Do active Mutual Fund Managers Deserve their Fees? A Return-Based Style Analysis on their Performance

9 
Introduction 
In the present thesis we discuss one of the most famous topics in the literature on mutual funds 
performances: do fund managers really deserve their fees? Our focus is exclusively on the top performing 
funds which invested in S&P 500 stocks in the period from March 31
st
 1999 to December 31
st
 2019. Our 
goal is to determine if at least the most successful managers, belonging to the 1% highest percentile of 
the distribution of 5001 different funds, have measurable skills that can benefit the investors after 
adjusting the performance for the risk and the operational expenses. In order to perform our analysis, we 
employed a technique called Return-Based Style Analysis that helped us in the determination of the 
portfolio style of each manager. Afterwards, we reported the Alpha returns that they achieve, calculated 
as the difference between their performance and the reference benchmark and representative of the 
manager’s contribution to the results. In conclusion, we estimate if the Alphas, also known as active 
returns, are driven by the exposure to factors or should be reconducted to the skills of the managers.  
Our study is part of the line of research that follows the findings of Fama and French (2010), who show 
that the largest majority of mutual fund managers have a negative or statistically not significant impact 
on returns. Only a small subgroup of managers, belonging to the high tail of the distribution of returns, 
is deemed able to achieve positive extra returns over the benchmark and to transfer them to the investors.  
The originality of our approach is to focus only on the top 1% of the universe of managers, discarding 
all the other managers who didn’t perform as well as them. We use traditional tools, like factor investing 
and RBSA, blending them together to determine whether, at least the top 1% of the managers show actual 
skills. This small subset has not been deeply analyzed yet by other researchers, who instead focused on 
larger groups of managers without a-priori selection: conclusions have never been directly drawn to the 
top 1%, but only the majority of the managers. 
We estimate this extra return at an average of 3.98% among the subset of skilled managers and our 
analysis aims at drawing a clearer and more detailed picture on this talented subgroup. The results of our 
research confirms that there is only a fraction among the high-performing managers with significant 
skills that should be compensated with appropriate fees. In our sample of 30 funds meeting all the 
selection constraints that we have imposed, the fraction of skilled managers is 60%, meaning that our 
estimation is that only 0.60% of the universe managers is able to generate significant extra returns. The 
remaining managers, instead, didn’t achieve Alphas which were significantly different from zero,
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showing that these managers didn’t add a relevant contribution to the performance and, based on our 
definition, are not skilled enough to deserve their compensation. 
Another relevant aspect investigated in the thesis is linked to the investment style of the analyzed mutual 
fund managers, measured with the tools of factor investing. We discuss the persistence of the risk 
premiums connected to the exposure to the six factors (Size, Low Volatility, High Quality, Momentum, 
Dividend Yield and Value), especially in relation to the recent diffusion of Smart Beta funds.  
The research is structured in five chapters and each of them is focused on a specific aspect of the analysis. 
In the first chapter we describe the main framework of the paper and review the literature pertaining to 
our study. The focus is on the dichotomy between active and passive funds and the introduction of Smart 
Beta funds as an alternative with characteristics in common with both types of funds. We discuss 
previous models and research assessing the performance of mutual fund managers and the differences 
among them. We also introduce the setting and tools used in the following chapters.  
We begin chapter 2 with an in-depth analysis of the six factors, their discovery and the explanation of 
their risk premiums. Furthermore, we describe the ongoing debate among academics and practitioners 
on the persistence of the factors, together with different theories suggesting the existence of other 
additional factors. The six factors represent the basis in the construction of the six indexes of chapter 3, 
which includes also a detailed assessment of their performance in relation to the S&P 500 benchmark.  
Additionally, in chapter 3 we explain the rules in the selection of the stocks in the construction of our 
customized indexes, the weighting criteria and the rebalancing constraints. Furthermore, we compare 
and comment on the performance of the indexes, both relative to the S&P 500 and among themselves. 
The results reported are consistent with the major studies on factor premiums and factor investing.  
The following chapter includes the main findings of the analysis and starts with a detailed explanation 
of the technique (RSBA) and the list of all the constraints in the selection of the pool of managers. We 
develop a step by step description of the model used in the multiple variable regression and determine 
the investment style of fund managers. We evaluate their skills by calculating the R
2
 coefficient, whose 
complement to one represents the share of the variance of the performance not explained by the factors. 
We measure the performance of the managers by calculating their Alpha return over customized style 
portfolios and we conduct T-tests on the significance of the results. Our analysis shows that among the 
most successful managers a relevant portion of them - 18 out of 30 - deserve their compensation fees and 
are able to produce significant net returns for the investors. Furthermore, we make additional remarks on
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what could distort our analysis, such as statistical biases and luck component in the excess returns. In the 
end, we compare our results to those of other researchers.  
