Chance of Beating the Stock Market – A 20-Year Look Back
Kenneth J. Simpson*
May 2010
Abstract
This study examines the likelihood of picking investment portfolio managers who can
beat market returns in the future, based on their past performance. The study selected the
best-performing U.S. equity mutual funds for the 10 years ending December 31, 1999,
and then examined their performance over the next 10 years ending December 31, 2009.
The study divides portfolio performance into policy (asset allocation) and strategy
(market timing and security selection) and seeks to determine the chances that strategy
can add to the return of the market. It finds that, depending on the method used to test
the funds, the chances of beating the market returns in the future by using funds or money
managers with prior excellent track records is at best 3 in 10 and statistically close to
zero. The preponderance of evidence suggests that the market handicaps stocks
efficiently enough that portfolio managers with excellent track records are unlikely to
repeat their performance and beat the market in the future.
* Kenneth J. Simpson received his Bachelor of Science degree in Accounting and Master of
Accountancy degree, with emphasis in Management Advisory Services, from Brigham Young
University. He has been a licensed CPA (Certified Public Accountant) since 1987 and holds the
Personal Financial Specialist (PFS) designation issued by the American Institute of Certified
Public Accountants. He is currently a Managing Director of Onyx Financial Advisors and may be
reached at 2400 East 25th Street, Idaho Falls, ID 83404, Phone: (208) 522-6400, email:
[email protected].
This study is not intended as a recommendation to buy or sell any investment and does not
constitute investment advice.
Objective of the Study
A common question asked, in one form or another, by investors is: “Are there investment
professionals who can beat the market?” Given the seemingly irrational fluctuation in
stock prices from day to day, it would seem logical that very smart money managers with
tremendous resources and expertise should be able to beat the average market return.
Every investment disclosure statement includes words to the effect: “Past performance is
not a guarantee of future results”. However, past performance still seems to be the single
dominant factor, for both individual investors and investment advisors, in the selection of
money managers. After all, many argue, what else have you got to rely on?
The objective of this study is to determine the chance of beating the stock market in the
future by investing with money managers who have beaten the market in the past.
Background Research
Over the past 60 years, significant research has been conducted on the determinants of
stock market returns. Beginning with the work published in 1952 by Harry Markowitz 1
on diversification and portfolio risk, Modern Portfolio Theory began to emerge.
In 1964, William Sharpe’s work on the Capital Asset Pricing Model (CAPM) was
published2. At this time, the CAPM starts to be used as a way to dissect the return earned
on an investment portfolio into the component parts of the return. First, the risk-free
component is accounted for. All investors have choices where to invest their money,
within a range of perceived low to high risk investments. It is reasonable to assume that
no investor would put money in a high risk investment unless he believed he could earn a
higher return than could be earned on a low risk investment. Although no investment is
truly risk free, one month U.S. Treasury Bills (T-Bills) have been used as an estimate of a
risk free return. The backing of the U.S. government gives them the highest credit rating
and, because the interest rate reprices every month, they have very little interest rate risk.
Next, the market component is determined. A calculation is made of the amount of
exposure the portfolio has to the stock market by determining how sensitive the change in
value of the portfolio is to changes in the value of the stock market as a whole. In
essence, this is determining how much you are getting paid to hold stocks instead of one
month T-bills. This is referred to as beta (β).
The last component is the part of the return that cannot be explained by the model. This is
referred to as alpha (α). This is the part of the return that successful money managers
attribute to their management skills.
In a simple example, assume you have two portfolios of stocks. Both are invested over
the same period of time, so both have earned the same risk free return. As well, assume
that both portfolios are invested in similar, although not identical, stocks and that the
sensitivity of both portfolios to changes in the overall stock market is similar to the stock
2
market in general; i.e., β = 1. During this period of time, assume that one month T-bills
earned 2% and assume that the stock market in total earned 8%. During this same time
period, portfolio A earned 8.5% and portfolio B earned 9.75%. Thinking about this
within the context of the CAPM, an investor may draw the following conclusion: The
manager of B did a better job of selecting stocks to buy, or when to buy them, than did
the manager of A.
Table 1. Breakdown of return using CAPM
A
B
Risk Free
2.00
2.00 Return of one month T-bills
β – risk factor
6.00
6.00 1*(total market less T-bills)
α
0.50
1.75 Unexplained return
Total Return
8.50
9.75
In 1992, Eugene Fama and Kenneth French expanded on the work of the CAPM3. Fama
and French determined that if you add two more risk factors to the CAPM, most of the
unexplained return goes away. These two additional risk factors are the amount of large
versus small company stocks (size factor) and the amount of value stocks versus growth
stocks (value factor) held in the portfolio. They developed the Three-Factor Asset
Pricing Model, which their research shows accounts for approximately 95% of the return
of the portfolio.
Going back to the example in Table 1, let us further assume that although both portfolios
hold all U.S. stocks, B is a small value company portfolio and A is a large growth
company portfolio. Also assume that during this period, within the stock market in total,
small companies had higher returns than did large companies and value companies had
higher returns than did growth companies. This being the case, we would expect
portfolio B to do better than A simply because the investment policy of portfolio B
happened to include a concentration within the market that performed better than the
market in total. Table 2 shows how the breakdown of the total return for both portfolios
might look in this example, under the Three-Factor Model.
