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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 risk-adjusted 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 Moore 2

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


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


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 top-

performing 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


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-by-

year 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


(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


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.


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).


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


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 five-

year 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.


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

Total net assets (millions of $)

711.96 523.37 296.90 667.68

Expense Ratio 1.15% 1.40% 1.44% 1.19%

Age (years) 8.80 7.77 7.15 8.60

Manager Tenure (years)

4.73 4.95 4.54 4.73

Cumulative Performance

39.35% 67.48% 62.68% 42.94%

Monthly SD 3.13% 5.03% 5.43% 3.43%

2 A full explanation of my data preparation process is in Appendix II.


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.


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 = 𝛽1𝑆𝑖𝑧𝑒 + 𝛽5𝐸𝑥𝑝 + 𝛽9𝐴𝑔𝑒 + 𝐵=𝑇𝑒𝑛𝑢𝑟𝑒 +𝜀ABCD

Outcome Variable can represent cumulative performance, monthly standard deviation, or risk-

adjusted 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 = 𝛽1𝑆𝑖𝑧𝑒 + 𝛽5𝐸𝑥𝑝 + 𝛽9𝐴𝑔𝑒𝐼𝑛𝑑 + 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-


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 one-

standard 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),


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 risk-

adjusted 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


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


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 one-

standard 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 well-

documented 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


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.

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.”

5

4

2

4

1

4

6

4

9

7 7 7

0

1

2

3

4

5

6

7

8

9

10

Size Expense Ratio Manager Tenure Age

Cumulative Performance (all Categories and Time Periods)

Positive

Negative

Not significant


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.

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 x-axis.

6

3

2

3

1

5 5

3

8

7

8

9

0

1

2

3

4

5

6

7

8

9

10

Size Expense Ratio Manager Tenure Age Indicator

Cumulative Performance (all Categories and Time Periods) w/ Age Indicator

Positive

Negative

Not significant

4

13

1

8

0 0

7

1

11

2

76

0

2

4

6

8

10

12

14


Monthly SD (all Categories and Time Periods)

Positive

Negative

Not significant


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 x-axis, utilizing the age indicator variable instead of the standardized age variable.

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.

4

13

12

0 0

45

11

2

10

8

0

2

4

6

8

10

12

14

Size Expense Ratio Manager Tenure Age Indicator

Monthly SD w/ Age Indicator (all Categories and Time Periods)

Positive

Negative

Not significant

3

0

4

1

2

12

2

6

10

3

9

8

0

2

4

6

8

10

12

14


Performance/SD (all Categories and Time Periods)

Positive

Negative

Not significant


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.

2

0

45

2

12

4

1

11

3

7

9

0

2

4

6

8

10

12

14


Performance/SD with Age Indicator (all Categories and Time Periods)

Positive

Negative

Not significant


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.

1

3

1

2

1

0

1

2

3

2

3

1

0

0.5

1

1.5

2

2.5

3

3.5

Size Expense Ratio

Manager Tenure

Age

Large-Cap Performance

Positive

Negative

Not significant1

3

2 2

1 1 1

2

3

1

2

1

00.5

11.5

22.5

33.5

Size Expense Ratio

Manager Tenure

Age

Large-Cap Performance (Age Indicator)

Positive

Negative

Not significant

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.


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.

2

5

1

3

0 0

3

1

3

0

1 1

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Large-Cap Monthly SD

Positive

Negative

Not significant

2

5

1 1

0 0

2

33

0

2

1

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Large-Cap Monthly SD (Age Indicator)

Positive

Negative

Not significant

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.


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).

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.

3

0

2

1

0

5

0

22

0

3

2

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Large-Cap Performance/Monthly SD

Positive

Negative

Not significant

2

0

2

3

0

5

3

0

3

0 0

2

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Large-Cap Performance/Monthly SD (Age Indicator)

Positive

Negative

Not significant


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.

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.

2

0 0

1

0

3 3

1

3

2 2

3

0

0.5

1

1.5

2

2.5

3

3.5

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Performance

Positive

Negative

Not significant

3

0 0 00

3 3

0

2 2 2

5

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Performance (Age Indicator)

Positive

Negative

Not significant


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.


