The motivation for this article is the investigation of the use of a promising class of neural network (NN) models, higher order neural networks (HONNs), when applied to the task of forecasting and trading the 21-day-ahead realised volatility of the FTSE 100 futures index. This is done by...

The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate. This is done by benchmarking three different neural network designs representing a Higher Order...

The motivation for this article is to investigate the use of a promising class of Neural Network (NN) models, Higher Order Neural Networks (HONNs), when applied to the task of forecasting the 1-day ahead Value at Risk (VaR) of the brent oil and gold bullion series with only autoregressive terms...

The motivation for this paper is to investigate the use of alternative novel neural network (NN) architectures when applied to the task of forecasting and trading the euro/dollar (EUR/USD) exchange rate, using the European Central Bank (ECB) fixing series with only auto-regressive terms as...

ABSTRACT This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions,...

ABSTRACT In this paper a hybrid genetic algorithm–support vector regression (GA‐SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting US inflation and unemployment. GA‐SVR genetically optimizes...

A clear motivation for this paper is the investigation of a correlation filter to improve the return/risk performance of spread trading models. A further motivation for this paper is the extension of trading futures spreads beyond the 'Fair Value' type of model used by Butterworth and Holmes...

In this article, a mixed methodology that combines both the Autoregressive Moving Average Model (ARMA) and Neural Network Regression (NNR) models is proposed to take advantage of the unique strength of ARMA and NNR models in linear and nonlinear modelling. Experimental results with real data...

In the current paper, we present an integrated genetic programming (GP) environment called java GP modelling. The java GP modelling environment is an implementation of the steady-state GP algorithm. This algorithm evolves tree-based structures that represent models of inputs and outputs. The...