Every time someone comes up with a “new and improved” way to invest or predict which active managers will shine, it seems that subsequent research finds that it’s really just about focusing on different betas. The latest example is a new study (“Deactivating Active Share”) from AQR, a quantitative money manager, which documents that it’s (still) all about beta when it comes to looking for clues about which active managers are more likely to deliver statistically significant alpha.
Under scrutiny is a methodology known as Active Share, which was outlined in the widely discussed 2009 paper “How Active is Your Fund Manager? A New Measure That Predicts Performance”, by Martin Cremers and Antti Petajisto (C&P 2009). The basic idea is that funds that strayed further from a benchmark, the higher the potential to deliver market-beating results (alpha). According to C&P 2009, mutual funds with high Active Share rankings have a history out of outperforming their benchmarks. The key message: avoid the closet indexers and load up on strategies with high Active Share ranks.
For some managers, a high Active Share measure is a badge of honor, as it seems to show a fund’s dedication to raising the odds that it will beat beta. But as the AQR analysis shows, reality is more nuanced. “Overall, our conclusions do not support an emphasis on Active Share,” the AQR authors write. “Predicting investment performance is difficult and there do not seem to be any silver bullets.”
Consider, for instance, how Active Share statistics compare when sorted by benchmark, as per AQR’s analysis. As the chart below shows, high Active Share ranks among equity funds are concentrated in portfolios with a small-cap bias.
“This presents a clear problem,” the AQR authors explain.
[Research] papers that sort funds on Active Share (as do C&P) end up sorting funds on their benchmarks. In practice, few investors would evaluate all managers on a particular dimension and then accept whichever benchmark falls out. Instead, they would start with a benchmark and select a manager from within that benchmark.
Another complication is that the various benchmarks delivered a range of return results.
The differences, estimated over 1990-2009, are substantial, with annualized alphas ranging from -3.35% for Russell 2000 Growth to +1.44% for S&P 500 Growth…. One could speculate that in this sample period small-cap benchmarks were easier to beat for investors who could access value, size and momentum as defined in the academic literature. This is consistent with findings of other studies critical of Active Share that have observed that its performance predictability can be explained by a bias towards the small-cap sector.
The bottom line is that “it is more realistic to rank funds separately within each benchmark,” the AQR study advises.
This way we are directly comparing high and low Active Share funds that share the same benchmark universe…. Once we control for benchmarks, the performance difference between Stock Pickers and Closet Indexers (raw, benchmark-adjusted, or alphas), while positive, is not statistically different from zero.
In short, the choice of betas drives results. Focusing on alpha, by contrast, is a relatively minor affair. Even worse, depending on how you search for alpha, the task may end up misleading us, as the AQR research shows.
All of which reminds me of an observation by Professor John Cochrane in his 2011 Presidential Address to the American Finance Association:
I tried telling a hedge fund manager, “You don’t have alpha. I can replicate your returns with a value-growth, momentum, currency and term carry, and short-vol strategy.” He said, “‘Exotic beta’ is my alpha. I understand those systematic factors and know how to trade them. You don’t.” He has a point. How many investors have even thought through their exposures to carry-trade or short-volatility “systematic risks,” let alone have the ability to program computers to execute such strategies as “passive,” mechanical investments? To an investor who has not heard of it and holds the market index, a new factor is alpha. And that alpha has nothing to do with informational inefficiency.
Most active management and performance evaluation just is not well described by the alpha-beta, information-systematic, selection-style split anymore. There is no “alpha.” There is just beta you understand and beta you don’t understand, and beta you are positioned to buy vs. beta you are already exposed to and should sell. [emphasis added]