Timid Performance Fees in Mutual Funds, No Signal for Investors
M Teresa Corzo Santamaría, Carlos Martínez Ibarreta, Juan Rodriguez Calvo
Universidad Pontificia Comillas. ICADE.
Abstract
In this paper, we test for the implications on investors’ performance of the fee structure
that has prevailed in Spain since 2008. We compare the risk-adjusted performance
measures for mutual funds with and without performance fees within the same classes
of investment policies. Using a dynamic panel data model, we conclude that funds that
charge performance fees earn superior risk-adjusted returns; however, they fail to attract
investors. We offer a plausible explanation for this fact. Our results do not support the
presence of economies of scale or learning economies, but we find evidence of smart
money and are the first to report this finding for Spain. Because almost all funds in this
market that charge a performance fee also charge a regular management fee, we
consider the role exercised by performance fees to be timid.
JEL codes: G20, G23, G11, C23
Keywords: mutual funds, performance fees, risk adjusted returns, costs, smart money
1
1. Introduction
In 2008, Spain undertook a regulatory change in the mutual fund industry to provide
incentives to mutual fund managers in an effort to align the interests of managers and
those of the final investors and to increase the competitiveness of the sector. This
research focuses on this special structure of performance fees in the mutual fund
industry, and the main contribution is the evaluations of whether the mixed fees system
introduced affected investors’ risk adjusted returns and funds’ inflows.
Since 2008, the fee structure applied in Spain has allowed for the charging of fees on
assets, return or both, with some limits. The regulation 1 stipulates the annual maximum
permissible for each type of fee: If the fund charges fees based only on performance,
then the maximum is 18% of the fund’s returns; if the fund charges mixed fees, then
there is a maximum of 1.35% charged on assets managed and a maximum of 9%
charged on a fund’s returns. Mixed funds are mutual funds that charge management fees
totally or partially on returns. This mixed structure is followed in Spain by almost all
funds that have chosen to charge performance fees. (Hereafter, we will refer to the
performance fee as PERFEE.) Of the more than 1400 funds analyzed in this work, we
find that 234 charged mixed fees, and just four funds in our sample charged purely the
PERFEE. Thus, we consider the use of performance fees in this context to be timid. The
remainder of the funds in our sample, and the vast majority of funds in the Spanish
industry, charged fixed fees based totally on assets under management (we will
henceforth refer to these funds as ASSETFUNDS.)
To the best of our knowledge, the particular fee structure in place in Spain since 2008
differs from other specifications studied so far. 2 There is no necessity for the symmetry
of the performance-based fee, and no requirement is established for a reference
portfolio; therefore, fees are paid starting with the first cent of positive returns. The new
regulation, in addition to modifying the maximum fees, also establishes a high water
mark. Performance fees will be charged in year t only if the end-of-year net asset value
(NAV) of the fund is higher than at the end of year t-1; if it is lower, no performance
fees will be charged, and the NAV of year t-1 will become a water mark. After three
years of NAVs under the water mark, this water mark will be reset and will correspond
to the fund’s NAV at the end of the year t+2. An interesting and questioning essay
concerning the way Spanish managers apply the high water mark rule was published by
Morningstar (Saenz de Cenzano, 2014), raising many points given the regulation’s lack
of clarity.
The Spanish mutual fund industry is characterized by being far from perfect
competition: There is a strong presence of banks on the supply side and many
1
The applicable regulation is the Royal Decree 1309/2005, partially modified later on by the Royal
Decree 749/2010, and the Instruction 6/2008 CNMV.
2
The fees we study here are different from the known fulcrum fees used in the United States. Fulcrum
fees refer to incentive fees centered on an index. As Elton et al. (2003) show, the difference between
fulcrum fees and never-negative fees is the existence of the upper limit on fees. Spanish fees also have an
upper limit, but they are not centered on an index.
2
unsophisticated investors subject to tax binds in the demand side. A detailed description
of this market’ features and some ethical considerations can be found in Marco (2009)
and Toledo and Marco (2010). However, since the introduction of MIFID in 2008, we
have been witnessing a process of modernization in the investment industry and an
increasing professional presence, yielding as a result an evolving financial market with
important changes happening quickly.
Using a dynamic panel data model with daily and monthly data from 2008 to 2012, in
this paper, we analyze eight mutual fund categories (investment policies) in which funds
that charge mixed fees have enough representation. As stated above, in Spain, almost all
funds that charge performance fees also charge mixed fees. We will refer to this group
as PERFUNDS to differentiate it from ASSETFUNDS. We compare the groups and
analyze whether there is any difference in the risk-adjusted performance (as suggested
by Diaz Mendoza et al. 2014), and in the cash inflows attracted. In addition, we are able
to test for the presence of economies of scale, learning curves, and risk and cost effects
on performance.
The contribution of this research is threefold. First, we analyze the broadest and most
current dataset studied so far for the Spanish market by combining data from
Bloomberg and the Spanish Financial Markets Regulator (CNMV). We cover a time
period with a significant change in regulation, and we observe the industry reaction to
this change and its repercussion on performance measures.
From a theoretical point of view, Das and Sundaram (1998) indicate that under the
assumption of risk aversion, the optimal contract must be linear and include a base fee
for the amount of assets under management and an additional remuneration depending
on the returns above those of a benchmark portfolio. Our fund sample complies with
this definition, except they do not need to beat a benchmark; positive returns are enough
to receive the additional remuneration.
Second, with a dynamic panel data model and using risk-adjusted performance
measures (also called alphas), the results obtained in this paper allow us to conclude that
performance fees have somehow motivated managers to obtain better results, once all
risk factors have been taken into account, but they have failed to attract money. We
offer a plausible explanation related to the prevalence of mixed fees on the market
analyzed and the weakened message sent to investors. As the novel research by Ferson
and Lin (2014) shows, investors’ disagreement and heterogeneity are economically
significant in the behavior of fund investors. Positive alphas do not indicate that an
investor would want to buy a fund.
Finally, an interesting finding first reported for the Spanish market is the presence of
smart money and a significant short-term performance reversion.
The closest studies to ours in relation to the Spanish mutual fund industry are DiazMendoza et al. (2014) and Toledo and Marco (2010). Diaz-Mendoza et al. (2014)
analyze funds under the previous Spanish regulation and the sample years considered
3
(1999-2009); their paper differs from ours in several respects being the most important
one the different focus: they analyze whether the way expenses are charged is relevant
to mutual fund performance and to the performance-expenses relation, while we test for
a broader set of hypothesis adding the perspective of the effect of the mixed structure on
money inflows. Also, the samples are different (data periodicity, time period under
study, funds selected for the study), and the econometric approach, cross-sectional
regressions versus dynamic panel data. The paper by Toledo and Marco (2010), takes a
descriptive tilt and uses funds’ sample annual observations from 1993 to 2001,
nonoverlapping with ours and with a very different regulatory environment.
The rest of the paper is organized as follows. Next, in the second section, we briefly
review the literature and describe the main relationships tested. In section 3, we explain
the construction of the variables under investigation, and in sections 4 and 5, we detail
the empirical estimations and discuss the results. Finally, in section 6, we conclude.
References can be found at the end.
2. Performance Fees and Mutual Funds
Although many articles have theoretically analyzed the optimality of a performancebased fee structure (see Diaz-Mendoza et al., 2014, for a detailed list), the extensive
empirical literature on mutual fund performance has found somewhat disparate results
in relation to management ability. Some studies, such as Gruber (1996) and Cahart
(1997), find that active managers fail to outperform passive benchmarks. Other studies,
such as Wermers (2000) and Elton et al. (2003), indicate that active managers hold
stocks that beat their characteristic benchmark portfolios.
