mutual fund performance in europe

8/13/2019 Mutual Fund Performance in Europe

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Master Thesis

Mutual Fund Performance in Europe

The Role of Fund Size

Moritz Felde

ID 444650

Supervisor: Dr. Rogr Otten

Maastricht University

Faculty of Economics and Business Administration

Department of Finance

The Netherlands

August 2007


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Mutual Fund Performance in Europe The Role of Fund Size

II

List of Contents

LIST OF FIGURES............................................................................................................... III

LIST OF TABLES................................................................................................................. IV

1 INTRODUCTION .................................................................................................................1

2 LITERATURE REVIEW .....................................................................................................4

2.1USMUTUAL FUNDS...........................................................................................................42.2EUROPEAN MUTUAL FUNDS ..............................................................................................62.3FUND SIZE .........................................................................................................................82.4CONCLUSION ...................................................................................................................11

3 THE MUTUAL FUND MARKET.....................................................................................13

3.1THE FRENCH MUTUAL FUND MARKET ............................................................................163.2THE GERMAN MUTUAL FUND MARKET...........................................................................173.3THE ITALIAN MUTUAL FUND MARKET ............................................................................183.4THE SPANISH MUTUAL FUND MARKET ...........................................................................203.5THE UKMUTUAL FUND MARKET ...................................................................................20

4 DATA....................................................................................................................................22

5 METHODOLOGY..............................................................................................................28

5.1BENCHMARKS ..................................................................................................................28

5.2PERFORMANCE MEASUREMENT.......................................................................................325.3PORTFOLIO FORMATION ..................................................................................................34

6 TIME SERIES ANALYSIS ................................................................................................36

6.1THE EFFECT OF FUND SIZE IN FRANCE ..............................................................................366.2THE EFFECT OF FUND SIZE IN GERMANY ..........................................................................416.3THE EFFECT OF FUND SIZE IN ITALY .................................................................................466.4THE EFFECT OF FUND SIZE IN SPAIN .................................................................................506.5THE EFFECT OF FUND SIZE IN THE UK ..............................................................................546.6EXTREME PORTFOLIOS.....................................................................................................596.7SMALL CAP FUNDS ..........................................................................................................63

6.8SMALL FUNDS IN LARGE FUND FAMILIES........................................................................65

7. ROBUSTNESS TESTS ......................................................................................................68

7.1PERFORMANCE DEVELOPMENT........................................................................................687.2TIMING.............................................................................................................................717.3SURVIVORSHIP BIAS ........................................................................................................747.4OFFSHORE DOMICILE.......................................................................................................767.4ACTIVE VS.PASSIVE ........................................................................................................78

8 CONCLUSION....................................................................................................................79

REFERENCES .......................................................................................................................81


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III

List of Figures

Figure 3.1 World Mutual Fund Assets by Region, 2006 .....................................................13

Figure 3.2 Total Net Assets of European Mutual Funds, 2006 in billion .........................14

Figure 3.3 The European Investment Fund Market Breakdown of Nationally DomiciledFunds, 2006 ........................................................................................................15

Figure 3.4 Per Capita Assets invested in Mutual Funds, 2006 in .....................................16

Figure 3.5 French Mutual Funds (retail and non-retail) under Management segmented by

Type of Fund, 2001-2003 in m........................................................................17

Figure 3.6 German Mutual Funds (retail and non-retail) under Management segmented by

Type of Fund, 1999-2003 in m........................................................................18

Figure 3.7 Italian Mutual Funds (retail) under Management segmented by Type of Fund,1999-2003 in m................................................................................................19

Figure 3.8 Spanish Mutual Funds (retail and non-retail) Under Management segmented by

Type of Fund, 1999-2003 in m........................................................................20

Figure 3.9 UK Mutual Funds (retail and non-retail) under Management segmented by Type

of Fund, 1999-2003 in m .................................................................................21

Figure 4.1 Size Histogram of German Equity Mutual Funds, 2005 in m (large picture) .25

Figure 4.2 Size Histogram of German Equity Mutual Funds, 2005 in m (small picture).26


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IV

List of Tables

Table 4.1 Summary Statistics .............................................................................................23

Table 4.2 Development of the average and median size fund from 1991 to 2005 in m..25Table 4.3 Investment Focus: Time-Series Averages of Cross-Sectional Averages ...........27

Table 5.1 Summary Statistics for Benchmark Portfolios Domestic Funds .....................29

Table 5.2 Summary Statistics for Benchmark Portfolios European Funds .....................30

Table 5.3 Summary Statistics for Benchmark Portfolios Global Funds..........................31

Table 6.1 Portfolio Returns France Domestic ....................................................................37

Table 6.2 Portfolio Returns France Europe ........................................................................38

Table 6.3 Portfolio Returns France Global.........................................................................40

Table 6.4 Portfolio Returns Germany Domestic ................................................................42

Table 6.5 Portfolio Returns Germany Europe ....................................................................43

Table 6.6 Portfolio Returns Germany Global .....................................................................45

Table 6.7 Portfolio Returns Italy Domestic ........................................................................46

Table 6.8 Portfolio Returns Italy Europe............................................................................48

Table 6.9 Portfolio Returns Italy Global ............................................................................49

Table 6.10 Portfolio Returns Spain Domestic ......................................................................51

Table 6.11 Portfolio Returns Spain Europe..........................................................................52

Table 6.12 Portfolio Returns Spain Global...........................................................................53

Table 6.13 Portfolio Returns UK Domestic .........................................................................55

Table 6.14 Portfolio Returns UK Europe .............................................................................56

Table 6.15 Portfolio Returns UK Global ..............................................................................57

Table 6.16 Extreme Portfolio Returns France ......................................................................60

Table 6.17 Extreme Portfolio Returns Germany ..................................................................61

Table 6.18 Extreme Portfolio Returns Italy..........................................................................61

Table 6.19 Extreme Portfolio Returns Spain ........................................................................62

Table 6.20 Extreme Portfolio Returns UK ...........................................................................63

Table 6.21 Small Cap Fund Returns.....................................................................................64

Table 6.22 Fund Family Returns ..........................................................................................66

Table 7.1 Performance Development .................................................................................69

Table 7.2 Timing (Henriksson and Merton (1981).............................................................72

Table 7.3 Timing Treynor and Mazuy (1966) ....................................................................73

Table 7.4 Test for Survivorship Bias ..................................................................................75


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V

Table 7.5 Offshore Domicile ..............................................................................................77

Table 7.6 Offshore Domicile Domestic Spread Portfolios .................................................77

Table 7.7 Active/Passive ....................................................................................................78


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1

1 Introduction

"You will see below-average expenses, high-quality

management, diversified portfolios, and, of course, a long

and generally successful track record."

This quote, referring to the five largest equity mutual funds in the US, is from Russel

Kinnel, director of mutual fund research at Morningstar. While Russel Kinnel stresses the

advantages of large equity mutual funds, the performance history of Fidelity Magellan,

formerly the worlds largest mutual fund, does not look very convincing. After the fund

was closed to investors in 1997, its new portfolio manager Robert Stansky took control of a

fund that was no longer manageable due to its enormous size (Nocera 1996). As a result,

the unhealthy growth in assets under management is now regarded as the primary cause of

the fund falling to fifth place in size in 2005. Prior, a record of poor performance led

investors to pull out an estimated $ 11,6 billion from the fund between March 2004 and

March 2005.

Building on these two opposite outlooks on mutual fund size and its effect on

performance, this study explores return differences between small and large mutual funds.

Fund size has become of critical concern in Europe, more so since the EU enacted the first

UCITS (Undertakings for Collective Investments In Transferable Securities) directive in

1985. Equipped with a so-called European Passport obtained in Europes mutual fund

centre, Luxembourg, funds can be distributed in all EU member countries. Mutual funds

managers are therefore able to reach a substantially larger number of customers compared

to a purely national fund distribution. Thus, the question arises whether prospective

advantages of larger mutual funds outweigh the potential disadvantages associated with

fund size. Regardless of the qualitative viewpoint, one effect seems certain: Investors

allocate money to those funds that have a convincing track record. Therefore, out-

performing funds attract more capital than others resulting in the funds growing larger

(Beckers &Vaughan, 2001).

