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Investment Analysis and Portfolio ManagementOtten, R., and D. Bams (2002) European mutual fund performance, European Financial Management, 8, 75-
101.
1. Introduction
By 1998 the US mutual fund industry reached record levels ($5.2 trillion in assets). Most academic studies
conclude that the net performance of mutual funds (after expenses) is inferior to that of a comparable
passive market proxy. However, some contradictory studies emerged. Some found that mutual funds had
enough private information to offset the expenses they made. Also, there was evidence of persistence in
mutual fund performance over short-term horizons. Carhart (1997) however argues that this effect is mainly
due to momentum strategies, and not to superior fund management.
Malkiel and Gruber claimed that many studies were subject to survivorship biasà when adjusting for this
effect, mutual funds underperform the market proxy, by the amount of expenses they charge the investor
à Investing in a low-cost index fund is preferred over choosing an actively managed fund.
The European market for mutual funds lags the US market when it comes to both size and market
importance. Still, the European market has experienced large inflows.
This study focuses on the performance of European funds (both dead and surviving) only investing in their
domestic market.
Ø Give an overview of the largely unexploited European mutual fund area. à fund performance evaluated
using a unique survivorship bias controlled databases consisting of 506 mutual funds from 5 European
countries. Both unconditional and conditional versions of the Carhart 4-factor model are applied.
Ø Investigate whether past performance predicts future performance (“hot hands effect”). Also, the
influence of several characteristics is considered.
è Results suggest that European mutual funds (especially small cap funds) are able to add value, as
indicated by their positive alphas. If management expenses are added back, 4 out of 5 countries show out-
performance at an aggregate level. Finally, there’s strong persistence in mean returns for funds investing in
the UK.
2. The European Mutual Fund Industry
By the end of 1998, there was $2.66 trillion of assets under management in EMF. As a proxy for the European
market, the 6 most important EMF markets are considered.
Although the 6 most important EMF markets together account for less than a half of the US mutual fund
market, the European number of funds exceed the US number of funds. à the average size of the EMF is
much smaller than the average size of the US fund. Another difference between the US and the European
mutual fund market is the dominance of equity-oriented funds in the US; while European investors also
invest much in the bond funds à might be due to a different equity culture, strong presence of banks, and
a different pension system. However, the asset allocation of European mutual funds through time has been
changing: the percentage of assets invested in equity mutual funds has been rising. This increase has been
at the expense of money market funds.
è the European (equity) mutual fund market is smaller than the market in the USS: however, Europeans do
not necessarily have less exposure to the equity market as they can also purchase equity themselves or
through other institutions.
à the statistic is calculated as the total market value of equity mutual
funds divided by the domestic market capitalization. The European mutual
fund sector is not as important as its American counterpart indicating that
individuals possibly purchase equities through other channels. The
increasing importance of the mutual fund sector can be derived from the
increasing percentage through time, in both Europe and USA.
3. Data
3.1 European mutual funds
Database contains the 5 most important mutual funds countries (together they cover >85% of total assets
in European funds). Only pure domestic equity funds with at least 34 months of data are considered à506
open-ended equity mutual funds with monthly logarithmic returns from 01/1991 to 12/1998.
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Survivorship issues can influence results severely, that is when a database consists only of funds that have
data available during the whole sample period à bad track records are “cancelled” à overestimation of
the average performance as only surviving funds are evaluated. Here, dead funds were included until they
disappeared, after which the portfolios are re-weighted accordingly.
The % of disappearing funds is: Germany (5%), Italy (6%), NL
(11%), UK (25%). Restricting the sample to only surviving
funds would lead us to overestimate average returns by
0,12% (Germany), 0,45% (Italy), 0,11% (NL), and 0,15% (UK).
3.2 Benchmarks
All stocks that are in the Worldscope universe for each
country are considered. All stocks are ranked based on size and the bottom 20% of total market
capitalisation is assigned to the small portfolio. SMB is the return difference between small and large. For
the HML factor, all stocks are ranked on their book to market ratio. The top 30% of market capitalisation is
assigned to the high ratio portfolio, while the bottom 30% to the low ratio portfolio. HML is obtained by
subtracting the low from the high book-to-market return. The momentum factor portfolio is obtained by
ranking all the stocks on their prior 6-month return. The return difference between the top 30% and bottom
30% by market capitalization gives the Pr6m factor returns.
4. Performance Measurement
4.1 Mutual Fund Performance Models
In studies that use CAPM single index model, the intercept (Jensen alpha), is usually interpreted as a measure
of out- or under-performance relative to the used market proxy. Such a CAPM model however assumes that
a fund’s investment behaviour can be approximated using only one single market index à better to use a
multifactor model due to the diversity of investment styles.
In the Fama and French 3-factor model, besides a value-weighted market proxy, two additional risk factors
are used, size and book to market. This model, however, is still unable to explain the cross-sectional variation
in momentum sorted portfolio returns. à Carhart adds a fourth factor that captures the momentum
anomaly à results in a performance attribution model, where the coefficients and premia on the factor-
mimicking portfolios indicate the proportion of mean return
attributable to 4 elementary strategies:
In the table on the right above, the premium on the SMB factor
is negative à small stocks suffered during the period examined.
