Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules
Philip Hans Franses and
Kasper van Griensven
Additional contact information
Kasper van Griensven: ABN-AMRO Bank, Amsterdam, The Netherlands
Studies in Nonlinear Dynamics & Econometrics, 1998, vol. 2, issue 4, 8
Abstract:
We examine the performance of artificial neural networks (ANNs) for technical trading rules for forecasting daily exchange rates. The main conclusion of our attempt is that ANNs perform well, and that they are often better than linear models. Furthermore, the precise number of hidden layer units in ANNs appears less important for forecasting performance than is the choice of explanatory variables.
Keywords: technical analysis; neural networks (search for similar items in EconPapers)
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (16)
Downloads: (external link)
https://doi.org/10.2202/1558-3708.1033 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:2:y:1998:i:4:n:2
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html
DOI: 10.2202/1558-3708.1033
Access Statistics for this article
Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach
More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla (peter.golla@degruyter.com).