Volume 10, Issue 1, 2017, Pages 336 - 346
A Performance Comparison of Neural Networks in Forecasting Stock Price Trend
Authors
Binghui Wu*, 1, 2, vcmd@163.com, Tingting Duan2, duantingting417@163.com
1School of Economics and Management, Southeast University, Nanjing, Jiangsu Province 211189, China
2Finance Department, Lanzhou University of Finance and Economics, Lanzhou, Gansu Province 730020, China
*Corresponding author.
Corresponding Author
Binghui Wuvcmd@163.com
Received 22 April 2016, Accepted 31 October 2016, Available Online 1 January 2017.
- DOI
- 10.2991/ijcis.2017.10.1.23How to use a DOI?
- Keywords
- Stock market; BP neural network; Elman neural network; CSI 300 Index; relative error
- Abstract
The stock price shows the character of complex non-linear system, along with changes of internal and external environmental factors in stock market. As a form of artificial intelligence, neural network can fully reveal the complex relationship between investors and price fluctuations. After comparing network structures of different neural networks, the conclusions show Elman neural network has an obvious advantage over BP neural network in predicting price trend of Chinese stock market both in theory and practice.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Download article (PDF)
View full text (HTML)
Cite this article
TY - JOUR AU - Binghui Wu AU - Tingting Duan PY - 2017 DA - 2017/01/01 TI - A Performance Comparison of Neural Networks in Forecasting Stock Price Trend JO - International Journal of Computational Intelligence Systems SP - 336 EP - 346 VL - 10 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2017.10.1.23 DO - 10.2991/ijcis.2017.10.1.23 ID - Wu2017 ER -