A Stock Market Forecasting Model in Peru Using Artificial Intelligence and Computational Optimization Tools

Miguel Angel Cano Lengua, Mirko Jerber Rodríguez Mallma, Erik Alex Papa Quiroz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationProceedings of the 5th Brazilian Technology Symposium - Emerging Trends, Issues, and Challenges in the Brazilian Technology
EditorsYuzo Iano, Rangel Arthur, Osamu Saotome, Guillermo Kemper, Reinaldo Padilha França
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030575472
StatePublished - 2021
Externally publishedYes
Event5th Brazilian Technology Symposium, BTSym 2019 - Campinas, Brazil
Duration: 22 Oct 201924 Oct 2019

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference5th Brazilian Technology Symposium, BTSym 2019

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Artificial neural networks
  • Computational optimization
  • Stock market forecasting


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