Predicting bitcoin’s volatility using machine learning

• Κύριος συγγραφέας: Κανελλόπουλος, Μιχαήλ
• Άλλοι συγγραφείς: Kanellopoulos, Michail
• Γλώσσα: English
• Έκδοση: 2022
• Θέματα: Machine learning; Neural networks; Volatility; Classification; Feature importance; Cryptocurrency; Μηχανική μάθηση; Νευρωνικά δίκτυα; Μεταβλητότητα; Κατηγοριοποίηση; Αξιολόγηση χαρακτηριστικών; Κρυπτονόμισμα

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.