Protecting Retail Investors from Order Book Spoofing using a GRU-based Detection Model

TL;DR

This paper introduces a GRU-based detection model to identify spoofing in order book data, aiming to protect retail investors from market manipulation in unregulated markets.

Contribution

It presents a novel, extendable GRU model that detects spoofing using market variables, suitable for unregulated trading environments and early detection.

Findings

Model performs well in early spoofing detection

Effective on granular order book data from unregulated markets

Potential to inform investors and regulators about illicit activities

Abstract

Market manipulation is tackled through regulation in traditional markets because of its detrimental effect on market efficiency and many participating financial actors. The recent increase of private retail investors due to new low-fee platforms and new asset classes such as decentralised digital currencies has increased the number of vulnerable actors due to lack of institutional sophistication and strong regulation. This paper proposes a method to detect illicit activity and inform investors on spoofing attempts, a well-known market manipulation technique. Our framework is based on a highly extendable Gated Recurrent Unit (GRU) model and allows the inclusion of market variables that can explain spoofing and potentially other illicit activities. The model is tested on granular order book data, in one of the most unregulated markets prone to spoofing with a large number of non-institutional traders. The results show that the model is performing well in an early detection context, allowing the identification of spoofing attempts soon enough to allow investors to react. This is the first step to a fully comprehensive model that will protect investors in various unregulated trading environments and regulators to identify illicit activity.