Larry Swedroe on a study into whether alpha predictors hold out-of-sample.
Larry Swedroe, Director of Research, The BAM Alliance
An important question facing mutual fund investors is whether funds with positive alpha, net of fees and costs, can be distinguished, ex-ante, from funds with negative alpha. Most individual investors must believe this to be true, because the vast majority of their assets are invested in actively managed funds.
Reaching this conclusion requires that investors believe there are variables (such as past performance or active share) associated with future alphas.
While there have been some papers that claim to have found the holy grail of performance predictors, the research shows that skill-based alphas tend to be erased by fund fees and diseconomies of scale arising from fund flows and arbitrage (once anomalies are discovered, they tend to quickly disappear as the markets become ever more efficient).
As my co-author Andrew Berkin and I explain in “The Incredible Shrinking Alpha,” there are major trends that have led to an increase in arbitrage activity over the last several decades, making stock markets more efficient and alpha generation more difficult).
Christopher Jones and Haitao Mo contribute to the literature with their November 2016 study, “Out-of-Sample Performance of Mutual Fund Predictors.” They began by identifying 20 different predictors from 17 papers. Unfortunately, four of those predictors were impossible to replicate, as they relied on proprietary or hand-collected data, leaving 16 for which the authors were able to analyze out-of-sample performance.
Their study covered the period January 1961 to January 2013 and more than 3,500 mutual funds. On average, the authors’ quintile sort produced an in-sample alpha spread of 1.96% a year with an average t-statistic of 2.28.
Following is a summary of their findings:
- There is a significant positive relationship between alpha predictors and ﬂows; in other words, investors believe the predictors have value.
- There is a negative relationship between alpha predictors and fees. This relationship becomes increasingly negative out-of-sample. If predictors deteriorate, it isn’t because of rising fees.
- Using two different tests, the out-of-sample alphas fell between 78-87%. The out-of-sample performance of mutual fund alpha predictors is, at best, marginal.
- Publication of the paper on which each predictor is based leads to higher levels of arbitrage (as measured by aggregate short interest, aggregate share turnover and aggregate hedge fund asset size), which is then associated with smaller alpha spreads. Learning from a published academic study that a set of securities offers positive alpha induces greater investment (arbitrage) to those securities, which raises their prices and eliminates the alpha.
Another interesting finding was that in the period between the end of the data sample and publication of the finding on predictability (about four years on average), although predictability declined between about 30% and 40%, some predictability still existed. Predictability is no longer reliable after publication. The finding of a gradual decline in predictability allowed the authors to rule out the likelihood that the original findings resulted from data mining.
Jones and Mo noted that “whether academic ﬁnance research is useful or not in practice largely rests on whether or not its ﬁndings continue to hold out of sample.” Unfortunately, they reached the following conclusion: “Performance persistence, at least for equity funds, is mostly a phenomenon of an earlier era. The primary advice that one can give, again for equity funds, is to avoid high fees.”
Importantly, they added: “Poor out-of-sample performance is at least mostly the result of an overall trend towards greater market eﬃciency.” And finally, they offered this: “The obvious implication of our ﬁndings is that investment practitioners, who are known to use at least some of these measures to guide portfolio selection, may be engaging in a now-futile exercise.”
The quest for performance predictability has been as unsuccessful as the quest by King Arthur’s knights for their own Holy Grail, the cup from Christ’s Last Supper.
The bottom line is that investors are not well-served by selecting mutual funds based on past performance, whether in the form of alpha or absolute returns.
Instead, investors should focus on other characteristics, such as the fund’s underlying market philosophy, portfolio construction rules, exposure to (or loading on) well-documented factors, trading strategy, total costs (not just expense ratio) and, for taxable investors, tax management strategies—all of which are important to providing investors with the best odds of achieving their financial goals.
Alpha vs. Actual Returns
There’s one more important point we need to cover. When it comes to picking mutual funds, investors should care less about alpha (by whatever measure) than about actual returns. I want to own a fund that provides me with exposure to factors I care about, such as market beta, size, value and momentum. I’m then happy to have minimal alpha, so long as I get the beta (loading on a factor) I am seeking, which leads to higher returns.
In other words, I would rather own a low-cost, passively managed small value fund that provides me with high loadings on those factors, and minimizes or even eliminates the negative exposure to momentum that is typical of value funds and has no alpha, than an active fund with less exposure to those factors, even if it generates a positive alpha, because that positive alpha would have to be great enough to overcome the loss of returns due to its lower loading on the factors.
This commentary originally appeared June 23 on ETF.com
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