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Title:Uporaba umetne inteligence pri upravljanju portfelja delnic
Authors:ID Fister, Dušan (Author)
ID Jagrič, Timotej (Mentor) More about this mentor... New window
ID Perc, Matjaž (Comentor)
Files:.pdf DOK_Fister_Dusan_2022.pdf (5,58 MB)
MD5: 7557B3FCDC89CCE2587FE24D4DF296EB
 
Language:Slovenian
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:EPF - Faculty of Business and Economics
Abstract:Izziv dela predstavlja snovanje, načrtovanje in praktična izvedba avtomatiziranega trgovalnega sistema, ki neodvisno in brez posredovanja uporabnikov sprejema in izvaja trgovalne odločitve. Jedro trgovalnega sistema predstavlja trgovalna strategija, ki spremlja pretekle ter aktualne podatke borznih kotacij, izvaja tehnično analizo in, če je tega sposobna, se prilagaja sprotnim razmeram na finančnih trgih. Obravnavamo dve skupini trgovalnih strategij, klasične, ki niso sposobne sprotnega prilagajanja niti učenja, in dve trgovalni strategiji na osnovi naprednih algoritmov umetne inteligence, eno izmed njih predstavnico umetnih nevronskih mrež najnovejše tretje generacije. Izvedemo obširna simulacijska eksperimentiranja na osnovi nemškega delniškega trga v zadnjih desetih letih, zasnujemo in izvedemo pa tudi eksperimentiranja na namenski strojni opremi, ki močno pohitri kompleksnost časovnega izvajanja, ter eksperimentiranja na analognem elektronskem vezju, s pomočjo katerega se podrobno seznanimo z načinom propagiranja informacij umetnih nevronskih mrež tretje generacije. Rezultati eksperimentov prinašajo tako vsebinske kot tehnične ugotovitve, najpomembnejšo med njimi, da se enoten model ki hkrati trguje z večjim številom finančnih instrumentov obnaša podobno kot kopica posamično prilagojenih modelov na točno določen finančni instrument, kakor tudi novo ugotovljene izkušnje vezane na propagiranje in izrabo najnovejše generacije umetnih nevronskih mrež.
Keywords:umetna inteligenca, portfelj delnic, umetne nevronske mreže, mehanski trgovalni sistem
Place of publishing:Maribor
Publisher:[D. Fister]
Year of publishing:2022
PID:20.500.12556/DKUM-81480 New window
UDC:336.76:004.8(043.3)
COBISS.SI-ID:129178883 New window
Publication date in DKUM:14.11.2022
Views:1087
Downloads:277
Metadata:XML DC-XML DC-RDF
Categories:EPF
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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:31.03.2022

Secondary language

Language:English
Title:Artificial intelligence for managing the portfolio of stocks
Abstract:Challenge of this work is about the design, planning and practical implementation of the automated trading system that independently and user-interference free generates the trading decisions and realizes them on the open market. The core of the trading system is a trading strategy that monitors past and current financial data, performs the technical analyses and, if capable adapts to the latest conditions on financial markets. Two groups of the trading strategies, the classics, traditionally not capable of adapting nor learning, and two adapting and learning capable trading strategies, based on the latest artificial intelligence methods, one of them a representative of the third-generation neural networks, are implemented. Comprehensive simulation experiments and tests are concluded using the data on German stock market in past ten years, with additional digital and purely analogue hardware experiments on the dedicated equipment, that demand significantly lower time complexities on one hand, and offer an outstanding chance to get familiarized with the concept of information propagation in the third-generation artificial neural networks. Results of experiments communicate both substantive and technical findings, more important among them, that the universal model that manages several financial instruments concurrently behaves similar as a bunch of specific models that are specialized for only a single financial instrument at a time, as well as newly discovered experiences on propagation and exploitation of the latest third-generation neural networks.
Keywords:artificial intelligence, portfolio of stocks, artificial neural networks, mechanical trading system


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