The stock market plays a prominent role in the economy, used as both as an investment area to make wealth and as an overall indicator of its health. Thus, people have been trying to organize and predict the stock market and which stocks would be winners as seen by Peter Sander [1]. Research by Mantegna [2] and Onnela [3] showed that the market has a clear structure and could be represented as Minimal Spanning Trees. Qian [4] reported that the Hurst Exponent, a modeling method of correlation, could be applied to the study of financial markets. This study seeks to model these methods and utilizing Random Matrix Theory, determine whether these methods are valid and, if possible, applicable to a smaller subset of stocks. After review of the gathered data, it was found that while the Hurst Exponent and Minimal Spanning Trees do show structure, they cannot accurately predict future stock performance. In addition, there is no benefit to following a small group of stocks verse the market as a whole with the only exception being the index.