  Market manipulation is a strategy used by traders to alter the price of
financial securities. One type of manipulation is based on the process of
buying or selling assets by using several trading strategies, among them
spoofing is a popular strategy and is considered illegal by market regulators.
Some promising tools have been developed to detect manipulation, but cases can
still be found in the markets. In this paper we model spoofing and pinging
trading, two strategies that differ in the legal background but share the same
elemental concept of market manipulation. We use a reinforcement learning
framework within the full and partial observability of Markov decision
processes and analyse the underlying behaviour of the manipulators by finding
the causes of what encourages the traders to perform fraudulent activities.
This reveals procedures to counter the problem that may be helpful to market
regulators as our model predicts the activity of spoofers.
