Download Mutual Fund Age, Performance, and the Optimal Track Record

Mutual Fund Age, Performance, and the Optimal Track Record
March 7, 2016
Olivia Moore
Department of Economics
Stanford University
Stanford, CA 94305
[email protected]
Under the direction of Prof. John Shoven and Prof. Monika Piazzesi
ABSTRACT
This paper investigates the relationship between mutual fund age and performance, and what this
might imply about the track record that investors should require when evaluating mutual funds.
While this relationship has been explored mostly through the lens of performance persistence,
primarily by Berk and Green (2004), the age/performance relationship alone is still relatively
unexplored, particularly in regard to new funds. This relationship has significant implications for
the decisions of mutual fund investors. If age and performance have a negative correlation, then
waiting several years for a mutual fund to establish a track record before investing may be
unwise, as the fund’s best performance will already be behind it. In this paper, I utilize data from
the CRSP mutual fund database to quantify the age/performance, age/risk, and age/risk-adjusted
performance relationships for Morningstar’s listed U.S. equity funds. I conclude that in general,
older funds are riskier, but this relationship reverses for very young funds, which exhibit more
risk. I also find that older funds have slightly lower risk-adjusted returns, but this relationship
again reverses for very young funds. Though both of these relationships are somewhat weak, my
results imply that investors may not want to flock to new funds in the hope of outsized riskadjusted returns—requiring a track record does not come at a significant cost and may, in fact,
help investors avoid undue risk.
Keywords: mutual fund performance; age and performance; Morningstar funds
Acknowledgements
I would like to thank Professor John Shoven, Professor Monika Piazzesi, and Orie Shelef for
their guidance and support throughout the many different stages of my research—this thesis
would not have been possible without all of your help. Thank you to Marcelo Clerici-Arias for
his advice throughout the writing process. I would also like to thank my family and friends for
their encouragement.
March 7, 2016
Contents
I.
Introduction
II.
Literature Review
Persistence in Mutual Fund Performance
Correlation between Performance and Fund Characteristics
Morningstar Mutual Fund Performance
III.
Methodology
Data
Empirical Strategy
IV.
Analysis and Results
All Funds
Large-Cap Funds
Mid-Cap Funds
Small-Cap Funds
V.
Conclusions
VI.
Appendix
Descriptive Statistics
Data Preparation
Regressions
VII.
References
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I. Introduction
In this paper, I will attempt to answer the question of whether or not there is a significant
relationship between age and performance for actively managed equity mutual funds. This is
interesting primarily in the context of investor behavior, as many investors require a multi-year
track record before investing in a fund, which could be costly if new funds perform better than
established funds. I investigate this question using performance information and fund
characteristics (expense ratio, fund size, age, and manager tenure) from the CRSP database for
all Morningstar U.S. equity funds between 1990 and 2015. I find that on a risk-adjusted basis,
younger funds generally perform better than older funds. However, this relationship reverses
when an age indicator is used, which distinguishes funds younger than three years from all other
funds. I find that these very young funds have lower risk-adjusted performance, primarily
because they exhibit more risk. Therefore, requiring a track record for new funds should not
come at a significant cost to investors, and is likely a wise decision in many cases.
Though experienced individual and institutional investors often consider a track record to
be an important component of the fund evaluation process, less experienced investors may also
encounter issues related to mutual fund age and performance. Investors who contribute to a
401(k) plan or other retirement or pension plan, for example, are often given a set of pre-selected
mutual funds to invest in. If fund administrators require each of the mutual funds in this set to
have a certain track record, and younger funds actually perform better, then investors may be
hurt by these limited offerings. While the average track record required by investors has not been
quantified in academic research, anecdotal evidence suggests that it is typically at least two
years, sometimes more. Morningstar, for example, will not assign its star ratings to a fund until it
has at least three years of performance history. If age and performance have a negative
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correlation, then waiting several years for a mutual fund to establish a track record may hurt an
investor’s returns, as the fund’s best performance will already be behind it. This would support
Berk and Green’s (2004) research, as they conclude that once a fund establishes itself as a strong
performer, future performance will be decline as a result of fund inflows. However, it is also
possible that there is a positive relationship between age and performance due to a manager’s
ability to make more informed decisions once they have more experience running a fund,
assuming that manager tenure is closely correlated with fund age. It could also be the case that
age and performance are negatively correlated, but age and risk are also negatively correlated,
meaning that on a risk-adjusted basis, young funds that may have a higher absolute return might
not actually be better investments. In either of these cases, the requirement of a multi-year track
record for mutual fund investors would be rational.
II. Literature Review
This literature review examines and synthesizes prior research on mutual fund
performance and the different characteristics that can influence performance, particularly age and
size, as well as research on potential biases that may come into play when attempting to analyze
mutual fund performance. The existing literature can be divided into two major categories:
persistence in mutual fund performance, and efforts to attribute performance to variables such as
age and assets under management.
Persistence in Mutual Fund Performance
I was primarily interested in persistence of performance because it may relate to age. If
fund performance persists, then we might see a positive age/performance relationship. The topperforming funds will continue performing well as they age, and the bottom-performing funds
are more likely to cease operations and “drop out” of the pool at a lower age, making it look as
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though age and performance are related. Goetzmann and Ibbotson (1995) provide one of the
early analyses of mutual fund persistence that incorporates the potential effects of this
survivorship bias. The authors note that prior studies “lend strong support to the conventional
wisdom that the track record of a fund manager contains information about future performance”
(p. 679), which implies that performance is persistent. However, when they expand their research
to both defunct and surviving funds and utilize temporal disaggregation to perform a year-byyear analysis, they find a slightly different result. The authors find that poor performers are more
likely to disappear from the sample, confirming the importance of adjusting for potential
survivorship bias. They also find that though performance persistence for these funds is “robust
to adjustments for risk” (p. 680), much of this persistence is due to poorly performing funds that
repeatedly lagged the benchmark. The utilization of temporal disaggregation also leads the
authors to an interesting conclusion: they find that, in most years, performance was persistent,
but occasionally there is a surprising reversal of this trend. This led Goetzmann and Ibbotson to
postulate that persistence could be either correlated across managers and caused by the
implementation of a common investment strategy, or caused by the fact that poor performers are
not always “fully disciplined” (p. 680) by the market. As these poor-performing funds are
allowed to continue operating and therefore remain in the dataset, they increase the appearance
of persistence in performance overall.
