Machine learning in finance

Summary

This course aims to give an introduction to the application of machine learning to finance. These techniques gained popularity due to the limitations of traditional financial econometrics methods tackling big data. We will review and compare traditional methods and machine learning algorithms.

Content

Keywords

Machine Learning, Deep Learning, NLP

Learning Prerequisites

Required courses

 Econometrics

Recommended courses

Introduction to finance

Important concepts to start the course

Basic linear algebra.

Basic probabilistic and statistical concepts.

Learning Outcomes

By the end of the course, the student must be able to:

• Elaborate a machine learning algorithm
• Assess / Evaluate the performance of different models
• Formulate hypotheses behind different models
• Propose optimal methods for problems seen
• Optimize techniques / algorithms used
• Construct a parsimonious model
• Implement machine learning algorithms
• Exploit information contained in data

Transversal skills

• Give feedback (critique) in an appropriate fashion.
• Demonstrate the capacity for critical thinking
• Use a work methodology appropriate to the task.

Teaching methods

Lectures and exercise sessions

Projects

Expected student activities

• Participate in lectures
• Participate in exercises sessions
• Solve the problem sets
• Work on a project and present outcomes
• Write a final exam

Assessment methods

(Project report+Project presentation+Exam)/3

Supervision

Resources

Bibliography

Dixon M. F, Halperin I. and Bilokon P. (2020): "Machine Learning in Finance", Springer

Ressources en bibliothèque

• Machine Learning in Finance / Dixon

Moodle Link

• https://go.epfl.ch/FIN-407

Prerequisite for

• Courses using statistical dynamic models