Big Data Algorithms And Prediction: Bingos And Risky Zones In Sharia Stock Market Index

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.