SHS Web Conf.
Volume 39, 2017Innovative Economic Symposium 2017 (IES2017)
|Number of page(s)||8|
|Section||Strategic Partnerships in International Trade|
|Published online||06 December 2017|
Stock price development forecasting using neural networks
1 Institute of Technology and Business in České Budějovice, School of Expertness and Valuation, Okružní 10, 370 01 České Budějovice, Czech Republic
2 University of Žilina, Faculty of Operation and Economics of Transport and Communications, Univerzitná 8215/1, 01026 Žilina, Slovakia
* Corresponding author: firstname.lastname@example.org
Stock price forecasting is highly important for the entire market economy as well as the investors themselves. However, stock prices develop in a non-linear way. It is therefore rather complicated to accurately forecast their development. A number of authors are now trying to find a suitable tool for forecasting the stock prices. One of such tools is undoubtedly artificial neural network, which have a potential of accurate forecast based even on non-linear data. The objective of this contribution is to use neural networks for forecasting the development of the ČEZ, a. s. stock prices on the Prague Stock Exchange for the next 62 trading days. The data for the forecast have been obtained from the Prague Stock Exchange database. These are final prices at the end of each trading day when the company shares were traded, starting from the beginning of the year 2012 till September 2017. The data are processed by the Statistica software, generating multiple layer perceptron (MLP) and radial basis function (RBF) networks. In total, there are 10,000 neural network structures, out of which 5 with the best characteristics are retained. Using statistical interpretation of the results obtained, it was found that all retained networks are applicable in practice.
Key words: forecasting / stock / price development / artificial neural networks
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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