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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

URORA: an autonomous agent-oriented hybrid trading servic

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Author(s):
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]
Total Authors: 7
Affiliation:
[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
Total Affiliations: 6
Document type: Journal article
Source: NEURAL COMPUTING & APPLICATIONS; v. 34, n. 3, SI SEP 2021.
Web of Science Citations: 0
Abstract

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)

FAPESP's process: 19/14429-5 - Visual analytics of heterogeneous brain networks using multilevel methods
Grantee:Alan Demétrius Baria Valejo
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support Opportunities: Research Projects - Thematic Grants