Smart Beta Investing: Just a Marketing Story?
Smart beta is an impressive investment branding story. The name neatly encapsulates the idea of beating conventional indices consistently but with little effort and much lower costs. Intellectually seductive, it conveys the concept of market exposure that avoids the costs and behavioral failures of active managers. And it simultaneously gives the impression of an edge over most of the passive conventional index-tracking strategies. If only the actual performance matched the image. But the popularity of smart beta tells us even more about investment narratives and the psychology of investors.
There are two clear issues for smart beta. The difficulty is proving a strategy on subsequent out-of-sample experience after it has been optimized against historical data. No matter how long the historical period used in developing a strategy, the real test is what happens after. Another key issue is the seductive appeal of individual words and narratives. Terms such as emerging markets, new energy, and dynamic all convey the concept of buying the future and have an emotional appeal. It is this emotion that investors need to be alert to.
Separating Marketing from Reality
Stories are embedded in much of the presentation of smart, or alternative, beta. Many of the exchange-traded funds (ETFs) that focus on smart beta come with strongly emotive labels. Examples range from names like “Buyback Achievers” to terms like “progressive” and “dynamic.” What’s not to like? Representativeness is a strong psychological bias, making it easy for investors to convince themselves that they are buying a rosy future. It is a more acute version of the emerging-markets syndrome — that is, growth must be a good investment. It triggers emotional responses much as hedge funds once did.
Representativeness can make us think that a growth area represents a good investment, much as we think that gold shares should behave like gold itself. Unconscious impressions and beliefs compromise our analysis. We can readily understand that the argument that gold-mining businesses should, in some way, be leveraged plays on the gold price. Yet, gold-mining stocks have spent five years mainly not correlating. They look as if they should be a play on the gold price but have proved not to be. Similarly, we can imagine a future in which there are lots more solar panels yet fail to calculate whether anyone will make money on them.
Research suggests little linkage between share performance and economic growth, not least because valuation can change dramatically, as we have seen with the 75% fall in China’s stockmarket P/E over 10 years, broadly canceling out earnings growth. And it is worth looking at the truly long term to get a perspective on economic growth. China may actually have been the largest global economy for all but 1 century of the last 20.
There is a danger in an investment strategy’s becoming simply a story. Investors are too easily seduced by phrases like “the China story still has a long way to run.”
The danger of alternative beta is not just that many of the strategies are created by data mining and thus are optimized against an experience that may not repeat. Certainly, this apparent endorsement of history gives specious credibility. But the more insidious risk is that we are too readily taken in by a marketing concept that conveys the idea of systematically better investment performance with less risk and lower costs. It has created an excitement that the old brand of “factor tilts” never had.
Be Careful of Compelling Stories
Thematic approaches to smart beta require investors to hold beliefs about the future. Some fund labels are essentially narratives in themselves and deserve deeper examination. There is a danger that an investment strategy (or ETF) can become simply a story. Fund managers use stories to rationalize underperformance, but with smart beta, the strategy itself is often the story, summed up in the label. Typically, investors have made a deep emotional and long-term commitment to it.
Professors Richard Taffler and David Tuckett highlight the importance of stories in re-affirming conviction amid a sea of data. These are stories that managers tell themselves, tell colleagues, and use in client reporting. Taffler and Tuckett also highlight the importance of meta-narratives, or overarching distillations of an investment philosophy. Linking information together in the form of a narrative not only assists recall but also helps us make sense of something. We also try to incorporate our own life events in a personal narrative. This preference for stories over otherwise apparently unrelated facts and possibilities creates a bias. Think of analyst reports that cover poor results or a setback with the phrase “poised for growth,” a story that moves us on from disappointing facts to hopes. We see these phrases and narratives so frequently that we can easily miss their persuasiveness.
The data-mining issue remains a challenge. Time after time, we have seen that optimizing a strategy on historical data — modeling in a sample that is known — can subsequently turn out quite differently in the real out-of-sample world. An illustration of this phenomenon is the record of stock market behavior when Congress is in session or out of session. Academic studies have found that 46 years of empirical data show that over long periods, the stock market performed dramatically better on days when Congress was out of session as compared with days when it was in session, with the split of annualized returns approximately 16.1% to 0.3%. However, a fund set up to apply this long-term pattern has actually underperformed the S&P 500 over the five years since its inception, which shows how dangerous it can be to turn long-term anomalies into fund strategies.
Still, Some Thoughtful Applications
The clearest area where behavioral economics supports some of the smart beta approaches is in the persistent underperformance of some of the very largest companies. Strategies that more evenly weight a portfolio may rightly be avoiding overexposure to these underperforming megacaps. Poor executive incentivization appears to be a factor in the failure of many of the largest companies to deliver shareholder value. In some cases, they may pay compensation on the basis of beating or matching an ever-narrowing group of global peers. Think, for example, of the five oil majors and incentives that mean three out of five attain median performance or better and may pay bonuses on that basis. These types of soft incentives are prevalent across megacaps. For smart beta strategies and active managers, a long-term bias against megacaps may be justified.
Undoubtedly, there are flaws in traditional indices. And lowering portfolio management costs can assist long-term returns, something that smart beta helps with. But smart beta looks mainly like a branding approach. Investors should question the labels and recognize the emotion that may be involved in the narratives.
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Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.
Hindsight bias, data mining, curve fitting – these are problems not just for quantitative strategies, but any strategy. Active or passive. The active manager is basing his knowledge, research and experience of the past and assuming the future will be the same. The quant is no different. The buy and holder is assuming that in the future, markets will be higher than they are today.
There is no way around these issues, no matter what you choose to do.
Very good and crisp. I look forward to more. Thanks. Norman Toy
I am curious to know which published studies/research prompted you to unequivocally write about mega-cap underperformance. Also executive compensation being the primary reason for underperformance appears puzzling considering the exceptionally big size of mega-cap balance sheets and the ever-increasing media attention on the salaries of executives at these companies.
Should regulators prohibit the prefix “smart” beta, as it clearly implies a positive value judgment that, as you suggest, could subconsciously impact investor decisions ?
Should regulators insist on a value-neutral qualifying term, such as modified beta, or adjusted beta ?