Elton, Gruber, and Blake (1996) examine a similar question from the lens of the active
versus passive mutual fund manager debate, as investors might rationally select an actively
managed fund at a greater cost than an index fund if they can predict that this fund will
consistently outperform the passive benchmark. The authors reference prior studies that show
that persistence is not necessarily guaranteed—including Hendricks, Patel, and Zeckhauser
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(1993), who find that performance persisted mostly over the short term, and Brown, Goetzmann,
Ibbotson, and Ross (1992), who find that survivorship bias could be responsible for the
appearance of performance persistence. Elton, Gruber, and Blake focus on common stock funds
that were present in the Wiesenberger Investment Companies Service dataset in 1977 with at
least $15 million or more in total net assets. Following these funds through 1993 allowed the
authors to eliminate survivorship bias, as the data captured downward-trending monthly returns
before a fund expired. Using this data, the authors rank funds based on their risk-adjusted returns
and find that performance was persistent in both the short and long run, and that a portfolio
could, hypothetically, be constructed using past returns that outperformed the benchmark in the
future. However, beyond a brief investigation into the effect of expenses on performance, the
authors do not explore what characteristics might be related to this persistence, attributing it
mainly to manager talent or differential information across funds.
Carhart (1997) further expands on this research with a closer analysis of the different
characteristics related to performance, and finds slightly different results. In his study, Carhart
looks at the monthly performance of diversified equity funds between January 1962 and
December 1993 (a total of 1,892 individual funds and 16,109 fund years), following each fund
from its inception through its dissolution to ameliorate the effects of survivorship bias. He
employs the Capital Asset Pricing Model and his own Carhart four-factor model (which
incorporates the key variables from Fama and French’s three-factor model) to analyze
performance. He concludes that persistence in mutual fund performance “does not reflect
superior stock-picking skill” (p. 57), but is instead the result of differences in expense ratios and
transaction costs, as well as common factors in stock returns. Similarly to Goetzmann and
Ibbotson, Carhart finds that underperformance by the worst performing funds is particularly
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persistent, and can be adequately explained by the factors listed above. This contradicts the
findings of Elton, Gruber, and Blake, who hypothesize that the performance persistence they find
could be attributed to manager skill, and also contradicts the findings of authors of earlier papers,
such as Wermers (1996), who thought that momentum strategies were responsible for persistence
in the short term. Carhart finds that transaction costs eliminate gains from a momentum strategy,
and also finds a negative relationship between both expenses and turnover, and performance.
Berk and Green (2004) further expand on performance in the context of skilled or
unskilled managers, but come to a different conclusion than Carhart. Like Carhart, Berk and
Green (2004) find that there is a lack of persistence of returns across mutual fund managers.
However, unlike Carhart, the authors believe that this is not necessarily inconsistent with the idea
that there are skilled mutual fund managers, but is caused by the fact that investors chase past
performance. Skilled managers who exhibit strong performance typically experience an influx of
funds, which limits their ability to continue outperforming as the costs of management are
“increasing and convex in the amount of funds under active management” (p.1273). With more
assets under management, a manager may “spread his information-gathering activities too thin”
(p.1273) or be forced to execute larger trades that are extremely costly or that move the price of
the security. Berk and Green’s research, as well as past studies examining mutual fund
performance persistence, is relevant to my research because of the potential implications of
performance persistence (or lack of persistence) on the age/performance relationship, as well as
the question of what the ideal track record is for an investor to require before investing in a fund.
If managers are skilled, but the effect of this skill on performance is eroded as the fund ages, then
requiring a several year track record may not be wise, as the manager’s prime years of
outperformance will have passed by the time an investor buys into the fund.
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Correlation between Performance and Fund Characteristics
Another significant field of research involves the study of correlations between fund
performance and significant variables, like size, manager tenure, expense ratios, turnover, and
other characteristics. Much of this research has concentrated on the relationship between
performance and the characteristics of single stocks in the context of a broader mutual fund that
holds these stocks, including Grinblatt, Titman, and Wermers’ 1997 study. The relationship
between the characteristics of an entire fund and the fund’s performance is less well-explored,
though there have been several recent papers examining this topic. Golec (1996) uses data from
Morningstar’s Mutual Fund Sourcebook between 1988-1990 to calculate alpha, beta, and
residual return standard deviation for each fund. He finds several significant results—funds with
lower fees, more diversified portfolios, managers with MBA degrees, and managers who have
long tenures at a fund tend to have higher returns. In particular, he finds that the most significant
variable in terms of predicting performance is the manager’s tenure, though manager age is
negatively related to alpha, providing evidence for the claim that “younger managers cope more
easily with the job’s demands” (p.140). Golec also finds that the correlation between fund age
and performance is negative, though statistically insignificant, and that fund size and
performance have an indeterminate relationship. These results are somewhat contradictory, as we
might expect older funds that perform poorly will have older managers with a longer tenure at
the fund, but these variables are apparently not always consistent. Golec also finds that while
administrative expenses are negatively correlated with performance, larger management fees do
not necessarily mean the fund is likely to perform more poorly, as in some cases a “large
management fee signals superior investment skill which leads to better performance” (p.133).
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Wermers (2000) also investigates the relationship between performance and key
variables, focusing on the manager’s strategy or style, transaction costs and expenses, and
turnover. He utilizes the data available in the CDA Investment Technologies dataset, as well as
the CRSP mutual fund database, from 1975 to 1994, and finds that in this time range, mutual
funds with stock portfolios in his sample outperformed a broader market index by 130 basis
points a year. Sixty basis points of this outperformance can be attributed to the characteristics of
stocks held by the funds, and the other 70 basis points is due to manager talent in selecting stocks
that “beat their characteristic benchmark portfolios” (p.1689). Wermers finds a positive
correlation between turnover and performance, as the funds with higher turnover in his sample
tended to have higher performing stocks, which outweighed the larger transaction costs and
expenses. This, according to Wermers, supports the value of actively managed funds, as even
funds with higher expenses and higher turnover can outperform the benchmark on a net
performance basis if their managers make better investments.
III. Methodology
Data
In this study, I will be using data from the Center for Research in Security Prices (CRSP),
provided by Wharton Research Data Services. The CRSP data set includes, among other data,
performance information and characteristics for all publicly traded mutual funds from January
1961 onward. In order to avoid inconsistencies that could result by aggregating different types of
funds, I restricted my sample to domestic U.S. equity funds listed on the Morningstar website,
and downloaded separate sets of data for all funds listed in the large, mid, and small market
capitalization categories. According to Morningstar, domestic equity funds have at least 70% of
assets in U.S. stocks. They are categorized by their style: value, blend, or growth, and their
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median market capitalization: small, medium, or large. I focused my analysis only on the
distinction between market capitalizations, as I believe that this might be more closely related to
performance than the investment style, as the distinctions between funds of different investment
styles can often be relatively minor. According to Morningstar, the market capitalization of a
fund is determined in the following way: individual stocks are first assigned a large, mid, or
small-cap label, with stocks in the group that composes the top 70% of the market capitalization
for a geographic area labeled as “large-cap,” the next 20% as “mid-cap,” and the balance as
“small-cap.” An asset-weighted average is then performed using the size scores of each fund’s
underlying stocks, and the fund is assigned an overall label.
