 The standard deviations, attached in appendix 1, were determined by taking the historical prices for each of the stocks on a weekly basis, adjusting for dividends paid, then calculating the returns.  It must be noted that the standard deviation calculated on weekly prices were weekly standard deviations and had to be adjusted to be yearly standard deviations.  Once these values were obtained for all the relevant stocks and ETFs, a T-Test was performed to evaluate whether or not these samples were statistically different from one another.  Using excel, the two tailed T-Test formula was used, testing the weekly standard deviations for the past year, the results are shown below

Specifically, this shows that at the alpha = .05 level, these are not statistically different.  The T-Test value came back at .959, so at an an alpha of .01, we would say that these are statistically different.  
	Now that we can say with some certainty that the portfolio comprised of the top ten holdings gives a reasonable estimation of the ETF as a whole, we postulate the question of whether or not we can create a better combination of these portfolios by using a sharp optimal portfolio rather an the current weights.  The process to form a sharp optimal portfolio was rather involved, but the major difficulty was in creating the covariance table and array weights shown below

	Once these were created, a simple excel solver function was all that was necessary to form the sharp optimal portfolio.  I constrained the weights of each stock to be within 5% and 25% of the total, as I felt these were reasonably close to the weights of the normalized portfolio.  The changes between the weights of the normalized portfolio are shown below.
	These revised weights give the portfolio a higher sharpe ratio, indicating a higher return per unit of risk, however does this ratio and weighting system translate into higher returns.  Over a period of fifteen days, the prices were tracked of each stock, then the weights were applies to determine the return of this sharp optimal portfolio versus the returns of the ETF.  The results of the tracking are shown below

	Surprisingly, the sharp optimal portfolio did perform slightly better than the ETF, although, once again, it is doubtful that the differences are statistically significant.  We can only be sure that using these tools to optimize your portfolio will help remove the idiosyncratic risk associated with the market.  This was quite useful exercise, and I am using it on my 401k!


	As a major land developer in the greater El Paso area, Hunt Companies struggles with the traditional negative impressions associated with land development.  The modern consumer struggles with their own environmental consciousness and desire for ‘green’ consumption is placed directly at odds with price sensitivity.  It is often said that the perfect innovation increases demand and decreases cost, and I believe that with the current portfolio of companies, Hunt has an opportunity to do just that.
