Download How to Invest in the U.S. Overall Market?

2014
Course:
Author:
ANR:
Master Thesis Finance
Frank van der Linden
222759
Supervisor:
Co-Reader:
dr. L. Baele
dr. F. Castiglionesi
How to Invest in the U.S. Overall Market?
“A comparison between actively- and passively managed funds”
[Abstract]
This study examines the best strategy to invest in the U.S. overall market, by comparing the
performance of a passively managed market fund against two strategies of actively managed market
funds during the period 2001-2013. Additionally, the performance is measured during periods of
recession and expansion. The two approaches of actively managed market funds that are used in this
study are market-like (large blends) funds, as they have the same investment style as the market, and a
replicated market fund, based on sector funds. The results show that neither market-like funds nor the
replicated market fund are able to outperform the passively managed market fund as they obtain alphas
of respectively -1.68% and -0.04%. A large part of this underperformance is attributable to high expense
ratios. During recessions (expansions) the same tendency is observable, as market like funds
underperformed the market by -1.22% (-2.14%) and the replicated market by -1.29% (-0.22%). The
results might clarify why passively managed funds experience large net cash inflows, whereas the
opposite is the case for actively managed funds. Complementary, I found that sector funds on average
outperform the market and are negatively related to turnover ratio, whilst positively related to fund size
and fund age.
Table of Contents
Chapter 1:
Introduction and Problem Definition----------------------------------------
2
Chapter 2:
Theoretical and Literature Review-------------------------------------------
4
2.1.1: Mutual Fund Industry------------------------------------------------------------2.1.2: How Funds are Sold and the Corresponding Costs------------------------2.1.3: Development and Trends in the Fund Industry--------------------------------
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6
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2.2.1: Mutual Fund Performance: Efficient Market and Factor Models---------2.2.2: Mutual Fund Performance: Empirical Evidence and Hypotheses---------2.2.3: Luck?--------------------------------------------------------------------------------
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2.3: Fund Characteristics----------------------------------------------------------------
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Methodology----------------------------------------------------------------------
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3.1.1: Factor Models---------------------------------------------------------------------3.1.2: Assumptions------------------------------------------------------------------------
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Data---------------------------------------------------------------------------------
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4.1: Market (Benchmark) Screening---------------------------------------------------4.2: Market-like Mutual Fund Screening---------------------------------------------4.3: Mutual Fund Sector Screening----------------------------------------------------4.4: Performance and Economic Circumstances-------------------------------------
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Results------------------------------------------------------------------------------
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5.1.1: Carhart’s Four Factor Model--------------------------------------------------5.1.2: Market-like Fund Performance-------------------------------------------------5.1.3: Sector Fund Performance--------------------------------------------------------
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5.2: Fund Characteristics and Performance-----------------------------------------5.3: Mutual Fund Performance during Recessions and Expansions---------------
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Chapter 3:
Chapter 4:
Chapter 5:
Chapter 6:
Conclusions and Recommendations------------------------------------------ 38
6.1: Conclusions--------------------------------------------------------------------------6.2: Recommendations--------------------------------------------------------------------
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39
Bibliography ---------------------------------------------------------------------------------------
40
Appendix 1
42
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Chapter 1: Introduction and Problem Definition
During the last decades the role of investment companies is of growing importance in the United States
(U.S.) and the rest of the world. According to the Investment Company Institute (ICI, 2014), more than
$17 trillion at year- end 2013 is managed by U.S. investment companies. Most of these investments are in
mutual funds, which are responsible for almost 90 percent of the assets, and therefore playing a
significant role in the total U.S. fund industry. Moreover, the ICI fact book (2014) pointed out that mutual
funds is the most popular investment vehicle among investors, since the financial crisis in 2007.
Although mutual funds are playing a central role in the investment world, it is still a debatable and
relevant topic whether mutual funds are performing well over time. Previous studies investigated the
performance of actively managed mutual funds compared to passively managed funds. Most of these
studies found that in general, mutual funds underperformed compared to their passively managed
benchmarks, which thus indicates that in general, managers of actively managed funds do not have the
ability of stock picking talent. However, there are studies that analyzed the performance of certain mutual
funds with a specific fund policy. Among them were Kacperczyk, Sialm and Zheng (2005), who
examined the performance of industry concentrated funds in comparison to diversified funds. Skilled fund
managers could benefit from their informational advantage and therefore obtain superior performance by
picking the right stocks. The results indicate that this might be the case, as industry concentrated funds
outperformed diversified funds. Finally, economic circumstances also play a role on mutual fund
performance. According to Kosowski (2006), actively managed funds perform better during recessions
compared to periods of expansion.
In this study I will elaborate more on the results mentioned above, as I will investigate the best strategy to
invest in the U.S. overall market during the time period 2001-2013. In order to do this, the performance of
two strategies of actively managed market funds will be compared to a passively managed market fund.
The first strategy entails actively managed funds, which are “market-like”. This means they have the
same investment style as the market, as they tend to invest across the spectrum of U.S. sectors and consist
of both growth and value stocks. The latter approach is based on sector mutual funds. The market will be
replicated based on sector funds, by using the same sector weightings as the passively managed market
benchmark. In addition, fund performance will be measured during recessions and expansions in the time
period 2001-2013. Finally, the relation between several fund characteristics including the expense ratio,
turnover ratio, management fee, fund size and fund age on fund performance will be examined. The
research question that will be answered in this study is formulated as follows:
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“What is the best strategy to invest in the U.S. overall market and how is this influenced by different
economic circumstances?”
The remaining is structured as follows. In chapter 2, background information will be provided regarding
investment companies and trends in the investment industry will be described. Additionally, a literature
review will be provided, which gives some more information about the efficient market hypothesis and
mutual fund performance. Based on the literature review, four hypotheses are formulated, which
contributes in answering the research question of this study. Chapter 3 describes the methodology used in
this study in order to measure fund performance and the relationship between performance and several
fund characteristics. Chapter 4 provides descriptive statistics regarding the passively and actively
managed fund(s), and the regression results are presented in chapter 5. By means of these results the
hypotheses can be answered. Finally, in chapter 6 I will conclude this study by enumerating all findings,
in order to end up by answering the research question. Additionally some recommendations will be given
for further research.
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Chapter 2: Theoretical and Literature Review
2.1.1: Mutual Fund Industry
This chapter starts with a thorough explanation of the fund industry in the United States (U.S.).
Furthermore I will provide empirical evidence regarding mutual fund performance and specify some fund
characteristics, which might have an impact on fund performance.
According to the Investment Company Act of 19401, U.S. investment companies can be classified into
two different types, namely: Unit Investment Trusts (UITs) and Managed Investment Companies.
Investment companies are financial intermediaries that collect money from investors in order to compose
a portfolio consisting of a wide range of securities. Investors in this kind of funds basically own a
proportion of the portfolio based on the amount invested. Moreover, this investment strategy gives
investors the opportunity to obtain the same (diversification) benefits of large-scale investing. Investment
companies do have responsibilities towards their investors. First of all, they are obliged to provide
shareholders with periodical reports, which give a clear overview of important statistics regarding the
fund. On top of that, the investment company is responsible for professional managing the fund, which
means that portfolio managers should aim for superior performance and optimal diversification.
An UIT is an unmanaged fund, whereby a pool of money is invested in a fixed portfolio. In order to
establish an UIT, a sponsor is required, which are typically brokerage companies. This kind of firms will
buy portfolios of securities, which are deposited in a trust. Finally, they will sell the shares or “units” in
the trust to the public. Fund’s trustees such as banks or trust companies are responsible for the payment of
shareholders, when income is realized. Costs related for buying an UIT, comes into two ways. First of
all, investors have to pay a management fee to the trustee. This is often very low as it contains an
unmanaged fund. Next to that, investors have to pay a premium over their shares, which is the reward for
sponsors.
Unlike UITs, funds of managed investment companies are not fixed and available in two classes: closedend and open-end. Funds of both types are managed by management companies, which are appointed by
the board of directors of the funds. Management companies are compensated in the form of management
fees, which is normally higher compared to unmanaged UITs. Often, management companies are also the
founders of the funds. The differences between open-end funds and closed-end funds arise by trading
these kinds of funds. Open-end funds or also known as mutual funds can be bought from and redeemed
1
In order to stabilize financial markets after the stock market crash of 1929, the Investment Company Act (1940) was created,
which defines the limitations and responsibilities of all sorts of investment companies. It is enforced and regulated by the
Securities and Exchange Commission (SEC).
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through the investment company at net asset value (NAV). They do no trade on organized exchanges.
Companies of closed-end funds sell a fixed number of shares at a one-time initial public offering. After
the public offering these shares can only be purchased at organized exchanges and are not redeemable.
This means that holders of these shares can only sell their part to other investors via brokers at prices that
probably differ from NAV. The number of shares outstanding for mutual funds will change on a daily
basis, whereas this is not the case for closed-end funds. Therefore, closed-end funds could invest more in
illiquid assets, as they do not face liquidity problems. Finally, it is also possible to invest in funds which
have characteristics of both open-end and closed-end funds. These are called interval funds. This type of
fund issues or redeems shares at pre-determined intervals.
This study will primarily focus on mutual funds (open-end funds) and therefore I will describe them more
extensively. Currently, there are multiple flavors of mutual funds, which are based on the investment
policy. The fund’s policy can be found in the prospectus and often comprises one of the following
categories: money market funds; fixed income funds; balanced and income funds; asset allocation funds;
equity funds; index funds and specialized sector funds. As this study does not cover all policies, I will
only discuss the relevant types.
Equity funds mostly consist out of stocks, although it is possible that a small portion is invested in other
types of assets. Additionally, an equity fund often holds about 5% in the money market in case of share
redemptions. Equity funds can be distinguished based on their objective. Besides the funds with the
objective of investing in emerging and international markets, there are also more traditional objectives
such as growth and/or income. Growth funds invest in stocks which are prospects of large capital gains,
whereas income funds holds value stocks which focuses on current income, such as dividend stocks. Next
to that, it is also possible to invest in funds which have the objective to invest in stocks concentrated on a
particular industry.
In contrary to the actively managed mutual funds, it is also possible to invest in index mutual funds or
Exchange-Traded Funds (ETFs) which are characterized as passively managed. The objective of index
funds is to track a certain index, for example the S&P500, Total U.S. Stock Market or utilities sector. This
is done by buying the same stocks as the proportion of the security’s representativeness in the index.
ETFs are kind of similar, but trade like a stock. As mutual funds can be traded once a day when NAV is
calculated, ETFs can trade throughout the day. This might cause that ETFs slightly deviates from its
NAV. An advantage of investing in passively managed funds compared to actively managed funds is the
significantly lower expenses.
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2.1.2: How Funds are Sold and the Corresponding Costs
Currently, there are two ways how actively managed mutual funds are sold to the public. First of all,
shares can be indirectly sold via brokers, which require a commission for their services. These fees are
often called loads and are paid by investors either at the purchase of shares (front-end load) or when they
sell their shares (back-end load). The size of the commission is however determined by the fund and not
part of the 12b-1 fees. In addition, brokers can also be compensated by means of a sales charge, which are
paid out of the 12b-1 fees. These fees are paid annually and therefore investors should consider whether
to invest for a short or long time horizon in order to make a choice between funds with load payments or
12b-1 fees. The second approach of selling fund shares is via direct marketing, which entails that shares
are sold for example through various media channels or fund supermarkets. This selling method also
contains several costs primarily to create awareness among investors, such as advertising cost and
promotional literature costs (annual reports and funds prospectuses). The elements of 12b-1 fees
mentioned above are together with costs related for operating the fund such as administrative and
advisory costs, part of the operating expenses. These costs are as one would expect higher for actively
managed funds compared to passively managed funds. According to the Investment Company Institute
(fact book 2014, p.91), the asset-weighted average expense ratios in 2013 were respectively 0.89% for
actively managed funds and 0.12% for passively managed funds.
Besides the direct costs with regards to mutual funds, there are also indirect costs such as taxes on
investment income. If the fund granted a pass-through status, taxes on capital gains and dividends should
be paid by investors. According to the Investment Company Act of 1940, mutual funds can only be
qualified as pass-through entity as they fulfill certain criteria primarily related to capital gain distributions
and diversification. This tax construction entails one important disadvantage for the individual investor,
as they are partly restricted to time their capital gain realizations, which results in inefficient tax
liabilities.
