison, Glenn. “CareerConcerns of Mutual Fund Managers.” Quar-terly Journal of Economics, 1999, 114(2), pp.389–432.

Coase, Ronald H. “The Nature of the Firm.”Economica, 1937, 4, pp. 386–405.

Coval, Joshua D. and Moskowitz, Tobias J.“Home Bias at Home: Local Equity Prefer-ence in Domestic Portfolios.” Journal of Fi-nance, 1999, 54(6), pp. 2045–73.

Coval, Joshua D. and Moskowitz, Tobias J. “TheGeography of Investment: Informed Tradingand Asset Prices.” Journal of Political Econ-omy, 2001, 109(4), pp. 811–41.

Daniel, Kent; Grinblatt, Mark; Titman, Sheri-


dan and Wermers, Russ. “Measuring MutualFund Performance with Characteristic-Based Benchmarks.” Journal of Finance,1997, 52(3), pp. 1035–58.

Elton, Edwin J.; Gruber, Martin J. and Blake,Christopher R. “A First Look at the Accuracyof the CRSP Mutual Database and a Com-parison of the CRSP and Morningstar MutualFund Databases.” Journal of Finance, 2001,56(6), pp. 2415–30.

Falkenstein, Eric G. “Preferences for StockCharacteristics as Revealed by Mutual FundPortfolio Holdings.” Journal of Finance,1996, 51(1), pp. 111–35.

Fama, Eugene F. and French, Kenneth R. “Com-mon Risk Factors in the Returns on Stocksand Bonds.” Journal of Financial Econom-ics, 1993, 33(1), pp. 3–56.

Fama, Eugene F. and MacBeth, James D. “Risk,Return, and Equilibrium: Empirical Tests.”Journal of Political Economy, 1973, 81(3),pp. 607–36.

Fredman, Albert J. and Russ, Wiles. How mutualfunds work, 2nd Edition. New York: NewYork Institute of Finance, 1998.

Gompers, Paul A. and Metrick, Andrew. “Insti-tutional Investors and Equity Prices.” Quar-terly Journal of Economics, 2001, 116(1), pp.229–59.

Grinblatt, Mark and Titman, Sheridan. “Mu-tual Fund Performance: An Analysis ofQuarterly Portfolio Holdings.” Journal ofBusiness, 1989, 62(3), pp. 393– 416.

Grinblatt, Mark; Titman, Sheridan and Werm-ers, Russ. “Momentum Investment Strategies,Portfolio Performance, and Herding: A Studyof Mutual Fund Behavior.” American Eco-nomic Review, 1995, 85(5), pp. 1088–105.

Grossman, Sanford J. and Hart, Oliver D. “TheCosts and Benefits of Ownership: A Theoryof Vertical and Lateral Integration.” Journalof Political Economy, 1986, 94(4), pp. 691–719.

Gruber, Martin J. “Another Puzzle: The Growthin Actively Managed Mutual Funds.” Journalof Finance, 1996, 51(3), pp. 783–810.

Hart, Oliver. Firms, contracts, and financialstructure. Clarendon Lectures in Economics.Oxford and New York: Oxford UniversityPress, Clarendon Press, 1995.

Hart, Oliver and Moore, John. “Property Rightsand the Nature of the Firm.” Journal of

Political Economy, 1990, 98(6), pp. 1119–58.

Hechinger, John. “Deciphering Funds’ HiddenCosts,” Wall Street Journal. New York,March 17, 2004, D1–D2.

Holmstrom, Bengt and Roberts, John. “TheBoundaries of the Firm Revisited.” Journalof Economic Perspectives, 1998, 12(4), pp.73–94.

Hong, Harrison; Kubik, Jeffrey D. and Solomon,Amit. “Security Analysts’ Career Concernsand Herding of Earnings Forecasts.” RANDJournal of Economics, 2000, 31(1), pp. 121–44.

Hong, Harrison; Lim, Terence and Stein, JeremyC. “Bad News Travels Slowly: Size, AnalystCoverage, and the Profitability of MomentumStrategies.” Journal of Finance, 2000, 55(1),pp. 265–95.

Investment Company Institute. Mutual fund factbook. Washington, DC: Investment CompanyInstitute, 2000.

IvKovic, Zoran. “Spillovers in Mutual FundFamilies: Is Blood Thicker Than Water?”Unpublished Paper, 2002.

Jegadeesh, Narasimhan and Titman, Sheridan.“Returns to Buying Winners and Selling Los-ers: Implications for Stock Market Effi-ciency.” Journal of Finance, 1993, 48(1), pp.65–91.

Jensen, Michael C. “The Performance of MutualFunds in the Period 1945–1964.” Journal ofFinance, 1968, 50, pp. 549–72.

Lowenstein, Roger. “Frightened Funds: Is Therea Master in the House?” Wall Street Journal.November 20, 1997, p. C1.

Lynch, Anthony W. and Musto, David K.“How Investors Interpret Past Fund Returns.”Journal of Finance, 2003, 58(5), pp. 2033–58.

Malkiel, Burton G. “Returns from Investing inEquity Mutual Funds 1971 to 1991.” Journalof Finance, 1995, 50(2), pp. 549–72.

Milgrom, Paul and Roberts, John. “An EconomicApproach to Influence Activities in Organi-zations.” American Journal of Sociology, vol.94, supplement, 1988, pp. 154–79.

Newey, Whitney K. and West, Kenneth D. “ASimple, Positive Semi-Definite, Heteroske-dasticity and Autocorrelation ConsistentCovariance Matrix.” Econometrica, 1987,55(3), pp. 703–08.


Perold, Andre and Salomon, Robert S. “TheRight Amount of Assets under Manage-ment.” Financial Analysts Journal, 1991,47(3), pp. 31–39.

Pozen, Robert C. The mutual fund business.Cambridge and London: MIT Press, 1998.

Prather, Larry J. and Middleton, Karen L. “AreN � 1 Heads Better Than One? The Case ofMutual Fund Managers.” Journal of Eco-nomic Behavior and Organization, 2002,47(1), pp. 103–20.

Sharpe, William F. “Capital Asset Prices: ATheory of Market Equilibrium under Condi-tions of Risk.” Journal of Finance, 1964,19(3), pp. 425–42.

Simester, Duncan and Wernefelt, Birger. “De-terminants of Asset Ownership: A Studyof Carpentry Trade.” Unpublished Paper,2000.

Sirri, Erik R. and Tufano, Peter. “Costly Search

and Mutual Fund Flows.” Journal of Fi-nance, 1998, 53(5), pp. 1589–622.

Stein, Jeremy C. “Information Production andCapital Allocation: Decentralized versus Hi-erarchical Firms.” Journal of Finance, 2002,57(5), pp. 1891–921.

Wermers, Russ. “Mutual Fund Performance: AnEmpirical Decomposition into Stock-PickingTalent, Style, Transactions Costs, and Ex-penses.” Journal of Finance, 2000, 55(4), pp.1655–95.

Williamson, Oliver E. “Corporate Finance andCorporate Governance.” Journal of Finance,1988, 43(3), pp. 567–91.

Williamson, Oliver E. Markets and hierarchies:Analysis and antitrust implications. NewYork: Simon and Schuster, 1975.

Zheng, Lu. “Is Money Smart? A Study of Mu-tual Fund Investors’ Fund Selection Ability.”Journal of Finance, 1999, 54(3), pp. 901–33.


Does Fund Size Erode Mutual Fund Performance? The...

Documents

Transcript of Does Fund Size Erode Mutual Fund Performance? The...