FIN9004 Machine Learning for Finance

June 20-24, 2022

ECTS credits: 5
Level of course: PhD
Course type: Elective
Study location: Bodø
Course coordinator: Thomas Leirvik
Faculty: Professor Doron Avramov, Herzliya University, Israel
Teaching language: English
Teaching semester: Spring 2021
Costs: No tuition fees. Costs for semester registration and course literature apply.
Course evaluation: Evaluation using final survey.

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:


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​ 


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.​



Must fulfill the requirements for admission to the PhD program.


Mode of delivery:




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. 



Application deadline: May 2nd, 2022

Practical information

The city of Bodø

Bodø is home to around 50,000 people and is one of the fastest growing cities in the country, with a lively urban scene.


Photo: David Grandorge

Getting to Bodø

  • Bodø is the hub of Nordland and can be reached by plane, train and boat. 
  • Bodø's airport is located in the city itself, making it quick and easy to fly in and out. Oslo Gardemoen is a 90 minute flight away.

Photo: Ernst Furuhatt /

Transport in Bodø

  • The main campus is located at Mørkved, about 9 kilometers from the centre of Bodø.
  • It is easy to take the bus from the airport or city center to campus.

Accomodation in Bodø

  • At campus there is a student hotel “Nordavind” which offers short-time rent. For more information. 
  • You may also stay at a hotel in the centre of Bodø.
  • We advise you to book accommodation as early as possible as hotels in Bodø at times are fully booked