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master's thesis
Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa
Zagreb: University of Zagreb, Faculty of Economics and Business, 2024. urn:nbn:hr:148:868133
Čelan, Klara
University of Zagreb
Faculty of Economics and Business
Department of Managerial Economics

Institutional repository: REPEFZG

Cite this document

Čelan, K. (2024). Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa (Master's thesis). Zagreb: University of Zagreb, Faculty of Economics and Business. Retrieved from https://urn.nsk.hr/urn:nbn:hr:148:868133

Čelan, Klara. "Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2024. https://urn.nsk.hr/urn:nbn:hr:148:868133

Čelan, Klara. "Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa." Master's thesis, University of Zagreb, Faculty of Economics and Business, 2024. https://urn.nsk.hr/urn:nbn:hr:148:868133

Čelan, K. (2024). 'Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa', Master's thesis, University of Zagreb, Faculty of Economics and Business, accessed 01 December 2024, https://urn.nsk.hr/urn:nbn:hr:148:868133

Čelan K. Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa [Master's thesis]. Zagreb: University of Zagreb, Faculty of Economics and Business; 2024 [cited 2024 December 01] Available at: https://urn.nsk.hr/urn:nbn:hr:148:868133

K. Čelan, "Modeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa", Master's thesis, University of Zagreb, Faculty of Economics and Business, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:148:868133

Metadata
TitleModeliranje i prognoziranje prinosa odabranih europskih burzovnih indeksa
Title (english)Modeling and forecasting the returns of selected European stock indices
AuthorKlara Čelan
MentorIvana Lolić (mentor)
Committee memberIvana Lolić (predsjednik povjerenstva)
Committee memberPetar Sorić (član povjerenstva)
Committee memberZrinka Orlović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Economics and Business
(Department of Managerial Economics)
Zagreb
Defense date and country2024-07-05, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Mathematics and Statistics
Abstract
Razvoj financijske ekonometrije i prediktivne analitike omogućio je da se na financijskim tržištima koje karakterizira slabi oblik hipoteze o efikasnosti tržišta prognoziraju buduće cijene i prinosi s obzirom na zabilježene povijesne podatke. Predmet je ovog rada modeliranje i kratkoročno prognoziranje prinosa pet odabranih europskih dioničkih burzovnih indeksa (DAX, FTSE 100, CAC 40, IBEX 35, Euro Stoxx 50) korištenjem naivnih modela, modela jednostavnih pomičnih prosjeka, autoregresijskih modela i modela geometrijskog Brownovog gibanja s ciljem ocjene njihove prediktivne moći. Empirijska je analiza provedena na javno dostupnim podatcima s platforme Yahoo Finance korištenjem programskog jezika Python. Inicijalna primjena modela dodatno je evaluirana dijeljenjem podataka na poduzorke za treniranje i testiranje modela. Grafička i kvantitativna komparativna analiza pomoću RMSE pokazatelja potvrđuju mogućnost uspješnog kratkoročnog prognoziranja dnevnih prinosa relevantnim modelima.
Abstract (english)
The development of financial econometrics and predictive analytics has enabled forecasting future prices and returns based on histrical data in financial markets that imply weak form of the efficient market hypothesis. This paper focuses on modeling and short-term forecasting returns of five selected European stock market indices (DAX, FTSE 100, CAC 40, IBEX 35, Euro Stoxx 50) using naïve models, simple moving average models, autoregressive models, and geometric Brownian motion in order to evaluate their predictive power. The empirical analysis was conducted using publicly available data from Yahoo Finance, utilizing Python programming language. The initial application of the models was further evaluated by splitting the data into training and testing subsets. Graphical and quantitative comparative analysis using the RMSE indicator confirms the possibility of successful short-term forecasting of daily returns by applying relevant models.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:148:868133
Study programmeTitle: Business economics
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: sveučilišni magistar ekonomije/sveučilišna magistra ekonomije (sveučilišni magistar ekonomije/sveučilišna magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) icon
Created on2024-07-07 19:33:20