Abstract
The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.
Highlights
The stock market is instrumental in measuring the strength or weakness of a country's economy
Proposed models are for the general index of Qatar stock exchange and for the usages in other several sectors
After applying several models, such as: Quadratic trend model, double exponential smoothing model, and Autoregressive Moving Average (ARIMA), it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index
Summary
- Model forecasting of phase composition of electrolytic alloys Co-Ni-Mn (part 1) V V Schmidt and I G Zhikhareva. - Combined deterministic – stochastic forecasting of monthly river flows for water management E Peksova Szolgayova, R Vyleta, J Szolgay et al. ICMAE'17 IOP Conf. 1, 2 Department of Mathematics, Statistics and Physics, College of Arts and Science, Qatar University, P.O. Box 2713, Doha, Qatar
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