The Sharpe Ratio of a portfolio is its return divided by the standard deviation of its return. It is a measure of the incremental return for taking on added risk (as measured by standard deviation). Portfolios with higher Sharpe Ratios reward added risk with higher returns.
A ``zero cost'' or ``dollar neutral'' is one where no outlay of money is needed. The procees of the short sales are used to fund the long purchases. In reality, even such strategies require some outlay, if only to meet margin calls and brokerage fees.
If there is a particular holding horizon that is important, make sure that the ending date of the sampling data is at least that distance from the current date. For example, if today is 1/1/2010, and the holding period is 2 years, one might have starting date for the sampling data of 1/1/2005 and ending date for the sampling period of 1/1/2008. The application will then show the effectiveness of using 3 years worth of data (1/1/2005 to 1/1/2008) in choosing a trading strategy for the following 2 years (1/1/2008 to 1/1/2010). The application can then be rerun with an ending date for the sampling data of 1/1/2007 to creating a ranking for the out of sample period (1/1/2007 to 1/1/2010) which uses the most current 3 years for actual trading.
The optimal portfolio for both the sample and out of sample period are computed. The portfolio is represented by the weights that would be attached to each of the stocks in the portfolio. The Sharpe Ratio of the portfolio is also computed.
The most important number is that listed at the top of the analysis. This is the "Spearman Rank Correlation Level of Significance" which is a statistical test of the power of the sample weights to predict out of sample weights. The lower this number, the more likely the two weights have statistical significance and not just a fluke. For example, a value of 5%, means that correlation observed between the sample weights and out of sample weights would only happen by chance 5% of the time given truly independent processes. This correlation may be positive or negative, the sample weights may be a negative predictor of out of sample weights.