Semester:SS 21
Art:Modul/LV/Prüfung
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:2
Lektionen / Semester:30.0 L / 22.5 h
Selbststudium:67.5 h
Art:Modul/LV/Prüfung
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:2
Lektionen / Semester:30.0 L / 22.5 h
Selbststudium:67.5 h
Modulleitung/Dozierende
- Ass.-Prof. Dr. Sebastian Stöckl
(Modulleitung)
Studiengang
Masterstudium Finance (01.09.2020)Lehrveranstaltungen
Beschreibung
- This course builds on what you have learnt in Innovative Finance: Data Science and Machine Learning 1.
- Based on a large real-world dataset, we will host our own Kaggle competition, where groups of students will compete against each other in a machine learning contest using financial data.
- The challenge will be different each time, so we might forecast stock returns, classify stocks according to how green they are based on tweets and facebook posts or dynamically put together portfolios of cryptocurrencies that are expected to outperform in subsequent periods.
- The course is structured as a lab, where we tackle all real-world issues related to the current challenge together, but will also run small competitions to get the most out of our data.
- Grading will NOT be based on placement in the contest but focus on contribution to the final output and team work.
Lernergebnisse
After successful completion of the course:
- Students understand the practical problems when applying statistical methods to real world financial data.
- Students are familiar with the necessary tools to tackle real-world problems based on large (and possibly unstructured) datasets.
- Students can apply the relevant methods to solve real-world problems with the tools available to them.
- Students are able to effectively communicate the results from their projects to a wider audience.
Kompetenzen
Lehrmethoden
- Lectures are interactive “labs” devoted to hands-on programming.
- Moodle is used throughout the course to disseminate course material and for information and discussion.
Prüfungsmodalitäten
see lecture(s) within the module
Prüfungen
- PWW-MA_Innovative Finance: Data Science and Machine Learning II SE (SS 21, bewertet)