Data science and convex optimization methods for empirical finance

Executive Master in Business Administration

Data d'inizio: 3 Febbraio 2021

Data di fine: 6 Febbraio 2021

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 (


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