URORA: an autonomous agent-oriented hybrid trading servic

Autor(es)

Nobre, Renato A. ^{[1, 2]} ; do Nascimento, Khalil C. ^[1] ; Vargas, Patricia A. ^[3] ; Baria Valejo, Alan Demetrius ^[4] ; Pessin, Gustavo ^[5] ; Villas, Leandro A. ^[6] ; Rocha Filho, Geraldo P. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Brasilia UnB, Dept Comp Sci, Brasilia, DF - Brazil

^[2] Univ Milan, Comp Sci Dept, Milan - Italy

^[3] Heriot Watt Univ, Edinburgh Ctr Robot, Edinburgh, Midlothian - Scotland

^[4] Fed Univ Sao Carlos UFSCar, Dept Comp, Sao Carlos - Brazil

^[5] Vale Inst Technol, Robot Lab, Ouro Preto, MG - Brazil

^[6] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: NEURAL COMPUTING & APPLICATIONS; v. 34, n. 3, SI SEP 2021.
• Citações Web of Science: 0

Resumo

Stock markets play an essential role in the economy and offer companies opportunities to grow, and insightful investors to make profits. Many tools and techniques have been proposed and applied to analyze the overall market behavior to seize such opportunities. However, understanding the stock exchange's intrinsic rules and taking opportunities are not trivial tasks. With that in mind, this work proposes AURORA: a new hybrid service to trade equities in the stock market, using an autonomous agent-based approach. The goal is to offer a reliable service based on technical and fundamental analysis with precision and stability in the decision-making process. For this, AURORA's intelligence is modeled using a rational agent capable of perceiving the market and acting upon its perception autonomously. When compared with other solutions in the literature, the proposed service shows that it can predict the gain or loss of value at the price of a stock with an accuracy higher than 82.86% in the worst case and 89.23% in the best case. Furthermore, the proposed service can achieve a profitability of 11.74%, overcoming fixed-income investments, and portfolios built with the Markowitz Mean-Variance model. (AU)

• Processo FAPESP: 19/14429-5 - Análise visual de redes cerebrais heterogêneas usando métodos multilíveis
• Beneficiário: Alan Demétrius Baria Valejo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Modalidade de apoio: Auxílio à Pesquisa - Temático