杏吧论坛

Dr Christoph Czichowsky COL 3.11 and Prof Mihail Zervos COL 4.02

This course is available on the MSc in Applicable Mathematics, MSc in Financial Mathematics, MSc in Quantitative Methods for Risk Management, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (杏吧论坛 and Fudan) and MSc in Statistics (Financial Statistics) (Research). This course is not available as an outside option.

Students must have completed Stochastic Processes (ST409).

Students are expected to have done ST409; students who haven't done ST409 need to obtain permission from the lecturer by providing a statement explaining why and how they know the material covered in ST409. Students are also expected to have basic Python programming skills and good command of linear algebra and calculus.

This course introduces a range of computational problems in financial markets and illustrates how they can be addressed by using tools from machine learning. In particular, portfolio optimisation, optimal trade execution, pricing and hedging of financial derivatives and calibration of stochastic volatility models are included. The course considers some theoretical results on machine learning basics such as empirical risk minimisation, bias-complexity tradeoff, model selection and validation as well as more advanced topics such as deep learning, feedforward neural networks, universal approximation theorems, stochastic gradient descent, back propagation, regularisation and different neural network architectures. Practical implementation in Python and training of neural networks for the above problems in financial markets are also addressed.

20 hours of lectures, 10 hours of seminars and 5 hours of seminars in the LT.

This course is delivered through a combination of seminars and lectures totalling to 35 hours across Lent Term. 

Students will be expected to produce 1 problem sets in the Week 6.

The main formative assessment will be in the form of weekly exercise sets, which will be discussed in the seminars. Some of the topics of these will be similar to what is expected in the summative assessment (coursework and exam). Students will be expected to submit one piece of formative coursework in the mid