Prerequisites 
The course is designed as a firstyear PhD 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 PhD students in finance and related fields to empirical asset pricing research methods using big data. 
Course content 
The course provides students with empirical asset pricing tools to use big data to analyze modern topics in financial economics. The course starts with a quick overview of asset pricing, empirical asset pricing, and how to work with big financial data. The course then covers the factor zoo, multiple testing adjustments, replication, machine learning in asset pricing, and asset pricing with frictions. In addition to the theoretical discussion, the students will gain access to a large data set of global equity returns and use this data to solve several mandatory exercises, which constitute an essential part of the course. Each student must make their own solution to each exercise and be able to explain this solution and present it. Students are allowed to discuss the exercises and solution methods, but students are not allowed to copy each other. 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. 

Lecture plan 
16 February 2023 Watch the videos, download the data, and play with the data (before and in parallel with Lectures 12) so you are ready for Exercise 2 Lecture 3: Working with big asset pricing data (video, TIJ 3h) WRDS, CRSP, Compustat, JKPfactors, global data
23 February 2023  912 Lecture 1: A primer on asset pricing (hybrid, LHP 3h) Stochastic discount factors, tradable and nontradeable factors, factor models
2 March 2023  1417 Lecture 2: A primer on empirical asset pricing (hybrid, LHP 3h) How to make and use factors, time series and crosssectional regressions, predictability in the time series and the cross section, further methods Discussion of Exercise 1 (Betadollar neutral portfolios)
9 March 2023  912 Lecture 4: The factor zoo and replication (hybrid, LHP, TIJ 3h) Replication crisis, frequentist and Bayesian multiple testing adjustments Discussion of Exercise 2: Construct Value Factors
16 March 2023  916 (incl lunch) Lecture 5: Machine learning in asset pricing (hybrid, LHP, TIJ 6h) Validation, hyperparameters, penalized regressions, trees, neural networks, feature importance, asset pricing applications Discussion of Exercise 3: Factor replication analysis Work on Exercise 4
17 March 2023  912 Lecture 6: Asset pricing with frictions (hybrid, LHP 3h) Transaction costs, market liquidity risk, funding liquidity risk, frictions meet machine learning
30 March 2023  910 ONLINE VIA ZOOM Lecture 7: Discussion of Exercise 4: Highdimensional return prediction (hybrid, TIJ 1h) 
Learning objectives 
The course objectives are to: • Work with big financial data, including making factors • 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 • Analyze financial market frictions 
Exam 
There is no final exam, but students must satisfactory complete of all the mandatory exercises. The class is graded as pass/fail. 
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. 
Course Literature 
Course participants are expected to have read the assigned reading before each class. The lecture plan lists the preliminary readings, but final readings will be listed on the “canvas” website.
Lecture 1: Notes are selfcontained, but familiarize yourself with • Ch. 6 and 12 in Cochrane, J. H. (2009). Asset pricing. Princeton university press. • Kozak, Nagel, and Santosh (2018). Interpreting factor models. The Journal of Finance 73(3), 11831223.
Lecture 2: Notes are selfcontained, but contain a list of classic references that you should be aware of. See in particular: • Cochrane (2011). Presidential address: Discount rates. The Journal of Finance 66(4), 10471108.
Lecture 3: SelfcontainedLecture 4: • Harvey, Liu, and Zhu (2016). . . . and the crosssection of expected returns. The Review of Financial Studies 29 (1), 568.• Jensen, Kelly, and Pedersen (2021), “Is There a Replication Crisis in Finance?”• See references in notes as background readingLecture 5: Notes are selfcontained, but read the first paper here and familiarize yourself with the next two references:• Gu, Kelly, Xiu (2020). Empirical asset pricing via machine learning, Review of Financial Studies.• Kozak, S., S. Nagel, and S. Santosh (2020). Shrinking the crosssection. Journal of Financial Economics 135 (2), 271292.• Friedman, Hastie, Tibshirani. The elements of statistical learning. Lecture 6: • Ch. 12.23 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), 125.• Also familiarize yourself with other references in notes.Lecture 7: No reading
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.


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CBS students: Choose CBS PhD students and the course fee will be deducted from your PhD budget.
Students from other Danish universities: Choose Danish Electronic Invoice (EAN). Fill in your EAN number, attention and possible purchase (project) order number. Do you not pay by EAN number please choose Invoice to pay via electronic bank payment (+71).
Students from foreign universities: Choose Payment Card. Are you not able to pay by credit card please choose Invoice International to pay via bank transfer.
Please note that your registration is binding after the registration deadline.