Using the mutual fund lists provided by Morningstar for each of these categories, I
identified 2,011 funds in the small-cap category, 1,625 funds in the mid-cap category, and 4,731
funds in the large-cap category.1 I then downloaded the CRSP-provided monthly return, net of
fees, for each of the funds in each market capitalization category over five different time periods:
1990-1994, 1995-1999, 2000-2004, 2005-2009, and 2010-2014. The decision to focus on fiveyear time periods versus another time interval was not extremely significant—five years seems
to be a reasonable amount of time over which to assess at least medium-term performance. I
started looking at five-year periods in 1990 because prior to this time, the number of listed
mutual funds was less robust. I wanted more than 40 mutual funds in each five-year sample, and
for mid and small-cap funds, this only occurred after 1990. Since the 1990-2015 time range
captures several different unique market events, such as the tech bubble and subsequent dot-com
crash in the late 1990s and early 2000s, the collapse of the housing market in 2008-2009, and the
subsequent economic recovery, I believe that comparing results across different five-year periods
1
See the appendix for descriptive statistics on my entire pool of data, as well as for each time period and
fund type.
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within this range will minimize any effects that occur only in a specific year or as a result of a
specific market event.
I also obtained, from CRSP, the values of several characteristics of interest for each fund
at the beginning of each five-year time period mentioned above: the total net assets, expense
ratio, date of inception for the fund, and date that the fund’s current manager was hired, as well
as a variable indicating whether or not it was an index fund. Using this data, I calculated age,
manager tenure, cumulative performance, monthly standard deviation, and a rough measure of
risk-adjusted performance (cumulative performance / monthly standard deviation) for each fund.
I also assigned each fund an age indicator variable, which had a value of 0 if the fund was less
than three years old at the start of each relevant five-year period, and a value of 1 if the fund was
three years or older. I also cleaned the data—eliminating all duplicates, index funds, and funds
that did not survive the entire five-year period. 2 The basic descriptive characteristics for my final
set of funds is as follows:
Large-Cap
Mid-Cap
Small-Cap
All Funds
711.96
523.37
296.90
667.68
1.15%
1.40%
1.44%
1.19%
Age (years)
8.80
7.77
7.15
8.60
Manager Tenure
(years)
Cumulative
Performance
4.73
4.95
4.54
4.73
39.35%
67.48%
62.68%
42.94%
3.13%
5.03%
5.43%
3.43%
Total net assets
(millions of $)
Expense Ratio
Monthly SD
2
A full explanation of my data preparation process is in Appendix II.
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As illustrated in the table above, the market capitalization of each fund was closely
related to total net assets—the large-cap funds in my sample had average total net assets of
$711.96 million, while the mid-cap funds had an average of $523.37 million and the small-cap
funds had an average of $296.90 million. This relationship was reversed for the expense ratio—
small-cap funds were the most expensive (144 basis points, on average), followed by mid-cap
funds (140 basis points), and large-cap funds (115 basis points). The large-cap funds tended to be
slightly older, with an average age of 8.80 years, compared to 7.77 years for mid-cap funds and
7.15 years for small-cap funds. However, manager tenure did not seem to be closely related to
market capitalization, as each of the three market capitalization categories had an average
manager tenure between 4.50 and 5.00 years.
Average cumulative performance over each of the five-year time periods was highest for
mid-cap funds (67.48%), followed by small-cap funds (62.68%), and trailed significantly by
large-cap funds (39.35%). Small-cap funds were the riskiest, with an average monthly standard
deviation of 5.43%, followed by mid-cap funds with 5.03% and large-cap funds with 3.13%. For
each fund type, and for the entire pool of funds, total net assets tended to increase over time,
though the average value dropped slightly during periods of financial turmoil—such as the
bursting of the dot-com bubble in the early 2000s and the housing market crash in 2008. Expense
ratios also tended to increase over time, though they dropped slightly after the most recent
financial crisis. Unsurprisingly, performance was closely correlated with major financial events
during each of the relevant time periods, and monthly standard deviation seemed to be highest
during time intervals that contained dramatic market events, such as 2005-2009.
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Empirical Strategy
For my initial calculations, I performed three sets of linear regressions for each market
capitalization category (small, medium, and large-cap) over each of the five relevant periods of
interest (1990-1994, 1995-1999, 2000-2004, 2005-2009, and 2010-2014). I first regressed the
cumulative performance measure against my set of relevant characteristics (total net assets,
expense ratio, age, and manager tenure), and then regressed these same dependent variables with
monthly standard deviation, and then with risk-adjusted performance (cumulative performance /
monthly standard deviation). The regression equation is as follows:
(1) 𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 = 𝛽 1 𝑆𝑖𝑧𝑒 + 𝛽5 𝐸𝑥𝑝 + 𝛽9 𝐴𝑔𝑒 + 𝐵= 𝑇𝑒𝑛𝑢𝑟𝑒 + 𝜀ABCD
Outcome Variable can represent cumulative performance, monthly standard deviation, or riskadjusted performance, as I run this regression with each of those dependent variables. Size is the
standardized measure of total net assets at the start of the relevant time period, Exp is the
standardized measure of the expense ratio at the start of the relevant time period, Age is the
standardized measure of age (in years) at the start of the relevant time period, and Tenure is the
standardized measure of manager tenure (in years) at the start of the relevant time period. This
regression was performed for each of the three main categories of funds (small, medium, and
large-cap) over the five relevant five-year time periods.
In my second set of calculations, I replaced the standardized age variable with my
indicator variable for age. I then performed the same set of regressions, with the equation below:
(2) 𝑂𝑢𝑡𝑐𝑜𝑚𝑒𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 = 𝛽 1 𝑆𝑖𝑧𝑒 + 𝛽5 𝐸𝑥𝑝 + 𝛽9 𝐴𝑔𝑒𝐼𝑛𝑑 + 𝐵= 𝑇𝑒𝑛𝑢𝑟𝑒 + 𝜀CGHIJKL
The variables in equation (2) have the same interpretation as the variables in equation (1),
including the three different interpretations of the Outcome variable. The AgeInd variable, which
replaces Age, has a value of 1 if the fund is over three years old at the start of the relevant five-
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year time period and a value of 0 if the fund is less than three years old at the start of the relevant
time period.
IV. Analysis and Results
All Funds
From the first set of regressions (equation 1 run across all three fund types and all five
time periods for each of the three dependent variables), I found several significant and persistent
relationships between performance, risk, and risk-adjusted performance and my relevant fund
characteristics. For one-third of my observations, total net assets were positively correlated with
performance (exhibited in Figure 1.1), and were also positively correlated with standard
deviation for 27% of my observations (Figure 1.3). However, the relationship between total net
assets and risk-adjusted performance was inconclusive (Figure 1.5), and the relationship between
total net assets and performance was small in magnitude. In the most extreme case, a onestandard deviation increase in total net assets would have resulted in only a 19-basis point
improvement in performance over the relevant five-year time period. This is consistent with
Golec’s (1996) conclusions, as he found a fairly insignificant size/performance relationship. The
relationship between total net assets and standard deviation was of a slightly greater magnitude-in the most extreme case, a one-standard deviation increase in total net assets would result in an
increase in monthly standard deviation of 0.43 percentage points.
