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master's thesis
Optimizacija porftelja dionica korištenjem dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:471029
Krvavica, Niko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Krvavica, N. (2022). Optimizacija porftelja dionica korištenjem dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:471029

Krvavica, Niko. "Optimizacija porftelja dionica korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:471029

Krvavica, Niko. "Optimizacija porftelja dionica korištenjem dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:471029

Krvavica, N. (2022). 'Optimizacija porftelja dionica korištenjem dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 12 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:471029

Krvavica N. Optimizacija porftelja dionica korištenjem dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 July 12] Available at: https://urn.nsk.hr/urn:nbn:hr:168:471029

N. Krvavica, "Optimizacija porftelja dionica korištenjem dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:471029

Metadata
TitleOptimizacija porftelja dionica korištenjem dubokog učenja
Title (english)Optimizing Stock Portfolios Using Deep Learning
AuthorNiko Krvavica
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberMirko Poljak (član povjerenstva)
Committee memberTomislav Marković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu opisan je sustav za konstruiranje i upravljanje portfeljom dionica. Konstruiranje diversificiranog portfelja dionica je napravljeno korištenjem konvolucijskog autoenkodera i Leiden-ovog algoritma za otkrivanje zajednica. Upravljanje konstruiranim portfeljom dionica napravljeno pomoću metoda iz dubokog potpornog učenja. Arhitekutra agenta koji je donosio odluke je bila izmjenjena arhitektura Transformera. Opisani sustav je ispitan na vremenskim nizovima cijena dionica koje pripadaju S&P 500 indexu.
Abstract (english)
This paper describes a system for constructing and managing a stock portfolio. The construction of a diversified stock portfolio was done using a convolutional autoencoder and Leiden’s community detection algorithm. Stock portfolio managment was done using methods from deep reinforcement learning. The architecture of agent in deep reinforcement learning was modified architecture of the Transformer. The described system has been tested on stock price time series belonging to the S&P 500 index
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:471029
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 20:53:51