AURORA: an autonomous agent-oriented hybrid trading service

被引:0
|
作者
Renato A. Nobre
Khalil C. do Nascimento
Patricia A. Vargas
Alan Demétrius Baria Valejo
Gustavo Pessin
Leandro A. Villas
Geraldo P. Rocha Filho
机构
[1] University of Brasília (UnB),Department of Computer Science
[2] Heriot-Watt University,Edinburgh Centre for Robotics
[3] Federal University of São Carlos (UFSCar),Department of Computing
[4] Robotics Laboratory,Vale Institute of Technology
[5] University of Campinas (UNICAMP),Institute of Computing
[6] Università degli Studi di Milano,Computer Science Department
来源
关键词
Time-series; Stock market prediction; Long short-term memory; Recurrent neural network;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
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页码:2217 / 2232
页数:15
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