Table 2. Breakdown of return using Three-Factor Model
A
B
Risk Free
2.00
2.00
Return of one month T-bills
β – risk factor 6.00
6.00
1*(total market less T-bills)
Size factor
0.30
1.00
Added return from small stocks
Value factor
0.00
0.55
Added return from value stocks
α
0.20
0.20
Unexplained return
Total Return
8.50
9.75
In this simplified example, an investor may draw the conclusion that nearly all of the
return in both portfolios was the result of the policy of each portfolio and that there was
virtually no unexplained return or alpha. Under this analysis, it would appear that neither
3
manager added any value outside of the investment policy. Portfolio B did better simply
because it took on more risk.
There is some disagreement about whether the Fama/French size and value factors truly
are risk factors. The data however, definitely show that small companies have
outperformed large companies and value companies have outperformed growth
companies over long periods of times. It also seems logical that a large company like
Microsoft could obtain capital at a lower cost than could a small company or that WalMart (a growth company) could obtain capital at a lower cost than K-Mart, coming out of
bankruptcy (a value company). In the end, the cost of capital to a company, less fees and
expenses, is the return on investment to an investor. Presumably, this difference in cost
of capital is at least an assumption of higher risk (a risk factor) on the part of the capital
markets.
In 1986, Gary Brinson, L. Randolph Hood, and Gilbert Beebower released a study of 91
pension plans (the Brinson study)4. Their study focused on which investment decisions
had the greatest impact on the return earned by the investor. They drew a distinction
between the investment policy and the investment strategy.
The investment policy is the asset allocation decision. First, how much of the portfolio
will be invested in bonds versus stocks? Second, within bonds, how much should be
invested in short-term, long-term, investment grade, and so on? Third, within stocks,
how much should be invested in U.S., international, large, small, value, and growth
companies?
The investment strategy is composed of market timing and securities selection. In other
words, the manager of a portfolio with an overall policy of 50% stock and 50% bonds
may decide to vary from this at times and over or underweight stocks, or hold cash. The
manager may also decide to over or underweight certain stocks at different times, relative
to the market.
The Brinson study concludes that 93.6% of the total return earned came from the
investment policy decision and only 6.4% came from investment strategy. This is
consistent with what one would expect based on the Fama/French findings. The Brinson
study suggests that the investment policy decision should be the primary focus for any
investor, as it will drive the vast majority of the overall performance of the portfolio.
Morningstar’s research arrives at that same conclusion. In their 2009 Ibbotson SBBI
publication, they state the following: “It is safe to say that, on average, the pension funds
and balanced mutual funds are not adding value above their asset allocation policy due to
their combination of timing, security selection, management fees, and expenses. Thus,
about 100 percent of the total return is explained by asset allocation policy”5.
Based on the research cited here, and other research with similar findings, most financial
advisors today agree that the single largest determinant of the performance of a
diversified investment portfolio will be the asset allocation (investment policy) decision.
4
What they still do not agree on is whether or not you can expect to add additional return,
without taking on more market risk (beat the market), by using an active management
strategy.
Even though the active strategies in the Brinson study underperformed the market on
average by -1.10%, the range of added return from active strategies was from -4.17% to
+3.69%. L. Randolph Hood points out in a follow-up article6 that the original Brinson
study did not suggest that active management was not important and notes that some
active managers in the study did beat the market. He notes that anything you could do to
avoid losing active strategies and to find winning active strategies would be very
important in improving your overall investment return.
There is an obvious way to avoid losing active strategies and that is simply to avoid
active strategies altogether and only use passive investment strategies. However, by
doing this, you also give up any chance of beating the market by finding an active
strategy that will outperform the market in the future. It is to this end that the present
study has been undertaken; to try to determine what the chances are of finding an
excellent money manager in the future, based on their excellent performance in the past.
Data and Method
Mutual funds were used for this study for two reasons. The first was the availability of
audited data and the second was that mutual funds are used by a large percentage of
investors. Whether a money manager is managing a portfolio for a pension, a mutual
fund, or a separately managed account, all of the decisions relating to investment policy
and strategy are the same.
The sample data to be studied were selected from the Morningstar database of mutual
funds as of December 31, 1999. There are 11,131 funds in this population. The
population was screened for funds with inception dates prior to December 1989; with
U.S. stock holdings over 80%, bond holdings under 5%, and other holdings under 5%
(the balance including cash and non-U.S. stock). This was done to limit the sample to
U.S. equity funds with a 10-year history. There are 513 funds that meet these criteria.
Next, the sample was sorted by Morningstar categories. Forty-nine funds were removed
with Domestic Hybrid and Specialty categories. That left 464 funds within the 9-box
Morningstar style categories ranging from large to small and value to growth.
Next, the 464 funds were sorted by their 10 year annualized return (not adjusted for
loads), within each of the nine Morningstar categories. The funds in each category were
compared to the following indexes:
5
Table 3. Performance by Asset Class - 10 years ending December 31, 1999
Category
Index
Index 10yr Number
Number
Annualized of Funds that beat
Return
the Index
Large Value
Russell 1000 Value
15.60 %
86
16
Large Blend
Russell 1000
18.13 %
105
21
Large Growth
Russell 1000 Growth
20.32 %
80
20
Mid-Cap Value
Russell Midcap Value
13.89 %
31
8
Mid-Cap Blend
Russell Midcap
15.92 %
34
13
Mid-Cap Growth Russell Midcap Growth 18.95 %
56
23
Small Value
Russell 2000 Value
12.47 %
21
7
Small Blend
Russell 2000
13.40 %
21
8
Small Growth
Russell 2000 Growth
13.51 %
30
27
Of the 464 funds, 143 outperformed the prior 10-year annualized return of the
comparable market index. Two of the Small Growth funds were dropped from the study
because the monthly return data were incomplete, resulting in a sample of 141 funds for
the study.
