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master's thesis
Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:573849
Radović, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Radović, F. (2021). Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:573849

Radović, Filip. "Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:573849

Radović, Filip. "Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:573849

Radović, F. (2021). 'Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:573849

Radović F. Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:573849

F. Radović, "Istraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:573849

Metadata
TitleIstraživanje utjecaja alternativnih podataka na predviđanje volatilnosti cijena valute Bitcoin
Title (english)Exploring the Impact of Alternative Data on Bitcoin Price Volatility Prediction
AuthorFilip Radović
MentorKlemo Vladimir (mentor)
Committee memberKlemo Vladimir (predsjednik povjerenstva)
Committee memberMarin Šilić (član povjerenstva)
Committee memberGoran Delač (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-09-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu istražuje se i definira novi sustav predobrade podataka u cilju predviđanja cjenovnih serija. Taj je sustav zasnovan na događajima i funkcionira tako da uzorkuje događaje s tržišta i agregira ih prema unaprijed definiranim promjenama na tržištu. Također, u radu se procjenjuje utjecaj različitih značajki s posebnim fokusom na značajke dobivene s Twittera. Testira se više funkcija agregacije i ocjenjuje se njihova izvedba. Osim toga predstavljeno je i objašnjeno nekoliko metoda predobrade za vremenske serije. Rezultati rada pokazuju da sustav zasnovan na događajima ima potencijala i da način agregacije financijskih vremenskih serija može imati utjecaja na uspješnost predikcija modela. Osim toga pokazano je da značajke s Twittera nemaju utjecaj na tržište u predviđanju instrumenta BTC-PERPETUAL u visokofrekventnim uvjetima.
Abstract (english)
This thesis explores and defines a new system of preprocessing data for price series prediction tasks. The system is called an event-based system and functions by sampling events from the market and aggregating data according to predefined changes in the market. The impact of many features is explored with a focus on features that are extracted from Twitter. Multiple aggregation functions are tested, and their performance is evaluated. Finally, several preprocessing methods for preprocessing time series are introduced and explained. This thesis indicates that event-based systems have potential and should be explored in more detail. It is also shown that the type of event aggregation can impact model prediction accuracy. Finally, it is shown that features from Twitter are not good indicators of price change in a high-frequency setting.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:573849
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 on2023-01-13 16:00:12