Experimental evidence for the interplay between individual wealth and transaction network

Tseng, Jie-Jun; Li, Sai‐Ping; Wang, Sun-Chong

doi:10.1140/epjb/e2009-00424-8

Cited by 16 publications

(17 citation statements)

References 26 publications

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“…For degrees in the range of – (over of all nodes with nonzero balance), the average balance is a monotonously increasing function of the degree, and it is approximated by the power-law , indicating that the accumulated wealth and the number of distinct transaction partners an individual has are inherently related. Similar scaling was reported by Tseng et al, who conducted an online experiment where volunteers traded on a virtual market [48] .…”

Section: Resultssupporting

confidence: 80%

“…It states that the growth of the wealth of each individual is proportional to the wealth of that individual. In line with this principle, several statistical models were proposed to account for the heterogeneous wealth distribution [42,[47][48][49].…”

Section: B Dynamics Of Transactionsmentioning

confidence: 99%

“…On the other hand, the "average" behavior shows a correlation between the balance and the increase of the balance: the uncorrelated assumption (α = 0) clearly gives a much worse approximate than the exponents that presume preferential attachment (α > 0). et al, who conducted an online experiment where volunteers traded on a virtual market [49].…”

Section: B Dynamics Of Transactionsmentioning

confidence: 99%

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Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

et al. 2014

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The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.

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Section: Resultssupporting

confidence: 80%

Section: B Dynamics Of Transactionsmentioning

confidence: 99%

Section: B Dynamics Of Transactionsmentioning

confidence: 99%

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Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

et al. 2014

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“…Our platform, Taiwan Political Exchange (TAIPEX), is a futures market with continuous double auction trading mechanism. Beginning 2004, we have carried out several experiments on this platform and have found many interesting features in these experimental markets [6,7,8]. Aside from the price and volume time series which resemble real market data, we have indeed also found power-law behavior in the degree distribution of the traders' transaction network, the wealth distribution of traders and community size, etc some of which have never been able to be extracted from real markets before.…”

Section: Introductionmentioning

confidence: 93%

Statistical properties of agent-based models in markets with continuous double auction mechanism

Tseng

Lin

et al. 2010

Physica A: Statistical Mechanics and its Applications

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Real world markets display power-law features in variables such as price fluctuations in stocks. To further understand market behavior, we have conducted a series of market experiments on our web-based prediction market platform which allows us to reconstruct transaction networks among traders. From these networks, we are able to record the degree of a trader, the size of a community of traders, the transaction time interval among traders and other variables that are of interest. The distributions of all these variables show power-law behavior. On the other hand, agent-based models have been proposed to study the properties of real financial markets. We here study the statistical properties of these agent-based models and compare them with the results from our web-based market experiments. In this work, three agentbased models are studied, namely, zero-intelligence (ZI), zero-intelligenceplus (ZIP) and Gjerstad-Dickhaut (GD). Computer simulations of variables based on these three agent-based models were carried out. We found that although being the most naive agent-based model, ZI indeed best describes the properties observed in real markets. Our study suggests that the basic ingredient to produce the observed properties from real world markets could in fact be the result of a continuously evolving dynamical system with basic features similar to the ZI model.

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“…Jiang and Zhou reveal that the daily trading networks in Shenzhen stock market exhibit patterns of power-law degree distributions and disassortative architectures [2]. Tseng et al uncover a power-law correlation between the average degree and the size of placed orders [3,4]. Wang et al find that the trading networks in the Shanghai Futures Market exhibit similar features such as scale-free, small-world effect, hierarchical organization, and power-law betweenness distribution [5].…”

Section: Introductionmentioning

confidence: 99%

Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

Jiang

Xie

et al. 2015

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• We construct and investigate time series of security trading networks. • Correlation relationships are uncovered between trading network metrics and financial variables. • The structure of trading networks contains rich information about the dynamics of securities. a b s t r a c t Traders develop and adopt different trading strategies attempting to maximize their profits in financial markets. These trading strategies not only result in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 rolling time windows with a length of 5 min, construct a trading network within each window, and obtain a time series of over 11,600 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the system's performance.

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Experimental evidence for the interplay between individual wealth and transaction network

Cited by 16 publications

References 26 publications

Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

Statistical properties of agent-based models in markets with continuous double auction mechanism

Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

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