A Stock Decision Support System Based on ELM

  • Chengzhang Zhu
  • Jianping Yin
  • Qian Li
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)


People often tend to use a reliable way to predict the stock market in order to get a substantial return on investment. However, with plenty of uncertainty and noise, prediction is full of challenging and risk when it comes to stock markets. This chapter combines extreme learning machine (ELM) and the Oscillation box theory together to construct a stock decision support system, which can help people make decisions on stock trading through suggestion buy or sell stock. In experiments, 4 typical stock movements have been tested trading and 400 stocks in S&P500 are used to detect the performance of the system. Results show that our method is much better than buy-and-hold strategy.


Stock predict ELM Oscillation box theory 



This work was supported by the National Natural Science Foundation of China (Project No. 60970034, 61170287 and 61232016).


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.School of EconomicsMinzu University of ChinaBeijingChina

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