Using a fuzzy association rule mining approach to identify the financial data association

Ho, G.T.S., Ip, W.H., Wu, C.H. and Tse, Y.K. ORCID: https://orcid.org/0000-0001-6174-0326 2012. Using a fuzzy association rule mining approach to identify the financial data association. Expert Systems with Applications 39 (10) , pp. 9054-9063. 10.1016/j.eswa.2012.02.047

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	0957-4174
Last Modified:	07 Nov 2022 09:59
URI:	https://orca.cardiff.ac.uk/id/eprint/130803

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