To analyze if the risk-taking behavior of mutual funds is related to fund’s belonging to a fund family, I employ two methods that follow the hypotheses presented in section 3. The first method studying the risk-taking differences of family and non-family funds is related to the regression model, and is simply the family dummy applied in the regression model (presented in Subsection 4.1.3). The second method examines the risk-taking differences across fund families and is consistent with the method used by Elton et al. (2007).
4.2.1 Measuring fund family membership effect on risk-taking
To examine if the risk-taking behavior differs between family and non-family funds, I employ a family dummy (FAMILY) as the ninth explaining variable in the regression model presented in the previous section. The family dummy denotes for 1 if the fund is managed by a fund company functioning under a retail bank operating in Finland, otherwise 0.
Funds of foreign fund families are defined as non-family funds as the dataset does not necessarily cover all the funds belonging to the family. Also funds of smaller fund companies that do not have a retail bank background are denoted as non-family funds originating from the fact that these companies have fairly narrow distribution channels compared to funds of retail banks’ fund management companies. The framework is chosen since the retail bank- backed funds are significantly more popular in Finland than non-bank funds, measured both by assets under management and by the number of shareholders. Also, service providers offering simultaneously retail banking services have continuously the advantage of attracting fund investors due to the service package covering both banking and investing services; for the investor, this means decreased search costs since he doesn’t have to go outside the bank in order to find investment opportunities [Sirri and Tufano (1998)].
The inclusion of the dummy and the examination of family membership effect are justified since the importance of distribution channel has been found to be highly significant in the Finnish mutual fund market [see e.g. Knuutila, Puttonen and Smythe (2007)]. As family and non-family funds can be hypothesized to differ by their organizational characteristics, family membership should thus have effect on fund’s risk-taking behavior.
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4.2.2 Measuring risk concentration in fund families
The second method is applied to examine if high and low risk funds are clustered into certain fund families. From the aggregate sample, I separate the families that are managed by a retail bank-backed fund company operating in Finland. The final sample for examining the risk concentration in fund families consists of nine families whose funds are sold through a retail bank and managed by a fund company that essentially operates under the retail bank in question.
The purpose is to analyze the differences in variance across fund families. I first sort out the median relative volatilityobservation for each period t 13. Second, I denote each observation with a relative volatility above (below) the median as HIGH (LOW) risk observation in the respective period. The observations are then grouped to family subsamples. Then, I examine if the distribution for HIGHs and LOWs in a fund family is different from the one expected by chance. If low and high risk funds are randomly assigned to different fund families, the following normally distributed test statistic can be applied [Elton et al. (2007)]14:
(5) ∑ , ∑ , / ∑ , where
(6) , ,
(7) , ,
(8) , ,
, denotes for the number of HIGHs obtained for fund family h when there are g funds in the family. A two tailed t-test is run in order to examine the possible concentration of risk into certain fund families.
13 Elton et al. (2007) use simply standard deviation to sort out LOW and HIGH risk observations. I apply relative
volatility as the sample contains equity funds with different geographical concentrations. The relative volatility measure levels down the market-related risk component in volatility and emphasizes the part of volatility that the fund has materialized over general market volatility during the period.
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The funds are defined as being in the same family if they have the same management company. Nine subsamples fulfilling the requirements set for the sample size are sorted out from the aggregate sample and henceforth, are separately denoted as Family N, where N ϵ [1,9]. Each management company (family) is part of a retail bank operating in Finland. Many families have grown significantly in the beginning of the 21st century and before that include fairly low number of member funds, which is why I examine risk concentration within families only for the period from 01/2002 to 7/2009.
The disadvantage of the family analysis is that the family subsamples are fairly small, a characteristic that will be addressed when analyzing the results. Elton et al. (2007) study the risk concentration for fund groups having separate investment objectives; as for my analysis, I do not separate funds with different objectives in order to preserve valid number of funds for families. In turn, I use relative volatilitymeasure to proportion the risk level of sample funds. The measure maintains a similar perspective for LOW and HIGH risk comparison across families than the method of Elton et al. and at the same time takes into consideration the possible style differences of the sample equity funds. For a point of comparison, Elton et al. calculates the test statistic also for all funds with different objectives by using standard deviation and still finds statistically significant risk clustering in fund families.