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Market manipulation is tackled through regulation in traditional marketsbecause of its detrimental effect on market efficiency and many participatingfinancial actors. The recent increase of private retail investors due to newlow-fee platforms and new asset classes such as decentralised digitalcurrencies has increased the number of vulnerable actors due to lack ofinstitutional sophistication and strong regulation. This paper proposes amethod to detect illicit activity and inform investors on spoofing attempts, awell-known market manipulation technique. Our framework is based on a highlyextendable Gated Recurrent Unit (GRU) model and allows the inclusion of marketvariables that can explain spoofing and potentially other illicit activities.The model is tested on granular order book data, in one of the most unregulatedmarkets prone to spoofing with a large number of non-institutional traders. Theresults show that the model is performing well in an early detection context,allowing the identification of spoofing attempts soon enough to allow investorsto react. This is the first step to a fully comprehensive model that willprotect investors in various unregulated trading environments and regulators toidentify illicit activity.

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