Uporaba algoritmov po vzorih iz narave pri napovedovanju cene delnic in optimizaciji portfelja

magistrsko delo

Nejc Cvörnjek (Author), Miran Brezočnik (Mentor), Timotej Jagrič (Mentor), Gregor Papa (Co-mentor)

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;magistrske naloge;

Data

Language:	Slovenian
Year of publishing:	2015
Typology:	2.09 - Master's Thesis
Organization:	UM FS - Faculty of Mechanical Engineering
Publisher:	[N. Cvörnjek]
UDC:	004.89.012:336.763(043.2)
COBISS:	18622742
Views:	1726
Downloads:	218
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Usage of nature-inspired algorithms for stock price predictions and portfolio optimization
Secondary 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.
Secondary keywords:	financial market;portfolio theory;aritificial neural networks;genetic algorithms;Markowitz model;optimization;multiobjective optimization;
URN:	URN:SI:UM:
Type (COBISS):	Master's thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za strojništvo, Gospodarsko inženirstvo, Strojništvo
Pages:	XII, 59 f.
ID:	8739083