magistrsko delo
Nina Šilovinac (Author), Marko Gosak (Mentor), Rene Markovič (Co-mentor)

Abstract

V magistrskem delu predstavimo inovativen komputacijski pristop, ki temelji na teoriji kompleksnih mrež, in ga uporabimo za kvantitativni opis dinamike trgovanja z delnicami. Z avtomatiziranim pridobivanjem podatkov, njihovim filtriranjem in obdelavo smo podatke pripravili za izgradnjo dveh vrst funkcionalnih finančnih mrež. Prva temelji na korelirani dinamiki dnevnega trgovanja, druga pa na korelacijah v dolgoročnih trendih, opisanih z mesečnim povprečjem. Obe mreži smo vizualizirali, izračunali njune topološke lastnosti in jih primerjali z drugimi realnimi mrežami. Proučevali smo tudi dinamično spreminjanje funkcionalnih finančnih mrež in ugotovili, da so iz sprememb topoloških lastnosti razvidni pretresi finančnih trgov, kot je bila finančna kriza v letu 2007. S principi multipleksne mreže smo proučevali tudi zvezo med korelirano dinamiko dnevnega trgovanja in mesečnih trendov. Na koncu smo tudi analizirali, kako se sovisno trgovanje kaže z vidika posameznih držav in ovrednotili deležnike za stabilnost posameznih delnic. Naše ugotovitve kažejo, da ima uporaba sodobnih teoretskih orodij s področja kompleksnih mrež velik potencial na področju ekonofizike in kvantitativnega finančništva.

Keywords

ekonofizika;kompleksne mreže;delniški trg;korelirana dinamika;časovne mreže;trgovanje;funkcionalne mreže;dolgoročni trendi;dnevno trgovanje;topološke lastnosti;realne kompleksne mreže;finančna kriza;multipleksna mreža;kvantitativno finančništvo;karakterizacija;fizika;magistrsko delo;Šilovinac;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UM FNM - Faculty of Natural Sciences and Mathematics
Publisher: [N. Šilovinac]
UDC: 53:336.76(043.2)
COBISS: 24225032 Link will open in a new window
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Downloads: 97
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Other data

Secondary language: English
Secondary title: ǂThe ǂapplication of complex network approaches for the characterization of the correlated stock market dynamics
Secondary abstract: In the thesis we present an innovative computational approach that is based on the complex network theory and utilize it for a quantitative description of stock market dynamics. By means of data mining, filtering and processing we prepared the data for the construction of two types of functional financial networks. The first one is based on the correlated dynamics of daily returns, whereas the second one on the long-term trends described by the monthly average. We visualized both networks, computed their topological features and compared them with other real-life networks. The dynamic evolution of both functional networks was studied as well and it turned out that changes in topological characteristics go in hand with financial crashes, such as the crisis in 2007. Furthermore, on the basis of multiplex network approaches we studied the relationship between the correlated dynamics of daily returns and the monthly averages. Finally, we also analyzed the mutual trading interactions with respect to individual countries and identified the key pillars for the stability of individual stocks. Our findings point out that the utilization of advanced theoretical tools from the realms of the complex network theory possesses a huge potential on the field of econophysics and quantitative economics.ž
Secondary keywords: magistrska dela;ekonofizika;kompleksne mreže;delniški trgi;korelirana dinamika;časovne mreže;master theses;econophysics;complex networks;stock markets;correlated dynamics;temporal networks;
URN: URN:SI:UM:
Type (COBISS): Master's thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za naravoslovje in matematiko, Oddelek za fiziko
Pages: VIII, [51] f.
ID: 10982701