Stock market index prediction using artificial neural network

Amin Hedayati Moghaddam, Moein Hedayati Moghaddam, Morteza Esfandyari


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. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 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.



NASDAQ; ANN; prediction

Full Text:



  • There are currently no refbacks.

Copyright (c) 2017 The bi-annual academic publication of Universidad ESAN

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The Universidad ESAN, with more than 50 years of experience in the higher education field and post graduate studies, desires to contribute to the academic community with the most outstanding pieces of research. We gratefully welcome suggestions and contributions from our readers in order to help
us hit our goals.