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Faculty of mathematics, Semnan university, Semnan, Iran.
Abstract
In this research, the aim is to assess and analyze a method to predict the stock market. However, it is not easy to predict the capital market due to its high dependence on politics but by data modeling, it will be somewhat possible to predict the stock market in the long period of time. In this regard, by using the semi-parametric regression models and support vector regression (SVR) with different kernels and measuring the predictor errors in the stock market of one stock based on daily fluctuations and comparing methods using the root of mean squared error and mean absolute percentage error criteria, support vector regression model has been the most appropriate fit to the real stock market data with radial kernel and error equal to 0.1.
Roozbeh, M., Rouhi, A., Jahadi, F. & Zalzadeh, S. (2022). Support Vector Machines Regression Model and Comparison with Semi-parametric Regression. Andishe_ye Amari, 26(2), 21-32.
MLA
Roozbeh, M., Rouhi, A., Jahadi, F., & Zalzadeh, S. "Support Vector Machines Regression Model and Comparison with Semi-parametric Regression", Andishe_ye Amari, 26, 2, 2022, 21-32.
HARVARD
Roozbeh M., Rouhi A., Jahadi F., Zalzadeh S. (2022). 'Support Vector Machines Regression Model and Comparison with Semi-parametric Regression', Andishe_ye Amari, 26(2), pp. 21-32.
CHICAGO
M. Roozbeh, A. Rouhi, F. Jahadi & S. Zalzadeh, "Support Vector Machines Regression Model and Comparison with Semi-parametric Regression," Andishe_ye Amari, 26 2 (2022): 21-32,
VANCOUVER
Roozbeh M., Rouhi A., Jahadi F., Zalzadeh S. Support Vector Machines Regression Model and Comparison with Semi-parametric Regression. Andishe_ye Amari. 2022;26(2):21-32 (In Persian).
