Short and Long-Term Forecasting Using Artificial Neural Networks for Stock Prices in Palestine: A Comparative Study
MetadataShow full metadata
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.
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.
Quest'opera è distribuita con Licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia.