Accession Number:

ADA355005

Title:

Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy

Descriptive Note:

Master's thesis,

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s):

Report Date:

1998-09-01

Pagination or Media Count:

112.0

Abstract:

This research examines and analyzes the use of neural networks as a forecasting tool. Specifically a neural networks 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 models forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of using neural 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 SP500. 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.

Subject Categories:

  • Administration and Management
  • Economics and Cost Analysis
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE