Predicting the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques

Article type: Research Article

Authors: Rakotomarolahy, Patrick

Affiliations: Mention Mathematics and Their Applications, Faculty of Sciences, University of Fianarantsoa, BP 1264 Andrainjato, Fianarantsoa, Madagascar | E-mail: [email protected]

Correspondence: [*] Corresponding author: Mention Mathematics and Their Applications, Faculty of Sciences, University of Fianarantsoa, BP 1264 Andrainjato, Fianarantsoa, Madagascar. E-mail: [email protected].

Abstract: This paper proposes prediction of the bitcoin return direction with logistic, discriminant analysis and machine learning classification techniques. It extends the prediction of the bitcoin return direction using exogenous macroeconomic and financial variables which have been investigated as drivers of bitcoin return. We also use google trends as proxy for investors interest on bitcoin. We consider those variables as predictors for bitcoin return direction. We conduct an in-sample and out-of-sample empirical analysis and achieve a misclassification error around 4% for in-sample evaluation and around 41% in out-of-sample empirical analysis. Ensemble learning trees based outperforms the other methods in both in-sample and out-of-sample analyses.

Keywords: Bitcoin return direction, macroeconomic and financial variables, google trends, logistic regression, discriminant analysis, machine learning

DOI: 10.3233/MAS-210530

Journal: Model Assisted Statistics and Applications, vol. 16, no. 3, pp. 169-176, 2021