Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

Yancong Xie; Hongxun Jiang

首页> 外文期刊>Journal of Computers >Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

【24h】

Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

机译：

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

News items have significant impact on stock markets but the ways is obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we collect 2,302,692 news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this dataset, a specific domain stop-word dictionary and a precise sentiment dictionary are formed. Thirdly, we propose a forecasting model using SVM. On the algorithm of SVM implementation, we also propose two parameter optimization algorithms to search for best initial parameter setting. Result shows that parameter G has the main effect, while parameter C's effect is not obvious. Furthermore, support vector regression (SVR) models for different Chinese stocks are similar whereas in support vector classification (SVC) models best parameters are quite differential. Series of contrast experiments show that: a) News has significant influence on stock market; b) Expansion input vector for additional situations when that day has no news data is better than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree of fitting in predicting stock fluctuation while such result has some time lag; d) News effect time lag for stock market is less than two days; e) In SVC, historic stock data has a most efficient time lag which is about 10 days, whereas in SVR this effect is not obvious. In addition, based on the special structure of the input vector, we also design a method to calculate the financial source impact factor. Result suggests that the news quality and audience number both have significant effect on the source impact factor. Besides, for Chinese investors, traditional media has more influence than digital media.

著录项

来源
《Journal of Computers》 |2017年第6期|500-510|共11页
作者
Yancong Xie; Hongxun Jiang;
展开▼
作者单位

Information Systems School, Queensland University of Technology, Brisbane, Australia & School of Information, Renmin University of China, Beijing, China;

School of Information, Renmin University of China, Beijing, China;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
Impact factor; parameter optimization; support vector machine; stock market; text mining;

相似文献

外文文献
中文文献

1. Forecasting stock returns based on information transmission across global markets using support vector machines [J] . Thenmozhi M., Chand G. Sarath Neural computing & applications . 2016,第4期

机译：Forecasting stock returns based on information transmission across global markets using support vector machines
2. Studies from China University of Mining and Technology Provide New Data on Support Vector Machines (A Multiprocess Joint Modeling Method for Performance Prediction of Nonlinear Industrial Processes Based On Multitask Least Squares Support ...) [J] . Robotics & Machine Learning Daily News . 2022,第3期

机译：Studies from China University of Mining and Technology Provide New Data on Support Vector Machines (A Multiprocess Joint Modeling Method for Performance Prediction of Nonlinear Industrial Processes Based On Multitask Least Squares Support ...)
3. Researchers from Changchun University of Technology Publish Findings in Support Vector Machines (Sound Based Fault Diagnosis Method Based on Variational Mode Decomposition and Support Vector Machine) [J] . Robotics & Machine Learning Daily News . 2022,第26期

机译：Researchers from Changchun University of Technology Publish Findings in Support Vector Machines (Sound Based Fault Diagnosis Method Based on Variational Mode Decomposition and Support Vector Machine)
4. 基于支持向量机灵敏度的轧制过程模型仿真（Model Simulations for Rolling Process by Sensitivity Based on Support Vector Machine） [C] . Chinese Control Conference vol.2; 20040810-13; Wuxi(CN) . 2004

机译：基于支持向量机灵敏度的轧制过程模型仿真（Model Simulations for Rolling Process by Sensitivity Based on Support Vector Machine）
5. Identification of Human Age Using Trace Element Concentrations in Hair and the Support Vector Machine Method [O] . Lv Jinmei(吕金妹), Wang WY, Zhang FY, 2011

机译：Identification of Human age Using Trace Element Concentrations in Hair and the support Vector machine method

Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method

摘要

著录项

相似文献

相关主题

期刊订阅