The most
common form of active management is stock picking. On average, stock
pickers will always lose by the amount of their costs and expenses.
Some will do better and some will do worse than an appropriate or blended
benchmark of risk factors. Since the average return of the market is
the average return of all investors, the average investor gets the average
return. Although you may think that you can choose or be the investor
who beats the others, the probability of a money manager outperforming
other managers each year is equal to getting heads on the toss of a
coin: 50/50. This is because the markets are random, just like a coin
toss. The chances of a manager beating a market over the long term (more
than 10 years) were 1 in 36 in one study, and 1 in 39 in another 30-year
study! You would be better off betting on one number on the roulette
table in Vegas, where odds are 1 in 38. So far, we have collected over
200 articles measuring the performance of active managers, starting
with Alfred Cowles in 1933, and the results are not good for active
management.
3.2
Definitions
3.2.1
Stock Pickers
Stock
pickers are active investors who bet they can beat a market by picking
stocks they believe will outperform an index. To be precise, the only
proper comparison to their result is the portfolio they choose. All other
portfolios will end up with different risk and return characteristics.
Generally, stock pickers take on more risk than the index because they concentrate their bets on fewer stocks than those in the index. When
they allocate their portfolio differently than the index, they are guaranteed
to obtain a different return as well as a different risk level. Sometimes it is more and
sometimes it is less, but we can always assume it will be different when
looking at both risk and return. Since it takes at least 20 years of risk
and return data to confirm skill over luck, stock pickers face a virtually impossible task in their ability to ensure continued success against the
appropriate market index. However, indexes are a source of 20-year risk
and return data, and consequently are the only logical choice for establishing
efficient portfolios of various levels of expected risks and returns.
3.2.2
Adjusted Performance
The performance of
stock pickers must be examined on an adjusted basis. This means that all
factors must be considered before we can determine if the stock picker
has achieved a benefit over an appropriate index or benchmark. When comparing
active management to an index, we must:
1.
Make sure we are talking about the entire portfolios for the exact same
period of time.
2.
Confirm proper accounting of the returns, including the cash flows in
and out of the account.
3.
Consider the state and federal taxes paid on short and long-term capital
gains and dividends.
4.
Consider all fees when assessing net return. Most funds report gross performance
before deduction of fees and commissions.
5.
Adjust for the portfolios' exposure to market risk, size risk, and value
risk factors.
6.
Consider the level of diversification of the two portfolios.
7. Analyze the standard deviations or volatility measurements.
8.
Consider if the over and underperformance is within the bounds of what
would be expected randomly.
9.
Be sure to compare results to an appropriate benchmark. Proper benchmark
specification avoids inflated performance reports.
10.
If looking at a group of stock pickers, be sure to include the returns
of those pickers that did not survive the duration of the period, usually
due to significant losses.
11.
Look at all active managers in an asset class, both those who stayed
in business and those who did not.
12.
Check to make sure the stock-picking manager did not drift in its designated style during
the period in question.
Making these comparisons requires a high degree of understanding of each
of the concepts listed above. To simplify your analysis, you may consider
that the only way to end up with a different performance than the index
is to own investments different than the index. Since an index is the
only source of long-term risk and return data, why would any investor
choose something other than the index? The only question should be: What
mix of indexes is appropriate for you?
Lets take a closer look at why stock pickers lose on average.
3.3
Problems
3.3.1 Why Stock Pickers Fail
The main reason that stock
pickers fail is that stock prices are moved by news, and news is unpredictable
and random in nature. Therefore, the movements of stock prices are unpredictable
and random.
This simple logic makes it impossible for any human being to consistently
pick stocks that outperform the averages of a market.
Secondly, the news that moves stock prices is incorporated into the new
price within minutes of its release. This adds a major hurdle for stock
pickers. It means they must compete with thousands of highly intelligent
and well-informed traders on a minute-by-minute basis.