Apart from Russel Kinnel, numerous researchers and practitioners refer to

advantages of fund size. The most obvious is the opportunity of large funds to profit from

economies of scale. Fixed overhead expenses can be spread over a larger asset base, which

might include costs for resources and research, among others. Normally, this should cause

the costs per Euro invested to decrease and therefore result in a superior net performance.

Additionally, Smith (1994) refers to larger funds being able to obtain more shares in


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oversubscribed IPOs than smaller ones. In contrast, other researchers point at potential

diseconomies of scale for mutual funds. Perold and Salomon (1991) conjecture that

diseconomies of scale stemming from the relation between market impact and transaction

size, erode mutual fund performance. Equipped with a large asset base, fund managers lose

the ability to quickly move in and out of positions. Not only does this reduce the fund

managers flexibility, it also reduces the anonymity of trades. As Indro (1999) points out,

large funds managers are carefully monitored by outsiders, who suspect managers to

execute highly sophisticated deals. Therefore, investment intentions of large funds are

quickly revealed.

Apart from Otten and Bams (2002), previous research on fund size and its effect on

performance focused on the US market. The purpose of this study is therefore to expand

existing US studies on fund size and its influence on performance by investigating the

European market. The main analysis in this study therefore aims to investigate whether

large or small funds available for purchase in five European markets (France, Italy,

Germany, Spain and the UK) display a better risk-adjusted performance. Within the course

of this investigation, three models for estimating risk-adjusted returns are employed. The

one-factor model by Sharpe (1964) and Lintner (1965) is augmented by Fama and Frenchs

(1993) three-factor model as well as Carharts (1997) four-factor model. Additionally, two

implications from a study conducted by Chen et al. (2004) are tested for the European

market. First, it is examined whether the potentially eroding effects of fund size on

performance are more severe for funds investing in small cap firms. Second, economies of

scale for US mutual funds are believed to be found on the fund family level rather than on

the individual fund level. This finding together with the assertion that large fund families

face incentives to push their smaller funds yields the implication that small funds of

large families should perform well. This second implication is also tested for the European

fund market.

The results of this study suggest that equity mutual funds in the four investigated

continental European countries are on average able to expropriate economies of scale.

Diseconomies of scale are found for UK equity mutual funds. As predicted by the liquidity

hypothesis, diseconomies of scale are found for funds investing in small cap stocks, only.

Apart from French funds exclusively investing in the small cap universe, smaller small cap

funds outperform their larger peers in all investigated countries. By way of contrast, the

second hypothesis from Chen et al. (2004), relating to small funds of large familiesoutperforming their peers, is not supported by the data.


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The remainder of this paper is organized as follows. Chapter two presents an overview of

the literature on which this research is based. Chapter three contains information on the

worldwide, European and country specific mutual fund markets. Chapter four introduces

the two datasets on which this study is based and presents a basic overview of descriptive

statistics. In chapter five, the methodology as well as the different benchmarks in use are

described. Chapter six presents an analysis on performance differences between large and

small equity mutual funds, in chapter seven it is checked for robustness and chapter eight

concludes.


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2 Literature Review

This literature review attempts to depict an overview over a selection of seminal and

influential studies in the area of mutual funds. Since all of the early and most of the recentworks on mutual funds spotlighted the US market, this review starts out by reviewing the

most important studies on this market. The subsequent sections will in contrast centre on

studies on the European mutual fund market and on studies focusing on fund size, as these

are the subjects which this study attempts to combine.

2.1 US Mutual Funds

Scholars have long attempted to asses the performance of mutual fund managers. While

early studies are only able to make performance assessments on the basis of relative risk,

the first academic to assess mutual fund performance in an absolute way is Jensen (1968).

With the help of the capital asset pricing model developed by Sharpe (1964) and Lintner

(1965), he is able to refer to performance that is attributable to risk. Everything beyond that

is considered abnormal and has become known as Jensens alpha. The sample of 115

mutual funds in the period from 1945-1964 is on average not able to outperform market

averages on a risk-adjusted basis. Furthermore, Jensen doubts whether any individual fund

in the sample is able to do so.

Other researchers such as Sharpe (1966) and Friend et al (1970) are in line with

Jensens findings of mutual funds not being able to compensate for the costs of their

operation. Although not generally contradicting these findings, Carlson (1970) points out

that the question whether mutual funds outperform the market crucially depends on the

time period of investigation and the market proxy used.

A shortcoming of these studies was, according to Chang and Lewellen (1984) that

the use of the single-factor capital asset pricing model used in the early studies fails to

account for a fund managers ability to engage in macro market-timing-activities. In

their study of 67 mutual funds between 1971 and 1979 they develop a statistical procedure

incorporating the potential presence of market-timing by the fund manager. Nevertheless,

the results they obtain show no qualitative difference to the previous ones. This is in

accordance with Henriksson (1984), who finds that mutual fund managers are on average

not able to successfully time the return on the market portfolio.

According to Ippolito (1989), the entire first generation of papers relating to

mutual fund performance is characterized by the joint conclusion of mutual funds


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performing poorly. In the very same paper, while performing a study comparable to the

one of Jensen (1968), he obtains results contradicting prior findings. After having

investigated a sample of 143 mutual funds during the period from 1964-1984, he finds that

even net of fees and expenses mutual funds are able to generate an abnormal return of 0.81

per cent per year. When additionally investigating the effect of active investment strategies

on turnover, fees and expenses, he finds that more active funds generate returns high

enough to compensate for the higher costs they charge.

Grinblatt and Titman (1989) in their study on quarterly portfolio holdings of a large

sample of mutual funds also obtain results indicating that mutual fund managers do possess

enough private information to generate abnormal gross returns. This effect is most

pronounced for aggressive-growth funds, growth funds and funds with the smallest net

asset values. Because these funds are also the ones charging the highest costs, Grinblatt

and Titman (1989) do not find any category of funds to generate positive abnormal net

returns.

Malkiel (1995) criticises earlier research on mutual funds referring to the fact that

these studies utilize datasets containing all mutual funds in existence at the time of the

study. The bias associated with such a sample is called survivorship bias. He examines a

sample of mutual funds between 1971 and 1991 free from survivorship bias and finds that

the bias effect had previously been underestimated. In his performance analysis he finds

mutual funds on average underperforming the market in net as well as in gross terms. Most

strikingly, he finds no relationship between beta and returns within the framework of the

capital asset pricing model. He thus concludes that most investors would be better off

buying passive rather than active funds.

Gruber (1996) poses the question why, after all that has been found out about

mutual fund performance, investors still buy actively managed mutual funds. He explains

what he calls the mutual fund puzzle with the help of a model in which he decomposes

investors into groups with different skill and incentives.

Wermers (2000) finds that most stocks held by mutual funds outperform the value

weighted CRSP index. In aggregate he finds mutual funds outperforming the index by a

yearly 130 basis points. By decomposing the 130 basis points into different parts, Wermers

attributes 71 basis points to the stock picking talent of the fund managers. 55 to 60 stem

from mutual funds picking stocks with characteristics associated with higher average

returns than the benchmark. Nevertheless, because the outperformance of 130 basis points


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roughly equals the magnitude of the mutual funds expenses and transaction costs, they

generate no abnormal net returns.

Other studies emerging since the beginning of the 1990s have shifted their attention

from performance to persistence. Hendricks, Patel and Zeckhauser (1993) investigate

quarterly return data of a sample of no-load, open-end, growth-oriented equity funds from

1974 to 1988. They detect persistence in short-term superior fund performance and label

this characteristic hot hands. If these hot hand funds are detected ex ante, risk-adjusted

returns can be improved by 6 per cent per year. Moreover, the counterpart to hot hands,

so-called icy hands is detected. Funds that underperformed in the previous period tend to

underperform in the following period. These results are confirmed by numerous fellow

scholars, among them Goetzmann and Ibbotson (1994) as well as Brown and Goetzmann

(1995). Using a probit analysis, the latter find that poor performance increases the

probability of a fund disappearing and that persistence stems mostly from those funds

lagging the S&P 500. Moreover, the presence of persistence seems to depend on the time

period of investigation. This latter finding is confirmed by Malkiel (1995) who finds strong

patterns of persistence in the 1970s but is unable to detect them during the 1980s.

