One could easily make the argument that Rob Arnott, founder and CEO of Research Affiliates, is the face of smart-beta indexing. In addition to his firm's own indexes, he has partnered with index providers such as FTSE, Russell, S&P Dow Jones Indices and Citigroup to create benchmarks based on his research, and frequently appears at conferences and in the media to speak about the Fundamental Index concept.
In the run-up to the 2nd annual Inside Smart Beta conference June 8-9, 2017 in New York, InsideETFs' Head of Europe John Swolfs sat down with Arnott to discuss the current state of the "smart-beta union.”
Inside ETFs: What was it about cap-weighted investment strategies that didn’t appeal to you, that made you look in a different direction for an alternative way to invest?
Rob Arnott: While capitalization weighting makes an assumption that market prices are correct, intuitively they're not. The price of any stock represents its fair value, plus or minus an error. The market may judge the price too high, it may judge it too low. The market is constantly trying to figure out whether the price is too high or too low. In the very long run, the errors are mean-reverting; they correct over time, creating the challenge of how do you do better than that.
And what's interesting is in the smart-beta arena—at least by original definition of smart beta—the engine for all of these strategies is the same: It's a rebalancing of alpha. You're contra-trading against the performance chasers, the lemmings. And isn't that cool, to have a strategy that will contra-trade against the performance chasers?
So that's why I reject cap-weighting, and that's why I believe that for the patient, long-term investor, it's actually relatively easy to beat the market.
Inside ETFs: You stated you think patient investors can easily outperform traditional benchmarks. Do you feel that traditional benchmarks of the S&P 500 or a Russell 2000 are a fair comparison for factor or smart-beta strategies?
Arnott: That's actually a trickier question than it sounds. The market is cap weight. So people will always want to know, is this idea going to add value? And adding value, for most people, means adding value relative to the market.
In the early days of fundamental index, we were encouraging people to use it as a benchmark. Our rationale was simple: If you have a dual benchmark, cap weight and fundamental index, you're going to find that most of your managers fail to beat fundamental index. And you're going to be drawn toward making greater use of fundamental index, or other smart-beta strategies.
That effort fell flat on its face. Nobody wants a benchmark that's hard to beat; nobody. So we quickly realized that if you recognized that investors really just want to beat the market, it's OK; if your benchmark is the market, it's OK. In addition, you aren't seeing a push from the factor community to shift the benchmark.
Inside ETFs: That makes sense; you'd want an easier benchmark. You wouldn't want anything that might stand in your way of showing that your strategy might be working.
Arnott: Exactly. And fundamental index, in some markets, shows a residual alpha even net of multivariate Fama-French factor attribution. And in some markets, neutral or slightly negative.
Does that mean it's not working? No. It means it's actually capturing the factor alphas pretty darn well without incurring a lot of trading cost to do so. So it's actually capturing those factor returns. Factor strategies don't do that. Ouch!
Now, one of the other things you made mention of in one of your questions was factor or smart-beta strategies. I liked your choice of words—factor or smart beta.
Inside ETFs: With so many products, and strategies being labeled “smart beta,” can we lump both factors and so-called smart beta together?
Arnott: I don't think of factor tilts as smart beta. Most factor-tilt strategies start with cap weight and then put on a factor tilt. Cap weight is not smart beta. It doesn't break the link with price. And if you put on a factor tilt, the factor tilt might be smart, it might be stupid. It might be smart, but poorly timed because it's currently expensive. But it's not smart beta; it doesn't break the link with price.
So I think the redefinition of smart beta to encompass practically everything robs it of any meaning. I mean, if smart beta encompasses everything, it means nothing.
Inside ETFs: Research Affiliates constantly says, “Keep the smart in smart beta.” What exactly do you mean by "the smart"?
Arnott: To our way of thinking, the original way the term was used was that strategies that break the link between the price of a stock and its weight in the portfolio have a structural alpha.
Structural alpha is that you're not going to automatically overweight overvalue and underweight undervalue companies. Now, you may make other mistakes. You may have negative alpha from time to time, but overvalued companies will not automatically be overweight just by dint of their price.
That's the Achilles' heel of cap weight. If a share price doubles, the weight of the portfolio doubles. Now, is that a good reason to double the weight in the portfolio? Of course not.
Now, the other thing that's interesting is that you can reverse-engineer the mathematics, and you can find that, tacitly, within capitalization weighting: If the price of a stock doubles and its weight in the portfolio doubles, then by definition, all else equal, you must be expecting a higher return after the price doubled than before it doubled. You must be expecting a higher return; otherwise, the weight wouldn't be higher.
The market overpays for good news and over-discounts bad news, with great persistence. Anything we can do that counteracts that is likely to win. And anything that contra-trades against the market's most extreme bets is likely to win.
So the alpha engine for equal weight and for fundamental index is the same: It's all driven by contratrading against the market's most extreme bets. It's all driven by long-horizon mean reversion.
A fundamental index doesn't win because of the fundamentals. Fundamentals have nothing to do with it. It's because we're anchoring on some convenient, stable anchor, the fundamental size of a business, and using that anchor to contra-trade against the market's extreme bets.
