Supervised and Unsupervised Methods for Stock Trend Forecasting

Stock forecasting is a major component of any finance institution because predictions of future prices, indices, volumes and many more values are often incorporated into the economic decision-making process. Although there are many different approaches out there, this paper will compare unsupervised classification techniques such as k-means clustering with supervised learning algorithms such as support vector machines (SVMs). In our study, a list of stock prices taken from historical data of the S&P 500 is used as our testbed. These prices will be categorized as increasing or decreasing in price on a weekly basis. The goal of this study is to determine the best method for forecasting the trend of stock prices.