NDLI: Fuzzy neural networks for modelling commodity markets

Content Provider	IEEE Xplore Digital Library
Author	Rast, M.
Copyright Year	2001
Description	Author affiliation: Inst. Math., Ludwig-Maximilians-Univ., Munich, Germany (Rast, M.)
Abstract	The paper describes a method for forecasting the price of crude oil. This market sector is important for both, forecasting the market for trading the commodity (crude oil or its derivatives) itself, and for its effect on other market segments. For the presented approach a certain effect of two observable market states is used, which allows for establishing a combination of two neural networks, or specialist models, each of which is specialized on a different market state. The model used is a fuzzy neural network which is trained to determine the state of the market and then uses the output of the respective specialist model for forecasting. The two states are called contango and backwardation respectively and can quite easy be determined by looking at the prices of the two futures contracts which are due next. Establishing a model based on the market states allows to increase the accuracy of prediction (in comparison to a classical model).
Sponsorship	Int. Fuzzy Syst. Assoc.
Starting Page	952
Ending Page	955
File Size	262370
Page Count	4
File Format	PDF
ISBN	0780370783
DOI	10.1109/NAFIPS.2001.944733
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Petroleum Economic forecasting Predictive models Neural networks Contracts Accuracy Finance Shock waves Economic indicators
Content Type	Text
Resource Type	Article

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