Universität Wien FIND

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040514 KU Python for Finance II (MA) (2020S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

This course will take place via video conferencing at the originally scheduled times. Detailed announcements will be available on Moodle.

Tuesday 05.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 12.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 19.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 26.05. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 09.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 16.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 23.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 30.06. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Aims, contents and method of the course

The course enables participants to gain further experience in Python and its applications in Finance. It is expected that students have prior knowledge of Python equivalent to the contents of Python for Finance I. Participants get to know and apply methods from machine learning and natural language processing, with a focus on practical applications of these methods. Students learn to gather textual data from different internet sources, clean the data, and process the data to produce quantitative measures that might be relevant for an investment strategy. Subsequently, this data and other standard finance data sources are used to develop an investment or trading strategy based both on standard econometric data analysis and machine learning methods. Besides these specific methods, students will also gain further general knowledge with respect to Python programming and managing a programming project.

Assessment and permitted materials

The grade will be based on homework exercises that participants are expected to present in class, class participation, and a course project in which students apply the methods learnt in the course.

Minimum requirements and assessment criteria

40% homework exercises
20% class participation
40% course project

Minimum requirement for a positive grade: a total of 50%.

Examination topics

All material covered in class.

Reading list

Bird, S., Klein, E., Loper, E., Natural Language Processing with Python, 2019. https://www.nltk.org/book/

Géron, A., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st (2017) or 2nd (2019) edition. O'Reilly Media.

Raschka, S., Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd (2017) or 3rd (2019) edition. Packt Publishing.
or
Raschka, S., Machine Learning mit Python und Scikit-Learn und TensorFlow : das umfassende Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning, 2. Auflage, 2018. MITP.

Zatloukal, K., ML & Investing Part 1: From Linear Regression to Ensembles of Decision Stumps, 2018. https://www.osam.com/Commentary/ml-investing-linear-regression-to-decision-stumps

Zatloukal, K., ML & Investing Part 2: Clustering, 2019. https://osam.com/pdfs/research/ML-and-Investing-Part-2-Clustering.pdf

AQR Capital Management, Can Machines "Learn" Finance? 2019. https://images.aqr.com/-/media/AQR/Documents/Alternative-Thinking/AQR-Alternative-Thinking-2Q19-Can-Machines-Learn-Finance.pdf

Association in the course directory

Last modified: Mo 07.09.2020 15:19