2.1.3: Development and Trends in the Fund Industry
In order to obtain a broad overview of trends and activities in the U.S. investment company industry, the
latest edition (2014) of the investment company fact book2 is used, published by the Investment Company
Institute (ICI)3.
2
3
http://www.ici.org/pdf/2014_factbook.pdf
The ICI is the national association of U.S. Investment Companies
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According to the ICI, almost $30 trillion is invested in mutual funds worldwide, year-end 2013. The
mutual fund market in the U.S is the largest, with more than $15 trillion assets under management and
thus about 50% of the market. The ETF market is significantly smaller with only $2.3 trillion assets under
management worldwide. The U.S. stands for $1.7 trillion and thus responsible for 72% of the total assets
in the market.
Table 1 gives a broad overview of the evolvement of the fund industry in the U.S. for the period 1996 –
2013. The final column represents the total assets under management by all types of investment
companies and has grown from almost $4 trillion in 1996 to $17 trillion in 2013, which is an increase of
more than 350%. The rapid growth was primarily caused by the development of the mutual fund industry,
which is responsible for approximately 90% of total assets under management. In addition, ETFs are
gaining popularity in the last decade and plays a more significant role in the fund industry. On the other
hand, UITs and closed-end funds are responsible for only 2% of total assets under management and thus
are not very attractive investment vehicles. Moreover, there is hardly any growth in these types of
investment companies observable.
When screening the evolvement of the fund industry in table 1, I notice growth in almost every year.
However, there is a large decline observable in the years 2002, 2008 and 2011, which might be caused by
the collapse of the market as part of the dot-com bubble and financial crisis. I will elaborate more on the
performance of funds in recessions, later on.
Table 1: Investment Company Total Net Assets by Type
This table provides an overview of assets under management for several types of investment companies, namely: Mutual Funds,
Closed-end Funds, Exchange-Traded Funds (ETFs) and Unit Investment Trusts (UITs). Amounts are in Billions of dollars at
year-end, covering the period 1996-2013. Source: ICI Fact book (2014)
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As mentioned above, the fund industry has exploded since 1996. During 2012 and 2013 assets under
management has grown by approximately 15% on yearly basis. Figure 1, contributes to the fact of the
increasing popularity of investment companies. The investments in stocks have drastically decreased in
the last decade, while investments in bonds are also following a negative trend. On the other hand, large
investments in the fund market are observable.
While funds can be seen as the most attractive investment vehicle present-day, it is also interesting to look
more closely on the cash in- and outflows of the several fund types in order to check whether some trends
are observable. This is done in figure 2, whereby the cumulative cash flows concerning the period 2007 2013 for actively managed funds and passively managed funds are graphically displayed. When looking
at figure 2, some obvious trends are observable. Actively managed U.S. equity mutual funds are
experiencing a yearly increasing cash outflow, while the opposite is observable for passively managed
funds as inflows are steadily increasing every year. Although figure 2 represents only a small period in
mutual fund history, it definitely show signs of the increasing popularity for passively managed funds. As
already mentioned, it might be that the financial crisis plays a role for this particular trend as it is
completely covered by the time period. On the other hand, fund performance could also be the underlying
reason, which might indicate that actively managed funds are underperforming their passively managed
peers.
Figure 1: U.S. Household Net Investments in Funds, Bonds and Equities
This figure provides an overview of investments by households in funds, bonds and equities. Amounts are in billions of dollars
and covering the period 2003 – 2013. Source: ICI Fact Book (2014).
* Data for long-term registered investment companies include mutual funds, variable annuities, ETFs, and closed-end funds.
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Figure 2: Cash Flows of Actively and Passively Managed Funds
This figure gives an overview of the cumulative cash in- and outflows for index funds and ETFs (passively managed), and mutual
funds (actively managed) for the time period 2007-2013. Amounts are in billions of dollars. Source: ICI Fact Book (2014).
2.2.1: Mutual Fund Performance: Efficient Market and Factor Models
The reason why investors invest in mutual funds is that they believe fund managers have the ability of
picking the right stocks and thus are able to outperform their passively managed benchmark. This means
that they believe fund managers will obtain a better performance, than if they invest the money by
themselves. Currently, there is still an ongoing debate about whether mutual funds could outperform their
passively managed counterparts. Many researchers investigated this phenomenon, but often provide
contradictory results.
In 1970 Eugene Fama developed the Efficient Market Hypothesis (EMH), which was an academic breakthrough that changed the world. Fama stated that an efficient market is one where prices always fully
reflect available information. Basically this means that stocks always represent their fair value and
investors should be unable to beat the market by picking the right stocks or by market timing. In this case,
higher returns are only possible by investing in more risky assets. The Capital Asset Pricing Model
(CAPM) developed by Markowitz (1952) in his Modern Portfolio Theory, and after a few modifications
introduced by Sharpe (1964), Lintner (1965) and Black (1972), is a model that is in line with the EMH.
The CAPM describes expected returns based on the beta, which is the riskiness of the stock towards the
market. Furthermore, investors are compensated for the time value of money.
While the CAPM has been used for a long time as a standard model for predicting future expected
returns, research by Banz (1981) and Rosenberg, Reid and Lanstein (1985) showed that expected returns
are also explained by respectively size and the book-to-market ratio. These studies provide evidence that
small capitalization stocks tend to outperform large capitalization stocks and value stocks (high book-to9
market ratio) tend to outperform growth stocks (low book-to-market ratio). A study by Fama and French
(1992) confirmed these findings and added these factors in a new three factor model based on the CAPM,
which was better able to predict expected returns than the CAPM. As mentioned before, there are doubts
whether the efficient market holds or not. If the efficient market holds, the additional factors size and
value are proxies for risk. The question is, what kind of risk it measures. Fama and French (1996)
proposed the possibility of distress risk as they observed large deviations in returns for small
capitalization stocks and value stocks. In case that returns of both kind of stocks are highly correlated, it
means that they are subject to systematic risk and therefore it requires a risk premium. A recent study by
Campbell, Hilscher and Szilagy (2008) about distress risk showed however a negative relation between
distress risk and excess return, which might indicate that the market is not efficient at all. This would
mean that stocks are mispriced and outperformance is possible. A possible explanation for mispricing of
stocks can be found in behavioral finance. According to La Porta, Lakonishok, Schleifer and Vishny
(1994) naïve investors tend to invest in growth (glamour) stocks, because these are often fast growing
upside potential firms. An excess demand for glamour stocks leads to mispricing of these stocks and a
lower demand for value stocks. In order to attract investors to buy value stocks, prices go down, which
leads to undervaluation of value stocks.
While the Fama French three-factor model has been frequently used in academic research, it is still
debatable whether it is a good one. Although it captures anomalies such as the size and value effect,
literature shows that more anomalies plays a role which are not integrated in the three-factor model. A
study by De Bondt and Thaler (1985) shows that portfolios that performed poorly during the previous 3 to
5 years will outperform portfolios that performed well in the same period, for the next 3 to 5 years. It is
however unclear whether these results can be attributed to systematic risk or an overreaction of prices.
The short-term momentum effect discovered by Jegadeesh and Titman (1993), shows that stocks which
performed well in the past 3 to 12 months tend to do well in the next 3 to 12 months and vice versa. In
response to these findings, Carhart (1997) developed a four-factor model based on the three-factor model
of Fama and French, which includes the momentum effect as additional factor.
2.2.2: Mutual Fund Performance: Empirical Evidence and Hypotheses
In this part I will elaborate more on mutual fund performance, supported by empirical studies. In addition,
hypotheses are formulated which will contribute to answer the main question of this study: “What is the
best strategy to invest in the U.S. overall market and how is this influenced by different economic
circumstances?” As mentioned in the previous section, the EMH is highly controversial with regards to
many academic studies. If the EMH holds, outperformance by mutual funds should not be observable and
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thus underperform at a rate equal to the additional expenses compared to the market. On the other hand, if
the EMH is not completely true, there is a possibility that mutual funds can outperform the market.
Jensen (1967) was one of the first, who investigated the performance of mutual funds for a sample of 115
funds ranging from 1945 – 1964. In order to measure the performance of the fund, he derived “Alpha”
from the CAPM model. “Jensen’s Alpha” is a risk-adjusted performance measure (also defined as
abnormal return) showing over- or underperformance in a portfolio compared to the predicted CAPM
return4. Basically it represents a fund manager’s predictive ability to pick the right stocks. The results of
his study show that on average mutual funds underperform relative to their passively managed
benchmark, even when gross returns are used. Ippolito (1989) found however contradictory results in his
study about mutual fund performance during the period 1965 – 1984. The results show that on average
mutual funds obtain a positive alpha net of expenses, but it does not offset load charges. Although
Ippolito’s (1989) results show that outperformance is possible, most academic studies are in line with
Jensen (1967) and show signs that the market is efficient. Malkiel (1995) is one of them, and performed a
kind of similar study as Jensen (1967) did, covering a more recent period 1971 – 1991. Additionally, he
shows that survivorship-bias plays a role by measuring the performance of mutual funds. Therefore, he
adjusted his dataset accordingly. Although the data used in his study is more accurate, the results confirm
Jensen (1967), as he observed underperformance by mutual funds both net and gross of expenses. Finally,
a more recent study by Fama and French (2010) covering the period 1984 -2006 contributed to the fact
that mutual funds on average underperform by about their expenses, which is consistent with the efficient
market hypothesis.
As mentioned in the introduction section, this study investigates the best way to invest in the U.S. overall
market and therefore I am only interested in the performance of market-like and sector mutual funds
relative to the market. I define market-like mutual funds as funds with the same investment style as the
market. According to Morningstar5, large blend funds are fairly representative for the total U.S. stock
market in terms of size, growth rates, and price. Large blend portfolios tend to invest across the spectrum
of U.S. industries and consist of both growth and value stocks.
Hypothesis 1: “Market-like” mutual funds underperform relative to the market.
Market-like funds will on average obtain a negative alpha with respect to the market for the time period
2001 – 2013. Alpha is measured based on net returns, thus excluding all relevant expenses6.
4
Alpha is also applicable to the three-factor model of Fama and French and the four-factor model of Carhart. It has the same
meaning, but is based on a model with more factor variables.
5 http://admainnew.morningstar.com/directhelp/Glossary/Operations/Common/Morningstar_Categories.htm
6 Fund loads are not taken into account throughout this study and thus should not be interpreted as part of the relevant expenses.
11
As mentioned above, several academic studies claim that on average actively managed funds are
underperforming relative to a passively managed benchmark. This does not mean that all funds are
underperforming. In order to detect outperformance by funds, it is necessary to perform a more extensive
study of fund performance on a more specific level.
There are studies providing more insights on mutual fund performance. According to Grinblatt and
Titman (1989), some funds do show characteristics of stock picking talent. They first looked at the
abnormal return (alpha) gross of expenses and discovered that growth funds and aggressive growth funds
outperformed the market. Moreover, funds with the smallest NAV also obtained positive alphas. These
positive abnormal returns however vanished as they take expenses into consideration. Apparently these
funds have the highest expenses, which abolished the managers stock picking talent. Barras, Scaillet and
Wermers (2010), also take a closer look on fund performance. Their study covered 2076 funds in the U.S.
during 1976 – 2006, and divides them among three groups based on performance, namely: unskilled,
zero-alpha and skilled. Their results show that on average the number of funds that are able to outperform
the market is 0.6% and thus negligible. Furthermore, 24% of the funds are unskilled, whereas 75.4% are
zero-alpha funds. By observing the particular funds that were able to outperform, they noticed that most
of these funds were aggressive growth funds. Next to that, results pointed out that fund expenses have a
major impact on performance, as skilled funds raised to 10% and unskilled shrank to less than 5% of the
sample when using gross returns.
Kacperczyk et al. (2005) examined the performance of industry concentrated funds in comparison to
diversified funds. According to the theory it might be expected that diversified funds should outperform
industry concentrated funds, because of the diversification benefits. On the other hand, it is also possible
that skilled fund managers could benefit from their informational advantage and obtain superior
performance by picking the right stocks. The results show that this might be the case, because industry
concentrated funds are outperforming diversified funds with an average abnormal return of 0.33%, net of
expenses.
Hypothesis 2: Sector funds outperform relative to the market.
The Industry Classification Benchmark (ICB) defines 10 different sectors. These 10 different sector funds
will obtain on average a positive alpha relative to the market for the time period 2001-2013. Alpha is
based on net returns, thus excluding all relevant expenses.
