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

Abstract

Traders in a stock market exchange stock shares and form a stock trading\nnetwork. Trades at different positions of the stock trading network may contain\ndifferent information. We construct stock trading networks based on the limit\norder book data and classify traders into $k$ classes using the $k$-shell\ndecomposition method. We investigate the influences of trading behaviors on the\nprice impact by comparing a closed national market (A-shares) with an\ninternational market (B-shares), individuals and institutions, partially filled\nand filled trades, buyer-initiated and seller-initiated trades, and trades at\ndifferent positions of a trading network. Institutional traders professionally\nuse some trading strategies to reduce the price impact and individuals at the\nsame positions in the trading network have a higher price impact than\ninstitutions. We also find that trades in the core have higher price impacts\nthan those in the peripheral shell.\n