John Stossel of ABC’s 20/20, reported a story on the perils of picking
stocks. Stossel interviewed Professor Burton Malkiel of Princeton University,
author of the book, "A Random Walk down Wall Street." In the interview,
Professor Malkiel said that stock markets historically deliver a performance
of 9.5% to 10% compounded per year over the long haul. Inquired Stossel:
“To beat that average, should an investor listen to the Wall Street
professionals?”
“No,” replied Professor Malkiel. “All the information
an analyst can learn about a company, from balance sheets to marketing
material, is already built into the stock price, because all of the other
thousands of analysts have the same information. What they don’t
have is the knowledge that will move the stock, knowledge such as a news
event, which is unpredictable and impossible to forecast.”
Indeed, an analyst can only guess about a future event, which is no different
than throwing a dart at a newspaper while blindfolded to find a stock.
Both events are unpredictable. The main purpose of the financial research
industry is to try to predict the future course of events since that is
the only thing that will drive future stock prices. If thousands of highly
intelligent, sophisticated analysts with degrees from top universities
and access to the best computing power available can’t predict the
future, what use are they? How can one company or research analyst have
more knowledge than another without it being inside information, which
would be illegal?
3.3.2The
Stock Picker's Graveyard
“Survivorship
bias” is one of the many reasons that stock pickers’ returns
look better than they actually are. Survivorship bias is when mutual fund
managers tout their fund’s performance based on comparisons with
an “average” mutual fund. This average is calculated from
a list of funds that have survived during a particular period. Funds that
did not survive the period are not included in the calculation. According
to the Center for Research on Securities Prices (CRSP) at the University
of Chicago, if only data from surviving funds is considered, the growth
of a dollar for the surviving funds appears to be 19% better since 1962.
If only “live growth and income funds” are considered over
this period, $100 appears to grow to about $2,500. However, the only way
to properly account for all active managers is to include those mutual
funds that did not survive. When taking these dead funds into account,
CRSP found that the average stock picker’s $100 investment grew
to only about $2,100.
Rex
Sinquefield
Difficulties
of Stock Picking
Part 1
Rex
Sinquefield
Difficulties
of Stock Picking
Part 2
The Center for Research on Securities Prices (CRSP)
at the University of Chicago has the only complete database of both live
and dead mutual funds.
Mark
M. Carhart, currently Co-Head of Quantitative Research, Goldman Sachs
Asset Management, New York, developed this unique database for his 1995
Ph.D. dissertation at the University of Chicago Graduate School of Business.
In his dissertation, Survivor Bias and Persistence in Mutual Fund Performance,
he noted that the explosion in new mutual funds has been "...accompanied
by a steady disappearance of many other funds through merger, liquidation,
and other means...this data is not reported by mutual fund data services
or financial periodicals and in most cases is electronically purged from
current databases. This imposes a selection bias on the mutual fund data
available to researchers: only survivors are included.."
In estimating
the performance of an equal-weight index of equity mutual funds, Dr. Carhart
found that analyzing only surviving funds biases performance upward by
about one percent per year.
The CRSP database includes 24,725 mutual funds from 1961 to 2004. In that
time, 8,710 of the 24,725 mutual funds died. That means that more than 35% percent of
mutual funds data is not included in the average returns of active managers.
The active managers who run them happily bury most of the data about dead
funds. Is it possible that the 8,710 dead mutual funds had high returns
for their investors?
The financial press goes to great lengths to inform the public about active
managers with good luck, e.g., "Last Year's Top Ten Mutual Funds."
This kind of media reporting provides no data about those managers
who lose money taking chances in the market, then shutting their
doors and erasing their bad returns from the record. Well, we have bad
news for those mutual fund managers: we are exhuming their results. We
paid CRSP
$1,000 (it is expensive to dig up old corpses) to prepare a list of the
top
200 worst performing dead mutual funds going back to 1961. To our
surprise, this had never been done before. Here are the top twenty of
the worst performing dead mutual funds:
These two video clips are an explanation of survivorship bias by
Rex Sinquefield, Co-Chairman of Dimensional Fund Advisors.
Click on 1 then 2 to the left
In addition to funds
that die, there is an indeterminable number of funds that are aborted.
