Big Data Asset Pricing

Details: A Concentrated Class, Partly Hybrid

• CBS website: https://phdsupport.nemtilmeld.dk/ 

• 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: The course is partly hybrid and partly in-class, over about 6-8 weeks. There is one lecture per week, except that lectures 5-6 are held on two consecutive days, where participants from abroad must show up physically at Copenhagen Business School. 

• Time. The class is held in the Spring semester at Copenhagen Business School, likely around February/March.

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

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

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

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

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

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

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

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

• 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.