Machine learning in finance

FIN-407 / 6 credits

Teacher: Malamud Semyon

Language: English

Summary

This course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization and hedging. A particular focus will be on deep learning and the practical details of applying deep learning models to financial data.

Content

 

1- Introduction to machine learning in finance

  • Goals of machine learning
  • Applications of machine learning
  • Optimizaing algorithms

2- Neural Networks

  • Kernel Methods and Feature Learning
  • Feedforward networks
  • Recurrent Neural Networks
  • Transformers

 

3- Supervised learning

  • Regression
  • Classification
  • Applications to asset pricing and forecasting

 

4- Unsupervised learning

  • Clustering
  • Linear and Non-Linear PCA; autoencoders

 

5- Introduction to Natural Language Processing

  • Text representation
  • Sentiment analysis
  • Topic modelling
  • Application to index building

 

 

Keywords

Machine Learning, Deep Learning, NLP

Learning Prerequisites

Required courses

Introduction to 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 (each 33.333333% )

Supervision

Assistants Yes

Resources

Bibliography

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

 

Ressources en bibliothèque

Moodle Link

Prerequisite for

  • Courses using statistical dynamic models

In the programs

  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Machine learning in finance
  • Courses: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Machine learning in finance
  • Courses: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
  • Semester: Spring
  • Exam form: Written (summer session)
  • Subject examined: Machine learning in finance
  • Courses: 3 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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