Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
This work addresses the challenge of predicting crypto price movements for investors and analysts, though it is incremental as it applies existing analytical techniques to a new domain.
The paper tackles the problem of understanding crypto-token price dynamics by analyzing the Ethereum transaction network, showing that novel tools based on topological data analysis and functional data depth can provide critical insights on price strikes that are inaccessible with traditional methods.
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on crypto-tokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.