No attempt was made to eliminate passive funds from the sample. There is a large range
in management strategies. Some active strategies have very high turnover, almost to the
level of day-trading, while other active managers have very low turnover, even trying to
mimic an index (closet indexers). There are also several different passive approaches.
For example, Vanguard Extended Market Index (VEXMX) seeks to track a specific index
as closely as possible (index fund). Another fund in the sample, DFA US Micro Cap
(DFSCX), uses a passive approach but does not seek to track an index. Instead, this fund
is designed to capture the returns of the smallest companies in the U.S. stock market.
Instead of trying to draw a line between active and passive strategies, the study looks at
all the funds simply to try to determine the chances of a repeat excellent performance
regardless of the strategy.
See appendix A for a list of the 141 funds included in the sample. Note that many of the
funds changed names over the 20-year period. The fund ticker symbol has been used as
the key sort field.
Limitations of Study
Einstein’s quest was to develop a “Theory of Everything” that would, once and for all,
explain all the forces of the universe. This study is not that ambitious. There are many
limitations to this study and the results should be viewed within the context of those
limitations. Here are some of the limitations that should be kept in mind with regard to
this study:
1. The Morningstar Categories assigned to each fund are based on the determination
of Morningstar and may or may not be the best classification for each fund.
6
2. Within an asset class category, a significant variation in weights exists between
large versus small and value versus growth companies.
3. Many of the funds have style drift, moving back and forth between different
categories over time. This may be considered a change in investment policy, but
it more likely represents a strategy decision on the part of the manager.
4. The Russell indexes that are used may or may not be the best indexes to measure
fund performance against. They were picked because they are well known and
good data are available for an index in each of the nine Morningstar style boxes
over the 20-year period covered in the study.
5. This study only covers one period of time. The results would be more robust if
multiple periods of time were studied under the same parameters.
Analysis and Findings
The 141 funds in the sample represent the best performing funds within their asset classes
over a 10-year period. These funds were studied over the next 10-year period ending
December 31, 2009, to determine how many of them could repeat their excellent
performance. This should give us some idea of the odds of picking future outperforming
funds based on past performance.
Test One – How many of the funds survived the next ten years?
Of the 141 funds in the sample, only 104 funds (74%) survived to December 31, 2009.
This study did not examine the non-survivors to determine why they did not survive.
Some attrition would be the result of mergers and acquisitions by fund management
companies as well as a host of other reasons. However, because most funds advertise
based on their performance record, it seems unlikely that a management company with a
winning long-term performance would let go of the fund’s symbol.
Test Two – How many beat the market based on asset class indexes?
Of the 141 funds that beat their market index for the 10 years ending December 31, 1999,
only 44 (31%) repeated and beat the same index for the next 10 years ending December
31, 2009. It is interesting to note that 31% (143 funds out of 464 funds) is the same
percentage of funds that beat their index in the 10 years ending December 31, 1999.
These two results are not, however, directly comparable because the period ending
December 31, 1999 includes survivor bias (the 464 funds include only those funds that
survived to December 31, 1999).
7
Table 4. Performance by Asset Class - 10 years ending December 31, 2009
Category
Index
Index 10yr Number
Number
Annualized of Funds that beat
Return
the Index
Large Value
Russell 1000 Value
2.47 %
16
1
Large Blend
Russell 1000
-.49 %
21
4
Large Growth
Russell 1000 Growth
-3.98 %
20
12
Mid-Cap Value
Russell Midcap Value
7.57 %
8
2
Mid-Cap Blend
Russell Midcap
4.98 %
13
2
Mid-Cap Growth Russell Midcap Growth -.52 %
23
3
Small Value
Russell 2000 Value
8.27 %
7
3
Small Blend
Russell 2000
3.51 %
8
6
Small Growth
Russell 2000 Growth
-1.38 %
25
11
You cannot assume from this test that 31% of the funds beat the market through active
management. As noted in the Data and Methods section of this study, the data include
passive as well as active managed funds. In fact, some of these funds, including the DFA
US Micro Cap and Vanguard Small Cap Index funds, are included in the 44
outperforming funds and both have passive strategies. In the example of these two funds,
their outperformance must be explained by the fact that they are not holding exactly the
same segment of the market as the comparable index. In other words, they have a
different asset allocation policy. Therefore, it is reasonable to assume some number less
than 31% of the funds outperformed through active strategies.
Many of the funds changed asset class category from 1999 to 2009. The results in this
test would assume that investors picking a fund in 1999 would have done so based on the
asset class they wanted to invest in. In each asset class, the investors could choose a
passive market strategy fund or pick a fund that they thought could beat the market. As it
turned out, only 3 in 10 would have picked a fund that did that.
Of the 44 funds that outperformed the market index, only 22, or 16%, of the total did so
with a standard deviation in monthly returns less than the index. This would indicate that
half of the funds that outperformed did so by taking on more risk than the market within
the asset class.
