The evidence in favor of indexing is convincing, perhaps overwhelming. But finance is conspicuously light on definitive laws and so it’s not surprising that research on active management continues to offer encouragement for those who think they can beat the odds and generate alpha through time. But there are two ways to read these pro-active management studies. One interpretation (probably the more popular view) is using these papers to rationalize alpha’s charms as widely available for those who are committed to working harder. The alternative view, which animates my thinking, is to see these studies as evidence that chasing alpha demands a lot of extra time and effort for an uncertain payoff that’s probably out of reach for most of us in the long run.
Consider the so-called active share research from professors K. J. Martijn Cremers and Antti Petajisto (“How Active Is Your Fund Manager? A New Measure That Predicts Performance”). It’s surely one of the more convincing studies in recent years in terms of offering a methodology for trying to predict alpha for mutual funds and other portfolios. Not surprisingly, the paper has inspired a wave of favorable reviews for embracing active management. In an article I wrote last year for Financial Advisor, I summarized the research:
The paper can be thought of as a refinement of tracking error, which has been used for years for evaluating investment strategies. Tracking error calculates how closely a portfolio follows its benchmark (or not) by measuring the volatility (standard deviation) of the difference between the returns of a fund and its benchmark. But tracking error does a poor job of distinguishing between the two main types of active management: individual security selection and factor timing, such as industry-rotation or market-timing strategies.
A superior metric for judging active management’s results is comparing a portfolio’s holdings to an appropriate benchmark, according to Cremers and Petajisto. Their active share measure quantifies the divergence in a fund’s securities versus an appropriate index. The readings range from 0% (no deviation in holdings and weightings versus an index) to an active share rating of 100% (zero overlap with the index). The closer to 100, the stronger the degree of active management and (the authors emphasize) the higher the odds of delivering alpha.
It’s an idea that’s been informally discussed for years in active management circles. If there’s any chance of beating an index, the portfolio must differ from the benchmark in a meaningful way. That alone is no silver bullet, although the reasoning is bound up with the recognition that a manager who’s willing to make more than trivial bets harbors above-average confidence in those choices. Think Warren Buffett or George Soros, for instance. No wonder that concentrated-portfolio strategies-holding only the “best picks”-resonate strongly within the active management community.
The problem is that the practical hurdles of applying active share analysis are more than trivial. For one thing, most of the funds with the most encouraging active share scores are relatively small portfolios. That’s hardly a shock. As portfolios grow larger, the gravitational pull of the market becomes stronger and so the closet-indexing risk rises with assets under management. The message is that there’s limited capacity for the funds with the best prospects. That’s also a warning that if too many investors pile in, the expected alpha is likely to fade if not evaporate entirely.
Meanwhile, there are practical challenges for deploying active share analysis on a routine basis. On that point, I’ll quote myself once more via Financial Advisor:
Even if you think active share can help you pick managers, there’s the problem of crunching the numbers on a timely basis. The formula outlined by Cremers and Petajisto is simple enough to calculate in Excel. The stumbling block is the obligatory dose of fresh data on a fund’s holdings. In fact, you’ll need recent data on lots of funds. A sensible use of active share rankings as a screening tool inspires sifting through a broad list of portfolios within a strategy. Imagine that you’re searching for strong managers in the small-cap domestic-equity blend mutual fund category. According to Morningstar Principia, nearly 200 actively managed products are available. Assuming there are 50 to 100 holdings per fund, an intensive round of statistical analysis awaits.
Even if you overcome this hurdle, don’t get too comfortable. There will be ongoing maintenance. If active share shines brightest in smaller funds, you should plan on bailing out of products that grow too large. In turn, you must redeploy the proceeds into smaller portfolios that rank high on alpha-generating prospects. In other words, you’ll need to run active share evaluations regularly.
Another pro-active management study that’s receiving attention is a Barclays paper from last month—“The Science and Art of Manager Selection.” Paul Sullivan of The New York Times discussed the paper recently, reporting that the Barclays research “aims to lay out the risks of trying to read past performance into future returns when selecting active managers.” The Barclays authors advise that “for those for whom active management may be suitable, this paper explains how we go about identifying, analyzing, selecting and monitoring investment management organizations.”
Unfortunately, the process outlined by Barclays is nothing if not complicated. No one will confuse the due diligence laid out in this paper as streamlined. Between reviewing historical records on funds, running performance attribution analyses, focusing on “return gap analysis,” etc., the road to success sounds like a full-time career. For most folks, that’s asking too much.
Even for professionals, the question is one of deciding if the time is better spent developing robust expectations about return and risk for the major asset classes. Keep in mind that even if you dedicate yourself to identifying superior active managers, you’ll still need to do all the asset allocation analysis that drives the lion’s share of results for indexing strategies. That alone is a time-consuming challenge if you’re looking for robust estimates.
The truth is that most investors don’t have the time or expertise to do both. The primary allure of indexing in the context of a multi-asset class portfolio is that it focuses on exploiting the main drivers of risk and return: beta. If we can develop some productive intuition about expected risk premiums for the various betas, we’ll have a solid foundation for achieving investment success. It’s not easy, but this necessary task is sure to be a lot more difficult if you add active management analysis to this strategic chore.
The truth is that relatively few investors are able to pull off this dual task, and for a simple reason: alpha sums to zero. There’s only so much market-beating performance to go around, and most of it goes to a handful of really smart investors. Meanwhile, the positive alpha is financed exclusively by investors who suffer negative alpha. Beta, of course, generally ends up in the middle; if you factor in trading costs, beta tends to deliver above-average results, as I noted here. Some things never change, no matter how many research papers suggest otherwise.