Neural Networks and Stocks

One of the projects I was working on during my (now-finished) Spring 2006 term at school was an independent research course focusing on using neural networks to make predictions about stock price fluctuations. I finished the paper, and have put it up on the I2R Articles page. Here is the abstract:

This research paper is the result of a three-month-long independent study focusing on the ability of feedforward and recurrent neural networks in predicting stock price fluctuations of companies within the "Consumer Discretionary" category of the Global Industry Classification Standard (GICS). The paper focuses on predictions for Circuit City Stores Inc. (CC) for 1-, 5-, and 20-business day periods through the use of input patterns based on fundamental and technical indicators from the stock market and economy. The project is unique in its focus on companies selling to consumers. The results of the project are promising, with statistically significant results appearing for 1-day single-layer feedforward and Elman networks, as well as networks based on fundamental inputs and predicting for 20-day periods.

The paper goes through a great deal of basics, but also has some interesting results. Feel free to check it out, and let me know what you think by e-mailing me.