As noted, many of the funds transitioned from one asset class to another from 1999 to
2009. One explanation for this would be the foresight of the money manager to change
the investment policy and move the portfolio to an asset class that is believed to do better
in the future than the asset class it was in. Investors may be comfortable buying a fund
that does this, if they are trying to find a manager who will seek the best return overall,
regardless of asset allocation. This brings us to test number three.
Test Three – How many managers transitioned to a better asset class?
Of the 104 surviving funds, only 3 transitioned to an asset class (Morningstar Category)
that did better than the asset class they started in on December 31, 1999. The rest either
8
stayed consistent, or moved to a worse performing asset class. In addition, the Russell
2000 Value index had the highest return of the nine market indexes included in the study
for the 10 years ending December 31, 2009, and only 5 of the surviving 104 funds were
able to beat that index. These results would appear to suggest that it is very unlikely that
fund managers are any good at picking which segment of the market will be the best
performing in the future.
Test Four – How many managers added alpha value?
Regression models like the Fama/French Three Factor Model are used to break down the
return of a portfolio and determine how much of the return can be explained by the asset
allocation policy. In other words, how much of the portfolio’s return is the result of
holding large versus small and value versus growth company stocks? Even though alpha
is a measure of the unexplained portion of the return of a portfolio, many claim that alpha
is the amount of the return that is the result of the manager’s strategy.
Let us assume that a portfolio manager has determined an investment policy (the asset
allocation decision) and that the investor has agreed with that policy. The only thing left
is to decide what strategy is going to be used. An investor could choose a passive
strategy, which would result in an alpha near zero or could choose an active strategy.
The active strategy will try to achieve an above market return through a combination of
market timing and security selection.
With this in mind, the Fama/French Three Factor Model was used to do a regression
calculation on each fund to determine how many fund managers could provide additional
return above the market in both 10-year periods.
R = Rf + β(Rm – Rf) + s(SMB) + h(HML) + α
where:
R is the return of the portfolio
Rf is the risk free return
β is the exposure to the total stock market
Rm is the total stock market return
s is the exposure to small company stocks
SMB is small minus big company stock factor
h is exposure to value company stocks
HML is high minus low book-to-market stock factor
α is the unexplained return (alpha)
Because this test analyzes the alphas for each fund in each 10-year period, this could only
be done on the 104 surviving funds. Therefore, there is survivor bias in this test, meaning
that the results would most likely show a lower percentage of funds with positive alpha if
it was possible to test the full population.
9
Of the 104 funds, 81 funds (78%) had a positive alpha for the 10 years ending December
31, 1999. This high number would be expected due to the fact that these funds were
selected because they had outperformed their market indexes over that period of time.
Only 13 out of 104 funds (13%) had an alpha t-statistic greater than two.
In this calculation, the term t-statistic refers to a statistical measure of the degree of
reliability of the alpha number. When the t-statistic is higher, more reliance can be
placed that the alpha is a strong indication that something other than market factors is
affecting the return. Even when alpha is positive, if the t-statistic is less than two, you
cannot rely on alpha to represent a meaningful number that adds value above the market
factors.
For the 10 years ending December 31, 2009, 46 funds (44%) had a positive alpha and
only 31 funds (30%) had a positive alpha in both 10-year periods. There was not a single
fund that had a positive alpha with a t-statistic greater than 2 in both 10-year periods.
The best that could be said was that there was about a 3 in 10 chance of picking a fund
that will outperform the market in the future, based on excellent performance in the past.
Based on the t-statistic, the odds are close to zero.
Conclusion
The results of this study suggest that the odds of picking a portfolio manager (or
management team) that can outperform the market in the future based on past
performance are very unlikely and, in all reality, the chances are probably somewhere
between 30% and zero. This is consistent with the research on the determinants of
portfolio returns and other similar studies. If the best money managers cannot reliably
beat average market returns from period to period, the question remains why? What
makes this profession different than others?
The answer may lie in the handicapping factor. We normally think of handicapping
when it comes to sporting events. This is the practice of assigning advantage or penalty
to a player or team in order to equalize the chances of winning. Handicapping is also
done every day to every publicly traded stock on the stock market by millions of
investors, mostly through thousands of financial professionals – handicappers, if you will.
These handicappers are constantly studying companies and evaluating all accessible
information. As new information becomes available, they immediately factor it into the
price at which they are willing to buy or sell the company’s stock and the price of the
stock moves up or down. You can see this every day as you watch the price of a
company’s stock move within seconds after newsbreaks about their earnings, events,
competing companies, and market conditions in general. In 2008, we saw how fast
market prices reacted as problems in the banking industry came to light.
How good are these handicappers? This study and many others would suggest they are
very good – definitely not perfect, but good enough that the odds of profiting from their
imperfections are very slim. This would also explain why monkeys throwing darts seem
10
to do as well as professionals at picking winning stocks. We would expect that if the
handicappers have done a good job handicapping the stocks before the darts are thrown,
then any random group of stocks should have as good a chance of out-performing as any
other random group of stocks.
To put this into a sports analogy, in the 2008-2009 NBA season, Kobe Bryant scored a
total of 2,201 points and Derek Fisher scored 814 total points. Suppose you could buy an
ownership interest in any of the NBA players you wanted and that it would pay you one
dollar for every point scored in the upcoming season. In other words, treat points scored
like dollars of earnings of a company. A share of Bryant Inc. would most likely sell for
more than a share of Fisher Inc. If you paid $2,201 per share for Bryant Inc. and $814
per share for Fisher Inc. and both players scored the same points in the coming season as
the prior season, you would have the same return on both players. Those betting on these
players would use all information available (health issues, playing schedule, new
competition, and so on) to determine how much they are willing to pay for each player in
the NBA and a market price would develop.
