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Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants by Chen, Wei; Xu, Huilin; Jia, Lifen; Gao, Ying is a Economics, Econometrics and Finance article available to read on EtoBox.

What is Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants about?

In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use th

Who reads Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants?

Author

Chen, Wei; Xu, Huilin; Jia, Lifen; Gao, Ying

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-2070)

Published

2021

Language

EN

Field

Economics, Econometrics and Finance (Social Sciences)