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 casefor Malaysia’s Kuala Lumpur Stock exchange (KLSE). FTSE BURSA Malaysia EMASSharia price index (FTBMEMA) is one of its Sharia indexes. In an effort to find whichother indices may forecast this Sharia index, we selected 23 relevant indexes and twoexchange rates. Momentum indicators for short, medium and long term have beencalculated for the variables. The objective of this study is to find predictive indicatorsfor FTBMEMA out of the population of 188 original and derived variables. Difficultyarises in reducing the number of variables for regression or other predictive modelslike neural networks. In this preliminary study, data mining attribute selectionalgorithms along with cross validation criteria have been used, through the use of Javaclass library Weka (JCLW), for reducing the number to statistically relevant variablesfor our regression estimation in an effort to forecast various performance parametersfor FTBMEMA like performing either in a mean performance range, having jackpotsand bingos or falling into danger zones. Provided the extent of the required predictiveaccuracy, the results may bring additional insights for diversifying and hedgingvarious types of investment portfolios as well as for maximizing returns by portfoliomanagers.