COMPARISON OF NEURAL NETWORK AND ORDINARY LEAST SQUARES MODELS IN FORECASTING CHINESE STOCK PRICES

Ojoung Kwon, California State University, Fresno, U.S.A.
Sasan Rahmatian, California State University, Fresno, U.S.A.
Alicia Iriberri, California State University, Fresno, U.S.A.
Zijian Wu, California State University, Fresno, U.S.A.

Published in

JOURNAL OF INTERNATIONAL FINANCE AND ECONOMICS
Volume 19, Issue 1, p17-32, March 2019

ABSTRACT

With the recent entrance of China’’s stock market into the global financial system, a renewed interest emerges in discovering the one framework that will reliably forecast changes in stock market prices. This research presents evidence of the forecasting performance of ANN models compared to linear regression models, specifically ordinary least square (OLS) models. Using a 10-year data set from 2002 to 2012 of 154 companies in the A-share of the Shanghai Stock Exchange, the study demonstrates the use of ANNs in forecasting price changes in China’’s stock market. The data set used includes observations on daily closing prices as well as data on 25 indicators. A t-test was calculated to compare the performance of the ANN model with the performance of the OLS model in predicting daily stock price change in China’’s stock markets. The results of the t-test indicate that the ANN model performed significantly better.

Keywords

Business Intelligence, Financial Forecasting, Investment Strategies, Behavioral Finance, Technical Analysis, Data Mining, Neural Networks, and Artificial Intelligence


About the Article

Abstract, Keywords, Page Numbers, etc

About the Journal

Managing Editors, Indexing, Best Practices

About The Publisher

History, Partners, Conferences

Access the Full Article

Log-in to IABE to access full article

Search IABE

Search IABE's articles by Title, Author, or keyword

Contact Us

Send a message to IABE