Exchange rate forecasting: comparison of various architectures of neural networks
| Autor: | V. K. Bhalla, Ajay Kumar Dhamija |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Network architecture
Artificial neural network Computer science business.industry Computer Science::Neural and Evolutionary Computation Perceptron Machine learning computer.software_genre Exchange rate Models of neural computation Artificial Intelligence Radial basis function Trading strategy Artificial intelligence business computer Software |
| Zdroj: | Neural Computing and Applications. 20:355-363 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | This paper evaluates the predictive accuracy of neural networks in forecasting exchange rate. The multi-layer perceptron (MLP) and radial basis function (RBF) networks with different architectures are used to forecast five exchange rate time series. The results of each prediction are evaluated and compared according to the networks and architectures used. It is found that neural networks can be effectively used in forecasting exchange rate and hence in designing trading strategies. RBF networks performed better than MLP networks in our simulation experiment. This experiment suggests that it is possible to extract information hidden in the exchange rate and predict it into future. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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