InsideETFs: Does this same approach work across all asset classes, or is the outperformance confined to equities?
Arnott: Long-horizon mean reversion works across all asset classes. We have strategies in place in stocks, fundamental index; bonds, fundamental index. Commodities, Dow Jones has the Dow Jones RAFI Commodity Index. Currencies; we're rolling out a RAFI global macro strategy that's across equity, bond, currency and commodity markets. So it works across all markets. Long-horizon mean reversion works everywhere.
Now, that's not to say it works over the same time horizon everywhere or works equally well everywhere. But it does work everywhere.
Every one of these markets is populated by human beings. And a funny thing about human beings is they all have emotion, and they all tend to overreact. What a shock that is.
Inside ETFs: There's been a lot of talk recently about diversification and moving away from the nine style boxes and using factors as a new way to build a core portfolio. What are your thoughts on that as a new way for investors to build the core of their portfolio? Is that the right approach?
Arnott: I think factors are a powerful tool. I think, as with any tool, there's risk of overuse. Would you want to build a house without using a hammer? Of course not. Would you want to build a house solely by using a hammer? You've got to be kidding. So using factors as part of your tool kit, sure; it's a powerful tool. But using it as the singular, the central approach to investing? You've got to be kidding.
We're writing a series of papers this year called “Alice in Factor Land.” And we point out in the first paper in that series, which is coming out shortly, the incredible shrinking factor return. We point out that the returns on factor long/short paper portfolios don't show up in mutual fund and ETF data. The returns for factors that are realized in mutual funds and ETFs are much smaller than the returns that exist in the paper portfolios.
That's worrisome. So do we replace an existing suite of strategies and ideas with style boxes? With factor strategies? No, these are individually useful tools, none of which stands on their own as a uniquely perfect way to build our portfolio.
So I think there's going to be a lot of tears in the factor landscape. Not spread across the whole factor landscape, but in those who dive into it carelessly, thinking that this is the holy grail.
InsideETFs: When you come to our Smart Beta conference in June, you'll be talking about relative value and its effects on future returns. What were your findings on that? How will that impact investors' portfolios going forward?
Arnott: The findings last spring were that quality was expensive. Momentum was expensive. Low vol was off-the-charts expensive. That value was cheap, and that small-cap was cheap.
And what happened in the second half of the year? Small-cap outperformed, value outperformed. Low vol fell off a cliff. Momentum and quality struggled. So our batting average was five out of five for the second half of last year. I don't ever expect an idea to have that kind of batting average. So I view that as far more luck than skill.
But coming into this year, momentum has flipped. It's not that the momentum stocks suddenly got cheaper, it's that momentum is no longer a tail wind for the FANGs (Facebook, Apple, Netflix, Google] and the bubble stocks. It's now a tail wind for the value stocks. The value stocks have turned. That's now where the momentum is, and momentum says buy value.
OK, well, that's interesting. Quality after the tough run in the second half of last year is now trading a little cheap. Well, that's nice. Value is still trading a little cheap in the U.S. and very cheap outside the U.S. Well, that's nice. Low volatility is still trading at nosebleed valuations. Early last year, three of the four FANG stocks made their way into some of the low-vol strategies. How can you have a low-vol strategy with Facebook, Amazon and Netflix in it, and think you're reducing your downside risk? It doesn't make sense.
And those are now gone. They're not in the portfolios anymore. Guess what? Their beta just popped back up. What a surprise.
But these strategies are almost all still priced at premium multiples. So that's worrisome. If you're paying premium multiples, do you really expect lower downside risk? So they're no longer trading in the top two or three percentiles of historic valuations, but they're still kind of in the 10th percentile of historical valuation multiples. That's still high.
So I'd still be very wary about overrelying on low-vol strategies. I wouldn't say, don't use them. I would say, be careful. Don't expect them to help you much in the next bear market.
Inside ETFs: What’s the biggest misconception or mistake investors make when it comes to smart-beta and factor investing? What's the biggest mistake you see across the board, day in and day out?
Arnott: The biggest mistake in every element of the investing world—and it's the same whether you're looking at equity investing, or manager selection, or mutual fund selection, or stock picking or bond investing—is performance chasing. It's endemic.
We all want more of whatever has given us great joy and profit. But that's what we ought to be thinking about selling. We all want less of whatever has given us pain and losses, but if you want to buy low, you have to buy something that's caused pain and losses. And it goes against human nature. We didn't survive on the African plains by running toward a lion. It goes against human nature.
To profit in investing, you need to be willing to buy low and sell high. And buying low and selling high means buying what's inflicted pain and losses, and selling what’s given us great joy and profit. It just goes against human nature. And it's as true in smart beta as it is in every other element of investing.
Inside ETFs: Do smart-beta or factor strategies then inherently look to take away that kind of human element of investing?
Arnott: The original definition of smart beta—strategies that break the link with price—have an inherent buy-low/sell-high discipline, because if a stock falls in price, you're going to want more of it. If it soars in price, you're going to want less of it. And so, the alpha engine for all of the true smart-beta strategies is this buy-low/sell-high discipline. That's not true of factor-tilt strategies, which are built on a foundation of cap weight.