If historical returns provide useful insights when it comes to identifying skillful active managers, then surely those funds with the longest track records of success are most likely to be the “winners” of the future. I tested this hypothesis using data from Jeremy Siegel’s Stocks for the Long Run, an investment classic.
The 2014 edition contains a list of the 10 best-performing domestic mutual funds over the 41-year period from 1972 through 2012. Here’s the list for that period with their returns compared to the return of the S&P 500 Index.
|
Fund
|
Return (%) 1972-2012
|
|
Sequoia
|
14.2
|
|
Mutual Shares Z
|
13.7
|
|
Fidelity Magellan
|
13.6
|
|
Columbia Acorn
|
12.9
|
|
T. Rowe Price Small Cap
|
12.9
|
|
Fidelity Contra
|
12.4
|
|
Davis NY Venture Fund A
|
12.4
|
|
Invesco A
|
12.3
|
|
Fidelity Advisors Diversified O
|
12.2
|
|
Janus Fund D
|
12.1
|
|
S&P 500 Index
|
10.0
|
While the returns of these funds were clearly impressive, Siegel noted that “chance may have played a role.” For example, he determined that the odds that a fund would outperform the S&P 500 by 4% or more were one-in-12. The full sample Siegel examined was 86 funds. Thus, randomly, seven should have been expected to outperform at that level, yet only one did.
On the other hand, Siegel determined that Fidelity Magellan’s performance from 1977 through 1990, under the direction of Peter Lynch, wasn’t due to luck. The fund outperformed the S&P 500 by an incredible 13% per year. The probability of that occurring was one in 500,000.
That leaves us with a question: What, if anything, do the returns of these 10 best-performing funds over a very long period tell us about future performance? Is the past predictive of future results? The fact that these 10 funds had assets under management of $200 billion at the end of 2015 seems to tell us that investors certainly believed that persistence in performance exists.
The evidence
The table below, based on Morningstar data as of December 31, 2015, provides evidence on how Siegel’s 10 “superstar” funds have performed over the most recent five- and 10-year periods relative to comparable offerings from two leading providers of passively managed mutual funds, Vanguard (with their index funds) and Dimensional Fund Advisors (with their structured-asset-class portfolios). (Full disclosure: My firm, Buckingham, recommends Dimensional funds in constructing client portfolios.)
|
Fund
|
Symbol
|
AUM Billions
($)
|
Expense Ratio
(%)
|
Annualized Return
2011-2015
(%)
|
Annualized Return
2006-2015
(%)
|
|
U.S. Large Growth
|
|
|
|
|
|
|
Sequoia
|
SEQUX
|
6.7
|
1.00
|
11.9
|
7.6
|
|
Janus Fund D
|
JANDX
|
8.2
|
0.77
|
12.1
|
7.2
|
|
Davis NY Venture Fund A
|
NYVTX
|
13.7
|
0.86
|
9.6
|
5.5
|
|
Fidelity Contra
|
FCNTX
|
109.6
|
0.64
|
11.7
|
8.7
|
|
Fidelity Magellan
|
FMAGX
|
15.8
|
0.68
|
10.9
|
5.5
|
|
Average
|
|
|
0.79
|
11.2
|
6.9
|
|
Vanguard Growth Index Fund
|
VSGAX
|
15.6
|
0.09
|
10.2
|
8.3
|
| |
|
|
|
|
|
|
U.S. Large Value
|
|
|
|
|
|
|
Fidelity Advisor Diversified Stock O
|
FDESX
|
1.8
|
0.50
|
11.1
|
6.6
|
|
Mutual Shares Z
|
MUTHX
|
15.4
|
