Syllabus

Title
6203 Y2E Machine Learning in Finance
Instructors
Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Contact details
paul.eisenberg@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/01/21 to 02/21/21
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Elective - Advanced Topics in Financial Mathematics
Dates
Day Date Time Room
Thursday 03/04/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 03/11/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 03/18/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 03/25/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 04/08/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 04/15/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 04/22/21 01:00 PM - 04:30 PM Online-Einheit
Thursday 04/29/21 01:00 PM - 03:00 PM Online-Einheit

On this page:

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Contents

The lecture will be held online unless activity on campus becomes available again. In this case, the course will be conducted in synchronous hybrid mode. To this end, the participants will be split into groups (number of groups depends on the number of participants). Only one group will be allowed to be physically present at the lecture while other groups can watch the course live online (the course will be streamed). If this becomes possible, details will be sent out to students accordingly either via mylearn or email.

Students are expected to be active in the class.
 
Attendance during classes is mandatory (online or on campus).

 

The lecture introduces several fundamental concepts from machine learning and treats important  applications  in finance. It will cover topics like

-Neural networks

-Universal approximation theorems,

-Stochastic gradient descent,

-Backpropagation.

The financial applications include

-deep hedging,

-deep portfolio optimization,

-deep simulation and

-deep calibration.
 

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Learning outcomes

After completing this class the student will have the ability to...

-theoretically understand  neural networks, stochastic gradient descent, reservoir computing, etc.

-apply modern machine learning techniques to solve problems arising in quantitative finance, like hedging, portfolio optimization, prediction and calibration tasks.

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Attendance requirements

For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

The lecture will be held online unless activity on campus becomes available again. In this case, the course will be conducted in synchronous hybrid mode. To this end, the participants will be split into groups (number of groups depends on the number of participants). Only one group will be allowed to be physically present at the lecture while other groups can watch the course live online (the course will be streamed). If this becomes possible, details will be sent out to students accordingly either via mylearn or email. Students may choose to continue the class as an online course in any case.

Students are expected to be active in the class.
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Teaching/learning method(s)

This class is taught as a lecture complemented with exercises and a coding project.

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Assessment

Exercise Series (25%)

Coding project (20%)

Final exam (55%)

There will be a total of 40 points (10 Exercises, 8 Coding project, 22 Exam) and the final grade is based on this points.

Grade:

  1. 80,1% or more (33-40 points): 1
  2. 70,1% - 80% (29-32 points): 2
  3. 60,1% - 70% (25-28 points): 3
  4. 50,1% - 60% (21-24 points): 4
  5. 0 - 50% ( 0-20 points): failed
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Prerequisites for participation and waiting lists
  • Successful completion of at least 42 ECTS credits from the first year compulsory common courses
  • Allocation to the elective
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Readings
1 Author: Josef Teichmann (joint lecture project with Christa Cuchiero, Matteo Gambara, Hanna Wutte)
Title:

Machine Learning in Finance

https://people.math.ethz.ch/~jteichma/index.php?content=teach_mlf2019

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Recommended previous knowledge and skills
Successful completion of the first-year courses in the Master QFin.
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Other

Last edited: 2021-05-20



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