Stock price prediction of Chinese company using support vector machine
DOI:
https://doi.org/10.54691/bcpbm.v38i.4207Keywords:
Stock prediction; SVM; Random forest.Abstract
The accuracy of the prediction in the stock market has gained increasing importance in both research and investment in the market. Support vector machine which can be called SVM is one of important algorithms that plays a role dealing with classification problem and regression measure and the SVM with Pearson VII function-based universal kernel gained great accuracy of prediction of stock price in this paper with the lowest value of mean absolute error of 0.0006 among six selected measures such as random forest to make the prediction and the highest correlation coefficient (0.9999). Based on the collected data and the result of experiment, the efficiency of applying SVM in prediction of stock price has been strongly approved.
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Minaee S, Kalchbrenner N, Cambria E, et al. Deep learning--based text classification: a comprehensive review[J]. ACM Computing Surveys (CSUR), 2021, 54(3): 1-40.
Awad M, Khanna R. Support vector machines for classification[M]//Efficient learning machines. Apress, Berkeley, CA, 2015: 39-66.
Yingjun Chen, Yijie Hao, Integrating principle component analysis and weighted support vector machine for stock trading signals prediction, Neurocomputing (2018)
Ding G, Qin L. Study on the prediction of stock price based on the associated network model of LSTM[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(6): 1307-1317.
Minaee S, Kalchbrenner N, Cambria E, et al. Deep learning--based text classification: a comprehensive review[J]. ACM Computing Surveys (CSUR), 2021, 54(3): 1-40.
Kim K. Financial time series forecasting using support vector machines[J]. Neurocomputing, 2003, 55(1-2): 307-319.
O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, “Choosing Multiple Parameters for Support Vector Machines,” Machine Learning, vol. 46, pp. 131-159, 2002.
T. Eitrich, B. Lang, “Efficient optimization of support vector machine learning parameters for unbalanced datasets,” Journal of Computational and Applied Mathematics, vol. 196, issue 2, pp. 425-436, 2006.
S. Boughorbel, J. P. Tarel, N. Boujemaa, “The LCCP for Optimizing Kernel Parameters for SVM,” International Conference on Artificial Neural Networks, pp. 589-594, 2005.
Tharwat A. Parameter investigation of support vector machine classifier with kernel functions[J]. Knowledge and Information Systems, 2019, 61(3): 1269-1302.
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