engleski

Stock market analysis and price prediction using deep learning and artificial neural networks

This paper aims to present a deep learning and artificial neural network application in the field of stock market trading, specifically, in the analysis and forecasting of the stock market prices as an additional tool to reduce risk and increase profits. The goal of the paper is to introduce a new form of technology, show its potential, and encourage further research into this subject in the Republic of Croatia. To achieve this goal and to present the potential of this technology, two different artificial neural network prototypes were built, trained, and tested on the available set of historical stock price data from the Zagreb Stock Exchange. Two artificial networks were build using Python programming language: Long Short-Term Memory (LSTM) network and Multilayer Perceptron (MLP) network. Both artificial neural networks were built and the data sets they were trained and tested on were taken from the Zagreb Stock Exchange, containing historical stock prices of Croatian telecommunication companies, Optima Telekom (OPTE), and Hrvatski Telekom (HT). The results, i.e. price predictions, of both neural networks are presented in two parts. First, for OPTE stock prices and then for HT stock prices.

Artificial intelligence, deep learning, artificial neural networks, stock market trading, stock prices, Republic of Croatia

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