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COMPARISON OF NEURAL NETWORKS AND REGRESSION TIME SERIES IN ESTIMATING THE DEVELOPMENT OF THE AFTERNOON PRICE OF GOLD ON THE NEW YORK STOCK EXCHANGE
Gold is a very important commodity in today's global world. Therefore, the price of gold and its development is a fundamental question for many researchers. This paper aims to perform a regression analysis of the development of the afternoon price of gold on the New York Stock exchange using artificial neural networks and linear regression. Data from a period longer than ten years are used. This is a total of 2,578 pieces of data. We use linear regression with the linear, exponential, polynomial, logarithmic, numbers of weighted distances, multiple negative-exponential extermination and spline function. Multilayer perceptron neural networks and neural networks of the radial basis function are generated. A total of 1,000 neural structures are generated, 5 of those with the best characteristics are retained Regarding simple linear regression, the curve obtained via the spline function mirrors the development of the gold price best. However, better results are achieved by all 5 preserved neural networks.
Release: 2020/1 Pages: 61-72 JEL classification: C22, C45, C53
DOI:
Keywords: artificial neural networks, regression time series, prediction, gold price, commodity, future price development
Section:
Contacts:
Ing. Veronika Machová, MBA
School of Expertness and Valuation, The Institute of Technology and Business in České Budějovice
Okružní 517/10, 370 01 České Budějovice, Czech Republic
machova@mail.vstecb.cz
Ing. Tomáš Krulický, BBA
Faculty of Operation and Economics of Transport and Communications, University of Žilina
Univerzitná 8215/1, 010 26 Žilina, Slovakia
krulicky@mail.vstecb.cz
Ing. Jakub Horák
Faculty of Operation and Economics of Transport and Communications, University of Žilina
Univerzitná 8215/1, 010 26 Žilina, Slovakia
horak@mail.vstecb.cz
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