Using a comprehensive data set on (surviving and non-surviving) UK equity mutual funds, we use a cross-section bootstrap methodology to distinguish between ‘skill’ and ‘luck’ for individual funds. This methodology allows for non-normality in the idiosyncratic risk of the funds — a major issue when considering those funds which appear to be either very good or very bad performers, since these are the funds which investors are primarily interested in identifying. Our study points to the existence of 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, our analysis strongly rejects the hypothesis that most poor performing funds are merely unlucky. Most of these funds demonstrate ‘bad skill’. Recursive estimation and Kalman ‘smoothed’ coefficients indicate temporal stability in the ex-post performance alpha's of winner and loser portfolios. We also find performance persistence amongst loser but not amongst winner funds

Semiconductor nanowires of silicon have been synthesized within the pores of mesoporous silica using a novel supercritical fluid solution-phase approach. Mesoporous silica, formed by the hydrolysis of tetramethoxysilane (TMOS) in the presence of a triblock copolymer surfactant, was employed for the nucleation and growth of quantum-confined nanowires. The filling of the silica mesopores with crystalline silicon and the anchoring of these nanowires to the sides of the pores were confirmed by several techniques including electron microscopy, powder X-ray diffraction, 29Si magic angle spinning nuclear magnetic resonance, infrared spectroscopy, and X-ray fluorescence. Effectively, the silica matrix provides a means of producing a high density of stable, well-ordered arrays of semiconductor nanowires in a low dielectric medium. The ordered arrays of silicon nanowires also exhibited discrete electronic and photoluminescence transitions that could be exploited in a number of applications, including nanodevices and interconnects.

This paper surveys and critically evaluates the literature on the role of management effects and fund characteristics in mutual fund performance. First, a brief overview of performance measures is provided. Second, empirical findings on the predictive power of fund characteristics in explaining future returns are discussed. Third, the paper reviews the literature on fund manager behavioural biases and the impact these have on risk taking and returns. Finally, the impact of organizational structure, governance and strategy on both fund risk taking and future performance is examined. While a number of surveys on mutual fund performance are available, these have not focused on the role of manager behavioural biases, manager characteristics and fund management strategic behavior on fund performance and risk taking. This review is an attempt to fill this gap. Empirical results indicate that finding successful funds ex-ante is extremely difficult, if not impossible. In contrast, there is strong evidence that poor performance persists for many of the prior “loser fractile” portfolios of funds. A number of manager behavioural biases are prevalent in the mutual fund industry and they generally detract from returns

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Access to the full text of the published version may require a subscription. Rights Abstract:We apply a recent nonparametric methodology to test the market timing skills of UK equity mutual funds. The methodology has a number of advantages over the widely used regression based tests of Treynor-Mazuy (1966) and Henriksson-Merton (1981). We find a relatively small number of funds (around 1.5%) demonstrate positive market timing ability at a 5% significance level, while around 10-20% of funds exhibit negative (perverse) timing and most funds do not time the market. Our findings indicate that the few skillful market timers possess private market timing signals so their performance cannot be attributed to publicly available information. In terms of fund classifications, there are a small number of successful positive market timers amongst equity income and general equity funds, while a few small company funds time a small company rather than a broad market index. We also apply regression based tests of volatility timing and find evidence that a slightly larger (around 5%) of funds successful time market volatility.

Access to the full text of the published version may require a subscription. Rights Abstract:We evaluate the academic research on mutual fund performance in the US and UK concentrating particularly on the literature published over the last 20 years where innovation and data advances have been most marked. The evidence suggests that ex-post, there are around 2-5% of top performing UK and US equity mutual funds which genuinely outperform their benchmarks whereas around 20-40% of funds have genuinely poor. Key drivers of relative performance are, load fees, expenses and turnover. There is little evidence of successful market timing. Evidence on picking winners suggests past winner funds persist, particularly when rebalancing is frequent (i.e. less than one year) -but transactions costs and fund fees imply that economic gains to investors from actively switching into winner funds may be marginal. However, recent research using more sophisticated sorting rules (e.g. Bayesian approaches) indicate possible large gains from picking winners, when rebalancing monthly. The evidence also clearly supports the view that past loser funds remain losers. Broadly speaking results for bond mutual funds are similar to those for equity mutual funds but hedge funds show better ex-post and ex-ante risk adjusted performance than do mutual funds. Sensible advice for most investors would be to hold low cost index funds and avoid holding past 'active' loser funds. Only very sophisticated investors should pursue an active investment strategy of trying to pick winners -and then with much caution.

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