Quantifying immediate price impact of trades based on the k-shell decomposition of stock trading networks

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell. Copyright c ⃝ EPLA, 2016 Introduction. – The availability of large-scale data on economic and financial activities provides great challenges and new opportunities for us to gain a deeper understanding of the dynamics of complex economic and financial systems [1,2], in which the structure and evolutionary dynamics of complex economic and financial networks play an essential role [3–6]. In financial and economic networks, the nodes represent financial or economic agents, such as economies, companies, financial institutions, traders, etc., while the links represent interactions between two nodes, such as investment, trade, lending, economic cooperation, and so on [7–11]. The buy-sell interactions among traders in economic systems can be described by trading networks, in which the nodes represent the traders and the edges stand for the trading relationships. The main statistical properties (a)E-mail: [email protected] of several trading networks have been investigated, such as the Austrian money flow trading network [12], the trading network in a web-based experimental prediction market [13,14], the daily trading networks in the Shenzhen stock market [15], and the trading networks in the Shanghai Futures Market [16]. Usually, these trading networks are scale-free with power-law degree distributions and disassortative. The statistical properties of trading networks can be utilized to track and detect abnormal trades implemented by price manipulators in financial markets. Kyriakopoulos et al. performed random matrix analysis to identify accounts with financial misconduct [12], Tumminello et al. identified trader clusters with a very high degree of synchronization in trading which implies to some extent the presence of price manipulation [17], Sun et al. found significant differences in the topological properties between manipulated and non-manipulated stocks [18,19], and Jiang