There will be those who would think the handicappers or the market have mispriced the
players and they may put large bets on those players. If they guess right, they win; if they
bet on a player and he gets hurt, they can lose big. You can see how a monkey throwing
darts at a chart of NBA players should have as good a chance as anyone at picking a
group of players who will beat their own average in the coming season after this
handicapping is done.
This is exactly what is going on every day in the stock market. In order to outperform the
stock market, a money manager has to assume that he knows something the rest of the
market doesn’t or that the handicappers are wrong more times than they are right. In
addition, he has to assume that the margin of error is large enough to overcome the fees
and expenses that come with active management. The preponderance of evidence
suggests this not to be the case.
For the investor, the implications from this study would be to spend your time developing
the right investment policy and then to implement that policy through passive strategies
as efficiently as you can. This would include strategies that capture the returns of the
market within your asset allocation policy, while reducing fees and trading costs as much
as possible.
11
1
Harry Markowitz, Portfolio Selection – The Journal of Finance, March 1952.
William Sharpe, Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk,
Journal of Finance, September 1964.
3
Eugene Fama and Kenneth French, The Cross-Section of Expected Stock Returns – The Journal of
Finance, June 1992.
4
Gary P. Brinson, L. Randolph Hood, and Gelbert L. Beebower, Determinants of Portfolio Performance –
Financial Analysts Journal, July/August 1986.
5
Morningstar 2009 Ibbotson SBBI Classic Yearbook, p. 104.
6
L. Randolph Hood, Determinants of Portfolio Performance – 20 Years Later – Financial Analysts Journal,
September/October 2005.
2
12
Appendix A
List of the 141 funds studied over the 10 year period ending December 31, 2009. Funds are sorted by the Morningstar Category and 10 year annualized return for the 10 years ending December 31, 1999.
Data for 10 Years ending December 31, 1999
Ticker
LMVTX
NYVTX
VQNPX
GMCTX
SLASX
SRVEX
MPEQX
AHDEX
SAFQX
VGRIX
GTCEX
NBSSX
SHFVX
WQCEX
FALDX
NEFOX
MSIGX
FDETX
AVLFX
AGBBX
FSRPX
GPAFX
CUCAX
USBOX
HRCPX
FMAGX
FDFFX
CHTRX
FGRIX
DDSTX
OPPSX
PPSPX
MFRFX
ABCAX
FFIDX
VMRGX
FDEQX
SPECX
MGCAX
MEGBX
Fund Name
Legg Mason Value Prim
Davis NY Venture A
Vanguard Growth & Income
GMO U.S. Core III
Selected American
Victory Diversified Stock A
MAS Equity Instl
AHA Diversified Equity
Safeco Equity No Load
Chase Vista Growth & Inc A
Glenmede Tax Managed Equity
Neuberger Berman Focus
Smith Barney Fundmntl Val A
Wright Major Blue Chip Eqty
Federated American Leaders A
New England Grow and Inc A
Russell 1000 Value
Oppenheimer Main St Gr&Inc A
Fidelity Destiny II
AIM Value A
Alliance Growth B
Fidelity Sel Retailing
Guardian Park Avenue A
Warburg Pincus Cap Appr Comm
Quantitative Grth & Inc Ord
Heritage Capital Apprec A
Fidelity Magellan
Fidelity Retirement Growth
AIM Charter A
Fidelity Growth & Income
Dreyfus Disc Stock
Oppenheimer Growth A
Principal Pres S&P 100 PlusA
MFS Research A
AIM Blue Chip A
Fidelity
Vanguard Morgan Growth
Fidelity Disciplined Equity
Russell 1000
Spectra
Managers Captl Appreciation
MFS Emerging Growth B
Morningstar
Category
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Value
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Blend
Large Growth
Large Growth
Large Growth
Tot Return
Annlzd 10 Yr
21.56
18.77
18.48
18.46
18.07
17.54
17.52
17.44
17.19
17.15
16.81
16.67
16.18
16.04
15.84
15.63
15.60
22.58
21.56
21.28
20.67
20.04
19.87
19.68
19.10
19.09
18.92
18.88
18.43
18.37
18.33
18.28
18.27
18.23
18.21
18.16
18.14
18.14
18.13
29.21
25.26
24.93
Data for 10 Years ending December 31, 2009
