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Support vector regression with modified firefly algorithm for stock price forecasting

  • Jun Zhang
  • Yu-Fan Teng
  • Wei ChenEmail author
Article
  • 71 Downloads

Abstract

The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.

Keywords

Stock price forecasting Support vector regression Firefly algorithm Opposition-based learning Chaotic 

Notes

Acknowledgments

This research was supported by the Beijing Social Science Fund (No. 18YJB007). The author Yu-Fan Teng acknowledges the support by Graduate Science and Technology Innovation Foundation from the Capital University of Economics and Business, Beijing, China.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of InformationCapital University of Economics and BusinessBeijingChina

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