MarketWatch
May 10, 2013, Mark
Hulbert INVESTORS CAN’T BEAT THE MACHINES
Commentary: Computer-dominated
trading takes over
By Mark Hulbert, MarketWatch
‘It has always been difficult for investors to
consistently beat index funds. It has been nearly impossible lately.
And there’s a double whammy: The small number of advisers who
outperform the market rarely can keep doing so.
One big culprit, experts say: the rise of sophisticated computer
trading programs.
Consider the 51 advisers out of more than 200 on the Hulbert Financial
Digest’s list who beat the market in the decade-long period that ended April
30, 2012, as measured by the Wilshire 5000 Total Market index, including
reinvested dividends.
Of that group, just 11 — or 22% — have outperformed the overall market
since then. On average over the last year, they have lagged the Wilshire index
by 6.2 percentage points.
That’s no better than the percentage that applies to all advisers,
regardless of past performance. In other words, going with a recent market
beater doesn’t increase your odds of future success.
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“Before the era of computer-dominated trading, it was slightly easier
to identify winning advisers in advance, because you could more easily
understand and evaluate what they were doing,” says Lawrence G. Tint, chairman
of Quantal, a risk-management firm for institutional investors, and former U.S.
CEO of Barclays Global Investors.
Going with a recent market beater
doesn’t increase your odds of future success. 
Getty Images
One major reason why machines are winning is our inability to process
lots of financial data, which is getting more complex and voluminous every
year.
Terrance Odean, a finance professor at the University of California,
Berkeley, has extensively studied the behavior and performance of individual
traders. He points out that there used to be another human being on the other
side of the trade when an individual bought or sold a stock. “Now it’s a
supercomputer you’re competing with,” says Odean.
“Individuals are no longer playing against Grandmasters; they’re
playing against Deep Blue,” he says, referring to the famous battle in the
1990s between chess’s Grandmasters and International Business Machines’ IBM +0.61% supercomputer Deep
Blue. Individual investors “will almost certainly lose.”
Another reason traders are losing out to machines is their general
inability to assess complex data. They look at the same set of facts on
different occasions and reach different conclusions, and they unwittingly let
their emotions dominate their intellect.
Daniel Kahneman, professor emeritus of psychology and public affairs
at Princeton University and the 2002 Nobel laureate in economics, has widely
studied this phenomenon. In his 2011 book “Thinking Fast and Slow,” he reviewed
more than 200 academic studies over the past five decades that analyzed
head-to-head contests between human beings and mechanical algorithms.
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Kahneman reports that man consistently loses out to machine in a wide
variety of pursuits, ranging from medicine to economics, business, psychology
and even things like predicting the winners of U.S. football games and judging
the quality of Bordeaux wine. In each of these domains, he reports, “the accuracy
of experts was matched or exceeded by a simple algorithm.”
Betting on the pros
Some traders hold out the hope that they can beat the market by
following the lead of an investment adviser. But it is close to impossible to
identify these advisers in advance, according to Tint.
“The average reader of The Wall Street Journal simply won’t be able to
identify these market-beating advisers,” he says. After all, “repeated studies
have shown that even the best institutional investors have been unable to
identify them in advance.”
Tint adds that there is an above-average chance that an awful adviser
will continue to perform terribly. This creates the mathematical illusion that
there also is persistence among high-ranking managers and that we can beat an
index fund by following one of those top performers, he argues. But all it
really tells us is that it’s a good idea to avoid a terrible adviser.
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This persistence at the bottom of the rankings is well-illustrated by
the adviser on the Hulbert Financial Digest’s monitored list who, one year ago,
was at the very bottom for trailing 10-year performance: Charlie Buck’s
Situational Strategies. Sure enough, it has been a bottom performer in the 12
months since then, falling 33% vs. a 17% gain for the overall stock market. The
newsletter’s publishers didn’t respond to requests for comment.
There’s another reason why it is so hard for top-performing advisers
to beat the index over the long term, says Tint, even when their numbers were
powered by genuine ability rather than sheer luck.
Once
the adviser turns in impressive performance, lots of new money flocks to his
fund, diluting the ability to continue performing well.
That phenomenon appears to be what contributed to the downfall of legendary
fund manager Bill Miller of Legg Mason Value Trust. At the end of 2005, Miller
had one of the hottest hands in U.S. mutual-fund history, beating the Standard
& Poor’s 500-stock index for each of the previous 15 years. His fund
attracted lots of new money, and he found it impossible to continue his
remarkable record.
From 2006 to 2011, he lagged the market in all but one year, and in
2012 he resigned as manager of that fund.
Miller, in an interview, says it is “mathematically true” that there
is a portfolio size “beyond which it is difficult, if not impossible, to beat
the market.” Assets under management in Legg Mason Value Trust grew markedly
over the years.
