Finance Lab: Fintech, Investments & AI Use (WP)*
Niveau
Second cycle, master's
Learning outcomes of the courses/module
The participants:
• expand their financial knowledge, in particular in the area of investment alternatives and portfolio theory.
• prepare their own asset class analyses with the help of AI.
• make and test their own investment decisions on the computer (via demo accounts on virtual platforms).
• discuss innovations in the field of fintech.
• develop their analytical thinking skills and apply their financial knowledge, in particular in the area of investment alternatives and portfolio theory.
• expand their financial knowledge, in particular in the area of investment alternatives and portfolio theory.
• prepare their own asset class analyses with the help of AI.
• make and test their own investment decisions on the computer (via demo accounts on virtual platforms).
• discuss innovations in the field of fintech.
• develop their analytical thinking skills and apply their financial knowledge, in particular in the area of investment alternatives and portfolio theory.
Prerequisites for the course
-
Course content
• Investment alternatives and modern portfolio theory
• Analysis of different asset classes
• Developing one's own investment decisions based on risk-return profiles and personal preferences
• New fintech developments
• Analysis of different asset classes
• Developing one's own investment decisions based on risk-return profiles and personal preferences
• New fintech developments
Recommended specialist literature
• Berk, Jonathan; DeMarzo, Peter: Corporate Finance. Pearson (latest edition)
• Hull, John C.: Options, Futures and Other Derivatives. Pearson (latest edition)
• Wooldridge, Jeffrey: Introductory Econometrics: A Modern Approach. South-Western: Thomson (latest edition)
• Hull, John C.: Options, Futures and Other Derivatives. Pearson (latest edition)
• Wooldridge, Jeffrey: Introductory Econometrics: A Modern Approach. South-Western: Thomson (latest edition)
Assessment methods and criteria
Portfolio assessment
Language
English
Number of ECTS credits awarded
4
Semester hours per week
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
4