These funds are referred to as incubator funds, and are basically experiments
within a fund firm that never develop into a publicly available mutual
fund.
Upon their inception, the funds are not available to the public; therefore,
they are safe from public scrutiny. After a time, the fund shop rolls
out only the best performing funds. And some of the best performing funds have unusual reasons for performing so well, like limited access to IPOs, click here to see a SEC case on this issue.
Table
3-2
The stock picking
managers of these incubator funds tried something new and ended up with
a failure. This little known fact has yet to be quantified in the average
returns of stock pickers.
Finally, there are
the revolving doors of stockbrokers who are churning through clients and
constantly rotating from one firm to another. Their records are quickly
extinguished, never to be counted in the average of stock pickers.
In addition to dead mutual funds, there is a huge graveyard full of
dead companies. Table 3-2 lists the largest bankruptcies
from 1980 to 2008.
Two examples of bankruptcy filings help remind investors how easy it is
to pick the wrong stock. The first example is Bethlehem Steel. Bethlehem
Steel was founded by legendary steel tycoon Charles Schwab in 1904 in
Bethlehem, Pennsylvania. The company produced some of the nation’s
first steel railroad rails, revolutionized high-rise building construction
with the introduction of structural I-beams in 1908, and built the country’s
first aircraft carrier in 1925. Landmarks such as the Golden Gate Bridge,
the Chicago Merchandise Mart, Rockefeller Center and the U.S. Supreme
Court were constructed with Bethlehem steel. Peacetime employment reached
a peak of 157,000 workers in 1957, and profits hit a record $426 million
in 1988 and in 2001 the mammoth steel company filed for Chapter 11 bankruptcy.
Bethlehem was one of the stocks in the Dow Jones Industrial Average for
nearly 70 years until its replacement by Johnson & Johnson in 1997.
Its stock price reached a peak of nearly $60 in late 1959, and remained
in a broad trading range over the next 40 years as the firm struggled
to improve its competitive position through various acquisitions, divestitures,
and cost cuts.
Indeed, only three members of the original 30-stock Dow Average established
in October 1928 are still included in 2005: General Electric, General Motors,
and Standard Oil of New Jersey, now ExxonMobil.
Another example is Polaroid Corp., which was founded in 1937 by 28-year
old Harvard dropout Edwin Land. Land was a brilliant physicist and tireless
inventor who accumulated 535 patents by the time of his death in 1991
(second only to Thomas Edison). An early quest to solve the problem of
headlight glare led to a patented process for polarized glass, and a variety
of optical products for military and commercial use. The self-developing
Polaroid Land camera he ultimately developed was a marketing sensation
when introduced for $89.50 in 1948. A steady stream of improvements including
instant color film stoked consumer interest in the 1950s and 1960s.
Figure
3-1
Polaroid shares were
a favorite among aggressive investors, soaring more than 40-fold from
their initial public offering in 1957 to an all-time high of $149.50 in
1972 ($74.75 adjusted for a subsequent two-for-one split). The SX-70 camera,
which ejected prints that developed externally, was introduced the same
year. In addition, there was talk of a forthcoming instant movie system.
What’s more, in 1991, a successful patent infringement lawsuit against
Eastman Kodak appeared to vanquish the sole competitive threat in instant
photography. But Polaroid was unable to capitalize on the $925 million
judgment, and struggled to broaden its product line amidst the proliferation
of inexpensive 35mm cameras, one-hour photo kiosks and digital photography.
Eventually Polaroid filed for bankruptcy in 2001.
The moral of these stories is that success today does not ensure survival
tomorrow; therefore, investors need to diversify so that these kinds of
events will not destroy their portfolio.
In the book Creative Destruction, McKinsey & Company consultants Richard
Foster and Sarah Kaplan researched the original S&P 500, which was
created in 1957. The survival of companies is similar to the survival
of mutual fund managers. Figure 3-1 shows that in the
41 years from 1957 to 1998, only 74 of the original 500 companies were
still in existence and only 12 of those outperformed the S&P 500 Index
over the 1957 to 1998 period. The study found that the odds of picking
a winning stock that beat the S&P 500 Index was one in 42.
