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http://hdl.handle.net/20.500.12358/25745
TitleShort and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study
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Abstract

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.

Authors
Safi, Samir K.
White, Alexander
TypeJournal Article
Date2017
Subjects
artificial neural network
time series, forecasts
arima
stock prices
Published inElectronic Journal of Applied Statistical Analysis
SeriesVolume: 10, Number: 1
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  • Staff Publications- Faculty of Economics and Administrative Sciences [292]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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