Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15846
Title: Using a fuzzy association rule mining approach to identify the financial data association
Authors: Ho, GTS
Ip, WH 
Wu, CH
Tse, YK
Keywords: Financial data association
Fuzzy association rule
Fuzzy set theory
Hang Seng Index
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 10, p. 9054-9063 How to cite?
Journal: Expert systems with applications 
Abstract: In the rapidly changing financial market, investors always have difficulty in deciding the right time to trade. In order to enhance investment profitability, investors desire a decision support system. The proposed artificial intelligence methodology provides investors with the ability to learn the association among different parameters. After the associations are extracted, investors can apply the rules in their decision support systems. In this work, the model is built with the ultimate goal of predicting the level of the Hang Seng Index in Hong Kong. The movement of Hang Seng Index, which is associated with other economics indices including the gross domestic product (GDP) index, the consumer price index (CPI), the interest rate, and the export value of goods from Hong Kong, is learnt by the proposed method. The case study shows that the proposed method is a feasible way to provide decision support for investors who may not be able to identify the hidden rules between the Hang Seng Index and other economics indices.
URI: http://hdl.handle.net/10397/15846
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2012.02.047
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