The fifth and last chapter expands our focus on Smart Beta funds, highlighting the main concerns 
regarding the persistence of the risk premiums of factors and mentioning the new specific risks pertaining 
to factor investing. Furthermore, we review some studies attempting to identify skillful managers based 
on their personal background, without relying on their past performance. We include the description of 
two common statistical biases (the selection and the survivor biases), their effects and the relative 
corrections. Finally, we conclude remarking the relevance of the fee structure of the funds on the 
investors’ investment decisions, showing that, based on the results of our paper, retail investors would 
be better off allocating most of their money to passive funds and a small share to Smart Beta strategies. 
On the other hand, institutional investors with appropriate resources and capable of determining if a 
manager is skillful or not, should include active funds in their holdings too.  
In the last section we summarize the conclusions of our thesis and indicate some suggestions for further 
research, like an alternative managers’ selection process and the inclusion of other factors in the style 
analysis of mutual funds.
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Chapter 1 - The Main Framework 
1.1    Introduction 
In this chapter we introduce the main concepts and framework that will be developed in the thesis. We 
review the most relevant literature on our topics and discuss the implications for our work. We begin 
from the characteristics and the main distinctions between the two management styles that can be 
observed in most mutual funds in the world: active or passive management. We consider the benefits and 
drawbacks for the investors of both types of funds, setting the framework for the principal question of 
the paper: do the most successful active managers deserve their fees?  
We briefly introduce the methodology in the selection of the mutual fund managers, who are chosen 
among the best of their category
1
, and how the analysis will be developed in the determination of their 
skills. The approach, called Returns-Based Style Analysis (RBSA) is concisely outlined with its 
advantages and inner limitations. 
In the description of RBSA, we pose great attention on the construction of factor style indexes to create 
the appropriate benchmark for each one of the 30 mutual funds under examination. We review the 
literature and previous researches on performance measurement, Total Alpha Return, style factors and 
factor investing, presenting the six most famous factors and describing how we employ them in the 
analysis. In conclusion, we recall the recent growth of funds whose strategies are based on factor 
investing, the so called “Smart Beta Funds.” 
1.2    Active Versus Passive Management 
When it comes to deciding where to invest their money, investors have to make a choice between two 
main categories of funds: active or passive portfolio managed funds. There are many differences between 
the two, highlighting two completely opposite views on the markets and their functioning. In our paper 
we do not include bond funds or funds investing both in stocks and bonds, both just mutual funds with 
stocks and cash as holdings. 
First of all, active managers try to outperform a reference benchmark by taking active positions in the 
market and expressing their views, selling or buying securities based on their forecasts. On the other 
hand, passive managed portfolios have the goal to replicate the performance of the selected benchmark 
 
1
 Identified in the “high tail of the distribution of managers’ returns”, as analyzed by Fama and French (2010).
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as closely as possible, without assigning different weights to the securities in the portfolio construction 
with respect to the benchmark allocation
2
. No active position is taken by the passive managers, implying 
that their portfolio composition mimics the weights assigned to the securities of the reference index. 
Passive portfolios are, for example, index portfolios, as well as most Exchange Traded Funds (ETFs). 
Their goal is to replicate the performance of the market, of another reference index or of a set of indexes. 
Passive equity mutual funds were less common than active equity mutual funds, but their share in the 
market has increased during the past years to the point that the assets under management are the same as 
active equity mutual funds.
3
 In our entire work we are always considering US mutual funds, since the 
US market is the most developed in the world and investors have the chance to invest in a wide set of 
companies. 
Passive investors usually face low fees and expenses, because the managers are not requested to beat the 
market and achieve an extra-return, but they only need to follow the market performance as closely as 
possible. The expected return of a passive mutual fund is reflected by the performance of the market, less 
the small costs that the managers face, such as rebalancing and transaction costs. The net return adjusted 
for fees will then decrease a bit more depending on the annual management fees
4
.  
Active investors, instead, set their goal at outperforming the reference benchmark. Their holdings differ 
from the index because they look for higher returns, buying and selling securities that they deem as 
undervalued or overvalued. The performance of these funds depends on how they allocate their 
endowment among the asset classes and how they implement the security selection. Their ability is 
reflected into the returns that they achieve.  
In our paper we try to determine if active managers have substantial skills and if they are the true 
determinant in achieving superior returns, or if their results can be simply explained by the historical 
premiums recorded by the so-called “style factors,” which will be introduced in chapter 2. Active 
managed funds usually have higher turnover ratios
5
 than passive funds, since the managers need to adjust 
their position more often, depending on their expectations of the market. Subsequently, active funds incur 
in higher transaction and rebalancing costs due to the larger number of trades with respect to passive 
investing. The costs connected to active mutual funds management are higher, in a range between 0.5% 
and 1%, occasionally up to 3% (Morningstar, 2019). Furthermore, an additional cost which, in general, 
 
2
 Passive managers have the mandate to track the benchmark, maintaining the portfolio allocation as close as possible to 
the index and reducing deviations from it. 
