Machine Learning for Stock Selection

In this paper, we propose a new method called Prototype Ranking (PR) designed for the stock selection problem. PR takes into account the huge size of real-world stock data and applies a modified competitive learning technique to predict the ranks of stocks. The primary target of PR is to select the top performing stocks among many ordinary stocks. PR is designed to perform the learning and testing in a noisy stocks sample set where the top performing stocks are usually the minority. The performance of PR is evaluated by a trading simulation of the real stock data. Each week the stocks with the highest predicted ranks are chosen to construct a portfolio. In the period of 1978-2004, PR's portfolio earns a much higher average return as well as a higher risk-adjusted return than Cooper's method, which shows that the PR method leads to a clear profit improvement.