High frequency data should be the primary object of research because practitioners determine their trading decisions by observing tick-by-tick data. This leaves the practitioner with the problems of dealing such vast amounts of data using the right quantitative tools and models. This module provides in-depth understanding on 1) how markets are organized and regulated, 2) how traders analyze the big data from the high frequency markets, 3) how to design algorithmic trading strategies and 4) how to perform risk analysis in the context of high frequency finance. In addition to the theoretical aspects, students gain practical skills needed to analyse big data in finance, design and deploy algorithmic trading strategies. Further, apply the appropriate analysis and modelling techniques for financial risk analysis in the context of high frequency finance.
Exam info and full course description can be found in the course catalogue.
Course specific:
To apply for the course, you must have passed a Bachelor's degree in Business Economics, Business Administration, or an equivalent degree.
Furthermore, participants must have a good understanding of quantitative and statistical techniques that are relevant to undergraduate finance/economics. For example, understanding of probability, conditional probability, random variables, probability distribution, correlation and covariance, and matrix algebra. No programming background is required, but it helps.
General:
Exchange students: nomination from your home university
Freemovers: documentation for English Language proficiency
You can read more about admission here.
Dr. Raju is a Reader in Financial Technologies in the Department of Computing, Goldsmiths, University of London. Prior to joining University of London, Dr. Raju was Assistant Professor in Finance at University of Southampton. He has a background in finance, economics, statstics mathematics, computer science, and engineering.
