Mathematical Aspects of High-Frequency Trading

Student: Fadeev Mikhail

Educational Programme: Stock Market and Investment (Master)

Year of Graduation: 2016

Due to technological innovations taking place in financial markets of the world in recent years, phenomenon of high-frequency trading became widespread. In this paper machine learning algorithms are used to research microprocesses taking place in the financial markets, using real life high-frequency tick data of MMC "Norilsk Nickel" stocks. To achieve this goal an analysis of unevenly spaced time-series was conducted and a system for market data interpretation, microtrends recognition and trading signals generation was created.

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