Jan

31

Many Statements, by Craig Mee

January 31, 2007 |

Since 1980, it has only been three years since we have not seen a year (as of '06) where there has been a significant sept. - dec. clear out of longs (using a variety of available weekly trend change indicators). These years are '03, '96 and '93. Of these years, the Dow finished relatively flat for one of them, which was in '93, and the bid for the other two were in '96 and '03.

In particular in '96, after the market had three relatively strong years, it still posted strong gains in '97 without a sept. - dec. sell off.

In all three years, however, the April hoodoo's came through the following year before recovering, maybe not a significantly strong sample, but maybe of interest nevertheless.

As the chair suggests, it may be a case where as the volume attracts volume, strength attracts strength.

J.T. Holley comments: 

So remember, as good as counting is, it ain't gonna change what happens tomorrow. So protect your stack (your portfolio) and never lose your stake (all your net worth and ability to invest). No odds are worth losing everything. You gotta live to play another day. -Sb

There are 52 cards in a deck with four suits.

66 stocks make up the Dow Composite with Industrials, Transports, and Utilities (3 suits). Playing Dow Theory is like playing the classic 21 cards a piece "War," meaning totally random and no strategy involved unless you're a cheater.

If you really want to step back and take in the big picture, there are 3000 stocks (operating or not) roughly on the NYSE and 2000 stocks on the NASDAQ NMS. This is what makes up the deck we call the Willshire 5000. That's it. All you have to do is know how many players are sitting down at the table like American, Fidelity, Vanguard with billion dollar portfolios, and then remember that it's like blackjack with ten decks. Most of the big guys don't take positions with numbers around five's and three's. This means that there is maybe 0.55% or 0.33% of a mutual fund that has 300 stocks in its coffers with one or two reserved for cash. I'm sure if you'd like to do the work, you could form enough hypotheses and tests to come up with something fruitful. I mean it's only 5000 stocks right?

Victor Niederhoffer adds:

These days, there is talk about it's having been 978 days from the last 10% decline, how the maximum is just 1050 et al, and ha ha, the day of reckoning is coming, and things like the decline in oil prices are going to cause the Fed to join the doomsday camp and knock the stock market down by raising rates because things are so good with energy prices going down that they have to act et al. Thus, no matter which way energy goes, it's bad for inflation. There are many statistical errors with the above reasoning that go far beyond the always suspect former Tennessee research outfit that was bearish all through the 90's because stocks were above book value the way they were in the 1929 era. It's a good statistical exercise that we all might profit from considering that it involves conditional expectations and predictions given a certain extreme range of an independent variable without knowing the form of the distribution itself. For others, the statement might better be bruited about among the 150 reasons to be bearish and to go against the drift among the good colleagues of the weekly financial columnist and his fellow performers. For those who would like to shed some light on the line of reasoning itself, what's required is a bit of counting. What is the life expectancy and the expectation given that you have reached a certain number of days without an x% move? Books on survival statistics are a great help here with our favorite being The Statistical Analysis of Failure Time Data by John Kalbfleish (Wiley Series). Not knowing the secrets of Rebecca, pi, or key level analysis, one has counted such expectations. The good news here is that like the crocodile or the oak tree, or Chorus Line, or Shakespeare himself, the longer a stock market goes without a big decline, the greater the expectation of going further once a threshold is reached.

J.T. Holley offers: 

Instead of utilizing survival statistics to attempt to predict the non-randomness of a -1% day or worse, how about looking at the frequency of the 2% up days that are hopefully gaining? We took a long spell of oct. '03 to june of '06 some 682 days in between the last time. Then the latest run is 156 days without one. It's always eyeopening to see how in the trenches, the smaller (quarters or thirds) up days on a histogram are winning the war in the quarterly battles of the war called drift.


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