Sometimes corporate marketing can seriously wear out the potential golden-egg-laying goose. Take factor investing. It's "overhyped," "oversold" and marketed as "a panacea." So says Robert Arnott, chair and founder of Research Affiliates, who, in an interview with ThinkAdvisor, explains reasons for factor investing's bad performance last year.
Indeed, he discusses the three biggest risks examined in a paper he's written with colleagues, "Alice's Adventures in Factorland: Three Blunders that Plague Factor Investing," due for April publication in the Journal of Portfolio Management.
Arnott, 64, a pioneer in unconventional portfolio strategies, including tactical asset allocation and tax-advantaged equity management, is known for testing the conventional wisdom. In so doing, he often discovers opportunities for profitable investing.
He has been dubbed the "Godfather of Smart Beta," referring to strategies meant to rebalance portfolios on the basis of characteristics others than market cap. But "smart beta" is a term, Arnott argues in the interview, that has been co-opted merely as a marketing tool ("…pretty soon [it] was attached to everything under the sun.")
He also talks to ThinkAdvisor about the multiple market bubbles he sees — Twitter, for instance — and offers his long-term forecast for the U.S. economy, and stock and bond markets. Best place to invest now? Emerging economies, he maintains.
Arnott is currently researching the risks of government deficit spending posed by adherence to the faddish "modern monetary theory," which he explores in the interview too.
His recently published research papers include "Can Momentum Investing Be Saved?" ("So-called experts on momentum have generally delivered terrible returns to their clients.") and "A Backtesting Protocol in the Era of Machine Learning," about AI.
Arnott founded Research Affiliates in 2002 to focus on innovative approaches to active asset allocation, portfolio construction and other quantitative strategies. Based in Newport Beach, California, the firm, with more than $175 billion in assets under management, develops investing strategies distributed by partner firms, including BlackRock, Pimco and Schwab, in a range of formats, such as ETFs and mutual funds.
In August 2018, Arnott stepped down as CEO to increase his focus on research and portfolio management. He manages the Pimco All Asset and All Asset All Authority family of funds and the Pimco RAE suite of funds.
Chicago-born, he graduated from the University of California, Santa Barbara with a B.S. summa cum laude in economics, applied math and computer science. He has published more than 100 journal articles, about a dozen of which have picked up prestigious awards.
ThinkAdvisor recently interviewed Arnott, on the phone from his Miami office. One area of discussion was target-date funds. He says the industry has the strategy, well, essentially backwards.
Here are highlights of our conversation:
THINKADVISOR: You've made a career of testing the conventional wisdom. What's the upshot?
ROB ARNOTT: It's fun looking at ideas and trying to discern what works and what doesn't. Wherever there's a gap between conventional wisdom and the real world, that's a profit opportunity for investors.
You're not a cheerleader for mainstream stock-and-bond portfolios. Rather, you favor investments like commodities, REITs, emerging markets, high-yield bonds. What do you like right now?
I see emerging economies as the place to be. The best markets out there seem to be emerging markets stocks; the worst seems to be U.S. small-cap growth. The spread between the expected returns of those two is nearly 10% a year for the next 10 years. That's enormous.
What, for example, makes emerging economies so attractive?
They'll have a soaring population of mature adult workers in their 40s and 50s who'll be setting money aside for retirement.
Do you think emerging markets will beat the U.S. stock market this year?
I have no clue. But I'd bet very long odds that emerging markets investments will beat U.S. stocks on a 10-year horizon and very likely a five-year horizon.
You've just written a paper saying that understanding the risks of factor investing is "essential before adopting [that] investment framework." What are the three main risks?
The biggest risk is that many factors aren't real. They've worked historically, but that doesn't mean they'll work in the future. The second risk is of losing your edge, and alpha gets arbitraged away. The third risk is that factors, by dint of being popular, can become expensive so that you're paying a premium for the stocks that meet the factor criteria. By paying that premium, you wind up with a very expensive portfolio where the alpha has disappeared.
What do you think of factor investing broadly?
It has merit. There are some interesting ideas out there, but I think they're overhyped and oversold to an alarming extent. Every factor is suspect. If you use multiple factors, you diversify your risk a little bit. But it's been sold as a panacea.
How many factors are there?
Anywhere from hundreds to thousands. Some big ones are momentum, quality, size, value. There's a whole other array that looks at degree of leverage of the company, volatility of earnings, dividends, buybacks — and the list goes on and on. The thing is that every single one of these works in back tests. But all that tells us is that people publish only the ones that did work. It doesn't tell us that factors work.
Why did factor investing perform poorly last year?
One reason [lies] in construction and implementation. How many of the academics who designed factors have actually traded stocks for a living and know the potential ugliness of careless trading? That's a key part of the issue.
What's another cause?
The factors themselves were identified as a consequence of aggressive data mining. If you mobilize the entire academic community to search for factors because that's the easiest way to get tenure and you discover a factor that hasn't been published before — great! You become the expert on that factor and get tenure! So there's a powerful incentive to look for relationships that may or may not have merit.
That doesn't sound scientific.
All too often in the factor world, people did the research, found something that seemed to work and then created their hypothesis — they concocted behavioral explanations.
You've been dubbed the "Godfather of Smart Beta." What are your thoughts now about these strategies?
I didn't coin the expression, smart beta. The credit for that goes to [the firm] Towers Watson. Smart beta used to mean something — strategies to break the link between a stock's price and its weight in the portfolio so that you're rebalancing alpha. But it doesn't mean that anymore.
Why not?