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Predicting Stock Price Using Two-Stage Machine Learning Techniques

Author

Listed:
  • Jun Zhang

    (Capital University of Economics and Business)

  • Lan Li

    (Capital University of Economics and Business)

  • Wei Chen

    (Capital University of Economics and Business)

Abstract

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS). In the first stage, the future values of technical indicators are forecasted by SVR. In the second stage, ENANFIS is utilized to forecast the closing price based on prediction results of first stage. Finally, the proposed model SVR-ENANFIS is tested on 4 securities randomly selected from the Shanghai and Shenzhen Stock Exchanges with data collected from 2012 to 2017, and the predictions are completed 1–10, 15 and 30 days in advance. The experimental results show that the proposed model SVR-ENANFIS has superior prediction performance than single-stage model ENANFIS and several two-stage models such as SVR-Linear, SVR-SVR, and SVR-ANN.

Suggested Citation

  • Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:4:d:10.1007_s10614-020-10013-5
    DOI: 10.1007/s10614-020-10013-5
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