I took a market analysis statistics class a couple of years ago (very interesting class but hard as nails) and the conclusion we came to at the end of the semester was that statistical analysis doesn't work on the stock market because of unit root (I can't recall what this means) and non-stationary problems. The non-stationary problems are obvious -- the market doesn't stop for you to analyze it so picking your end point for the data is almost impossible (impossible may be too strong a word) and picking your beginning point is very difficult. I plotted stock market data on a distribution curve a while back and it looks like a skewed curve with smaller normal curves inside the skewed curve. The trick is to do your analysis within those minor normal curves. If you can manage to do that then you will be very successful. Also, as the day wears on the data becomes more skewed and the sizes of the normal curves become very unpredictable, which is a very important detail. Do the most of your trading during the morning and trade light in the PM or don't trade at all, assuming you're using a statistical based method. Today's action is a perfect example of what the curve looks like. I attached a rough sketch of what market data looks like statistically. I hear a lot of people say they trade well in the morning and give it all back in the afternoon -- this is why. If someone knows what Unit Root Problems mean, and can explain it in understandable terms I'd love to read about it. I looked it up on the internet but the terminology is beyond my statistical comprehension.