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Title: Application of Artificial Neural Network to Stock Forecasting – Comparison with SES and ARIMA
Authors: Alhassan, J. K.
Abdullahi, M. B.
Lawal, J
Keywords: : Artificial Neural Networks; Forecasting; Stock; Single Exponential Smoothening; Autoregressive-Integrated-Moving-Average
Issue Date: 2014
Publisher: Scientific Press, International Limited
Series/Report no.: Volume 4 Number 2;
Abstract: Stock market also known as equity market is a public entity which is a loose network of economic transactions, not a physical facility or discrete entity for the trading of company stock or shares and derivatives at an agreed price. Artificial Neural Network (ANN) is a field of Artificial Intelligence (AI), which is a common method to identify unknown and hidden patterns in data which is suitable for stock market prediction. In this study we applied a time-delayed neural network model for forecasting future price of stock by using Artificial Neural Network (ANN) methodology. We compared ANN with Single Exponential Smoothening (SES) and Autoregressive-Integrated-Moving-Average (ARIMA) models, the ANN forecasting tool proved to be more precise than the SES and ARIMA as it had a smaller Root Mean Squared Error (RMSE) of 0.686 as compared to the RMSE of the SES which was 2.7400 and ARIMA which was 1.6570.
ISSN: 1792-8850
Appears in Collections:Computer Science

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