From the second set of regressions (equation 2 run across all three fund types and all five
time periods for each of the three dependent variables), I found that substituting the age indicator
for the standardized age variable did not yield significantly different results—total net assets still
exhibited a weakly positive relationship with both performance and risk (Figures 1.2 and 1.4),
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and an inconclusive relationship with risk-adjusted performance (Figure 1.6). The positive
relationship between size and performance is congruent with Berk and Green’s (2004) theory
that well-performing funds tend to grow larger, as assets chase past performance. However, this
theory would also predict that in the long run, the inflow of assets into these top-performing
funds would cause them to perform worse. The positive relationship between size and standard
deviation could be supported by a similar theory, as managers who have more assets need more
places to invest them and may therefore be more likely to turn to risky investments.
The relationship between performance and expense ratio was inconclusive (Figure 1.1),
as it was insignificant for approximately half of my observations and evenly split between
positive and negative for my remaining observations. However, there was a strong positive
relationship between expense ratio and monthly standard deviation (Figure 1.3), as this
correlation was positive for 87% of observations. The magnitude of this relationship was also
large for many of the observations, as in the most extreme case a one-standard deviation increase
in the expense ratio would result in an increase in monthly standard deviation of 1.52 percentage
points. Expense ratio also had a strong relationship with risk-adjusted performance (Figure 1.5),
but this relationship was negative in 80% of my observations—which is again consistent with
Golec’s (1996) results. All of these relationships held when the age indicator was used instead of
the standardized age variable. The relationship between expense ratio and standard deviation
could be explained by the fact that more active managers typically charge higher expense ratios,
and these active managers may also be more likely to trade frequently and make more unique
and potentially risky bets on the market. The relationship between expense ratio and riskadjusted performance is not surprising, as there is a strong base of literature supporting the fact
that, on average, active managers cannot outperform passive index funds, since these active
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managers charge higher fees that are incorporated into net returns. However, in my samples, the
negative expense ratio/risk-adjusted performance relationship seems to be driven by the fact that
more expensive funds are riskier, as their pure performance is actually on par with cheaper
funds, which is a somewhat surprising result.
Manager tenure had a weakly negative relationship with performance, as this relationship
was statistically significant and negative for 40% of my observations (Figure 1.1). This
relationship holds when the age indicator is substituted for the standardized age variable (Figure
1.2). Manager tenure also has a negative relationship with standard deviation (Figure 1.3), as the
correlation is negative for almost half of the observations and holds when the age indicator
variable is used (Figure 1.4), though it is weakened slightly. There was not a significant and
persistent relationship between manager tenure and risk-adjusted performance (Figures 1.5 and
1.6). The negative relationship between manager tenure and both performance and standard
deviation could be explained by a theory of manager attrition. Once a manager has established
her reputation and potentially exhausted many of her best trade ideas, she may be less likely to
exert extraordinary effort in managing of her fund. As a result, she may therefore tend to “follow
the market” more closely and end up with a fund that has lower returns but also lower risk.
However, the magnitude of these relationships was small. In the most extreme case, a one
standard-deviation increase in manager tenure resulted in only a 15-basis point decrease in
performance over the five-year period and a 0.32 percentage point decrease in monthly standard
deviation.
Age did not exhibit a significant and persistent relationship with performance (Figure
1.1), as the relationship was insignificant for approximately half of the observations, and the
balance was split evenly between positive and negative correlations. This would imply that there
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is no reason to expect a significant difference in raw performance based on the age of the fund.
The relationship remained inconclusive when the age indicator variable was used (Figure 1.2).
This contradicts my initial hypothesis, as I suspected that age and performance would exhibit a
negative relationship, but is consistent with Golec’s (1996) conclusions, as he found a slightly
negative but insignificant correlation between age and performance. However, age did exhibit a
slightly positive relationship with standard deviation (Figure 1.3), implying that older funds are
riskier, as this correlation was positive for 53% of observations. In the most extreme case, a onestandard deviation increase in age resulted in a 0.56 percentage point increase in monthly
standard deviation, so this relationship was not negligible. This relationship is not welldocumented in existing literature, but it could be again caused by the fact that the investment
team of an older fund may have exhausted many of its initial trade ideas, and may need to turn to
riskier investments to be able to fully invest the fund’s assets or maintain outperformance.
However, this relationship reversed when the age indicator variable was used (Figure 1.4), as
33% of observations were negative and more than half were insignificant—the frequency of
positive observations declined significantly. This would imply that though younger funds are less
risky than older funds in general, funds that are younger than three years old are riskier than
funds older than three years. This could be due to the fact that newly established funds are
experimenting with different trade ideas and attempting to earn outsized returns for marketing
purposes, so they may make riskier investments.
The relationship between age and risk-adjusted performance was also significant—with
the standardized age variable, this relationship was negative for 40% of observations (Figure
1.5), implying that older funds have worse risk-adjusted performance. For my sample of funds,
this seems to be caused by the fact that older funds are riskier, and does not suggest that they
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have lower pure performance than younger funds. However, this relationship again reverses
when the age indicator variable is substituted for the standardized age variable, as it is positive
for 33% of observations (Figure 1.6), and the frequency of negative observations drops to 7%.
This suggests that though older funds have worse risk-adjusted performance overall, very young
funds actually have worse risk-adjusted performance than funds older than three years. This
could be due to the fact that, as mentioned above, the managers of younger funds may take
riskier bets in an attempt to prove themselves, which can result in more risk and lower returns if
these bets don’t pay off.
Figures 1.1 through 1.6 below illustrate the relationship between performance, risk, and
risk-adjusted performance and the four fund characteristic variables, examined across all fund
categories and all of the five relevant time periods. Each fund characteristic has fifteen different
observations divided between “Positive,” “Negative” and “Not significant.”
Cumulative Performance (all Categories and Time Periods)
10
9
9
8
7
7
7
6
7
6
Positive
5
5
4
4
4
4
4
Not significant
3
2
Negative
2
1
1
0
Size
Expense Ratio
Manager Tenure
Age
Figure 1.1. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between cumulative performance and each of the four fund characteristics on the x-axis.
March 7, 2016
Moore
Cumulative Performance (all Categories and Time Periods) w/
Age Indicator
10
9
9
8
8
8
7
7
6
6
5
5
Positive
5
Negative
4
3
3
3
Not significant
3
2
2
1
1
0
Size
Expense Ratio
Manager Tenure
Age Indicator
Figure 1.2. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between cumulative performance and each of the four fund characteristics on the x-axis,
utilizing the age indicator variable instead of the standardized age variable.
