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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Main Authors: Ozer Ozdemir, Memmedaga Memmedli, Akhlitdin Nizamitdinov
Format: Article
Language:English
Published: Econometric Research Association 2013-09-01
Series:International Econometric Review
Subjects:
Online Access:http://www.era.org.tr/makaleler/11130001.pdf
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spelling 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
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