Financial researchers have uncovered many relationships between investment factors and security returns. For investors, an important question is whether these relationships will continue after the research on them has been published. Said another way, should we expect a premium to continue outside of the sample period if everyone knows about it?
As my co-author Andrew Berkin and I explain in our new book, “Your Complete Guide to Factor-Based Investing,” unless such a relationship is persistent across long periods of time and economic regimes, pervasive around the globe, robust to various definitions, investable (survives transaction costs) and has logical, risk-based and/or behavioral-based explanations, we should be skeptical about its persistence going forward.
This is especially true for technical trading rules that rely solely on historical prices and, thus, don’t have risk-based explanations (which cannot be arbitraged away).
If anomalies are the result of behavioral errors—or even investor preferences—and the publication of research about them draws the attention of sophisticated investors, it is possible that post-publication arbitrage would cause premiums to disappear. Investors seeking to capture an identified premium could quickly move prices in a way that reduces the return spread between assets with high and low exposure to the factor.
However, limits to arbitrage (such as aversion to shorting and its high cost) can prevent arbitrageurs from correcting pricing mistakes. And the research shows that this tends to be the case when mispricings exist in less-liquid stocks where trading costs are high.
Publish & Anomalies Perish?
Two recent studies—the 2015 paper “When Anomalies Are Publicized Broadly, Do Institutions Trade Accordingly?” by Paul Calluzzo, Fabio Moneta and Selim Topaloglu; and the paper “Does Academic Research Destroy Stock Return Predictability” by R. David McLean and Jeffrey Pontiff, published in the January 2016 issue of the Journal of Finance—provide us with insights into the question of return predictability.
The authors of both studies found that, post-publication, institutional investors and hedge funds—and to a lesser degree, active mutual funds—trade on the published information, exploiting the pricing mistakes of retail investors.
Thus, institutional trading and anomaly publication are integral to the arbitrage process, which helps bring prices to a more efficient level. Both studies also found that, on average, anomalies shrink by about one-third. Where anomalies occur in more liquid, easily traded stocks, anomalies can even disappear.
Bollinger Bands & Popularity
Jiali Fang, Ben Jacobsen and Yafeng Qin contribute to the literature addressing the effect of publication on anomaly returns through their study “Popularity versus Profitability: Evidence from Bollinger Bands,” which appeared in the Summer 2017 issue of The Journal of Portfolio Management.
Among numerous technical indicators, methods that involve Bollinger Bands are some of the most widely used. In 1983, John Bollinger introduced Bollinger Bands on the Financial News Network (which eventually became CNBC), where he was chief market analyst. Bollinger Bands generally include three parameters, with the following default settings:
- A middle band = 20-day moving averages of underlying prices
- An upper band = the middle band plus two standard deviations of the underlying prices
- A lower band = the middle band minus two standard deviations of the underlying prices
According to Fang, Jacobsen and Qin, who cite a 2001 book that he authored, Bollinger used 20 days to “capture reasonable intermediate-term price fluctuations and, in statistical terms, the ±2 standard deviations should contain about 95% of the price variations. This means that the price falling outside the bands signals a potential market change.”
They continue: “The basic application of Bollinger Bands, namely, the volatility breakout method, generates a buy (sell) signal when the underlying price closes outside the upper (lower) band.”