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undergraduate thesis
Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:137373
Jukić, Petar
University of Zagreb
Faculty of Electrical Engineering and Computing

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Jukić, P. (2021). Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:137373

Jukić, Petar. "Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:137373

Jukić, Petar. "Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:137373

Jukić, P. (2021). 'Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:137373

Jukić P. Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:137373

P. Jukić, "Procjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:137373

Metadata
TitleProcjena trenda u financijskim vremenskim nizovima zasnovana na strojnom učenju
Title (english)Machine Learning Based Trend Assessment in Financial Time Series
AuthorPetar Jukić
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberStjepan Begušić (član povjerenstva)
Committee memberTomislav Kovačević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Rad se bavi procjenom trenda u financijskim vremenskim nizovima zasnovano na strojnom učenju. Pregledom dosadašnjih znanstvenih radova na tu temu, prepoznati su problemi definicije uzlaznog/silaznog trenda, tj. oznaka, kao i problemi određivanja značajki i odabira adekvatnog modela. U radu je nad vremenskim nizom cijena dionica konstruiran posmični prozor koji za generiranje značajki i oznaka. U tako definiranom eksperimentalnom postavu, koristeći model nižeg kapaciteta, logističke regresije, isprobani su i uspoređeni različiti načini označavanja trenda. Koristeći odabrani indikator konvergencije/divergencije pomičnog prosjeka, treniran je stroj potpornih vektora, klasifikator većeg kapaciteta. Treniranje i validacija modela napravljena je na povijesnim cijenama dionica Njujorške burze. Uz zadovoljavajuće performanse modela strojnog učenja, predložena strategija trgovanja koja se oslanja na izlaz modela.
Abstract (english)
The paper deals with trend assessment in financial time series based on machine learning. A review of previous scientific work on this topic has revealed problems in defining an upward/downward trend (labels), as well as problems in determining features and an adequate model. A rolling window was constructed to generate features and labels. In the experimental setup thus defined, using a lower capacity model (logistic regression), different ways of labelling the trend were identified and compared. Using the selected moving average convergence/divergence indicator, a higher capacity support vector classifier was trained and validated. The training and validation of the model was performed using NYSE historical stock prices. Upon satisfactory performance of the machine learning model, a trading strategy was proposed based on the trained model output.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:137373
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 20:43:18