Data mining for the global natural resources funds development. (2016)

• Record Type: Journal Article
• Authors: Liao, Shu-Hsien; Li, Chien-Wen; Tsai, Yi-Fang
• Is Part Of: International journal of intelligent information and database systems. Volume 9:Number 3/4(2016)
• Page Start: 289
• Page End: 314
• Publication Date: 2016
• Subjects: data mining -- association rules mining -- natural resources funds -- co-movement -- investment portfolios -- risk management -- fund development -- investment value -- big data analytics -- product design -- investment decision making -- commodity prices; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

After the financial crisis in 2008, easy money policy and fiscal policy implemented by countries have managed to aid the recovery of the global economic. Recovering economic activities and infrastructure projects have also increased the demand for raw material, pushing up commodity prices. Under these related demand drives, the exceptional performance of natural resources funds has proven its investment value. However, it is difficult to select proper funds or make investment choice with limited funds. Therefore, this study implements the big data analysis, the association rules, using SPSS Modeler as a tool to discover the co-movement knowledge of global natural resources fund market. This provides investors with feasible suggestions while creating their investment portfolios as well as different ways of investing their funds. On the other hand, from investment corporations' point of view, this study provides three sets of natural resources funds combinations for possible product design and investment decision making.