Learning Unfair Trading: A Market Manipulation Analysis from the Reinforcement Learning Perspective

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Abstract

Market manipulation is a strategy used by traders to alter the price of financial assets. 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 price manipulation, but cases can still be found in the markets. In this paper we model spoofing and pinging trading from a macroscopic perspective of profit maximisation, 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 perpetrators by finding the causes of what encourages these traders to perform fraudulent activities. Procedures can be applied to counter the problem as our model predicts the activity of the manipulators.
Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-109
Number of pages7
ISBN (Print)9781509025831
DOIs
Publication statusPublished - 30 Jun 2016
Event10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016 - Natal, Brazil
Duration: 23 May 201625 May 2016

Conference

Conference10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016
Country/TerritoryBrazil
CityNatal
Period23/05/201625/05/2016

Keywords

  • Asset price manipulation
  • Generative model
  • MDP
  • Pinging
  • Spoofing

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