Data science and convex optimization methods for empirical finance
Executive Master in Business Administration
This module covers recent applications of data science and optimisation methods to key questions in empirical finance. It provides a self-contained general introduction to convex optimization theory, including infinite-dimensional settings, and explains how it is used to address a number of important open issues in empirical finance, such as:
Real data asset allocation problems with frictions,
The detection of factor structures in cross-sections of assets,
Portfolio sorting techniques for characteristics-based return factors,
Model-free pricing kernels and optimal portfolios for large assets cross-sections.
We provide necessary mathematical backgrounds for understanding key notions and objects in these domains and we study interactively corresponding implementations in Python within Nuvolos (http://nuvolos.cloud).
For more info: firstname.lastname@example.org