Most investors
operate under the misguided assumption that great companies are excellent investments. They believe that these companies
can defy the poor odds of beating the market. In fact,
almost the entire investment industry thrives on recommending a handful of “great stocks
to buy now”.The firms represented in Figure 3-1a are widely
considered to be industry leaders. They have been included at some
point among the top ten "Most Admired Companies" in Fortune's
annual survey.
The
figure depicts
the results of an IFA study which sets forth the total return comparisons
for the S&P 500 index, IFA Index Portfolios and those of highly regarded companies. October 1, 2002 was chosen as a starting point because it was near the lowest point of the S&P 500 over the last 10 years. As you
can see, not one of the admired companies came close to
the total return of 104% for the S&P
500 Index in the 5-year time period from October 1, 2002 through
September 30, 2007. The closest was Berkshire Hathaway, with a 59% return. The company's CEO, Warren Buffett, was named the World's Number One Billionaire for 2007, with a fortune valued at $62 Billion. The time proved to be even more profitable
for the IFA Index Portfolios (IP).
The small value heavily tilted all-equity IP 100 had a total return of 184%, while
the all-equity IP 90 gained 172%. IP 80, with 10% fixed income
to dampen volatility, gained 150%, while IP 70, with 20% fixed
income gained 130%. IP 60 had 30% fixed income and still returned
112%. Even more interesting, the Emerging Market Value Index, that has had about the same risk (standard deviation) as Berkshire Hathaway over a recent twenty year period, had a total return of 586% (not shown in the chart below). Index investors enjoyed a wonderful increase
in their portfolios, while investors of these admired stocks
missed out on an enormous profit opportunity.
Figure
3-1a
In Figure
3-1b, you can see the risk and return of the 30 Dow Jones Industrial
stocks, which include several of the stocks listed above, but over a 20
year period. Please note that for the risk taken, not one company exceeded
the return that would be expected based on the diagonal line that estimates
the appropriate return for the risk taken. This line is known as the Capital
Markets Line.
Figure 3-1b
Further research
demonstrating that good companies make bad investments is found in
a 1987 study titled, “In Search of Excellence: The
Investor’s Viewpoint,” investment analyst Michelle
Clayman compared the returns of 29 “excellent companies” with
39 “unexcellent companies.” Clayman’s idea
for this study originated from the 1982 best-seller In Search
of Excellence by Tom Peters and Bob Waterman, which described
43 successful U.S. companies of which 36 were publicly traded.
The book awarded companies an “excellent status” by
virtue of their profitability, employee satisfaction and overall
good working conditions, inspiring stock pickers nationwide to
believe that they too could use winning companies to make winning
investments. Clayman compared 29 of Peters and Waterman’s “innovative
and excellent” companies with 39 “unexcellent” companies
she selected. Her criteria for unexcellent companies included
those with terrible profitability and “Dark Ages” management.
Examples of her excellent companies included powerhouses such
as Johnson and Johnson, Intel, Merck and Disney; unexcellent
companies were made up of companies like U.S. Steel, American
Motors, Westinghouse Electric and F.W. Woolworth.
Figure 3-1c shows
that Clayman's excellent companies were stronger by every economic measure
than her unexcellent ones for the five-year period between 1981 through
1985. “The excellent companies have qualities we would all love
to see in our own companies, ”she observed.
Figure 3-1c
Clayman found, however,
that the unexcellent companies showed significantly greater returns over
the five years than their healthier counterparts. Figure 3-1d
illustrates that between 1981 and 1985, the unexcellent companies earned
investors a 298% total return while the excellent companies earned only
a 182% total return. The two portfolios had almost identical standard
deviations, so what made the unexcellent portfolio deliver such higher
returns to its investors? The discrepancy arises because the higher cost
of capital for unexcellent companies is paid to investors. Similar to
individuals who approach banks for loans, borrowers with strong credit
and payment histories will receive loans with lower interest rates (lower
costs of capital) than that of a riskier borrower. Less stable companies
end up paying higher costs of capital in exchange for their higher risk,
which translates to a lower stock price relative to book value and a higher
expected return for investors in those risky companies.
