AURORA: an autonomous agent-oriented hybrid trading service

Renato A. Nobre, Khalil C.do Nascimento, Patricia A. Vargas, Alan Demétrius Baria Valejo, Gustavo Pessin, Leandro A. Villas, Geraldo P. Rocha Filho

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalNeural Computing and Applications
Early online date25 Sep 2021
DOIs
Publication statusE-pub ahead of print - 25 Sep 2021

Keywords

  • Long short-term memory
  • Recurrent neural network
  • Stock market prediction
  • Time-series

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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