Inter-transaction association rule mining in the Indonesia stock exchange market

Abstract

Stock exchanges have a major impact on Indonesia economy condition as well as on the global economy. Stock activities forecasting is still a challenging issue which is a high demand for stock actors. Therefore, there is still a need to develop an application that is capable to accurately predict directions of stock price movement. This research proposes a data mining technique to model relationship between company stocks with other company stocks listed in the Indonesia Stock Exchange in a form of association rules. It is expected that extracted rules can be of a help to predict future stock prices movements with significant level of accuracy.