Implementation of a synthetic adequacy fund-investor rating system
Promotor(s) : Bodson, Laurent
Date of defense : 23-Jun-2016/28-Jun-2016 • Permalink :
|Title :||Implementation of a synthetic adequacy fund-investor rating system|
|Author :||Jacobs, Marie-Laure|
|Date of defense :||23-Jun-2016/28-Jun-2016|
|Advisor(s) :||Bodson, Laurent|
|Committee's member(s) :||Hübner, Georges
|Keywords :||[en] Fund scoring system|
[en] Investor’s profile
[en] Fund ranking
[en] Performance measure
[en] European funds
|Discipline(s) :||Business & economic sciences > Finance|
|Institution(s) :||Université de Liège, Liège, Belgique|
|Degree:||Master en ingénieur de gestion, à finalité spécialisée en Financial Engineering|
|Faculty:||Master thesis of the HEC-Ecole de gestion de l'Université de Liège|
[en] Nowadays, there are many scoring systems available for investors and there is a growing interest to take into account the investor’s risk profile. Nevertheless, they rely on a single investor’s profile. Therefore, Gambit Financial Solutions has developed a fund scoring system named SAFIR that takes into account the heterogeneity of the investor’s profile. This system allows assessing any fund capacity to deliver the levels of risk and return that best match the investor objectives and risk preferences. However, until now, neither the definition of the universe of funds nor the weighting of the equation were based on scientific evidence. Yet both tremendously impinge on the results since SAFIR is computed relatedly to the other funds of the universe and the weighting impacts the final score attributed to a fund. Consequently, the aim being pursued in this Master thesis was to find the weighting of the equation of the fund scoring system developed by Gambit Financial Solutions that gives the best risk-adjusted performance measure depending of the investor’s profile.
Using data from Morningstar, a database was created and was composed of 11,041 open-end funds having an investment area and domicile in Europe, and with a base currency in euro. The database was divided into six periods of 52 weeks containing all the open-ended funds with a complete return history during the given period in order to avoid the survivorship bias. On each of the periods, the style analysis was performed in order to define the asset allocation of each fund. Funds with a quite diversified asset allocation were selected in the sample.
The quality of a performance measure was assessed in terms of precision and stability. Sixty-six different combinations of weights were inputted in the equation in order to see which combination of weights gave the best performance measure. Moreover, two performance measures per investor’s profile were used. Finally, four different association measures between the ranking of SAFIR for a given investor’s profile and the associated performance measures were computed over different time periods in order to assess the precision and stability of SAFIR.
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