Monthly SD (all Categories and Time Periods)
14
13
12
11
10
8
8
7
Positive
7
6
6
Negative
Not significant
4
4
2
2
1
0
0
Size
Expense Ratio
1
0
Manager Tenure
Age
Figure 1.3. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between monthly standard deviation and each of the four fund characteristics on the xaxis.
19
March 7, 2016
Moore
Monthly SD w/ Age Indicator (all Categories and Time Periods)
14
13
12
11
10
10
8
8
Positive
Negative
6
5
4
Not significant
4
4
2
2
2
1
0
0
Size
Expense Ratio
0
Manager Tenure
Age Indicator
Figure 1.4. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between monthly standard deviation and each of the four fund characteristics on the xaxis, utilizing the age indicator variable instead of the standardized age variable.
Performance/SD (all Categories and Time Periods)
14
12
12
10
10
9
8
8
Positive
6
6
Negative
Not significant
4
4
3
3
2
2
2
1
0
0
Size
Expense Ratio
Manager Tenure
Age
Figure 1.5. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between performance/monthly standard deviation and each of the four fund characteristics
on the x-axis.
20
March 7, 2016
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21
Performance/SD with Age Indicator (all Categories and Time
Periods)
14
12
12
11
10
9
8
Positive
7
Negative
6
5
4
4
Not significant
4
3
2
2
2
1
0
0
Size
Expense Ratio
Manager Tenure
Age
Figure 1.6. This illustrates the number of observations (of a total of 15) that exhibited a positive, negative, or not
significant correlation between performance/monthly standard deviation and each of the four fund characteristics
on the x-axis, utilizing the age indicator variable instead of the standardized age variable.
Large-Cap Funds
After considering all funds together, I then examined large, mid, and small-cap funds
separately to discern whether or not particular types of funds exhibited stronger individual
trends. The sample sizes were smaller for these groups, since each market capitalization category
only had five observations for each fund characteristic—one for each time period. In general,
most of the trends observed for the entire pool of funds were also consistent for funds of specific
market capitalizations, though mid and small-cap funds were more likely to have insignificant
coefficients, implying that large-cap funds might be driving many of the significant relationships
in the broader pool of funds.
March 7, 2016
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22
As Figure 2.1 illustrates, the total net assets coefficient was insignificant for the majority
of observations for large-cap funds (and split evenly between positive and negative for the
remaining observations), implying that the size/performance relationship is inconclusive. This
inconclusive relationship persisted with the age indicator variable (Figure 2.2). Expense ratio
was positively correlated with performance for 60% of observations, indicating that the more
expensive funds actually performed better. This was not observed for the overall pool of funds,
for which this relationship was inconclusive. This positive relationship could be due to alpha
earned by more active managers, who tend to charge a higher fee, and it persisted when the age
indicator variable was used. The relationship between manager tenure and performance was
inconclusive for both the standardized age and age indicator variable, though it trended slightly
more positive with the age indicator variable. This inconclusive result for large-cap funds again
differed from the results for the entire pool of funds, which exhibited a negative relationship
between manager tenure and performance. However, the negative relationship observed for the
pool of all funds was fairly weak. The age variable exhibits an inconclusive relationship with
performance (for both standardized age and the age indicator), which matches the results for the
full pool.
Large-Cap Performance
3.5
3
3
Large-Cap Performance (Age
Indicator)
3
3
2.5
2
2 2
Positive
2
1.5
1 1
1 1
1
1
Negative
Not significant
0.5
0
0
Size
Expense Manager
Ratio
Tenure
Age
3.5
3
2.5
2
1.5
1
0.5
0
3
3
2
1 1
1 1
2
1
2 2
Positive
1
Negative
Not significant
Size
Expense Manager
Ratio
Tenure
Age
Figure 2.1 (left) and Figure 2.2 (right). Figure 2.1 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance and each of the four fund characteristics on the x-axis. Figure 2.2 exhibits
the same relationships utilizing the age indicator variable instead of the standardized age variable.
March 7, 2016
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23
As illustrated in Figure 2.3 and Figure 2.4, the relationships observed between standard
deviation and fund characteristics for the entire pool of funds held true for large-cap funds. Total
net assets had an insignificant relationship with standard deviation for 60% of observations under
both the standardized age and age indicator variables. The positive relationship previously
observed for all funds between expense ratio and standard deviation was particularly strong for
large-cap funds, as all of the observations exhibited a statistically significant positive relationship
between these two variables using both the standardized age and age indicator variables. The
manager tenure and standard deviation relationship was negative for 60% of the observations
using the standardized age variable, which is similar to what I observed for the entire pool,
though this relationship became slightly less significant when the age indicator variable was
used. The age variable was, again, positively correlated with standard deviation when the
standardized age variable was used, and negatively correlated with standard deviation when the
age indicator variable was used, indicating that older funds are typically riskier, but very young
funds are riskiest.
Large-Cap Monthly SD
6
Large-Cap Monthly SD (Age
Indicator)
5
5
6
4
5
3
3
3
3
2
2
1
1
0
1
1 1
Positive
4
Negative
3
Not significant
0
3
3
2
Positive
2 2
2
1
0 0
5
1
0
1
1
0 0
Negative
Not significant
0
Size
Expense
Ratio
Manager
Tenure
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure Figure 2.3 (left) and Figure 2.4 (right). Figure 2.3 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between monthly standard deviation and each of the four fund characteristics on the x-axis.
Figure 2Figure 2.4 exhibits the same relationships utilizing the age indicator variable instead of the standardized age variable.
March 7, 2016
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24
For risk-adjusted performance, the results for large-cap funds were again similar to the
results for the overall pool of funds, with the exception of the total net assets variable. A slight
positive relationship was observed between total net assets and risk-adjusted performance, which
would imply that bigger funds tend to have stronger risk-adjusted performance. This effect was
only present, however, for 60% of the observations—the other 40% were insignificant, and when
the age indicator variable was used, this relationship became less significant. These results
suggest that for large-cap funds, the correlation between size and risk-adjusted performance may
be greater than for funds of other market capitalizations. The expense ratio variable was again
very significant—it was negatively correlated with risk-adjusted return for all observations under
both the standardized age and age indicator variables, indicating that more expensive funds
perform worse on a risk-adjusted basis. This is consistent with the result I observed for the entire
pool of funds. The relationship between manager tenure and risk-adjusted performance was
inconclusive for both age variables, as I observed for the broader pool. I again found that the
age/risk-adjusted performance relationship is slightly negative when the standardized age
variable is used (older funds generally do worse on a risk-adjusted basis), but that this
relationship reverses and becomes slightly positive when the age indicator variable is used (very
young funds do worse on a risk-adjusted basis than all other funds).
