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undergraduate thesis
Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža
Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. urn:nbn:hr:126:730563
Žohar, Hrvoje
Josip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics

Institutional repository: Repository of School of Applied Mathematics and Informatics

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Žohar, H. (2024). Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics. Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:730563

Žohar, Hrvoje. "Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:730563

Žohar, Hrvoje. "Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, 2024. https://urn.nsk.hr/urn:nbn:hr:126:730563

Žohar, H. (2024). 'Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, accessed 16 November 2024, https://urn.nsk.hr/urn:nbn:hr:126:730563

Žohar H. Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics; 2024 [cited 2024 November 16] Available at: https://urn.nsk.hr/urn:nbn:hr:126:730563

H. Žohar, "Predviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, School of Applied Mathematics and Informatics, Osijek, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:126:730563

Metadata
TitlePredviđanje kretanja cijena dionica primjenom LSTM rekurentnih neuronskih mreža
Title (english)Predicting Stock Price Movement Using LSTM Reccurent Neural Networks
AuthorHrvoje Žohar
MentorDomagoj Matijević (mentor)
GranterJosip Juraj Strossmayer University of Osijek
School of Applied Mathematics and Informatics
Osijek
Defense date and country2024-10-30, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Mathematical Logic and Accounting
Abstract
U ovom radu istražujemo primjenu long short-term memory rekurentnih neuronskih mreža na predviđanje cijene ZB trend investicijskog fonda Zagrebačke banke koristeći cijene dionica tvrtki sastavnica fonda, tehničku analizu i Fourierove transformacije. Fokusiramo se na sastavne metode tehničke analize te dizajn i treniranje neuronskih mreža. Rezultati pokazuju da je model sposoban pronaći obrasce u povijesnim podacima i dati uvid u buduće kretnje za razvoj investicijskih strategija.
Abstract (english)
In this thesis we explore the applications of long short-term memory recurrent neural networks for predicting price movements Zagrebačka bank’s ZB trend mutually traded fund using the prices of the fund’s constituent company stock positions, technical analysis and Fourier transforms. We focus on fundamental technical analysis methods and the design and training process of neural networks. Our results show that the model is capable of capturing patterns in the data give insight into future price movements to develop investing strategies.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:730563
Study programmeTitle: Bachelor of Mathematics and Computer Science
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) matematike i računarstva (sveučilišni/a prvostupnik/prvostupnica (baccalaureus/baccalaurea) matematike i računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
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Created on2024-11-04 12:29:32