While according to these studies, persistence seems to exist at least for certain time

periods, Carhart (1997) attributes Hendricks, Patel and Zeckhausers (1993) Hot Hands

momentum. He forms portfolios based on the funds lagged one year performance and

assigns the difference in performance to common stock factors, expense ratios and

transaction costs. Persistence is only detected in the underperformance of the two worst

performing deciles of funds. He thus merely detects Icy and no Hot Hands.

2.2 European Mutual Funds

All of the above mentioned studies have one commonality: They solely focus on US

American mutual funds. After all, the US mutual fund market has been extensively

researched, while publications on European funds are scarce.

In a study on performance persistence of European mutual funds, Grnbichler and

Pleschiutschnig (1999) find persistence in risk-adjusted returns. Moreover, their finding

stemming from an investigation of a sample of 333 European equity mutual funds is not

weakened by the criticism of Carhart (1997). Neither employing the 4-factor model nor

changing the technique to measure performance persistence mitigates their results.

In a paper by Otten and Schweitzer (2002) the authors compare the US and theEuropean mutual fund industry. The European sample, covering 506 funds for purchase in


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the five most important mutual fund countries ranging from 1991-1997, is restricted to

domestically investing equity funds. With respect to performance the authors find that

European mutual funds generate risk-adjusted returns superior to those of US mutual

funds. Furthermore, small cap mutual funds in Europe as well as in the US outperform all

other funds and their respective benchmarks.

In the same year, based on data including the same number of funds but one more

year of return data as in the previous study, Otten and Bams (2002) present a

comprehensive overview over the European mutual fund industry. Contrary to what is

found for the US market, they find positive and significant risk-adjusted gross returns in

four out of five markets. Net of costs, mutual funds in these four markets on aggregate still

perform better than the market. Moreover, this effect is significant for UK funds. With

respect to performance persistence, the results of Otten and Bams (2002) contradict those

of Grnbichler and Pleschiutschnig (1999). Apart from mutual funds in the UK, only weak

evidence is found for performance persistence.

Other studies on European mutual funds deal with individual countries, only.

Dermine and Rller (1992) as well as Mc Donald (1973) focus on the French mutual fund

industry. While the former investigate economies of scale and scope, the latter study

focuses on the performance of only eight French mutual funds from the middle until the

end of the 1960s. On average, these funds offered superior risk-adjusted returns.

In a study on the German mutual fund market, Krahnen, Schmidt and Theissen

(1997) investigate the performance of eleven domestically investing German mutual funds

during the period 1987-1993. The authors find cross-sectional variation in performance,

which results in changes in market share. An absolute performance measure is not within

the scope of their study.

This issue is instead covered in a very recent study by Stotz (2007). He performs a

comprehensive performance evaluation study based on a sample of 141 funds during the

period of 1989-2005. He estimates risk-adjusted returns using the CAPM based one-factor

model of Jensen (1986) as well as Carharts (1997) 4-factor model. He finds a risk-

adjusted underperformance of 130 to 190 basis points for the average domestically

investing, actively managed German equity fund. Nevertheless, fund managers investing in

small-cap growth stocks with high momentum are able to generate positive though not

significant Carhart alphas. With respect to mutual fund managers stock picking ability,

Stotz (2007) thus arrives at the same sobering conclusion as other researchers based on theUS fund market did.


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Cesari and Panetta (2002) study the performance of 82 Italian equity funds during the

period 1985-1995. They find positive, risk-adjusted alphas of 7 to 109 basis points yearly,

after CAPM estimation. However, based on net returns, Jensens alpha becomes close to

zero and statistically insignificant. By the same token, Casarin, Pelizzon and Piva(2003)

find no positive risk-adjusted net returns for Italian equity funds during the period of 1988-

1999. Furthermore, only about ten per cent of the funds in their sample are able to generate

positive and significant alphas.

Quite recently, Vicente and Ferruz (2005) as well as Sainz, Grau and Doncel (2006)

investigate the Spanish mutual fund market with respect to performance persistence and

performance, respectively. The former investigate a sample of Spanish equity funds

investing in the domestic market. While they found no added value in holding actively

managed equity funds, they found strong evidence of contrary persistence in mean returns.

This finding goes in the direction of Carhharts (1997) finding of gambling behaviour by

some fund managers. Sainz, Grau and Doncel (2006) confirm Vicentes and Ferruz (2005)

results of Spanish mutual funds not being able to generate positive abnormal returns.

Moreover, the authors find no evidence of mutual fund managers being able to successfully

engage in market timing.

Blake and Timmermann (1998) study the UK mutual fund market. Based on a

sample of 2300 UK Mutual funds in existence between 1972 and 1995, they find the

average UK equity fund to underperform the market by 180 per year on a risk-adjusted

basis. Fletcher and Marshall (2005), in their study on the performance of UK international

unit trusts, find the average Jensen alpha to be negative and close to zero. While five out of

six fund categories display non superior performance when compared to the benchmark,

trusts investing in European countries perform superior to the benchmark, regardless of the

model used.

2.3 Fund Size

The first scholars to find an effect that relates to the size of mutual funds are Grinblatt and

Titman (1989). On the one hand, they hypothesize that small funds could outperform larger

ones because their impact on security prices when moving in and out of securities is

smaller. On the other hand, they anticipate larger funds could outperform smaller ones

because of lower transaction costs and other economies of scale. When forming quintile

portfolios based on the funds net asset values as of the beginning of their period ofinvestigation, Grinblatt and Titman (1989) obtain results consistent with their first


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hypothesis. The smallest quintile of funds outperformed the other quintiles and is the only

quintile that is able to maintain positive abnormal returns significant at the one per cent

level. Even after controlling for the fact that the smallest quintile contains more aggressive

growth and growth funds than the others, the size effect remains. However, the authors

second hypothesis is also partly confirmed. Transaction costs are inversely related to fund

size, eroding the effect of the smaller funds superior performance on a net return basis.

Five years later, Grinblatt and Titman (1994) again investigate the same sample of

domestically investing mutual funds as in their 1989 study. They use net asset value as one

of five independent variables in a cross-sectional regression. By using two different

benchmarks, the coefficient on the net asset value variable is negative but insignificant

regardless of the benchmark in use. According to their second study, fund size has thus no

effect on performance.

Ciccotello and Grant (1996) obtained results in line with the previous studies on the

effect of size on mutual fund performance. They investigate a sample of 626 mutual funds

in the period of 1992-1992. Methodologically, they adopted Grinblatt and Titmans (1989)

approach and sorted the funds in quintiles based on their net asset value at one specific

point in time. They chose the end of their period of investigation to measure net asset value

in order to test the first of their two hypotheses, stating that that a)large funds should have

greater historical returns than small funds. They also follow Grinblatt and Titmans (1989)

approach of measuring fund size as of the beginning of their period of investigation in

order to test their second hypothesis stating that b)small funds should have greater future

returns than large funds. Indeed, as predicted by hypothesis one, large funds outperformed

small funds on the basis of past returns. This is in accordance with Becker and Vaughan

(2001), who found that outperforming funds were able to attract significantly more money

from investors than others. The second hypothesis, relating to a funds future returns is

only partially confirmed by the data. Fund size has no effect on future fund performance

except for funds with an aggressive growth investment objective. The authors denote this

result intuitive, because logically the inflow of assets should pose a problem to a fund

manager with limited investment choices.

Within the framework of a pooled cross-sectional/time-series regression, Droms

and Walker (1994) relate mutual fund performance to a large set of fund characteristics.

Unlike the studies on size and performance previously mentioned, they investigate a

sample of international funds available to US investors, not of domestically investing USfunds. After having scrutinized 60 coefficients for asset size, the authors obtain a clear cut


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result: none of the coefficients is significantly different from 0. This lets them conclude

that asset size is unrelated to the risk-adjusted returns on mutual funds in the US.

