„Machine Learning in Finance“ for students of M.Sc. Data Science
In the winter term 2023/24 Prof. Christian Jäkel offers a new course „Machine Learning in Finance“ in the M.Sc. Data Science, specialization Machine Learning / Artificial Intelligence.
The aim of the lecture is to draw an arc from the mathematical foundations to concrete applications in mathematical finance. The starting point of this lecture is detailed financial data (tick data on foreign exchange, real estate, stock market indices, commodities, cryptocurrencies and stocks). The application and connection of building blocks of the artificial intelligence, which are readily available in various program repositories such as Sci-Kit, Keras and Tensorflow, enable the students to solve relevant and concrete tasks. The lecture is divided into a number of modules (fractional differentiation, cross validation, back testing, structural breaks, principal components, time series with memory, etc.). The code for the individual modules programmed by the students combine to a main unit in a Github. For further information, please consult reference [1].
References: [1] Advances in Financial Machine Learning, M. Lopez De Prado, WILEY (2018).
[2] Machine Learning in Finance: From Theory to Practice, Matthew F. Dixon, Igor Halperin, Paul Bilokon, Springer (2020).
[3] Machine Learning for Asset Managers (Elements in Quantitative Finance), Marcos Lopez De Prado, Cambridge University Press (2020).
[4] Machine Learning in Asset Pricing, Stefan Nagel, Princeton Lect. in Finance (2021).
[5] Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing, Michael Robbins, McGraw Hill (2023).
[6] Time Series Analysis with Long Memory in View, Uwe Hassler, WILEY (2018).