ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price
ANN (Artificial Neural Network) models and Spline techniques have been applied to economic analysis, to handle economic problems, evaluate portfolio risk and stock performance, and to forecast stock exchange rates and gold prices. These techniques are improving nowadays and continue to serve as p...
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doaj-3399a0e0b27542ce80f6765e2e23e39f2020-11-24T21:27:50ZengEconometric Research AssociationInternational Econometric Review1308-87931308-88152013-09-01525369ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold PriceOzer Ozdemir0Memmedaga Memmedli1Akhlitdin Nizamitdinov2Anadolu UniversityAnadolu UniversityAnadolu UniversityANN (Artificial Neural Network) models and Spline techniques have been applied to economic analysis, to handle economic problems, evaluate portfolio risk and stock performance, and to forecast stock exchange rates and gold prices. These techniques are improving nowadays and continue to serve as powerful predictive tools. In this study, we compare the performance of ANN models and Bayesian Spline models in forecasting economic datasets. We consider the most commonly used ANN models, which are Generalized Regression Neural Networks (GRNN), Multilayer Perceptron (MLP), and Radial Basis Function Neural Networks (RBFNN). We compare these models using BayesX and Statistica software with three important economic datasets: on the exchange rate of Turkish Liras (TL) to Euro, exchange rate of Turkish Liras (TL) to United States Dollars (USD), and Gold Price for Turkey. With these three economic datasets, we made a comparative study of these models, using the criterions MSE and MAPE to evaluate their forecasting performance. The results demonstrate that the penalized spline model performed best amongst the spline techniques and their Bayesian versions. Amongst the ANN models, the MLP model obtained the best performance criterion results.http://www.era.org.tr/makaleler/11130001.pdfArtificial Neural NetworksBayesian Spline ModelsExchange Rates |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ozer Ozdemir Memmedaga Memmedli Akhlitdin Nizamitdinov |
spellingShingle |
Ozer Ozdemir Memmedaga Memmedli Akhlitdin Nizamitdinov ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price International Econometric Review Artificial Neural Networks Bayesian Spline Models Exchange Rates |
author_facet |
Ozer Ozdemir Memmedaga Memmedli Akhlitdin Nizamitdinov |
author_sort |
Ozer Ozdemir |
title |
ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price |
title_short |
ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price |
title_full |
ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price |
title_fullStr |
ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price |
title_full_unstemmed |
ANN Models and Bayesian Spline Models for Analysis of Exchange Rates and Gold Price |
title_sort |
ann models and bayesian spline models for analysis of exchange rates and gold price |
publisher |
Econometric Research Association |
series |
International Econometric Review |
issn |
1308-8793 1308-8815 |
publishDate |
2013-09-01 |
description |
ANN (Artificial Neural Network) models and Spline techniques have been applied to
economic analysis, to handle economic problems, evaluate portfolio risk and stock
performance, and to forecast stock exchange rates and gold prices. These techniques are
improving nowadays and continue to serve as powerful predictive tools.
In this study, we compare the performance of ANN models and Bayesian Spline models
in forecasting economic datasets. We consider the most commonly used ANN models,
which are Generalized Regression Neural Networks (GRNN), Multilayer Perceptron
(MLP), and Radial Basis Function Neural Networks (RBFNN). We compare these
models using BayesX and Statistica software with three important economic datasets: on
the exchange rate of Turkish Liras (TL) to Euro, exchange rate of Turkish Liras (TL) to
United States Dollars (USD), and Gold Price for Turkey. With these three economic
datasets, we made a comparative study of these models, using the criterions MSE and
MAPE to evaluate their forecasting performance. The results demonstrate that the
penalized spline model performed best amongst the spline techniques and their Bayesian
versions. Amongst the ANN models, the MLP model obtained the best performance
criterion results. |
topic |
Artificial Neural Networks Bayesian Spline Models Exchange Rates |
url |
http://www.era.org.tr/makaleler/11130001.pdf |
work_keys_str_mv |
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