Indro et al (1999) investigate a sample of 683 actively managed US mutual equity

funds over the period 1993-1995. They are the first scholars to refer to a required minimum

size of a mutual fund. Without a fund having reached a minimum net asset volume, the so-

called break-even size of a fund, the cost of gathering and processing information are too

high to be outweighed by returns. Moreover, the authors find declining marginal returns to

information activities that become negative once a fund has reached a certain net asset

volume. The authors attribute these diseconomies of scale to higher transaction costs on

large purchases/sale orders, administrative stress, deviations from desired investment style,

the opportunity costs of not implementing trades and the lack of the freedom to act without

signalling intent. The central argument is that as a mutual fund attracts more and more

money from investors, there are less investment opportunities left for him to take

advantage of.

In line with Indro et al (1999), Christopherson et al (2002) conjecture that there

might exist an inverse relationship between net asset value and performance for small-cap

investment managers. Their study contains data on small-cap managers, most of them

offering a variety of products, not only mutual funds. The general problems connected with

trading stock are, according to the authors, augmented when it comes to moving in and out

of small-cap stocks. In the vein of previous studies, they sort managers into size quintiles.

The methodological difference to previous studies lies in Christopherson et al (2002)

grouping managers in quintiles in every sub period. By doing so, they control for the fact

that well performing managers attract more capital than mediocre ones and are thus able to

isolate an uncontaminated size effect. The analysis reveals that indeed there exists an

inverse relationship between risk-adjusted performance of a small-cap manager and the

level of assets under management and growth in assets under management, respectively.

Hence, investors are urged to move out of small-cap investments once they have grown too

large.

Otten and Bams (2002) investigate the influence of fund characteristics on risk-

adjusted performance. They regress several fund characteristics on the funds four-factor

alphas. Over all investigated countries, they observe a positive effect of size on risk-

adjusted performance and deduce that European mutual funds still have to grow to reach an

efficient size. Nevertheless, the authors frankly admit that this finding might be biased by


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self-induced correlation. This is the case if over-performing funds attract more capital from

investors than other funds.

Using a sample of mutual funds covering the extensive time period 1962-1999,

Chen et al (2004) make use of the CAPM as well as the Carhart (1997) four-factor model

to test the relation between fund size and performance. Their results reveal a statistically

significant inverse relationship between fund size and performance. However, as the

authors emphasize, the size effect is only visible while controlling for fund family size. A

larger fund family size is associated with higher returns. This effect is attributed to larger

fund families being able to realize economies of scale associated with trading commissions

and lending fees. Because fund family size is positively correlated with fund size, the

negative size effect would not be visible without controlling for fund family size. Inspired

by the stipulations of the liquidity hypothesis stating that net asset volume should affect

small cap investments more than others, the authors control for the size of the investment

target. The results they obtain point to the confirmation of the liquidity hypothesis.

While small-cap funds are largely affected by the adverse effect of size on their

performance, the performance of large-cap funds is unaffected by the size of their asset

base. Another interesting finding of Chen et al (2004) relates to the number of stocks held

in the mutual funds portfolios. The median fund in the smallest size quintile held about 16

stocks, while the median fund in the largest quintile held about 66 stocks. In connection

with the fact that the largest funds are many more than four times bigger than the smallest

funds, this means that large funds on average have to take larger positions in individual

firms than smaller ones. This lends further support to the unfortunate liquidity concerns for

large funds.

Recently, Chan et al (2005) present a study on the relation between fund size and

the performance of Australian equity managers. Along the lines of previous research, they

report a negative influence of fund size on manager performance. While digging deeper

towards the origin of these diseconomies of scale, the authors receive results suggesting

that high fund inflows exert purchasing pressure on the manager which results in him

picking inferior investments and ultimately erodes performance.

2.4 Conclusion

The previous section portrays an overview of the various findings concerning

performance and fund size in earlier studies. As the studies are arranged in a chronological


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order, it becomes obvious that the research techniques have become more enhanced and

results have become more sophisticated over time.

In conclusion, some studies report an adverse relationship between size and

performance, while others detect no effect at all. Nevertheless, all of the studies

partitioning on a funds investment objective found fund size to negatively influence a

funds risk adjusted returns. This is consistent with the liquidity hypothesis as it is stated

in several of the abovementioned publications. As assets under management grow, fund

managers seem to experience difficulties meeting sophisticated investment decisions.


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3 The Mutual Fund Market

In the year 1774, the Dutch merchant banker and broker Adriaan van Ketwich aspired to

provide investment diversification opportunities also to those investors with limited means.He encouraged potential investors to subscribe to the Eendragt Maakt Magt and thereby

laid the foundations for todays mutual funds. Despite this early attempt, the first mutual

fund (Massachusetts Investors Trust) was established in Boston in the year 1924; exactly

150 years after Ketwich had the initial idea.1

Since then, the worldwide mutual fund industry looks back to a history of

astonishing growth. In the USA, mutual fund assets amounted to $ 47,6 billion ( 34,9

billion) in the year 1970 and have grown to $ 10,4 trillion ( 7,6 trillion) in the year 20062.

This corresponds to a yearly growth rate of roughly 16 per cent from 1970 to 2006.

Moreover, the US mutual fund market accounts for 48 per cent of the worldwide $ 21,8

trillion ( 16 trillion) of assets invested in mutual funds. As can be seen from figure 3.1,

Europe is the second biggest fund market with a share of 36 per cent of global assets,

followed by Africa/Asia Pacific and the other Americas. Although the US fund market is

the biggest fund market worldwide, its share of global mutual fund assets is steadily

declining. While it accounted for 58 per cent of global assets in 1999, the other fund

markets and especially the European market, have taken away some of the US global

market share.

Figure 3.1 World Mutual Fund Assets by Region, 2006

Other Americas

5%Africa and Asia

Pacific 12%

Europe 36%

United States

48%

Source: ICI

1ICI 2007 Investment Company Fact Book

2Ibid, ICI 1996 Investment Company Fact Book, own computations


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14

As described above, the European mutual fund market gains world wide market share.

Moreover, European mutual funds net asset volume today accounts for almost 60 per cent

when measured in relation to the GDP of the European Union (EU-15). This ratio

amounted to app. 36 per cent in the year 2002 and documents the rapid growth in European

mutual funds net asset value as compared to GDP.3

Net assets of European mutual funds are displayed in figure 3.2. While net assets

under management declined after the bubble period from 2000 to 2002, the market grew

continuously from 2002 to 2006. From 2005 to 2006 the market experienced a growth of

15,1 per cent and now amounts to almost 6 trillion of assets under management.4

Figure 3.2 Total Net Assets of European Mutual Funds, 2006 in billion

33383775

4206

5189

5974

0

1000

2000

3000

4000

5000

6000

7000

2002 2003 2004 2005 2006

Source: EFAMA

As presented in figure 3.3, mutual funds domiciled in Luxembourg, France and Germany

together account for over one half of the European mutual fund market. Of these countries,

Luxembourg is the most important country as a domicile for European mutual funds with a

market share of 24,4 per cent. This huge rate seems at odds with Luxembourgs overall

size relative to the other EU countries. When asked to comment on this apparent paradox,

practitioners put forward several reasons to explain Luxembourgs importance in the

European mutual fund market.

First of all, the Luxembourg authority responsible for fund investments acts, the

CSSF, is supposed to be quicker and more flexible than the national authorities in other EU

member states. Furthermore, the CSSF is believed to be more liberal with respect to

innovative products. As European fund markets are highly competitive, providers

appreciate the quick and uncomplicated steps they are able to take in Luxembourg.

