Title: Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network
Authors: Sainan Wang; Luda Wang; Shouping Gao; Zhi Bai
Addresses: Hunan Ecectrical College of Technology, Xiangtan, 411101, China ' Xiangnan University, Chenzhou, 423000, China; School of Information Science and Engineering, Central South University, Changsha, 410075, China ' Xiangnan University, Chenzhou, 423000, China ' Hunan Ecectrical College of Technology, Xiangtan, 411101, China
Abstract: The stock market is an important part of the capital market, which plays a significant role in optimising capital allocation, financing and increasing the value of assets and other areas. Hence, the correct model for estimating and predicting the stock price has a very important practical significance to provide investors with investment decision reference. In this paper, a novel chaotic hybrid PSO-based RBF neural network model (CHPSO-RBFNN) has been proposed for forecasting the stock price, which can effectively prevent the RBF neural network from the local minimum trap and provide great learning ability. The presented methodology was tested with stock 601998, and the results showed that CHPSO-RBFNN can improve the prediction of accuracy and a high efficient and accurate stock prediction model compared to the traditional RBFNN and PSO-RBFNN methods.
Keywords: stock price; RBF neural network; optimisation; chaotic hybrid particle swarm algorithm.
International Journal of Applied Decision Sciences, 2017 Vol.10 No.2, pp.89 - 100
Available online: 18 May 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article