TY - GEN
T1 - A Stock Market Forecasting Model in Peru Using Artificial Intelligence and Computational Optimization Tools
AU - Cano Lengua, Miguel Angel
AU - Rodríguez Mallma, Mirko Jerber
AU - Papa Quiroz, Erik Alex
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - It is proposed the development of a forecast model capable of predicting the behavior of the price indices and quotes of the shares traded on the Lima Stock Exchange, based on the use of artificial intelligence techniques such as artificial neural networks and fuzzy logic based on computational optimization methods. The proposed model considers the forecast, in addition to the historical quantitative data of the share price, the inclusion of qualitative macroeconomic factors that significantly influence the behavior of the time series of the stock markets. It is about harnessing the ability of artificial neural networks to work with nonlinear quantitative data and their capacity for learning and also take advantage of the fuzzy logic technique to simulate the way of reasoning of human beings by defining judgment rules or knowledge base and their evaluation through inference mechanisms. The main contribution is to demonstrate that the proposed model is capable of obtaining more optimal approximations in the forecast of the financial time series.
AB - It is proposed the development of a forecast model capable of predicting the behavior of the price indices and quotes of the shares traded on the Lima Stock Exchange, based on the use of artificial intelligence techniques such as artificial neural networks and fuzzy logic based on computational optimization methods. The proposed model considers the forecast, in addition to the historical quantitative data of the share price, the inclusion of qualitative macroeconomic factors that significantly influence the behavior of the time series of the stock markets. It is about harnessing the ability of artificial neural networks to work with nonlinear quantitative data and their capacity for learning and also take advantage of the fuzzy logic technique to simulate the way of reasoning of human beings by defining judgment rules or knowledge base and their evaluation through inference mechanisms. The main contribution is to demonstrate that the proposed model is capable of obtaining more optimal approximations in the forecast of the financial time series.
KW - Artificial neural networks
KW - Computational optimization
KW - Stock market forecasting
UR - http://www.scopus.com/inward/record.url?scp=85098187491&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-57548-9_7
DO - 10.1007/978-3-030-57548-9_7
M3 - Contribución a la conferencia
AN - SCOPUS:85098187491
SN - 9783030575472
T3 - Smart Innovation, Systems and Technologies
SP - 79
EP - 86
BT - Proceedings of the 5th Brazilian Technology Symposium - Emerging Trends, Issues, and Challenges in the Brazilian Technology
A2 - Iano, Yuzo
A2 - Arthur, Rangel
A2 - Saotome, Osamu
A2 - Kemper, Guillermo
A2 - Padilha França, Reinaldo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 October 2019 through 24 October 2019
ER -