Sometimes the hype is real.
My recent articles decrying active management and poking holes at seeming outperformance have filled my email box with a bit of backlash. You could sum them all up by saying, “Nadig hates everything that’s not 10 basis point vanilla indexing.”
It’s a fair comment, I suppose, since I am an academic finance geek at heart.
But I want to be clear—it’s not that I don’t think it’s possible to have consistent, risk-adjusted outperformance, I just think it’s incredibly difficult, and easy to fake.
Difficult, because you’re playing in the same sandbox as the rest of the market, and easy to fake because as long as the market at large is in any kind of trend, you can “beat the market” just by adjusting your beta.
Take a bit more risk, and you’ll outperform in up markets. Take a bit less risk, you’ll outperform in down markets.
That’s why we always talk about “risk-adjusted” alpha here at ETF.com. What that means is that when we look at a fund, we don’t just look at whether it beat its segment benchmark, we run a regression analysis of the fund versus that benchmark and look at beta capture, and ultimately, alpha.
We test that alpha, and we only report it if it passes a fairly strict significance test. Unless the math says “we’re 90 percent sure the outperformance of this fund is not based on random chance,” we assume it’s random.
If you’re not a stats person, that may sound harsh, but it’s actually pretty loose by stats standards (95 percent is more common).
So do any funds ever throw off real alpha? Let’s look at one in particular that I find intriguing: the ProShares Large Cap Core Plus ETF (CSM | B-89).
CSM follows the Credit Suisse 130/30 Index, which is one of my favorite wonky academic takes on “smart beta.” The premise is pretty simple: Start with the S&P 500, and based on a bunch of quantitative metrics, figure out which stocks are “better” and which stocks are “worse.”
The metrics used are largely the same ones everyone is looking at: momentum, profitability, growth, value metrics and a few technical indicators. You run everything through a giant black box, and you get a rank ordering of the good and the bad.
That’s all well and good, and plenty of folks construct big portfolios from black boxes like that. The problem is, most of those black boxes just overweight the good stuff and ignore the bad stuff, leading to highly skewed portfolios with crazy industry concentrations and cap tilts.
The reason CSM is more interesting is that it was developed by a bunch of academics (notably Andrew Lo and Pankaj Patel, from MIT and ISI Group, respectively, back in 2007) who specifically wanted to avoid all of those noisy skews. So they constrain their methodology by positing:
- An overall beta of 1 to the market (meaning it doesn’t just play the beta manipulation game)
- Any “bet” on a stock can only be +/- 40 bps of its weight in the S&P 500
The minus sign there is critical. It means a bunch of stocks that have a tiny weight in the S&P 500 actually get negative weights in the 130/30 index. That’s where the 130/30 comes in—you raise cash from the short selling, and you use that cash to buy more of what you like, to get your overall exposure back up to a beta of 1.
In terms of implementation, the actual ETF—CSM—takes a hybrid approach, gaining most of its short exposure through swaps, and getting its long exposure through a combination of plain old purchases of shares and swaps.
As these types of strategies go, it’s actually been pretty effective, and not terribly expensive. With an expense ratio of 45 bps, CSM is hardly insane, although the median tracking difference would suggest the true cost is a bit more like 80 bps.
The tracking is in fact so tight around that 80 bps, it makes me fairly certain what you’re seeing is the cost of the swap positions. The 30-50 bps strikes me as about right for that, so keep that in mind when assessing the “true cost” here.
And what do you get for that? Well, it turns out, you get alpha—actual statistically significant, risk-adjusted outperformance, at least over the last year.
We often get accused of not leaving room for active management in our analytics methodology. Well, this is how you make room for active management. You actually have to deliver on a better mousetrap.
CSM is holding very tight to its promise of beta 1, and beating the market. It does this all while keeping surprisingly tight sector exposure too. Note that our segment benchmark here isn’t the S&P 500; rather, it’s the 281-stock MSCI USA Large Cap Index. Measured against the S&P 500, these sector skews are even less dramatic:
At the end of the day, it’s frankly a chart pretty much anyone can love:
Since inception in 2009, CSM has returned 144 percent, crushing both the S&P 500 and the MSCI large-cap index. In the last year, it’s up 24.6 percent, versus 20.3 percent for the S&P 500. Even in the worst period I could cherry-pick since inception, it underperformed by just more than 1 percent—not much more than its true cost differential:
And that’s literally the worst period I could find. In almost every other period of longer than a few weeks since inception, CSM has quietly gone about the boring, beta-1 business of beating the market with pretty minimal variation from the S&P 500.
Is it guaranteed? Of course not. Nothing in investing is ever guaranteed. In fact, there are real risks here that aren’t captured by these traditional stats.
The 130/30 may have a beta of 1, but they do use leverage and shorting as core pieces of the methodology. Theoretically, if everything went terribly wrong, and the shorted stocks started running like crazy precisely when the levered long positions all go pear-shaped, you could do much worse than you would in a simple large-cap portfolio. However, the construction methodology makes that kind of magical misalignment pretty unlikely.
For now, at least, CSM is proof that better mousetraps occasionally do get created, and at least for a while, seem to work.
At the time this article was written, the author held no positions in the security mentioned. Contact Dave Nadig at [email protected] or follow him on Twitter @DaveNadig.