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Title:S principi kompleksnih mrež do karakterizacije korelirane dinamike delniških trgov
Authors:ID Šilovinac, Nina (Author)
ID Gosak, Marko (Mentor) More about this mentor... New window
ID Markovič, Rene (Comentor)
Files:.pdf MAG_Silovinac_Nina_2018.pdf (7,06 MB)
MD5: 82C3F7336C2D36D1F5E979C9693C1C2D
PID: 20.500.12556/dkum/9a8ea19e-ba0e-4adc-aca9-d97705c87c53
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
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
Place of publishing:Maribor
Publisher:[N. Šilovinac]
Year of publishing:2018
PID:20.500.12556/DKUM-72769 New window
UDC:53:336.76(043.2)
COBISS.SI-ID:24225032 New window
NUK URN:URN:SI:UM:DK:5ZKODRUC
Publication date in DKUM:13.12.2018
Views:1479
Downloads:175
Metadata:XML DC-XML DC-RDF
Categories:FNM
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:08.11.2018

Secondary language

Language:English
Title:The application of complex network approaches for the characterization of the correlated stock market dynamics
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.
Keywords:econophysics, complex networks, stock market, correlated dynamics, temporal networks


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