How does a hedge fund decide which positions to take?
What is risk, and how can it be modeled?
Is there an optimal way to combine individual predictive models?
This course gives students the tools to answer these questions.
It takes a dual perspective on quantitative investment: forming expectations of future returns, and managing the risks those expectations carry.
The course is organised into three modules, covering volatility modelling, machine learning methods for combining predictors, and feature engineering.
It pairs theoretical foundations with real-world applications drawn from market practice.
