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Information Journal Paper

Title

TEHRAN STOCK EXCHANGE INDEX RETURN FORECASTING BY RADIAL BASIS FUNCTION NEURAL NETWORKS

Pages

  75-92

Keywords

RBFQ2

Abstract

 Until now some methods have been used for stock return FORECASTING and index return FORECASTING, and artificial intelligence and neural networks are one of them. We sought to evaluate the performance of redial basis neural networks to predict the index return. To this purpose, Tehran Stock Exchange index has been used and the performance of radial basis function neural network and perceptron neural networks are compared. Performance testing of neural networks based on least square error approach in both the in sample FORECASTING and out sample FORECASTING. Result of this study showed that for in sample approach radial basis function neural network has better performance and for out sample FORECASTING perceptron. Each one of these methods has strength and weakness of them but in the way we want to use them we can chose each one base on our usage. Base on this research choosing between NNS can be clear for investors and users.

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  • Cite

    APA: Copy

    TEHRANI, R., & MORADPOOR, S.. (2012). TEHRAN STOCK EXCHANGE INDEX RETURN FORECASTING BY RADIAL BASIS FUNCTION NEURAL NETWORKS. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 3(10), 75-92. SID. https://sid.ir/paper/197836/en

    Vancouver: Copy

    TEHRANI R., MORADPOOR S.. TEHRAN STOCK EXCHANGE INDEX RETURN FORECASTING BY RADIAL BASIS FUNCTION NEURAL NETWORKS. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)[Internet]. 2012;3(10):75-92. Available from: https://sid.ir/paper/197836/en

    IEEE: Copy

    R. TEHRANI, and S. MORADPOOR, “TEHRAN STOCK EXCHANGE INDEX RETURN FORECASTING BY RADIAL BASIS FUNCTION NEURAL NETWORKS,” FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), vol. 3, no. 10, pp. 75–92, 2012, [Online]. Available: https://sid.ir/paper/197836/en

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