The optimal set features determination in discriminant analysis by the group method of data handling
Systems Analysis Modelling Simulation - Special issue on automatic model generation
Investment management: tactical asset allocation
The handbook of brain theory and neural networks
A comparison between neural networks and chaotic models for exchange rate prediction
Computational Statistics & Data Analysis
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
The accuracy of a procedural approach to specifying feedforward neural networks for forecasting
Computers and Operations Research
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A hybrid model for exchange rate prediction
Decision Support Systems
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Efficient prediction of exchange rates with low complexity artificial neural network models
Expert Systems with Applications: An International Journal
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Heuristic self-organization in problems of engineering cybernetics
Automatica (Journal of IFAC)
Information Systems Frontiers
| Hi-index | 12.05 |
Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound.