Support Vector Machine-based Stock Prediction Analysis
DOI:
https://doi.org/10.54097/hbem.v3i.4625Keywords:
Support Vector Machines; Stock Forecasting.Abstract
Changes in the stock market are closely related to the dynamics of the market development and economic research of the whole country. Correctly predicting the stock price trend is not only beneficial for investors to make correct investment management decisions but also of great importance to promote the effective allocation of resources and improve market effectiveness. To this end, the empirical prediction and analysis of a stock development market using a support vector machine show that the accuracy of stock prediction using a support vector classification machine is as high as about 90%, indicating that the accuracy of stock prediction based on support vector machine is ideal and meaningful.
Downloads
References
Li, Hang. Statistical learning methods. Beijing: Tsinghua University Press,2012.
Xixi Xu. Research on stock price prediction and investment strategy based on support vector machine. Xi'an: Northwest University Academic, 2018.
Shahi Tej Bahadur, Shrestha Ashish, Neupane Arjun, Guo William. stock Price Forecasting with Deep Learning: A Comparative Study. Mathematics,2020,8(9).
Zhang, X.-G. On statistical learning theory and support vector machines. Journal of Automation, 2000,01:36-46.
Gu Yaxiang. Ding S. F. Advances in support vector machine research. Computer Science, 2011,02:14-17.
Tang, Inv. Research on support vector machine algorithm based on statistical learning theory. Huazhong University of Science and Technology, 2005.08.
Chen, Yi-Bing. Zhang L. L., Nie G. L. et al. financial time series forecasting based on improved support vector regression machine. Mathematical Practice and Perceptions, 2012, 42(04): 38-44.
Jun Wei. An efficient support vector machine parameter optimization algorithm. Computer Technology and Development. 2015,25(12):97-104.
Liu M, Zhou S, Wu H. A new hybrid kernel function support vector machine. Computer Applications, 2009, 29(S2):167-168.
Yang XW, Hao ZF. Algorithm design and analysis of support vector machines. Beijing: Science Press, 2013.
Ren, Y. F. Some research on SVM model improvement. Nanjing University of Posts and Telecommunications, 2013.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
