Abstract
Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k-means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.
Highlights
It is well-known that financial systems are complex and their evolution depends heavily on the behavior of their agents
Our analysis shows that systemic events change people’s behavior in both systems, but quite differently—there was no big change in Behavioral structure of users in cryptocurrency market ethereum with slight increase of number of pessimistic users, while bitcoin users appeared to be more optimistic
Our developed methodology to classify different strategies that exist in the cryptocurrency market, using a combination of unsupervised machine learning method (k-means clustering) and supervised learning method (SVM), has allowed us to derive distinct and robust clusters of users having different behavior
Summary
It is well-known that financial systems are complex and their evolution depends heavily on the behavior of their agents (users). This realization can be traced back to the times of Adam Smith in the late 1700s. It was soon realised that human behavior is more heterogeneous and complex than what efficient market theory has assumed It spurred the invention of many agent-based models which simulate diverse users (agents) interacting according to a set of prescribed rules. These models are able to explain many stylized facts in financial time series that previous models have failed to reproduce. These models perform well in explaining facts observed in financial data, they are based on hypothetical assumptions that a person’s behavior can be subjected to confounding interpretations
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