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Hypothesis 3: Replicating the U.S. market by creating a portfolio consisting of sector funds will
outperform the market.
The new constructed portfolio consists of identically the same sector weightings as the chosen market
benchmark, but based on mutual funds with a specific sector objective. The average alpha will be positive
relative to the market, for the time period 2001 – 2013. Alpha is based on net returns, thus excluding all
relevant expenses.
Finally, Moskowitz (2000) and Kosowski (2006) observed that mutual fund performance is also
influenced by economic circumstances. According to Kosowski (2006), previous studies did not take the
value added by active mutual fund managers in recessions into consideration. However, results show that
in times of recession, mutual funds are performing better compared to expansion periods, as he observed a
difference in the yearly alpha of 3% to 5%. According to Ferson and Schadt (1996) the fund flow effect
could be a possible explanation for these results, as funds will hold more liquid assets during recessions
for possible redemptions. This will lower the funds riskiness, which improves the performance of the
fund.
Hypothesis 4: Mutual funds perform better relative to the market in recessions compared to periods of
expansions.
The National Bureau of Economic Research (NBER) distinguishes periods of recessions and expansions
which are often used in academic research. During periods of recession mutual funds should have on
average a higher alpha compared to expansion periods. The mutual funds that will be taken into
consideration are: market-like funds, sector funds and the new constructed market fund (based on sector
funds).
2.2.3: Luck?
Academic research provides evidence that only a small proportion of actively managed funds are able to
outperform their passively managed benchmark. Most of these funds try to attract new investors by
mentioning good historical returns in their intensive marketing campaigns. Historical returns are however
no guarantee for good performance in the future. Moreover, there is a possibility that good historical
performance is just because of luck and thus has nothing to do with the stock picking ability of the fund
manager.
Fama and French (2010) investigated by means of a bootstrap simulation whether good performing funds
are based on luck or managerial skills. During the period 1984 – 2006, they analyzed the performance of
3156 funds and found that only a few funds had enough skill to cover their costs. These skilled funds are
13
however hard to detect as most of the funds covered by this study are unskilled. Barras et al. (2010)
conducted a similar study as Fama and French did, covering the period 1976 – 2006. Although they had a
different approach to detect skilled or unskilled funds, the results are comparable. In addition, they show
significant differences among the time periods, as prior to 1996 the fraction of skilled funds were higher
compared to the period 1997-2006.
As shown by academics, there is a relation between luck and fund performance, which means that
outperformance might not be the result of stock picking talent. Therefore, studies should be aware of this
phenomenon when interpreting the results.
2.3: Fund Characteristics
In this section I will elaborate more on a couple of fund characteristics which might be related with fund
performance. The relation between performance and fund characteristics could give more insights in the
difference between good- and bad performing funds. The key characteristics covered in this study are the
expense ratio, turnover ratio, management fee, fund age, and fund size.
The expense ratio are costs incurred for operating the mutual fund as a fraction of the total assets under
management. As mentioned earlier, it includes administrative expenses, advisory fees and 12-b1 fees.
According to the ICI (Fact book 2014, p.91), on average, a downward trend in expenses is observable
over the last 13 years for both actively managed mutual funds and passively managed index funds. The
ICI mentioned two possible explanations for the decline in expense ratios. Firstly, expense ratios are
inversely related to fund size. Several fees included in the expense ratio such as agency fees and
accounting and audit fees are fixed. Therefore an increase in fund size will result in a relatively smaller
expense ratio. Secondly, the mutual fund market has exploded over the last decade. This has led to an
increase in the amount of funds and more competition, as the number of fund sponsors increased.
Additionally, other investment vehicles such as ETFs stimulate competition, which might result in lower
expense ratios. Many studies investigated the relation between expense ratios and performance, which led
to different results. There are studies that did not find a significant relation between expense ratio and
performance. On the other hand, several studies exhibit a negative relation, meaning that if the expense
ratio goes up, performance will be negatively influenced (Malkiel,1995), (Carhart, 1997), (Chalmers,
Edelen, and Kadlec, 1999), (Kacperczyk et al, 2005). Rationally, a positive relation is expected between
expense ratio and performance, because normally you pay for performance. The same holds for
management fees, which is part of the expense ratio. Managers are compensated for professional
managing the fund. The more skilled a manager is, the higher the compensation probably should be.
14
The turnover ratio entails all the trading activity (all stocks bought and sold) during the year divided by
the net asset value of the funds. A high turnover ratio means that the fund is very active in terms of
trading stocks, but activity goes together with increasing costs (taxes and transaction costs). The turnover
ratio of passively managed funds is in general fairly small, whereas the opposite is the case for actively
managed funds. The average turnover ratio over the last 34 years for equity funds was 61% (ICI Fact
book 2014, p.36), but this includes index funds which are often indicated as passively managed. This
means that the actual average turnover ratio of actively managed funds is higher. If fund managers do
have stock picking talent, then a positive relation between turnover ratio and fund performance is
expected. Literature shows however that most fund managers do not have the ability of picking the right
stocks. On top of that, there are studies who found a negative relation between turnover ratio and
performance (Carhart, 1997), (Chalmers et al., 1999). This means that a high turnover ratio dilutes value.
Fund size or Total Net Assets (TNA) is also an important characteristic, which might be related to fund
performance. Researchers found mixed results regarding the relation between fund size and performance.
According to Elton, Gruber and Blake (2012), an increase in fund size could lead to economies of scale
which is beneficial as it results in a lower expense ratio. These results are in line with the ICI, mentioned
earlier. On the other hand, a study by Chen, Hong, Huang and Kubik (2004) shows that fund size is
negatively related to performance. They argue that diseconomies of scale might play a role in explaining
this negative relationship, which results in suboptimal investment decisions and higher trading costs.
Lastly, more recent research by Philips, Pukthuanthong and Rau (2013) shows however that fund size
does not affect performance.
Finally, there might be a link between fund age and performance. In theory, older funds might perform
better as they are more experienced, have better resources and also a better understanding of the market.
On the other hand, it could deteriorate performance as fund becomes larger in size and thus might be
more complex. This is in line with the results of Webster (2002) who founds a negative relationship
between fund age and performance.
15
Chapter 3: Methodology
3.1.1: Factor Models
This chapter will provide more insights in the methodology that will be used to measure fund
performance. In addition, I will provide a model that examines the relation between fund performance and
associated fund characteristics.
In order to measure a fund’s abnormal return, I will use a multi-factor model namely, Carhart’s fourfactor model, which is an extension of the basic CAPM model. The intercept of the regression between
the excess return and the factors included in this model is used as performance measure, and often
denoted as alpha.
The CAPM is a simplistic model which describes the relationship between risk and expected return. The
idea behind the CAPM is that investors should be compensated for taking additional risk and for the time
value of money. The formula is written as follows:
Rit - Rft = αi + βi(Rmt – Rft) + εit
[1]
The dependent variable, describes the excess return of fund i at time t, which is a function of the excess
return of the market (Rm – Rf) , a risk measure βi, an intercept αi, and an error term εi. The Rf refers to the
one-month U.S. treasury bill. As mentioned above, α is used as a performance measure, which is often
referred to Jensen’s Alpha, as Jensen was the first who used this measure by evaluating fund performance
in his study in 1967. A positive alpha means the fund is outperforming its benchmark and a negative
alpha means that the fund is underperforming compared to its benchmark. The additional risk a fund
might have compared to its benchmark is described by beta.
Fama and French (1992) showed that the CAPM is not a proper model, as beta alone cannot explain
expected returns. Their three-factor model is an extension of the CAPM model and includes the additional
factors size and value. However, Jegadeesh and Titman (1993) found another factor that explains
expected returns, which is known as the short-term momentum effect. Carhart (1997) added the
momentum factor to the three-factor model and developed the four-factor model, which is denoted as
follows:
Rit - Rft = αi + β1(Rmt – Rft) + β2SMBt + β3HMLt + β4MOMt + εit
[2.1]
αi = (Rit - Rft) - β1(Rmt – Rft) - β2SMBt - β3HMLt - β4MOMt - εit
[2.2]
Rewriting gives:
16
This model contains three additional factors compared to the CAPM. First of all, Small Minus Big (SMB),
which corrects for size as small capitalization stocks tend to have higher returns compared to large
capitalization stocks. Secondly, High Minus Low (HML), which corrects for value as high book-tomarket stocks tend to outperform low book-to-market stocks. Finally, Momentum (MOM) refers to short
–term persistent in the performance of stock. I will use this model to examine the performance of all
relevant actively managed funds compared to a passively managed market benchmark, in order to
investigate the best strategy to invest in the U.S. overall market. Therefore, I will perform a panel
regression on monthly data (2001-2013) of excess returns on the four factors of the passively managed
benchmark.
Lastly, I will employ a model that provides more insights of the relationship between fund performance
and several fund characteristics. Therefore, I will perform a panel regression by using the alphas obtained
from the regression of equation 2.2, on the fund characteristics relevant for this study. This model is
given below and will be used for the characteristics expense ratio, turnover ratio, fund size, fund age and
management fees.
𝛼𝑖𝑡 = 𝛾0 + ∑𝑁
𝑛=1 𝛾𝑛 𝑋𝑖𝑡 + εit
[3]
Equation 3 exhibits the relation between fund performance and fund characteristics. α refers to the abnormal return
of fund i at time t, whereas X refers to the factor loading of fund i at time t. The regression coefficients are denoted
by ϒ, which measures the effect of fund characteristic n on performance, while ϒ0 is the intercept.
3.1.2: Assumptions
For all regressions stated above, I will make use of the Ordinary Least Squares (OLS) approach. This
approach crucially depends on a few assumptions (Gauss-Markov assumptions) which are required for
interpreting the regression results. Under the Gauss-Markov assumptions, the OLS estimator is the best
linear unbiased estimator for β.
The first assumption refers to linearity, which means that there is a linear relationship between the
dependent and independent variable. The second assumption excludes multicollinearity between the
explanatory variables, which means that the variables are statistical independent and thus not correlated to
each other. It could have a negative impact on the trustworthiness of the regression results, In case of
high correlations between the independent variables. The third assumption states that all the error terms
have the same variance, which refers to homoscedasticity. When the variance of the errors is not constant,
17
it is referred to heteroskedasticity. This could lead to results which are invalid as most models are based
on a constant variance of the error terms. Finally, the error terms should be independent, thus not
correlated to each other (no autocorrelation) or to the independent variables, and additionally follow a
normal distribution with a mean equal to zero and a constant variance. When one or several assumptions
mentioned above are violated, it will negatively influence the reliability of the regression, as it is possible
that the t-test will conclude too easily that the model is useful.
18
Chapter 4: Data
This chapter will elaborate more on the data which contributes to answer the main question of this study.
This is done by providing descriptive statistics of the passively managed market fund and actively
managed funds. I will first start finding a suitable passively managed market benchmark. Secondly, I will
provide descriptive statistics for all market-like funds and all sector funds, which can be categorized into
10 different groups. Finally, I will elaborate more on the relationship between several fund characteristics
and fund performance and will describe periods of recession and periods of expansion. The data will be
almost completely derived from Morningstar and the Center for Research in Security Prices (CRSP)
mutual fund database.
4.1 Market (Benchmark) Screening
When investors consider investing in a portfolio that tracks almost the complete U.S. market index, this is
only possible by buying a market ETF or index fund. According to Morningstar, there are 10 market
funds, which have the objective to track almost the complete U.S. market. There are however differences
between these funds in terms of composition, expense ratio and inception date, as can be seen in table 2.
In order to find a suitable market benchmark, it is first of all essential that it is covering the years relevant
for this study. The second part of the screening is based on the number of holdings and expense ratio. A
larger number of holdings entails that the fund has a broader coverage of the total U.S. market. Therefore,
I selected the Vanguard Total Stock Market Index Admiral as benchmark, because it has 3684 holdings,
which is a very good representative for the total U.S. market. On top of that, the fund has on average, one
of the lowest expense ratios and is covering the required period 2001-2013.
