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Title

FORECASTING STOCK PRICES VIA COMBINED NEURAL NETWORKS

Pages

  125-138

Abstract

 Forecasting is a process of picturing the future condition according to existing data. For more than a decade, time series forecasting has been focused on, especially in finance management. The artificial neural network is a flexible learning method for time series forecasting. STOCK PRICE forecasting is possible by detecting the behavioral patterns of the process of the STOCK PRICE fluctuation. In this paper, we focus on the development of an innovative stock market price forecasting model based on artificial neural network .The model contains two levels. First level NEURAL NETWORKS, or the independent base forecasters, forecast STOCK PRICEs, VOLUME OF TRANSACTIONS, and PRICE CHANGES. In second level, another neural network as combiner does the final forecasting according to the outputs of first level. For this purpose, we have used three types of data reported daily in Tehran stock exchange (TSE). One important aim of our research is to use various types of stock market data in order to achieve better forecasts. The practical results of the study show that combined NEURAL NETWORKS model has a high precision and it is suitable for STOCK PRICE forecasting.

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    APA: Copy

    MIRZAZADEH, HOJAT, & TAVAKOLI MOHAMADI, MOHAMAD. (2011). FORECASTING STOCK PRICES VIA COMBINED NEURAL NETWORKS. JOURNAL OF INDUSTRIAL MANAGEMENT, 6(15), 125-138. SID. https://sid.ir/paper/171091/en

    Vancouver: Copy

    MIRZAZADEH HOJAT, TAVAKOLI MOHAMADI MOHAMAD. FORECASTING STOCK PRICES VIA COMBINED NEURAL NETWORKS. JOURNAL OF INDUSTRIAL MANAGEMENT[Internet]. 2011;6(15):125-138. Available from: https://sid.ir/paper/171091/en

    IEEE: Copy

    HOJAT MIRZAZADEH, and MOHAMAD TAVAKOLI MOHAMADI, “FORECASTING STOCK PRICES VIA COMBINED NEURAL NETWORKS,” JOURNAL OF INDUSTRIAL MANAGEMENT, vol. 6, no. 15, pp. 125–138, 2011, [Online]. Available: https://sid.ir/paper/171091/en

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