Figure 3-1d
3.3.4 Studies and Observations that Show the Daunting
Odds of Stock Picking
Merton
Miller
Merton
Miller from the Nova Special, The Trillion
Dollar Bet
The basic problem
with stock picking is revealed when we examine how stock pickers are
unable to beat a market over the long run. In a random and efficient
stock market, active investors are just gambling or playing a game of chance. The
money managers that run actively managed mutual funds are essentially gamblers,
paid by the unsuspecting shareholders, with a high average annual
fee of about 1.5%.
Gambling may be fun when you go to Vegas, but it is not how investors should invest
their hard earned money. Consider it this way: index funds investors invest like the owner of a casino, while the active investors
behave like the gamblers in the casino. Attempting to predict the stocks, times, or
managers that will perform the best is NOT a profitable expenditure
of time or money. Assembling a portfolio of indexes is a very different
story that has a guarantee of obtaining a low-cost, tax-efficient market
rate of return, which is better than about 95% of stock pickers over
10 or 30 year periods.
The
Trillion Dollar Bet
A survey of both the popular
and academic literature provides a crystal clear picture of the daunting
odds of stock picking. Robert Jeffrey and Robert Arnott published
a study titled “Is your
Alpha Big Enough to cover its Taxes?” In the study, 71
large cap
growth and growth and income active mutual fund managers were compared
to the S&P 500 over a period of 10 years from 1982 to 1991. Most
invested in styles that closely represented the S&P 500, but not one
was exact. Only two of these 71 managers beat the index. That
is a mere 3%! Had they all just invested in the S&P 500, they
would have equaled its return. For those investors who were invested
in either of the two funds that did beat the S&P 500, very few
enjoyed the full returns of these funds. This is because huge cash
inflows showed up in the last year of the time period, a typical sign
indicative of manager or stock picking. See Figure 3-2.
Figure
3-2
The odds
of throwing a two (snake eyes) at the craps table are the same as the
results of this study, one in 36. The least likely rolls of a pair of
dice are two and 12. The odds in roulette are one in 38 for picking a
one-number winner. Gambling in Las Vegas may lead to more success than
trying to find a manager who beats a chosen index at the beginning of
the period. Says John Bogle, founder of Vanguard: “Investors earn
a net return, after all of the costs of our system of financial intermediation.
Just as gambling in a casino is a zero-sum game before the croupiers rake
in their share and a loser’s game thereafter, so beating the stock
and bond markets is a zero-sum game before the intermediation costs, and
a loser’s game thereafter.”
The
odds of throwing a two (snake eyes) at the craps table are the same
as the results of this study, one in thirty-six or 2.77%.
Two and twelve are the least likely rolls of a pair of dice. Try
it on the image to the right.
Rex
Sinquefield
"Managers
Don't Beat Markets"
Rex Sinquefield, Co-Chairman DFA
Click
on the button in the bottom left corner and see how long it takes
you to get snake eyes. Dice
Experiments
To illustrate the
daunting odds of success for stock pickers, take a look at these studies.
Alfred
Cowles conducted one of the first recorded studies of stock pickers’
performance in a July 1933 article titled “Can Stock Market Forecasters
Forecast?” He concluded that it was “doubtful.”
In another study titled “Bogle on Equity Fund Selection,”
Bogle determined that only nine out of 355 equity funds beat their benchmark
over a period of 30 years. Interestingly, that is 2.5% or a one in 39
chance of choosing the correct mutual fund in advance. Another study using
CRSP data showed similar results. See Figures 3-3 and 3-4.
Figure
3-3
How do mutual fund managers do over thirty years?
In a similar analysis by a different firm and using a different
database and a slightly different time period, very similar
results were determined.
