Abstract
Aiming at Shenzhen component index in the stock market change trend prediction problem, a new method based on Principal component analysis and Support vector machine was proposed. Principal component analysis was used to calculate the influence factors of the share price of Shenzhen component index and the closing price of Shenzhen component index, and then the influence factors were reduced from high dimension to low dimension. From 1991 to 2018, More than 6,700 groups of data related to the Shenzhen component index were selected to test the method. The average relative error of the Shenzhen component index closing price calculated is 7.35%, the experimental results showed that. The convergence speed and accuracy of the experiment are improved by the principal component analysis method. As a result, based on principal component analysis and support vector machine, it can be able to make use of the data of known Shenzhen component index to speculate on the trend of closing prices. And it has a certain application prospect in the field of stock forecasting research.
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References
Lu, Z.: Why stock forecasting is difficult to work. Public Financ. Advis. 19(7), 90–91 (2009). (in Chinese)
Zhang, S.: Application of time series method in stock prediction. Shang 13(37), 186 (2014). (in Chinese)
Chen, H., Wang, X.: Stock price prediction based on PCA and CS-RBF neural network. Value Eng. 14(31), 142–143 (2014). (in Chinese)
Wang, G., Xu, X.: Stock prediction method based on wavelet analysis and neural network time series. Financ. Econ. 13(12), 161–163 (2013). (in Chinese)
Liu, Z.: Application of grey forecasting in stock forecasting. China Market 13(46), 143–144 (2013). (in Chinese)
Zhao, C., Geng, C.: Stock prediction and research based on neural network model. J. Econ. Res. 14(10), 97–99 (2014). (in Chinese)
Li, W., Sun, T.: Applying of GM(1, N) model in stock forecasting. Value Eng. 28(11), 152–154 (2009). (in Chinese)
Chen, X.: Research on stock prediction model based on data mining and neural network technology. Sci. Technol. Transf. Highlights 18(20), 57 (2008). (in Chinese)
Zhang, B.: Application of data mining in stock forecasting. Contemp. Econ. 17(8), 5–7 (2017). (in Chinese)
Wang, H., Liu, L., Liu, L.: Application research of BP neural network based on GRA and PCA. Manag. Rev. 19(10), 50–54 (2007). (in Chinese)
Shi, F., Wang, X., Yu, L., et al.: MATLAB Neural Network Analysis of 30 Cases. Beijing University of Aeronautics and Astronautics Press, Beijing (2013). (in Chinese)
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Wang, Y., Chen, T., Liu, W. (2020). Prediction of Shenzhen Component Index Based on PCA and SVM. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_56
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DOI: https://doi.org/10.1007/978-981-15-3250-4_56
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