| Summary: | Decentralized Finance is the new socioeconomic system growing with an extremely fast
pace and changing the way financial interactions are being conducted. In comparison
with the growing importance of digital assets and blockchain technology, there is still
little understanding of Decentralized Finance as a system. In this thesis we analyze
transaction datasets from Bitcoin and Ethereum blockchains to obtain a comprehensive
understanding of digital assets - from studying the behaviour of each part to investigating
the whole structure and deriving the relations between micro and macro properties of
the cryptocurrency systems. Using the Complex Networks approach we explained the
system's overall structure and dynamics, and uncovered the mechanism behind network
formation. It was found that there is fitness preferential attachment among nodes in
the bitcoin network that leads the system to scale-free behaviour. We proposed the
quantifiable definition of fitness and supported our finding by simulating a synthetic
network and reproducing the main properties of the bitcoin network. After having a
good understanding about the structure of the system, we zoom in into its parts by
studying the behavioral patterns among the system's users (people). We develop the
methodology based on Machine Learning models to define distinct behavioral types in
the cryptocurrency systems and find that despite differences between the bitcoin and
ethereum systems, there are four common strategies that users follow in both markets.
Based on our finding, we model the dynamics of people's behaviour in market as an
Absorbing Markov Chain. This approach allowed us to present the behavioral switches
in a comprehensive and intuitive way. Moreover, we were able to obtain the predictions
on the longevity of users in the system according to their behaviour. Finally, we use
the Granger causality test to derive the relations between all system characteristics. We
attempt to explain the effect of behavioral switches on the structural properties and price;
we find that indeed, switches of users from certain behavioral groups causes a change in
price which affects the size of the network as well. We hope that the work and results
presented in this thesis will advance the understanding of the new field of Decentralized
Finance and expect that the research approach and methodologies developed for this
study will be helpful to investigate various complex systems as well.
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