Trading networks, abnormal motifs and stock manipulation

TL;DR

This paper analyzes complex trading networks to identify abnormal motifs associated with stock price manipulation, revealing patterns in volatility, trading volume, and returns that support manipulation detection.

Contribution

It introduces a network-based approach using abnormal motifs to detect potential stock price manipulation, providing new insights into manipulation patterns.

Findings

Abnormal motifs are formed by few traders, indicating potential manipulation.

Variables like volatility and trading volume peak around abnormal events.

Positive excess returns follow buyer-initiated suspicious trades.

Abstract

We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares to the latter. Specifically, three abnormal network motifs are investigated, which are found to be formed by a few traders, implying potential intention of price manipulation. We further investigate the dynamics of volatility, trading volume, average trade size and turnover around the transactions associated with the abnormal motifs for large, medium and small trades. It is found that these variables peak at the abnormal events and exhibit a power-law accumulation in the pre-event time period and a power-law relaxation in the post-event period. We also find that the cumulative excess returns are significantly positive after buyer-initiated suspicious trades and exhibit a mild price reversal after seller-initiated suspicious trades. These findings can be better understood in favor of price manipulation. Our work shed new lights into the detection of price manipulation resorting to the abnormal motifs of complex trading networks.