Better results of artificial neural networks in predicting ČEZ share prices

Abstract

The specific objective of the article is to propose a methodology for predicting future price development of the ČEZ, a.s., share prices on Prague Stock Exchange using artificial neural networks and time series exponential smoothing to validate the results on a part of the time series, and to compare the success rate of these two methods. The data used in our analysis is the data on the share prices for the period of 2014-2019. Multilayer perceptron (MLP) and radial basis function (RBF) networks are generated, with the time series time lag of 1, 5, and 10 days. In the case of exponential smoothing of time series, multiplicative models (triple smoothing of time series) are used. Based on residuals and absolute residuals, the best model for share prices’ prediction is chosen. In the case of time series smoothing, the method of exponential smoothing appears to be more successful; however, predictions of the best neural network are significantly more accurate. The resulting neural network can be used in practice to predict future development of ČEZ share prices. The neural network is able to self-train for a certain period of time to provide current and more accurate predictions.