Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators

PurposeThe purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).Design/methodology/approachThe data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into trai...

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Bibliographic Details
Published in:Journal of economic and administrative sciences 2021-04, Vol.37 (2), p.253-271
Main Authors: Aslam, Faheem, Mughal, Khurrum S, Ali, Ashiq, Mohmand, Yasir Tariq
Format: Article
Language:English
Subjects:
Econometrics
Financial services
Forecasting
Investments
Investors
Islamic financing
Islamic law
Neural networks
Performance evaluation
Regression analysis
Securities markets
Stochastic models
Stock exchanges
Time series
Volatility
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Summary:PurposeThe purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).Design/methodology/approachThe data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.FindingsThe results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.Practical implicationsThe findings of this study may have important implications for investment and risk management by using index-based products.Originality/valueNumerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.
ISSN:1026-4116
2054-6246
DOI:10.1108/JEAS-04-2020-0038