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Course 2022-2023 a.y.


Department of Finance

Course taught in English

Go to class group/s: 31

DSBA (8 credits - I sem. - OBCUR  |  SECS-S/06)

Classes: 31 (I sem.)

Class-group lessons delivered  on campus

Suggested background knowledge

Students should have mastered the first-year courses on machine learning, databases, and big data processing. Good command of standard data science and machine learning frameworks in Python (pandas, scikit-learn) is expected; no knowledge in finance is required.

Mission & Content Summary

The digital age has created mountains of data that continue to grow exponentially. The International Data Corporation estimates that the world generates more data every two days than all of humanity generated from the dawn of time to the year 2003. This big data revolution is deeply reshaping the financial industry and the way financial economists do research. This course will cover normative finance, banking and monetary theory and will highlight how big data and AI changed the way finance is practiced by focusing on problems currently confronting finance professionals. The course is a mix of lectures, paper presentations and coding labs during which students will investigate a variety of empirical questions from different areas within finance including: FinTech, asset management, trading, banking, corporate finance, corporate governance, venture capital, and private equity



  1. Introduce finance / banking theory and present main concepts
  2. Illustrate how big data and AI can improve financial decision-making
  3. Provide students with a foundation for performing data analytics in finance-related roles both inside the financial sector (e.g., commercial and investment banking, venture capital, private equity, asset management) and outside the financial sector (e.g., consulting, general management, corporate development).

This course is not intended to be a substitute for an econometrics course or for a machine learning course. Instead, this course is designed as a complement to these courses. Thus, the course assumes that students have prior exposure to statistics and data analysis.

Intended Learning Outcomes (ILO)
At the end of the course student will be able to...
  • Master key theoretical concepts in finance, banking, and monetary economics
  • Understand how big data and AI changed the way finance is practiced
  • Test and select adequate machine learning strategies for financial applications
  • Identify origins of bias in machine learning models for credit screening, firm failure
  •  Understand key technological, strategic, and regulatory aspects of new FinTech business models
At the end of the course student will be able to...
  • Handle, clean and analyze structured and unstructured financial data
  • Apply advanced ML techniques to answer real-world financial questions currently confronting finance professionals
  • Choose adequate technologies, data sources and machine learning models to support a FinTech business idea or an academic research project

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Group assignments

This course is designed for a high level of participation and interaction. We'll have face-to-face lectures, complemented by hands-on lab classes in which we develop prototypes of what was discussed in the lectures, run simulations, or let the models compete against each other. Furthermore, there are long-term project which give the students plenty of opportunity to develop and demonstrate your own ideas. Due to the high degree of in-class interactivity and extensive computer work, attendance is strongly recommended.

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • x    
  • Individual assignment (report, exercise, presentation, project work etc.)
  • x    
  • Group assignment (report, exercise, presentation, project work etc.)
  •     x

    Your course grade is based on four components:

    1. Several data labs account for 30% of the course grade. You will work in teams on the labs. Your lowest lab score will be dropped.
    2. A final project or paper presentation accounts for 30% of the course grade
    3. A final written exam accounts for 30% of the grade
    4. Class participation, as measured by quality of course engagement, comprises the final 10% of the course grade

    Teaching materials

    All relevant teaching materials are made available via Bboard. We use slack to communicate with all class participants; more detailed information about this will be available on the Bboard course page. The full list of academic articles and textbooks will be communicated in class.

    Last change 15/07/2022 16:38