Support Vector Machines Regression Model and Comparison with Semi-parametric Regression

‎‎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.