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Entropy
Volume 26
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10.3390/e26030177
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Article

Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets

by
Rodrigo Colnago Contreras
1,2,*,†,
Vitor Trevelin Xavier da Silva
2,†,
Igor Trevelin Xavier da Silva
2,†,
Monique Simplicio Viana
3,
Francisco Lledo dos Santos
4,
Rodrigo Bruno Zanin
4,
Erico Fernandes Oliveira Martins
4 and
Rodrigo Capobianco Guido
1
1
Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, São Paulo State University (UNESP), São José do Rio Preto 15054-000, SP, Brazil
2
Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos 13566-590, SP, Brazil
3
Department of Computing, Federal University of São Carlos, São Carlos 13565-905, SP, Brazil
4
Faculty of Architecture and Engineering, Mato Grosso State University, Cáceres 78217-900, MT, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Entropy 2024, 26(3), 177; https://doi.org/10.3390/e26030177
Submission received: 4 January 2024 / Revised: 3 February 2024 / Accepted: 12 February 2024 / Published: 20 February 2024
(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)
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Abstract

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested.
Keywords: feature selection; genetic algorithm; Bitcoin; time series; forecasting; machine learning feature selection; genetic algorithm; Bitcoin; time series; forecasting; machine learning

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MDPI and ACS Style

Contreras, R.C.; Xavier da Silva, V.T.; Xavier da Silva, I.T.; Viana, M.S.; Santos, F.L.d.; Zanin, R.B.; Martins, E.F.O.; Guido, R.C. Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets. Entropy 2024, 26, 177. https://doi.org/10.3390/e26030177

AMA Style

Contreras RC, Xavier da Silva VT, Xavier da Silva IT, Viana MS, Santos FLd, Zanin RB, Martins EFO, Guido RC. Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets. Entropy. 2024; 26(3):177. https://doi.org/10.3390/e26030177

Chicago/Turabian Style

Contreras, Rodrigo Colnago, Vitor Trevelin Xavier da Silva, Igor Trevelin Xavier da Silva, Monique Simplicio Viana, Francisco Lledo dos Santos, Rodrigo Bruno Zanin, Erico Fernandes Oliveira Martins, and Rodrigo Capobianco Guido. 2024. "Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets" Entropy 26, no. 3: 177. https://doi.org/10.3390/e26030177

APA Style

Contreras, R. C., Xavier da Silva, V. T., Xavier da Silva, I. T., Viana, M. S., Santos, F. L. d., Zanin, R. B., Martins, E. F. O., & Guido, R. C. (2024). Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets. Entropy, 26(3), 177. https://doi.org/10.3390/e26030177

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