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Identification of Insider Trading on High Frequency Data.

Student: Solomasov Rostislav

Supervisor: Dmitry Malakhov

Faculty: International College of Economics and Finance

Educational Programme: Double degree programme in Economics of the NRU HSE and the University of London (Bachelor)

Year of Graduation: 2021

Insider trading is a process that can be hardly examined since the information available to the informed agent is unobservable to any authority until being disclosed in accordance with the regulations. Due to such time inconsistency, it might be hard to identify illegal trades being executed in the market based on such private information, which provides an opportunity for certain agents to regularly outplay the market. This paper aims to simulate the actions of a single insider within a trading day in the high-frequency stock market held by the Moscow Stock Exchange with a single risky asset. The goal then is to construct a model that can estimate insiders’ activity given signal and thus to perform ex-post analysis of the trading history. The results of the proposed examination are expected to reveal orders executed by a potential informed agent and to help identify illegal activities within the market with respect to the information that can be obtained publicly and the estimated behavior of the insider. For achieving this goal, it is proposed to use time series modeling to estimate the true value of the asset from past trading day data and then use obtained parameters in analog of theoretical Kyle (1985) model to derive insider’s trading strategy.

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