After several days of no breakouts to speak of, today’s strength has created several picturesque charts.

The volume on the initial breakout in McAfee, Inc. (MFE: 39.73 +1.53%) is impressive. The volume on today’s move is also notable.

Raven Industries, Inc. (RAVN: 44.43 +5.86%)

Its all about the volume on Sapient Corporation (SAPE: 9.30 +4.97%) .

During times when volume is slack and there is not much going on, I like to scan for stocks that have recently broken out or broken down, and have re-tested their breakout/breakdown pivot points. These types of setups allow entries into stocks that have already begun making a strong move, but are taking a breather. It is not an anticipatory setup. The breakout or breakdown has already occurred, and the nimble trader is afforded a lower-risk entry just before the stock begins to move again.

Urban Outfitters, Inc. (URBN: 34.71 +0.75%) is a good example of the breakout and re-test. The volume on the breakout was good, while volume on the re-test has been slack. That is exactly what one wants. Watch the MACD here. It should stall out and begin moving back over the signal line if this trade is going to work.

Teleflex Incorporated (TFX: 64.23 +1.07%) offers two levels of support: One at 62.50 and one at 60.00. Also note the Golden Cross of the 50 day moving average over the 200 day moving average from beneath. Volume on the breakout was good.

Arena Resources, Inc. (ARD: 45.84 +1.93%) is a good example of this setup for a breakdown, short play. This one has the added benefit of 50 day average resistance. Even with Gustav coming ARD does not look very sure about shaking off its breakdown and beginning a new uptrend.

I want to introduce to all of you a statistical measure which has come to have a significant influence on the trading systems I am developing. Ralph Vince describes the measure very well in his book Portfolio Management Formulas. This measure is called the Z-Score, or the Runs Test.

I want to skip most of the statistical jargon and get right to the meat of the issue, but I will be happy to answer specifics in the comment section.

To understand why the Z Score or Runs Test is important, we need to digest Rob Hanna’s statement from his recent post How to Trade the Choppiest Environment in 50 Years. Rob writes, “As you can see, buying after strong days and selling after weak ones worked well for 40 years. In 2000 that changed, and the last year and a half is the worst it has ever been with regards to follow through. This would suggest that strategies that may have worked well for forty years or more could be suffering greatly now.”

Also, it is important to read Dr. Brett Steenbarger’s recent post Short-Term Reversal Patterns Among Global Equity Indexes.

Both authors conclude that short-term trend following is not working very well. We can test their conclusion by applying the Z-Score to the data from the indices. I should mention that both Bhh from IBDIndex and Damian from Skill Analytics have been instrumental in helping me flesh out the rest of the ideas presented below.

The Z-Score can determine whether wins or losses are dependent on previous wins or losses. Think of dependency in this way: Do wins begat more wins? Do losses begat losses? If so, this relationship would be described as a positive dependency. What if wins begat losers, and losers begat wins? This would be a negative dependency.

While Z-Score has traditionally been used to analyze the win and loss streaks of a trading systems, it seems that another application for the measure may be to analyze the win and loss streaks of the indices in order to determine whether there is any dependency. Are the sequences of wins and losses containing more or less streaks (of wins and losses) than would be expected in a truly random sequence? When digesting this, consider the fair coin, where one flip is equally as likely to be heads as it is tails. We want to determine if the indexes are trading as a fair coin, or one that is biased to heads or tails, or both.

Below are the Z-Scores for the S&P 500 (SPX), using all data available from yahoo, which goes back to 1950. In January of 1993, the S&P 500 SPDRs was introduced. I will quit using SPX data and use SPY data from 1993 forward.

All Data, 1950 to Present: Z-Score -9.359014

This negative Z-Score implies a positive dependency at a confidence level of much higher than 99.73%. In short, a positive close on the S&P 500 generally begat more positive closes, and losing days generally begat more losing days, over this broad time span.

1960 to Present: -7.196132

1970 to Present: -3.795268

Note that the positive dependency is decreasing, yet from 1970-Present, the confidence level is still above 99.73%.

1980 to Present: Z-Score .3465443

1990 to Present: Z-Score 1.692151

1993 to Present (With SPY Data): Z-Score 2.115444

Note that there has been a switch. The positive Z-Score implies a negative dependency, where buying begats selling, and selling begats buying. Be careful though with this data, as the score must be above 1.64 to have a confidence level of greater than 90%.

2000 to Present: Z-Score 1.3608623

2003 to Present: Z-Score 1.1696094

2006 to Present: Z-Score .4530397

2007 to Present: Z-Score 1.3030246

October 2007 to Present: Z-Score .3994188

Note that from 2000 on, the Z-Scores move lower. The highest score from this period, 1.303, gives a little better than an 80% confidence level. I interpret this data to mean that the S&P 500 is basically moving through a random walk, although the confidence level is not high enough to draw any firm conclusions.

January 2008 to Present: Z-Score -0.628093

February 2008 to Present: Z-Score -0.400456

March 2008 to Present: Z-Score -0.246511

April 2008 to Present: Z-Score -0.403907

May 2008 to Present: Z-Score -0.288564

June 2008 to Present: Z-Score 0.0022265

July 2008 to Present: Z-Score -0.09631

From January 2008 to the present, we begin to once again see negative Z-Scores. A negative score implies a positive dependency, where selling begats selling and buying begats buying. The scores are not significant enough to exhibit a high level of confidence.

Conclusions

The recent data show no definitive dependency, either positive or negative. This means that buying because the market has closed up or selling because it has gone down has not been working as well as in the past. Also, buying weakness or selling strength, in order to catch a reversal, has not been working as well either.

Right now, betting on the market, as represented through the SPY, is similar to betting on the flip of a fair coin. This data, while it may not prove the conclusion of Hanna and Steenbarger, certainly does not disprove it.

Implications for Further Research

It seems to me that keeping shorter and intermediate time frame of Z-Scores, update daily across the indices, could give the trader a head’s up that market conditions may be changing to be more favorable to trend-following or contrarian strategies.

I’m just hanging out, executing strategy trades.

My signals say this weakness should be bought, but again, I’m only looking for short-term swings lasting around 5 days. And, I use stops, religiously.

I’m going to give you all another list of stocks which meet the criteria for a good short-term swing.

Linear Technology Corporation (LLTC: 32.99 +0.76%)

Gartner, Inc. (IT: 26.93 +2.16%)

PAREXEL International Corporation (PRXL: 31.58 +0.64%)

Websense Inc. (WBSN: 22.26 +1.09%)

Ness Technologies, Inc. (NSTC: 12.50 +4.17%)

CSG Systems International, Inc. (CSGS: 19.17 +2.13%)

Federal Signal Corporation (FSS: 15.95 +4.45%)

HealthExtras, Inc. (HLEX: 32.18 +0.16%)

If you want to know what is happening, or if you are having a hard time in this market, check out this excellent article: How to Trade the Choppiest Environment in 50 Years.

After reading the linked material, consider it your homework to review the setups listed above, keeping in mind the overall point of the article.