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Support Vector Machines Regression Model and Comparison with Semi-parametric Regression
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Mahdi Roozbeh *   , Arta Rouhi , Fatemeh Jahadi , Saeed Zalzadeh |
| Semnan University |
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Abstract: (1764 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. |
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| Keywords: Regression Model, Stock Forecasting, Support Vector Regression Model. |
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Full-Text [PDF 255 kb]
(1844 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2021/07/4 | Accepted: 2022/03/30 | Published: 2022/09/8
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