  Bitcoin has attracted attention from different market participants due to
unpredictable price patterns. Sometimes, the price has exhibited big jumps.
Bitcoin prices have also had extreme, unexpected crashes. We test the
predictive power of a wide range of determinants on bitcoins' price direction
under the continuous transfer entropy approach as a feature selection
criterion. Accordingly, the statistically significant assets in the sense of
permutation test on the nearest neighbour estimation of local transfer entropy
are used as features or explanatory variables in a deep learning classification
model to predict the price direction of bitcoin. The proposed variable
selection methodology excludes the NASDAQ index and Tesla as drivers. Under
different scenarios and metrics, the best results are obtained using the
significant drivers during the pandemic as validation. In the test, the
accuracy increased in the post-pandemic scenario of July 2020 to January 2021
without drivers. In other words, our results indicate that in times of high
volatility, Bitcoin seems to autoregulate and does not need additional drivers
to improve the accuracy of the price direction.
