The stock market attracts risk takers who are willing to bet their money on whether a stock price will increase or decrease. What if you had an edge in this gambling mecca? Machine learning techniques are among the newest methods of stock market price trend prediction. Using a support vector machine (SVM), as implemented by WEKA, and eleven attributes in each data point, the data was classified as either BUY or DNB (Do Not Buy), depending on whether the price would increase or decrease. Many previous experiments’ goals were to find attributes that correlated with future stock price trends. These attributes were used to create the dataset used in this study, along with a nominal sector attribute, to determine if the addition of this variable improves the accuracy of the stock price trend prediction model.