Smart beta is growing rapidly, but who’s reviewing the research?

Dan Bortolotti’s April 1st post about Dr. Molti Fattore’s data mined index strategy was an April Fool’s joke, but the beauty of the post was that it could have easily been true. Some ETF providers were likely drooling at the thought of a “factor lasagna” that they could package and sell for 0.75%.

Data around the average active managers’ failure to outperform a low-cost index fund has resulted in investors’ assets shifting into low-cost index funds. Market cap weighted index funds have quickly become commodities, resulting in the lowest fund fees in history. An S&P 500 index fund with a 0.05% MER was once an anomaly, and it is now an expectation. In an effort to differentiate their products, index providers have started producing factor-based research which can be implemented in smart beta index portfolios, or funds which have been designed to outperform a simple cap-weighted index by capturing a specific part of the market. Of course, a smart beta fund is no longer a commodity and will accordingly command a higher fee.

The problem with the rapid proliferation of smart beta products is that mashing a handful of back-tested factors together does not necessarily result in a robust portfolio. The research behind the factors needs to be impeccable, and the implementation of the research requires significant care and expertise.

Momentum and quality are two factors that have been showing up in smart beta and factor ETFs. The momentum premium has been well-documented but it does not have a sensible explanation, raising the question of whether it is likely to persist. Momentum as an investment factor also decays quickly, making it very difficult to capture without a high level of portfolio turnover. High turnover increases costs and erodes any premium that may have been available. This is an obvious challenge with implementation.

Quality, based on earnings variability, has presented a past premium. However, when profitability is controlled for unusual items and taxes on the income statement, the variability of profitability contains little information about future profitability. In short, the quality factor does not have significant explanatory power over returns compared to well-documented factors such as size, value, profitability, and investment, and it is not useful to add it as an additional factor in a portfolio.

There is a tremendous amount of data available about markets, and it is relatively easy to find patterns that appear to point to higher expected returns based on a factor. Implementing an investment portfolio based on this type of research requires significant due diligence to mitigate the risk that observed differences in returns have not simply happened by chance. An excellent example is the Scrabble score weighted index reported in this paper by Clare and Motson. They found that if they weighted an index based on the Scrabble score produced by the ticker of each stock, they significantly outperformed a market cap weighted index. The Scrabble factor is, of course, not reliable data, but not all poorly thought out smart beta strategies will be so easy for investors to spot.

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