The Vanguard Total Stock Market Index Admiral Fund (Market Benchmark) is established by the
Vanguard Group in 2000 and comparable in terms of holdings with the Vanguard Total Stock Market
Investor shares. The Admiral shares are however trading at lower costs, but on the other hand the initial
investment for these stocks are higher. According to the prospectus 7 , the investment strategy of the
Admiral fund is to track the CRSP U.S. Total Market Index, which represents almost the complete
investable U.S. stock market traded on the New York Stock Exchange and NASDAQ. This means that the
index fund is broadly diversified and includes all types of market capitalizations. As can be seen in table
2, the Admiral fund is one of the largest and most popular U.S. total stock market fund with total net
assets under management of more than $86 billion.
7
http://quote.morningstar.com/fundfiling/Prospectus/2014/4/28/t.aspx?t=VTSAX&ft=485BPOS&d=06216af438ffc8ba705e2cf6062d0094
19
In the next chapter, I will replicate the market based on sector funds to check whether this is a good
investment strategy. In order to do so, the sector weightings of the market are required. These can be
found in table A.1 of appendix 1.
Table 2: Market Fund Screening
Fund Size (TNA)
Expense
Market Fund
Inception Date
Holdings
(Billions of Dollars)
Ratio
Schwab US Broad Market ETF
3-11-2009
2007
2.91
0.06%
iShares Core S&P Total U.S. Stock Market ETF
20-1-2004
1501
1.04
0.19%
iShares Dow Jones U.S. ETF
12-6-2000
1259
0.85
0.2%
iShares Russell 3000 ETF
22-5-2000
2979
5.41
0.2%
iShares NYSE Composite ETF
30-3-2004
1748
0.07
0.25%
SPDR Russell 3000 ETF
4-10-2000
2511
0.56
0.21%
Vanguard Total Stock Market ETF
24-5-2001
3648
39.17
0.09%
Vanguard Russell 3000 Index ETF
20-9-2010
3049
0.09
0.15%
Vanguard Total Stock Market Index Admiral
13-11-2000
3684
86.54
0.09%
Vanguard Total Stock Market Index Investor
27-4-1992
3684
105.01
0.18%
This table shows characteristics of 10 different passively managed U.S. market funds including: the inception date, number of
holdings, total net assets under management and expense ratio. The expense ratio is the average of 2001 – 2013, except for funds
which did not exist since 2001. Then it is the average since existence. Holdings and TNA are based on values at year-end 2013.
4.2: Market-like Mutual Fund Screening
In order to check whether mutual funds outperform the passively managed benchmark (Vanguard Total
Stock Market Index Admiral), I first made a selection of market-like actively managed funds. As
mentioned in Chapter 2, large blend funds are fairly representative for the overall U.S. stock market.
Additionally, a fund should be open for retail investors during the period 2001-2013 in order to be
relevant for this study.
The CRSP mutual fund database provides 1140 funds which fulfill the criteria mentioned above. These
funds are providing 107.614 monthly return observations. Moreover, this dataset is free of survivorshipbias, which means that it includes funds which do not exist anymore. This contributes to more realistic
results, as “dead” funds will have a negative impact on mutual fund performance.
Table 3 provides descriptive statistics for the market benchmark and market-like mutual funds. The table
shows that the passively managed benchmark has better characteristics in terms of the average yearly
excess return and standard deviation compared to actively managed market-like funds. The excess return
of the market is more than twice as high, while the standard deviation or riskiness of the market and
market-like funds are comparable. Additionally, the dataset is separated into two sub periods as shown in
table 3. However, the statistics of market-like funds are still inferior compared to the market. The Sharpe
ratio, which measures the risk-return trade-off, confirms the findings above. A higher Sharpe ratio
indicates better risk-return characteristics and vice versa. The final column of table 3 shows that the
market has a higher Sharpe ratio in all periods. During the period of 2001–2006, market-like funds
20
exhibits a negative result, which means that additional risk, would lead to extra losses. Therefore a riskless asset would even better perform than market-like funds.
Table 3: Market-like Mutual Fund Statistics
Period
Excess Return
Max
Median
Min
SD
Sharpe Ratio
Market 2001 -2013
5.04%
29.60%
4.63%
-44.44%
16.05%
0.314
2001-2006
2.59%
8.11%
0.74%
-10.24%
14.16%
0.183
2007-2013
7.41%
11.54%
1.46%
-17.71%
17.56%
0.422
Market-like Funds 2001 - 2013
2.44%
44.16%
0.72%
-35.47%
16.63%
0.147
2001-2006
-0.06%
44.16%
0.43%
-31.47%
14.84%
-0.004
2007-2013
4.93%
28.71%
1.10%
-35.47%
18.17%
0.271
This table provides an overview of descriptive statistics for the chosen market benchmark (Vanguard Total Stock Market Index
Admiral) and market-like funds. It includes the average yearly excess return (net of expenses), maximum, median and minimum
monthly return, yearly standard deviation (SD) and Sharpe ratio (excess return / SD). The statistics are provided for the complete
period 2001 – 2013 and for the sub periods 2001-2006 and 2007-2013. Market-like results are based on 1140 funds, which are all
open for retail investors and responsible for 107.614 monthly observations.
The final part of the market-like mutual fund screening is based on several fund characteristics which
might be related with fund performance. This includes: the yearly expense ratio, turnover ratio,
management fee, fund size (TNA) and fund age. Data for these characteristics are available via CRSP
mutual fund database and give some more insights in the funds, as shown in table 4. The next chapter will
elaborate more on the relationship between these characteristics and performance.
The expense ratio represents the operating expenses including the 12b-1 fees of a fund. When observing
table 4, it turns out that the expense ratios of market-like funds are relatively high when comparing it to
the market. This seems quite logical, as mutual funds are actively managed, which is related to higher
expense ratios.
Secondly, the turnover ratio measures the trading activity of a portfolio to the assets of the portfolio. The
ratio of market-like funds varies a lot as the difference between the maximum and minimum value is
enormous. The average turnover ratio for market-related funds is 85%. This means that the yearly trading
activity is on average almost completely the average value of the portfolio. As expected, the turnover ratio
of the market is quite low, as it entails a passively managed fund. When fund managers do have stock
picking talent a high turnover ratio could be beneficial. The downside of a high turnover ratio is that
capital gains or losses are constantly realized, which is very tax inefficient as the investor cannot time his
tax obligation. Furthermore, a higher turnover ratio is related to higher transaction costs, which dilutes
value.
The next column describes the management fees, which are costs related for professionally managing the
fund and part of the expense ratio. It are costs incurred for the time and expertise of the fund managers.
Normally, you would expect a positive relationship between performance and management fees, as good
performing funds could demand higher fees. Table 4 shows that the management fee is on average 0.41%
21
Fund age is another characteristic which might have an impact on the performance of funds. The funds
age is ranging from 0 – 88 years old, with an average of 10. While funds getting older, it entails that it is
more experienced. Therefore, there might be a positive relationship between age and performance.
Finally, the average total net asset value (TNA) is presented in table 4. There consist large deviations in
size between the market-like funds ranging from 0.1 million till 91.391 million dollar. The average size of
market-like funds is 524 million dollar.
Table 4: Fund Characteristics of the Market and Market-like Funds
Market
Mean
Max
Min
SD
Expense Ratio
0.09% 0.15%
0.05%
0.04%
Turnover Ratio
5%
12%
2%
2%
Management Fee
0.07% 0.14%
0.03%
0.04%
Fund Age (Years)
7
13
1
4
Fund Size (TNA in Millions of Dollars)
30110
86541
3894
25000
Market-like Funds
Expense Ratio
1.59% 38.24%
0.09%
0.61%
Turnover Ratio
85%
1770%
0%
97.40%
Management Fee
0.41% 3.98% -38.38% 1.71%
Fund Age (Years)
10
88
0
11
Fund Size(TNA in Millions of Dollars)
524
91391
0.1
2895
ln Size
3.28
11.42
-2.30
2.70
ln Age
1.87
4.48
-5.90
1.07
In this table fund characteristics are presented for the market (Vanguard Total Stock
Market Index Admiral) and market-like funds. It includes: the expense ratio, turnover
ratio, management fee, fund age (years) and fund size (TNA in millions of dollars).
On top of that, the natural logarithms of size and age are included for market-like
funds. Market-like results are based on 8777 yearly observations, which stems from
1135 funds during the period 2001-2013.
* The management fee may include waivers and reimbursements. This could lead to negative management fees.
4.3: Mutual Fund Sector Screening
The final step obtaining the best investment strategy for the market is based on mutual funds with a
specific sector objective. CRSP provides lots of data regarding mutual funds, including the Lipper
objective codes. Based on these codes, 10 sector classifications can be distinguished. According to
Lipper’s definitions document 8, the classifications are structured following the Industry Classification
Benchmark (ICB) developed by the Financial Times Stock Exchange (FTSE) and Dow Jones. Table 5
provides an overview of the 10 different sectors and their corresponding sector code. These codes will be
used throughout this study. To obtain a classification a fund is required to have an exposure with a
threshold set at 75% of the portfolio.
8
http://www.lipperweb.com/docs/Research/Methodology/Lipper_Global_Classifications_Definitions2012.pdf
22
Table 5: Sector Classification Scheme
Sector
Code
Basic Materials
BM
Consumer Goods
CG
Consumer Services
CS
Energy
E
Financials
F
Healthcare
H
Industrials
ID
Information Technology
IT
Telecommunication Services
TS
Utilities
UT
This table gives an overview of the 10 different sectors,
according to the industry classification benchmark (ICB).
Next to that, the sector codes are presented.
A problem that arises using the Lipper sector codes is the geographical focus of the different sector funds.
According to Lippers Fund Classification Roadmap9, some sectors do not contain many domestic funds
and therefore international funds are also included. The global oriented sectors are marked with a “G”,
while domestic oriented sectors are marked with a “D”, as shown in table A.2 of appendix 1. Although, it
entails a small portion of the dataset (27%), it will have an impact on the results, because this study
focuses on the U.S. domestic market only. Therefore, the results might be slightly biased.
In addition to the classification codes, CRSP mutual fund database provides 89.608 monthly return
observations (net of expenses), which stems from 1160 different sector funds (survivorship-bias free).
Table 6 gives an overview of the descriptive statistics of the market and 10 different sectors. When
looking at the average yearly excess returns of all sectors, it slightly underperforms relative to the market.
About the half of the sectors show better return characteristics compared to the market, while the other
half perform poorly as they partly obtain a negative excess return. Especially the consumer goods and
industrial sector have high excess returns of respectively 11.16% and 10.47%.
There are however some deviations in terms of riskiness and therefore it is better to take a closer look at
the Sharpe ratio. Figure 3 gives a better view of the relevant Sharpe ratios including the market, marketlike funds and sector funds. When looking at figure 3, large differences are observable. It turns out that
investing in consumer goods and industrial funds are the most attractive investments, while investing in
basic materials, information technology and telecommunication services dilutes value. Next to that,
market-like funds have a low Sharpe ratio compared to the market and most sectors, which makes them
not very attractive investments. Moreover, when comparing the average Sharpe ratio of all sectors (0.255)
with the market (0.314), it turns out that the market has better risk-return characteristics. The next chapter
will use a more sophisticated model to evaluate fund performance.
9
http://www.lipperweb.com/docs/Research/Fiduciary/2009_02_Fund%20Classification%20Roadmap.pdf
23
Table 6: Descriptive Statistics for the Market and 10 different Sectors
Excess
Sharpe
Sector
Return
Max
Median
Min
SD
Ratio
Market
5.04% 29.60%
4.63%
-44.44% 16.05%
0.314
BM
-0.34% 19.31% -0.09%
-29.49% 17.02%
-0.02
CG
11.16% 55.15%
1.11%
-38.33% 16.01%
0.697
CS
6.73% 21.48%
0.97%
-23.27% 19.25%
0.350
E
9.21% 23.67%
1.28%
-33.66% 25.24%
0.365
F
7.11% 36.35%
1.28%
-36.40% 22.85%
0.311
H
4.12% 28.32%
0.75%
-26.36% 17.85%
0.231
ID
10.47% 22.75%
1.41%
-26.07% 20.62%
0.508
IT
-2.51% 36.70%
0.44%
-42.03% 31.13% -0.081
TS
-2.65% 60.66%
0.52%
-52.77% 27.92% -0.095
UT
4.24% 11.01%
1.05%
-19.32% 14.68%
0.289
Average
4.75% 31.54%
0.87%
-32.77% 21.26%
0.255
In this table the descriptive characteristics for the 10 relevant sectors and
the market (Vanguard Total Stock Market Index Admiral) are presented. It
includes the average yearly excess returns (net of expenses), the maximum,
median and minimum monthly return, the standard deviation (SD) and the
Sharpe ratio (excess return / SD). The data is based on 89.608 monthly
observations during the time period 2001-2013, which stems from 1160
different sector funds.
