Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review

By Wei Huang, Kin Keung Lai, Yoshiteru Nakamori, Shouyang Wang. 2004

159 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Omer Berat Sezer
2. A. Ozbayoglu
3. M. Ugur Gudelek
4. Longbing Cao

In Electrical and Electronic Engineering

1. Longbing Cao
2. A. Ozbayoglu
3. M. Ugur Gudelek
4. Omer Berat Sezer
5. Weiwei Jiang

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Longbing Cao
5. Wael El-Deredy
6. Paulo J. G. Lisboa
7. Abir Jaafar Hussain
8. Adam Knowles

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “Among those soft computing methods, EC and ANN had the most overall interest.”
• AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “• Modeling foreign exchange rates by time series and sequence analysis, ANN or DNNs and analyzing the relationships between multi-foreign currency rates as MTS [79, 101].”
• Financial time series prediction using polynomial pipelined neural networks (2008, background): “The problem associated with these models is that the pre-specification of the models restricts their usefulness since there could be lot of input patterns that can be considered (Huang et al., 2004).”
• Soft computing techniques applied to finance (2008, background): “Usually the prime tool for this task are neural networks [10– 13], but soft computing methods can be combined in this context to obtain creative solutions.”