The most obvious purpose of incentive compensation is performance encouragement,
and we find a number of theories that offer explanations for the effectiveness of this
practice. Grinold and Rudd (1987) provide an early study in this vein. As Elton et al.
(2003) explain, the principal reason is that according to the theory of incentive
contracting (related to the so-called agency theory), incentive fees align managers’
interests with investors’ interests. The model shows that managers paid through
incentive compensation will outperform managers who receive fixed fees. Other
theories, such as the signaling theory, state that performance fee contracts screen or
signal managers who are less risk averse, and these contracts may be used by investors
who wish to hire and encourage aggressive managers.
Given that the literature has not reached a conclusion about the effect of performance
fees on funds’ performance and that we have faced a non-standard structure of
performance fees in Spain since 2008, the main hypothesis tested in this work is:
H1: PERFUNDS should have a higher risk-adjusted fund performance than their
partners, the non-incentive fees mutual funds.
Along with this hypothesis, and following the reasoning of Elton et al. (2003) and Sirri
and Tufano (1998), we hypothesize that if incentive contracts have superior
4
performance, then they should attract more new cash-flows than funds without incentive
contracts (not previously tested in the Spanish market):
H2: PERFUNDS attract more new cash flow than ASSETFUNDS.
In addition, our analysis allows us to test and control for the effect of other variables in
risk-adjusted returns; these other factors have been demonstrated to influence costs and
returns. First, we study the size and age of the funds. The argument about the size of the
fund states that an increase in the size of the fund should produce economies of scale
and reduce the expense ratio, thus improving the performance of the fund (e.g., Ferris
and Chance, 1987). Counterevidence is found by Berk and Green (2002). Evidence so
far in the European markets has not been definitive, and in Spain, Toledo and Marco
(2010) find no economies of scale with a sample covering years 1993-2001. Then, do
investors achieve higher returns when they invest in larger funds? We test the following
hypothesis:
H3: There are economies of scale in the Spanish mutual fund industry.
The fourth hypothesis is related to the learning curve. It is rational to expect that the
oldest funds, which have survived different crisis periods, should face better investment
talent and operational efficiency than newer funds. However, the empirical evidence
contradicts this opinion; Berk and Green (2002) find no learning curve in the US
market, and Toledo and Marco (2010) find no learning curve in the Spanish market
when measuring it by its effect in a lower management cost. Based on this previous
evidence, we hypothesize that:
H4: There is no learning curve in our sample, and the oldest funds do not show better
performance results.
One of the most widely documented effects in mutual fund studies is the effect of the
expense ratio (measured as a ratio between the total costs and the value of the net asset
holdings of the fund) or the total costs on various measures of performance. From a
theoretical point of view, funds that incur high costs can survive only if their
performance compensates for those loads; additionally, we can assume that good
managers will try to be rewarded for their work. In this line, some papers find that for
the best governed funds, there is a positive relationship between fees and performance
(Gil-Bazo and Ruiz-Verdú, 2009, Berkowitz and Kotowitz, 2002), although this is not
the case for the vast majority of funds. The negative relationship between funds’
performance and costs is a typical result in the literature (e.g., Barber and Odean 2000;
Brown et al. 2004; Barber et al. 2005). This result has been documented in Spain (e.g.,
Diaz Mendoza et al. 2014; Marco 2007; Martínez 2003) and in other markets (e.g.,
Gruber 1996; Cahart 1997; Tufano and Sevick 1997; Malhotra and Mc leod 1997). A
wide discussion on investing costs can be found in the paper by French (2008). In H5,
we test for this effect:
H5: Cost has a negative effect on performance results.
5
Finally, we include an additional hypothesis to test for persistence in the results. In this
respect, previous research seems to converge. Cahart (1997) suggests that any
persistence in performance is short term. Fama and French (2008), after gathering all of
the relevant literature, find that persistence is sensitive to the way funds are ranked, is
temporary, and largely disappears after 1992.
H6: Performance exhibits short-term persistence.
Some considerations apply. It is possible that in Spain, given the financial culture and
the strong presence of banks and large institutions in this sector, mutual fund managers
do not directly receive the performance fee reward earned; instead, the management
house wins it. This fact would distort the motivation structure because the investment
decision maker may not be rewarded according to his performance and thus the
incentive system will be biased.
3 Data and Variables
3.1 The Funds Sample
Worldwide, the mutual fund industry is among the most successful financial
innovations. The Spanish mutual fund industry has also been very successful, managing
since its inception approximately 25% of savings; however, the assets under
management decreased during our period of study. By the end of 2012, mutual fund
assets amounted to 126,530 million euro, a decrease of 27.7% from 2008 numbers
(Inverco, 2012). The fall was steady during the 2010-2012 period. As an exception, we
primarily observe that only the investment in fixed-income international markets,
emerging markets and indexed management increased. Additionally, during these years
in Spain, the number of mutual fund shareholders decreased by 26%. Overall, the past
few years have been very difficult ones for the mutual fund industry in Spain. The
country’s struggle is in contrast with the worldwide evolution of mutual funds, which
raised assets under management by more than 50% during this period.
Spanish mutual funds registered in the Spanish Financial Markets Regulator (Comisión
Nacional del Mercado de Valores, CNMV) comprise the universe of funds used for this
research. This database is composed of 2417 Spanish funds under the supervision of the
Spanish regulator. The funds finally included in our analysis have been selected
according to the following criteria. Consistent with the official classification 3 , the
Spanish mutual funds are divided into 15 categories; 14 of them have funds with a
mixed structure of fees (i.e., PERFUNDS). However, in some of them, the percentage
of PERFUNDS is very low. We choose to study those categories where the percentage
of PERFUNDS to the total number of funds in the category was at least 5% in
December 2011. There are 8 categories that fulfill this requirement. The most important
ones are Absolute Return and Global, where PERFUNDS account for more than 30% of
the category.
3
These categories are established with regard to the fund investment objective, which determines the
composition of the portfolio; see Instruction 1/2009 CNMV.
6
Because the different categories lack an official index as benchmark, we look at the
funds’ registered brochures to select one or two indexes that may serve as such. We find
that many funds do not include any reference. The indexes chosen to track each
category risk are included in Table 1.
The funds sample we will analyze accounts for more than 40% of the universe of
mutual funds and approximately 81% of PERFUNDS; thus, we consider the sample to
be representative of the performance fund universe. We observe that during our sample
period, the percentage of PERFUNDS in the different categories remained stable, and
we do not observe any increase as a result of the 2008 change in regulation. In this
table, we have not included passive management and guaranteed funds because the
search for alpha is not an objective for them, and performance fees do not play an
incentive role.
Table 1: The Funds Sample
Number of funds and their corresponding categories. In the column on the right hand side, the
market benchmark used is specified.
