Currently cryptocurrencies and Decentralized Finance (DeFi), which enable financial services on public blockchains, represents a new growing trend in finance. In contrast to financial markets, ruled by traditional corporations, DeFi is completely transparent as it keeps records of all transactions that occur in the network and makes them publicly available. The availability of the data represents an opportunity to analyze and understand the market from the complexity that emerges from the interactions of the actors (users, bots and companies) operating in the embedded market. In this paper we focus on the Ethereum network and our main goal is to show that the properties of the underlying transaction network provide further and useful information to forecast the evolution of the market. We aim to separate the non redundant effects of the blockchain transaction network properties from classic technical indicators and social media trends in the future price of Ethereum. To this end, we build two machine learning models to predict the future trend of the market. The first one serves as a base model and considers a set of the most relevant features according to the current scientific literature including technical indicators and social media trends. The second model considers the features of the base model, together with the network properties computed from the transaction networks. We found that the full model outperforms the base model and can anticipate 46 more rises in the price than the base model and 19 more falls.

翻译：当前，加密货币与去中心化金融（DeFi）作为在公共区块链上实现金融服务的模式，正成为金融领域的新兴趋势。与传统企业主导的金融市场不同，DeFi具有完全透明性，它会记录网络中发生的所有交易，并将其公开可查。数据的可得性为从参与者（用户、机器人和公司）在嵌入式市场中互动所产生的复杂性角度分析和理解市场提供了机遇。本文聚焦于以太坊网络，主要目标是证明底层交易网络的属性能为预测市场演变提供额外且有用的信息。我们旨在将区块链交易网络属性的非冗余效应与经典技术指标及社交媒体趋势对以太坊未来价格的影响区分开来。为此，我们构建了两个机器学习模型来预测市场的未来趋势。第一个模型作为基准模型，根据当前科学文献选取了最具相关性的特征集合，包括技术指标和社交媒体趋势。第二个模型则在此基础上，加入了从交易网络中计算出的网络属性。研究发现，完整模型的性能优于基准模型，能够比基准模型多预测46次价格上涨和19次价格下跌。