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:
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Real data asset allocation problems with frictions,
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The detection of factor structures in cross-sections of assets,
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Portfolio sorting techniques for characteristics-based return factors,
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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: [email protected]
