Segmenting Bitcoin Transactions for Direction of Price Movement Prediction

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy than existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants on asset price movement. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies