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Empirical analysis: stock market prediction via extreme learning machine

  • Extreme Learning Machine and Applications
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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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Notes

  1. To inter-day value traders and news traders, they usually take the overnight news articles into consideration, as they analyze the mid- or long-term impacts of news, such as daily, weekly and even monthly. However, for high frequency trading, they usually make use of intra-day short-term signals. Since before the main trading hours, there is an auction session in H-share market, the impact of the news overnight is assumed to be reflected in the auction prices and absorbed before continuous session starts. Therefore, regarding to our platform’s prediction frequency, we only keep intra-day news. This approach also follows the method in many previous works in computer science, e.g., Schumaker et al. [31].

  2. Software is downloaded on ictclas.org.

  3. Levenberg–Marquardt algorithm has its implementation in MATLAB 7.14 toolbox.

  4. Notation #(X) indicates the number of object X.

  5. Online dynamic reconfiguration is not discussed in the paper, since all the four models are not online algorithms and training can be setup as an overnight job. We record the training time of each model and present them in Table 9. As we can see from the results, the training time of four models is much longer than validation and testing time, and the time of BP-NN is much longer than the time of the other three models.

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Acknowledgments

This work was partly supported by National Natural Science Foundation of China (Grant No. 61300137); the Guangdong Natural Science Foundation, China (Grant No. S2011040002222); the Fundamental Research Funds for the Central Universities, SCUT (Grant No. 2012ZM0077). This work was also supported by Shenzhen New Industry Development Fund under grant No. JCYJ20120617120716224.

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Xiaodong Li, Haoran Xie & Ran Wang

  2. Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518067, China

    Ran Wang

  3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Yi Cai & Huaqing Min

  4. School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Jingjing Cao

  5. State Key Lab of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Feng Wang

  6. AIMS Lab, Department of Computer Science, Shanghai Jiaotong University, Shanghai, 200240, China

    Xiaotie Deng

  7. Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, Shanghai, 200433, China

    Xiaotie Deng

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  1. Xiaodong Li
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  2. Haoran Xie
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  3. Ran Wang
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  4. Yi Cai
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  6. Feng Wang
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  8. Xiaotie Deng
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Correspondence to Yi Cai.

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Li, X., Xie, H., Wang, R. et al. Empirical analysis: stock market prediction via extreme learning machine. Neural Comput & Applic 27, 67–78 (2016). https://doi.org/10.1007/s00521-014-1550-z

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