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

Faheem Aslam (Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan)
Khurrum S. Mughal (Research Department, State Bank of Pakistan, Karachi, Pakistan)
Ashiq Ali (Management Sciences, Riphah International University Islamabad Campus, Rawalpindi, Pakistan)
Yasir Tariq Mohmand (Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan)

Journal of Economic and Administrative Sciences

ISSN: 1026-4116

Article publication date: 10 September 2020

Issue publication date: 29 April 2021

168

Abstract

Purpose

The purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).

Design/methodology/approach

The 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.

Findings

The 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 implications

The findings of this study may have important implications for investment and risk management by using index-based products.

Originality/value

Numerous 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.

Keywords

Acknowledgements

Disclaimer: The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of their institution.

Citation

Aslam, F., Mughal, K.S., Ali, A. and Mohmand, Y.T. (2021), "Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators", Journal of Economic and Administrative Sciences, Vol. 37 No. 2, pp. 253-271. https://doi.org/10.1108/JEAS-04-2020-0038

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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