Protecting Retail Investors from Order Book Spoofing using a GRU-based Detection Model. (arXiv:2110.03687v1 [q-fin.ST])

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.



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