Stock Return Forecast with LS-SVM and Particle Swarm Optimization

被引:11
作者
Shen, Wei [1 ]
Zhang, Yunyun [2 ]
Ma, Xiaoyong [1 ]
机构
[1] North China Elect Power Univ, Sch Business & Adm, Beijing 102206, Peoples R China
[2] North China Elect Power Univ, Dept Econ & Adm, Baoding 071003, Peoples R China
来源
2009 INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, PROCEEDINGS | 2009年
关键词
Stock Return Forecast; Least Square Support Vector Machines; Dynamic Inertia Weight; Particle Swarm Optimization; NETWORKS;
D O I
10.1109/BIFE.2009.42
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce Least Square Support Vector Machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and Dynamic Inertia Weight Particle Swarm Optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this paper is as following: First, forecast daily stock Return of Shanghai Security Exchanges of China using Back Propagation Neural Network (BPNN) and LS-SVM. Secondly, forecast the stock return using LS-SVM optimized by W- PSO. Finally, make a comparative analysis of the three algorithms. We reached conclusion that, in terms of forecast accuracy, LS-SVM outperforms BPNN, and when LS-SVM is optimized by W-PSO, the best result is achieved.
引用
收藏
页码:143 / 147
页数:5
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