Figure 3: Sharpe ratios
This figure gives an overview of the Sharpe ratios including: the market (Vanguard Total Stock Market Index Admiral), marketlike funds and 10 sectors. The Sharpe ratio is measured by the excess return (net of expenses) divided by the standard deviation.
Values are based on the period 2001-2013.
Sharpe Ratio
1,000
0,800
CG
0,600
ID
0,400
UT F
CS E
Market
H
Market-Like
0,200
0,000
TS IT
BM
-0,200
The last part of the sector screening is based on the same fund characteristics as I used in the market-like
fund screening. When observing the fund characteristics in table 7, it is notable that the turnover ratio is
quite high with an average value of 196% for all sectors. This means that the yearly trading activity of
sector funds is almost twice the average value of the portfolio. When comparing this ratio to that of
market-like mutual funds, it is significantly higher.
24
Secondly, the management fee and expense ratio is comparable to market-like funds with values of
respectively 0.47% and 1.76% for all sectors. This is much higher compared to the passively managed
market fund.
Finally, the average total net asset value (TNA) is presented. Investors invest most of their money in the
domestic oriented sectors such as: financials, healthcare, energy and information technology. One
exception holds for the global utilities sector, which is according to table 7, the most popular sector to
invest in. The other sectors are less attractive, which might explain the small amount of these kinds of
sector funds (appendix 1, table A.2).
Table 7: Overview of Fund Characteristics Sector Funds
Management
Fund
Fund
Fee
Age Max Min SD Size Max
Min SD
Max
Min
SD
Expense
Ratio
Max
Min
SD
Turnover
Ratio
All Sectors
1.76%
9.54%
0.09%
0.67%
196%
4925% 1% 372%
0.47%
3.38%
-32.33%
3.71%
9
74
0
7
189
8758
0.1
506 3.11 9.06 -2.3 2.33 1.85 4.30 -3.60 0.98
BM
1.61%
2.95%
0.61%
0.57%
204%
3455% 3% 431%
0.33%
1.14%
-7.28%
1.23%
6
28
1
8
128
3745
0.1
388 2.25 8.23 -2.3 2.72 1.37 3.35 0.11 0.79
CG
1.53%
2.57%
0.14%
0.55%
253%
1907% 7% 339%
0.31%
1.10%
-4.80%
1.19%
14
28
2
9
89
1475
0.1
211 2.56 7.30 -2.3 2.42 2.39 3.35 0.65 0.71
CS
1.64%
2.75%
0.14%
0.53%
505%
3788% 5% 752%
0.52%
1.05%
-15.34%
1.28%
17
30
3
7
71
1197
0.1
158 2.33 7.09 -2.3 2.24 2.70 3.40 1.18 0.50
E
1.63%
2.95%
0.14%
0.53%
200%
1707% 9% 294%
0.61%
1.05%
-15.64%
1.29%
14
33
0
7
203
3240
0.1
385 3.85 8.08 -2.3 2.01 2.44 3.49 -1.74 0.76
F
1.70%
6.02%
0.09%
0.55%
132%
2336% 1% 274%
0.60%
1.65%
-30.68%
1.52%
15
52
0
6
196
8294
0.1
587 3.38 9.02 -2.3 2.20 2.59 3.94 -2.90 0.58
H
1.85%
4.59%
0.14%
0.63%
160%
1848% 4% 201%
0.54%
1.96%
-32.33%
1.65%
14
32
3
4
162
8578
0.1
534 2.95 9.06 -2.3 2.26 2.61 3.48 1.18 0.28
ID
1.49%
2.59%
0.14%
0.52%
336%
2786% 5% 502%
0.30%
1.05%
-10%
1.41%
17
30
0
8
103
1428
0.1
208 2.77 7.26 -2.3 2.47 2.64 3.39 -1.68 0.73
IT
2.03%
9.54%
0.14%
0.84%
201%
4925% 2% 330%
0.47%
3.38%
-14.59%
1.09%
16
74
1
8
168
5209
0.1
460 3.12 8.56 -2.3 2.29 2.69 4.30 0.29 0.34
TS
1.79%
2.95%
0.14%
0.56%
385%
3606% 2% 682%
0.48%
1.95%
-15.60%
1.57%
14
30
5
5
84
3329
0.1
289 1.68 8.11 -2.3 2.42 2.59 3.40 1.62 0.41
UT
1.53%
3.22%
0.14%
0.49%
159%
3158% 1% 399%
0.54%
1.11%
-14.44%
1.12%
19
67
1
9
264
4686
0.1
565 3.92 8.45 -2.3 2.07 2.84 4.21 -0.39 0.46
Average
1.68%
4.01%
0.18%
0.58%
254%
2952%
0.47%
1.54%
-17.55%
1.34%
15
40
2
7
147
4118
0.1
379
Sector
Max
4% 420%
Min SD
ln
ln
Size Max Min SD Age Max Min SD
3
8
-2
2
2
4
0
1
In this table several fund characteristics are presented for all sectors together and every sector separately. It includes: the expense ratio, turnover ratio, management fee, fund age (years), fund size (TNA in Millions of Dollars) and the natural
logarithms of size and age. All results are averages for the period 2001 – 2013, except for the standard deviation (SD), maximum and minimum results. The results are based on 6270 yearly observations, which stems from 891 different
sector funds.
* The management fee may include waivers and reimbursements. Reimbursements could lead to negative management fees
4.4: Performance and Economic Circumstances
The final part of this study will elaborate more on the performance of funds by different economic
circumstances. The National Bureau of Economic Research (NBER) provides data regarding recessions
and expansions since 1854 and is often used in academic studies. A recession is generally known as a
decline in real GDP for two consecutive quarters. The NBER gives however a different meaning to a
recession. According to their latest publication10 (2010), “a recession is a significant decline in economic
activity spread across the economy, lasting more than a few months, normally visible in real GDP, real
income, employment, industrial production and wholesale-retail sales.” During the time period 2001 –
2013 there were two periods of recession and two periods of expansion, as shown in table 8. The
recessions might be caused by the dot-com bubble (1997 – 2000) and the financial crisis (2007- 2011).
When observing table 8, I notice that there are on average significant deviations in excess returns and
standard deviations between the market, market-like funds and sector funds. During recessions, the excess
10
http://www.nber.org/cycles/sept2010.html
25
returns are largely negative for both the passively managed market fund and the actively managed mutual
funds, while riskiness or standard deviation is very high. Additionally, the Sharpe ratios are largely
negative, indicating that more risk leads to higher losses.
During expansion periods the performance indicators looks more positive. Especially, the second
expansion period has led to outstanding results for all funds as excess returns (net of expenses) exceeds or
almost exceeds 20%, while the level of riskiness is quite low. According to the Sharpe ratios, the market
exhibits the best risk-return characteristics, while this is much lower for actively managed funds. The next
chapter will elaborate more on the performance of actively managed funds relative to the market in times
of recessions and expansions.
Table 8: Descriptive Statistics during Recessions and Expansions
Market
Market-Like Funds
Sector Funds
Excess
Sharpe Excess
Sharpe Excess
Sharpe
Return
SD
Ratio
Return
SD
Ratio
Return
SD
Ratio
Period
Recession 1
(03-2001 - 11-2001)
-10.58% 21.18% -0.500 -12.32% 21.47% -0.574 -14.73% 38.21% -0.386
Recession 2
(12- 2007 - 06 -2009) -22.06% 24.94% -0.885 -23.15% 24.76% -0.935 -21.94% 35.89% -0.611
Recessions Average
-16.32% 23.06% -0.708 -17.74% 23.12% -0.767 -18.34% 37.05% -0.495
Expansion 1
(12-2001 - 11-2007)
5.54%
11.97% 0.463
3.25% 12.70% 0.256
4.49% 19.82% 0.227
Expansion 2
(07-2009 - 12-2013)
21.15% 14.46% 1.463 18.45% 15.01% 1.229 18.29% 17.28% 1.058
Expansions Average
13.35% 13.22% 1.010 10.85% 13.86% 0.783 11.39% 18.55% 0.614
This table distinguishes two periods of recession and two periods of expansion during the time period 2001 –
2013 (source: NBER). For every period the average yearly excess returns (net of expenses), standard deviation
(SD) and Sharpe ratio (excess return / SD) are provided for the market (Vanguard Total Stock Market Index
Admiral), market-like funds and sector funds.
26
Chapter 5: Results
This chapter will provide an extensive analysis of the regression results and is structured as follows. First
of all, I will explain how the four factors included in Carhart’s model are constructed. Moreover,
descriptive statistics of these factors and a basic interpretation of the numbers are provided. Secondly, I
will elaborate more on mutual fund performance of the market-like funds, sector funds and the replicated
market fund. Then the regression results which describe the relationship of several fund characteristics on
performance are provided. Finally, this chapter will take a closer look on mutual fund performance during
periods of recession and expansion. Eventually, this chapter should provide answers on the hypotheses
and the main question of this study.
5.1.1: Carhart’s Four Factor Model
In order to measure fund performance, I will use Carhart’s four factor model. The monthly factor inputs
for the market will be partially retrieved from the web page of Kenneth French. 11 These factors are
constructed by means of the returns of all CRSP firms incorporated in the U.S. and listed on the NYSE,
AMEX and NASDAQ. As the market benchmark used in this study is tracking this index, hardly any
deviations are expected. The market factor is the only factor where the excess return of the Vanguard
Admiral Market funds is used, as these slightly deviates with the data from French’ database. This is
primarily caused by the fact that the Admiral Fund takes expenses into consideration, hence implementing
these data will lead to more realistic results.
The SMB12 factor is constructed by first sorting the stocks into 6 different portfolios based on market
capitalization, 3 categories of small capitalization stocks and 3 categories of large capitalization stocks.
Finally, the average return of the 3 large cap. portfolios will be subtracted from the average return of the 3
small cap. portfolios. The HML factor is constructed in a similar way. First, the stocks will be sorted into
4 different portfolios. 2 portfolios of value stocks (high book-to-market) and 2 portfolios of growth stocks
(low book-to-market). Then the average return of the growth stocks is subtracted from the average return
of the value stocks, in order to get the HML factor. The MOM13 factor is created by dividing the stocks
into two portfolios based on size (large and small cap.). The median NYSE market cap. is used as
breakpoint when dividing the stocks into the two categories. Subsequently, a next sorting procedure will
occur based on returns of the previous 2 till 12 months. The stocks which fall in the top 30% of returns
are defined as “high prior return portfolios” and stocks which fall in the bottom 30% percentile are
11
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
Study by Fama and French (1992) provides the methodology for constructing the SMB and HML factors.
13 Carhart (1997) provides the methodology for constructing the MOM factor .
12
27
defined as “low prior return portfolios”. Finally, the MOM factor can be obtained by taking the average of
the two (high and low market cap.) high prior return portfolios minus the two low prior return portfolios.
Table 9 provides a yearly overview regarding the four different factors. When observing the table, the
market obtained in most years a positive excess return. The negative returns might be related with
historical events as the years 2001 and 2002 are in the aftermath of the dot-com bubble and 2008 was in
the middle of the financial crisis. On average the market obtained a positive excess return of 0.42% for
the period 2001-2013. The same holds for the SMB and HML factors with averages of respectively 0.45%
and 0.31%. This means that small capitalizations stocks and value stocks obtained higher returns than
their counterparts. Finally, deviations are observable regarding the MOM factor. There are years of shortterm persistence in performance, as showed by the positive numbers in table 9. On the other hand also
negative years are observable, which contradicts a momentum effect. On average, the momentum effect is
positive, but negligible.