0.77
|
8.5
|
5.0
|
|
Invesco Comstock A
|
ACSTX
|
12.3
|
0.82
|
10.1
|
5.9
|
|
Average
|
|
|
0.70
|
9.9
|
5.8
|
|
Vanguard Value Index Fund
|
VVIAX
|
37.9
|
0.09
|
11.7
|
6.5
|
|
DFA U.S. Large Cap Value III Portfolio
|
DFUVX
|
2.9
|
0.13
|
12.1
|
6.8
|
| |
|
|
|
|
|
|
U.S. Small Cap Growth
|
|
|
|
|
|
|
T. Rowe Price Small Cap
|
OTCFX
|
8.8
|
0.91
|
10.9
|
8.6
|
|
Columbia Acorn
|
LACAX
|
7.3
|
1.08
|
7.6
|
6.6
|
|
Average
|
|
|
1.00
|
9.3
|
7.6
|
|
Vanguard Small Cap Growth Index Fund
|
VSGIX
|
15.6
|
0.08
|
10.2
|
8.4
|
The following is a summary of the data:
- Compared to the benchmark Vanguard index funds, over the most recent five-year period, five of the 10 top-performing active funds outperformed. Equal-weighting the active funds in each of the three asset classes (large growth, large value and small growth) and also equal-weighting the three asset classes, a portfolio of the 10 active funds would have returned 10.1%. An equal-weighted Vanguard index fund portfolio would have returned 10.7%, outperforming the active fund portfolio by 0.6%. The underperformance was basically explained by the difference in expense ratios (an averaged of 0.74%).
- Compared to the benchmark Vanguard index funds, over the most recent 10-year period, just three of the 10 top-performing active funds outperformed. Equal-weighting the active funds in each of the three asset classes (large growth, large value and small growth) and also equal-weighting the three asset classes, a portfolio of the 10 active funds would have returned 6.8%. An equal-weighted Vanguard index fund portfolio would have returned 7.7%, outperforming the active fund portfolio by 0.9%. Again, the difference in returns is basically explained by the difference in expense ratios.
- Compared to the benchmark DFA large-value fund, over the most recent five-year period, all three of Siegel’s top-performing active funds underperformed. An equal-weighted portfolio of the three active funds underperformed the DFA fund by 2.2 % per year (9.9% versus 12.1%). Note that the underperformance was greater than the 0.57% difference in expense ratios.
- Compared to the benchmark DFA large-value fund, over the most recent 10-year period, none of the three active funds outperformed. An equal-weighted portfolio of the three active funds underperformed the DFA fund by 1.0% per year (5.8% versus 6.8%). Once again, the underperformance was greater than the 0.57% difference in expense ratios.
Factor analysis
I’ll now take another look at the performance of Siegel’s 10 top-performing actively managed funds using the analytical tools and data available at Portfolio Visualizer. Factor analysis helps provide important additional insights into a fund’s performance because Morningstar asset-class categories are very broad and actively managed funds can style drift. The table below shows the results of the three-factor (beta, size and value), four-factor (adding momentum) and six-factor (adding quality and low beta) analyses. The data covers the period from October 2005 through September 2015. Each t-statistic is in parentheses.
