Big Data Asset Pricing


  1. Beta-dollar neutral portfolios

  2. Construct value factors

  3. Factor replication analysis

  4. High-dimensional return prediction

  5. Research proposal

All exercises are available here

Details: A Concentrated Hybrid Class

  • Instructors: Lasse Heje Pedersen (LHP) and Theis Ingerslev Jensen (TIJ)

  • Prerequisites: The course is designed as a first- or second-year Ph.D. course. The prerequisites are knowledge of asset pricing theory and econometrics at a M.Sc. level and an ability to work independently with data using a programmatic computer language such as Matlab, R, or Python. Students must participate in the whole course and do all problem sets.

  • Aim: The class aims to introduce Ph.D. students in finance and related fields to research methods in big data asset pricing.

  • Format: Hybrid (the course can be followed online) over 6 weeks. There is one lecture per week, except that lectures 5-6 are held on two consecutive days, where participants from abroad are encouraged to show up physically at Copenhagen Business School.

  • Time. The class in held in the Spring of 2023 at Copenhagen Business School, likely around March.

  • Lecture plan (preliminary, 3h means 3 hours):

    • Lecture 1: A primer on asset pricing (3h)

    • Lecture 2: A primer on empirical asset pricing; discussion of Exercise 1 (3h)

    • Lecture 3: Working with big asset pricing data (3h)

    • Lecture 4: The factor zoo and replication crisis; discussion of Exercise 2 (3h)

    • Lecture 5: Machine learning in asset pricing; discussion of Exercise 3; work on Exercise 4 (6h)

    • Lecture 6: Asset pricing with frictions (3h)

    • Lecture 7: Discussion of Exercise 4 (1h)

  • Exercises: must be handed in before the lecture in which they are discussed. Exercise 5 should be handed in 2 weeks after the last lecture.