Detecting Collusive Cliques in Futures Markets Based on Trading Behaviors from Real Data

TL;DR

This paper presents a method to detect collusive trading groups in futures markets by analyzing trading behavior correlations, successfully identifying suspicious cliques in real market data, and deploying a surveillance tool.

Contribution

The paper introduces a novel correlation-based approach for identifying collusive cliques in futures markets, with real-data validation and practical deployment.

Findings

Effective detection of collusive cliques in real data

Successful deployment in Shanghai Futures Exchange

Enhanced market surveillance and risk management

Abstract

In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the hidden collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management.