Category
Fixed Income
International (FII)
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
International Mixed
(FIIM)
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Equity Euro Mixed
(EEM)
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Equity Euro (EQE)
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
International Equity
(INTE)
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Absolute Return
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Global
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Total funds analyzed
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
Universe of funds
registered
# ASSETFUNDS
# PERFUNDS
% PERFUNDS
2008
2009
2010
2011
64
7
10,94%
70
8
50
5
10,00%
59
8
51
4
7,84%
55
6
48
4
8,33%
53
8
11,43%
133
9
6,77%
237
14
5,91%
330
33
10,00%
134
36
26,87%
333
98
29,43%
1301
205
15,76%
2681
227
8,47%
13,56%
111
8
7,21%
186
14
7,53%
242
32
13,22%
151
52
34,44%
188
57
30,32%
987
176
17,83%
2333
223
9,56%
10,91%
103
7
6,80%
178
16
8,99%
241
35
14,52%
153
49
32,03%
195
62
31,79%
976
179
18,34%
2259
228
10,09%
15,09%
93
7
7,53%
161
12
7,45%
260
28
10,77%
148
45
30,41%
208
63
30,29%
971
167
17,20%
2259
207
9,16%
2012
Benchmark
used
40
JP Global
5 Aggregate Bond
12,50% Index TR EUR
52 MSCI World and
JP Global
11
Aggregate Bond
21,15% Index TR EUR
71
EUR IBOXX and
6
Eurostoxx TMI
8,45%
127
7 Eurostoxx TMI
5,51%
211
MSCI World
29
13,74%
115
MSCI World
45
39,13%
192
MSCI World
69
35,94%
808
172
21,29%
2466
211
8,56%
In 2008, the CNMV also changed the categories, i.e., the funds’ classification. This
change has no major effect on our study because all of the categories analyzed existed
7
before the change. Two exceptions apply: The INTE (International Equity) category did
not exist as such before 2009, it was created aggregating 5 previous categories
(International Equity Europe, International Equity US, International Equity Japan,
International Equity Emerging and other International Equity), and the Absolute Return
category was created before it was included in the Global category. Then, for 2008, we
used the AR funds listed in 2009, not having changed the category, and classified them
as AR funds in 2008 (although they were officially Global funds in 2008).
We find that in the sample, the standard performance fees range between 5% and 9%.
There are some funds with a marginal performance fee (below 1%), that were included
in the sample (9 funds, 0.37% of the total and 4.3% of PERFUNDS). Only four funds in
our sample charge solely the performance fee, and they are classified in the Global
category.
For every fund, we gather the following data: daily net asset value (NAV), total assets
for the period 2008-2012, and the inception date plus the historical annual NAV for the
period 2001-2012.
Our sample is free of survivorship bias; the dataset includes all funds that existed in the
categories under study during the period. Survivorship bias implies an important
distortion in the results, as documented by Brown and Goetzmann (1995), Elton et al.
(1996) and Otten and Bams (2004), among others. We have considered funds with at
least one natural year of NAVs, and we have filtered the database, removing the funds
with fewer than 100 shareholders or fewer than 3 million assets under management.
(These funds, according to the Spanish regulation, are under a restructuration process.)
The final filtered sample consists of 2773 fund-year observations.
3.2 Variables and Descriptive Statistics
To analyze the relationships outlined previously in section 2 we detail below the
variables constructed and the main statistics.
The daily RAW RETURNS earned by the investors on fund i in day t is denoted by R it
and is calculated as ln (NAV t /NAV t-1 ). This RAW RETURN is the calculated net of the
total costs borne by the fund. Then, we calculate the net returns (NETRET) as the RAW
RETURNS net of the RISK-FREE RATE:
𝑅𝑖𝑡 − 𝑅𝑓𝑡
where R ft is the risk-free rate; we use as a proxy for the risk-free rate the daily estimate
of the one-year interest rate on the Spanish Treasury bills.
The alpha of the fund, or risk-adjusted fund performance is the measure of
outperformance or underperformance relative to the market proxy used and will be
estimated according to three models:
8
1) The first model widely used to evaluate risk-adjusted performance is the Capital
Asset Pricing Model (CAPM) based on the work by Sharpe, Lintner, Treynor
and Mossin:
𝑅𝑖𝑡 − 𝑅𝑓𝑡 = 𝛼𝐶𝐴𝑃𝑀𝑖,𝑡 + 𝛽𝑖𝑡 �𝑅𝑚𝑡 − 𝑅𝑓𝑡 � + 𝜀𝑖𝑡
[1]
where R mt is the log-return of the market benchmark used and we call the alpha
obtained by this model 𝛼𝐶𝐴𝑃𝑀 .
The market benchmarks used in each analyzed category can be found in Table 1 and
have been selected taking into account the investment policy of the registered funds.
2) The second model used to evaluate risk-adjusted performance is the three-factor
model proposed by Fama and French (1993). In addition to a value-weighted
market proxy, size and book-to-market are used as risk factors. We call this
alpha, 𝛼𝐹𝐹 :
𝑅𝑖𝑡 − 𝑅𝑓𝑡 = 𝛼𝐹𝐹𝑖,𝑡 + 𝛽0𝑖,𝑡 �𝑅𝑚𝑡 − 𝑅𝑓𝑡 � + 𝛽1𝑖,𝑡 𝑆𝑀𝐵𝑡 + 𝛽2𝑖,𝑡 𝐻𝑀𝐿𝑡 + 𝜀𝑖𝑡
[2]
where SMB t is the difference in return between a small cap portfolio and a large cap
portfolio at time t, and HML t is the difference in return between a portfolio of high
book-to-market stocks and a portfolio of low book-to-market stocks at time t.
3) The third model is the Cahart (1997) four-factor model, which includes a
momentum factor, WML t , that captures the Jegadeesh and Titman (1993)
momentum anomaly:
𝑅𝑖𝑡 − 𝑅𝑓𝑡 = 𝛼𝐶𝐴𝐻𝐴𝑅𝑇𝑖,𝑡 + 𝛽0𝑖,𝑡 �𝑅𝑚𝑡 − 𝑅𝑓𝑡 � + 𝛽1𝑖,𝑡 𝑆𝑀𝐵𝑡 + 𝛽2𝑖,𝑡 𝐻𝑀𝐿𝑡 + 𝛽3𝑖,𝑡 𝑊𝑀𝐿𝑡 +
+𝜀𝑖𝑡
[3]
WML t is the difference in return between a portfolio of past winners and a portfolio of
past losers at time t. 𝛼𝐶𝐴𝐻𝐴𝑅𝑇 is this model resulting alpha.
These models can be interpreted as performance attribution models where the
coefficients and premia on the factor-mimicking portfolios indicate the proportion of
mean return attributable to each strategy (Otten and Bams, 2004).
LNASSETS is the natural logarithm of fund assets, and we use it to test for a possible
size effect (H3). LNSHOLDERS is the natural logarithm of the total number of
shareholders in a fund. The variable AGE refers to the age of each mutual fund. To
check for learning curves in the mutual fund industry, we include this variable in our
analysis (see H4).
To measure the volume of net flows received (DASSETS), we construct the following
variable, adjusting for the annual raw return of the fund (Sirri and Tufano 1998):
𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 = (𝐴𝑠𝑠𝑒𝑡𝑡 − 𝐴𝑠𝑠𝑒𝑡𝑡−1 (1 + 𝑅𝑡 ))⁄𝐴𝑠𝑠𝑒𝑡𝑡−1
9
Each year, the risk (RISKt ) handled by the manager is measured by the daily standard
deviation of the previous 12 months’ daily returns. This measure is then annualized.
Finally we include the performance results of the previous year (α
persistence in the results (H6).
t-1)
to test for
The descriptive statistics of the variables used in the model can be found next in Table 2
A, where we observe that the funds’ mean annual RAW RETURN is almost zero,
although it covers a wide range, from -68,27% to 101,21%; during these years, stocks
experienced a very negative evolution (e.g., -31,5 % for Ibex 35, -28,7% for Eurostoxx
50, and -16% for the MSCI index), but fixed-income strategies performed better (e.g.,
IBOXX rose 35,26%), and as mentioned, the sample overall is composed of funds
following different investment policies.
