Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study


To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine.

DOI Code: 10.1285/i20705948v10n1p14

Keywords: Artificial Neural Network; Time Series; Forecasts, ARIMA; Regression; Stock Prices


Abdallah M. Al-Habeel,, Professor of Statistics, Al-Azhar University, Palestine.

Raed Salha, Associate Professor of Statistics, The Islamic University of Gaz, Palestine.

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