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 illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.

中文翻译：

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细分比特币交易以预测价格变动

像比特币这样的加密货币已经受到金融交易所的广泛关注。不幸的是，基于套利的金融市场价格预测模型对加密货币无效。在本文中，我们利用标准机器学习模型和区块中公开的交易数据来预测比特币价格变动的方向。我们使用从比特币区块链和各种在线来源合并的数据来说明我们的方法。这为我们提供了 2011 年 9 月 13 日至 2017 年 5 月 5 日期间的比特币交易历史记录（区块 ID、区块时间戳、交易 ID、发送者地址、接收者地址、交易金额）以及市场交易价格。研究表明，与文献中现有的比特币价格变动预测模型相比，根据投资者类型对公开交易进行细分有助于实现更高的预测准确性。这种交易细分凸显了投资者类型在影响金融市场方面的作用。从管理上来说，金融交易的细分有助于我们了解金融和加密货币市场参与者在资产价格变动中的作用。这些发现为数字货币新时代的风险管理、金融监管和投资策略提供了进一步的启示。