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Predicting Stock Price Using Two-Stage Machine Learning Techniques

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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.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No.71720107002), the Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan (CIT&TCD20190338), and the Humanity and Social Science Foundation of Ministry of Education of China (No. 19YJAZH005), and the Young Academic Innovation Team of Capital University of Economics and Business of China (No. QNTD202002).

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  1. School of Management and Engineering, Capital University of Economics and Business, Beijing, China

    Jun Zhang, Lan Li & Wei Chen

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  1. Jun Zhang
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Correspondence to Wei Chen.

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Zhang, J., Li, L. & Chen, W. Predicting Stock Price Using Two-Stage Machine Learning Techniques. Comput Econ 57, 1237–1261 (2021). https://doi.org/10.1007/s10614-020-10013-5

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