Std Dev
10 Yr
20.70
18.05
16.26
15.35
18.61
15.76
17.11
15.96
16.60
16.05
15.52
21.25
15.04
15.74
14.95
15.03
12.89
17.49
18.30
18.40
20.45
22.71
16.67
18.06
17.95
16.99
17.47
20.01
16.51
14.87
16.01
17.59
16.10
17.17
15.76
15.31
18.11
16.39
13.53
28.97
25.23
30.36
Fund Name
Legg Mason Cap Mgmt Value C
Davis NY Venture A
Vanguard Growth & Income
Morningstar
Category
Large Blend
Large Blend
Large Blend
Tot Return
Annlzd 10 Yr
‐3.20
2.43
‐1.47
Std Dev
10 Yr
21.73
16.34
16.34
Selected American Shares S
Victory Diversified Stock A
Large Blend
Large Blend
2.24
2.67
16.19
17.39
AHA Diversified Equity I
Large Blend
1.11
15.46
JPMorgan Growth & Income A
Glenmede Strategic Equity
Neuberger Berman Focus Inv
Legg Mason ClearBridge Fdmtl Value A
Wright Major Blue Chip Equities
Large Value
Large Growth
Large Blend
Large Blend
Large Blend
‐0.28
‐1.17
0.59
1.69
‐3.07
15.77
15.11
25.86
17.72
15.58
Natixis Harris Associates Large Cp Val A
Large Blend
Oppenheimer Main Street A
Fidelity Advisor Capital Development O
Large Blend
Large Growth
‐1.38
2.47
‐0.82
‐1.43
17.70
16.21
16.10
17.08
AllianceBern Growth B
Fidelity Select Retailing
RS Large Cap Alpha A
Credit Suisse Large Cap Growth Comm
Quant Long/Short Ordinary
Eagle Capital Appreciation A
Fidelity Magellan
Fidelity Independence
AIM Charter A
Fidelity Growth & Income
Dreyfus Disciplined Stock
Large Growth
Consumer Discretionary (MCG)
Large Blend
Large Growth
Large Blend
Large Growth
Large Growth
Large Growth
Large Blend
Large Blend
Large Blend
‐5.06
3.04
‐2.38
‐3.94
‐5.14
0.13
‐2.27
0.04
‐0.91
‐4.09
‐1.65
19.71
20.10
17.94
18.47
17.37
19.74
18.87
25.03
16.96
15.95
16.00
MFS Research A
Large Blend
‐1.06
17.36
Fidelity
Vanguard Morgan Growth
Fidelity Disciplined Equity
Large Blend
Large Growth
Large Blend
Alger Spectra A
Managers AMG Essex Large Cap Growth
MFS Growth B
Large Growth
Large Growth
Large Growth
‐0.93
‐0.86
‐0.17
‐0.49
‐1.80
‐7.31
‐4.96
16.94
18.28
15.53
16.36
21.66
21.86
22.97
Appendix A
List of the 141 funds studied over the 10 year period ending December 31, 2009. Funds are sorted by the Morningstar Category and 10 year annualized return for the 10 years ending December 31, 1999.
Data for 10 Years ending December 31, 1999
Ticker
TWCUX
EQPGX
HACAX
FDGRX
IDETX
FCNTX
MCSCX
PVOYX
FBGRX
MIGFX
AFGPX
RBCGX
INIDX
INNDX
SMMIX
JANSX
SHRAX
FPPTX
SOPFX
FSDAX
MUHLX
FSRFX
SSHFX
RPFEX
FSAIX
FLISX
LOMCX
LMASX
GABVX
MPGFX
VCAGX
PARNX
FEAFX
CHCLX
VEXMX
GABAX
WHGRX
SACPX
ACEGX
Fund Name
American Cent Ultra Inv
Fidelity Adv Eqty Grth Instl
Harbor Capital Appreciation
Fidelity Growth Company
IDEX JCC Growth A
Fidelity Contrafund
MainStay Capital Apprec B
Putnam Voyager A
Fidelity Blue Chip Growth
MFS Massachusetts Inv Grth A
Alger Growth B
Reynolds Blue Chip Growth
AXP Growth A
AXP New Dimensions A
AIM Summit
Janus
Smith Barney Aggr Growth A
Russell 1000 Growth
FPA Capital
Strong Opportunity
Fidelity Sel Defense & Aero
Muhlenkamp
Fidelity Sel Transportation
Sound Shore
Davis Growth Opportunity B
Fidelity Sel Air Transport
Russell Midcap Value
Invesco Leisure
CGM Capital Development
Legg Mason Spec Invmnt Prim
Gabelli Value
Mairs & Power Growth
Chase Vista Capital Grth A
Parnassus
First Eagle Fund of Amer Y
Alliance A
Vanguard Extend Mkt Index
Gabelli Asset
Wayne Hummer Growth
Salomon Bros Capital O
Russell Midcap
Van Kampen Emerg Growth A
Morningstar
Category
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Value
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Blend
Mid‐Cap Growth
Tot Return
Annlzd 10 Yr
24.44
24.22
23.70
23.63
23.00
22.38
22.15
22.08
22.04
21.92
21.54
20.93
20.87
20.70
20.59
20.58
20.49
20.32
18.95
17.22
15.47
15.32
15.12
14.77
14.36
14.02
13.89
22.20
19.98
18.83
18.57
18.33
16.97
16.79
16.56
16.47
16.33
16.31
16.25
16.10
15.92
27.92
Data for 10 Years ending December 31, 2009
Std Dev
10 Yr
27.38
21.94
23.28
22.34
