Forecasting financial markets using Neural Networks
By: an analysis of methods and accuracy /  Jason E. Kutsurelis
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1998
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Format: Electronic resource
Number of pages: 99
Description: This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of usingneural networks as a forecasting tool for the individual investor. This study builds upon the work done byEdward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.
Notes: Thesis advisor(s): Katsuaki Terasawa, William R. Gates.

"September 1998."

Thesis (M.S. in Management) Naval Postgraduate School, September 1998.

Includes bibliographical references (p. 97).

"Approved for public release, distribution unlimited"--Cover.

Also available in print.

Mode of access:  World Wide Web.

System Requirements: Adobe Acrobat reader.

US Navy (USN) author.

dk/dk cc:9116 10/27/98.