It’s ‘what’ you trade that matters
You probably didn’t know that the S&P 500 Index
closed at an all-time high last Friday. You also were most likely unaware that the S&P had
broken the 2000 level early this year and now approximates a lofty 2350.
How can this be? The S&P 500 Index that I am describing is the
un-weighted version of the index, tracked by Carl Swenlin in his excellent Decision
Point site. Incredibly, the un-weighted version of the index has
outperformed the traditional, weighted average by over 88% since November,
2000. Nor is the S&P the only index to display inferior performance as
the result of weighting. The un-weighted NASDAQ 100 Index recently crossed
the 4000 barrier, now standing at 4123–145% above its weighted
equivalent.
One lesson I learned early in my investing and trading career, aided by
well-known author and speculator Victor
Niederhoffer–is that markets reward the assumption of risk. The
seemingly safest strategies are rarely the ones that are best-rewarded: just
look at the long-term rewards from stocks vs. bonds and cash, as beautifully
detailed in the book Triumph
of the Optimists by Elroy Dimson and colleagues. At a shorter term
level, we can see how market returns following periods of out performance are
dramatically lower than those following market weakness. (See my
last
article for one example of this phenomenon). The name stocks that
represent the greatest weightings in the S&P 500 and NASDAQ 100 indices are
the issues that most investors gravitate toward in their portfolios as safe blue
chips. Yet these have significantly underperformed the lesser lights of
those indices. Long-term charts reveal that they have underperformed the
relatively anonymous universe of small and mid-cap issues as well. Since
late 1999, the Dow Jones Industrial Average and S&P 500 weighted average of
large cap stocks have lost ground, but the S&P Midcap and Small Cap Indexes
have approximately doubled in value.
When you come to think of it, the decision to weight an index in one way or
another–or the decision to weight it at all–merely reflects the preferences of
those composing the indexes. The S&P 500 Index today is not at all
similar to the index of the mid 1970s; many issues have entered and exited the
universe, and sectors less weighted back then (finance) are more highly weighted
now. Microsoft
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weighted of the stocks in the current index, but neither existed in the
1970s. In short, indices are the constructions of index makers, not
timeless embodiments of Finance.
In spite of this elementary fact–and the fact that the most popular indices
under perform their more unheralded counterparts–traders gravitate toward what
is known. The e-Mini contract for the S&P 500 is consistently one of the
most actively traded futures contracts, and the exchange-traded funds for the
S&P 500
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volume lists among equities. This raises a fascinating question:
Might traders and investors profit by constructing synthetic market indices that
have the potential to outperform their favorite trading vehicles? If
market indices are nothing more than constructions, why not construct our own to
match our trading needs?
Clearly this is not a new idea. The major exchanges are always
developing new products to meet the needs of traders. Most of these are
designed to capture the equity performance of a particular country or market
sector. In a recent talk I attended, Peter Steidlmayer–best known as a
developer of the Market
Profile graphic that arranges price and time in the shape of statistical
distributions–encouraged listeners to boldly go where no index developers have
gone before and create indices solely to maximize their tradability. Such
an index might contain very different kinds of components, such as the U.S.
Dollar, crude oil, 10-year Treasury Notes, and shares in the largest
international companies. Constructing the index to meet the needs of
daytraders might mean, for instance, weighting the components for their
volatility–and then periodically re-weighting them so that the most volatile
components always carry the most impact on the index price. The result
would be a trading vehicle that has better trading properties than any of its
individual elements.
Here’s a practical illustration of the potential of synthetic indices.
In my last article, I showed how the synthetic e-Conomy Index that I had created
was a better predictor of future price changes in the QQQQ than the QQQQ
itself. The synthetic index acted as a “lead fish” that guided
the school of issues weighted in the NASDAQ 100. It turns out, however,
that the e-Conomy Index is a better trading vehicle than the QQQQ in its own
right. For instance, the previous article showed how the upper half of
five-day returns in the QQQQ produce a worse next five-day return in the QQQQ
(.26%) than the lower half of five-day returns (.36%). This is the classic
pattern of strength following weakness and vice versa.
When we look at the e-Conomy Index since August, 2004, this pattern is
greatly enhanced. The upper half of five-day returns in the e-Conomy Index
return .57% in the next five days of the e-Conomy Index (93 occasions up,
60 down), but the lower half of five-day returns yield an eye-popping 2.23% over
the next five days (121 occasions up, 33 down). If one wanted to trade
reversal patterns following market weakness, the e-Conomy Index is simply a
better vehicle than the QQQQ. It is not hard to imagine other synthetic
indices that could outperform SPY and QQQQ with respect to reversing market
strength–or reversing both strength and weakness.
The majority of trading advice in articles and books emphasize how
traders should trade. Rarely do we see serious consideration to what
traders should trade. The traditional trading vehicles are well-studied by
the data miners; they are highly efficient as a result. Perhaps we will
find that synthetic indices possess their own unique trading properties, opening
the door to new sources of edge. That’s my takeaway from Steidlmayer’s
work, and I think it’s of critical significance to those of us in search of
alpha.
Brett N. Steenbarger, Ph.D. is Associate Clinical
Professor of Psychiatry and Behavioral Sciences at SUNY Upstate Medical
University in Syracuse, NY and author of The
Psychology of Trading (Wiley, 2003). As Director of Trader Development
for Kingstree Trading, LLC in Chicago, he has mentored numerous professional
traders and coordinated a training program for traders. An active trader of the
stock indexes, Brett utilizes statistically-based pattern recognition for
intraday trading. Brett does not offer commercial services to traders, but
maintains an archive of articles and a trading blog at www.brettsteenbarger.com.
He is currently writing a book on the topics of trader development and the
enhancement of trader performance.