|
Fund
|
Symbol
|
Three-Factor Alpha
(%)
|
Four-Factor Alpha
(%)
|
Six-Factor Alpha
(%)
|
|
Sequoia
|
SEQUX
|
4.3
(2.1)
|
4.2
(2.1)
|
1.0
(0.5)
|
|
Janus Fund D
|
JANDX
|
-2.0
(-1.8)
|
-1.6
(-1.3)
|
-1.6
(-1.3)
|
|
Davis NY Venture Fund A
|
NYVTX
|
-0.1
(-0.1)
|
0.2
(0.2)
|
-0.8
(-0.8)
|
|
Fidelity Contra
|
FCNTX
|
3.2
(2.3)
|
1.7
(1.6)
|
0.1
(0.1)
|
|
Fidelity Magellan
|
FMAGX
|
-3.2
(-2.7)
|
-1.8
(-1.6)
|
-1.7
(-1.5)
|
|
Fidelity Advisor Diversified Stock O
|
FDESX
|
-1.5
(1.6)
|
-1.2
(-1.3)
|
-1.8
(-1.8)
|
|
Mutual Shares Z
|
MUTHX
|
1.2
(1.1)
|
1.2
(1.1)
|
-1.2
(-1.0)
|
|
Invesco Comstock A
|
ACSTX
|
1.1
(0.8)
|
1.9
(1.5)
|
0.4
(0.3)
|
|
T. Rowe Price Small Cap
|
OTCFX
|
-0.7
(-0.5)
|
0.6
(0.5)
|
1.6
(1.2)
|
|
Columbia Acorn
|
LACAX
|
1.9
(1.5)
|
1.0
(0.8)
|
-0.3
(-0.2)
|
|
Average
|
|
0.4
|
0.6
|
-0.4
|
When we examine the results from the three-factor analysis, we find that five of the 10 top-performing active funds generated positive alphas. The average annual alpha of all 10 was 0.4%. Only three of the funds showed statistically significant alpha at the 5% level, two being positive and the other being negative.
When we look at the results from the four-factor analysis, we find that seven of the 10 active funds generated positive alphas. The average alpha of all 10 funds was 0.6%. Just one of the active funds showed statistically significant positive alpha at the 5% level.
When we include all six factors in our analysis, we find that four of the 10 active funds now show positive alphas. And the average annual alpha was negative at -0.4%. In addition, none of the alphas were statistically significant. In other words, the positive alphas we found in the three- and four-factor analyses were because of the active funds’ loading on quality and low-beta stocks, not a result of individual stock-selection or market-timing skill.
Summary
The bottom line is that, despite the evidence provided by a very long period of outstanding performance (1972-2012), over the most recent five- and 10-year periods, as a group these superstars (currently with $200 billion in AUM) have underperformed comparable passively managed funds from both Vanguard and DFA. In other words, Jeremy Siegel was justifiably guarded (warning that their results might have been a result of luck) when it came to determining whether the past performance of the 10 top performers would persist.
While it’s often hard to determine if a fund’s success is a result of skill or luck, we do know that the success of actively managed funds contains the seeds of its own destruction. Jonathan Berk, in his paper “The Five Myths of Active Management,” suggested the following thought process: “Who gets money to manage? Well, as investors know who the skilled managers are, money will flow to the best manager first. Eventually, this manager will receive so much money that it will impact the manager’s ability to generate superior returns, and expected return will be driven down to the second-best manager’s expected return. At that point, investors will be indifferent between investing with either manager, so funds will flow to both managers until their expected returns are driven down to the third-best manager. This process will continue until the expected return of investing with any manager is driven down to the expected return investors can expect to receive by investing in a passive strategy of similar riskiness (the benchmark expected return). At this point, investors are indifferent between investing with active managers or just indexing, and an equilibrium is achieved.”
Berk went on to point out that the manager with the most skill ends up with the most money. He then added: “When capital is supplied competitively by investors but ability is scarce only participants with the skill in short supply can earn economic rents. Investors who choose to invest with active managers cannot expect to receive positive excess returns on a risk-adjusted basis.” If they did, “there would be an excess supply of capital” to those managers.
Finally, there is a second reason successful active management sows the seeds of its own destruction. As a fund’s assets increase, either trading costs will rise or the fund will have to diversify across more securities to limit trading costs. However, the more a fund diversifies, the more it looks and performs like its benchmark index. It becomes what’s known as a closet index fund. If it chooses this alternative, the fund’s higher total costs have to be spread across a smaller amount of holdings that distinguish its composition from the index, increasing the hurdle of outperformance.
Disclosure: The returns data included is from Morningstar. Performance is historical and does not guarantee future results. Information comes from sources deemed reliable but its accuracy cannot be guaranteed. It should not be assumed that any of the securities listed were or will prove to be profitable.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.
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