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

Abstract

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.

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

“…-Financial markets evolve in a selforganized manner with the interacting elements forming complex networks at different levels, including international markets [1][2][3][4], individual markets [5][6][7][8], and security trading networks [9][10][11][12][13][14][15][16]. There are well-documented stylized facts of stock return time series within individual markets unveiled by the random matrix theory (RMT) analysis [6,17]: (1) The largest eigenvalue reflects the market effect such that its eigenportfolio returns are strongly correlated with the market returns; (2) Other largest eigenvalues contain information of industrial sectors; and (3) The smallest eigenvalues embed stock pairs with large correlations.…”

Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

“…It contributes to the field of market surveillance. Another work is to study the correlations between market indicators and network topological metrics to unveil how the trading behaviors affect the market performance [25]. It contributes to the behavioral finance research.…”

“…Li [21] HSI fluctuation patterns Important topological nodes Hong Kong stock market Bakker [22] Investment behavior of trust traders Impacts of trust networks on the stabilization of market NA Jiang [23] Trading behavior Power-law degree distribution Shenzhen stock exchange Jiang [24] Trading behavior Abnormal motifs Shenzhen stock exchange Li [25] Trading behavior Correlations between financial variables and network metrics A specific stock…”

Tail dependence networks of global stock markets

“…Caraiani (2012) constructed the network using the Pearson correlation coefficient as the edge information in the European stock market and found that the stock network shows scale-free features and the clustering coefficients show multiple fractal features. Li et al (2015) used the Pearson correlation coefficient as a trading channel for Baosteel's equity information and found that there are strong simultaneous correlations between the topological metrics of trading networks that characterize the patterns of order execution and the financial variables for the stock and its warrant. Chen, Luo, Sun, and Wang (2015) constructed the network using the Pearson correlation coefficient as the edge information on the SSE stock market in China and found that there is a correlation between the industry's proximity and industry return, as well as between stock centre and stock returns.…”