Dr Tengyao Wang

This course is compulsory on the MSc in Statistics (Financial Statistics) and MSc in Statistics (Financial Statistics) (Research). This course is available on the MSc in Data Science and MSc in Quantitative Methods for Risk Management. This course is not available as an outside option.

Students must have completed Statistical Inference: Principles, Methods and Computation (ST425) and Financial Statistics (ST436).

The course covers a wide range of modern financial data analytics, with some built on the course ST436 Financial Statistics using the "R" environment, bridging advanced concepts in financial statistics to hands on practices.

Topics include:

• Decision Trees, Random Forests and Gradient Boosting;
• Neural Networks and deep learning in financial data analysis;
• Extension to the LSTM architecture;
• Factor model and cointegration;
• Granger Causality;
• Portfolio allocation in high frequency data;
• All refresh vs pairwise refresh times;
• Gross/Maximum exposure constraints for vast portfolios.

Consolidation of all concepts and practices will be done through case studies, on top of bi-weekly exercises.

This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours across Winter Term.

This includes 20 hours of lectures (10 two-hour sessions), together with 10 hours of seminars (10 one-hour sessions) that go through the practical aspects of the course.

Students will be expected to produce 5 problem sets in the WT.

• De Prado, M. L. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
• Dr. Param Jeet, Prashant Vats (2017). Learning Quantitative Finance with R. Packt Publishing.
• Ruey S. Tsay (2005). Analysis of Financial Time Series, 2nd Edition. Wiley Series in Probability and Statistics.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics.
• Matthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance: From Theory to Practice. Springer.
• Stefan Jansen (2020). Machine Learning for Algorithmic Trading, 2nd Edition. Packt Publishing.

Lecture notes will be provided on Moodle.

Exam (70%, duration: 2 hours) in the spring exam period.
Group project (30%) in the WT.