The impact of business cycles on multi-factor models
Feneuil, Brieuc
Promotor(s) : Bodson, Laurent
Date of defense : 4-Sep-2017/11-Sep-2017 • Permalink : http://hdl.handle.net/2268.2/3498
Details
Title : | The impact of business cycles on multi-factor models |
Translated title : | [fr] L'impact des cycles financiers sur les modèles multi-factoriels |
Author : | Feneuil, Brieuc |
Date of defense : | 4-Sep-2017/11-Sep-2017 |
Advisor(s) : | Bodson, Laurent |
Committee's member(s) : | Boussaid, Nabila
De Kempeneer, Laurent |
Language : | English |
Number of pages : | 100 |
Keywords : | [fr] Business cycles [fr] Conditional multi-factor model [fr] cross-section analysis [fr] Stationarity |
Discipline(s) : | Business & economic sciences > Quantitative methods in economics & management |
Target public : | Researchers Professionals of domain Student General public |
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
[fr] Many empirical studies to date paint a picture of the economy as having a consistent form at
every single time-series. Contrary to that view, we have seen financial markets undergo lots
of movements and some of these unpredictable. These fluctuations can range from local
disturbances to yearlong tendencies.
This thesis demonstrates empirically the effects of considering different business cycles on
the accuracy of traditional (multi-)factor models, especially in the European Monetary Union.
Indeed, when a market shifts to another state, factor sensitivities and factor premiums do not
remain static. Therefore, statistical proof is put forward to support the fact that for some
specific cycles conditional versions have better explanatory power in the cross-section of
stock returns. Next to this, some market anomalies showed to still be present in certain states.
Before considering the integration of new factors, the developed conditional model aims to
improve the predictability of future stock returns. Regarding this, some leading indicators
have been attributed key roles in the final model.
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