Figure
3-4
A study by Brad Barber of the University of California, Davis, titled
“Who Gains from Trade? Evidence from Taiwan,” showed that
82% of the 925,000 active traders on the Taiwan stock exchange lost $8.2
billion per year from 1995 to 1999.
Another stock picking study titled “The Importance of Investment
Policy,” conducted by Ronald Surz, Dale Stevens, and Mark Wimer
found that the market timing and stock picking done by active managers
had a predictably negative effect on returns. Active management created a negative
drag compared to a portfolio of index funds that most closely replicated
the active manager’s asset allocation. Adjustments were not made
for taxes in taxable accounts. These findings indicate
that asset allocation contributes to more than 100% of the expected return
of an actively managed portfolio.
The case against active management is clearly and logically spelled out
by Nobel laureate William Sharpe in an article titled “The Arithmetic
of Active Management.” In the article, Sharpe clearly states that
before costs, the return on the average actively managed dollar will equal
the return on the average passively managed dollar, and after costs the
return on the average actively managed dollar will be less than the return
on the average passively managed dollar.
The findings of another study by Sharpe titled “Asset Allocation:
Management Style and Performance Measurement, an Asset Class Factor Model
can Help Make Order out of Chaos” supported the hypothesis that
the average mutual fund cannot beat the market before costs. That’s
because such funds constitute a large and presumably representative part
of the market. Annualized, the mean underperformance is approximately
0.89% per year—an amount that is approximately equal to the costs
incurred by a typical mutual fund.
In a study titled “Are Investors Reluctant to Realize their Losses?”
Terrance Odean, using 10,000 random discount brokerage accounts, demonstrates
that the trading volume of discount brokerage clients is excessive. Overconfident
investors overestimate the amount of profit they can make and will thus
engage in costly trading, even though the profits will not cover the associated
costs. Overconfident investors also believe they have discreet, useful
information when in reality they have no such knowledge. Odean found that
stocks that investors purchased underperformed securities they sold!
In a follow-up study titled “Trading is Hazardous to Wealth: The
Common Investment Performance of Individual Investors,” Odean along
with Barber analyzed 66,465 individual trading accounts. They found that
from 1991 to 1996, investors that traded the most earned an annual return
of 11.4%. In the same time period, the market returned 17.9%. The simple
conclusion: Active investment strategies will underperform passive or
indexed investment strategies.
Figure
3-5
In another study by
Odean and Barber titled “Too Many Cooks Spoil the Profits: The Performance
of Investment Clubs,” 166 investment clubs were followed from February
1991 through December 1996. Many people belong to investment clubs, which
are touted as a valuable way for investors to learn about the markets.
Of the total investment clubs, 57% underperformed the market. See Figure
3-5.
In a study titled “The Performance of Mutual Funds in the Period
1945 to 1964,” Michael C. Jensen tested the predictive ability of
115 mutual fund managers in the period 1945 to 1964. He was interested
in gauging their ability to earn higher returns than those that would
be expected given the level of risk of each of the portfolios. What he
found was that on average the 115 mutual funds were not able to predict
security prices well enough to outperform a buy-the-market-and-hold policy.
In addition, there was very little evidence that any individual fund was
able to do significantly better than that which was expected from mere
random chance. Jensen’s conclusions held up even when fund returns
gross of management expenses were measured.
In a study titled “Mutual Fund Performance and Manager Style,”
James Davis looked at the relationship between fund performance and manager
style. Two specific issues were addressed. First, did any particular investment
style reliably deliver abnormal performance? Second, when funds with similar
styles were compared, was there any evidence of performance persistence?
The results of the study were not good news for investors who purchased
actively managed mutual funds. According to the findings, no investment
style generated positive abnormal returns over the 1965 to 1998 sample
period.
Edwin Elton, Martin Gruber, M. Hlavka and Sanjiv Das studied all 143 equity
mutual funds that survived from 1965 to 1984. These funds were compared
to a set of indexes comprised of large cap, small cap, and fixed income,
that most closely matched the actual investment choices of the funds.
The result: on average these funds underperform the indexes by a whopping
1.6% per year, before federal and state taxes. Not a single fund generated
a positive performance that was statistically significant.
