Protocolo de Negociação Baseado em Aprendizagem-Q para Bolsa de Valores

• Ano de defesa: 2013
• Autor(a) principal: Cunha, Rafael de Souza
• Orientador(a): LABIDI, Sofiane
• Banca de defesa: Abdelouahab, Zair
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: Bolsa de Valores; Sistemas Multi-Agente; Aprendizagem-q; Protocolo de negociação
• Palavras-chave em Inglês: Stock Exchange; Multi-Agent Systems; Q-Learning; Negotiation Protocol
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/501

In this work, we applied the technology of Multi-Agent Systems (MAS) in the capital market, i.e., the stock market, specifically in Bolsa de Mercadorias e Futuros de São Paulo (BM&FBovespa). The research focused mainly on negotiation protocols and learning of investors agents. Within the Stock Exchange competitive field, the development of an agent that could learn to negotiate, could become differential for investors who wish to increase their profits. The decision-making based on historical data is motivation for further research in the same direction, however, we sought a different approach with regard to the representation of the states of q-learning algorithm. The reinforcement learning, in particular q-learning, has been shown to be effective in environments with various historical data and seeking reward decisions with positive results. That way it is possible to apply in the purchase and sale of shares, an algorithm that rewards the profit and punishes the loss. Moreover, to achieve their goals agents need to negotiate according to specific protocols of stock exchange. Therefore, endeavor was also the specifications of the rules of negotiation between agents that allow the purchase and sale of shares. Through the exchange of messages between agents, it is possible to determine how the trading will occur and facilitate communication between them, because it sets a standard of how it will happen. Therefore, in view of the specification of negotiation protocols based on q-learning, this research has been the modeling of intelligent agents and models of learning and negotiation required for decision making entities involved.