This paper analyzes recursive and rolling neural network models to forecast one-step-ahead sign variations in gold price. Different combinations of techniques and sample sizes are studied for feed forward and ward neural networks. The results shows the rolling ward networks exceed the recursive...

This paper addresses new trends in quantitative geography research. Modern social science research - including economic and social geography - has in the past decades shown an increasing interest in micro-oriented behaviour of actors. This is inter alia clearly reflected in spatial interaction...

Context modifies the influence of any trading indicator. Ceteris paribus, a buyer would be more cautious buying in a selling market context than in a buying market. In order for automated, adaptive systems like neural networks to better emulate and assist human decision-making, they need to be...

Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting...

This paper attempts an eclectic synthesis on long-term growth of Indian economy. Paper establishes a model from different theories of growth within a unified framework. Artificial Neural Network (ANN) has been used to analyze determinants of growth in Indian economy from 1971 to 2008. ANN...

In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) in S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts with Generalized Autoregressive...

Managing electrical energy supply and generating exact amount of electricity are complex tasks. The most important part of managing energy supply is that of forecasting the future load demand. This is usually carried out by constructing a number of models on relative information such as previous...

The study presents an application of neural network methods for forecasting Foreign Direct Investment (FDI) in the six Asian economies, namely, India, Malaysia, Singapore, Hong Kong, the Philippines and Thailand. Using the dataset for the period 1970-2009, it finds that Artificial Neural Network...

This paper uses a competitive neural network model to examine whether the separation of monetary policy and banking supervision has an impact on inflation. Our results show that countries with similar organizations of banking supervision and monetary policy indeed have similar levels of...

In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à-viz conventional modelling is illustrated for an application field with noisy data of...