Secondly, practitioners refer to potential economies of scale. The distribution of funds in

3EFAMA Annual Report 2006-2007

4This and the following numbers do not include non-UCITS funds


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15

27 EU member states is easier and cheaper if centrally organised in one country. Thirdly,

registration in Luxembourg has various special advantages over the registration in single

countries. This refers to less strict reporting standards as compared to the Netherlands and

tax advantages as compared to e.g. the Netherlands or Germany. In the Netherlands for

example, there used to be a capital tax on reclaiming dividends of 40 to 50 basis points

which is absent in Luxembourg. Furthermore, trading in Luxembourg takes place only

once a day, while in other European countries fund promoters have to report intraday

NAVs. According to a practitioner from Barclays, this has the advantage of a more clear

view on the in and outflows of a fund meaning that the day before you know what you

will be selling the day after.5

Figure 3.3 The European Investment Fund Market Breakdown of Nationally Domiciled

Funds, 2006

Luxembourg

24,4%

France

19,7%

Germany

13,4%

UK

10,3%

Ireland9,5%

Italy

5,1%

Spain

3,8%

Austria

2,2%

Switzerland

2,0%

Sweden

1,9%

Belgium

1,7%

Denmark

1,6%

Others

4,5%

Source: EFAMA

Apart from a comparison of fund domiciles by country, it is interesting to have a look at

national European markets real size. Since Germany as the largest European member state

counts about 176 times more inhabitants than Luxembourg does, it is clear that

Luxembourg can not play a role in this comparison.6

By the year 2003, Germany was indeed the largest European fund market based on

assets distributed, accounting for 27,1 per cent of the European Fund market. The

Germans are followed by the French and the British, accounting for 25,2 per cent and 14,3

per cent of the European fund market, respectively.

Of the European overall mutual fund market amounting to 5.974 billion of assets, the

largest part is captured by equity funds, representing 44 per cent of the market. They are

5All information referring to practitioners come from telephone interviews conducted February 2

nd, 2007

6CIA World Fact Book 2006


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16

followed by bond funds, capturing 24 per cent of market share and money and balanced

funds, both representing 16 per cent of the market.

Although not a major domicile of mutual funds, the UK plays an important role in

the European equity-linked fund market. While the share of the other European countries

(except Germany) in the equity-linked fund market is comparable to their share in the

overall fund market displayed in figure 3.3, the UKs share in the equity-linked fund

market (19 per cent) is almost twice as high as in the overall fund market. This happens on

the expense of Germany, whose share of 6 per cent in the equity-linked fund market is less

than half of its share in the overall mutual fund market.

As can be seen from figure 3.4, there is still a large gap between most European

countries per capita assets in mutual funds and the US. With 16.916, France comes close

to the US per capita asset holdings of 20.573. Nevertheless, all of the other European

countries per capita assets in mutual funds range below half of the US level. Obviously,

the European mutual fund industry is in a huge backlog. This gives reason to expect further

growth in the industry and an augmentation of mutual funds influence on the development

of capital markets.

Figure 3.4 Per Capita Assets invested in Mutual Funds, 2006 in

4492

6511

9517 9535

20573

5572 5700 6199

16916

0

5000

10000

15000

20000

25000

Netherland

s

German

y

Spain

UK Italy

Austria

Switz

erland

Fran

ceUS

A

Source: BVI

3.1 The French Mutual Fund Market

As mentioned in the previous section, the French mutual fund market is the second biggest

in Europe. With respect to the type of funds held in France, the large share of money

market funds is remarkable. As displayed in figure 3.5, the share of money market funds


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during the period 2001-2003 ranged between 42 per cent and 48 per cent of total assets

held in mutual funds. This huge share is especially noteworthy considering the

unfavourable taxation of short-term investments as compared to long-term investments

until the year 2002.

Figure 3.5 French Mutual Funds (retail and non-retail) under Management segmented byType of Fund, 2001-2003 in m

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2001 2002 2003

Other

Unsegmented Off shore Funds

Property (n/a)

Money Market

Bond

Balanced

Equity

Source: Datamonitor

Of all the distribution channels for mutual funds, banks are by far the most important ones.

In 2003, 80 per cent of French mutual funds are distributed by banks. The remaining 20 per

cent are almost evenly split between insurers and independent distributors.

When segmenting the fund market by distributor, the huge diversity of the market

in France becomes apparent. About seven key players, among them BNP Paribas or AXA,

control about two thirds of the market. This makes the French market the second most

diversified one in Europe after the UK. However, ranked by performance, four out of five

of the best fund groups controlled less than five funds. This effect is due to a huge number

of smaller foreign promoters who had to open up funds domiciled in France in order to

compete with domestic promoters. In fact, these promoters were forced to do so for

otherwise unfavourable tax conditions. Of all the five European countries under

consideration, France has the smallest share of equity funds in the market. What is more,

this share declined between 2001 and 2003, reflecting the drop in equity prices and a more

security-oriented investment strategy.

3.2 The German Mutual Fund Market

Germany has the largest mutual fund market in Europe. Akin to other European fund

markets, assets under management in the German mutual fund market declined during the


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period 2001-2002 but experienced steady growth since then. As presented in figure 3.6, the

balanced fund market accounted for the largest stake in the market. They are followed by

money market and equity funds which, as in France, only account for a small share of all

assets invested. Their relative stake declined between 2000 and 2002 in reaction to the

equity price drop and investors longing to decrease their exposure the equity markets.

Figure 3.6 German Mutual Funds (retail and non-retail) under Management segmented byType of Fund, 1999-2003 in m

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999 2000 2001 2002 2003

Other


Property

Money Market

Bond

Balanced

Equity

Source: Datamonitor

The lions share of the funds sold in Germany is sold by Banks and ranged between two-

thirds and three-quarters from 2000 to 2002. While the rest of the market is mostly shared

between independent intermediaries and insurers, the latter suffer from a decrease of more

than 50 per cent of market share from 2000 to 2002. Furthermore, almost unrecognized a

few years ago, investment companies attempt to gain market share in the German mutual

fund sector and accounted for a share of about 5 per cent in 2002.

About 60 per cent of the German mutual fund market is controlled by 5 key

players. Among them, DWS and DEKA together account for 40 per cent of the German

market. Since they both belong to huge Banks, they profit from a huge branch network

triggering the distribution of mutual funds. Compared to other European mutual fund

markets such as the UK, France or Italy, the German market is quite concentrated. As in

the case of France, fund groups with a smaller number of funds in the portfolio did

extremely well over the last years.

3.3 The Italian Mutual Fund Market

With a compound annual growth rate of -7,1 per cent, the Italian mutual fund market wasthe worst performing market in Europe over the period 2001 to 2003.


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Similar to other European markets, the largest decline in mutual fund assets is found in the

equity segment. While this is a reaction to decreasing equity prices throughout all

European markets between 2000 and 2002, the increase in equity assets of 5 per cent in

2003 is again very low. As money was withdrawn from equity accounts, people began to

invest more money into secure investments like money market funds. These funds

experienced an increase in assets under management of 421 per cent during the period

1999 to 2003. This is why the Italian retail money market fund market and the traditionally

strong bond fund market are the largest of their category in Europe.

Figure 3.7 Italian Mutual Funds (retail) under Management segmented by Type of Fund,1999-2003in m

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999 2000 2001 2002 2003

Other

Unsegmented Offshore Funds

Property(n/a)

Money Market

Bond

Balanced

Equity

Source: Datamonitor

Although still the most important distribution channel for mutual funds in Italy, Banks are

steadily losing market share to other participants like intermediaries. The so-called mixed

system accounts for a market share of 40 per cent, almost as much as banks with a market

share of 49 per cent.

The fund market in Italy is very fragmented with the largest player (Nextra

Investment Management) accounting for a small 19 per cent of the total market. Other

smaller and medium size players control 49 per cent of the market while the remaining 32

per cent are left to smaller suppliers of mutual funds.

Larger fund promoters have in contrast to other European markets been very

successful in Italy. Between 1999 and 2003, none of the fund groups holding less than five

funds in the portfolio has more than once appeared in the top five between 1999 and 2003.


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3.4 The Spanish Mutual Fund Market

After a three year period of declining assets, the year 2003 marked a 21 per cent increase in

assets within the Spanish mutual fund sector. While the share of balanced and equity funds

declined during the period of weak stock markets from 2000-2002, the money market

segment remained stable at about 25 per cent of the total market.

