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

B560034101

Course Title

Corporate FinTech

Teaching language

English

ECTS value

10

Responsible study board

Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management

Date of Approval

09-10-2018

Course Responsible

Name Email Department
David Florysiak florysiak@sam.sdu.dk

Offered in

Odense

Level

Master

Offered in

Spring

Duration

1 semester

Mandatory prerequisites

None.

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:
  1. Introduction to Corporate FinTech
  2. Financial data analytics
  3. Corporate FinTech cases and applications

Learning goals

To fulfill the purposes of the course the student must be able to:

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

  • Identify new business cases that employ financial data analytics to increase firm value

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

The course will consist of lectures, exercise sessions, and programming sessions.

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

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 exam

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 in situ exam

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 exam 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. 


Courses offered

Period Offer type Profile Programme Semester

Teachers

Name Email Department City
David Florysiak florysiak@sam.sdu.dk Odense

URL for MySchedule

20
Monday
16-05-2022
Tuesday
17-05-2022
Wednesday
18-05-2022
Thursday
19-05-2022
Friday
20-05-2022
08 - 09
09 - 10
10 - 11
11 - 12
12 - 13
13 - 14
14 - 15
15 - 16
Show full time table