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

Samir Safi; Alexander White

doi:10.1285/i20705948v10n1p14

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

Samir Safi, Alexander White

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

To compare the forecast accuracy, Artificial Neural Networks and Autoregressive Integrated Moving Average 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 short and long-term time series of stock closing prices from Palestine.

Original language	English
Pages (from-to)	14-28
Number of pages	15
Journal	Electronic Journal of Applied Statistical Analysis
Volume	10
Issue number	1
DOIs	https://doi.org/10.1285/i20705948v10n1p14
Publication status	Published - 2017
Externally published	Yes

Keywords

ARIMA
Artificial Neural Network
Forecasts
Stock Prices
Time Series

ASJC Scopus subject areas

Statistics and Probability
Modelling and Simulation

Access to Document

10.1285/i20705948v10n1p14

Cite this

@article{fa84ee7e55e14c2f82b004d7546a9408,

title = "Short and long-term forecasting using artificial neural networks for stock prices in Palestine: A comparative study",

abstract = "To compare the forecast accuracy, Artificial Neural Networks and Autoregressive Integrated Moving Average 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 short and long-term time series of stock closing prices from Palestine.",

keywords = "ARIMA, Artificial Neural Network, Forecasts, Stock Prices, Time Series",

author = "Samir Safi and Alexander White",

note = "Publisher Copyright: {\textcopyright} Universit{\'a} del Salento.",

year = "2017",

doi = "10.1285/i20705948v10n1p14",

language = "English",

volume = "10",

pages = "14--28",

journal = "Electronic Journal of Applied Statistical Analysis",

issn = "2070-5948",

publisher = "ESE - Salento University Publishing",

number = "1",

}

TY - JOUR

T1 - Short and long-term forecasting using artificial neural networks for stock prices in Palestine

T2 - A comparative study

AU - Safi, Samir

AU - White, Alexander

N1 - Publisher Copyright: © Universitá del Salento.

PY - 2017

Y1 - 2017

N2 - To compare the forecast accuracy, Artificial Neural Networks and Autoregressive Integrated Moving Average 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 short and long-term time series of stock closing prices from Palestine.

AB - To compare the forecast accuracy, Artificial Neural Networks and Autoregressive Integrated Moving Average 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 short and long-term time series of stock closing prices from Palestine.

KW - ARIMA

KW - Artificial Neural Network

KW - Forecasts

KW - Stock Prices

KW - Time Series

UR - http://www.scopus.com/inward/record.url?scp=85018784554&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85018784554&partnerID=8YFLogxK

U2 - 10.1285/i20705948v10n1p14

DO - 10.1285/i20705948v10n1p14

M3 - Article

AN - SCOPUS:85018784554

SN - 2070-5948

VL - 10

SP - 14

EP - 28

JO - Electronic Journal of Applied Statistical Analysis

JF - Electronic Journal of Applied Statistical Analysis

IS - 1

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this