Distinguishing manipulated stocks via trading network analysis

TL;DR

This study analyzes trading networks constructed from transaction data to identify characteristics distinguishing manipulated stocks from non-manipulated ones, revealing specific network patterns associated with manipulation.

Contribution

It introduces a novel approach using trading network analysis to detect stock manipulation, highlighting distinct network features of manipulated stocks.

Findings

Manipulated stocks have higher lower bounds in power-law tails.

Manipulated stocks exhibit higher average degrees in trading networks.

Manipulated stocks show lower correlation between price return and seller-buyer ratio.

Abstract

Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a trading network is constructed to characterize the relations among its investors. In trading networks, nodes represent investors and a directed link connects a stock seller to a buyer with the total trade size as the weight of the link, and the node strength is the sum of all edge weights of a node. For all these trading networks, we find that the node degree and node strength both have tails following a power-law distribution. Compared with non-manipulated stocks, manipulated stocks have a high lower bound of the power-law tail, a high average degree of the trading network and a low correlation between the price return and the seller-buyer ratio. These findings may help us to detect manipulated stocks.