Corporate FinTech
Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management
Teaching language: English
EKA: B560034112, B560034102
Censorship: Second examiner: None
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master
Course ID: B560034101
ECTS value: 10
Date of Approval: 09-10-2018
Duration: 1 semester
Course ID
Course Title
Teaching language
ECTS value
Responsible study board
Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management
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).
This course requires that the student has prior knowledge of financial markets, financial instruments (e.g., options, futures, forwards) and, in particular, basic corporate finance. The latter includes knowledge about, e.g., the valuation of real assets and capital structure issues - e.g., for uncertain cash flow streams the student must be able to use risk neutral valuation to compute their values in a binomial model and apply this to address debt and equity holder conflicts. Standard concepts from finance such as e.g. the principle of no arbitrage, pay-off diagrams, and discounting must also be mastered. All these competences are acquired in the course "Finansiering, investering og virksomhedsstrategi" (course no. 9161001) based on the textbook:
- David Hillier, Mark Grinblatt and Sheridan Titman: Financial Markets and Corporate Strategy, European Edition, Irwin/McGraw-Hill, latest edition.
The student must have an elementary background in mathematics and probability theory. In particular one should be able to compute expectations, conditional probabilities, variances, and covariances of random variables whether their distribution is discrete (e.g., binomial) or continuous (e.g., normal). Finally, it is highly recommended that the student is familiar with simple optimization methods (e.g., first-order conditions and Lagrange optimization). These are all competences acquired in the courses "Matematik" (course no. 9105701) and "Statistik" (course no. 9116001) which are based on the textbooks:
- 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.
This course is directed toward finance professionals and data scientists alike. Participants learn the fundamentals of how to setup a financial problem and formulate it in a way such that a data scientist is able to effectively develop machine learning or artificial intelligence applications to solve the problem at hand. This requires the finance professional to understand how data scientists work and vice versa. The aim of this course is to equip students with an interdisciplinary skillset that is required for future finance professionals and data scientists.
In this course, several methods for financial data analytics relevant for solving problems in corporate finance settings are discussed. The focus is on applying these in cases and practical applications. Students also learn how 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. Developing applications may be in Excel, R, or Python, depending on the task at hand. No advanced programming knowledge is required. Introductions to R or Python are provided where necessary. Selected applications may include:
- 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.
In the applied part of course and in the assignments, that focus on cases and applications, students may emphasize the more qualitative finance professional view, which focusses on formulating the problem for the data scientist or on the more quantitative data scientist view, which focusses on developing financial data analytics applications.
Content
This course consists of three main blocks:
- 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
Scheduled classes:
4 hours of lectures weekly for 11.5 non-consecutive weeks.
The lecturing period can be extended due to intervening project or assignment work.
In addition, there is project work in intervening periods of the semester.
Workload:
The students' workload is expected to be distributed as follows:
Lectures - 46 hours
Preparation, lectures - 159 hours
Assignments - 60 hours
Examination - 5 hours
Total - 270 hours
Examination regulations
Exam
Name
Exam
Timing
Home assignments (part 1):
Exam: During the semester
Reexam: August
Final exam (part 2):
Exam: June
Reexam: August
Tests
Home assignments (part 1):
Name
Home assignments (part 1):
Form of examination
Take-home assignment
Censorship
Second examiner: None
Grading
Pass/Fail
Identification
Student Identification Card - Exam number
Language
English
Duration
One week per assignment. Date for submission will appear in the examination plan.
Length
No limitations.
Examination aids
All exam aids allowed.
Assignment handover
Course page in Blackboard.
Assignment handin
Via SDUassignment in the course page in Blackboard.
ECTS value
1
Additional information
Part 1 consists of four weekly sub-assignments solved in groups of up to three students. The instructor is in charge of approving the groups.
At least three sub-assignments must be answered satisfactorily in order to pass part 1..
Re-examination
Form of examination
Oral examination
Identification
Student Identification Card - Date of birth
Preparation
None.
Duration
20 minutes.
Additional information
The reexamination in part 1 is an oral exam without preparation based on the assignments used in the ordinary exam.
EKA
B560034112
Final exam (part 2)
Name
Final exam (part 2)
Form of examination
Written examination on premises
Censorship
Second examiner: None
Grading
7-point grading scale
Identification
Student Identification Card - Exam number
Language
English
Duration
5 hours.
Length
No limitations.
Examination aids
All exam aids allowed. However, it is not allowed to communicate with anybody.
Assignment handover
In the examination room.
Assignment handin
Via SDUassignment in the course page in Blackboard.
ECTS value
9
Additional information
Exam for International exchange students: 10-hour take-home assignment.
The examination tests the students' achievement on all specified targets.
Re-examination
Form of examination
Oral examination with preparation
Identification
Student Identification Card - Date of birth
Preparation
20 minutes.
Duration
20 minutes.
Examination aids
All exam aids are allowed at the preparation.
Additional information
The examination is based on a randomly drawn topic, but it can also include questions in other topics from the syllabus.
EKA
B560034102
External comment
Courses that are identical with former courses that are passed according to applied rules cannot be retaken.
The student is automatically registered for the first examination attempt when the student is registered for a course or course element with which one or more examinations are associated. Withdrawal of registration is not possible, and students who fail to participate in an examination have used one examination attempt, unless the University has made an exemption due to special circumstances.