FIN-418 / 3 credits

Teacher: Ackerer Damien Edouard

Language: English

Remark: MA3 only


Summary

This course is introduces machine learning techniques for financial applications in algorithmic trading, derivatives pricing, model calibration, hedging, and risk management. The course format is hands on coding sessions in Python (Keras, Tensorflow, and Scikit-learn).

Content

Keywords

Machine learning, neural networks, algorithmic trading, 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 common machine learning algorithms and their uses in finance
  • Implement code in Python ML libraries such as Keras
  • Assess / Evaluate the performance of a trained ML model
  • Choose which methods can solve a given financial problem
  • Optimize standard solutions by complementing them with faster and/or flexible algorithms
  • Solve numerically untractable control problems in finance
  • Construct special features and models for financial data
  • Investigate the information content of alternative 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 Yes
Assistants No
Forum Yes
Others Zoom meeting by arrangement

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: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for finance
  • Lecture: 3 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: During the semester (winter session)
  • Subject examined: Machine learning for finance
  • Lecture: 3 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14EXTRANEF126    
14-15    
15-16    
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Monday, 13h - 16h: Lecture EXTRANEF126