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Adaptive supervised learning decision networks for traders and portfolios

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)

Abstract

We propose a trading and portfolio management system named Adaptive Supervised Learning Decision Network, which learns the best past investment decision directly instead of making a good prediction first and then an investment decision based on the prediction. Without any extra efforts, this network can be realized directly by any existing adaptive supervised learning neural networks. Here, we propose to use a recent proposed adaptive Extended Normalized Radial Basis Function (ENRBF) network with Matched Competitive Learning (MCL). We demonstrate with experimental results that the proposed approach can bring in appreciable profit on trading in the foreign exchange market.

Original languageEnglish
Title of host publicationProceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)
PublisherIEEE
Pages206-212
Number of pages7
ISBN (Print)0780341333
DOIs
Publication statusPublished - 24 Mar 1997
Event1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, United States
Duration: 23 Mar 199725 Mar 1997

Publication series

NameProceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering

Conference

Conference1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, CIFEr
Country/TerritoryUnited States
CityNew York
Period23/03/9725/03/97

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