FIN-418 / 2 credits

Teacher: Ackerer Damien Edouard

Language: English

Remark: MA3 only


Summary

The goal of this course is to introduce machine learning techniques for financial applications in asset pricing, derivatives pricing, model calibration, hedging, and risk management. The course format is hands on coding sessions in Python with Tensorflow and Keras.

Content

Keywords

Machine learning, neural networks, asset pricing, derivatives pricing, hedging, risk management, textual analysis

Learning Prerequisites

Recommended courses

  • Introduction to finance
  • Econometrics
  • Derivatives
  • Advanced derivatives
  • Investments

 

Important concepts to start the course

  • Good programming knowledge of Python
  • Basic Probability and Statistics knowledge
  • Some knowledge of finance and financial derivatives

 

Learning Outcomes

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

  • Describe the principal types of machine learning algorithms
  • Implement code in Tensorflow
  • Assess / Evaluate an algorithm performance
  • Identify what methods to use for a given financial problem
  • Optimize the evaluation of standard pricing and calibration methods
  • Solve numerically complex dynamic control problems in finance
  • Construct flexible models for financial predictions and stress-testing
  • Investigate textual data with algorithms

Transversal skills

  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Use a work methodology appropriate to the task.
  • Evaluate one's own performance in the team, receive and respond appropriately to feedback.

Teaching methods

  • Hands on coding with prepared Jupyter notebooks to be completed during the sessions.

Assessment methods

  • 100% group project

Supervision

Office hours No
Assistants No
Forum Yes
Others Sykpe call

Resources

Virtual desktop infrastructure (VDI)

No

Moodle Link

In the programs

  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for finance
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for finance
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for finance
  • Lecture: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22