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An Endogenous Feedback and Entropy Analysis in Machine Learning Model for Stock’s Return Forecast

Edson Vinicius Pontes Bastos1,*, Jorge Junio Moreira Antunes2, Lino Guimarães Marujo1, Peter Fernandes Wanke2, Roberto Ivo da Rocha Lima Filho1

1 Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia-COPPE, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-598, Brasil
2 Instituto de Pós-Graduação em Administração-Coppead, Universidade Federal do Rio de Janeiro (UFRJ), 21941-918, Brasil

* Corresponding Author: Edson Vinicius Pontes Bastos. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3175-3190.


Stock markets exhibit Brownian movement with random, non-linear, uncertain, evolutionary, non-parametric, nebulous, chaotic characteristics and dynamism with a high degree of complexity. Developing an algorithm to predict returns for decision-making is a challenging goal. In addition, the choice of variables that will serve as input to the model represents a non-triviality, since it is possible to observe endogeneity problems between the predictor and the predicted variables. Thus, the goal is to analyze the endogenous origin of the stock return prediction model based on technical indicators. For this, we structure a feed-forward neural network. We evaluate the endogenous feedback between the predicted returns and technical analysis indicators based on the generated residues. It is possible to predict the return. The high accuracy of the model indicates that, during the test period, there is a hit rate close to 76%. Regarding endogeneity, the term of interest and the return are the variables that influence the largest number of indicators. The results will help investors build investment strategies based on this expert system applied to forecasting.


Cite This Article

E. V. P. Bastos, J. J. M. Antunes, L. G. Marujo, P. F. Wanke and R. I. D. R. L. Filho, "An endogenous feedback and entropy analysis in machine learning model for stock’s return forecast," Intelligent Automation & Soft Computing, vol. 36, no.3, pp. 3175–3190, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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