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Pregled bibliografske jedinice broj: 1069737

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


Medić, Tomislav; Pejić Bach, Mirjana; Jaković, Božidar
Stock market analysis and price prediction using deep learning and artificial neural networks // Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business / Šimurina, J. ; Načinović Braje, I. ; Pavić, I. (ur.).
Zagreb: Faculty of Economics & Business, University of Zagreb, 2020. str. 450-462 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1069737 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Medić, Tomislav ; Pejić Bach, Mirjana ; Jaković, Božidar

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business / Šimurina, J. ; Načinović Braje, I. ; Pavić, I. - Zagreb : Faculty of Economics & Business, University of Zagreb, 2020, 450-462

Skup
11th International Odyssey Conference on Economics and Business

Mjesto i datum
Online, 16-20.06.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove
Ekonomski institut, Zagreb

Profili:

Avatar Url Božidar Jaković (autor)

Avatar Url Mirjana Pejić Bach (autor)

Citiraj ovu publikaciju

Medić, Tomislav; Pejić Bach, Mirjana; Jaković, Božidar
Stock market analysis and price prediction using deep learning and artificial neural networks // Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business / Šimurina, J. ; Načinović Braje, I. ; Pavić, I. (ur.).
Zagreb: Faculty of Economics & Business, University of Zagreb, 2020. str. 450-462 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Medić, T., Pejić Bach, M. & Jaković, B. (2020) Stock market analysis and price prediction using deep learning and artificial neural networks. U: Šimurina, J., Načinović Braje, I. & Pavić, I. (ur.)Proceedings of FEB Zagreb 11th International Odyssey Conference on Economics and Business.
@article{article, year = {2020}, pages = {450-462}, keywords = {Artificial intelligence, deep learning, artificial neural networks, stock market trading, stock prices, Republic of Croatia}, title = {Stock market analysis and price prediction using deep learning and artificial neural networks}, keyword = {Artificial intelligence, deep learning, artificial neural networks, stock market trading, stock prices, Republic of Croatia}, publisher = {Faculty of Economics and Business, University of Zagreb}, publisherplace = {Online} }