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New methods for ordering multivariate data: an application to the performance of investment funds
Author(s) -
Zani Sergio,
Riani Marco,
Corbellini Aldo
Publication year - 1999
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/(sici)1526-4025(199910/12)15:4<485::aid-asmb411>3.0.co;2-j
Subject(s) - outlier , econometrics , bivariate analysis , multivariate statistics , centroid , volatility (finance) , profitability index , treasury , bond , mathematics , statistics , economics , computer science , finance , artificial intelligence , geography , archaeology
This paper deals with the performance evaluation of investment funds. The goal is the monitoring of the profitability of the funds using a set of variables referred to as short‐ and medium‐term performance, volatility and percentage of treasury bonds on total assets. The problem is the ordering of multivariate data and the search for the units lying far from the centroid. This question is related to the detection of multivariate outliers (Atkinson AC. Fast Very Robust Methods for the Detection of Multiple outliers. JASA 1994; 89 :1329–1339.; Riani M, Zani S. An Iterative Method for the Detection of Multiple outliers Metron 1997; 55 :101–117. In this analysis we use a variation of the robust‐bivariate boxplot for each pair of variables suggested in a previous paper first on the whole of the observations and then on each distinct category of funds (i.e. stock, mixed (balanced) and bond funds). The purpose is to split the units into a few subsets: those inside the inner region close to the centroid and those lying outside the outer contour (that is the most extreme observations). In the middle we can find groups of observations ordered according to their distance from a robust centroid. We apply our method first to the original variables and then to principal components and canonical variates. An analysis of longitudinal data is also considered. Copyright © 1999 John Wiley & Sons, Ltd.