STUDY OF NEURAL NETWORKS FOR FORECASTING THE VALUE OF COMPANY SHARES IN AN UNSTABLE ECONOMY
DOI:
https://doi.org/10.20998/2079-0023.2022.02.03Keywords:
forecasting, investment, neural network, long-term memory, convolutional neural network, hybrid model, variational decomposition, deep learningAbstract
These studies deal with analysis and selection of neural networks with various architectures and hybrid models, which include neural networks, to predict the market value of shares in the stock market of a country that is in the process of unstable development. Analysis and forecasting of such stock markets cannot be carried out using classical methods. The relevance of the research topic is due to the need to develop software systems that implement algorithmic support for predicting the market value of shares in Ukraine. The introduction of such software systems in the circuit of investment decisionmaking in companies that are interested in increasing the information transparency of the Ukrainian stock market will improve the forecasts of the market value of shares. This, in turn, will help improve the investment climate and ensure the growth of investment in the Ukrainian economy. The analysis of the results of existing studies on the use of neural networks and other methods of computational intelligence for modeling the behavior of stock market participants and market forecasting has been carried out. The article presents the results of a study for the using of neural networks with various architectures for predicting the market value of shares in the stock markets of Ukraine. Four shares of the Ukrainian Stock Exchange were chosen for forecasting: Centrenergo (CEEN); Ukrtelecom (UTLM); Kriukivs’kyi Vahonobudivnyi Zavod PAT (KVBZ); Raiffeisen Bank Aval (BAVL). The following models were chosen for the experimental study: long short-term memory LSTM; convolutional neural network CNN; a hybrid model combining two neural networks CNN and LSTM; a hybrid model consisting of a variational mode decomposition algorithm and a long-term memory neural network (VMD-LSTM); hybrid VMD-CNN-LSTM deep learning model based on variational mode (VMD) and two neural networks. Estimates of forecast quality based on various metrics were calculated. It is concluded that the use of the hybrid model VMD-CNN-LSTM gives the minimum error in predicting the market value of the shares of Ukrainian enterprises. It is also advisable to use the VMD-LSTM model to predict the stock exchanges of countries with an unstable economy.
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