Machine Learning for Stock Selection

author: Charles X. Ling, University of Western Ontario
published: Aug. 14, 2007,   recorded: August 2007,   views: 23518
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Description

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.

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Reviews and comments:

Comment1 david, February 5, 2008 at 2:27 p.m.:

i think that the people should be more familiar with financial stuff, then he will know the history is not able to tell the future in the stock market


Comment2 quoc, December 3, 2009 at 1:39 a.m.:

david- i started watching the video but then i saw your dumb comment. mere conclusions like that prevent yourself to act at all while others are doing something more productive things with it.


Comment3 kethek, September 10, 2012 at 3:30 p.m.:

Well no system can really predict the future, but technical analysis combined with machine learning is pretty cool stuff.

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