Complex stock trading network among investors
By Zhi-Qiang Jiang, Wei‐Xing Zhou. 2010
68 citations recorded by the source corpus.
We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extract detailed information of each executed order for each trading day and demonstrate that the trade size distributions for different trading days exhibit power-law tails and that most of the estimated power-law exponents are well within the Levy stable regime. Based on the records of order matching among investors, we can construct a stock trading network for each trading day, in which the investors are mapped into nodes and each transaction is translated as a direct edge from the seller to the buyer with the trade size as its weight. We find that all the trading networks comprise a giant component and have power-law degree distributions and disassortative architectures. In particular, the degrees are correlated with order sizes by a power-law function. By regarding the size of executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution.
Subfields: Economics and Econometrics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/36e2289baa989adef5e548dff4188bd2a2949236
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In Economics and Econometrics
In Statistical and Nonlinear Physics
Citations in the corpus
- Trading Network Predicts Stock Price (2014, background): “According to previous research 14,16,17 , the degree distributions of the trading network follow power-law.”