Breadth Volatility and Stock Market Cycles

Above is a measure that I’ve been working with lately that I call breadth volatility.  The indicator is created by tracking the percentage of stocks trading above various short term moving averages and then calculating the standard deviation of that series.  What we find is that breadth volatility tends to be low ahead of cycle peaks and crests as markets make–and rally from–bottoms. Going back to 2012, when breadth volatility has been in its highest quartile (greatest volatility of breadth), the next five days in SPY have averaged a gain of +.54%, much greater than the +.10% for all other occasions in the sample.

nterestingly, the breadth volatility measure shown above correlates only .41 with VIX going back to 2012.  That’s a significant correlation to be sure, but suggests that VIX only accounts for 16% of the variance in breadth volatility.  Indeed, when I’ve tracked breadth volatility for specific VIX regimes, the predictive results with SPY have been quite good.

As you can see from the chart above, we made a low in breadth volatility ahead of the recent price peak in stocks and now have been trending higher as the market has sold off.  As of yesterday’s close, we were not yet at levels associated with intermediate-term market bottoms.

The previous two posts have looked at variables that I have found useful in tracking short-term movement in the stock market:  buying/selling pressure and volatility.  A third variable I have found useful is the breadth of movement among stocks.

There are many ways of tracking breadth.  Of all of these, I have found advance-decline lines to be among the least useful in predicting forward price movement.  This reflects my more general experience that the most commonly tracked market measures are among the least useful, perhaps precisely because they are so widely tracked.

Above are three breadth measures that I have found to be useful.  The first (top chart) is the number of common stocks across all changes that are making fresh three month highs minus those making new three month lows.  (Data available from the Barchart site).  You can see that breadth has deteriorated in recent days, leading to the most recent market weakness.  You can also see that breadth has deteriorated since the early August decline, as the market rally grows increasingly selective.

The second measure (middle chart) covers all stocks listed on the NYSE and tracks the daily number that close above their upper Bollinger band minus those closing below their lower Bollinger band.  (Data available from the Stock Charts site).  This, too, shows a recent pattern of deterioration, even as stocks moved to new highs.

The third breadth measure (bottom chart) is specific to the universe of S&P 500 stocks.  It is a composite measure of the percentages of SPX stocks trading above their 3, 5, 10, and 20 day averages.  (Data available from the Index Indicators site).  What we see again is a tendency for this measure to peak ahead of price, as has happened most recently.

In general, I find that strong breadth leads to short-term upside momentum, followed by reversal.  Weak breadth leads to short-term downside momentum at important market bottoms (i.e., bottoms of longer-term market cycles) and short-term reversal at market corrections.  In a qualitative sense, these measures give me a picture on whether markets are getting stronger or weaker day over day–very useful information for gauging where we might be at in a market cycle.

Vocational interests are expression

Vocational interests are expressions of what an individual finds rewarding in terms of social interaction, external rewards (i.e. remuneration) and social status, and a sense of personal meaning. For example, many people go into finance or trading because they like the potential for high income; an example would be Charles Schwab. The same type of people, interestingly, would make great intelligence analysts

Over 100 years of world-wide professionally conducted empirical research on human performance, combined with a few more decades of study on human cognition, could be summarized as follows:

Cognitive ability enables one to make sense of data, integrate new information with previously learned information, and retrieve information to create new work products. But before your readers with perfect SAT scores or A-level exams start congratulating themselves, let’s acknowledge that being among “the smartest guys in the room” doesn’t mean you’ll be successful; just look at what high IQ did for Enron. And as you and I know from our work with traders, a lot of successful traders think people with academic smarts are, well, stupid. What distinguishes “smart” from “successful” is good decision-making. So the question is, why and how do people make good decisions?

Sector Correlations in the Stock Market and What They’re Telling U