Back-testing Stock Selection - Data Analysis Help Needed

Hey everyone!

I'm a finance grad and am doing my first big project back-testing some stock selection methods.

I have spent the last few weeks writing a big vba program to run the back-test and I have the following:

10 dates (5 years semi-annual) and 40 companies where, for a given date, if data is availalbe then I have
a) the stock price on that date
b) a valuation
from which I then calculate % difference to determine whether I value the stock at more or less than it's trading.

On each of these dates, I have a set of companies (fewer - approx 10 for the earlier dates since not all companies had sufficient data for a back-test that far back and a full 40 for the latest few dates) and I have for each company a 'spread' which is used to indicate whether to buy or sell the stock.

I have tried a non-parametric method of testing whether the stock-selection method works by ranking the stocks on each date by spread and creating an equally weighted portfolio of the top quartile and similarly for the lower quartile and then check the return over the next 6 months.

The results are as hoped with the quartile with the highest spread (valuation suggests that they're a 'buy') yielding the highest return over the following period and conversely, the lower quartile significantly under-performs relative to the top quartile and relative to an equally weighted holding of all stocks tested for that given sub-period.

I would now like to statistically test this relationship. A t-test comes to mind but I'm unsure about whether I should just take the top quartile versus lower quartile just for each sub-period and do 10 t-tests (similarly for buy vs equally weighted sample portfolio) ... or whether I should somehow do a test over the entire set of 10 dates (given that the number of companies on each date is different and so each portfolio is different.

Also, any other suggestions of nonparametric or other statistical methods to draw some juicyness out of the data will be much appreciated! :)