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Market manipulation is a strategy used by traders to alter the price offinancial securities. One type of manipulation is based on the process ofbuying or selling assets by using several trading strategies, among themspoofing is a popular strategy and is considered illegal by market regulators.Some promising tools have been developed to detect manipulation, but cases canstill be found in the markets. In this paper we model spoofing and pingingtrading, two strategies that differ in the legal background but share the sameelemental concept of market manipulation. We use a reinforcement learningframework within the full and partial observability of Markov decisionprocesses and analyse the underlying behaviour of the manipulators by findingthe causes of what encourages the traders to perform fraudulent activities.This reveals procedures to counter the problem that may be helpful to marketregulators as our model predicts the activity of spoofers.

Learning Unfair Trading: a Market Manipulation Analysis From the Reinforcement Learning Perspective