Figure 3.8 Spanish Mutual Funds (retail and non-retail) Under Management segmented byType of Fund, 1999-2003 in m

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999 2000 2001 2002 2003

Other


Property

Money Market

Bond

Balanced

Equity

Source: Datamonitor

The fund distribution in the retail segment lies almost exclusively within the handsof banks. In the year 2003, banks and savings banks together distributed 91,8 per cent of

assets in the mutual fund sector. Brokers cover the remaining bit of the Spanish retail

market. Among banks two key players dominate the distribution of Spanish mutual funds.

Santander Gestion de ActivosandBBVA Gestionaccount for a combined market share of

46 per cent. In comparison to other European mutual fund markets, this is a high

concentration for the two top players. However, the remaining market is quite dispersed

with none of the other distributors accounting for a market share of more than 6 per cent.In four out of five years between 1999 and 2003, the best performing fund promoter had

less than five funds in the portfolio. This effect is not unique in the Spanish market and

seems to be a Europe wide phenomenon.

3.5 The UK Mutual Fund Market

The UK mutual fund market is in many aspects different from the continental

European mutual fund markets. Over the period from 1999 to 2003, the British fund

market experienced a decline in assets under management. The compound annual growth


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rate of -1,7 per cent over that period means that the UK fund market was the second worst

performing one in Europe over that period.

The depressing performance of the UK mutual fund market over that period mainly

originates in the stock markets downturn during those years. As exhibited in figure 3.9,

the mutual fund markets development is highly dependent on the development of the

stock market. In fact, 93 per cent of the decrease in overall UK mutual funds between 2000

and 2002 was driven by equity funds. With an equity exposure of about two-thirds, the

UKs mutual fund market has by far the highest equity exposure of all European countries.

Of the countries investigated, Italy had Europes second highest equity exposure in 2003,

amounting to 27,9 per cent of total assets invested in mutual funds. This is still more than

two times smaller than the UKs relative equity stake in its mutual fund market.

Figure 3.9 UK Mutual Funds (retail and non-retail) under Management segmented by Typeof Fund, 1999-2003 in m

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1999 2000 2001 2002 2003

Other


Property

Money Market

Bond

Balanced

Equity

Source: Datamonitor

Another peculiarity about the UK mutual fund market concerns the distribution channels.

While Banks are responsible for the majority of mutual fund assets distributed in

continental Europe, they do not play a role in the UK. Instead, independent intermediaries

distribute about two thirds of the mutual funds in the UK. Direct distribution, private client

distribution and sales force tied agents share the rest of the UK mutual fund market.

Distribution in the retail segment, which accounts for roughly one-half of the

overall British mutual fund market, is very fragmented. The market leader Fidelity

Investment has a stake of only 8 per cent of the fund market, while the majority of the

funds are distributed by providers having less than 3 per cent market share. Hence, the UK

market is the most fragmented of all markets under investigation.


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4 Data

Within the framework of this study, two different datasets are used and combined. They

are both generously provided by Dr. Rogr Otten.The first dataset is supplied by Lipper and contains monthly observations of equity

mutual fund returns from January 1992 to March 2006. Included are equity mutual funds

listed in France, Germany, Italy, Spain and the UK. The dataset is free form survivorship-

bias because it not only contains live equity mutual funds but also those that were

previously merged or liquidated. Funds with less than twelve months of data are not

contained in the sample and returns are inclusive of distributions and net of fees. Returns

for each fund in the sample are originally displayed in the funds respective reporting

currency. Since this study attempts to take the view of a European investor, all returns are

converted into Euro. This is done by application of the interest rate parity. Furthermore,

discrete returns are converted into lognormal returns by utilization of the formula

Ln return = Ln (discrete return +1).

The second dataset consists of annual observations of cross sectional data on live

equity mutual funds listed in France, Germany, Italy, Spain and the UK. Unlike the first

dataset, observations in the different countries in the second dataset refer to different

periods of time. While the dataset displays observations of 40 fund characteristics from

1991 to 2005 for France and Italy, it only contains a limited number of years for Germany

(1995 to 2005) Spain (1995 to 2005) and the UK (1996 to 2005). For the purpose of this

study, the characteristic closing net asset value is most important. It is stated in absolute

monetary terms and builds the basis for portfolio formation later on. However, this process

will be described in detail in the next chapter.

Since this study attempts to investigate the effect of fund size as measured by the

net asset value of a fund on performance, the two before mentioned datasets are combined.

For the matching procedure, funds are first matched on the basis of their names, then on

the basis of their ISIN numbers. The resulting merged dataset is the one used in this study.

However, the merged dataset is not free of survivorship-bias. Unfortunately, the cross-

sectional dataset only contains data on funds in existence in the year 2005. Most likely,

underperforming funds have been the ones shut down in the past. This is why the merged

dataset is likely to overestimate actual returns. Unless indicated differently, this study

exclusively relies on the matched dataset.


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Table 4.1 exhibits summary statistics of the merged equity mutual fund sample. Most data

is available for funds distributed in Germany. With a total number of 2434 funds it offers a

broad basis for a performance analysis. Unfortunately, the UK fund sample is limited to

merely 1089 funds. This is due to the cross-sectional database containing only 1445 UK

funds. Additionally, merging the UK datasets comes with a second peculiarity. While 22

per cent of the UK funds in the return database invest domestically, this ratio doubles once

the datasets are merged. Hence, 43 per cent of UK funds in the merged sample invest in

domestic stocks. Apart from the total number of funds contained in the dataset, table 4.1

exhibits the average number of funds. It accounts for the fact that not all funds included in

the database exist in every year of the sample period. Especially in the early years of the

sample period the number of funds for which data is available is very low. Fortunately, the

number of UK funds is quite stable throughout the years. Therefore, problems connected to

the low absolute number of funds are somewhat mitigated.

Table 4.1 Summary Statistics

Total Number Average Number Excess Return Std.Dev.

France

All Funds 1815 1046,4 2,91% 20,28%

Global ex Europe 927 546,2 1,35% 21,57%

Europe ex Domestic 673 358,2 4,91% 19,87%

Domestic 215 140,3 4,61% 21,31%

Germany

All Funds 2434 1308,4 3,60% 21,36%Global ex Europe 1495 799,2 2,33% 22,58%


Domestic 107 76,2 4,23% 25,25%

Italy

All Funds 1613 823 1,69% 19,64%

Global ex Europe 1023 509,3 0,10% 21,39%


Domestic 97 56,2 5,59% 18,95%

Spain

All Funds 1701 877,8 1,94% 20,41%

Global ex Europe 1014 517,6 0,49% 22,89%

Europe ex Domestic 562 293,2 4,01% 19,63%Domestic 125 66,5 4,44% 18,62%

UK

All Funds 1089 698,1 5,56% 20,37%

Global ex Europe 495 320,6 3,97% 22,31%

Europe ex Domestic 129 87 8,34% 20,76%

Domestic 465 290,3 6,57% 19,26% Summary statistics for the merged sample. Sample period is from January 1992 to March 2006.Funds are separated by country of distribution and geographical investment focus. Excess returnsare in excess of the risk-free rate, lognormal, annualized, expressed in Euros, and net of expenses.For each portfolio, returns are calculated as equal-weighted average returns of all funds in aportfolio. Standard deviations are also annualized.

The lognormal net excess returns presented in column three demonstrate large differences

by country and geographical investment focus. They range from 0,1 per cent for Italian


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globally investing stocks to 8,34 per cent for UK funds with a European investment focus.

Throughout all five countries, investments in equity mutual funds with a global investment

focus yielded the lowest excess returns. Risk as measured by the standard deviation of

excess returns ranges from 18,62 per cent for domestic Spanish funds to 25,25 per cent for

domestic German funds. Overall, risk differences across countries and investment focuses

are quite small.

Table 4.2 displays the size of the average fund as well as the size of the median

fund for each year. The relation between the size of the average fund as calculated with the

arithmetic average to the median funds size is the same in all five countries. The median

fund is much smaller throughout all years under investigation. The ratio of average fund

size divided by median fund size varies from 1,6 to 4,9 and indicates that the distribution

of size is skewed. Some very large players seem to drive up the size of the average fund.

