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Currently cryptocurrencies and Decentralized Finance (DeFi), which enablefinancial services on public blockchains, represents a new growing trend infinance. In contrast to financial markets, ruled by traditional corporations,DeFi is completely transparent as it keeps records of all transactions thatoccur in the network and makes them publicly available. The availability of thedata represents an opportunity to analyze and understand the market from thecomplexity that emerges from the interactions of the actors (users, bots andcompanies) operating in the embedded market. In this paper we focus on theEthereum network and our main goal is to show that the properties of theunderlying transaction network provide further and useful information toforecast the evolution of the market. We aim to separate the non redundanteffects of the blockchain transaction network properties from classic technicalindicators and social media trends in the future price of Ethereum. To thisend, we build two machine learning models to predict the future trend of themarket. The first one serves as a base model and considers a set of the mostrelevant features according to the current scientific literature includingtechnical indicators and social media trends. The second model considers thefeatures of the base model, together with the network properties computed fromthe transaction networks. We found that the full model outperforms the basemodel and can anticipate 46 more rises in the price than the base model and 19more falls.

The predictive power of the Blockhain transaction networks: Towards a new generation of network science market indicators