An investor is looking at the screen showing stock market crash in 2010. Photo taken on: April 27th, 2010
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Has the investment industry’s marketing push outsmarted itself? For several years, huge effort has gone in to selling “smart beta” funds. It has worked, creating great excitement. Now, not at all surprisingly, the backlash has begun.

Investment theory may be a tad crunchy for Easter weekend, so let us keep this simple. Beta is the academic term for the return you get from passively investing in an index. Smart beta comes up with a strategy to beat the index, which can itself be made into an index with simple rules.

The advantage of doing this is that funds that track an index can be run far more cheaply than active funds, which face a far higher bill for research and managers’ salaries. So if a winning strategy can be reduced to an index, it should be possible to cut costs, and offer a superior return to investors.

Passive investing is popular at present because investors have worked out that low fees matter. So smart beta offers a future for active managers.

Smart beta strategies are now proliferating but most commonly stem from anomalies identified in the academic literature. Perhaps most importantly, there are Value (cheap stocks do better than expensive), Momentum (winners keep winning, and losers keep losing), and Low volatility (relatively stable stocks perform better). All will have periods when they do badly. All perform well in the long run (even if, as the chart shows, value has had a tough time recently). Other popular strategies involve weighting portfolios by companies’ sales, or revenues, or dividends.

From these building blocks, investment managers have now built multifactor funds in different proportions, and come up with a dizzying array of new factors. And they have sold a lot of funds on the back of it.

But there is a problem. In theory, and in practice, once a market anomaly has been observed, it cannot continue. There are two reasons why future performance may be worse than the historical backtest suggests, outlined by Pete Hecht, chief market strategist for Evanston Capital Management, in a recent paper. First, the back-test may have been “data-mined”. In other words, the researchers fiddled to find a formula that delivered the very best result for the period they were looking at. This may be due to dishonesty, or may happen unconsciously.

A second problem is arbitrage, and the very existence of smart beta funds feeds this problem. Once you know that cheap stocks outperform, the logical response is to buy cheap stocks. If many do this, cheap stocks’ price will rise until they no longer outperform.

Mr Hecht tested this theory using the formulas used in 1991 in a seminal paper by Gene Fama, the University of Chicago economist who won a Nobel Prize for his work on markets. This identified the value effect using three different measures of valuation.

Chart: Smart Beta

Mr Hecht took Mr Fama’s formulas for determining which stocks were cheap, and saw how the strategy would have performed starting in 1992 and carrying on to the present. In all cases, whether measured by straight performance or adjusted for risk, they did much worse after the paper’s publication than they had before it. The reduction in performance ranged from 30 to 71 per cent. The value effect had diminished.

That leads to another problem, identified by Rob Arnott in a paper for Research Affiliates, a pioneer of smart beta. A strong backtest at any point in time, he reasons, may be because the factor tested has become expensive.

Very perversely therefore, a strong backtest almost becomes a reason not to buy into a strategy. And if a strategy looks good now simply because it is expensive, that may be an active reason to fear that it will now perform badly. Conversely, it might imply that factors that have done poorly of late — and as the chart shows, value has badly lagged behind the market ever since the financial crisis — are now cheap and worth buying, for those with the intestinal fortitude to do so. Meanwhile it is worth checking whether low-volatility and high-momentum stocks, both still performing well, look over-expensive and due to revert to the mean.

A final issue: risk. Piling into one particular factor is inherently more risky. For Andrew Lo of Massachusetts Institute of Technology, one of the world’s most respected financial theorists, the problem with “smart beta” is that it can easily morph into “dumb sigma” — the Greek letter used for volatility.

Each factor involves taking a different risk — returns are rewards for taking risks. Investors should not take them without trying to balance those risks — which implies that these products are not “better mousetraps” that can be bought and left to fend for themselves, but rather that it will be necessary to watch them closely and manage them.

None of this means that smart beta is a bad idea. If you spot an anomaly, you should exploit it while it is there, as cheaply as possible — and that is what smart beta can do. But smart beta is still not quite as smart as it appears.

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