چکیده:
Use of linear and non-linear models to predict the stock price has been paid attention to by investors, researchers and students of finance and investment companies, and organizations active in the field of stock. Timely forecasting stock price can help managers and investors to make better decisions. Nowadays, the use of non-linear methods in modeling and forecasting financial time series is quite common. In recent years, one of the new techniques of data mining with support vector regression (SVR) has had successful application in time series prediction. In this study, using support vector regression model, we examined the Tehran Stock prices and the predicted results were compared with ARIMA classic model. For this purpose, of the Tehran stock companies, 5 companies were selected during the years 2002 to 2012. Using benchmarks to evaluate the performance of MSE, MAE, NMSE these two methods were compared and the results (in the case of non-linear data) indicate the superiority of SVR model compared to the ARIMA model.
خلاصه ماشینی:
"Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA Saeed hajibabaei* Nematollah hajibabaei** Syed mohammad hoseini*** Somaye hajibabaei**** sajad hajibabaei***** Received: 2013/04/16 Accepted: 2013/12/11 * MSc; Department of Art and Architecture, Hamedan Branch, Islamic Azad University, Hamedan, Iran(Corresponding Author) ** MSc Candidate; Department of MANAGMENT, Buin zahra Branch, Islamic Azad University, Buin zahra, Iran *** MSc Candidate; Department of Art and MANAGMENT, malayer Branch, Islamic Azad University, malayer, Iran **** MSc; Department of Accounting, Hamedan Branch, Islamic Azad University, Hamedan, Iran ***** MSc Candidate ; Department of Art and Architecture, Hamedan Branch, Islamic Azad University, Hamedan, Iran Abstract se of linear and non-linear models to predict the stock price hasbeen paid attention to by investors, researchers and students offinance and investment companies, and organizations active in the field of stock.
Keywords: stock investment, stock price forecasting, data mining, support vector regression, ARIMA models 1- Introduction Achieving the long-term and continuous economic growth demands optimal allocation of resources at the national economics level which is impossible to obtain without the help of financial markets, especially extensive capital market .
regarding the nonlinear data, we use a Gaussian RBF kernel function as applied in most applications of time series prediction (tay and cao, 1992) and a grid search method is used to select the model parameters in that taking two parameters constant, the values of the parameters will change based on the lowest MSE in data validation to select the best parameter value."