How Active is Your Fund Manager? A New Measure That Predicts Performance

Cremers, Martijn; Petäjistö, Antti

doi:10.2139/ssrn.891719

Cited by 454 publications

(692 citation statements)

References 28 publications

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“…Th e results found are robust when we use the four factors FFC model instead of the traditional CAPM. Th e conclusion that selectivity leads to higher returns corroborates with previous studies carried out in the Brazilian (Malacrida et al, 2007;Rochman and Eid, 2006) and international (Cremers and Petajisto, 2009;Daniel et al, 1997;Kacperczyk et al, 2005) markets. Performance was measured by excess average return, by the Jensen's alpha, by the Sharpe ratio, and by cumulative returns.…”

Section: Resultssupporting

confidence: 90%

“…Besides the methodology from Amihud and Goyenko (2013), there are others such as "active share", from Cremers and Petajisto (2009), "industry concentration index", and "return gap", both from Kacperczyk et al (2005).…”

Section: Other Measures Of Investment Fund Level Of Activitymentioning

confidence: 99%

“…Among these, worth mentioning are those from Brands, Brown, and Gallagher (2005), Cremers, Ferreira, Matos, and Starks (2011), Cremers and Petajisto (2009), and Kacperczyk, Sialm, and Zheng (2005. Th ese studies show that, in general, the greater the divergence between these funds' positions and the composition of market indices, the greater the performance presented.…”

Section: Introductionmentioning

confidence: 99%

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Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

Guzella

Campani

2017

Rev. contab. finanç.

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This paper aimed to investigate the impact of levels of selectivity on the performance of equity funds using a methodology applied for the first time ever (as far as we know) in the Brazilian market. As an indicator of the activity level of a fund, we proposed the coefficient of determination (R 2 ) of the regression of its returns over market returns. In total, 867 funds were analyzed in the period between November 2004 and October 2014. The hypothesis tested is that more selective funds perform better to compensate for their higher operating costs. This hypothesis was confirmed in the Brazilian market. Dynamic equally-weighted portfolios of funds were simulated, according to their past R 2 and alphas, with monthly rebalancing and 12-month moving windows. The portfolio of the most selective funds had a Sharpe ratio of 0.0494, on a monthly basis, while the portfolio of the least selective funds had a Sharpe ratio of -0.0314. Performance was also higher in evaluations involving excess returns, Jensen's alpha, and accumulated returns, as well as when compared to randomly selected portfolios. Moreover, past performance (as measured by Jensen's alpha) was also a predictor of future performance. Particularly, the portfolio composed by funds with a higher past alpha and lower past R 2 presented a Sharpe ratio of 0.1483 and a Jensen's alpha of 0.87% (significant at 1%), while the one composed of funds with a lower past alpha and lower activity level presented a Sharpe ratio of -0.0673 and an alpha of -0.32% (also significant at 1%).

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Section: Resultssupporting

confidence: 90%

Section: Other Measures Of Investment Fund Level Of Activitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

Guzella

Campani

2017

Rev. contab. finanç.

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“…Some recent studies have claimed to be able to separate good from bad funds by using more developed measures of skill rather than past performance (see e.g. Cohen et al (2005), Kacperczyk et al (2006), Baks et al (2006), and Cremers and Petajisto (2007) However, considering the results of previous surveys from the U.S. which indicate that the typical mutual fund customer is not very well-informed about his mutual fund investments, it is hard to believe in a smart money effect, even though there might be some persistence in mutual fund performance. In general, investors seem to be unaware of the risks, returns and especially the costs associated with actively managed funds (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Financial Literacy and Mutual Fund Investments: Who Buys Actively Managed Funds?

Müller

Weber

2010

Schmalenbach Bus Rev

112

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Using data from an online survey with more than 3,000 mutual fund customers we construct a financial literacy score based on quiz-like statements. Our objective measure of financial literacy is significantly correlated with several socioeconomic and demographic variables. We also document a positive correlation between financial literacy and better than average (BTA) thinking in terms of investment skills. With respect to mutual fund investments, there is mixed evidence on the influence of financial literacy. While more sophisticated participants pay lower front-end loads, are less biased in their past return estimates and less miscalibrated in their return forecasts for their own fund as well as for the whole stock market, no relationship exists between financial literacy and ongoing fund expenses. Moreover, financial literacy has only a slight impact on the decision to buy a passive fund rather than an actively managed fund. Our results indicate that the lack of financial literacy among most mutual fund customers cannot completely explain the growth in actively managed funds over the past. The higher level of BTA among more sophisticated investors is modestly responsible for this finding.

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“…This particular topic has gained importance in the mutual fund performance evaluation literature, and several significant studies have been published since the early 1990s acknowledging this reality (see, for instance Grinblatt and Titman, 1992;Brown and Goetzmann, 1995;Carhart, 1997;Hendricks et al, 1993;Elton et al, 1996;Hendricks et al, 1993, among others). More recently, Pätäri (2009) has provided an extensive literature review of mutual fund performance persistence, and Cremers andPetajisto (2009) andLoon (2011) have proposed new methods to report evidence of persistence, and also on how investors respond to previous performance rankings.…”

Section: Introductionmentioning

confidence: 99%

On the informativeness of persistence for evaluating mutual fund performance using partial frontiers

Matallín‐Sáez

Soler‐Domínguez

Tortosa‐Ausina

2014

Omega

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The last few years have witnessed a rapid evolution in the literature evaluating mutual fund performance using frontier techniques. The instruments applied, mostly DEA (Data Envelopment Analysis) and, to a lesser extent, FDH (Free Disposal Hull), are able to encompass several dimensions of performance, but they also have some disadvantages that might be preventing a wider acceptance. The recently developed order-m and order-α partial frontiers overcome some of the disadvantages (they are robust with respect to extreme values and noise, and do not suffer from the well-known curse of dimensionality) while keeping the main virtues of DEA and FDH (they are fully-nonparametric). In this article we apply not only the non-convex counterpart of DEA (FDH) but also order-m and order-α partial frontiers to a sample of US mutual funds. The results obtained for both order-m and order-α are useful, since a full ranking of the mutual funds' performance can be obtained. We merge these methods with the literature on mutual fund performance persistence. By combining the two literatures we derive an algorithm which establishes how the choice of m and α parameters intrinsic to order-m and order-α (respectively) relate to the existence of performance persistence and the contrarian effect.

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How Active is Your Fund Manager? A New Measure That Predicts Performance

Cited by 454 publications

References 28 publications

Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

Financial Literacy and Mutual Fund Investments: Who Buys Actively Managed Funds?

On the informativeness of persistence for evaluating mutual fund performance using partial frontiers

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