Table 9: Yearly Values Carhart Four Factor Model
Year
Mkt-Rf
SMB
HML
MOM
2001
-1.10%
1.65%
1.31%
-0.34%
2002
-1.93%
0.37%
1.04%
2.35%
2003
2.27%
1.71%
0.28%
-1.52%
2004
0.92%
0.44%
0.71%
0.02%
2005
0.28%
-0.13%
0.70%
1.20%
2006
0.84%
0.07%
1.01%
-0.54%
2007
0.11%
-0.66%
-1.01%
1.75%
2008
-3.70%
0.59%
0.20%
1.59%
2009
2.32%
0.68%
-0.03%
-5.36%
2010
1.48%
1.04%
-0.15%
0.50%
2011
0.20%
-0.41%
-0.58%
0.69%
2012
1.31%
0.05%
0.56%
-0.03%
2013
2.47%
0.50%
0.04%
0.51%
2001-2013
0.42%
0.45%
0.31%
0.06%
This table presents yearly averages of Carhart’s four factor
model, which will be used in the alpha regressions. It includes
a market factor (Mkt-Rf), size factor (SMB), value factor
(HML) and momentum factor (MOM). The market factor is
based on the return of the Vanguard Total Stock Market Index
Admiral subtracted with the one month U.S. treasury Bill. The
other factors are constructed by means of characteristics of all
stocks listed on the NYSE and NASDAQ
5.1.2: Market-like Fund Performance
To determine the best strategy for investing in the U.S. overall market, I first performed a regression of
the market-like funds excess returns on the monthly factors of the market model. The regression results
are presented in table 10.
When analyzing the results in table 10, I observe highly significant negative alphas for all periods. As
mentioned before, alpha is used as performance measure and can be interpreted as the abnormal return.
28
During the period 2001 – 2013 market-like funds obtained a yearly alpha of -1.93%, which means that the
passively managed market fund outperformed the actively managed market-like funds by almost 2%. By
separating the period into two sub periods, I find that, during the period 2001 – 2006 the performance of
market-like funds is lower compared to the period after. A t-test shows a significant difference of 0.90%
on yearly basis. When relating performance with the average expense ratio (table 4) of market-like funds,
it is notable that a large part of the negative alpha is due to the expense ratio of 1.59%. Without taken
expenses into consideration, the market would be almost efficient. The results are in line with earlier
studies by Jensen (1967) and Fama and French (2010)
The regression coefficients of the four factors are all statistically significant at the 1% level and partially
explain the differences in excess returns between the market and market-like funds. First of all, the market
factor obtained a coefficient of 1 meaning that market-like funds are equal in terms of riskiness compared
to the market. The size (SMB) and value (HML) factor coefficients are slightly negative which means that
in general market-like funds have more exposure to large capitalization stocks and growth stocks. As
reported in table 9, the values of the SMB and HML factors are on average positive for the period 20012013 which means that small capitalization stocks and value stocks outperformed respectively large
capitalization and growth stocks. This partly clarifies why the market had a higher excess return than
market-like funds. Moreover, market-like funds have a small exposure to momentum stocks, which
positively influences returns. Finally, the adjusted r-squared (0.89) is presented in the final column of
table 10, which shows that a large part of the variation in returns is explained by the model.
The analysis above confirms hypothesis 1, which stated that market-like mutual funds underperform
relative to the market. The passively managed market fund outperformed the actively managed marketlike funds by about 2% for the period 2001 – 2013. A large part of this outperformance is caused by the
high expense ratio of market-like funds, which is on average 1.59%.
Table 10: Market-like Funds Regressions Results
Market-like Funds
α (Monthy)
α (Yearly)
β1(MKT)
β2(SMB)
β3(HML)
β4(MOM)
R2 Adj
Total
-0.16%***
-1.93%***
1.00***
-0.05***
-0.06***
0.02***
0.89
2001-2006
-0.20%***
-2.44%***
1.00***
-0.05***
-0.04***
0.02***
0.85
2007-2013
-0.13%***
-1.52%***
1.01***
-0.03***
-0.12***
0
0.92
This table presents the regression results of market-like funds. It includes the monthly and yearly alpha, the regression coefficients of
the four factors (MKT, SMB, HML, MOM) and the adjusted R2.The results are based on 1140 funds during the period 2001 – 2013,
which are together responsible for 107.614 monthly observations. The level of significance is indicated with asterisks, whereby *
stands for 10%, ** for 5% and *** for 1%.
29
5.1.3: Sector Fund Performance
The next strategy for investing in the total U.S. market is based on sector concentrated funds. First of all, I
will investigate whether sector funds in general outperform the market during the period 2001-2013.
Besides, I will check the performance of each sector individually. This is done, by means of a regression
of the excess returns of the funds on Carhart’s four factor model. Finally, I will replicate the market by
means of sector weightings, which means that a market portfolio will be constructed based on sector
funds.
Table 11 provides an overview of the regression results of both sector funds in general and each sector
individually. Analyzing the general sector results, highly significant negative alphas are observable for all
periods. During the period 2001-2013 the market on average outperformed all sector funds by 4.25% on a
yearly basis. This means that sector funds are in general not attractive investments as they even
underperform market-like funds (-2.03%, t-test). By separating the period into two sub periods, I find that,
during the period 2007– 2013 the performance of market-like funds is lower compared to the period
before. A t-test shows a significant difference of 1.65% on yearly basis.
The differences in excess returns are again partially explained by the regression coefficients of the four
factors. According to the market factor, sector funds are slightly riskier than the market which led to
higher returns. Next to that, sector funds have a small exposure to small capitalization stocks, have more
exposure to growth stocks and do not follow a momentum strategy. This taken into account, in
combination with the average factor loadings shown in table 9 has led to a lower excess return by sector
funds compared to the market.
The alphas of the individual sectors are besides the consumer services (CS) and industrial (ID) sector, all
significant. When analyzing the sector results, positive alphas are observable for the consumer goods
(4.03%), health (1.49%) and utilities (1.11%) sector. All other sectors obtained negative results, whereby
the basic materials sector stands out with a negative alpha of 11.84%. An explanation for this remarkable
result can be found in the yearly number of funds (appendix 1, table A.2) and observations, as some years
are heavier represented due to more observations. Therefore, to obtain more realistic results, it is better to
look at the yearly alphas of the sectors, in order to check whether they on average outperform the market.
This is done in the next section.
30
Table 11: Sector Funds Regression Results
Sector Funds
α (Monthy)
α Yearly
β1(MKT) β2(SMB) β3(HML) β4(MOM) R2 Adj
Total
-0.35%***
-4.25%***
1.04***
0.16***
-0.16***
-0.08***
0.58
2001-2006
-0.09%***
-1.13%***
1.12***
0.15***
-0.28***
0
0.53
2007-2013
-0.22%***
-2.67%***
0.99***
0.12***
-0.04***
-0.08***
0.65
Individual Sectors
BM
-0.99%***
-11.84%***
0.75***
-0.05
-0.23***
0.02
0.35
CG
0.34%***
4.03%***
0.76***
0.01
0.15***
0
0.60
CS
-0.05%
-0.59%
0.96***
0.36***
0.11***
-0.06***
0.81
E
-0.26%**
-3.11%**
1.18***
0.02
0.04
0.14***
0.54
F
-0.18%***
-2.11%***
0.88***
0.24***
0.77***
-0.14***
0.68
H
0.12%***
1.49%***
0.82***
0.01
-0.27***
0.08***
0.52
ID
-0.02%
-0.20%
1.03***
0.35***
0.29***
-0.01
0.79
IT
-0.32%***
-3.86%***
1.37***
0.37***
-0.96***
-0.10***
0.83
TS
-0.24%***
-2.89%***
1.27***
-0.28***
-0.60***
-0.11***
0.66
UT
0.09%***
1.11%***
0.76***
-0.27***
0.05***
0.11***
0.56
Average
-0.15%
-1.8%
0.98
0.08
-0.07
-0.01
0.63
This table presents the regression results of all sector funds together and each sector individually. It includes the
monthly and yearly alpha, the regression coefficients of the four factors (MKT, SMB, HML, MOM) and the adjusted
R2.The results are based on 89.608 monthly observations during the period 2001 – 2013, which stems from 1160
different sector funds. The level of significance is indicated with asterisks, whereby * stands for 10%, ** for 5% and
*** for 1%.
Table 12 shows the average yearly alphas for the 10 different sectors. On top of that, the average alpha
per sector for the period 2001-2013 and the alphas of the regression results in table 11 are presented. The
yearly alphas are measured by implementing the factor regression coefficients of each sector (table 11)
and monthly factor loadings (French Database) in the model of equation 2.2 14 . When comparing the
average sector alphas of table 11 and table 12, it turns out that big differences exist for several sectors.
This means that the number of yearly observations has a significant impact on the regression results of
table 11. Therefore, using the yearly alphas lead to more realistic results.
When observing the average yearly alphas of the 10 sectors, more sectors outperformed the market. In
particular the basic material and energy sector, which were in the primary regressions highly negative.
When looking at the average of all sectors during the period 2001-2013, it turns out that sector funds
outperform the market by 0.6%. As I think these results are more plausible than the results in table 11, it
can be concluded that on average fund managers of sector funds have the ability to obtain superior
performance by picking the right stocks, and thus could exploit their informational advantage. This is in
line with the results of Kacperczyk et al. (2005) and confirms hypothesis 2.
The final step entails the replication of the market. This is done by multiplying the yearly sector alpha by
its corresponding sector weighting (Table A.1, Appendix 1), in order to get the weighted yearly alpha of
the market. The results are shown in the final column of table 12. The replicated market portfolio
underperformed mostly in the beginning and ending three years of the relevant time period, while in most
14
αi = (Rit - Rft) - β1(Rmt – Rft) - β2SMBt - β3HMLt - β4MOMt - εit
31
of the other years outperformance is observable. When looking at the average alpha for the total time
period, the new constructed actively managed market portfolio slightly underperformed the market by
0.04%. This means that without taking expenses into consideration, the replicated market portfolio would
outperform the market benchmark. The same holds for the weighted alpha based on the regression results
of table 11. According to this method, the replicated market portfolio underperformed by 1.19%, which is
considerable larger compared to the other method.
The previous method is based on fixed factor betas, retrieved from the sector regression results of the
entire period. This approach solves the over-representation in terms of yearly observations, but does not
take the style effects by funds into consideration, as funds will probably change their exposure to certain
stocks styles. Therefore, I also performed regressions based on two years of returns, (with the exception
of the first period), in order to capture most of these style effects. Together with the average market sector
weightings of these periods, the market weighted alpha is calculated, as can be seen in appendix 1, table
A.3. The results are in line with earlier results, as sector funds on average still outperform the market, but
only by 0.02%. Additionally, the replicated market fund still slightly underperforms the market with an
average of -0.12%.
Finally, hypothesis 3 can be answered. Replicating the market by means of sector funds on average lead
to a negative abnormal return, which means it underperformed relative to the passively managed market
fund. This contradicts hypothesis 3 and therefore it does not hold.
Table 12: Average Yearly Sector Alphas and Replicated Market Alpha
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
BM
22.13%
16.23%
18.09%
14.56%
4.63%
8.04%
18.32%
-11.18%
-0.58%
9.44%
-10.60%
-12.30%
-27.78%
CG
14.53%
9.18%
2.65%
3.99%
-5.91%
0.30%
7.71%
1.14%
9.08%
5.68%
4.08%
1.33%
2.60%
CS
0.09%
1.45%
-7.00%
-2.17%
-4.54%
-0.95%
-5.73%
-10.50%
2.63%
5.39%
3.57%
3.82%
5.09%
E
-3.69%
12.63%
-7.63%
12.80%
31.40%
-2.90%
27.29%
-12.62%
18.50%
-0.62%
-9.00%
-15.54%
-21.47%
F
-5.26%
9.71%
-4.80%
4.26%
-1.30%
-0.44%
-8.71%
-9.71%
-2.95%
5.01%
7.69%
-1.26%
-17.40%
H
2.23%
-18.59%
8.15%
1.75%
3.98%
-6.18%
-0.81%
9.44%
4.81%
-5.33%
4.93%
9.53%
15.30%
ID
-0.05%
-0.10%
-8.33%
2.22%
3.32%
-6.52%
8.26%
2.83%
-2.88%
3.77%
-4.86%
-1.17%
1.09%
IT
-11.79%
-4.90%
-0.35%
-5.43%
7.75%
-0.93%
0.43%
7.33%
-0.43%
-10.10%
-12.84%
-1.59%
-12.30%
TS
-11.54%
-6.53%
3.59%
11.23%
6.30%
9.40%
-2.57%
-5.53%
-5.81%
0.06%
-11.81%
2.14%
-9.96%
Weighted
Alpha
-2.16%
0.29%
-1.27%
3.07%
4.13%
-1.11%
3.79%
-1.65%
2.97%
0.16%
-1.94%
-1.09%
-5.34%
UT
-11.08%
-13.02%
6.92%
11.98%
5.20%
10.54%
8.37%
-2.68%
5.49%
-2.01%
5.94%
-4.73%
-4.57%
Total
3.77%
4.34%
-0.68%
2.24%
-1.94%
2.25% -0.19%
-3.47%
-1.62%
1.26%
0.60%
Average
-0.04%
Alphas
4.03%
-0.59%
-3.11%
-2.11%
1.49% -0.20%
-3.86%
-2.89%
1.11%
-1.80%
Table11 -11.84%
-1.19%
In this table the average yearly alphas per sector for the period 2001 - 2013 are presented. The yearly alphas are measured by implementing the factor
regression coefficients of each sector (table 11) and monthly factor loadings (French Database) in Carhart’s four factor model. On top of that, it
includes the yearly replicated market alpha, which is based on the market sector weightings (Appendix 1, table A.1). Lastly, the alphas are shown for the
total regressions results (table 11).
