Neural network model for stock forecasting

Date

1995-05

Journal Title

Journal ISSN

Volume Title

Publisher

Texas Tech University

Abstract

The purpose of the thesis is to use the predictive abiUty of the ANN to analyze some financial time series. Different kinds of stock price data of more than 7 years are collected and used for prediction. Other kinds of time series also are used for the purpose of testing and making comparison. The probabihstic neural network, PNN, is used primarily because of its one-pass fast learning algorithm when dealing with large data sets. The short-term trend of the stock prices is predicted using the powerful classification ability of PNN. The modification of the PNN is made to forecast the real value of time series by first grouping real values into classes, and then converting the predicted class membership to some corresponding value afterward. The thesis will not pursue any trading strategy development, but it would rather attempt to provide necessary and useful information for making such kind of trading decision. Combining the trend and real change of stock prices along with some other knowledge will allow applicant to seek more benefits in the financial market.

Description

Keywords

Stocks, Stock price forecasting

Citation