Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks
Chung-Ming Kuan () and
Tung Liu ()
Journal of Applied Econometrics, 1995, vol. 10, issue 4, 347-64
In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample prediction error relative to the random walk model. Copyright 1995 by John Wiley & Sons, Ltd.
References: Add references at CitEc
Citations: View citations in EconPapers (122) Track citations by RSS feed
Downloads: (external link)
http://links.jstor.org/sici?sici=0883-7252%2819951 ... 0.CO%3B2-Q&origin=bc full text (application/pdf)
http://qed.econ.queensu.ca:80/jae/1995-v10.4/ Supporting data files and programs (text/html)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:jae:japmet:v:10:y:1995:i:4:p:347-64
Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252
Access Statistics for this article
Journal of Applied Econometrics is currently edited by M. Hashem Pesaran
More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().