Bitcoin Transaction Behavior Modeling Based on Balance Data
Yu Zhang, Claudio Tessone

TL;DR
This paper investigates Bitcoin users' balance distribution, revealing a log-normal pattern, and identifies two distinct user behaviors—'poor' and 'wealthy'—that influence their trading patterns over time.
Contribution
It introduces a behavioral classification of Bitcoin users and analyzes how their trading patterns relate to their initial wealth levels, expanding understanding of crypto market dynamics.
Findings
Bitcoin balances follow a log-normal distribution.
Poor users tend to buy then sell all their holdings over time.
Wealthy users tend to hold and gradually sell part of their holdings.
Abstract
When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Blockchain Technology Applications and Security
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