The fund now run by Miller, called Legg Mason Opportunity Trust, has
less than 10% of the assets under management that his prior fund did at its
peak, and was one of the best-performing mutual fund last year, with a return
of 40%.
Miller says he thinks the primary cause of his hot hand turning cool
was simply “bad decision making,” and that, in addition to good decision making
on his part, luck played a big role in his fund being ranked so highly last
year.
What about Warren Buffett, chairman of Berkshire Hathaway, who has
beaten the market by a large margin over the past four decades? Isn’t he an
exception?
It is certainly possible that he has been more skillful than his
competitors. But with a portfolio that is now so huge, Buffett will have a more
difficult time in the future picking stocks that will perform better than an
index fund, Miller says. Buffett himself has said that he expects Berkshire’s
future returns to be only slightly better than the S&P 500’s.
Tint says he believes much of the value that Berkshire has added in
recent years has derived not from Buffett’s stock-picking skills but from the
huge amount of cash at his disposal and his negotiating skills, which combine
to give him an enormous advantage in managing his company and extracting very
favorable terms from those who need that cash and cannot easily get it
elsewhere.
The trading trap
The implication of all this for individual investors is
straightforward: Don’t trade. Short-term trading has become so dominated by
Wall Street’s computers that individuals—and professional managers—almost
certainly will lose out to them over time. The obvious alternative, experts
say, is to buy and hold diversified index funds with very low expenses.
The portfolio that is most widely diversified, of course, reflects all
publicly traded stocks, both in the U.S. and abroad. One exchange-traded fund
that provides such total-market exposure is the iShares MSCI ACWI Index Fund ACWI +0.19% , which is benchmarked to
MSCI’s All-Country World Index. The ETF has an expense ratio of 0.34%, or $34
per $1,000 traded.
For U.S. equities, one low-cost way to get total-market exposure is
through the Vanguard Total Stock Market ETF VTI +0.44% , which has an annual
expense ratio of just 0.05%, or $5 per $10,000 invested. Almost as diversified
is a fund that mimics the S&P 500. A low-cost vehicle for exposure to that
index is the iShares Core S&P ETF IVV +0.31% , with an expense ratio of
0.07%, or $7 per $10,000 invested.
If you’re interested in developed countries’ stocks, a low-cost choice
is the iShares MSCI EAFE ETF EFA +0.11% , which is benchmarked to
MSCI’s Europe Australasia and Far-East index. Its expense ratio is 0.34%, or
$34 per $10,000 invested.
The advice to trade as little as possible and be diversified also
applies to fixed-income investing, since bond investors are at a disadvantage
against machines in this arena as well. For fixed-income exposure in the U.S.,
a low-cost option is the Vanguard Total Bond Market ETF BND -0.25% , with an expense ratio of
0.1%, or $1 per $10,000 invested.
For exposure to international bonds, be on the lookout for a fund that
Vanguard says it is close to launching: The Vanguard Total International Bond
Fund, which the firm has indicated will have an expense ratio of 0.2%—which, if
so, would be among the lowest in the category.
Low-cost index funds are also an obvious choice for getting
diversified exposure to other asset classes. iShares offers an ETF that is
benchmarked to the Goldman Sachs Commodity Index: the iShares S&P GSCI
Commodity-Indexed Trust, with an expense ratio of 0.75%, or $75 per $10,000
invested. To invest just in gold, one popular ETF is the iShares Gold Trust GLD -0.86% , with an expense ratio of
0.25%, or $25 per $10,000 invested.
A new partnership
Is there still a role for man in a world where he so consistently
loses to machines?
Yes, according to Brad Barber, a finance professor at the University
of California, Davis, who has extensively studied performance and behavior of
individual traders. There are some things that computers either can’t do or
can’t do well, such as determining whether one of the myriad patterns that
emerge from data crunching makes sense.
“If you don’t understand the reason for a pattern, you’re vulnerable
to following a mindless algorithm that is quite likely to perform poorly,”
Barber says.
Computers are also ill-suited to thinking outside the box and devising
new hypotheses and models of what might be able to beat the market in the
future, Tint points out. He envisions a man-and-machine partnership in which we
use computers to rigorously test our hypotheses and trade on those that survive
statistical muster.
Of course, most people don’t have the computer hardware and extensive
databases required to take advantage of what computers have to offer.
And even professionals who do have access to such resources need to
first recognize the limitations of their decision-making abilities. Until they
know what they’re not well suited to do, they are likely to perform poorly —
even if they have powerful computers at their disposal. That’s because they are
likely to exaggerate what they bring to the table and play down the role that
computers can play.
Odean says some of the poorest performances in his studies were turned
in by traders who were the most confident of their abilities. This led them to
trade even more often and incur even more risk.
This often leads them to do precisely the wrong thing. A series of
academic studies over the past decade compared stocks that traders buy with
those that they sell, both in the U.S. as well as in some foreign countries.
The average stock traders sell goes on to outperform the average stock
they buy, he says. ‘
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