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Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA

Author

Listed:
  • Saeed Hajibabaei

    (Department of Art and Architecture, Hamedan Branch, Islamic Azad University, Hamedan, Iran.)

  • Nematollah Hajibabaei

    (Department of Managment, Buin Zahra Branch, Islamic Azad University, Buin Zahra, Iran.)

  • Seyed Mohammad Hoseini

    (Department of Art and Managment, Malayer Branch, Islamic Azad University, Malayer, Iran.)

  • Somaye Hajibabaei

    (Department of Accounting, Hamedan Branch, Islamic Azad University, Hamedan, Iran.)

  • Sajad Hajibabaei

    (Department of Art and Architecture, Hamedan Branch, Islamic Azad University, Hamedan, Iran.)

Abstract

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.

Suggested Citation

  • Saeed Hajibabaei & Nematollah Hajibabaei & Seyed Mohammad Hoseini & Somaye Hajibabaei & Sajad Hajibabaei, 2014. "Tehran Stock Price Modeling and Forecasting Using Support Vector Regression (SVR) and Its Comparison with the Classic Model ARIMA," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 18(2), pages 105-130, Spring.
    • Handle: RePEc:eut:journl:v:18:y:2014:i:2:p:105
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    References listed on IDEAS

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    1. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    2. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.

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