Large-Cap Performance/Monthly SD
(Age Indicator)
Large-Cap
Performance/Monthly SD
6
5
6
5
4
3
3
3
2
Positive
2
2 2
2
1
5
5
1
0
0
0
0
Expense
Ratio
Manager
Tenure
Not significant
3
3
3
2
3
Positive
2
2
2
1
0
0
0
0
0
Negative
Not significant
0
0
Size
Negative
4
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure 2.5 (left) and Figure 2.6 (right). Figure 2.5 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance/ monthly standard deviation and each of the four fund characteristics on the
x-axis. Figure 2.6 exhibits the same relationship utilizing the age indicator variable instead of the standardized age variable.
March 7, 2016
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25
Mid-Cap Funds
For mid-cap funds (as illustrated in Figure 3.1), the total net assets/performance
relationship was not significant for 60% of observations but was positive for the remainder,
implying that larger funds may perform better, as I observed for the broader pool of all funds.
The expense ratio/performance relationship was negative for 60% of the observations, which
contradicts the results for both the broader pool (where the relationship is inconclusive) and the
large-cap results (where the relationship was slightly positive). This means that more expensive
mid-cap funds tend to perform worse, which fits the conventional theory surrounding active and
passive management, as active managers who charge a higher fee will, on average, naturally
underperform passive managers who achieve the market return with a lower fee. The size and
expense ratio relationships remained mostly unchanged when the age indicator variable was
introduced, as illustrated by Figure 3.2. As seen in the broader pool of funds, the relationship
between manager tenure and performance was negative in the majority of cases for both the
standardized age and age indicator variables. This indicates that managers who have been
running their fund for longer tend to perform worse, which fits with the attrition theory explained
above. The age and performance relationship was inconclusive under both age variables, which
is consistent with the results for the overall pool.
Mid-Cap Performance (Age Indicator)
Mid-Cap Performance
3.5
3
3
3
6
3
3
2.5
2
2
2
4
Positive
2
1.5
1 1
1
0.5
5
5
Negative
Not significant
0
0
0
3
3
3
2
3
2
Positive
2
Negative
2
1
Not significant
0
0
0
0 0
0
0
Size
Expense Manager
Ratio
Tenure
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure 3.1 (left) and Figure 3.2 (right). Figure 3.1 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance and each of the four fund characteristics on the x-axis. Figure 3.2 exhibits
the same relationship utilizing the age indicator variable instead of the standardized age variable.
March 7, 2016
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26
As illustrated by Figures 3.3 and 3.4, the total net assets/standard deviation relationship is
largely inconclusive for both age variables, which is consistent with the results for the entire pool
of funds. This implies that bigger funds are not likely to be any more or less risky than smaller
funds. The expense ratio relationship is also consistent with that observed for the overall pool of
funds, as for mid-cap funds this relationship is negative for 80% of observations for both age
variables. This implies that more expensive funds are also riskier, which again fits with the
theory that more active managers might charge more and make riskier investments. The manager
tenure and standard deviation relationship is inconclusive using both standardized age and the
age indicator, as it is insignificant for 80% of observations. This contradicts the result for the
overall pool, where there was a slightly negative relationship, implying that managers who had
been at the fund longer would run less risky funds. The reason behind this discrepancy is not
immediately clear, though the sample size was smaller for mid-cap funds, and the relationship
was fairly weak for the entire pool of funds. The relationship between age and standard deviation
is positive for 60% of the observations with the standardized age variable, as observed in the
broader pool—implying that older funds are riskier. However, this relationship becomes
completely insignificant when the age indicator variable is used, implying that very young funds
are not necessarily riskier than all other funds—in the overall pool of funds, this relationship
became negative, implying that very young funds are riskier. Again, the reasoning behind this
discrepancy is not immediately clear, though it’s worth noting again that the sample size is much
smaller for the mid-cap funds than for the large-cap funds.
MarchMid-Cap
7, 2016 Monthly SD
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
4
4
Moore
27
Mid-Cap Monthly SD (Age
Indicator)
6
4
5
5
3
4
4
4
4
2
1
1
0
0
Negative
1
0
Positive
Not significant
0
Positive
3
2
1
Negative
1
1
0
0
Size
Expense
Ratio
1
0
Not significant
0 0
0
Size
Expense Manager
Ratio
Tenure
Age
Manager
Tenure
Age
Figure 3.3 (left) and Figure 3.4 (right). Figure 3.3 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance and each of the four fund characteristics on the x-axis. Figure 3.4 exhibits
the same relationship utilizing the age indicator variable instead of the standardized age variable.
As with the overall pool of funds, the relationship between total net assets and riskadjusted performance was inconclusive for mid-cap funds, as observed in Figures 3.5 and 3.6.
This implies that larger funds do not necessarily achieve higher risk-adjusted performance than
smaller funds, which could fit with Berk and Green’s theory that these funds begin to struggle
when they face an inflow of assets. The relationship between expense ratio and risk-adjusted
performance is negative for 60% of observations under both age variables, which is consistent
with the results for the overall pool and implies that more expensive funds are not necessarily
worth the additional cost as they do not generate higher risk-adjusted performance. The manager
tenure and risk-adjusted performance relationship is again inconclusive for both age variables, as
it was for the larger pool of funds. The age/risk-adjusted performance relationship is slightly
negative (with 40% of observations) when the standardized age variable is used, as observed for
the entire pool of funds, but instead of becoming positive when the age indicator variable is used,
the relationship instead becomes insignificant. This implies that, for mid-cap funds, very young
funds do not necessarily underperform all other funds on a risk-adjusted basis. The reasoning
behind this is not immediately clear, and may be elucidated by further research.
March 7, 2016
Moore
Mid-Cap Performance/SD
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Mid-Cap Performance/SD (Age
Indicator)
4
3
3
2
1
2
1 1
0
0
6
3
0
5
5
Positive
4
Negative
3
Not significant
28
4
3
Positive
2
2
1
4
1
Negative
1
0
0
0
0 0
Not significant
0
Size
Expense Manager
Ratio
Tenure
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure 3.5 (left) and Figure 3.6 (right). Figure 3.5 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance/monthly standard deviation and each of the four fund characteristics on the
x-axis. Figure 3.6 exhibits the same relationship utilizing the age indicator variable instead of the standardized age variable.
Small-Cap Funds
For small-cap funds, the relationships between performance and the four fund
characteristic variables (total net assets, expense ratio, manager tenure, and age variables)
conformed closely to the relationships observed for the overall pool, as illustrated in Figures 4.1
and 4.2. For 40% of the observations under both age variables, the relationship between total net
assets and performance was positive, implying that bigger funds perform better. The relationship
between expense ratio and performance was inconclusive for both the standardized age variable
and the age indicator, as observed for the overall pool. However, while the relationship between
manager tenure and performance was negative for the overall pool of funds, this relationship was
inconclusive for small-cap funds under both age variables. This implies that small-cap managers
do not necessarily perform worse when they have been running their fund for longer. Again, the
reasoning behind this relationship is not necessarily clear, but may be due to the fact that the
small-cap sample size was small and the negative relationship was primarily observed for largecap funds. The relationship between both age variables and performance was inconclusive,
which is consistent with the results for the overall pool of funds.