3
 The growth of passive funds has been particularly fast after the Financial Crisis. (Bloomberg, September 2019) 
4
 Management fees vary among funds, but the average in 2019 for passive mutual funds is about 0.2%. 
5
 Defined as the percentage of portfolio’s holdings replaced in a fiscal year.
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is not taken into consideration is the taxation of capital gains when a trade is closed. On the other hand, 
passive investors, who buy and hold securities for a long time, can benefit from lower taxation costs and, 
sometimes can benefit from tax reductions too if they satisfy country-specific regulations on holding 
periods. 
Our goal is to determine if the skills that the managers use in their activity in the market are backed by 
statistically significant returns, after accounting for the risk that they undertake. The risk is measured by 
the volatility of the returns and, in order to compare the results of different managers, we calculate the 
Sharpe Ratio, also known as risk-to-reward ratio. The prevailing literature is pretty much aligned: active 
fund managers have traditionally underperformed passive indexes, excluding short periods. In particular, 
Fama and French (2010) prove in their analysis that only “few active managers can cover their costs” 
and can guarantee an extra return above the market to their investors. In order to address this specific 
and small subgroup of managers in our analysis, we set strict criteria to select only the managers that 
have performed far better than their peers and also with respect to their reference index (S&P 500 Index) 
from March 31
st
 1999 to December 31
st
 2019.  
Active portfolio managers try to maximize the value that they add to their clients by using their skills in 
discerning how to undertake risk in order to achieve a higher return. The risk that they focus on is called 
“active risk,” which is the risk measured outside the benchmark. In fact, the performance of the 
benchmark is not under the managers’ control, who can instead show their skills deviating from the 
returns of S&P 500.  
Managers can then make a difference in two ways: timing the benchmark
6
 and selecting the best 
securities
7
. The first strategy is followed by managers who assign different weight allocations to the 
index sectors with respect to the benchmark, while the second entails the identification and selection of 
single stocks that outperform or underperform the market. We measure the aggregate ability of the 
managers in our analysis, after identifying the components that better reflect their investment style.  
The past literature on active portfolio management offers many approaches to the implementation of the 
managers’ strategies, such as Black and Litterman (1992) who suggest that the managers add value by 
expressing their views on the market and by optimizing the asset classes weights following their 
expectations. Grinold and Kahn (1995), instead, determine that the managers add value to the portfolio 
 
6
 This strategy goes under the name of “Active Beta” in finance. 
7
 Traditionally referred to as stock picking.
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by forecasting Alphas of the available securities, which are defined as the extra returns over the market 
return. 
Summing up, investors face the alternative choice between active and passive management when they 
decide where to invest their money and, in both cases, they have advantages and drawbacks. The biggest 
advantage of active investing is that the managers can express their views without being forced to allocate 
their assets like in the reference index. On the other side, active managers can make bad decisions and 
the returns might be poor or even below the market, in addition to the higher operational expenses and 
management fees.  
Our focus is to determine if at least the most successful active managers have skills significant enough 
to be rightfully compensated with the fees that they collect. As a consequence, it would be also 
worthwhile for an investor to assign her money to active managers instead of depositing her savings in 
the alternative represented by passive mutual funds.  
In the next paragraph we review some research on this topic, showing how, on average, active managers 
have underperformed their reference indexes. 
1.3    Active Managers and Total Alpha Returns 
As previously mentioned, our main reference is Fama and French’s paper (2010). In their work, the two 
economists evaluate whether, after subtracting the management costs, the returns achieved by mutual 
fund managers add value to their clients at a significant degree of confidence.  
Their findings are that “few funds produce benchmark-adjusted expected returns sufficient to cover their 
costs” and that “evidence of inferior and superior performance (nonzero true alpha)” is observed only 
“in the extreme tails of the cross-section of mutual fund alpha estimates” (Fama and French, 2010). This 
implies that only a small subgroup of the mutual fund managers that the authors analyze show results 
consistent with the hypothesis that managers can generate superior returns with their abilities. 
Furthermore, there is a small group of managers who significantly underperform the benchmark. The 
researchers calculate Alpha, known as abnormal expected return, to determine the outperformance of the 
managers over their benchmark.  
These are the main similarities between our analysis and the work of Fama and French (2010), which 
provides us with the overall conceptual framework to set up our analysis. However, our focus is more 
specific and we target only managers belonging to the high tail of the distribution, where the two