Reliable stock market movement prediction is a challenging task. The difficulty is mainly due to the close to random-walk behaviour of a stock time series. A number of published techniques have emerged in the trading community for prediction tasks. One of them is neural network, NN. In this...

There is a growing consensus that social and economic sustainability depends on limited natural capital. Ecological Footprint (EF) provides an alternative tool to account for natural capital. This study presents two models to research Wuhan's natural capital: first using Genetic Algorithm Neural...

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters...

Reliable stock market movement prediction is a challenging task. The difficulty is mainly due to the close to random-walk behaviour of a stock time series. A number of published techniques have emerged in the trading community for prediction tasks. One of them is neural network, NN. In this...

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The macroeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood...

The aim of this paper is to develop and apply Neural Network (NN) models in order to forecast regional employment patterns in Germany. NNs are statistical tools based on learning algorithms with a distribution over a large amount of quantitative data. NNs are increasingly deployed in the social...

The aim of the paper is the analysis of the sequential characteristics of the Athens Stock Exchange general index (ASE) using the time series metho-dology based on artificial intelligent techniques. The applied models include the Feed Forward Neural Network trained with the efficient Levenberg -...

The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach,based on the S-estimation method, to...

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...