BIG DATA ALGORITHMS AND PREDICTION: BINGOS AND RISKY ZONES IN SHARIA STOCK MARKET INDEX

Shahid Anjum () and Naveeda Qaseem ()
Additional contact information
Shahid Anjum: School of Business Universiti Teknologi Brunei
Naveeda Qaseem: University of Westminster

Journal of Islamic Monetary Economics and Finance, 2019, vol. 5, issue 3, 475-490

Abstract: Each country with a stock exchange normally calculates various indexes. So is the case for Malaysia’s Kuala Lumpur Stock exchange (KLSE). FTSE BURSA Malaysia EMAS Sharia price index (FTBMEMA) is one of its Sharia indexes. In an effort to find which other indices may forecast this Sharia index, we selected 23 relevant indexes and two exchange rates. Momentum indicators for short, medium and long term have been calculated for the variables. The objective of this study is to find predictive indicators for FTBMEMA out of the population of 188 original and derived variables. Difficulty arises in reducing the number of variables for regression or other predictive models like neural networks. In this preliminary study, data mining attribute selection algorithms along with cross validation criteria have been used, through the use of Java class library Weka (JCLW), for reducing the number to statistically relevant variables for our regression estimation in an effort to forecast various performance parameters for FTBMEMA like performing either in a mean performance range, having jackpots and bingos or falling into danger zones. Provided the extent of the required predictive accuracy, the results may bring additional insights for diversifying and hedging various types of investment portfolios as well as for maximizing returns by portfolio managers.

Keywords: Sharia Stock Market Index; WEKA Class Library; Big Data Mining; Attributes Selection; Prediction Analysis (search for similar items in EconPapers)
JEL-codes: C45 D53 E37 G17 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://jimf-bi.org/index.php/JIMF/article/view/1151/772 (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:idn:jimfjn:v:5:y:2019:i:3a:p:475-490

DOI: 10.21098/jimf.v5i3.1151

Access Statistics for this article

Journal of Islamic Monetary Economics and Finance is currently edited by Dr. Ali Sakti

More articles in Journal of Islamic Monetary Economics and Finance from Bank Indonesia Contact information at EDIRC.
Bibliographic data for series maintained by Lutzardo Tobing ( this e-mail address is bad, please contact ) and Jimmy Kathon ().