32
5.2: Fund Characteristics and Performance
In the previous section alphas are calculated for market-like funds and sector funds in general. These
alphas will be used to check whether there exist a relation between performance and several fund
characteristics. This will be done by a regression of the yearly alphas on the fund characteristics included
in the model. In order to obtain unbiased regression results, it is important that the independent variables
are not correlated. These so called collinearity problems could negatively influence the results. In table
A.4 of the appendix 1, I included the correlations of the relevant fund characteristics for both market-like
and sector funds. These tables show no collinearity problems, as highly correlated characteristics are not
identified. Finally, to obtain the best model, I also performed regressions of the squared values of the fund
characteristics and the natural logarithms of fund age and fund size. The results of these regressions show
that by using the logarithms of fund size and age, the explanatory power of the model improves.
Therefore, I used these variables in the final regressions.
Table 13 presents the regression results of market-like funds and sector funds. When looking at the results
in the upper part of table 13, insignificant coefficients for all fund characteristics are found for both the
individual as multiple variable regressions. This means that the fund characteristics are not related to the
performance of market-like funds. Moreover, the coefficient of determination is very low, which indicates
that the variables included in the model explain only a small part of the variation of the alphas.
When looking at the regression results of sector funds in the bottom part of table 13, partly contradictory
results are observable compared to market-like funds. The individual regressions show that a higher
turnover ratio have a statistically significant negative effect on performance, whereas larger funds and
more experienced funds are positively related to performance. Finally, the final row presents the multiple
regression results. As the adjusted R-squared is higher, this model does better explain the variation in
alpha, despite the fact that it is still quite low. The results are almost similar to the individual regressions
in terms of the level of significance, with the exception that the management fee is statistically significant
at the 5% level. The regression coefficient indicates that a higher management fee will negatively
influence performance. The interpretation of the other variables remains the same.
33
Table 13: Regressions Results of Fund Characteristics on Performance
Expense Ratio
Turnover Ratio
Management Fee
ln Size
ln Age
R2 Adj
Market-like Funds
-0.014***
-0.137
0.0002
-0.016***
-0.0005
0.0001
-0.016***
-0.0005
0.0002
-0.017***
0.00005
0
-0.016***
0.00032
0
-0.014**
-0.164
-0.0004
-0.0005
0.0003
0.0012
0.0006
All Sector funds
-0.049***
-0.02
0.0075
-0.041***
-0.0029***
0.0047
-0.050***
0
0.0013
-0.066***
0.0089***
0.0055
-0.046***
0.00748*** 0.0149
-0.104***
1.029
-0.0018***
-0.003**
0.01***
0.011***
0.0253
In this table the regression coefficients for market-like funds and sector funds in general are presented of several
fund characteristics including: expense ratio, turnover ratio, management fee and the natural logarithms of size
and age. On top of that, the intercept term (α) and the coefficient of determination (R2 Adj) is shown. The level of
significance is indicated with asterisks, whereby * stands for 10%, ** for 5% and *** for 1%.
α
As mentioned above, some fund characteristics are related to performance. However, it does not explain
the impact on performance, when a change in the independent fund characteristics occurs. Therefore, it is
better to look at the economic effects and magnitude of a change. This is done for the significant results of
the complete model of sector funds namely: turnover ratio, management fee and the natural logarithms of
size and age.
Table 14 provides the economic impact on performance for a change in turnover ratio, management fee
and the logarithms of age and size. As stated above, the turnover ratio negatively influences performance.
A change of one standard deviation leads to a decrease in performance by -0.67%. In order to investigate
whether this is relatively large, it is better to look at the magnitude. With a magnitude of 16%, a change in
turnover ratio clearly has a relatively large effect on performance. The same is done for the other
significant fund characteristics. It turns out, that an increase in management fee has a negligible negative
effect on performance, whereas the logarithms of size and age has a significant impact on performance. A
one standard deviation increase in age, which stands for 6 years, leads to an increase in performance by
1.08%, which corresponds to a magnitude of 25% on performance. Next to that, an $1140 million
increase in fund size, will lead to an increase in abnormal return of 2.33%, which corresponds to a
magnitude of 55% on performance. This means that the turnover ratio, size and age for sector funds are
economically significant and thus investors should seek for low turnover, large and experienced sector
funds.
34
Table 14: Economic Relevance Sector Fund Characteristics
Fund Characteristic
ΔSD
Economic Effect
Magnitude
Turnover Ratio
729%
-0.67%
16%
Management Fee
0.02%
-0.01%
0.2%
ln Size
1140
2.33%
55%
ln Age
6
1.08%
25%
This table provides the economic effects(SD*Regression Coefficient) and the magnitude of
the turnover ratio, management fee and the natural logarithms of size and age on sector
funds performance and are based on the regression results (table 13) and descriptive
statistics (table 7). Moreover, the one standard deviation increase of the particular
characteristic is shown, which is based on the mean value of that characteristic. The
magnitude is the economic effect divided by the mean alpha of sector funds (4.25%).
5.3 Mutual Fund Performance during Recessions and Expansions
The final part of this thesis is based on the performance of the actively managed funds relative to the
market during periods of recessions and expansions. As stated in the literature review, former studies
found evidence that the performance of mutual funds is closely related to economic circumstances, as
mutual funds perform better in recessions compared to expansions (Kosowski, 2006). This section will
elaborate more on this.
According to the NBER, there were two periods of recession and two periods of expansion in the time
horizon of 2001-2013. Table 15 provides the regression results of each period for both market-like funds
and sector funds. The results show that the alphas of almost all periods are highly statistically significant
and negative, which means that mutual funds are not able to outperform the market in both economic
conditions. The results support earlier findings that mutual funds underperform relative to the market.
Besides, it partly confirms Kosowski (2006), as market-like funds on average performed slightly better
during recessions compared to expansions. As mentioned earlier, some sectors might be highly
represented in the dataset, while others are not. Therefore the results might be biased and thus the same
regression is executed for each sector individually in order to obtain more realistic results.
35
Table 15: Mutual Funds Regression Results during Recessions and Expansions
α (Monthy)
α (Yearly) β1(MKT) β2(SMB) β3(HML) β4(MOM)
R2Adj
Market-like Funds
Recession 1
-0.12%***
-1.49%***
0.83***
-0.11***
0.06***
-0.19***
0.88
Recession 2
-0.08%***
-0.95%***
1.02***
-0.03***
-0,15***
0.003
0.91
Average
-0.10%
-1.22%
0.93
-0.07
-0.05
-0.09
0.90
Expansion 1
-0.16%***
-1.96%***
1***
-0.05***
-0.09***
0.04***
0.85
Expansion 2
-0.19%***
-2.32%***
1.01***
-0.02***
-0,09***
-0.02***
0.93
Average
-0.18%
-2.14%
1.01
-0.04
-0.09
0.01
0.89
Sectors Funds
Recession 1
-0.08%
-0.94%
0.81***
0.03
-0.21***
-0.43***
0.57
Recession 2
-0.33%***
-3.91%***
1.03***
0.34***
-0.11***
-0.08***
0.65
Average
-0.21%
-2.43%
0.92
0.19
-0.16
-0.26
0.61
Expansion 1
-0.07%**
-0.90%**
1.04***
0.21***
-0.23***
-0.06***
0.52
Expansion 2
-0.22%***
-2.60%***
0.94***
0.06***
0.02***
-0.03***
0.63
Average
-0.15%
-1.75%
0.99
0.14
-0.11
-0.05
0.58
This table shows the regression results of market-like funds and sector funds during recessions and expansions
in the period 2001-2013. It includes the monthly and yearly alpha, the four factors of Carhart’s model
(Market, Size, Value and Momentum) and the adjusted R-squared. The level of significance is indicated with
asterisks, whereby * stands for 10%, ** for 5% and *** for 1%.
Period
* Recession 1: 03-2001 – 11-2001; Recession 2: 12-2007 – 06-2009; Expansion 1: 12-2001 – 11-2007; Expansion 2: 07-2009 – 12-2003.
In table 16 the alphas of each sector for both periods of recession and expansion are presented. When
looking at the results, large differences in performance are observable despite the fact that not all results
are statistically significant. This might be caused by the sensitivity of sectors towards business cycles.
According to Morningstar’s sector structure 15 , sectors can be defined as cyclical or non-cyclical
(defensive). Cyclical sectors are very sensitive to business cycles, whereas non-cyclical sectors are not.
The non-cyclical sectors are consumer goods, health and utilities, and therefore it is expected that these
sectors will perform better than the other sectors, especially during recessions. The results show that this
partially holds as most of the non-cyclical sectors obtained a positive alpha, although some results are not
statistically significant. Next to that, it is likely that cyclical sectors will obtain better results during
expansions. According to table 16, this is hard to detect. Although, the alphas are slightly less extreme
compared to recessions, it is still the non-cyclical sector which significantly outperforms the market
during expansions. When looking at the average performance over the total period, sector funds
underperformed both during recessions (-1.59%) and expansions (-0.12%) .
Besides the individual sector performance, I again replicated the market. The results are however slightly
biased as some sectors provide insignificant results. The market weighted alpha is based on sector
weightings of the market and approximately covers the relevant period. They are presented in table A.5 of
appendix 1. Despite that some sectors are able to outperform the market during recessions, this is not the
case for the replicated market with an average alpha of -1.29%. During expansions mixed results are
observable, as the first expansion period shows an abnormal return of 1%, whereas the second period
15http://corporate.morningstar.com/au/documents/MethodologyDocuments/FactSheets/StockSectorStructure_Factsheet.pdf
36
contains a negative alpha of 1.44%. On average the replicated market fund underperformed the market by
0.22% in periods of expansion. Nevertheless, table 16 shows that in contrary to market-like funds, sector
funds and the replicated market fund on average perform better during expansions. Therefore hypothesis 4
does not hold, as it is only partially true.
Table 16: Performance of Sector funds and Replicated Market Fund during Recessions and Expansions
Period
BM
CG
CS
E
F
H
Recession 1
10.46%*
Recession 2
16.41%
7.58%
-7.48%**
2.72%
-15.5%***
-15.68%***
-1.47%
19.09%***
-17.93%***
Average
13.44%
5.15%
-11.49%
1.71%
Expansion 1
8.01%**
2.66%**
-2.59%***
Expansion 2
-14.23%***
2.84%***
4.03%***
Weighted
Average Market Alpha
ID
IT
TS
UT
11.94%***
-1.40%
2.75%**
-16.85%***
-24.88%***
-3.50%
-1.18%
2.29%
-8.63%***
1.09%
-1.26%
5.01%***
0.33%
-1.41%
-9.70%
7.12%
-5.02%
1.92%
-9.06%
-9.94%
-1.59%
-1.29%
6.55%***
1.95%***
0.76%
1.12%
-2.93%***
0.90%
2.18%***
1.86%
1.00%
-14.6%***
-0.12%
5.1%***
-0.97%
-3.22%***
-1.11%
1.22%**
-2.11%
-1.44%
Average
-3.11%
2.75%
0.72%
-4.03%
0.92%
2.93%
0.08%
-3.08%
-0.11%
1.70%
-0.12%
-0.22%
In this table the yearly alphas are presented for the 10 relevant sectors and the replicated market fund during recessions and expansions. The level of significance is indicated with asterisks,
whereby * stands for 10%, ** for 5% and *** for 1%.
* Recession 1: 03-2001 – 11-2001; Recession 2: 12-2007 – 06-2009; Expansion 1: 12-2001 – 11-2007; Expansion 2: 07-2009 – 12-2003.
