TY - JOUR
T1 - AURORA
T2 - an autonomous agent-oriented hybrid trading service
AU - Nobre, Renato A.
AU - Nascimento, Khalil C.do
AU - Vargas, Patricia A.
AU - Valejo, Alan Demétrius Baria
AU - Pessin, Gustavo
AU - Villas, Leandro A.
AU - Filho, Geraldo P. Rocha
N1 - Funding Information:
The authors thank the Coordination for the Improvement of Higher Education Personnel (CAPES) and the São Paulo Research Foundation (FAPESP) grants 19/14429-5, 15/50122-0 for the financial support to develop this research.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - Long short-term memory
KW - Recurrent neural network
KW - Stock market prediction
KW - Time-series
UR - http://www.scopus.com/inward/record.url?scp=85115690523&partnerID=8YFLogxK
U2 - 10.1007/s00521-021-06508-3
DO - 10.1007/s00521-021-06508-3
M3 - Article
AN - SCOPUS:85115690523
SN - 0941-0643
VL - 34
SP - 2217
EP - 2232
JO - Neural Computing and Applications
JF - Neural Computing and Applications
ER -