  As the number of publicly traded companies as well as the amount of their
financial data grows rapidly, it is highly desired to have tracking, analysis,
and eventually stock selections automated. There have been few works focusing
on estimating the stock prices of individual companies. However, many of those
have worked with very small number of financial parameters. In this work, we
apply machine learning techniques to address automated stock picking, while
using a larger number of financial parameters for individual companies than the
previous studies. Our approaches are based on the supervision of prediction
parameters using company fundamentals, time-series properties, and correlation
information between different stocks. We examine a variety of supervised
learning techniques and found that using stock fundamentals is a useful
approach for the classification problem, when combined with the high
dimensional data handling capabilities of support vector machine. The portfolio
our system suggests by predicting the behavior of stocks results in a 3% larger
growth on average than the overall market within a 3-month time period, as the
out-of-sample test suggests.
