Bitcoin price forecasting: A perspective of underlying blockchain transactions

•We study Bitcoin price forecasting from the perspective of the underlying blockchain transactions.•We propose a novel framework to examine the volatility of Bitcoin prices and forecast its price at different time scales.•We find the trading volumes between big and small exchanges of the cryptocurre...

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Bibliographic Details
Published in:Decision Support Systems 2021-12, Vol.151, p.113650, Article 113650
Main Authors: Guo, Haizhou, Zhang, Dian, Liu, Siyuan, Wang, Lei, Ding, Ye
Format: Article
Language:English
Subjects:
Bitcoin
Blockchain
Cryptocurrency
Cryptography
Deep learning
Digital currencies
Economic forecasting
Forecasting
Goats
Mathematical models
Price forecasting
Pricing
Wavelet transforms
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Summary:•We study Bitcoin price forecasting from the perspective of the underlying blockchain transactions.•We propose a novel framework to examine the volatility of Bitcoin prices and forecast its price at different time scales.•We find the trading volumes between big and small exchanges of the cryptocurrency can impact the Bitcoin price forecasting performance.•We find our proposed model can detect different trends with high forecasting performance, and we also study the impact of our model components on performance improvement. Cryptocurrency price forecasting plays an important role in financial markets. Traditional approaches face two challenges: (1) it is difficult to ascertain the influential factors related to price forecasting; and (2) due to the 24/7 trading policy, cryptocurrencies’ prices face very large fluctuations, thus weakening the forecasting power of traditional models. To address these issues, we focus on Bitcoin and identify the influential factors related to its price forecasting from the perspective of underlying blockchain transactions. We then propose a price forecasting model WT-CATCN, which leverages Wavelet Transform (WT) and Casual Multi-Head Attention (CA) Temporal Convolutional Network (TCN), to forecast cryptocurrency prices. Our model can capture important positions of input sequences and model the correlations among different data features. Using real-world Bitcoin trading data, we test and compare WT-CATCN with other state-of-the-art price forecasting models. The experiment results show that our model improves the price forecasting performance by 25%.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2021.113650