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Mathematical Problems in Engineering
Volume 2018, Article ID 3967525, 14 pages
https://doi.org/10.1155/2018/3967525
Research Article

A Bimodel Algorithm with Data-Divider to Predict Stock Index

School of Computer Science & Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China

Correspondence should be addressed to Jinsong Hu; nc.ude.tucs@sjhsc

Received 12 August 2017; Accepted 30 January 2018; Published 5 March 2018

Academic Editor: Daniela Boso

Copyright © 2018 Zhaoyue Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is not yet reliable software for stock prediction, because most experts of this area have been trying to predict an exact stock index. Considering that the fluctuation of a stock index usually is no more than 1% in a day, the error between the forecasted and the actual values should be no more than 0.5%. It is too difficult to realize. However, forecasting whether a stock index will rise or fall does not need to be so exact a numerical value. A few scholars noted the fact, but their systems do not yet work very well because different periods of a stock have different inherent laws. So, we should not depend on a single model or a set of parameters to solve the problem. In this paper, we developed a data-divider to divide a set of historical stock data into two parts according to rising period and falling period, training, respectively, two neural networks optimized by a GA. Above all, the data-divider enables us to avoid the most difficult problem, the effect of unexpected news, which could hardly be predicted. Experiments show that the accuracy of our method increases 20% compared to those of traditional methods.