Application of Genetic Algorithms in Stock Market Simulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F12%3A50000214" target="_blank" >RIV/62690094:18450/12:50000214 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.sbspro.2012.06.619" target="_blank" >http://dx.doi.org/10.1016/j.sbspro.2012.06.619</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sbspro.2012.06.619" target="_blank" >10.1016/j.sbspro.2012.06.619</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of Genetic Algorithms in Stock Market Simulation

Popis výsledku v původním jazyce

Development of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application ofgenetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basicmodel of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms us

Název v anglickém jazyce

Application of Genetic Algorithms in Stock Market Simulation

Popis výsledku anglicky

Development of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application ofgenetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basicmodel of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms us

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Procedia - social and behavioral sciences

ISSN

1877-0428

e-ISSN

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Svazek periodika

2012

Číslo periodika v rámci svazku

47

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

5

Strana od-do

93-97

Kód UT WoS článku

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EID výsledku v databázi Scopus

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