When investigating this further, this hypothesis is confirmed. In Germany 2005, the largest

fund (Fidelity European Growth Fund A) is more than 3 times larger than the second

largest fund (Templeton Growth Euro A Acc). Furthermore, the largest fund is more than

13 times larger than the tenth largest fund (Union Uni Europa). The corresponding ratios

in the other continental European countries are of the same size. In the UK these numbers

are considerably smaller, largest divided by second largest yields a ratio of 1,5, while

largest divided by tenth largest yields a ratio of 5,4.

As an example for the extreme skewness, a histogram of the size distribution for

the German market in 2005 is provided in figure 4.1. About 81 per cent of the funds have a

net asset volume of less than 250 million . As depicted in figure 4.2, 58 per cent of the

mentioned 81 per cent are even smaller than 50 million . This means that almost one half

of the funds in the German market 2005 are smaller than 50 million . To put that into

perspective, almost 50 per cent of the funds are more than 319 times smaller than the

largest fund (Fidelity European Growth Fund A) in the sample.

Patterns of size increases and decreases are similar throughout all five countries. The

average and median fund size increases monotonically from 1991 to 2000. The only

exceptions are the UK, where the average and median equity mutual funds net asset

volume already declines from 1999 to 2000 and Italy, where it decreases from 1991 to

1992. From 2000 to 2002, net asset volume of the average and median fund decreases in

France and Spain. After 2002, net assets in these countries continue to increase. In

Germany, Italy and the UK, net assets of the average and median fund decrease one more


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year, from 2000 to 2003. Subsequently, the average and median funds net assets continue

to rise in these three countries.

Table 4.2 Development of the average and median size fund from 1991 to 2005 in m

Year Ave. Size Median Size Ave. Size Median Size Ave. Size Median Size

1991 6,93 3,47 n/a n/a 23,04 4,74

1992 16,03 5,87 n/a n/a 17,16 6,32

1993 40,86 17,89 n/a n/a 50,77 19,41

1994 60,40 29,29 n/a n/a 63,49 28,98

1995 63,36 29,59 61,68 34,86 70,11 34,68

1996 79,07 43,22 94,57 41,39 85,05 46,79

1997 108,69 59,64 133,74 52,03 133,62 77,87

1998 118,56 53,18 163,23 53,46 169,08 78,18

1999 201,15 91,25 228,85 77,28 274,19 105,42

2000 298,32 113,25 299,91 91,84 329,85 129,35

2001 196,90 59,36 207,23 57,92 228,54 84,64

2002 148,46 39,98 149,59 41,70 158,73 52,78

2003 148,63 45,88 143,46 39,40 156,38 50,272004 181,22 52,31 174,75 49,09 188,13 56,88

2005 321,30 84,13 209,32 58,70 225,11 62,74

Year Ave. Size Median Size Ave. Size Median Size

1991 n/a n/a n/a n/a




1995 61,94 36,39 n/a n/a

1996 70,48 43,53 135,26 78,61

1997 102,43 61,05 178,92 65,99

1998 130,23 66,37 193,44 66,611999 171,83 96,10 229,82 85,90

2000 277,78 106,06 218,08 81,49

2001 201,96 62,96 179,45 57,67

2002 107,96 32,36 145,76 47,35

2003 118,02 36,08 125,64 46,12

2004 156,35 44,71 145,32 50,25

2005 277,32 78,73 165,55 49,48

Spain UK

France Germany Italy

Figure 4.1 Size Distribution of German Equity Mutual Funds, 2005 in m (large picture)

410

4820 9 16 3

0

50

100

150

200

250

300350

400

450

< 250 250 - 500 500 - 750 750 - 1000 1000 -

2000

> 2000

Note: The horizontal axis displays the funds size in million. The vertical axis displays the numberof funds that belong to a certain size category as specified by the horizontal axis.


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Figure 4.2 Size Distribution of German Equity Mutual Funds, 2005 in m (small picture)

238

81

29 29 33

0

50

100

150

200

250

< 50 50 - 100 100 - 150 150 - 200 200 - 250

Note: The horizontal axis displays the funds size in million. The vertical axis displays the numberof funds that belong to a certain size category as specified by the horizontal axis.

While the average and median funds size is of comparable size in the continental

European countries, UK funds are different. They used to be much larger than their

continental European counterparts prior to 1999. However, the average and median funds

size in 2005 is considerably smaller in the UK than in the continental European countries.

This means that the size differences over time among UK equity mutual funds are much

less volatile than in the other countries. The reason for this might be that the UK equity

mutual fund market is much more mature and looks back to a much longer history than the

other markets. However, a deeper analysis of this finding is beyond the scope of this study.

Table 4.3 exhibits equity mutual funds geographic investment focuses per country.

Displayed are time-series averages of cross-sectional averages. In the four continental

European countries most equity mutual funds invest with a global focus, while roughly

every seventh fund in these countries invests with a European perspective. These funds are

followed by funds investing in the USA and by those investing in Japan. Between eight

and ten per cent of the funds invest in the USA and between seven and nine per cent of the

fund invest in Japan. In France, Germany and Italy, the domestic market is the fifth most

invested in. This means that in the continental European countries, a relatively small share

of funds is confined to domestic investment. About every 20 th fund offered in these

countries invests in domestic stocks, only. Equity mutual funds investing in the ten most

popular investment regions constitute between 74 per cent (Spain) and 79 per cent (France)

of all funds sold in the respective countries. The remaining funds not listed in table 4.3

mainly invest in regionally very constrained, smaller and thus less liquid markets such as

Korea, Malaysia or Norway.


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Once more, the picture looks different in the UK. Over 40 per cent of the UK funds in the

sample invest in the domestic market. Other investment focuses such as Global, Europe or

Japan are represented in roughly the same proportions as in the continental European

countries. Furthermore, the UK market is more concentrated than the four other markets

under investigation. Equity mutual funds investing in the ten most popular investment

regions account for 97 per cent of all UK funds in the sample. Hence, only a very small

fraction of UK funds invests in investment regions different from the most popular ones.

Table 4.3 Investment Focus: Time-Series Averages of Cross-Sectional AveragesFrance Germany Italy

Global 16,33% Global 22,40% Global 20,09%

Europe 15,68% Europe 15,67% Europe 15,14%

United States of America 9,53% United States of America 9,16% United States of America 8,85%

Japan 8,29% Japan 7,86% Japan 8,35%

France 6,14% Germany 5,72% Italy 6,40%

Eurozone 6,10% Asia (ex-Japan) 4,96% Asia (ex-Japan) 5,10%Asia (ex-Japan) 4,55% Global Emerging Markets 3,87% North America 4,75%

United Kingdom 4,24% North America 3,81% Global Emerging Markets 4,54%

North America 4,15% United Kingdom 3,22% EuroZone 3,51%

Global Emerging Markets 4,05% EuroZone 3,17% Asia Pacific 2,55%

Spain UK

Global 17,91% United Kingdom 43,35%

Europe 13,87% Global 16,56%

United States of America 9,06% Europe exc UK 9,37%

Japan 8,59% Japan 7,34%

EuroZone 5,54% North America 4,58%

Spain 4,37% Far East 4,21%

Asia (ex-Japan) 4,23% United States of America 4,10%

North America 3,71% Europe 3,28%United Kingdom 3,71% Global Emerging Markets 2,53%

Global Emerging Markets 3,26% Asia (ex-Japan) 2,01% Note: This table reports summary statistics for the funds contained in the sample. Reported are time-seriesaverages of cross-sectional averages of the funds regional investment focus. Sample periods differ by countryand are the same as reported in table 4.1.


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5 Methodology

Methodologically, previous studies investigating the effect of fund size on performance

such as Grinblatt and Titman (1989) or Ciccotello and Grant (1996) define fund size as

stationary. However, several disadvantages are connected to this method. First, defining

size as stationary goes along with the implicit assumption that neither the mean nor the

variance of the size distribution change over time. With respect to equity mutual fund size,

this assumption is not realistic. Second, mutual funds should attract more capital than their

peers if they outperform them and vice versa. This means that funds are larger because

they performed better in the past. In their 2002 study, Otten and Bams refer to this effect as

self-induced correlation, meaning that size and risk-adjusted performance are potentially

positively correlated when looking backwards.

