A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. (1st September 2017)

• Record Type: Journal Article
• Authors: Chen, Yingjun; Hao, Yongtao
• Is Part Of: Expert systems with applications. Volume 80(2017)
• Page Start: 340
• Page End: 355
• Publication Date: 2017-09-01
• Subjects: Feature weighted SVM (FWSVM) -- Information gain -- Feature weighted K-nearest neighbor (FWKNN) -- Stock market indices; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.02.044
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This study investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this paper, we propose a basic hybridized framework of the feature weighted support vector machine as well as feature weighted K -nearest neighbor to effectively predict stock market indices. We first establish a detailed theory of feature weighted SVM for the data classification assigning different weights for different features with respect to the classification importance. Then, to get the weights, we estimate the importance of each feature by computing the information gain. Lastly, we use feature weighted K-nearest neighbor to predict future stock market indices by computing k weighted nearest neighbors from the historical dataset. Experiment results on two well known Chinese stock market indices like Shanghai and Shenzhen stock exchange indices are finally presented to test the performance of our established model. With our proposed model, it can achieve a better prediction capability to Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index in the short, medium and long term respectively. The proposed algorithm can also be adapted to other stock market indices prediction.