23.36
17.18
21.81
22.68
18.28
23.47
21.89
20.47
22.14
18.03
21.22
18.07
27.14
15.30
23.38
15.69
18.76
18.64
19.70
15.37
24.29
24.70
13.59
20.23
27.56
21.47
17.23
17.02
18.54
26.68
16.54
21.76
18.61
13.24
15.14
18.49
14.65
29.12
Fund Name
American Century Ultra Inv
Fidelity Advisor Equity Growth I
Harbor Capital Appreciation Instl
Fidelity Growth Company
Morningstar
Category
Large Growth
Large Growth
Large Growth
Large Growth
Tot Return
Annlzd 10 Yr
‐3.58
‐3.71
‐2.35
‐0.85
Std Dev
10 Yr
17.58
19.16
18.47
23.90
Fidelity Contrafund
Large Growth
3.17
13.82
Putnam Voyager A
Fidelity Blue Chip Growth
MFS Massachusetts Investors Gr Stk A
Alger Large Cap Growth B
Reynolds Blue Chip Growth
Large Growth
Large Growth
Large Growth
Large Growth
Large Growth
‐2.86
‐2.11
‐2.96
‐3.40
‐4.77
19.00
17.48
17.32
18.92
22.98
AIM Summit P
Janus J
Legg Mason ClearBridge Aggressive Gr A
Large Growth
Large Growth
Large Growth
FPA Capital
Wells Fargo Advantage Opportunity Inv
Fidelity Select Defense & Aerospace
Muhlenkamp
Fidelity Select Transportation
Sound Shore
Davis Opportunity B
Fidelity Select Air Transportation
Mid‐Cap Value
Mid‐Cap Blend
Industrials (LCB)
Large Value
Industrials (MCB)
Large Value
Large Growth
Industrials (MCV)
AIM Leisure Inv
Consumer Discretionary (LCG)
‐4.48
‐3.68
0.78
‐3.98
9.44
3.02
8.77
4.87
7.23
4.78
2.73
5.07
7.57
0.95
20.08
19.55
20.64
18.99
21.81
19.00
19.26
20.90
20.71
15.90
18.48
25.28
18.03
18.85
Legg Mason Cap Mgmt Special Inv C
Gabelli Value A
Mairs & Power Growth Inv
Mid‐Cap Blend
Mid‐Cap Blend
Large Blend
2.80
1.99
6.91
25.35
18.90
14.76
Parnassus
First Eagle of America Y
AllianceBern S/M Cap Growth A
Vanguard Extended Market Idx
Gabelli Asset AAA
Large Growth
Mid‐Cap Blend
Mid‐Cap Growth
Mid‐Cap Blend
Mid‐Cap Blend
1.47
5.63
‐1.73
1.71
4.05
20.76
12.94
25.42
21.74
16.15
Legg Mason ClearBridge Capital I
Large Growth
4.24
4.98
18.65
18.71
Appendix A
List of the 141 funds studied over the 10 year period ending December 31, 2009. Funds are sorted by the Morningstar Category and 10 year annualized return for the 10 years ending December 31, 1999.
Data for 10 Years ending December 31, 1999
Ticker
UNECX
FIDYX
PBHGX
FOCPX
CSTGX
PHSKX
NAPGX
PVISX
EAGAX
TWGTX
SHELX
BRWIX
HLGEX
FACAX
PHCPX
SEGAX
SCFIX
NBNGX
‐
SGWAX
FGSAX
KAUFX
HRTVX
ICSCX
USCIX
SKSEX
BSVIX
FAMVX
EEQFX
MPSCX
CMNWX
WGROX
DFSCX
SAFGX
PPCAX
NAESX
MASPX
RSEGX
POEGX
Fund Name
United New Concepts A
Invesco Dynamics
PBHG Growth
Fidelity OTC
AIM Constellation A
Phoenix‐Engemann Agg Gr A
Nicholas‐Apple Grth Eq A
Putnam Vista A
Evergreen Aggressive Gr A
American Cent Giftrust Inv
Shelby
Brandywine
One Group Mid Cap Growth I
Fortis Advant Capital Appr A
Phoenix‐Seneca Mid‐Cap A
SunAmerica Small Co Grth A
Seligman Capital A
Sit Mid Cap Growth
Dresdner RCM MidCap
SunAmerica Growth Opp A
Federated Growth Strat A
Kaufmann
Russell Midcap Growth
Heartland Value
UAM ICM Small Company
Barr Rosenberg US Sm Cap Ins
Skyline Special Equities
Berger Small Cap Value Inst
FAM Value
Eclipse Small Cap Value
Russell 2000 Value
MAS Small Cap Value Inst
WM Northwest A
Wasatch Growth
DFA U.S. 9‐10 Small Company
Safeco Growth No Load
JP Morgan U.S. Small Co
Vanguard Small Cap Index
Merrill Lynch Spec Value A
Russell 2000
RS Emerging Growth
Putnam OTC Emerging Growth A
Morningstar
Category
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
Small Value
Small Value
Small Value
Small Value
Small Value
Small Value
Small Value
Small Blend
Small Blend
Small Blend
Small Blend
Small Blend
Small Blend
Small Blend
Small Blend
Small Growth
Small Growth
Tot Return
Annlzd 10 Yr
24.82
24.08
23.64
22.86
21.16
21.14
20.86
20.66
20.39
20.32
20.12
19.95
19.84
19.79
19.71
19.64
19.62
19.60
19.40
19.19
19.04
19.03
18.95
16.39
16.20
15.66
14.10
13.86
13.34
12.78
12.47
18.62
18.29
16.73
15.07
14.91
14.59
14.20
14.05
13.40
28.52
24.22
Data for 10 Years ending December 31, 2009
