Master Thesis Online
Asset Allocation and Machine Learning: a performance analysis within distressed market conditions
Shtini, Sindi 
Promotor(s) :
Hambuckers, Julien
Date of defense : 16-Jan-2023/27-Jan-2023 • Permalink : http://hdl.handle.net/2268.2/16750
Master Thesis - Sindi Shtini.pdfDetails
| Title : | Asset Allocation and Machine Learning: a performance analysis within distressed market conditions |
| Author : | Shtini, Sindi
|
| Date of defense : | 16-Jan-2023/27-Jan-2023 |
| Advisor(s) : | Hambuckers, Julien
|
| Committee's member(s) : | Jamar, Julie
|
| Language : | English |
| Number of pages : | 49 |
| Keywords : | [en] Asset Allocation, Machine Learning |
| Discipline(s) : | Business & economic sciences > Finance |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en sciences de gestion, à finalité spécialisée en Banking and Asset Management |
| Faculty: | Master thesis of the HEC-Ecole de gestion de l'Université de Liège |
Abstract
[en] The objective of the thesis is to investigate the usefulness of popular investment algorithms and go beyond the traditional mean-variance optimization approach, through the use of a realistic investment universe of stocks and mutual funds. The subject of this thesis would consider asset allocation strategies generated from machine learning and robo-advisors, and compare their performance using state-of-the-art statistical approaches.
Cite this master thesis
All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.