Pregled bibliografske jedinice broj: 1069737
Stock market analysis and price prediction using deep learning and artificial neural networks
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: Ekonomski fakultet Sveučilišta u Zagrebu, 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 : Ekonomski fakultet Sveučilišta u Zagrebu, 2020, 450-462
Skup
11th International Odyssey Conference on Economics and Business
Mjesto i datum
Online, 16.06.2020. - 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