March 7, 2016
Moore
Small-Cap Performance
3.5
3
3
Small-Cap Performance (Age
Indicator)
3
3
2.5
5
2
Positive
1.5
1 1
1
1 1
1
0.5
4
4
2 2
2
Negative
Not significant
0
29
3
3
3
Positive
2
2
1
4
1
0
0
1
1 1
Negative
Not significant
0
0
0
Size
Expense Manager
Ratio
Tenure
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure 4.1 (left) and Figure 4.2 (right). Figure 4.1 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance and each of the four fund characteristics on the x-axis. Figure 4.2 exhibits
the same relationship utilizing the age indicator variable instead of the standardized age variable.
For small-cap funds, the relationships between the monthly standard deviation variable
and the fund characteristic variables were again fairly comparable to the relationships for the
entire pool of funds, as illustrated in Figures 4.3 and 4.4. Total net assets and standard deviation
showed an insignificant correlation for 80% of observations under both age variables, and
expense ratio and standard deviation were positively correlated in 80% of cases, implying that
more expensive funds are riskier. While the manager tenure variable was negative for 60% of
observations under the standardized age variable, which was consistent with the results for the
entire pool of funds, it became insignificant in 80% of cases with the age indicator variable,
suggesting that longer-tenure managers were not necessarily more likely to run less risky funds.
The age variable also exhibited a positive correlation with standard deviation for 40% of
observations, as seen for the entire pool of funds, implying that older funds are riskier. However,
the reversal of this relationship was less dramatic when the age indicator variable was used for
small-cap funds than for the entire pool of funds, suggesting that very young small-cap funds are
not necessarily more likely to be riskier than all other funds.
March 7, 2016
Moore
Small-Cap Monthly SD (Age
Indicator)
Small-Cap Monthly SD
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
4
4
3
1
0
Negative
Size
Not significant
0
Expense Manager
Ratio
Tenure
0
Age
4
4
4
4
Positive
2
1
0
5
3
2
30
3
2
1
2 2
1
1
0
0
Size
Expense
Ratio
1
1
Positive
Negative
Not significant
0
0
Manager
Tenure
Age
Figure 4.3 (left) and Figure 4.4 (right). Figure 4.3 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between monthly standard deviation and each of the four fund characteristics on the x-axis. Figure
4.4 exhibits the same relationship utilizing the age indicator variable instead of the standardized age variable.
For small-cap funds, the risk-adjusted performance results were very similar to the results
for the entire pool of funds, as illustrated in Figures 4.5 and 4.6. The total net assets and riskadjusted performance relationship was insignificant for 80% of observations under both age
variables. This implies that, as I found for the entire pool of funds, bigger funds are not
necessarily expected to have significantly different risk-adjusted performance than smaller funds.
The expense ratio/risk-adjusted performance relationship was negative for 80% of observations
with both the standardized age and age indicator variable, again implying that more expensive
funds do not necessarily provide better risk-adjusted performance. The relationship between
manager tenure and risk-adjusted performance was insignificant for 60% of observations, and the
remainder of observations were evenly split between positive and negative correlations. This is a
similar result to that of the overall pool—it does not appear that funds with managers who have
been in their positions for longer perform significantly better or worse than average on a riskadjusted basis. The age/risk-adjusted performance relationship leans negative when the
standardized age variable is used, implying that on average, older funds perform worse.
March 7, 2016
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31
However, as I observed for the entire pool of funds, this relationship reverses when the age
indicator is used instead, revealing that funds younger than three years old have worse riskadjusted performance than funds older than three years.
Small-Cap Performance/Risk
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
4
4
3
1
1
5
3
2
Positive
Negative
1 1
Not significant
0
Small-Cap Performance/Risk (Age
Indicator)
0
0
4
4
4
3
3
2
2
1
1
0
1
1 1
2
1
Positive
Negative
Not significant
0
0
Size
Expense Manager
Ratio
Tenure
Age
Size
Expense
Ratio
Manager
Tenure
Age
Figure 4.5 (left) and Figure 4.6 (right). Figure 4.5 illustrates the number of observations (of a total of 5) that exhibited a positive,
negative, or not significant correlation between performance/ monthly standard deviation and each of the four fund characteristics on the
x-axis. Figure 4.6 exhibits the same relationship utilizing the age indicator variable instead of the standardized age variable.
V. Conclusions
The primary conclusion of my paper is that though older funds tend to perform worse on
a risk-adjusted basis than younger funds, very new funds are an exception to this rule: funds that
are younger than three years old have lower risk-adjusted returns than funds older than three
years old. There is not a significant relationship between age and raw performance, but there is a
strong positive relationship between age and standard deviation, which reverses when the age
indicator variable is substituted for the standardized age variable. This implies that the lower
risk-adjusted returns observed for very young funds is primarily due to the fact that these funds
are riskier, not necessarily because their pure returns are worse. However, this conclusion
indicates that requiring a track record before investing in a mutual fund may be wise, as investors
in very young funds may be exposed to undue risk. Since the young funds used in my sample did
March 7, 2016
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32
survive for at least the entire five-year period of interest, there is likely some survivorship bias—
the one, two, and three-year old funds in my sample were likely stronger performers than the
average one, two, and three-year old funds. Therefore, the fact that risk-adjusted performance
has a negative correlation with the age indicator for the funds in my sample implies that the
relationship is likely even more negative for the average fund under three years old. This
relationship generally persisted across the five time periods of study, and was observed across
large, mid and small-cap funds. While this relationship was particularly significant for large-cap
funds, this may be because the sample size for large-cap funds was the largest, by far—whether
or not there is a true distinction in this relationship between funds of different market
capitalizations would need to be investigated more thoroughly by further research.
My secondary conclusions involve the total net assets, expense ratio, and manager tenure
variables, as I also found significant relationships with performance, risk, and risk-adjusted
performance for some of these variables. I found that total net assets and performance were
positively correlated, suggesting that bigger funds have better returns, which fits with the theory
that better-performing funds with more talented managers attract more assets. However, I also
found a positive relationship between size and standard deviation, implying that larger funds are
also riskier, which could be because they have more assets and may need to invest some of them
into more unique or risky investments. Since performance was stronger but risk was also higher
for bigger funds, the overall relationship between size and risk-adjusted performance was
inconclusive.