Table 2(A): Summary Statistics
RAW RETURNS are annualized returns calculated as ln(NAV t /NAVt-1 ). The RISK FREE
RATE is estimated using the daily data of the one-year interest rate on Spanish government
notes, and RISK t is the annual standard deviation of the previous 12 months’ daily returns.
ASSETS are assets under management, and DASSETS are the volume change rate of net flows
received. #SHAREHOLDERS is the number of shareholders, and AGE refers to the age of the
fund.
Variable
RAW RETURNS (annual)
RISK FREE RATE (annual)
RISKt (annual)
ASSETS (thousands)
DASSETS
# SHAREHOLDERS
AGE (days)
Mean
-0.06%
3.098%
16.05%
32,652
0.3528
1,672
4,003
Std. Dev.
0.1981
1.076
11.45%
76,771
5.5825
3,516
1,651
Description of quartiles for DASSETS and for AGE
Min
p25
DASSETS
-1.912
-0.2229
AGE (days)
727
2,864
Total Number of observations: 2773
Min
-68.27%
1%
0.45%
3002
-1.912
100
727
Max
101.21%
5.28%
65.06%
1,292,879
216.5
52,086
14,769
p50
-0.0887
4,057
p75
0.0519
5,136
Max
216.5
14,769
The average risk experienced by the funds in the sample is somewhat lower than the
risk of the Ibex-35 Index (approximately 22%) and the Eurostoxx 50 and MSCI Indexes
(approximately 21%) because they are not equity funds, as explained above. We can
also observe that the assets under management varied widely, with funds reaching more
than 1 billion euros and some others with just the minimum 3,000,000 euros. The oldest
fund in our sample is more than forty years old, and minimum age to be included in the
sample is one year of NAV.
Due to their impact on mutual funds studies literature, we also include the summary
statistics and the correlation structure of three variables: total costs (TOTCOSTS),
10
management fees (MNGFEE) and performance fee (PERFEE). PERFEE refers to the
performance fee, MNGFEE refers to the regular management fee charged on the fund,
and TOTCOST 4 refers to the total costs incurred by the fund during the year (fees,
expenses, trading costs). The TOTCOST during the sample period studied also includes
the inducements or retrocession fees received by the funds. The description of these
variables during our sample period and the relationships between them can be seen in
Table 2B.
Table 2(B): Summary Statistics and Correlation Structure of total costs
(TOTCOSTS), management fees (MNGFEE) and performance fees (PERFEE).
Correlation Structure
Variable
TOTCOSTS
All sample
MNGFEE
period
PERFEE
TOTCOSTS
2008
MNGFEE
PERFEE
TOTCOSTS
2009
MNGFEE
PERFEE
TOTCOSTS
2010
MNGFEE
PERFEE
TOTCOSTS
2011
MNGFEE
PERFEE
TOTCOSTS
2012
MNGFEE
PERFEE
Mean
1.6916
1.5389
7.9878
1.3825
1.5158
8.1795
1.7648
1.5216
8.1018
1.7470
1.5283
7.8746
1.7244
1.5652
7.7330
1.8118
1.5778
8.1700
Std. Dev.
0.6604
0.5614
2.3131
0.6859
0.5653
2.0179
0.7975
0.5531
2.5155
0.6185
0.5511
2.4521
0.5955
0.5511
2.3853
0.6136
0.5933
1.9032
Min
0.01
0
0.01
0.01
0
0.44
0.01
0
0.11
0.07
0
0.02
0.13
0
0.01
0.13
0
0.44
Max
5.95
2.25
18
5.95
2.25
15.25
5.95
2.25
18
3.83
2.25
18
3.5
2.25
10
3.63
2.25
10
TOTCOSTS
TOTCOSTS
MNGFEE
PERFEE
TOTCOSTS
MNGFEE
PERFEE
TOTCOSTS
MNGFEE
PERFEE
TOTCOSTS
MNGFEE
PERFEE
TOTCOSTS
MNGFEE
PERFEE
TOTCOSTS
MNGFEE
PERFEE
1
0.70
-0.16
1
0.01
0.02
1
0.78
-0.18
1
0.88
-0.14
1
0.94
-0.36
1
0.86
-0.19
MNGFEE
PERFEE
1
-0.37
1
1
-0.33
1
1
-0.36
1
1
-0.34
1
1
-0.41
1
1
-0.44
1
Annual data in %. We report summary statistics of PERFEE taking only into account PERFUNDS.
For TOTCOSTS, we observe a clear tendency to increase. The same tendency is
observed in the case of the MNGFEE but not for the PERFEE. The PERFEE remains
more or less stable during the sample period with a mean of approximately 8%. These
data suggest that because the funds were not earning the performance fee during this
period because of bear markets, they increased other fees to gain a margin. Furthermore,
due to these dropping financial markets, the assets under management did not grow (as
a whole), which likely caused the high management fees in an attempt to maintain
profits.
In addition, the analysis of the correlation structure of costs is revealing. MNGFEE are
positively and highly correlated with TOTCOST, while the opposite happens with
PERFEE, indicating that the funds under study still rely mainly in the management fee,
and performance fees are a residual bet in this market, for this reason we call them
timid. PERFEE and MNGFEE exhibit negative correlation which is consistent with the
particular fees’ structure analyzed in this work that poses a maximum in management
fees when the fund charges mixed fees.
4
These total costs are also called TER: total expenses ratio.
11
An interesting remark is that in our sample, MNGFEE account for 90% of TOTCOSTS,
but we find a high number of observations (13,9%) in which the TOTCOSTS are under
the MNGFEE. We find two reasons for this fact. First, retrocession fees earned by the
funds reduce the costs. Inducements affect many observations, particularly our category
of FGL, where there are many funds of funds. Second, the MNGFEE is a daily fixed
percentage, whereas the percentage used for TOTCOSTS is calculated using the year’s
average assets in the funds. This particularity suggests that in 2008, a year of sharp
declines in the financial markets, TOTCOSTS were lower than MNGFEE in 60% of the
observations! For these reasons, we test H5 using (the effect of cost on performance) on
MNGFEE instead of TOTCOSTS.
3.3. Risk-Adjusted Performance
The fund’s benchmark-adjusted net return to investors
Table 3: Summary Statistics and year-by-year risk-adjusted returns
The alphas below are estimated according to models [1], [2] and [3] described in section 3.2.
Monthly data
Model 1
Model 2
Daily data
αCAPM
Mean (all period)
-0.0566***
Std. Dev.
0.2461
Min
-2.1619
Max
2.0446
Mean (2008)
-0.3218***
Mean (2009)
0.1077***
Mean (2010)
-0.0237***
Mean (2011)
-0.0821***
Mean (2012)
-0.0219***
Mean (AR)
-0.0219***
Mean (FII)
-0.0169***
Mean (FIIM)
-0.0236***
Mean (EQE)
-0.1516***
Mean (INTE)
-0.0481***
Mean (EEM)
-0.0133***
Mean (Global)
-0.03145***
*** Statistical significance at 1% level
Annualized alfas
#Obs.
2773
484
656
682
577
374
277
138
150
570
787
339
512
Model 3
αCAPM
αFF
αCahart
-0.0215***
0.0945
-0.5154
0.5307
-0.0391***
0.0217***
-0.0199***
-0.0312***
-0.0609***
-0.0099***
-0.0228***
-0.0239***
-0.0485***
-0.0057***
-0.0204***
-0.0213***
-0.0164***
0.0926
-0.4776
0.3592
-0.0326***
0.0169***
-0.0032***
-0.0419***
-0.0382***
-0.0017***
-0.0253***
-0.0097***
-0.0226***
-0.0138***
-0.0257***
-0.0146***
-0.0138***
0.0913
-0.5384
0.3961
-0.0274***
0.0206***
-0.0086***
-0.0419***
-0.0225***
-0.0023***
-0.0283***
-0.0071***
-0.0022***
-0.0253***
-0.0173***
-0.0109***
As observed, risk-adjusted performance measures are negative and significant in all
periods, estimated by both daily and monthly data. This is a common result for this
market (e.g., Diaz Mendoza et al. 2014) and in general (Elton and Gruber, 2013).
