School or Department

Doctoral School in Finance and Economics

Course ID

Using machine learning & artificial intelligence in economics and finance

1.Course details

Semester:

1

Credit rating:

15 TU, 1 ECTS

Pre-requisite(s):

Lecturer(s):

Professor Ulf von Lilienfeld-Toal

Administrator:

Roswitha Glorieux

Tutor(s):

Seminar times and rooms:

Semester Course

Tutorial times and rooms:

TBA

Communications

It is important that students should regularly read their University e-mails, as important information will normally be communicated this way.

Mode of assessment:

Presentations, hand-in of code

and class participation

Examination Periods:

Course WebPage:

Moodle.uni.lu

2.Aims and objectives

Aims

The aim of this course is to give a broad introduction to Machine learning/Artificial intelligence introduction and describe when it can and when it cannot be applied in economics and finance.

The course consists of 3 parts. In the first part, main concepts of machine learning will be taught, for example Gradient boosting regressor and LASSO. In the second part, recent research papers in economics and finance will be discussed. Finally, students are asked to apply some machine learning techniques on economic questions and datasets of their choice.

Learning Objectives

On completion of this course students will have a basic understanding of some machine learning techniques. Furthermore, students will be able to critically evaluate when machine learning techniques make are applicable and when they are not the method of choice in economics and finance.

Finally, students will be able to analyse datasets using basic machine learning techniques.

3. Plan of semester Summer semester dates

from

To

Topic of lecture

Deadline for students’ work

tba

14

17

BLF 2.13

Introduction

tba

14

17

BLF 2.13

Basic Machine learning techniques

tba

14

17

BLF 2.13

Current research in economics and finance using machine learning

tba

14

17

BLF 2.13

Programming machine learning techniques

tba

14

17

BLF 2.13

Presentation of own machine learning results

4. Course details (by topics)

The course is highly interactive. Students are required to have read all relevant papers before coming to class.

5. Reference list/ Bibliography

Susan Athey and Guido Imbens (2015): Summer Institute 2015 Methods Lectures by (http://www.nber.org/econometrics_minicourse_2015/ )

Susan Athey (2017): Beyond prediction: Using big data for policy problems (Science), Vol. 355, Issue 6324, pp. 483-485 DOI: 10.1126/science.aal4321

Susan Athey and Guido Imbens: AEA Continuing Education Program: “Machine Learning and Econometrics” , https://assets.aeaweb.org/assets/production/files/6205.pdf

Sendhil Mullainathan and Ziad Obermeyer (2017): Does Machine Learning Automate Moral Hazard and Error? American Economic Review: Papers & Proceedings 2017, 107(5): 476–480

https://doi.org/10.1257/aer.p20171084

Mullainathan, S., & Spiess, J. (2017): Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives , 31 (2), 87-106. aeaweb.orgAbstract jepml.pdf

6. Further information about assessment

Examination(s)

1

Weighting:

50%

50%

Date:

TBA

During the course

Length:

1 hours

Structure:

Exam

Submission of computer code and discussion of research results

Pass/Fail