thesis

UK mutual fund performance

Abstract

Using a comprehensive data set on (surviving and non-surviving) UK equity mutual funds (April 1975 - December 2002), this study uses a bootstrap methodology to distinguish between `skill' and `luck' in fund performance. This methodology allows for non-normality in the idiosyncratic risks of the funds -a major issue when considering the `best' and `worst' funds and these are the funds which investors are most interested in. The study points to the existence of genuine stock picking ability among a relatively small number of top performing UK equity mutual funds (i. e. performance which is not solely due to good luck). At the negative end of the performance scale, the analysis strongly rejects the hypothesis that most poor performing funds are merely unlucky. Most of these funds demonstrate `bad skill'. The study also examines the economic and statistical significance of persistence. Sorting funds into deciles based on past raw returns or on past 4-factor alphas, strong evidence is found that past loser funds continue to perform badly in terms of their future 4-factor alphas while little evidence is found that past winner funds provide future positive risk adjusted performance. However, on investigating relatively small `fund-of-fund' portfolios of past winners, evidence of positive persistence is found. Using a cross-section bootstrap approach the study derives the empirical distribution of final wealth at a 10 year horizon and finds that if transactions costs are above 2.5% per fund round trip, a passive strategy seems at least as good as the active strategies examined while with transactions costs of 5% the passive strategy is most probably superior. The study also examines the market timing performance of the funds. Using a nonparametric test procedure the study evaluates both unconditional market timing and timing conditional on publicly available information. A relatively small number of funds (around 1%) are found to successfully time the market while market mistiming is relatively prevalent

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