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Manipulations are taking place widely on various capital, commodity, derivative and other markets. They are reported regularly and sometimes causing significant losses. But it doesn’t mean that the efforts intended to limit this sort of activity are insignificant. Surveillance budgets, as well as applied fines, are impressing. The annual volume of manipulative attempts and the efforts, intended to deter these attempts, are growing exponentially year after year. The imperfection and low versatility of detection methods are leaving space for successful attempts, making manipulative behavior still attractive. This paper is representing the model, based on the Game Theory and aimed to fit modern requirements of surveillance. The article defines basic problems in manipulation detection and proves model’s capability to solve them. However, the problem is reviewed on a general level allowing to elaborate the versatile model, but not a specific manipulative scenario. At the same time, the model allows complementing it with precise tools defining aspects related to actual manipulation. Manipulation and the shaping of it's economic results are reviewed in-depth, revealing it's core phenomenology.

Forecasting economic result of manipulation using game theory modeling