On Luck Versus Skill When Performance Benchmarks Are Style-Consistent – ValueWalk Premium
Luck Versus Skill

On Luck Versus Skill When Performance Benchmarks Are Style-Consistent

On Luck Versus Skill When Performance Benchmarks Are Style-Consistent by SSRN

Andrew Mason

University of Surrey – Surrey Business School

Sam Agyei-Ampomah

Cranfield University – School of Management

Andrew Clare

City University London – Sir John Cass Business School

Steve Thomas

City University London – Sir John Cass Business School

April 15, 2015

Journal of Banking and Finance, Forthcoming


We firmly believe that style-appropriate, investible benchmarks not only provide a more parsimonious way of describing manager performance, but also that their use better aligns performance evaluation with the real world performance targets of fund managers’. It is against such benchmarks that managers should be judged. With this principle foremost in our approach, we use style-consistent benchmarks to determine whether any observed alpha produced by a sample of U.S. equity funds is due to skill or to luck. We find that different segments of the market, ranging from large-cap growth to small-cap value, exhibit different levels of skill and luck. Our results also show that the use of standard multi-factor models underestimates managerial ability and overstates the proportion of funds whose abnormal performance can be attributed to chance rather than to skill, when compared against the use of style-consistent practitioner benchmarks. We also find that a single factor performance evaluation model that uses Russell style indices consistent with the style orientation of a fund and market practice provides a parsimonious way of accounting for fund performance. Finally, our findings should be of particular relevance in mutual fund markets where the risk factors commonly used in the academic literature to evaluate manager performance – SMB, B/M, MOM and others – are not readily available.

On Luck Versus Skill When Performance Benchmarks Are Style-Consistent – Introduction

It is difficult to understate the importance of the US mutual fund industry; 92 million individuals, or 54 million households in the U.S. own mutual funds and these funds hold 24% of U.S. corporate equity. At the end of 2012 the $13 trillion of mutual fund assets were approximately the same size as the assets of all commercial banks in the United States. (Investment Company Institute, 2013, Federal Reserve, 2013). Of this $13 trillion, $4.3 trillion are invested in U.S. domestic equities, and 83% is managed on an active basis. Given its importance it is essential to strive to explain the existence or absence of skill amongst those charged with the oversight of these actively managed assets. However, there is a significant amount of evidence in the finance literature that suggests that actively managed mutual funds underperform the market and/or their assigned benchmarks on average (at least net of fees).

For example, Lakonishok et al (1994) and Carhart (1997) (amongst many others) find little evidence of skill. Wermers (2000) finds that skill may exist at the gross of fee level but this does not filter through to the ultimate investor through net of fee returns. A number of researchers have found that performance persistence tends to exist amongst the poorest performers (see for example Goetzmann and Ibbotson (1994) or Berk and Tonks (2009)), although Grinblatt and Titman (1992), Goetzmann and Ibbotson (1994) and Fama and French (2010) document evidence of positive persistence for the very top performing funds.

Many of the studies that attempt to establish the existence, or otherwise of skill amongst active fund managers, apply standard multi-factor models as the relevant benchmarks for performance comparison, with the factors popularised by Fama-French serving as the major point of reference here. However, recent empirical studies have shown that alphas obtained from these standard multi-factor models can misstate managerial ability (see for example Cremers et al (2012), Argon and Ferson (2006), or Angelidis et al (2013)). However, as well as potentially misstating the degree of skill, the use of multi-factor benchmarks embodies the implicit suggestion that, in addition to the market portfolio, fund managers should invest in hedge portfolios that compensate for risks associated with small, growth and momentum stocks. However, the major drawback of this ‘advice’ from the academic community to the fund management community is that these hedge portfolios are not investible when one takes into account capacity constraints and transaction costs, particularly the transactions costs of shorting even the largest, most liquid stocks (see Huij and Verbeek (2009)). Christopherson et al (2009) provide an excellent description of the desirable characteristics of a financial market benchmark. A benchmark should provide a “naïve” representation of the set of investment opportunities facing investors; in our case a style group of mutual funds. The index should be investible and cover the practical opportunities for an investment style. It should be floatadjusted,
that is, it should be based on the market capitalisation of tradable shares. Perhaps more importantly, the benchmarks should have a clear, simple and transparent construction methodology that can be easily replicated by others. The risk factors that comprise the multifactor performance evaluation models are not replicable, investible benchmarks and as such their use in performance evaluation raises the question as to what exactly is being evaluated with their use.

Luck Versus Skill

Luck Versus Skill

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Comment (1)

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