Evidence of Skill and Persistence in Mutual Fund Returns

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Evidence of Skill and Persistence in Mutual Fund Returns

Larry Swedroe May 10, 2016






  • Consistent with prior literature, using the four-factor model, there’s no evidence of after-fee alpha added from active management and no strong evidence of persistence in performance.
  • The addition of the RMW and CMA factors to the standard four-factor model reveals persistent positive alpha after fees for mutual funds.
  • Over the period from 2000 through 2014, about 15% of managers overcame transaction costs and fees, 50% of fund managers could overcome transaction costs but not fees, and 35% of managers overcame neither transaction costs nor fees.
  • The top 5% of funds produced alphas of 3.7% (t-stat of 3.4). In contrast, the bottom 5% produced an alpha of -3% (t-stat of 3.3). The difference of 6.7% was highly significant with a t-stat of 4.7 (a measure of statistical significance).
  • The best-performing funds (those with positive alphas) had significant negative exposure to both the RMW and CMA factors, while the worst-performing funds (those with negative alphas) had significant positive exposure.



Puzzling Findings



The fact that all three portfolios have higher returns than the S&P 500 can be explained by the fact that from 2000 through 2014, small and value stocks outperformed the S&P 500 by fairly wide margins. For example, using MSCI indexes, while the S&P 500 returned 4.4%, small caps returned 9.4%, small value stocks returned 11.2% and large value stocks returned 6.6%. Since the study equally weights funds, these returns do not represent the experience of the average investor.

A third puzzling result is that the average annual return difference between the best 5% and the worst 5% of funds is only 10 basis points, even though the corresponding difference in annualized alphas is 6.7%. Ranking funds on alpha from their daily return regressions produced virtually no dispersion in average return. If an investor’s objective is to identify funds with high expected returns, the methodology employed here doesn’t seem to accomplish that objective.


Using Factor Models


Factor models are valuable tools. They are so important because they allow investors to identify the sources of returns of a portfolio/fund and also to determine if there is residual alpha after adjusting for a fund’s exposure to common factors. However, factor models can be abused in the same way that a racing car can be abused.

The results of this study provide an excellent example of why it’s important to not misuse factor models. In making any portfolio construction decisions, investors should consider not just whether a fund is generating alpha, but also the fund’s exposure to various factors. In the case of the study by Jordan and Riley, we saw that while the stock-picking skills of the top 5% of performers allowed them to generate significant alphas, investors didn’t benefit in the form of higher returns. This is because the benefits derived from their skill were offset by the negative loadings on factors with positive returns.


The Bottom Line


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