Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices

Mohammed Ayoub Ledhem

Journal of Capital Markets Studies, 2022, vol. 6, issue 2, 130-147

Abstract: Purpose - The purpose of this paper is to predict the daily accuracy improvement for the Jakarta Islamic Index (JKII) prices using deep learning (DL) with small and big data of symmetric volatility information. Design/methodology/approach - This paper uses the nonlinear autoregressive exogenous (NARX) neural network as the optimal DL approach for predicting daily accuracy improvement through small and big data of symmetric volatility information of the JKII based on the criteria of the highest accuracy score of testing and training. To train the neural network, this paper employs the three DL techniques, namely Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG). Findings - The experimental results show that the optimal DL technique for predicting daily accuracy improvement of the JKII prices is the LM training algorithm based on using small data which provide superior prediction accuracy to big data of symmetric volatility information. The LM technique develops the optimal network solution for the prediction process with 24 neurons in the hidden layer across a delay parameter equal to 20, which affords the best predicting accuracy based on the criteria of mean squared error (MSE) and correlation coefficient. Practical implications - This research would fill a literature gap by offering new operative techniques of DL to predict daily accuracy improvement and reduce the trading risk for the JKII prices based on symmetric volatility information. Originality/value - This research is the first that predicts the daily accuracy improvement for JKII prices using DL with symmetric volatility information.

Keywords: Deep learning; Jakarta Islamic Index (JKII); NARX neural network; Small and big data; Symmetric volatility information; Training algorithm (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:jcmspp:jcms-12-2021-0041

DOI: 10.1108/JCMS-12-2021-0041

Access Statistics for this article

Journal of Capital Markets Studies is currently edited by Prof Guler Aras

More articles in Journal of Capital Markets Studies from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().