Financial machine learning projects

FIN-423 / 3 credits

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: MA3 only

Summary

The objective of this course is to acquire experience in financial machine learning by solving real-world problems. Different groups of students will work on different industry projects during the semester. Lectures will discuss best practices and tools.

Content

Projects:

from local industry partners (bank, market maker, broker, asset manager, etc.)
working in different asset-class (commodities, crypto-currencies, equity, FX, etc.)
with distinct applications (trading signal, portfolio optimization, volatility prediction, factors extraction, etc.)
each group of students will work on one dedicated project during the semester

Machine learning:

review of standard methods (regularized linear regressions, tree methods, neural networks)
study the challenges of applying data-driven algorithms in finance
present various use-cases in financial engineering (model pricing and calibration, time-series simulation, etc.)
transform text as data using natural language processing tools
discuss selected advanced topics in reinforcement learning (e.g. derivatives hedging)

Keywords

finance
machine learning
projects

Learning Prerequisites

Required courses

Programming knowledge of Python
Basic probability and statistical knowledge
Basic knowledge of finance
Basic knowledge of machine learning

Recommended courses

Introduction to finance
Financial econometric
Derivatives
Investments

Learning Outcomes

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

Choose an appropriate model to solve a problem in finance
Assess / Evaluate and benchmark a model performance
Design flexible models for financial applications
Implement data processing and models in python code
Develop a fast system to replace slow numerical methods
Manipulate and transform data

Transversal skills

Manage priorities.
Make an oral presentation.
Write a scientific or technical report.
Demonstrate a capacity for creativity.
Take feedback (critique) and respond in an appropriate manner.
Continue to work through difficulties or initial failure to find optimal solutions.
Demonstrate the capacity for critical thinking

Teaching methods

Lectures, 2 hours per week for 14 weeks
Project sessions, 1 hour per week for 14 weeks

Expected student activities

Actively participate to the lectures and the presentations

Assessment methods

Class participation 20%
Project presentations 20%
Project report 60%

Supervision

Office hours	Yes
Assistants	Yes
Forum	Yes

In the programs

Semester: Fall
Exam form: During the semester (winter session)
Subject examined: Financial machine learning projects
Lecture: 2 Hour(s) per week x 14 weeks
Project: 1 Hour(s) per week x 14 weeks

Semester: Fall
Exam form: During the semester (winter session)
Subject examined: Financial machine learning projects
Lecture: 2 Hour(s) per week x 14 weeks
Project: 1 Hour(s) per week x 14 weeks

Reference week

	Mo	Tu	We	Th	Fr
8-9					EXTRANEF126
9-10					EXTRANEF126
10-11					EXTRANEF126
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22

Légendes:

Lecture

Exercise, TP

Project, other