A far more comprehensive study of 1,892 funds that existed in any period
between 1961 and 1993 became the dissertation of Mark Carhart while he
was earning his Ph.D from the University of Chicago. The study titled
“On Persistence in Mutual Fund Performance” found that when
adjusted for the common factors in returns, an equal-weighted portfolio
of the funds underperformed the proper benchmark by 1.8% per year, before
federal and state taxes.
In the first major study of bonds funds, Christopher Blake, Edwin Elton
and Martin Gruber examined 361 bond funds for the period starting in 1977.
They compared the actively managed bond funds to a simple index alternative.
The result: the actively managed bond funds underperformed the proper
benchmark by 0.85% per year, before federal and state taxes.
A study by Brad Barber, Reuven Lehavy, Maureen McNichols and Brett Trueman
titled “Prophets and Losses: Reassessing the Returns to Analysts’
Stock Recommendations” analyzed the returns to analysts’ stock
recommendations over the 1996 to 2000 period. The period was one of growing
doubt about the value of these recommendations, as analysts became increasingly
involved in the investment banking side of their business. The study showed
that the more highly recommended stocks earned greater market-adjusted
returns during the 1996 to 1999 period than did those that were less highly
recommended. However, the opposite was true for 2000, as the least favorably
rated stocks earned the highest returns. These missed predictions of stock
pickers prevailed during most of 2000 while the market was rising and
as it was falling. .
Henry Blodget took a
hard look at active management, and he came to this conclusion. "Academics
have essentially proved that active fund management, for the fund customer,
is a loser's game. The vast majority of active funds underperform passive
benchmarks. So the vast majority of customers of active funds pay billions
of dollars in exchange for, at best, nothing."
DFA looked at 31 institutional pension plans with $70
billion in total assets. The firm found that when the returns were properly
risk adjusted using the Fama/French Three-Factor Model, at least 95% of the returns
were explained by the three risk factors, and the value added by active
management was statistically insignificant, even before fees.
When Jeff Brown of
TwinCities.com wrote an article titled “Beating
Index Funds Takes Rare Luck or Genius,” he asked Morningstar
to look at the record of mutual funds. The independent investment research
firm determined that there are 1,446 large-cap blend funds that invest
in a similar asset class to the S&P 500. Over the 10-year period ending
October 2004, only 35 mutual funds matched or beat the performance of
the S&P 500. That’s only 2.4% or one in 41. See Figure
3-6. Morningstar also looked at the last three years, and only
22 out of the 1,446 funds consistently beat the S&P 500. Brown’s
sobering conclusion was that “if such a small percentage beat the
index, many of them do it with luck, and there’s no way to identify
those that really are brilliantly managed…well that’s why
index fund investing is so attractive.”
Jeff's
sobering conclusion was that, "If such a small percentage beat the
index, many of them do it with luck and there's no way to identify those
that really are brilliantly managed... . Well, that's why index-fund investing
is so attractive."
Figure
3-6
Here
are several more comparisons of active managers versus an index fund
or index. If you see more red than green, then indexers win.
Figure 3-6A-1
Figure 3-6A-2
False Discoveries of the Elusive Alpha
The
term “alpha” represents the difference between
the return on an investment and the return which could have been
achieved in an index with identical risk exposure, quantifying
a fund manager’s skill. A recent study by Laurent Barras,
Olivier Scaillet, and Russ Wermers investigates the presence
of true alpha in the results of 2,076 open-end domestic equity
mutual funds for the thirty-two years from January 1975 to December
2006.
The
study “False Discoveries in Mutual Fund Performance:
Measuring Luck in Estimated Alphas,” employs the use of t-statistic
hypothesis testing and statistical data to compare funds’ relative
performance, employing a “False Discovery Test” to
avoid errors which commonly plague statistical analysis and mitigate
the effects of false positive and negative results. Unlike many
previous studies of mutual fund performance, this method allows
for distinctions to be made between fund results based on luck
and those based on skill.