Presence of Luck Vs Skill

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Additionally, Cremers and Petajisto (2009) created a new metric known as “active share” which is the difference on long positions between shares an active fund invests in and the shares of the closest benchmark. The funds with the highest “active share” outperform their benchmark by 1.13-1.15% per year, indicating fund managers who hold more active portfolios have the ability to display their skill by picking stocks which provide superior returns after costs.

Since funds may be able to generate greater returns than benchmarks in times of downturn when investors value money more, Vincent Globe (2011) justified the ability for funds, which on average underperform, to charge higher fees. Moreover, Greenwood and Scharfstein (2013) suggest that the limited number of managers that outperform should be able to charge higher fees. In reality this does not happen as the average investor is unable to identify these managers.

While actively managed funds on average provide returns either equal to or below their benchmarks after fees, investors continue allocating capital to active funds because they believe professional management has potential to create abnormal returns, especially within poor economic conditions. Believing some funds are able to outperform, creating a facade for investors since the studies show the average fund will have a net return close to or below zero.

Limitations

This literature review has a limited sample size of research and has gaps within the timeline. Potentially leading to a misrepresentation of the overall findings of the field regarding skill of fund managers.

Beginning with Gruber (1996), Berk and Green (2004), Greenwood and Scharfstein (2013) and Liebscher and Mählmann (2016), they use theories with limited original empirical analysis, indicating a bias towards opinion.

Methodology was the largest empirical bias. Grinblatt et al (1995) and Bollen And Busse (2004) used outdated analysis methods. Wermers (2000) state in robustness tests their analysis may be skewed due to their dated sample data. Cuthbertson et al (2008) and Agyei-Ampomah et al (2015) have used limited methods, with the former relying on one method on a small sample whilst the latter used four different methods which led to no common conclusion. Lastly, Cremers and Petajisto (2009) use a best fit benchmark, resulting in a potential benchmark misfit.

The following studies; Carhart (1997), Kosowski et al (2006), Fama and French (2010), and Cao et al (2013) have survivorship bias and heteroscedasticity error, meaning short-lived funds have extreme variations in measurements of returns compared to long-live funds, possibly skewing the results. Berk and Binsbergen (2015) explicitly state their data may be influenced by external factors other than those mentioned in their robustness tests.

In future reviews, a larger sample size of literature might provide a more reliable representation on the research of this topic. Regarding papers, authors should avoid using outdated methods and small sample sizes as well as adjusting for all aforementioned biases.

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Conclusion

As seen in the progression from 1995 to 2016, there has been a shift in the viewpoint regarding skill in the performance of fund managers. In the early research, conclusions were made in favour of luck rather than skill, however as research developed new findings and methods proved skill is present with the top managers who could consistently outperform as well as extreme performances being credited to a combination of both. Most funds either match or underperform compared to the benchmark index, however investors perceive the active funds to have value due to the potential of abnormal returns.