This thesis develops models for accurate prediction of price changes on electronic stock exchanges by utilizing autoregressive and logistic methods. Prices on these electronic stock exchanges, also called ECNs, are solely determined by where orders have been placed into the order book, unlike traditional stock exchanges where prices are determined by an expert market maker. Identifying the significant variables and formulating the models will provide critical insight into the dynamics of prices on ECNs. Whereas previous research has relied on simulated data to test market strategies, this analysis will utilize actual ECN data. The models recognize patterns of asymmetry and movement of the shares in the order book to formulate accurate probabilities for possible future price changes. On traditional stock exchanges, price changes could only occur as quickly as human beings could enact them. On ECNs, computerized systems place orders on behalf of traders based on their preferences, resulting in price changes that reflect trader activity almost instantaneously. The quickness of this automation on ECNs forces the re-evaluation of commonly held beliefs about stock price dynamics. Previous strategies developed for trading on ECNs have relied mainly on price fluctuations to gain profits. This thesis uses the formulated models to design profitable strategies that use accurate prediction rather than price variability.