Short and Long-Term Forecasting Using Artificial Neural Networks for Stock Prices in Palestine: A Comparative Study

Simple item page
dc.contributor.authorSafi, Samir
dc.contributor.authorWhite, Alexander
dc.date.accessioned2019-12-18T14:43:07Z
dc.date.available2019-12-18T14:43:07Z
dc.date.issued2017-04
dc.description.abstractTo 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.
dc.description.departmentMathematics
dc.formatText
dc.format.extent16 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationSafi, S., & White, A. (2017). Short and long-term forecasting using artificial neural networks for stock prices in Palestine: A comparative study. Electronic Journal of Applied Statistical Analysis, 10(1), pp. 14-28.
dc.identifier.doihttps://doi.org/10.1285/i20705948v10n1p14
dc.identifier.issn2070-5948
dc.identifier.urihttps://hdl.handle.net/10877/9101
dc.language.isoen
dc.publisherUniversita del Salento
dc.rights.licenseQuest'opera è distribuita con Licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia.
dc.sourceElectronic Journal of Applied Statistical Analysis, 2017, Vol. 10, No. 1, pp. 14-28.
dc.subjecttime series
dc.subjectforecasts
dc.subjectARIMA
dc.subjectregression
dc.subjectstock prices
dc.subjectartificial neural network
dc.subjectMathematics
dc.titleShort and Long-Term Forecasting Using Artificial Neural Networks for Stock Prices in Palestine: A Comparative Study
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
15803-122382-1-PB.pdf
Size:
2.45 MB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.54 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

College of Science and Engineering