Combination Forecasting of Stock Index Time Series based on Cooperative Game Theory

Authors

Luo Wei, Hunan Railway Professional Technology College, Zhuzhou 412001, China;

Abstract

Abstract: In view of the characteristics of nonlinear, large amplitude, frequent fluctuations in China's stock market, a prediction method of intelligent composite stock index time series based on the cooperative game is presented. The prediction model of stock index time series is established by using neural network method based on the correlations among the various economic indicators, and the development trend and laws of stock index time series are established by using the improved ARIMA method. The two methods are combined by importing cooperative game method. Simulation results show that the prediction accuracy of the presented method is controlled within 6.6% effectively, which has greater advantage than the single model in the index evaluation such as RMSE, MAPE and F.

Recommended Citation

Wei, Luo (2018) "Combination Forecasting of Stock Index Time Series based on Cooperative Game Theory," Journal of System Simulation: Vol. 30: Iss. 6, Article 11.
DOI: 10.16182/j.issn1004731x.joss.201806011
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss6/11

First Page

2086

Revised Date

2016-11-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201806011

Last Page

2094

CLC

TP273+.21

Recommended Citation

Luo Wei. Combination Forecasting of Stock Index Time Series based on Cooperative Game Theory[J]. Journal of System Simulation, 2018, 30(6): 2086-2094.

DOI

10.16182/j.issn1004731x.joss.201806011