Std Dev
10 Yr
23.97
23.68
33.87
22.93
25.01
24.96
26.63
21.34
28.05
34.37
27.59
23.02
22.32
32.02
21.41
29.73
23.11
24.09
21.92
24.45
21.81
24.36
17.70
19.41
17.57
18.47
18.14
17.33
14.95
16.09
14.50
21.49
22.71
19.04
20.10
24.85
22.98
19.67
20.58
17.27
36.21
33.44
Fund Name
Waddell & Reed New Concepts A
AIM Dynamics Inv
Morningstar
Category
Mid‐Cap Growth
Mid‐Cap Growth
Tot Return
Annlzd 10 Yr
0.35
‐3.50
Std Dev
10 Yr
21.46
26.30
Fidelity OTC
AIM Constellation A
Virtus Mid‐Cap Growth A
Large Growth
Large Growth
Mid‐Cap Growth
‐1.91
‐4.53
‐7.24
26.85
20.04
29.35
Putnam Vista A
Mid‐Cap Growth
‐4.26
25.35
American Century Giftrust Inv
Large Growth
‐1.90
27.36
Brandywine
JPMorgan Mid Cap Growth Sel
Hartford Small Cap Growth L
Mid‐Cap Growth
Mid‐Cap Growth
Small Growth
‐0.79
2.32
‐2.59
17.68
20.86
30.37
Seligman Capital A
Sit Mid Cap Growth
Mid‐Cap Growth
Mid‐Cap Growth
‐0.67
‐2.82
25.69
24.83
Federated Mid Cap Growth Strategies A
Federated Kaufmann K
Mid‐Cap Growth
Mid‐Cap Growth
Heartland Value
ICM Small Company
Small Value
Small Blend
‐2.20
6.05
‐0.52
8.87
9.70
22.52
20.09
24.49
20.40
19.42
Skyline Special Equities
Small Value
8.85
20.63
FAM Value Inv
Mid‐Cap Blend
6.53
14.32
Morgan Stanley Inst US Sm Cp Value I
Principal Capital Appreciation A
Wasatch Core Growth
DFA US Micro Cap I
Small Value
Large Blend
Small Growth
Small Blend
8.27
5.94
4.73
8.09
6.30
19.3
19.53
19.42
22.44
23.72
Vanguard Small Cap Index
BlackRock Value Opportunities I
Small Blend
Small Blend
RS Small Cap Growth A
Small Growth
4.36
5.70
3.51
‐5.71
21.56
21.22
21.54
30.86
Appendix A
List of the 141 funds studied over the 10 year period ending December 31, 2009. Funds are sorted by the Morningstar Category and 10 year annualized return for the 10 years ending December 31, 1999.
Data for 10 Years ending December 31, 1999
Ticker
JAVTX
AAGFX
ADSPX
DGRBX
TSEGX
USAUX
DELTX
POPCX
SSEPX
LAGWX
MSEMX
WAAEX
MGSEX
OBEGX
FEGAX
PRNHX
EKABX
VEXPX
KSCAX
SLFRX
EGRTX
MONTX
NCLEX
Fund Name
Janus Venture
AIM Aggressive Growth A
Pilgrim SmallCap Opp T
MSDW Developing Growth B
Hancock Small Cap Growth B
USAA Aggressive Growth
Delaware Trend A
PIMCO Opportunity C
UAM Sirach Spec Eqty Instl
Lord Abbett Developing Gr A
MSDW Instl Small Company A
Wasatch Aggressive Equity
Managers Special Equity
Oberweis Emerging Growth
First American Sm Cap Grth A
T. Rowe Price New Horizons
Evergreen Small Co Growth B
Vanguard Explorer
Kemper Small Cap Equity A
Seligman Frontier A
Evergreen Growth C
Monetta
Nicholas Limited Edition
Russell 2000 Growth
Morningstar
Category
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Small Growth
Tot Return
Annlzd 10 Yr
24.13
23.04
22.11
21.46
21.45
20.64
20.59
20.55
20.31
19.44
19.43
18.64
18.39
18.28
18.06
18.01
17.61
15.67
15.51
15.50
15.16
14.62
13.61
13.51
Data for 10 Years ending December 31, 2009
Std Dev
10 Yr
27.25
28.43
27.81
30.88
27.72
30.78
26.20
29.14
27.01
25.28
24.77
23.87
21.14
34.17
23.15
24.34
27.84
21.07
23.97
24.09
22.31
23.21
17.48
21.14
Fund Name
Janus Venture J
Morningstar
Category
Small Growth
Tot Return
Annlzd 10 Yr
‐3.54
Std Dev
10 Yr
28.70
Morgan Stanley Mid Cap Growth B
Mid‐Cap Growth
‐1.47
26.31
USAA Aggressive Growth
Delaware Trend A
Allianz OCC Opportunity C
Large Growth
Small Growth
Small Growth
‐6.08
‐0.41
‐0.37
25.91
25.58
29.80
Lord Abbett Developing Growth A
Small Growth
0.55
23.42
Wasatch Small Cap Growth
Managers Special Equity Managers
Oberweis Emerging Growth
Small Growth
Small Growth
Small Growth
6.37
‐0.25
‐0.93
22.43
22.57
29.81
T. Rowe Price New Horizons
Evergreen Mid Cap Growth B
Vanguard Explorer
Small Growth
Mid‐Cap Growth
Small Growth
3.31
‐2.36
3.35
24.08
23.85
21.98
Seligman Frontier A
Evergreen Growth C
Monetta
Nicholas Limited Edition I
Small Growth
Small Growth
Large Growth
Small Growth
‐1.80
1.98
‐0.86
3.14
‐1.38
22.54
23.49
26.22
19.22
25.48