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Related Experiment Videos

Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming.

Yi Cao, Yuhua Li, Sonya Coleman

IEEE Transactions on Neural Networks and Learning Systems

|October 16, 2015

Summary

This study introduces a new method to detect wash trading, an illegal market manipulation tactic. The approach effectively identifies wash trade scenarios in real stock market data, enhancing market integrity.

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Area of Science:

Financial Markets
Computational Finance
Market Abuse Detection

Background:

Wash trading, a form of market abuse, artificially inflates trading volumes to create a false market impression.
Existing detection methods often rely on specific behavioral assumptions and lack real-world applicability.
Effective analysis and detection of wash trading in live markets remain a challenge.

Purpose of the Study:

To analyze and conceptualize the structural elements of wash trading collusion.
To propose a novel, effective method for detecting wash trading activities in financial markets.
To address the gap in current research for real-life wash trade detection.

Main Methods:

Utilizing a directed graph model to represent trader interactions in wash trading.
Developing a two-step detection process: identifying suspiciously matched orders and then detecting trader collusion.
Formulating both detection steps as a simplified knapsack problem solvable via dynamic programming.

Main Results:

The proposed method successfully detects primary wash trade scenarios.
Evaluation on seven stock datasets from NASDAQ and the London Stock Exchange demonstrates high effectiveness.
The approach proves robust across different market data.

Conclusions:

The novel dynamic programming-based method offers an effective solution for wash trade detection.
This research contributes to safeguarding capital market integrity by identifying manipulative trading practices.
The findings pave the way for more sophisticated tools to combat market abuse.