An indicator that helps predict the Nasdaq

Many traders are familiar with the VIX, a measure of implied volatility
derived from S&P 500 Index options. The VXN is the same measure,
applied to the NASDAQ 100 Index. The thought is that, as implied
volatility decreases, traders are becoming more complacent. This usually
occurs when index prices rise. When implied volatility rises significantly,
as is usually the case when the index declines, option traders are bracing for
further movement. The
(
VIX |
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PowerRating)
and
(
VXN |
Quote |
Chart |
News |
PowerRating)
are often viewed as sentiment measures,
gauging relative calm and worry in the marketplace. Indeed, one of the
cardinal TradingMarkets rules has been to refrain from buying stocks when the VIX is 5% or
more below its 10-day moving average and refrain from selling when the VIX is 5%
or more above its ten-day moving average. In other words, buying a
complacent market and selling a worried market have lost traders money on
average.

One of my favorite ways of informally testing the value of an indicator is to
check its predictive performance during market periods that are flat. Such
flat periods take price change out of the equation, accomplishing a kind of
statistical control. I then divide all the flat occasions into the highest
and lowest values of the indicator and see if those values predict what happened
in the following time period.

It just so happens that the NASDAQ 100 Index
(
QQQQ |
Quote |
Chart |
News |
PowerRating)
has gone nowhere in the
past two sessions, but the VXN has risen by 3.16% in that time. In other
words, traders have gotten more worried over the past two sessions despite an
absence of market movement. Let’s see what happens when traders get
worried in flat times.

Going back to March, 2003 (N = 774 trading days), I found 46 occasions in
which the NASDAQ 100 Index (QQQQ) has neither risen nor declined by more than
.10% over a two-day period. Two days after this flat period, QQQQ has
risen by an average .12% (23 up, 23 down)–not much different from its overall
performance over the three year period (.18%; 419 up, 355 down).

Now let’s divide those flat occasions in half based on the two-day change in
VXN. We’ll call the stronger VXN change occasions the complacent periods
and the weaker VXN change occasions the worried periods. When QQQQ is flat
over two days and traders are complacent (N = 23), the next two days in QQQQ
average a loss of -.07% (10 up, 13 down). When QQQQ is flat over two days
and traders are worried (N=23), the next two days in QQQQ average a gain of .31%
(13 up, 10 down). In short, as the TradingMarkets rule suggests, buying
complacence and selling worry has been a huge mistake for traders.

Such historical analyses as this, like those on my
research blog
, are not intended as mechanical trades. Rather, they
provide a road map for how a market has behaved in the past. This allows
you to gauge current market action in light of historical tendencies. If I
conduct several such analyses and find multiple strands of evidence for a
positive two-day bias in QQQQ, I will be on the alert for buy setups early the
next day and will act on these with confidence.

The best trades, I submit, are those with multiple, independent sources of
edge. The VIX and VXN are one source of edge worth tracking.

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
and a blog of market analytics at www.traderfeed.blogspot.com.
His book, Enhancing Trader Development, is due for publication this fall
(Wiley).