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:: Volume 26, Issue 2 (3-2022) ::
Andishe 2022, 26(2): 21-32 Back to browse issues page
Support Vector Machines Regression Model and Comparison with Semi-parametric Regression
Mahdi Roozbeh *, Arta Rouhi, Fatemeh Jahadi, Saeed Zalzadeh
Semnan University
Abstract:   (906 Views)

‎‎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 ‎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.

Keywords: ‎Regression Model‎, ‎Stock Forecasting‎, ‎Support Vector Regression Model‎.
Full-Text [PDF 255 kb]   (679 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2021/07/4 | Accepted: 2022/03/30 | Published: 2022/09/8
References
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Roozbeh M, Rouhi A, Jahadi F, Zalzadeh S. Support Vector Machines Regression Model and Comparison with Semi-parametric Regression. Andishe 2022; 26 (2) :21-32
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