Whether the industry as a whole has stock picking talents that justify the trading costs it
incurs and the management and performance fees and expenses that it charges is an
12
issue already considered. Active investment is in aggregate a zero sum game (the
aggregate alpha is zero before costs). After costs (that is, in terms of net return to
investors), active investment is a negative sum game (e.g., French 2008). Because the
value weighted portfolio of funds produces an alpha close to zero in gross returns, the
alpha estimated on the net returns to investors is negative based on the amount of fees
and expenses. Elton and Gruber (2013) summarize that most studies find small positive
gross alphas that are not high enough to cover fees and expenses.
4. Methodology and Empirical Estimation
4.1 Performance Model
To test the hypotheses outlined in section 2, we first estimate a dynamic panel data
model with lagged endogenous variables (Panel 1):
𝛼𝐶𝐴𝑃𝑀𝑖,𝑡 = 𝜆𝑖,0 + Γ𝑅𝑒𝑠𝑒𝑎𝑟𝑐ℎ𝑉𝑏𝑙𝑒𝑠𝑖,𝑡 + Θ𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑏𝑙𝑒𝑠𝑖,𝑡 + 𝜈𝑖,𝑡
[4]
where α CAPMi,t is the risk-adjusted performance calculated according to the CAPM
model [1] using daily data.
ResearchVbles include variables related to the fund’s PERFEE (H1), size (H3), age
(H4), expenses (H5) and risk-adjusted performance in the previous year α CAPMi,t-1 (H6).
Size is measured by LNASSETS and LNSHOLDERS. The variable AGE refers to the
age of each mutual fund. We have divided our sample into quartiles according to the
fund’s age. The first quartile is composed of the youngest funds, whereas the fourth
quartile is composed of the oldest. Expenses are measured by MNGFEE and
TOTCOSTS. To test for nonlinearities in the relationship between performance and
expenses, we estimate three different specifications of model 4. Estimation 1 includes
an interaction between PERFEE and MNGFEE, estimation 2 includes PERFEE
squared, and estimation 3 includes MNGFEE squared.
Following prior literature vector ControlVbles include the volatility of the current year
(RISKt ) and lagged volatility (RISK t-1 ), to capture the current and past effects of
volatility on performance. Previous empirical work shows that this variable proxies for
uncertainty about managerial ability and soak up heterogeneity in fund investors’ tax
bases (Ferson and Lin, 2014). A quartile dummy (DASSETSQ1 to DASSETSQ4) is
used to control for increases in size, where funds belonging to the first quartile are those
with the lowest increase in size. This way of measurement has been chosen for this
variable because of the extreme values that present in the upper and lower tails (see
table 2.A). We also include dummy variables to control for fund family membership for
the year and for absorbing funds. 5
Table 4(A): Results for Panel 1. Risk-adjusted performance and funds’
characteristics for the period 2008-2012.
5
Coefficients for year and absorbing fund dummies are not included in the table. The results are not
significant and are available upon request.
13
Regressions are estimated using Arellano and Bond’s (1991) two-step GMM difference
estimator for dynamic panel data with a lagged endogenous variable 6. The dependent variable is
the risk-adjusted performance calculated according to the CAPM model using daily data. RISK
is calculated as the standard deviations of daily returns obtained during the current year. RISK t-1
is calculated with the daily returns of the previous year. PERFEE is the performance fee earned
by the fund. MNGFEE is the management (or asset) fee charged, and TOTALCOSTS are the
costs incurred by the fund during the year including custody fees, transaction costs, and other
miscellaneous costs. Estimation 1 includes an interaction between PERFEE and MNGFEE,
estimation 2 includes PERFEE squared, and estimation 3 includes MNGFEE squared.
DASSETSQ1 to DASSETSQ4 are quartile dummies; DASSETSQ1=1 if the fund belongs to the
quartile with the lowest increase in wealth and 0 otherwise. LNASSETS is the natural logarithm
of assets in the fund, and LNSHOLDERS is the natural logarithm of shareholders. AGEQ1 to
AGEQ4 are quartile dummies, where AGEQ1 is composed of the youngest funds, and AGEQ4
is composed of the oldest ones. AR, FII, FIIM, EQE, INTE and EEM are the fund categories,
and Global is the category used as the base category. Year dummies are time dummies (one per
year,) and absorbing fund dummies take a value of 1 for funds that absorb another one. Robust
standard deviations have been computed to make an inference about coefficients’ significance.
***, ** and * represent the significance at the 1%, 5% and 10% levels, respectively. At the
bottom of the table, we report the p-values for the model’s adequacy tests.
6
Several models have been estimated using Arellano and Bond’s (1991) two-step GMM difference
estimator for dynamic panel data with a lagged endogenous variable. As noted by Roodman (2009), this
estimator is designed for situations similar to those that appear in this paper because (1) there are few
time periods and many individuals (5 years but 872 mutual funds); (2) a single left-hand-side variable is
dynamic, depending on its own past realization (ALPHA lagged one year); (3) some independent
variables are not strictly exogenous, i.e., correlated with past and possibly current realizations of the error
(as RISK or DASSETS may be).
14
Depending variable
Explanatory variables
αCAPM (t-1)
RISKt
RISKt-1
PERFEE
MNGFEE
PERFEE*MNGFEE
PERFEE^2
MNGFEE^2
TOTCOSTS
DASSETSQ1
DASSETSQ3
DASSETSQ4
LN ASSETS
LNSHOLDERS
AGEQ2
AGEQ3
AGEQ4
AR (Absolute Return)
FII (Fixed Income Intn'l)
FIIM (Intn`l Mixed)
EQE (Equity Euro)
INTE (Intn'l Equity)
EEM (Equity Euro Mixed)
Constant
Year Dummies
Absorbing Fund Dummie
# Observations
# Funds
# Instrum
Chi^2
Arellano-Bond test1
Arellano-Bond test2
Sargan Test
Hansen Test
Estimation 1
Estimation 2
Estimation 3
-0.0714*
-1.0196*
0.4993
-0.0019
-0.0599**
-0.0115
-0.0714*
-1.0195*
0.4822
-0.0102
-0.7989
-0.0717*
-1.0083*
0.4215
-0.1761
-0.6033*
αCAPM
αCAPM
αCAPM
0.0177
0.4325***
-0.0103
0.1101
0.3607***
-0.0032
-0.0257
0.1538
-0.0053
-0.0631
.02488
-0.0899
0.3249
0.2491
0.2843
0.3432*
0.0799
yes
yes
2773
872
72
0
0
0.4874
0.2073
0.0564
0.0800
0.4385***
-0.0145
0.1019
0.3593***
-0.0047
-0.0228
0.1591
-0.0113
-0.0734
0.2423
-0.0932
0.2906
0.2158
0.2404
0.3256*
0.1880
yes
yes
2773
872
72
0
0
0.5159
0.2377
0.0574
0.4328***
0.0118
0.1345
0.3709***
0.0203
-0.0553
0.1379
-0.0574
-0.1151
0.1883
-0.0094
0.3538
0.2823
0.3177
0.3576*
0.0841
yes
yes
2773
872
72
0
0
0.7352
0.3513
0.1107
Second, this same equation (model 4) is also estimated based on monthly data and
dependent variables α CAPMi,t , α FFi,t , α CAHARTi,t , and we will address these results as
Panel 2, Panel 3 and Panel 4. We present the results in Table 4(B). The estimations of
Panel 1 based on daily data are preferred because they perform better on adequacy tests
(Arellano/Bond, Sargan and Hansen tests), but we find some interesting results in the
models estimated with monthly data using the risk-adjusted measures of Fama and
French, and Cahart.
