| Περίληψη: | Being a revolutionary form of financial instrument, Bitcoin's rapid price
fluctuations inevitably prompt the query of whether its price can be
predicted. This is a crucial question, particularly in light of Bitcoin's brief
history and the ease with several factors may have an impact on its
price. This study investigates the direction prediction of Bitcoins volatility
using internal, blockchain data, such as the past prices of Bitcoin and
blockchain’s characteristics. Convolutional neural networks (CNN),
among other methodologies, have lately been used for automatic feature
selection and market forecasting. In this research, we propose a CNN based framework using blockchain features for predicting the direction of
Bitcoins volatility from a set of data from various sources. The proposed
methodology has been used to predict the next day's direction of
movement for Bitcoin on a variety of different variables. The evaluation
displays a significant accuracy of 57% in prediction's performance and a
mean absolute error (loss) of 43%, a result that suggests CNN's
framework significant when predicting Bitcoin's volatility. This research
proposes an interpretative approach to derive feature importance, which
represents the degree to which an input feature may discriminate
between distinct classes, in order to better understand how these
networks make their final selections. Moreover, we found that the
blockchain’s characteristics that had an impact in the performance of the
CNN algorithm were the total value of all transaction outputs per day,
the miner’s revenue divided by the number of transactions, the total
estimated value in USD of transactions, the miner’s revenue and the
total number of confirmed transactions per day.
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