I did not find any significant relationship between expense ratio and pure performance,
implying that more expensive funds do not necessarily perform worse. However, I did find a
very strong relationship between expense ratio and standard deviation, indicating that more
March 7, 2016
Moore
33
expensive funds are also often riskier, which may be because more active managers who charge
more to run their fund also pursue riskier investments or are otherwise less likely to conform to
the market portfolio. This also spurred a strong negative relationship between expense ratio and
risk-adjusted performance, which seems to be primarily driven by the greater risk exhibited by
more expensive funds. This result suggests that it may not actually be worthwhile for investors to
purchase more expensive funds, since on a risk-adjusted basis these funds will likely do worse
than less expensive funds.
The manager tenure variable exhibited a significant relationship with both performance
and standard deviation, but not with risk-adjusted performance. Though the relationship was
somewhat weak, and mostly limited to large-cap funds, manager tenure seems to be negatively
correlated with pure performance. This suggests that managers who have been at a fund for
longer tend to perform worse, which fits with a theory of manager attrition—as a manager’s
reputation is established, he or she will have fewer incentives to continue putting in the effort
required to outperform. However, I also observed a negative relationship between manager
tenure and standard deviation, implying that managers with a longer tenure tend to run less risky
funds. This may be explained by the fact that managers who have exhausted most of their
original ideas might regress towards the mean and make less risky investments that more closely
mimic the benchmark. Overall, therefore, there was no significant relationship between manager
tenure and risk-adjusted performance, as longer-tenure managers tended to perform worse but
were also less risky.
Overall, my results suggest that investors should take caution when investing in very
young funds and in more expensive funds, as on a risk-adjusted basis, these types of funds may
underperform. Though the magnitude of most of the observed relationships between
March 7, 2016
Moore
34
performance, risk, and risk-adjusted performance and my characteristics of interest were small,
this is not necessarily surprising. The efficient markets hypothesis predicts that all available
information will be incorporated into security prices. As a result, we might expect that if there
was a clear, persistent relationship between performance and any specific fund characteristic, this
outperformance would quickly be arbitraged away. Future research could investigate these trends
for a wider range of funds (not only U.S. equity funds) over several different time intervals, not
just five-year time periods, as well as explore whether there are any significant omitted variables
that may contradict my results.
March 7, 2016
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35
VI. Appendices
Appendix I: Descriptive Statistics
Performance and Fund Characteristic Averages Over Time
1990-1994
(N=1131)
576.80
1995-1999
(N=3110)
686.45
2000-2004
(N=4749)
619.39
2005-2009
(N=4691)
688.77
2010-2014
(N=4791)
686.60
1.02%
1.06%
1.24%
1.24%
1.16%
Age (years)
7.69
6.44
6.70
8.51
10.62
Manager Tenure
(years)
4.19
3.38
3.98
4.90
6.19
42.53%
88.14%
21.33%
12.82%
66.25%
2.23%
2.49%
3.38%
4.01%
3.46%
1990-1994
(N=1046)
599.64
1995-1999
(N=2860)
706.81
2000-2004
(N=4311)
638.93
2005-2009
(N=3909)
737.27
2010-2014
(N=3552)
761.31
0.99%
1.04%
1.22%
1.20%
1.19%
Age (years)
8.33
6.50
6.80
8.79
11.08
Manager Tenure
(years)
4.04
3.38
4.01
5.00
6.37
40.72%
83.33%
18.56%
13.66%
59.31%
2.07%
2.29%
3.10%
3.67%
3.17%
1995-1999
(N=110)
625.54
2000-2004
(N=158)
660.19
2005-2009
(N=366)
573.94
2010-2014
(N=597)
454.81
Total net assets
(millions of $)
Expense ratio
Cumulative
Performance
Monthly Standard
Deviation
Large-Cap Averages Over Time
Total net assets
(millions of $)
Expense Ratio
Cumulative
Performance
Monthly Standard
Deviation
Mid-Cap Averages Over Time
Total net assets
(millions of $)
1990-1994
(N=42)
295.52
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1.28%
1.36%
1.45%
1.43%
1.38%
Age (years)
8.86
6.12
6.59
6.87
8.80
Manager Tenure
(years)
4.76
3.73
3.85
4.55
5.72
64.28%
164.70%
31.53%
10.53%
100.47%
4.17%
4.90%
6.10%
5.65%
4.44%
1990-1994
(N=43)
295.12
1995-1999
(N=140)
313.89
2000-2004
(N=280)
296.00
2005-2009
(N=416)
305.73
2010-2014
(N=642)
289.02
1.37%
1.28%
1.45%
1.49%
1.44%
Age (years)
6.57
5.54
5.15
6.62
8.51
Manager Tenure
(years)
3.62
3.09
3.53
4.21
5.59
65.24%
127.46%
58.43%
6.49%
92.22%
4.24%
4.71%
6.18%
5.98%
4.93%
Expense Ratio
Cumulative
Performance
Monthly Standard
Deviation
Small-Cap Averages Over Time
Total net assets
(millions of $)
Expense Ratio
Cumulative
Performance
Monthly Standard
Deviation
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Appendix II: Data Preparation
I calculated fund age by subtracting the date of fund inception from the first date of the
time period of interest. I then calculated manager tenure in the same way, substituting the date of
manager inception for the date of fund inception. I eliminated all funds that did not survive the
entire five-year time period to be able to fairly compare the cumulative performance for each
fund, which included eliminating all funds that were created after the start of the period. I also
eliminated all index funds to focus solely on funds with an active manager, as well as duplicate
entries—funds with the same CRSP fund identification number that appeared in the dataset
twice, and funds that were missing a value for any of these characteristics.
After merging fund characteristics with fund performance, I calculated cumulative
performance for each fund by adding one to each of the returns and then multiplying the monthly
returns for each month in the relevant five-year time period. I also calculated a measure of
monthly risk by taking the standard deviation of this monthly performance across all months in
the sample. Finally, I calculated a measure of risk-adjusted performance by creating a new
variable that divided this cumulative return by the monthly standard deviation. For each fund
characteristic (total net assets, expense ratio, age, and manager age), I standardized the values in
order to allow for easier comparisons. For the age and manager age variables, the standardization
was based off the age variable in years, not days. I also generated an indicator variable for each
fund that had a distinct value depending on whether or not the fund was more than three years
old at the start of the relevant five-year time period.
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Appendix III: Regressions
Large-Cap Funds, 1990-1994
Large-Cap Funds, 1995-1999
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Large-Cap Funds, 2000-2004
Large-Cap Funds, 2005-2009
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Large-Cap Funds, 2010-2014
Mid-Cap Funds, 1990-1994
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Mid-Cap Funds, 1995-1999
Mid-cap Funds, 2000-2004
Mid-cap Funds, 2005-2009
Mid-Cap Funds, 2000-2004
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Mid-Cap Funds, 2005-2009
Mid-Cap Funds, 2010-2014
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Small-Cap Funds, 1990-1994
Small-Cap Funds, 1995-1999
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Small-Cap Funds, 2000-2004
Small-Cap Funds, 2005-2009
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Small-Cap Funds, 2010-2014
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VIII. References
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