Australian forex market anaylsis using connectionist models

Abraham, Ajith, Chowdhury, Morshed and Petrovic-Lazarevic, Sonja 2003, Australian forex market anaylsis using connectionist models, Journal of management: theory and practice, vol. 29, no. 8, pp. 18-22.

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Title Australian forex market anaylsis using connectionist models
Author(s) Abraham, Ajith
Chowdhury, Morshed
Petrovic-Lazarevic, Sonja
Journal name Journal of management: theory and practice
Volume number 29
Issue number 8
Start page 18
End page 22
Publisher Univerzitet u Beogradu, Fakultet Organizacionih Nauka
Place of publication Yugoslavia
Publication date 2003
ISSN 0354-8635
Keyword(s) forex prediction
neurocomputing
neuro-fuzzy computing
scaled conjugate gradient
Summary The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The forex market is difficult to understand by an average individual. However, once the market is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. This paper is an attempt to compare the performance of a Takagi-Sugeno type neuro-fuzzy system and a feed forward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. The exchange values of Australian dollar are considered with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pound. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that neuro-fuzzy technique performed better than the neural network.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
HERDC Research category C3 Non-refereed articles in a professional journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30012821

Document type: Journal Article
Collection: School of Information Technology
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