Empirical analysis: stock market prediction via extreme learning machine |
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Authors: | Xiaodong Li Haoran Xie Ran Wang Yi Cai Jingjing Cao Feng Wang Huaqing Min Xiaotie Deng |
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Affiliation: | 1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong 2. Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518067, China 3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China 4. School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China 5. State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China 6. AIMS Lab, Department of Computer Science, Shanghai Jiaotong University, Shanghai, 200240, China 7. Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, Shanghai, 200433, China
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Abstract: | How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. Comprehensive experimental comparisons between ELM and the state-of-the-art learning algorithms, including support vector machine (SVM) and back-propagation neural network (BP-NN), have been undertaken on the intra-day tick-by-tick data of the H-share market and contemporaneous news archives. The results have shown that (1) both RBF ELM and RBF SVM achieve higher prediction accuracy and faster prediction speed than BP-NN; (2) the RBF ELM achieves similar accuracy with the RBF SVM and (3) the RBF ELM has faster prediction speed than the RBF SVM. Simulations of a preliminary trading strategy with the signals are conducted. Results show that strategy with more accurate signals will make more profits with less risk. |
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