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Doctoral Thesis
DOI
10.11606/T.3.2009.tde-21102009-153332
Document
Author
Full name
Paulo André Lima de Castro
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2009
Supervisor
Committee
Sichman, Jaime Simão (President)
Barros, Leliane Nunes de
Costa, Oswaldo Luiz do Valle
Garcia, Ana Cristina Bicharra
Ribeiro, Carlos Henrique Costa
Title in Portuguese
Uma arquitetura para administração automatizada de ativos baseada em agentes competitivos.
Keywords in Portuguese
Sistemas multiagentes
Teoria de finanças
Abstract in Portuguese
Além da capacidade de negociar, a atuação conjunta de agentes autônomos para resolver problemas complexos requer objetivos ao menos parcialmente compartilhados, de modo a tornar a cooperação atraente para tais agentes. Por outro lado, agentes competindo entre si podem trazer benefícios sociais, à medida que os mais aptos forem identificados e recursos sejam canalizados para aumentar sua relevância social e deste modo contribuir mais fortemente para a eficiência de todo o grupo. Dentro deste conceito, este trabalho apresenta uma arquitetura multiagentes que busca através da competição entre agentes com interesses comuns obter maior ganho social. Esta arquitetura, nomeada COAST, foi desenhada para o complexo ambiente que é a administração automatizada de ativos. Um sistema COAST é capaz de administrar uma carteira de ativos, utilizando um modelo explícito de perfil de investidor também proposto neste trabalho. Busca-se não apenas a maximização dos retornos, mas também um efetivo controle de risco e retorno, seguindo as restrições dadas pelo perfil do investidor. Um sistema de simulação de mercado financeiro, denominado AgEx, foi criado para facilitar o trabalho de desenvolver agentes para a administração de ativos e simular sua atuação através do uso de dados reais de negociações ocorridas em mercados financeiros. Foram realizadas várias simulações onde se pode observar que a arquitetura COAST se mostrou bastante eficaz no domínio escolhido
Title in English
An architecture for automated asset management based on competitive agents.
Keywords in English
Finance theory
Multiagent systems
Abstract in English
In order to solve complex problems using several autonomous agents it is necessary that these agents present negotiation skills and share some common goals to make cooperation attractive. On the other hand, competition among agents may bring some social benefits, since the most effective agents can be identified and more resources could be associated to them, thus contributing better to the whole society. According to this idea, this work presents a multiagent architecture that tries to explore competition among agents with commons goals, as a way to achieve better social results. The architecture was named COAST and it was designed to manage assets in an autonomous way. COAST is able to manage assets using an explicit investor profile model that is also proposed in this work. COAST architecture tries not only to maximize profits but also an effective risk and return control, according to an investor profile. A financial market simulation tool, called AgEx, was also implemented. This tool may facilitate the development of agents to automated asset management and simulate their acting in stock markets was also developed. We have performed many simulated experiments to validate COAST and the obtained results show that the use of COAST architecture in this domain is quite effective.
 
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tese.pdf (1.69 Mbytes)
Publishing Date
2010-05-18
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • CASTRO, Paulo André Lima, and SICHMAN, Jaime Simão. Automated asset management based on partially cooperative agents for a world of risks [doi:10.1007/s10489-012-0366-8]. Applied Intelligence [online], 2013, vol. 38, n. 2, p. 210-225.
  • CASTRO, Paulo André Lima de, and SICHMAN, Jaime Simão. COAST: An architecture based on negotiation among competitive agents for automated asset management. In International Conference on Artificial Intelligence (ICAI), 13, Las Vegas, 2011.
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