The momentum portfolio shows that momentum strategies
only add value in 3 out of 5 countries, especially in Italy and UK .
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Because of the negative correlation between SMB and PR6m factors, it could be that stock momentum is
more pervasive amongst large stocks than small stocks. The small cross-correlations suggest that
multicollinearity doesn’t affect the estimated factor loadings.
The results provide evidence for the 4-factor model instead of the single index model: for 85% of the funds,
the null that SMB, HML and Pr6m are jointly 0 can be rejected at the 5% level. The remaining 15% mainly
concerns index funds for which the market index should be the sole benchmark to use.
4.2 Results
The positive SMB loadings for the majority of funds indicate that the
returns are driven relatively more by smaller stocks. The HML factor
seems to add a bit less explanatory power, as only half of the loadings
are significant at 5%. Overall, funds seem to follow a more value-
oriented style. Pr6m also shows up significantly in about half of the
cases, while the sign of the coefficient is mostly negative à
contrarian strategies.
European mutual funds seem to prefer smaller stocks and stocks with
high book.to-market ratios (value). In the US, small stocks and stocks
with low book-to-market ratios are preferred, possibly due to agency
problems within institutions. It also seems that European mutual
funds are not employing simple momentum strategies like the US
funds do. The results are mixed, since they suggest that European
funds are both contrarian and momentum oriented.
On an aggregate country level, negative alphas are reported for
Germany, while all other countries have positive alphas. Significant
outperformance can only be found in the UK funds.
Moreover, small cap funds deliver outperformance in 3 out of 4
countries. The results in the last column confirm that 28% of small
cap alphas are positive. à small cap funds seem to add value.
The percentage of positive alphas is very high for UK funds à may be
due to the negative exposure of most funds to the momentum
portfolio.
4.3 Robustness of The Results
Since some funds invest in higher yielding and risky bonds, which is not picked up by the risk-free rate, the
inclusion of a bond index in mutual fund performance assessment can be includedà introduction of the
excess return on a local government bond index in the equation à 5
factors. The results show that European mutual funds are only to a small
extent exposed to bond returns à no significant loadings on the bond
index. Also, the observed alpha estimates do not change significantly
when the bond index is included.
The opposite problem could be over-specification of the model. While
the Fama and French factors SMB and HML are both based on actual
investment strategies, the momentum factors is not that clearly defined
in asset management à test the model without the momentum variable.
The adj. R-square of the 3-factor model is equal to or lower than the adj.
R-square of the 4-facto model in all cases. à out-performance of small
cap funds is not driven by the inclusion of the Carhart momentum factor.
Biases can arise if managers trade on publicly available information à if
dynamic strategies are involved. Average alphas calculated using a fixed
beta estimate are unreliable if expected returns and risk vary over time
à conditional performance measurement.
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Conditional alphas are in column 4. Although in
over two thirds of the cases the hypothesis of
constant betas can be rejected at 5%, the
estimated conditional alphas do not differ that
much from the unconditional ones. On average
they increase and make several investment
style portfolios outperformers.
è results are driven by time-variation in betas.
The final robustness check is the influence the
fund-weighting scheme exerts on the results
àconstruct portfolios of funds based on
individual asset size and examine 4-factor
alphasè the use of equally weighted portfolios
does not severely influence the earlier results,
as cap weighting only strengthen the results.
4.4 Management Fees
So far, management fees were already deducted from the fund’s return. Most mutual funds are able to
follow the market, with alphas insignificantly different from 0. If, however management fees are deducted,
funds underperform the market by the amount of fees they charge to the investor. When adding
management fees back to fund returns, most countries exhibit positive alphas on the models that are
adapted. Only German funds still underperform, but insignificantly. The results suggest that European funds
(in contrast to US funds) are sufficiently successful in finding and implementing new information to offset
their expenses, and therefore add value for the investor.
5. Persistence
The hypothesis that mutual funds with an above-average return in this period will also have an above-
average return in the next period is called the hypothesis of persistence in performance. To investigate
whether persistence in mutual fund performance is also present for European funds, all funds within a
specific country were ranked based on the past 12-month return. Funds with the highest previous 12-month
return go into portfolio 1, and funds with the lowest return go into portfolio 3, 4, or 10. These equally
weighted portfolios are then held for a year (performance period) and then rebalanced again, based on their
last 12-month return. à done until a time series of monthly returns on these portfolios is created. For all
examined countries, there is a monotonically decreasing excess return moving from the high- to the low-
past performance portfolios.
Since it could be argued that the funds in portfolio 1 receive higher returns in compensation for higher risk,
the Carhart 4-factor model is used to control for risk factors. Controlling for market risk, book-to-market,
size and stock price momentum doesn’t consume the spread between the high and low portfoliosè Results:
France, Germany, Italy exhibit weak or no persistence. UK funds show strong persistence.
It may also be useful to use the Fama 3-factor model, dropping the momentum factor è Results: persistence
of France and Germany remains weak. Italian funds exhibit strong and significant persistence. This result
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