This paper investigates real-time detection of spoofing activity in limit
order books, focusing on cryptocurrency centralized exchanges. We first
introduce novel order flow variables based on multi-scale Hawkes processes that
account both for the size and placement distance from current best prices of
new limit orders. Using a Level-3 data set, we train a neural network model to
predict the conditional probability distribution of mid price movements based
on these features. Our empirical analysis highlights the critical role of the
posting distance of limit orders in the price formation process, showing that
spoofing detection models that do not take the posting distance into account
are inadequate to describe the data. Next, we propose a spoofing detection
framework based on the probabilistic market manipulation gain of a spoofing
agent and use the previously trained neural network to compute the expected
gain. Running this algorithm on all submitted limit orders in the period
2024-12-04 to 2024-12-07, we find that 31% of large orders could spoof the
market. Because of its simple neuronal architecture, our model can be run in
real time. This work contributes to enhancing market integrity by providing a
robust tool for monitoring and mitigating spoofing in both cryptocurrency
exchanges and traditional financial markets.

Questo articolo esplora i giri e le loro implicazioni.

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