15
Table 4(B): Results for Panels 2, 3 and 4. Risk-adjusted performance (α CAPMi,t ,
αCAHARTi,t ) and funds’ characteristics for the period 2008-2012.
α FFi,t ,
Regressions are estimated using Arellano and Bond’s (1991) two-step GMM difference
estimator for dynamic panel data with a lagged endogenous variable. The dependent variable is
the risk-adjusted performance calculated according to the CAPM model using monthly data.
RISK is calculated as the standard deviations of monthly returns obtained during the current
year. RISK t-1 is calculated with the monthly returns of the previous year. PERFEE is the
performance fee earned by the fund. MNGFEE is the management (or asset) fee charged, and
TOTALCOSTS are the costs incurred by the fund during the year, including custody fees,
transaction costs, and other miscellaneous costs. Estimation 1 includes an interaction between
PERFEE and MNGFEE, estimation 2 includes PERFEE squared, and estimation 3 includes
MNGFEE squared. DASSETSQ1 to DASSETSQ4 are quartile dummies; DASSETSQ1=1 if the
fund belongs to the quartile with the lowest increase in wealth and 0 otherwise. LNASSETS is
the natural logarithm of assets in the fund, and LNSHOLDERS is the natural logarithm of
shareholders. AGEQ1 to AGEQ4 are quartile dummies, where AGEQ1 is composed of the
youngest funds, and AGEQ4 is composed of the oldest ones. AR, FII, FIIM, EQE, INTE and
EEM are the fund categories. Global is the category used as the base category. Year dummies
are time dummies (one per year), and absorbing fund dummies take a value of 1 for funds that
absorb another one. Robust standard deviations have been computed to make inference about
coefficients’ significance. ***, ** and * represent the significance at the 1%, 5% and 10%
levels, respectively. At the bottom of the table, we report the p-values for the model’s adequacy
tests.
16
Depending variable
Explanatory variables
α(t-1)
RISK (sigma t)
RISK (sigma t-1)
PERFEE
MNGFEE
TOTCOSTS
DASSETSQ1
DASSETSQ3
DASSETSQ4
LN ASSETS
LNSHOLDERS
AGEQ2
AGEQ3
AGEQ4
AR (Absolute Return)
FII (Fixed Income Intn'l)
FIIM (Intn`l Mixed)
EQE (Equity Euro)
INTE (Intn'l Equity)
EEM (Equity Euro Mixed)
Constant
Year Dummies
Absorbing Fund Dummie
# Observations
# Funds
# Instrum
Chi^2
Arellano-Bond test1
Arellano-Bond test2
Sargan Test
Hansen Test
Panel 2
Panel 3
αFF
αCahart
-0.2008***
0.4922*
-0.0393
0.0131
-0.0271
0.1465**
0.0059
0.891
0.2472***
0.0596*
-0.0404
0.0539
0.1701*
-0.0404
0.1261
0.0271
0.0342
-0.1965
-0.0288
0.1284
-0.7657***
yes
yes
2773
872
72
0
0
0.7371
0
0
-0.2244***
0.2586
-0.0699
0.0334*
0.0489
0.1072*
0.0843
0.2339***
0.2875***
0.0036
0.0057
0.0901
0.1196
0.0058
0.1571
0.2292
0.0012
-0.0631
-0.0471
0.1616
-0.6805**
yes
yes
2773
872
72
0
0
0.2368
0
0
-0.1835***
0.7948**
0.1322
0.0346*
0.1169
0.1066*
0.1519*
0.3284***
0.2318***
-0.0161
0.0209
0.0622
0.0758
0.0104
0.0368
0.0991
0.0471
-0.396**
-0.2644*
0.0846
-0.6173*
yes
yes
2773
872
72
0
0
0.0968
0
0.0001
αCAPM
Panel 4
In light of the results of the estimations, it is possible to discuss whether the research
hypotheses are not supported by empirical evidence.
The first result we obtain is related to H1. Our PERFUNDS sample does not exhibit a
higher risk-adjusted fund performance than ASSETFUNDS, and the estimations do not
support H1 because the coefficient of PERFEE is not significant. However, it is
interesting to note that this result is significant and positive in panels 3 and 4, indicating
that adjusting for all pertinent risk factors, there is a positive and significant relationship
between PERFEE and funds’ alpha. This result is aligned with the findings of DiazMendoza et al. (2014) for the Spanish market.
17
Attempting to capture in depth the relationship between fees and fund performance,
Estimation 1 (Table 4(A)) also includes an interaction term between PERFEE and
MNGFEE to test whether PERFEE effects’ magnitude depends on the level of
MNGFEE, that is, whether funds with higher MNGFEE have enough incentives to earn
the PERFEE and whether funds with lower MNGFEE behave differently and have more
incentives to earn the PERFEE. However, this interaction term turns out not significant,
so no conclusion in this respect can be reached.
Estimations 2 and 3 (Table 4(A)) attempt to explore whether there is a non-linear and
quadratic relationship between fund performance and MNGFEE or PERFEE. Again, we
find no significant results. It could be argued that there should be an optimum level of
both fees to maximize fund performance, but our empirical evidence does not support
this reasoning.
Regarding H3, which predicts that there are scale economies in the mutual fund
industry, there is some mixed evidence. On one hand, the coefficient of LNASSETS
(fund size) is not significant, with the exception of Panel 2 (Table 4(B)), but on the
other hand, the coefficient of DASSETSQ4 (increase in assets in the fourth quartile) is
highly significant (p<0.01) and positive for all estimations. Thus, there is no conclusive
evidence that larger funds have better performance, but mutual funds with higher asset
growth do. This result indicates the presence of smart money for selecting
contemporaneously the funds that are performing better. The increasing professional
financial services’ effect in Spain during the sample period is likely driving this robust
finding. A coincidental previous finding in the literature is the paper about smart money
by Zheng (1999).
Next, we find the confirmation of H4 relative to the non-existence of a learning curve in
the sense that older funds do not show better performance than younger ones; none of
the funds’ age quartile dummies is significant. This specification of the funds’ age
allows us to test for a possible U-shaped/ inverted U-shaped relationship effect, and the
results also show that there are no such relationships. We conclude that funds’ size and
age are not relevant to explain their performance. Toledo and Marco (2010), for the
1993-2001 Spanish sample, also found lack of a learning curve and lack of economies
of scale; this feature has not changed ten years later.
The coefficient of the lagged alpha is significant at the10% significance level in the
model estimated with daily data, and it is significant at 1% in the model estimated with
monthly data, but it is always negative. Therefore, the empirical findings show that if a
fund had poor performance one year, it tends to do better in the next, and vice versa.
This result, in line with all of the previous literature about short-term persistence in
performance, also indicates performance reversion in our fund sample and allows us to
reject hypothesis H6; we do not find persistence in results, but we can say that there is
short-term performance reversion or a mean reversal effect.
