  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.
