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master's thesis
Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica
Pula: University of Pula, 2015. urn:nbn:hr:137:521520
Hrga, Ingrid
University of Pula
Faculty of economics and tourism "Dr. Mijo Mirković"

Institutional repository: Digital Repository Juraj Dobrila University of Pula

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Hrga, I. (2015). Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica (Master's thesis). Pula: University of Pula. Retrieved from https://urn.nsk.hr/urn:nbn:hr:137:521520

Hrga, Ingrid. "Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica." Master's thesis, University of Pula, 2015. https://urn.nsk.hr/urn:nbn:hr:137:521520

Hrga, Ingrid. "Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica." Master's thesis, University of Pula, 2015. https://urn.nsk.hr/urn:nbn:hr:137:521520

Hrga, I. (2015). 'Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica', Master's thesis, University of Pula, accessed 03 April 2025, https://urn.nsk.hr/urn:nbn:hr:137:521520

Hrga I. Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica [Master's thesis]. Pula: University of Pula; 2015 [cited 2025 April 03] Available at: https://urn.nsk.hr/urn:nbn:hr:137:521520

I. Hrga, "Primjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica", Master's thesis, University of Pula, Pula, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:137:521520

Metadata
TitlePrimjena stroja s potpornim vektorima za predviđanje kretanja na tržištima vrijednosnica
Title (english)Application of support vector machine for prediction of stock market movement
AuthorIngrid Hrga
MentorKrunoslav Puljić (mentor)
Committee memberVanja Bevanda
Committee memberKrunoslav Puljić
Committee memberManuel Benazić
GranterUniversity of Pula
(Faculty of economics and tourism "Dr. Mijo Mirković")
Pula
Defense date and country2015-09-23, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
Stroj s potpornim vektorima pripada danas samom vrhu klasifikacijskih algoritama strojnog učenja s uspješnom primjenom u rješavanju najraznovrsnijih problema. Kao prvi algoritam proistekao iz statističke teorije učenja, svoju izvrsnu sposobnost generalizacije zahvaljuje implementaciji principa strukturne minimizacije rizika, baziranog na simultanoj minimizaciji empirijskog rizika i VC dimenzije, odnosno kapaciteta klase funkcija koje učeći stroj implementira. U radu se istražuje mogućnost... More predviđanja smjera kretanja cijena na tržištima vrijednosnica primjenom stroja s potpornim vektorima pri čemu su kao ulazne varijable korišteni tehnički indikatori, dok je izlaznu varijablu predstavljao predznak prinosa na određeni dan u budućnosti. S obzirom da, osim o samome algoritmu, uspješnost klasifikacije ovisi i o ostalim elementima sustava, ispitivan je utjecaj odabira značajki, različitih kombinacija parametara, neravnoteže u podacima te duljine niza na rezultate klasifikacije. Uspoređivane su različite evaluacijske mjere, a rezultati predviđanja testirani su i u simulatoru trgovanja gdje se pokazalo da se bolji rezultat može dobiti kombinacijom više klasifikatora na način da svaki od njih uči rješavati svoj zadatak. Od tri burzovna indeksa, iako odabrana za eksperiment na temelju testova predvidljivosti vremenskog niza, samo su kod jednoga u konačnici postignuti zadovoljavajući rezultati s obzirom da se kao osnovna prepreka boljim rezultatima pokazala nedovoljna prediktivna moć odabranih tehničkih indikatora. Less
Abstract (english)
Support vector machine today belongs to the very top of the machine learning classification algorithm, with successful application in resolution of all kinds of problems. As the first algorithm was the result of the statistical learning theory, it owes its excellent generalization performance to the implementation of the structural risk minimalization principle, based on simultaneous minimalization of the empirical risk and VC-dimension, i.e. the capacity of the class of functions... More implemented by the learning machine. In this paper, the possibility of prediction of the stock market price movement was researched by means of implementation of the support vector machine, where technical indicators were used as input variables, while the sign of the excess of returns on a specific date in the future represented the output variable. Given that, apart from the algorithm itself, the classification performance also depends on other system elements, the impact of the choice of features on the classification results, i.e. different parameter combinations, data imbalance, as well as the series length, were also examined. Different evaluation measures were compared and the prediction results were also tested in a market trading simulator, where it was shown that a better result could be obtained by the combination of multiple classifiers, in the manner that each one learns how to solve its own task. Out of the three stock market indexes, although chosen for the experiment on the basis of the time series predictability tests, ultimately in only one of them satisfactory results were achieved, given that insufficient predictive power of selected technical indicators proved to represent the main obstacle in obtaining better results. Less
Keywords
Languagecroatian
URN:NBNurn:nbn:hr:137:521520
Study programmeTitle: Business Economics; specializations in: Financial Management, Marketing Management, Management and Entrepreneurship, Tourism and Development, Business Informatics
Course: Business Informatics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2016-04-20 08:50:56
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