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Bitcoin is a peer-to-peer electronic payment system that popularized rapidly in recent years. Usually, we need to query the complete history of Bitcoin blockchain data to acquire variables with economic meaning. This becomes increasingly difficult now with over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in social science. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort, which enables us to create datasets and visualizations for some key indicators of Bitcoin transactions, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the accumulated unspent transaction output (UTXO). We provide a computationally feasible approach to characterize Bitcoin transactions, which paves the way for the future economic studies of Bitcoin.
Investors tend to sell their winning investments and hold onto their losers. This phenomenon, known as the emph{disposition effect} in the field of behavioural finance, is well-known and its prevalence has been shown in a number of existing markets.
As the first decentralized digital currency introduced in 2009 together with the blockchain, Bitcoin offers new opportunities both for developed and developing countries. Bitcoin peer-to-peer transactions are independent of the banking system, thus f
Bitcoin as well as other cryptocurrencies are all plagued by the impact from bifurcation. Since the marginal cost of bifurcation is theoretically zero, it causes the coin holders to doubt on the existence of the coins intrinsic value. This paper sugg
Global stabilization of viscous Burgers equation around constant steady state solution has been discussed in the literature. The main objective of this paper is to show global stabilization results for the 2D forced viscous Burgers equation around a
The purpose of this paper is to write a complete survey of the (spectral) manifold learning methods and nonlinear dimensionality reduction (NLDR) in data reduction. The first two NLDR methods in history were respectively published in Science in 2000