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- Title
Structured Maximum Margin Twin Support Vector Machine and Its Application in Stock Trend Prediction.
- Authors
LIN Mingsong; YANG Xiaomei; YANG Zhixia
- Abstract
The stock price is affected by many factors, such as policy, macro-economy and the company's operating conditions, among which there is a certain degree of correlation. So the stock data of high noise and non- stationary feature makes stock prediction difficult. Based on the separability between classes of Mahalanobis distance, this paper proposes structured maximum margin twin suport vector machine (SMM-TWSVM). The method finds two nonparallel hyperplane for positive class samples and negative class samples respectively, so that the Euclidean distance of each class of samples from their own hyperplane is as small as possible, and the Mahalanobis distance of divorced class hyperplane is as large as possible. The experimental results of 8 benchmark datasets show that this method has a stable accuracy in the classification of noisy data, thus improving the prediction performance and anti-noise ability of the model. Meanwhile, it is applied to the prediction of the fluctuation tend of stock price, through the empirical analysis of 14 stocks such as Ping An of China and Shanghai Composite Index, Shanghai A Index, Shanghai 380 Index, the results show that compared with other comparison models, SMM-TWSVM shows better prediction results and has certain practical value.
- Subjects
SUPPORT vector machines; EUCLIDEAN distance; VALUE (Economics); FORECASTING
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 11, p346
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2303-0032