On Detecting Spoofing Strategies in High Frequency Trading

TL;DR

This paper analyzes spoofing in high-frequency trading by modeling market microstructure, identifying conditions for spoofing, and proposing real-time detection methods based on Wasserstein distance calibrated to real data.

Contribution

It introduces a micro-structural model of spoofing, characterizes optimal spoofing strategies, and develops real-time detection procedures using Wasserstein distance.

Findings

Identifies market conditions favoring spoofing behavior

Quantifies the impact of spoofing on market imbalance

Proposes real-time spoofing detection method

Abstract

Spoofing is an illegal act of artificially modifying the supply to drive temporarily prices in a given direction for profit. In practice, detection of such an act is challenging due to the complexity of modern electronic platforms and the high frequency at which orders are channeled. We present a micro-structural study of spoofing in a simple static setting. A multilevel imbalance which influences the resulting price movement is introduced upon which we describe the optimization strategy of a potential spoofer. We provide conditions under which a market is more likely to admit spoofing behavior as a function of the characteristics of the market. We describe the optimal spoofing strategy after optimization which allows us to quantify the resulting impact on the imbalance after spoofing. Based on these results we calibrate the model to real Level 2 datasets from TMX, and provide some monitoring procedures based on the Wasserstein distance to detect spoofing strategies in real time.