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Title:Uporaba algoritmov po vzorih iz narave pri napovedovanju cene delnic in optimizaciji portfelja
Authors:ID Cvörnjek, Nejc (Author)
ID Brezočnik, Miran (Mentor) More about this mentor... New window
ID Jagrič, Timotej (Mentor) More about this mentor... New window
ID Papa, Gregor (Comentor)
Files:.pdf MAG_Cvornjek_Nejc_2015.pdf (4,12 MB)
MD5: 436A5F0673B8AA5B0ADA9E2827F716FB
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:V magistrskem delu smo uporabili algoritme po vzorih iz narave za finančna modeliranja. Najprej smo uporabili umetne nevronske mreže za napovedovanje cene delnice, nato pa še genetske algoritme za optimizacijo portfelja delnic, ki smo jih primerjali s kvadratnim programiranjem. V raziskavi se je izkazalo, da lahko s umetnimi nevronskimi mrežami bolje ocenimo variančno-kovariančno matriko, kot če bi uporabili zgodovinske podatke. Pri reševanju problema optimizacije portfelja delnic se je izkazalo, da lahko z genetski algoritmi dobimo rezultate primerljive s kvadratnim programiranjem, saj rezultati med tehnikama, predvsem pri manjšem porteflju, v glavnem niso statistično značilni.
Keywords:finančni trg, teorija upravljanja portfelja, umetne nevronske mreže, genetski algoritmi, Markowitzev model, optimizacija, večkriterijska optimizacija
Place of publishing:Maribor
Publisher:[N. Cvörnjek]
Year of publishing:2015
PID:20.500.12556/DKUM-47591-c1f3235d-90e6-07c7-620b-d4c42a44eeca New window
UDC:004.89.012:336.763(043.2)
COBISS.SI-ID:18622742 New window
NUK URN:URN:SI:UM:DK:FHWMTVI7
Publication date in DKUM:30.03.2015
Views:2603
Downloads:295
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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Secondary language

Language:English
Title:Usage of nature-inspired algorithms for stock price predictions and portfolio optimization
Abstract:In a master work we used nature inspired algorithms for financial modeling. Firstly we use an artificial neural networks to predict stock prices and secondly, we used genetic algorithms for stock portfolio optmization. The results show better assessing covariance matrix with neural networks gives more accurate results in a portfolio optimization than if we are taking historical prices. We can assert that results obtained with a genetic algorithms are in general statistically the same as they are with quadratic programming, especially in cases with less stocks in a portfolio.
Keywords:financial market, portfolio theory, artificial neural networks, genetic algorithms, Markowitz model, optimization, multiobjective optimization


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