Course description

The course gives the student a fundamental understanding of both elementary and advanced statistical and machine learning methods used in finance, with a focus on applications and research methods. The course gives the students a thorough introduction to topics such as ridge regression, the Lasso, Adaptive Lasso, Bridge regression, Regression Trees, Random Forest, and Neural Networks. The course gives a thorough understanding of methods such as the ordinary least squares, its shortcomings, and how, for example, machine learning can improve the estimates and their errors. Machine learning typically prescribes a vast collection of high-dimensional models attempting to predict quantities of interest while imposing regularization methods.​

Learning outcomes:

Knowledge

The student will

• Be in the forefront of knowledge within which methods are most suitable for assessing the main themes in the course, such as the cross-section of returns.

• Be able evaluate the purpose of the chosen methodology for specific research topics covered by the course, such as how to construct a factor model, and how to most suitably analyze such a model with machine learning methods

• Be able to contribute with new thoughts in the areas of securities pricing, factor models and portfolio allocation​ 

Skills

The student should ...

• Be able to formulate issues for, plan, and conduct research and professional work related to financial themes using machine learning

• Be able to conduct research at a high international level using the most modern and relevant machine learning methods that are appropriate for financial economics

• Be able to handle complex academic issues and challenges in established knowledge for topics related to securities pricing, portfolio allocation and risk management​
  
  

General competence

The student should ...

• Be able to identify new and relevant issues in topics related to the dynamics of financial instruments, risk, return, and portfolio allocation

• Be able to communicate research through recognized national and international channels, such as presentations at conferences and publishing articles based on methods reviewed in the subject

• Be able to participate in debates in financial economics, and especially within topics related to applications of machine learning methods and portfolio allocation.​
  

Prerequisites:

Must fulfill the requirements for admission to the PhD program.

Mode of delivery:

Face-to-face.

Assessment:

Paper. Which is graded pass/not pass.

Course literature and recommended reading

1. Belloni, Alexandre, Victor Chernozhukov, and Christian Hansen, 2014, Inference on treatment effects after selection among high-dimensional controls, The Review of Economic Studies 81, 608-650.

2. Cochrane, John H., 2011, Presidential Address: Discount Rates, The Journal of Finance 66, 1047-1108.

3. Feng, Guanhao, Stefano Giglio, and Dacheng Xiu, 2017, Taming the factor zoo, Working Paper.

4. Frank, I.E. and Friedman, J.H. (1993) An Statistical View of Some Chemometrics Regression Tools, Technometrics 35, 109-135.

5. Freyberger, Joachim, Andreas Neuhierl, and Michael Weber, 2017, Dissecting characteristics nonparametrically, Working Paper.

6. Fu, Wenjiang J., 1998, Penalized regressions: the bridge versus the lasso, Journal of computational and graphical statistics 7, 397-416.

7. Gu, Shihao, Bryan T. Kelly, and Dacheng Xiu, 2018, Empirical Asset Pricing Via Machine Learning, Working Paper.

8. Hoerl, Arthur E., and Robert W. Kennard, 1970, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 12, 55-67.

9. Hoerl, Arthur E., and Robert W. Kennard, 1970, Ridge regression: applications to nonorthogonal problems, Technometrics 12, 69-82.

10. Huang, J., J. L. Horowitz, and F. Wei, 2010, Variable Selection in Nonparametric Additive Models, Annals of statistics 38, 2282-2313.

11. Kozak, Serhiy, Stefan Nagel, and Shrihari Santosh, 2018, Interpreting factor models, The Journal of Finance 73, 1183-1223.

12. Kozak, Serhiy, Stefan Nagel, and Shrihari Santosh, 2018, Shrinking the cross section, NBER Working Paper.

13. Luyang, Chen., Markus, Pelger†., and Jason Zhu. 2019. Deep Learning in Asset Pricing. Working paper.

14. Pástor, Ľuboš, and Robert F. Stambaugh. 2000. Comparing asset pricing models: An investment perspective. Journal of Financial Economics 56 (3): 335-81.

15. Pástor, Ľuboš. 2000. Portfolio selection and asset pricing models. The Journal of Finance 55 (1): 179-223

16. Zou, Hui, and Trevor Hastie, 2005, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301-320.

17. Lecture Notes machine learning and Bayesian econometrics

Lecture notes available from the lecturer.

The reading list is subject to amendments at semester start.