As with the overall pool of funds, the relationship between total net assets and risk-

adjusted 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.


1

4

0

3

0 0

1

0

4

1

4

2

00.5

11.5

22.5

33.5

44.5

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Monthly SD

Positive

Negative

Not significant 1

4

0 00 0

1

0

4

1

4

5

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Monthly SD (Age Indicator)

Positive

Negative

Not significant


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 large-

cap funds. The relationship between both age variables and performance was inconclusive,

which is consistent with the results for the overall pool of funds.

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.

0 0

1

0

1

3

1

2

4

2

3 3

00.5

11.5

22.5

33.5

44.5

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Performance/SD

Positive

Negative

Not significant

0 0

1

0

1

3

0 0

4

2

4

5

0

1

2

3

4

5

6

Size Expense Ratio

Manager Tenure

Age

Mid-Cap Performance/SD (Age Indicator)

Positive

Negative

Not significant


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.


2

1 1 1

0

1

2

1

3 3

2

3

0

0.5

1

1.5

2

2.5

3

3.5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Performance

Positive

Negative

Not significant

2

0 0

1

0

1 1 1

3

4 4

3

0

1

2

3

4

5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Performance (Age Indicator)

Positive

Negative

Not significant


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 risk-

adjusted 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 risk-

adjusted basis. The age/risk-adjusted performance relationship leans negative when the

standardized age variable is used, implying that on average, older funds perform worse.

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.

1

4

0

2

0 0

3

0

4

1

2

3

00.5

11.5

22.5

33.5

44.5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Monthly SD

Positive

Negative

Not significant 1

4

0

1

0 0

1

2

4

1

4

2

0

1

2

3

4

5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Monthly SD (Age Indicator)

Positive

Negative

Not significant


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 risk-

adjusted performance than funds older than three years.

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

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.

0 0

1

0

1

4

1

2

4

1

3 3

00.5

11.5

22.5

33.5

44.5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Performance/Risk

Positive

Negative

Not significant0 0

1

2

1

4

1 1

4

1

3

2

0

1

2

3

4

5

Size Expense Ratio

Manager Tenure

Age

Small-Cap Performance/Risk (Age Indicator)

Positive

Negative

Not significant


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


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


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.


VI. Appendices

Appendix I: Descriptive Statistics

Performance and Fund Characteristic Averages Over Time

1990-1994 (N=1131)

1995-1999 (N=3110)

2000-2004 (N=4749)

2005-2009 (N=4691)

2010-2014 (N=4791)


576.80 686.45 619.39 688.77 686.60

Expense ratio 1.02% 1.06% 1.24% 1.24% 1.16%

Age (years) 7.69 6.44 6.70 8.51 10.62


4.19 3.38 3.98 4.90 6.19


42.53% 88.14% 21.33% 12.82% 66.25%

Monthly Standard Deviation

2.23% 2.49% 3.38% 4.01% 3.46%

Large-Cap Averages Over Time

1990-1994 (N=1046)

1995-1999 (N=2860)

2000-2004 (N=4311)

2005-2009 (N=3909)

2010-2014 (N=3552)


599.64 706.81 638.93 737.27 761.31

Expense Ratio 0.99% 1.04% 1.22% 1.20% 1.19%

Age (years) 8.33 6.50 6.80 8.79 11.08


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%

Mid-Cap Averages Over Time

1990-1994 (N=42)

1995-1999 (N=110)

2000-2004 (N=158)

2005-2009 (N=366)

2010-2014 (N=597)


295.52 625.54 660.19 573.94 454.81


Expense Ratio 1.28% 1.36% 1.45% 1.43% 1.38%

Age (years) 8.86 6.12 6.59 6.87 8.80


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%

Small-Cap Averages Over Time

1990-1994 (N=43)

1995-1999 (N=140)

2000-2004 (N=280)

2005-2009 (N=416)

2010-2014 (N=642)


295.12 313.89 296.00 305.73 289.02

Expense Ratio 1.37% 1.28% 1.45% 1.49% 1.44%

Age (years) 6.57 5.54 5.15 6.62 8.51


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%


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.


