An enhanced LGSA-SVM for S&P 500 index forecast

被引：0

作者：

Wang, Jinxin ^{[1
]}

Liu, Zhengyang ^{[1
]}

Shang, Wei ^{[1
]}

Wang, Shouyang ^{[1
]}

机构：

[1] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2017年

关键词：

S&P 500; SVM; Gravitational Search Algorithm; Logistic Mapping; Opposition Based Learning; GRAVITATIONAL SEARCH ALGORITHM; NEURAL-NETWORKS; STOCK INDEXES; MARKET; VOLATILITY; FUTURES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The S&P 500 index is an important representative of worlds' financial market and is influenced by various economic factors. There is a call for automatically select antecedents of S&P 500 index's change in the fast-changing world economy. This paper proposes an enhanced GSA model named LGSA to solve the feature selection and parameter optimization of SVM models for the S&P 500 index movement prediction. The results show that the accuracy of LGSA-SVM model surpasses benchmark SVM, PSO-SVM and GA-SVM model. And the proposed approach could hopefully be adopted for other financial data series automatic forecasting.

引用

页码：4176 / 4183

页数：8

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