Forecasting financial markets using neural networks: an analysis of methods and accuracy
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Authors
Kutsurelis, Jason E.
Subjects
Neural networks
Finance
Time series analysis
Forecasting
Artificial intelligence
Finance
Time series analysis
Forecasting
Artificial intelligence
Advisors
Terasawa, Katsuaki
Gates, William R.
Date of Issue
1998-09
Date
September 1998
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
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 by Edward 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.
Type
Thesis
Description
Series/Report No
Department
Department of Systems Management
Organization
Naval Postgraduate School
Identifiers
NPS Report Number
Sponsors
Funder
Format
xii, 99 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.