Finally, for costs other than PERFEE, the results of the estimations show that the as the
MNGFEE increases, the fund performance decreases, whereas as the TOTCOSTS
18
increases, so does the fund performance; we find that this result is robust to different
specifications of the model. 7 This is an odd result. On one hand, it confirms H5, which
establishes the negative relationship between costs (proxied here by the MNGFEE) and
PERFEE (also found in Toledo and Marco, 2010). On the other hand, the fact that we
find a strong and robust positive relationship between performance and TOTCOSTS
indicates the aforementioned influence of inducements and the cost calculation method
(section 3.2). The significance of both effects is higher in the model estimated using
daily data for the risk-adjusted measures.
Finally, we can say that there is no influence of the former year’s level of risk on the
current year’s mutual fund performance, and the effect of the contemporaneous risk is
not conclusive. In relation to the different investment categories, we find that only the
funds belonging to the EEM category seem to earn superior returns to the reference
category. For the estimations with daily data (see Table 4(A)), there is a positive and
significant relationship between the funds in this category and their performance.
4.3. Explanatory model for funds’ assets increase
Finally, to test for H2 and determine whether PERFUNDS attract more new cash flow
than ASSETFUNDS, we include a complementary model (see Table 5) in which the
dependent variable is not the performance measure but DASSETS (the funds’ asset
growth). We estimate the following panel data model:
𝐷𝐴𝑆𝑆𝐸𝑇𝑆𝑖,𝑡 = 𝛾0 + γ1 𝑃𝐸𝑅𝐹𝐸𝐸𝑖,𝑡 + Θ𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑏𝑙𝑒𝑠𝑖,𝑡 + 𝜈𝑖,𝑡
[5]
where PERFEE is a dummy variable that takes a value of 1 if the fund “i” at year “t”
had a performance fee and 0 otherwise.
To control for other sources of variations, other variables have been added: the previous
year’s risk-adjusted return, the lagged risk of the fund, MNGFEES, LNASSETS,
LNAGE and the fund’s category. It is worth noting that a dummy to control for whether
the fund absorbed (or not) another fund this year has also been included. This fact
would obviously provoke a great growth of assets independent of PERFEE.
Table 5: Results for model 5. Panel estimation for funds’ assets increase 2008-2012.
The panel has been estimated by means of the pooled averaged estimator and employing robust
clustered standard errors because the Breusch-Pagan test failed to reject the null hypothesis of
the absence of random effects. The dependent variable is DASSETS. PERFEE is a dummy
variable that takes a value of 1 if the fund earns a performance fee and 0 otherwise. α CAPM is
the risk-adjusted performance calculated based on daily data according to the CAPM model.
RISK t-1 is calculated as the standard deviations of the previous year’s daily returns. MNGFEE is
the management (or asset) fee charged. LNASSETS is the natural logarithm of assets in the
fund, and AGE refers to the number of days since inception. Global, FII, FIIM, EQE, INTE and
7
We have also estimated the model presented in table 4 without MNGFEE and without TOTCOSTS, but
the signs and significance of coefficients did not change, and the adequacy tests turned out worse. The
results are available upon request.
19
EEM are the fund categories, and AR is the category used as a base category. Year dummies are
time dummies (one per year), and absorbing fund dummies take the value 1 for funds that
absorb another one. Robust standard deviations have been computed to make inferences about
coefficients’ significance. ***, ** and * represent the significance at the 1%, 5% and 10%
levels, respectively. The sample has been trimmed at both ends, 1% at each, to avoid outliers’
effect on assets’ growth.
Dependent variable
Explanatory variables
PERFEE (yes/no)
αCAPM (t-1)
RISKt-1
MNGFEE
LN ASSETS
AGE
Global
FII (Fixed Income Intn'l)
FIIM (Intn`l Mixed)
EQE (Equity Euro)
INTE (Intn'l Equity)
EEM (Equity Euro Mixed)
Constant
Year Dummies
Absorbing Fund Dummie
# Observations
# Funds
Chi^2
DASSETS
0.0309
-0.0416
0.1353
-0.0843***
0.0724***
-0.1080***
0.2475***
0.3171***
0.1638***
0.1999***
0.3189***
0.1660***
0.6704***
yes
yes
2717
865
0
Given that the coefficient of PERFEE is not significant, we can say that PERFUNDS do
not attract more new money than ASSETFUNDS, and we reject H2. PERFEE does not
seem to be a relevant variable for investors to choose a fund, although as we have seen
previously (Table 4), the results indicate that adjusting for all pertinent risk factors,
funds with PERFEE earn a superior alpha. However, according to the results in Table 5,
PERFEE does not play a role as a signal to investors.
As mentioned, in our sample, very few funds charge solely the performance fee (just
four); all of the other funds that charge PERFEE also charge MNGFEE. Somehow, this
mixed structure is chosen by managers who do not merely rely on their skills; they
prefer to retain a fixed compensation. We find that this structure weakens the message
sent to investors, as the signaling theory indicates, because it is not strong enough to
indicate which managers are willing to assume more risks and be rewarded only on their
skills. The paper by Haslem (2007) backs up our findings; he reports a prevailing lack
of strong competition in the mutual fund industry, which benefits non-performance
based management fee funds.
20
Indicating the same result, but from a completely different perspective, the study by
Ferson and Lin (2014) finds that traditional risk-adjusted performance measures are not
reliable guides for the attractiveness of an investment. They encounter that positive
alphas do not indicate that an investor would want to buy a fund and that a client is
likely to view the performance of a given fund differently from another client, making it
more appropriate to develop clientele-specific measures of funds’ performance, such as
the variance of disagreement across investors.
On the other side, Table 5 reveals a very significant negative effect for MNGFEE; this
effect is related to high investor awareness about the load that management fees
represent. Investors clearly prefer to invest in funds with lower MNGFEE. These results
are aligned with the previous findings in the literature in this respect.
We also observe that larger funds attract more money, although they do not earn
superior returns. The aforementioned feature of the industry’s strong presence of banks,
which benefit from placing mutual funds through their offices to an unsophisticated
demand, contributes to this result. The increasing professional advice and the
popularization of the model investment portfolios also have an effect. Once a fund is
included in a model investment portfolio, all trackers will buy the fund for their
portfolios, causing a feedback effect. Additionally, these industry characteristics and the
increasing use of advertising propel the introduction of new funds as we observe that
the funds that attract more new money are the younger ones. Our results do not support
any sort of learning curve effect in model 4, for the performance fee, or in model 5, for
the increase in size.
Finally, the 2008-2012 period was a bad period for the AR (Absolute Return) category,
i.e., the exiting money category, whereas all others comparatively received money
inflows.
5. Conclusions
In this study, we compared the performance of funds with and without incentive fees
within the same investment philosophy to gain insight into the effect of the particular
incentive fee structure applied in Spain in 2008. We observe that all but four funds that
charge a performance fee select a mixed structure and retain a management fee that is
often very high. Thus, we call these performance fees applied in our sample timid fees.
Using a dynamic panel data model, we find that after adjusting for all relevant risk
factors, a significant relationship emerges, and funds charging performance fees obtain
better results than their equivalent charging fees on assets under management. This
result verifies previous research for the Spanish market. However, we find that they do
not attract more new money, as the theory suggests. According to theory, incentive fees
should align managers’ interests with investors’ interests and attract the managers with
the best skills. These managers send a signal to investors through the performance fee.
Nevertheless, we interpret that the mixed structure prevailing in Spain weakens the
signal sent to investors. Another explanation for this fact is in line with the recent
21
research by Ferson and Lin (2014) about the appropriateness of developing clientele’s
specific measures of fund performance.
This paper also contributes to the literature by providing evidence for the presence of
smart money and is the first to do so for Spain. Mutual funds with higher asset growth
achieve better performance. The emerging modernization of this industry and the
increasing presence of professionals render these effects. Additionally, we find that
these funds attracting more money are the younger funds; the results show that younger
funds attract more money. This evidence is consistent with prior research because we do
not find economies of scale or learning economies: Larger and older funds do not obtain
better performance.
