Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Approximation and estimation bounds for artificial neural networks
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
An overview of neural networks: early models to real world systems
An introduction to neural and electronic networks
An investigation of the use of feedforward neural networks for forecasting
An investigation of the use of feedforward neural networks for forecasting
Local feedback multilayered networks
Neural Computation
Forecasting with neural networks
Information and Management
A comparison between neural networks and chaotic models for exchange rate prediction
Computational Statistics & Data Analysis
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stock Market Prediction with Backpropagation Networks
IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Detecting business cycle asymmetries using artificial neural networks and time series models
Computational Economics
Modeling exchange rates: smooth transitions, neural networks, and linear models
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
An Investigation into the Use of Intelligent Systems for Currency Trading
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An Abductive-Reasoning Guide for Finance Practitioners
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In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.