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See detailThe fund Synthetic Index : An alternative benchmark for mutual funds
Terraza, Virginie UL; Razafitombo, Hery

in Bankers, Markets, Investors [=BMI] (2011), 114

Evaluation of the performance of investment funds is a topic of considerable interest to practitioners and academic researchers. Performance indicators of fi nancial places have for long posed interesting ... [more ▼]

Evaluation of the performance of investment funds is a topic of considerable interest to practitioners and academic researchers. Performance indicators of fi nancial places have for long posed interesting challenges with regards to funds investors, but also to legal regulators authorities. The two major issues that need to be addressed in any performance study are how to choose an appropriate benchmark for comparison and how to adjust a fund’s return for risk. Indeed, investors desire information about representative market indexes as a norm to evaluate the performance of their portfolios. MSCI Indexes are frequently used by institutional investors around the world as benchmarks to decide allocation of funds across asset classes and regions. Despite this wide acceptance, MSCI country index has in its original application a number of drawbacks and limitations. The main problems can be traced to the presence of usual biases, such as sampling, survivorship and instant history biases (Fung and Shieh, 2002), involving problems into the aggregation procedure. Thus, one can explain why certain fi nancial places are less representative, specifi cally for funds distribution places. Some results also indicate problems related to misclassifi cation in mutual funds (Sharpe, 1992). Each of these phenomena can have a signifi cant impact on international diversifi cation for fund managers. Based on these empirical fi ndings, Ferreira, Miguel and Ramos (2007) examine cross-country mutual fund performance using several alternative benchmark models including a domestic and an international version of the Carhart (1997) four-factor model. Using multiple regressions, they obtain signifi cant determinants explaining funds performance, like the funds size, the fees, the management style… . In this article, we contribute to the existing discussion on alternative benchmarks to compare fi nancial places, by conducting an analysis on funds time variation structure. Contrary to previous literature, we propose to use directly the information contained in the NAV to extract performance characteristics of funds. Then, each domiciliation place is compared by constructing a fund synthetic index that will capture the time structure of mutual fund performance. Usual statistical approach consists to estimate fi nancial returns of each fund in each country, which involves dealing with huge data sets that may cause the calculation processes to become slow and cumbersome and the results diffi cult to be interpreted and used in further applications. To reduce data sets, and give conclusions for each fi nancial place, indicators of the mean of fund’s returns can be used. Then one can identify classes of domicile funds that are subject to common properties. But this classical approach, gives only approximate results because it is based on an aggregation of average performance and risk and a boxplot statistical format. The construction of fund synthetic portfolio avoids this issue. First, it avoids the logic of representativity through market capitalization that is often diffi cult to apply to the mutual fund universe. Second, it is based on factor analysis techniques to generate indexes that are able to capture a very large fraction of the information. More precisely, it permits to take into account the common properties of fund returns relative to their domicile while keeping the maximum of information given by the original data. Indeed, it may be very useful to use a transformation to form a simplifi ed data set retaining the characteristics of the original data set. Principal component analysis, abbreviated as PCA, is a method of statistical analysis useful in data reduction and interpretation of multivariate data sets by identifying factors of common behavior such that not much of the contained information is lost. In our context, we use this method to derive portfolio weights in order to construct a synthetic portfolio of each fi nancial place. For that, we propose to replace the matrix of returns and to derive an index which keeps the global representation of each fi nancial place. [less ▲]

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