Appendix III: Regressions

Large-Cap Funds, 1990-1994

Large-Cap Funds, 1995-1999


Large-Cap Funds, 2000-2004 Large-Cap Funds, 2005-2009


Large-Cap Funds, 2010-2014 Mid-Cap Funds, 1990-1994


Mid-Cap Funds, 1995-1999 Mid-cap Funds, 2000-2004 Mid-cap Funds, 2005-2009 Mid-Cap Funds, 2000-2004


Mid-Cap Funds, 2005-2009 Mid-Cap Funds, 2010-2014


Small-Cap Funds, 1990-1994 Small-Cap Funds, 1995-1999


Small-Cap Funds, 2000-2004 Small-Cap Funds, 2005-2009


Small-Cap Funds, 2010-2014


VIII. References Adkisson, J.A. and Fraser, Don R. 2003. “Reading the Stars: Age Bias in Morningstar Ratings,” Financial Analysts Journal, September 59 (5): 24-27. Berk, J. and Green, R. 2004. "Mutual Fund Flows and Performance in Rational Markets," Journal of Political Economy, December 112 (6): pp. 1269-1295. Blake, Christopher R. and Morey, Matthew R. 2000. “Morningstar Ratings and Mutual Fund Performance,” Journal of Financial and Quantitative Analysis, September 35 (3): pp. 451-483. Brown, Stephen J. and Goetzmann, William N. 1995. “Performance Persistence,” The Journal of Finance,” June 50 (2): pp. 679-698. Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance,” The Journal of Finance, March 52 (1): pp. 57-82. Daniel, K., Grinblatt, M., Titman, S., and Russ Wermers. 1997. “Measuring Mutual Fund Performance with Characteristics-Based Benchmarks,” The Journal of Finance, July 52 (3): 1035-1058. Del Guercio, D. and Tkac, Paula A. 2008. “Star Power: The Effect of Morningstar Ratings and Mutual Fund Flow,” Journal of Financial and Quantitative Analysis, December 43 (4): pp. 907-936. Elton, Edwin J., Gruber, Martin J., and Christopher R. Blake. 1996. “The Persistence of Risk- Adjusted Mutual Fund Performance,” Journal of Business, April 69 (2): 133-157. Gerrans, Paul. 2006. “Morningstar ratings and future performance,” Accounting and Finance, October 46 (4): pp. 605-628. Golec, Joseph H. 1996. “The effect of mutual fund managers’ characteristics on their portfolio performance, risk, and fees,” Financial Services Review, June 5 (2): pp. 133-147. Investopedia Staff. 2016. “Understanding the Mutual Fund Style Box”. Last update date

unknown. http://www.investopedia.com/articles/basics/06/stylebox.asp, accessed February 2, 2016.

Morey, Matthew R. 2002. “Mutual Fund Age and Morningstar Ratings,” Financial Analysts Journal, March 58 (2): 56-63. Morey, Matthew R. 2003. “Kiss of Death: A 5-Star Morningstar Mutual Fund Rating?” The Journal of Investment Management, September 3 (2): 41-52.


Morningstar, Inc. 2016. “Fund Category Performance: Total Returns”. Last updated 2/2016. http://news.morningstar.com/fund-category-returns/, accessed October 10, 2015.

Morningstar, Inc. 2016. “Morningstar Style Box”. Last update date unknown.

http://www.morningstar.com/InvGlossary/morningstar_style_box.aspx, accessed February 10, 2016.

Wermers, Russ. 1997. “Momentum Investment Strategies of Mutual Funds, Performance Persistence, and Survivorship Bias,” unpublished. Wermers, Russ. 2000. “Mutual Fund Performance: An Empirical Decomposition into Stock- Picking Talent, Style, Transaction Costs, and Expenses,” The Journal of Finance, August 55 (4): pp. 1655-1703.

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