Other results found, that relate to the control variables, are a clear and statistically shortterm performance reversion in results—meaning that if a fund had poor performance
one year, it will tend to perform better the next year, and vice versa—, the negative
relationship between management fees and performance results, and the inconclusive
evidence on the relationship between the risk endured by a fund and its alphas. This
evidence is aligned with previous literature on the topic.
We conclude that the mixed structures are not clear enough for investors and, in this
respect, do not attract money, although they may deliver better risk-adjusted
performance. Our study indicates an interesting avenue for the development of the
mutual fund industry in Spain: the opportunity and attractiveness of funds that charge
the performance fee alone. This challenge remains open.
REFERENCES
Barber BM, Odean T (2000) Trading is Hazardous to Your Wealth: The Common Stock
Investment Performance of Individual Investors. J. Finance 55 (2): 773-806 doi:
10.1111/0022-1082.00226
Barber BM, Odean T, Zheng L (2005) Out of sight, out of mind: The effects of
expenses on mutual funds flows. J. Bus. 78 (6): 2095-2120
Berkowitz M. K., Kotowitz Y (2002) Managerial quality and the structure of
management expenses in the US mutual fund industry. Int. Rev. Econ. & Finance
11(3):315-330 doi:10.1016/S1059-0560(02)00099-0
Brown S.J., Goetzmann W.N. (1995) Performance Persistence. J. Finance 50(2):679698 doi: 10.1111/j.1540-6261.1995.tb04800.x
Brown SJ, Goetzmann WN, Liang B (2004) Fees on fees in funds of funds. J. Invest.
Manag. 2 (4): 39-56.
Carhart M.M. (1997) On Persistence in Mutual Fund Performance. J. Finance 52(1):5782 doi:10.1111/j.1540-6261.1997.tb03808.x
22
Das SR, Sundaram RK (1998) On the Regulation of Fee Structure in Mutual Funds.
NBER Working Paper No. 6639 doi:10.3386/w6639
Díaz-Mendoza AC, Lopez-Espinosa G, Martinez MA (2014) The efficiency of
performance based mutual funds. Eur. Financial Manag. 20(4):825-855
doi:10.1111/j.1468-036X.2012.00654.x
Elton E. J., Gruber M.J., Blake C.R. (1996) Survivorship bias and mutual fund
performance. Rev Financial Stud. 9(4):1097-1120. doi: 10.1093/rfs/9.4.1097
Elton E. J., Gruber M. J., Blake C.R. (2003) Incentive fees and mutual funds. J. Finance
58(2):779-804 doi:10.1111/1540-6261.00545
Elton E. J., Gruber M. J. (2013) Mutual funds. In: Constantinides, G.M., Harris, M.
Stulz, R.M. (Eds.) Handbook of the Economics of Finance, vol. 2B, North Holland,
Amsterdam, pp.1011-1061 (chapter 15). doi:10.1016/B978-0-44-459406-8.00015-9
Fama E. F., French K.R (1993) Common risk factors in the returns on stock and bonds.
J Financial Econom. 33(1):3-56 doi:10.1016/0304-405X(93)90023-5
Fama, E. F., French, K.R. (2008) Mutual fund performance. Working paper. Dartmouth
College.
Ferris S.P., Chance D.M. (1987) The effect of 12b-1 plans on mutual fund expense
ratios: a note. J. Finance 42(4):1077-1082 doi:10.1111/j.1540-6261.1987.tb03929.x
Ferson W, Lin J (2014) Alpha and Performance Measurement: The Effects of Investor
Disagreement and Heterogeneity. J. Finance 69(4):1565-1596 doi:10.1111/jofi.12165
French K.R (2008) Presidential address: the cost of active investing. J. Finance
63(4):1537-1573 doi:10.1111/j.1540-6261.2008.01368.x
Gil-Bazo J, Ruiz-Verdú P (2009) The relation between price and performance in the
mutual fund industry. J. Finance 64(5):2153-2183 doi: 10.1111/j.15406261.2009.01497.x
Grinold R, Rudd A (1987) Incentive Fees: Who Wins? Who Loses? Financial Analysts
J. 43(1):27-38 doi: http://dx.doi.org/10.2469/faj.v43.n1.27
Gruber M.J. (1996) Another puzzle: the growth in actively managed mutual funds. J.
Finance 51(3):783-810 doi:10.1111/j.1540-6261.1996.tb02707.x
Haslem J.A., Baker H.K., Smith D.M. (2007) Identification and Performance of Equity
Mutual Funds with High Management Fees and Expense Ratios. J. Invest. 16(2):32-51
doi:10.3905/joi.2007.686410
INVERCO (2012) The Spanish Association of Mutual and Pension Funds. Memoria
2012 http://www.inverco.es/documentos/publicaciones/documentos/0009_MEMORIAS
%20INVERCO/C95_Memoria%202012.pdf Accessed 12 June 2014
23
Jegadeesh N, Titman S (1993) Returns to Buying Winners and Selling Losers:
Implications for Stock Market Efficiency. J. Finance 48(1):65-91 doi:10.1111/j.15406261.1993.tb04702.x
Malhotra DK, Mc Leod RW (1997) An Empirical Analysis of Mutual Funds Expenses.
J. Financial Res. 20(2): 175-190 doi: 10.1111/j.1475-6803.1997.tb00243.x
Marco R (2007) Rentabilidad y crecimiento patrimonial en el Mercado de fondos de
inversión. Revista de Economía Aplicada 44(15): 41-84
Marco R (2009) Spanish mutual fund fees and less sophisticated investors: examination
and ethical implications. Bus. Ethics: A Eur. Rev. 18(3): 224-240. doi: 10.1111/j.14678608.2009.01559.x
Martinez M. A. (2003) Legal constraints, transaction costs and the evaluation of mutual
funds. Eur. J. Finance 9(3):199-218. Doi:10.1080/13518470010011260
Sáenz de Cenzano, J (2013) Tenga cuidado con las comisiones sobre resultados en
algunos fondos españoles. Morningstar web.
http://www.morningstar.es/es/news/114308/tenga-cuidado-con-las-comisiones-sobreresultados-en-algunos-fondos-espa%C3%B1oles.aspx. Accessed 12 June 2014.
Otten R, Bams D (2004) How to measure mutual fund performance: economic versus
statistical relevance. Account. & Finance 44(2): 203-222 doi:10.1111/j.1467629X.2004.00105.x
Roodman, D. (2009). How to do xtabond2: An introduction to difference
and system GMM in Stata. Stata Journal, 9(1), 86-136.
Sirri E, Tufano P (1998) Costly search and mutual flows. J. Finance 53(5):1589-1622.
doi: 10.1111/0022-1082.00066
Toledo I, Marco R (2010) Costs associated with mutual funds in Spain. Int. J. Finance
& Econ. 15(2):165-179 doi:10.1002/ijfe.394.
Tufano P, Sevick M (1997) Board structure and fee-setting in the US mutual fund
industry. J Financial Econ. 46(3):321-355 doi:10.1016/S0304-405X(97)00033-0.
Wermers R (2000) Mutual Fund performance: An Empirical Decomposition into StockPicking Talent, Style, Transaction Costs, and Expenses. J. Finance 55(4):1655-1703
doi: 10.1111/0022-1082.00263.
Zheng L (1999) Is Money Smart? A Study of Mutual Fund Investors' Fund Selection
Ability. J. Finance 54(3):901-933 doi:10.1111/0022-1082.00131
24