
Corporate FinTech
Course ID
Course Title
Teaching language
ECTS value
Responsible study board
Date of Approval
Course Responsible
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Recommended prerequisites
Bachelor of Science in Economics, Mathematics and Economics or Business Administration (with finance courses corresponding to a BSc in Economics).
- David Hillier, Mark Grinblatt and Sheridan Titman: Financial Markets and Corporate Strategy, European Edition, Irwin/McGraw-Hill, latest edition.
- Knut Sydsaeter and Peter Hammond, Essential Mathematics for Economic Analysis, Pearson Education, latest edition.
- Malcow-Møller, N. og Allan Würtz "Indblik i Statistik", latest edition.
Aim and purpose
Financial Technology (FinTech) and recent financial innovations are disrupting the traditional financial industry. This course starts with a discussion on how the field of corporate finance may be reshaped by technology. For example, crowdfunding or initial coin offerings (ICOs) provide new financing channels and provide alternatives to traditional equity financing such as venture capital, private equity, or an IPO. Vast amounts of internal and publicly available data (e.g. from social media) increase the importance and create new opportunities of financial data analytics (e.g. big data, machine learning, artificial intelligence applications) to support financing and investment decisions.
- Using machine learning to predict M&A targets.
- Fraud detection in ICOs using natural language processing.
- Performance analysis of crypto-listed firms.
- Implementing trading strategies for crypto-listed firms.
Content
- Introduction to Corporate FinTech
- Financial data analytics
- Corporate FinTech cases and applications
Learning goals
Description of outcome - Knowledge
Demonstrate knowledge about the course’s focus areas enabling the student to:
- Explain and reflect upon different traditional and new sources of financing such as crowdfunding or initial coin offerings
- Explain and reflect upon using financial data analytics to support financing and investment decisions
- Explain and reflect upon on different financial data analytics methodologies
Description of outcome - Skills
Demonstrate skills, such that the student is able to:
- Acquire data, for example, from databases such as Bloomberg, ThomsonReuters, or S&P Capital IQ (Compustat), or using public APIs, or using web-scraping.
- Formulate financial problems taking the view of both finance professionals and data scientists
- Solve financial problems using data analytics, e.g. using basic machine learning algorithms
Description of outcome - Competences
Literature
Examples:
- Berk, Jonathan B., and Peter M. DeMarzo, Corporate Finance, 4th edition, Pearson Education, 2017.
- Hilpisch, Yves, Python for Finance: Mastering Data-Driven Finance, 2nd edition, O'Reilly Media, 2018.
- Florysiak, David and Schandlbauer, Alexander, The Information Content of ICO White Papers, 2018. Available at SSRN: http://ssrn.com/abstract=3265007
- Yermack, David, Corporate Governance and Blockchains, Review of Finance 21(1), 7-31, 2017.
Teaching Method
Workload
Examination regulations
Exam
Name
Timing
Home assignments (part 1):
Exam: During the semester
Reexam: August
Tests
Home assignments (part 1):
Name
Form of examination
Censorship
Grading
Identification
Language
Duration
Length
Examination aids
Assignment handover
Assignment handin
ECTS value
Additional information
Re-examination
Form of examination
Identification
Preparation
Duration
Additional information
EKA
Final exam (part 2)
Name
Form of examination
Censorship
Grading
Identification
Language
Duration
Length
Examination aids
Assignment handover
Assignment handin
ECTS value
Additional information
Exam for International exchange students: 10-hour take-home assignment.
The examination tests the students' achievement on all specified targets.