This study takes the attempt to circumvent this problem as described in the next

section. Subsequently, the various performance benchmarks will be described and

discussed.

5.1 Benchmarks

In order to estimate risk-adjusted returns for the equity mutual fund sample, this study

relies on the Capital Asset Pricing Model (CAPM) of Sharpe (1964), the three-factormodel of Fama and French (1993) and the four-factor model by Carhart (1997). In order to

construct these models, performance benchmarks are needed. They are an approach to

account for heterogeneity in fund styles. Market proxies are constructed by Dr. Rogr

Otten and obtained from the Worldscope universe of the respective country. All stocks that

are larger than $25 million are considered. Five different market proxies are used in this

study, each reflecting different geographical investment focuses. In a second step, the local

risk-free rate as proxied by the one-month interbank rate is subtracted from the market.

Thus, for each of the following investment focuses a distinct market proxy is employed:

Global, Europe, France, Germany, Italy, Spain and the UK. Since the risk-free rate is

different in each country, 15 different time-serieses of excess market return are obtained.

Apart from the market proxies, factor mimicking portfolios are constructed in order

to account for style specific returns. While some scholars like Fama and French (1993)

attribute these returns not to a specific style but to risk, a comprehensive discussion of the

factors correct interpretation is beyond the scope of this study. All stocks within an

investment region are sorted by size in order to calculate SMB. SMB is the difference

between the returns on a portfolio containing large stocks minus the returns on a portfolio


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containing small stocks. In order to calculate HML, stocks are ranked according to their

book-to-market ratio.HMLis the return differential between a portfolio containing the top

30 per cent of stocks with respect to the book-to-market ratio and the bottom 30 per cent of

stocks with respect to the book-to-market ratio. For the formation of the European MOM

factor, this study follows Rouwenhorst (1998) instead of Carhart (1997) and first ranks

stocks based on their prior 6-month return. MOM is the return differential between a

portfolio containing the top 30 per cent of stocks with respect to the past 6-month return

and a portfolio containing the bottom 30 per cent of stocks with respect to the past 6-month

return. In order to obtain a rolling momentum factor, these portfolios are rebalanced

monthly. Tables 5.1, 5.2 and 5.3 report summary statistics for the various portfolios that

are comprised in the performance benchmarks.

Table 5.1 Summary Statistics for Benchmark Portfolios Domestic Funds

Panel A: France Domestic

Monthly

Factor Excess Std t-stat for

Portfolio Return Dev Mean = 0 Market SMB HML MOM

Market -0,63% 5,78% -0,75 1,00

SMB 0,75% 2,74% 1,90 -0,34 1,00

HML 0,72% 4,11% 1,21 0,38 0,08 1,00

MOM 0,32% 6,97% 0,32 -0,71 0,34 -0,44 1,00

2001/1 - 2004/12

Panel B: Germany Domestic

MonthlyFactor Excess Std t-stat for


Market -0,02% 6,06% -0,02 1,00

SMB 0,09% 4,26% 0,19 -0,79 1,00

HML 0,99% 6,30% 1,33 -0,12 -0,09 1,00

MOM -0,56% 8,84% -0,54 -0,20 0,24 -0,71 1,00

1999/1 - 2004/12

Panel C: Italy Domestic

Monthly



Market -0,49% 5,51% -0,62 1,00SMB 0,14% 2,30% 0,43 -0,23 1,00

HML 0,62% 3,65% 1,18 -0,24 0,48 1,00

MOM 0,16% 5,64% 0,20 -0,62 0,38 0,36 1,00

2001/1 - 2004/12

Panel D: Spain Domestic

Monthly



Market 0,10% 5,47% 0,12 1,00

SMB 1,07% 3,26% 2,27 -0,65 1,00

HML 0,59% 4,39% 0,93 -0,35 1,00

MOM -0,29% 5,79% -0,35 -0,76 0,68 0,35 1,002001/1 - 2004/12

Cross-Correlations

Cross-Correlations

Cross-Correlations

Cross-Correlations


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Table 5.1 Continued

UK Domestic

Monthly



Market 0,36% 4,69% 0,76 1,00

SMB 0,58% 3,99% 1,43 0,38 1,00HML 0,22% 4,26% 0,51 0,21 0,04 1,00

MOM 0,46% 6,26% 0,72 -0,04 0,27 -0,55 1,00

1999/1 - 2005/12

Cross-Correlations

Summary statistics for loadings of the four fund portfolios sorted on different factorsand separated by country. The market factor is the return on the total universe of thedifferent investment regions according to Worldscope. The excess return iscalculated by subtracting the local one-month interbank rate. SMB is calculated bysubtracting the top 80% of market capitalization from the bottom 20% of marketcapitalization. HML is constructed by subtracting the bottom 30% of book-to-marketration from the top 30%. MOM is calculated by subtracting the prior month bottom30% performing stocks from the top 30% performing stocks. Returns and standard

deviations are stated as monthly. Time periods coincide with the respective timeperiods used in the time-series analysis. Panel (A) through (E) report statisticsreferring to France, Germany, Italy, Spain and the UK, respectively.

Table 5.2 Summary Statistics for Benchmark Portfolios European Funds

France Europe

Monthly



Market 0,73% 4,78% 1,68 1,00

SMB 0,20% 2,15% 1,00 -0,30 1,00

HML 0,52% 3,50% 1,64 -0,09 0,06 1,00

MOM 0,11% 5,23% 0,24 -0,11 0,21 -0,58 1,00

01.1996 - 12.2005 / 120 Observations

Germany Europe

Monthly



Market 0,74% 4,79% 1,69 1,00

SMB 0,20% 2,15% 1,00 -0,30 1,00

HML 0,52% 3,50% 1,64 -0,09 0,06 1,00

MOM 0,11% 5,23% 0,24 -0,11 0,21 -0,58 1,00

01.1996 - 12.2005 / 120 Observations

Italy Europe

MonthlyFactor Excess Std t-stat for


Market 0,65% 4,76% 1,49 1,00

SMB 0,20% 2,15% 1,00 -0,30 1,00

HML 0,52% 3,50% 1,64 -0,08 0,06 1,00

MOM 0,11% 5,23% 0,24 -0,11 0,21 -0,58 1,00

01.1996 - 12.2005 / 120 Observations

Cross-Correlations

Cross-Correlations

Cross-Correlations


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Table 5.2 Continued

Spain Europe

Monthly



Market 0,57% 4,93% 1,20 1,00

SMB 0,20% 2,15% 1,00 -0,29 1,00HML 0,52% 3,50% 1,64 -0,08 0,06 1,00

MOM 0,11% 5,23% 0,24 -0,11 0,22 -0,57 1,00

01.1997 - 12.2005 / 108 Observations

UK Europe

Monthly



Market 0,25% 4,89% 0,50 1,00

SMB 0,36% 2,10% 1,68 -0,20 1,00

HML 0,66% 3,86% 1,67 -0,07 0,03 1,00

MOM 0,05% 5,78% 0,09 -0,15 0,27 -0,58 1,00

01.1998 - 12.2005 / 98 Observations

Cross-Correlations

Cross-Correlations

Summary statistics for loadings of the four fund portfolios sorted on different factorsand separated by country. The market factor is the return on the total universe of thedifferent investment regions according to Worldscope. The excess return iscalculated by subtracting the local one-month interbank rate. SMB is calculated bysubtracting the top 80% of market capitalization from the bottom 20% of marketcapitalization. HML is constructed by subtracting the bottom 30% of book-to-marketration from the top 30%. MOM is calculated by subtracting the prior month bottom30% performing stocks from the top 30% performing stocks. Returns and standarddeviations are stated as monthly. Time periods coincide with the respective timeperiods used in the time-series analysis. Panel (A) through (E) report statisticsreferring to France, Germany, Italy, Spain and t

mutual fund performance in europe

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