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Academy of Architecture, Faculty of Communication, Culture and Society, Faculty of Economics, Faculty of Informatics

Data science and convex optimization methods for empirical finance

Executive Master in Business Administration

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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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