Machine learning in finance
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, as well as textual analysis. A particular focus will be on deep learning and the practical details of applying deep learning models to financial
Content
1- Introduction to machine learning in finance
- Goals of machine learning
- Applications of machine learning
- Optimizaing algorithms and Inductive Biases
2- Neural Networks
- Kernel Methods and Feature Learning
- Feedforward networks
- Recurrent Neural Networks
- Transformers
3- Supervised learning
- Regression
- Portfolio Optimization
- Applications to asset pricing and forecasting
5- Introduction to Natural Language Processing
- Text representation and Embeddings
- Sentiment analysis
- Topic modelling
- Large Language Models
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
- Write a final exam
Assessment methods
0.4 *Project report + 0.2 * homework + 0.4 * Exam
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