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

arXiv: https://arxiv.org/abs/1511.00740 · HTML: https://mlsanity.com/paper/1511.00740/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [A Criminology of Machines](https://mlsanity.com/paper/2511.02895/) (2025)
- [The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents](https://mlsanity.com/paper/2510.15995/) (2025)
- [Can Reinforcement Learning Efficiently Discover Price Manipulation?](https://mlsanity.com/paper/2607.06121/) (2026)

## Abstract (arXiv)

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.