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Empirical Asset Pricing - 5 ECTS

Date and time

Wednesday 2 March 2022 at 09:00 to Thursday 31 March 2022 at 16:00

Registration Deadline

Wednesday 2 March 2022 at 09:00

Location

Room TBA, Campus TBA, 2000 Frederiksberg Room TBA
Campus TBA
2000 Frederiksberg

Empirical Asset Pricing - 5 ECTS

Event Description

Faculty
Professor Lasse Heje Pedersen (LHP) and Theis Ingerslev Jensen (TIJ)

Course Coordinator
Professor Lasse Heje Pedersen

Prerequisites
The course is designed as a first-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 aim of the class is to introduce Ph.D. students in finance and related fields to empirical asset pricing research methods.

Course content
The course provides students with the empirical asset pricing tools to analyze equity returns and other data to understand risk and expected return.
The course is structured as follows. The first several weeks consist of online learning in which students watch recorded lectures, work on empirical problem sets, and discuss these on live online or hybrid meetings. Week 4 - 2 days (24 and 25 March) are live (and possibly hybrid for students at other universities).

The course content has three main elements:
1. The students will gain access to a large data set of global equity returns and use this data to analyze several topical issues in asset pricing. The lectures include videos with detailed instructions on how to access this data and how to use it to solve problem sets.
2. The course also has a more theoretical element that develops the tools in modern empirical asset pricing such as factor models, multiple testing adjustments, and machine learning.
3. The course has a number of problem sets that students must complete.

The exercises are an essential part of the course. Students taking this class for credit must hand in their solutions of all three exercises. The class is graded on a pass/fail basis and satisfactory completion of each of the exercises is needed to pass the class.

To pass each exercise, each student must individually send their solved exercise, electronically. Students are allowed to discuss the exercises and solution methods, but students are not allowed to copy each other. Each students must make their own solution and be able to explain this solution and present it. Students must disclose in their solutions if code has been copied from public sources (using public code is perfectly fine, but should be disclosed), and should disclose any other material used.

The written solution must consist of the following parts:
- A written description of the solution with discussion, tables, and figures in PDF. This PDF must include a cover page with name, university, and, for CBS students, university ID. Also, the cover page must include the signed statement on the code of ethics.
- The code.

Teaching style
Blended Learning

Lecture plan
Lecture 1: Introduction to empirical asset pricing (video/hybrid, LHP 3h)
Stochastic discount factors, factor models, and predictability in the time series and the cross section 
- Cochrane (2011). Presidential address: Discount rates. The Journal of Finance 66(4), 1047-1108.
- Kozak, Nagel, and Santosh (2018). Interpreting factor models. The Journal of Finance 73(3), 1183-1223.
- Fama and French (2015). A five-factor asset pricing model. Journal of financial economics 116(1), 1-22.

Lecture 2: How to work with asset pricing data (video, TIJ 3h)
Exercise 1: Construct Value Factors

Lecture 3.A: Discussion of Exercise 1 (hybrid, LHP+TIJ 1h)

Lecture 3.B: The factor zoo (video/hybrid, LHP 2h)
Replication crisis and multiple testing adjustments
- Harvey, Liu, and Zhu (2016). . . . and the cross-section of expected returns. The Review of Financial Studies 29 (1), 5-68.
- Jensen, Kelly, and Pedersen (2021), “Is There a Replication Crisis in Finance?”
Exercise 2: Factor Replication Analysis

Lecture 4.A: Discussion of Exercise 2 (hybrid, TIJ 1h)

Lecture 4.B: Machine learning in asset pricing (hybrid, TIJ+LHP 2h)
- Gu, Kelly, Xiu (2020). Empirical asset pricing via machine learning, Review of Financial Studies.
- Kozak, S., S. Nagel, and S. Santosh (2020). Shrinking the cross-section. Journal of Financial Economics 135 (2), 271-292.
- Friedman, Hastie, Tibshirani. The elements of statistical learning.

Lecture 4.C: Work in class on Exercise 3 (TIJ, 3h)
Exercise 3: High-Dimensional Return Prediction

Lecture 5: Empirical asset pricing with frictions and course conclusions (hybrid, LHP 3h)
- Ch. 12.2-3 in Campbell, J. Y. (2017). Financial decisions and markets: a course in asset pricing. Princeton University Press.
- Frazzini and Pedersen (2014), Betting Against Beta, Journal of Financial Economics 111 (1), 1-25.

Lecture 6: Discussion of Exercise 3 (video, TIJ 1h)

Learning objectives
The course objectives are to:
  • Apply factor models to estimate risk and expected return
  • Estimate stock return predictability via regressions and portfolio sorts
  • Evaluate potential replication crisis and the factor zoo
  • Implement multiple testing adjustments using frequentist and Bayesian methods
  • Apply machine learning to asset pricing data

Exam
Please se 'Course content - element 3'

Other
The course is offered through The Nordic Finance Network, and the Department of Finance at CBS will cover the course fee for PhD students from other NFN associated universities.

Start date
02/03/2022

End date
31/03/2022

Level
PhD

ECTS
5

Language
English

Course Literature
Course participants are expected to have read this literature before the course - please see above

Suggested readings 
Additional related research papers for background reading (preliminary list)
- Fama, E. F. and J. D. MacBeth (1973). Risk, return, and equilibrium: Empirical tests. The Journal of Political Economy 81(3), 607-636.
- Harvey, C. R. (2017). Presidential address: The scientific outlook in financial economics. The Journal of Finance 72 (4), 1399-1440.
Books for background reading
- Bali, T. G., Engle, R. F., & Murray, S. (2016). Empirical asset pricing: The cross section of stock returns. John Wiley & Sons.
- Campbell, J. Y. (2017). Financial decisions and markets: a course in asset pricing. Princeton University Press.
- Cochrane, J. H. (2009). Asset pricing. Princeton university press.
- Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press.
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.
- Ferson, W. (2019). Empirical Asset Pricing: Models and Methods.
- Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. New York: Springer series in statistics.
- Pedersen, L. H. (2015). Efficiently inefficient. Princeton University Press.

Event Location

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