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

TL;DR

This paper analyzes how traders' positions in stock trading networks, classified by $k$-shell decomposition, influence the immediate price impact of trades across different market types and trader categories.

Contribution

It introduces a network-based approach using $k$-shell decomposition to quantify the relationship between trading positions and price impact, highlighting differences between trader types and market environments.

Findings

Trades in the core network have higher price impacts.

Institutional traders use strategies to reduce price impact.

Individuals have higher impact than institutions at similar network positions.

Abstract

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.