prikaz prve stranice dokumenta Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala
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master's thesis
Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:481615
Pavelić, Mate
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Pavelić, M. (2021). Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:481615

Pavelić, Mate. "Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:481615

Pavelić, Mate. "Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:481615

Pavelić, M. (2021). 'Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:481615

Pavelić M. Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:481615

M. Pavelić, "Usporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:481615

Metadata
TitleUsporedba performansi algoritama strojnog učenja u cilju vrednovanja financijske imovine na američkom tržištu kapitala
Title (english)Performance comparison of machine learning algorithms for pricing financial assets in the US capital market
AuthorMate Pavelić
MentorPetra Posedel Šimović (mentor)
Committee memberZvonko Kostanjčar (član povjerenstva)
Committee memberStjepan Šebek (član povjerenstva)
Committee memberPetra Posedel Šimović (predsjednik povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Algoritmi strojnog učenja moćan su alat u financijama za izradu predikcija kretanja cijena dionica i konstrukciju portfelja. U ovome radu napravljena je analiza tri algoritma primijenjenih na povijesne podatke s američkog dioničkog tržišta u svrhu određivanja budućih cijena dionica. Korišteni su OLS, LASSO i Random Forest algoritmi. U predikcijskim performansama kao i u konstrukciji portfelja dominira algoritam Random Forest. Konstruirane su dvije vrste portfelja: long-short i long-only.
Abstract (english)
Machine learning algorithms are a powerfull tool to use in finance to make predictions for future stock price movements and portfolio construction. In this paper we conduct an analysis of three algorithms applied on historic dana from american stock market in order to predict future stock prices. Used algorithms were OLS, LASSO and Random Forest. Regarding predictive performance as well as portfolio construction, Random Forest algorithm was shown to be the best. Two types of portfolios were constructed: long-short and long-only.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:481615
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 09:18:27