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In this study the ability of artificial neural network ANN in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs.

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Daily stock exchange rates of NASDAQ from January 28, to 18 June, are used to develop a robust model. First 70 days January 28 to March 7 are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset four prior days and nine prior days were developed and validated.

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Journal of Economics, Finance and Administrative Science Volume 21, Issue 41 , December , Pages open access. Author links open the author workspace. Numbers and letters correspond to the affiliation list.

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Click to expose these in author workspace Moein Hedayati Moghaddam. Opens the author workspace Opens the author workspace b. Click to expose these in author workspace Morteza Esfandyari. Click to expose these in author workspace a Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran b Faculty of Managing and Accounting, College of Farabi, University of Tehran, Qom, Iran c Department of Chemical Engineering, Faculty of Engineering, University of Bojnord, ,Bojnord, Iran.

Open Access funded by Universidad Esan. Under a Creative Commons license. Abstract In this study the ability of artificial neural network ANN in forecasting the daily NASDAQ stock exchange rate was investigated. Elsevier About ScienceDirect Remote access Shopping cart Contact and support Terms and conditions Privacy policy. Cookies are used by this site. For more information, visit the cookies page.