* The Market Weighted Alpha is based on sector weightings of approximately the relevant period. They are presented in table A.5 of appendix 1.
37
Chapter 6: Conclusions and Recommendations
6.1: Conclusions
In this study, the best strategy to invest in the U.S. overall market and how this is influenced by different
economic conditions is investigated. In order to do this, the performance of a passively managed market
fund relative to actively managed funds is measured, during the period 2001-2013. After a screening
process of several passively managed market funds, the Vanguard Total Stocks Market Index Admiral has
been chosen as a benchmark for the market, as it represents almost the complete U.S. investable market
and has on average one of the lowest expense ratios. The following actively managed funds are taken into
consideration: market-like funds, sector funds and a replicated market fund based on sector funds.
In order to measure the performance of funds, Carhart’s four factor model is used. Firstly, a regression is
executed based on the monthly excess returns of 1140 market-like funds (survivorshipsbias-free). The
results show that market-like funds underperformed the market by 1.96% during the period 2001-2013.
All results are net of expenses, with the exception of fund loads, which are not taken into account in this
study. Secondly, the performance of sector funds is measured, which is done by using the excess returns
of 1160 sector funds survivorship-bias free. Sector funds which are categorized into 10 different sectors,
on average outperformed the market by 0.6%. This is line with earlier findings of Kacperszyk et al.
(2005). Finally, I replicated the market based on sector funds, by using the yearly sector weightings of the
market benchmark. However, the results show that the replicated market slightly underperformed the
passively managed market fund by 0.04%. All results based on sector funds might be slightly biased as
some results are not statistically significant and some sectors are global oriented. Most of the
underperformance is attributable to the high expense ratios, as market-like funds and sector funds have on
average an expense ratio of respectively 1.59% and 1.68%.
Additionally, I performed a regression of several fund characteristics, including the expense ratio,
turnover ratio, management fee, fund size and fund age on fund performance. Results indicated that there
is no significant relationship between these characteristics and the performance of market-like funds.
Sector fund performance show however a statistically and economically significant relation with turnover
ratio, size and age. The results show that investors of sector funds should seek for experienced and large
funds, with a low turnover ratio.
Furthermore, the performance of funds during recessions and expansions is measured. On average,
market-like funds, sectors funds and replicated market fund underperformed the market during recessions
by respectively 1.22%, 1.59% and 1.29%. The same tendency is observable during expansions with
underperformance of respectively 2.14%, 0.12% and 0.22%. When observing the individual sectors the
38
best performing sectors are non-cyclical sectors such as, consumer goods, health and utilities. The results
contradict findings of Kosowski (2006), as he found that mutual funds perform better during recessions
compared to expansions.
Finally, the main question of this study can be answered. The best strategy to invest in the U.S. overall
market is to invest in a passively managed market fund. The same holds during a recession or expansion.
On average, managers of market-like funds do not have the ability of stock picking talent, whereas the
opposite is the case for managers of sector funds. Underperformance is however mainly attributable to the
large expense ratios of actively managed funds. The above mentioned findings could clarify the observed
trends in the fund industry. This entails the large net cash inflow passively managed funds experience in
previous years, while actively managed funds experience cash outflows.
6.2: Recommendations
Further research could focus more on the value-weighted performance of mutual funds, as most of the
investments are concentrated on a small part of the relevant funds. I excluded this part in my study, as it
led to mostly insignificant results, which is probably caused by the fact that this study covered a too short
time period. Secondly, the global oriented funds in several sectors should be removed, in order to obtain
unbiased results. Finally, further research could focus on the relation between fund performance and fund
characteristics. The fund characteristics taken into consideration only explained a small part of the
variation in performance. This could mean that I forgot some important fund characteristics.
39
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41
Appendix 1
Year/Sector
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Average
BM
3.3%
3.6%
4.0%
4.0%
3.0%
3.0%
3.9%
3.3%
3.9%
4.2%
4.0%
4.0%
3.1%
3.6%
CG
6.4%
6.9%
7.0%
7.0%
8.0%
8.0%
8.9%
11.2%
9.8%
9.1%
10.1%
9.4%
10.1%
8.6%
Table A.1: Sector Weightings Market Fund
CS
E
F
H
ID
14.8% 5.6% 20.3% 14.2% 10.7%
15.7% 5.8% 22.3% 14.3% 10.8%
15.0% 5.0% 24.0% 13.0% 11.0%
14.0% 8.0% 23.0% 12.0% 11.0%
12.0% 9.0% 22.0% 13.0% 11.0%
12.0% 9.0% 23.0% 12.0% 11.0%
9.6% 12.0% 17.6% 12.1% 11.9%
9.1% 12.2% 14.9% 14.5% 11.4%
10.2% 11.0% 15.3% 12.6% 11.0%
11.5% 11.3% 16.3% 11.2% 11.5%
11.7% 11.4% 14.6% 11.8% 11.2%
12.4% 10.2% 16.5% 11.8% 11.1%
13.5% 9.2% 18.5% 11.8% 13.7%
12.4% 9.2% 19.1% 12.6% 11.3%
IT
16.6%
13.5%
15.0%
14.0%
16.0%
15.0%
16.8%
15.6%
19.5%
18.7%
18.6%
18.5%
15.0%
16.4%
TS
3.6%
3.1%
3.0%
3.0%
3.0%
3.0%
3.3%
3.4%
2.9%
2.8%
2.7%
2.7%
2.1%
3.0%
UT
4.5%
4.0%
3.0%
4.0%
3.0%
4.0%
3.9%
4.4%
3.8%
3.4%
3.9%
3.4%
3.0%
3.7%
Total
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
In this table the yearly sector weightings of the market fund (Vanguard Total Stock Market Index
Admiral) are presented for the period 2001-2013. The data is retrieved from the yearly fund
prospectus. (Source: sec.gov)
BM
(G)
2
2
2
2
2
2
4
6
11
34
46
62
59
234
3%
67
6%
CG
(G)
7
7
7
9
9
13
13
14
17
19
19
22
22
178
2%
23
2%
Table A.2: Sector Fund Observations
CS
ID
IT
(G)
E (D) F (D) H (D)
(G)
(D)
17
20
142
111
13
241
19
22
134
120
13
207
19
22
139
104
13
169
21
25
149
102
14
161
22
23
141
76
14
122
23
26
140
74
15
116
24
26
146
68
15
107
25
50
199
91
15
138
23
53
188
83
19
126
25
53
190
79
24
112
25
54
179
70
21
104
25
59
187
69
23
101
20
70
195
68
24
94
288
503
2.129 1.115
223
1.798
4%
7%
28%
15%
3%
24%
27
79
297
167
27
321
2%
7%
26%
14%
2%
28%
TS
UT
Year/Sector
(G)
(G)
Total
2001
53
50
656
2002
49
50
623
2003
37
48
560
2004
31
50
564
2005
31
47
487
2006
35
44
488
2007
35
39
477
2008
32
63
633
2009
31
61
612
2010
31
59
626
2011
31
53
602
2012
30
54
632
2013
30
54
636
Total
456
672
7596
Percentage
6%
9%
100%
No. of Funds
67
85
1160
Percentage
6%
7%
100%
No. of Monthly
Observations
2447 2032 3401 5745 25020 13217 2568 21803 5481 7894
89608
Percentage
3%
2%
4%
6%
28%
15%
3%
24%
6%
9%
100%
This table gives an overview for the 10 different sectors. It includes: the yearly number of funds, total number of
funds and number of monthly observations Furthermore the total observations per year for all sectors are included
and the sector proportion of the dataset for the total period. In addition, the first row describes whether the sector is
globally (G) or domestic (D) oriented.
42
Table A.3: Market Sector Weightings per Period and Corresponding Performance
Market Sector Weightings
Year
BM
CG
CS
E
F
H
ID
IT
TS
UT
Total
2001-2002-2003
3.63%
6.77%
15.17%
5.47%
22.20%
13.83%
10.83%
15.03%
3.23%
3.83%
100.00%
2004-2005
3.50%
7.50%
13.00%
8.50%
22.50%
12.50%
11.00%
15.00%
3.00%
3.50%
100.00%
2006-2007
3.45%
8.45%
10.80%
10.50%
20.30%
12.05%
11.45%
15.90%
3.15%
3.95%
100.00%
2008-2009
3.60%
10.50%
9.65%
11.60%
15.10%
13.55%
11.20%
17.55%
3.15%
4.10%
100.00%
2010-2011
4.10%
9.60%
11.60%
11.35%
15.45%
11.50%
11.35%
18.65%
2.75%
3.65%
100.00%
2012-2013
3.55%
9.75%
12.95%
9.70%
17.50%
11.80%
12.40%
16.75%
2.40%
3.20%
Performance (Alphas)
100.00%
Weighted Alpha
2001-2002-2003
8.04%*
2.16%
-4.00%***
-6.85%**
3.07%***
-2.86%***
-3.96%**
-4.96%***
-3.68%**
-10.13%***
-1.94%
2004-2005
-3.33%
-1.51%
1.01%
8.54%***
2.82%***
7.48%***
7.50%***
-2.42%***
11.47%***
3.43%***
3.12%
2006-2007
9.67%**
3.77%**
-2.58%**
8.44%***
-5.65%***
-3.44%***
2.16%
0.69%
4.56%***
9.17%***
0.56%
2008-2009
-0.55%
3.54%
-7.65%***
3.09%
-9.73%***
6.58%***
-2.82%
2.95%***
-5.61%**
2.35%**
-0.49%
2010-2011
-6.62%***
2.99%*
1.87%*
-7.69%***
3.64%***
1.73%**
-4.01%***
-6.56%***
-4.38%***
2.36%***
-1.59%
2012-2013
-10.49%***
-1.48%
6.49%***
-11.15%***
-3.16%***
3.80%***
4.94%***
-0.19%
3.55%*
-5.10%***
-0.36%
-0.55%
1.58%
-0.81%
-0.94%
-1.50%
2.22%
0.63%
-1.75%
0.98%
0.35%
-0.12%
Average
In this table the average market sector weigthings and corresponding performance (alphas) are presented per period for 10 different sectors. The sector alphas are measured by means of
Carhart's four factor model. Additionally, the weighted alphas are included, which stands for the performance of the replicated market. The level of significance is indicated with asterisks.
whereby * stands for 10%. ** for 5% and *** for 1%. This is only applicable on the bottom part of the table.
Table A.4: Correlation Matrix Market-like and Sector Funds
Expense Ratio
Turnover Ratio
Management Fee
ln Size
ln Age
Market-like
Expense Ratio
1
Turnover Ratio
0.1
1
Management Fee
0.0
0.0
1
ln Size
-0.4
-0.1
0.2
1
ln Age
-0.2
-0.1
0.2
0.5
1
Expense Ratio
Turnover Ratio
Management Fee
Fund Size ln Age
Sector Funds
Expense Ratio
1
Turnover Ratio
0.1
1
Management Fee
0.0
0.0
1
ln Size
-0.5
-0.3
0.2
1
ln Age
-0.3
-0.1
0.3
0.5
1
This table shows the correlations between the several fund characteristics for both market-like funds and
sector funds. It includes the expense ratio, turnover ratio, management fee and the natural logarithms of size
and age. It is based on yearly data covering the time period 2001-2013.
Table A.5: Used Market Sector Weightings for Recessions and Expansions
Weightings
BM
CG
CS
E
F
H
ID
IT
TS
UT
Total
Period
Recession 1
3.3%
6.4%
14.8%
5.6%
20.3%
14.2%
10.7%
16.6%
3.6%
4.5%
100%
2001
Recession 2
3.6%
10.5%
9.65%
11.6%
15.1%
13.55%
11.2%
17.55% 3.15%
4.1%
100%
2008-2009
Expansion 1 3.58% 7.63% 13.05%
8.13%
21.98% 12.73% 11.12% 15.05% 3.07% 3.65% 100%
2002-2007
Expansion 2 3.84% 9.70% 11.86% 10.62% 16.24% 11.84% 11.70% 18.06% 2.64% 3.50% 100%
2009-2013
In this table the market sector weightings are presented for two periods of recession and two periods of expansion. The data are based on
averages of the weightings period.
* Recession 1: 03-2001 – 11-2001; Recession 2: 12-2007 – 06-2009; Expansion 1: 12-2001 – 11-2007; Expansion 2: 07-2009 – 12-2003.
43