In this study the prediction capabilities of Artificial Neural Networks and typical econometric methods are compared. This is done in the domains of Finance and Economics. Initially, the Neural Networks are shown to outperform traditional econometric models in forecasting nonlinear behaviour. The comparison is extended to indicate that the accuracy of share price forecasting is not necessarily improved when applying Neural Networks rather than traditional time series analysis. Finally, Neural Networks are used to forecast the South African inflation rates, and its performance is compared to that of vector error correcting models, which apparently outperform Artificial Neural Networks.Prof. D.J. Marai
The goal of this paper is to compare and analyze the forecasting performance of two artificial neura...
Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of ...
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
Mattina of Finance Canada and Alain Paquet of UQAM for their helpful comments. The views expressed i...
In recent years, neural networks have received an increasing amount of intention among macroeconomic...
M.Com. (Financial Economics)Forecasting inflation is an important concern for economists and busines...
The modelling technique known as Artificial Neural Networks (ANNs) is investigated. ANNs have the ab...
Abstract: An artificial neural network (hence after, ANN) is an information-processing paradigm that...
In recent years, neural networks have received an increasing amount of attention among macroeconomic...
This study shows that neural networks have been advocated as an alternative to traditional statistic...
Forecasting macroeconomic and financial data are always difficult task to the researchers. Various s...
In recent years, neural networks have received an increasing amount of attention among macroeconomic...
Although rigorous econometric methods have been used to build an abundance of macroeconomic models, ...
This paper presents an empirical exercise in economic forecast using traditional time series methods...
The goal of this paper is to compare and analyze the forecasting performance of two artificial neura...
Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of ...
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
Mattina of Finance Canada and Alain Paquet of UQAM for their helpful comments. The views expressed i...
In recent years, neural networks have received an increasing amount of intention among macroeconomic...
M.Com. (Financial Economics)Forecasting inflation is an important concern for economists and busines...
The modelling technique known as Artificial Neural Networks (ANNs) is investigated. ANNs have the ab...
Abstract: An artificial neural network (hence after, ANN) is an information-processing paradigm that...
In recent years, neural networks have received an increasing amount of attention among macroeconomic...
This study shows that neural networks have been advocated as an alternative to traditional statistic...
Forecasting macroeconomic and financial data are always difficult task to the researchers. Various s...
In recent years, neural networks have received an increasing amount of attention among macroeconomic...
Although rigorous econometric methods have been used to build an abundance of macroeconomic models, ...
This paper presents an empirical exercise in economic forecast using traditional time series methods...
The goal of this paper is to compare and analyze the forecasting performance of two artificial neura...
Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of ...
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...