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Dual ETF Momentum Update

Scott’s Investments provides a free “Dual ETF Momentum” spreadsheet, which was originally created in February 2013. The strategy was inspired by a paper written by Gary Antonacci and available on Optimal Momentum . Antonacci’s book, Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk , also details Dual Momentum as a total portfolio strategy. My Dual ETF Momentum spreadsheet is available here , and the objective is to track four pairs of ETFs and provide an “Invested” signal for the ETF in each pair with the highest relative momentum. Invested signals also require positive absolute momentum, hence the term “Dual Momentum”. Relative momentum is gauged by the 12-month total returns of each ETF. The 12-month total returns of each ETF is also compared to a short-term Treasury ETF (a “cash” filter) in the form of the iShares Barclays 1-3 Treasury Bond ETF (NYSEARCA: SHY ). In order to have an “Invested” signal, the ETF with the highest relative strength must also have 12-month total returns greater than the 12-month total returns of SHY. This is the absolute momentum filter which is detailed in depth by Antonacci, and has historically helped increase risk-adjusted returns. An “average” return signal for each ETF is also available on the spreadsheet. The concept is the same as the 12-month relative momentum. However, the “average” return signal uses the average of the past 3-, 6-, and 12-(“3/6/12”) month total returns for each ETF. The “invested” signal is based on the ETF with the highest relative momentum for the past 3, 6 and 12 months. The ETF with the highest average relative strength must also have average 3/6/12 total returns greater than the 3/6/12 total returns of the cash ETF. Portfolio123 was used to test a similar strategy using the same portfolios and combined momentum score (“3/6/12”). The test results were posted in the 2013 Year in Review and the January 2015 Update . Below are the four portfolios along with current signals. “Risk-Off” is the current theme among all four portfolios: Return Data Provided by Finviz Click to enlarge As an added bonus, the spreadsheet also has four additional sheets using a dual momentum strategy with broker-specific commission-free ETFs for TD Ameritrade, Charles Schwab, Fidelity, and Vanguard. It is important to note that each broker may have additional trade restrictions, and the terms of their commission-free ETFs could change in the future. Disclosure: None.

Most Factor Anomalies Are Not Persistent

Smart-beta indices are constructed to exploit “anomalies” that reward exposure to risk factors beyond what would be expected as “necessary compensation” under the Capital Asset Pricing Model (“CAPM”). Of course, any factor that results in nominal outperformance must be considered on a risk-adjusted basis, since taking on higher risk should engender a greater reward – and investment researchers at S&P Dow Jones Indices think at least some factor “anomalies” aren’t anomalies at all, but just rewards for greater-than-understood risk-taking. Even still, among the remaining anomalies, the researchers think many are “disappearing,” “statistical,” or “attenuated” – and only a few are truly “persistent.” Writing on behalf of S&P Dow Jones, academic Hamish Preston and S&P Dow Jones Index Investment Strategy professionals Tim Edwards and Craig Lazzara express these views in an October 2015 research paper titled ” The Persistence of Smart Beta .” Disappearing Anomalies Disappearing anomalies don’t last. A great example shared by the paper’s authors is the so-called “Weekend Effect” that was popularized by Frank Cross in 1973. Mr. Cross discovered that if investors had bought stocks at their closing prices each Monday and sold them at their closing prices each Friday – avoiding the weekend and the Monday trading session – they would have dramatically outperformed a “buy and hold” strategy from 1950 to the time of his research. But then, almost immediately after the Weekend Effect became well known, the anomaly didn’t just disappear, it reversed. The Weekend Effect rebounded in 1984, only after another academic research paper called it into question – and then, when a paper called “The Reverse Weekend Effect” was published in 2000, the old Weekend Effect returned. As soon as investors gained knowledge of the Weekend Effect, it reversed. When knowledge of the reversal became widespread, the reversal reversed. Now, it’s taken as a given that the Weekend Effect was a coincidence – hence, it was a disappearing anomaly. Statistical Anomalies Perhaps a better approach is for investors to keep knowledge of anomalies they discover secret – that way, they may be less likely to disappear. This is what David Dolos did when he discovered that applying the price movements of the 1720 South Sea Bubble – second only to Tulip Mania in episodes of old-school irrational exuberance – to the Dow Jones Industrial Average inexplicably produced outsized returns. Mr. Dolos never told anyone about his discovery, and he reaped the rewards in anonymity until 2007, when the system broke down. Why? Well first off, David Dolos didn’t exist. The story is made up, and although the 1720 South Sea Bubble was real, the South Sea Bubble effect was data-mined into existence. As the paper’s authors note, modern computing power can easily produce “false positives” – i.e., anomalies that are purely statistical in nature. In order for an anomaly to be persistent, it must make logical sense. Attenuated Anomalies Momentum is one of the most popular factors. Academic research supports its outperformance, and the concept of momentum stocks – stocks that are going up – outperforming non-momentum stocks makes logical sense. The momentum anomaly is known to anyone who cares to know about it, and yet this knowledge hasn’t caused the anomaly to disappear – instead, it has reinforced it. The downside is that since investors have become aware of the momentum anomaly, its drawdowns have been bigger. This is what the S&P Dow Jones authors mean by an “attenuated anomaly.” In 1997, Mark Carhart published a study that showed adding momentum to the famous Fama-French three-factor model boosted returns. This caused more money to flow into momentum stocks, ultimately leading to bigger drawdowns during crashes. Persistent Anomalies Are there any truly persistent anomalies? The authors say there is at least one: Low volatility. But they conclude with a word of caution: “So far, the investment and attention directed toward low-volatility strategies has not been sufficient to temper their returns or attenuate their risk/return profile.” So far. As the well-known disclaimer goes: ” Past performance does not necessarily predict future results. ” For more information, download a pdf copy of the white paper. Jason Seagraves contributed to this article.

Is It Time For Smart-Beta ETFs To Enter The Bond Markets?

By Detlef Glow The new year has started, but the financial markets are still affected by topics from the old year. One of the topics that has come up again is the liquidity of bonds in general-and bond funds in particular. From my point of view nearly all that can be said has been said about this topic. After all this discussion about liquidity in the bond markets and the possible implications for bond funds, especially exchange-traded funds (ETFs), one might raise the question of whether these issues could be addressed with smart-beta products. These products concentrate on the liquidity of securities in addition to using the two main drivers of performance-duration and credit risk. Since the liquidity of the underlying securities is already an issue for ETFs that track the broad indices, even “plain-vanilla” products are nowadays not far from being smart-beta products. That is because of the optimization techniques used to replicate the returns of the underlying index using the tradable securities in the index basket. In this regard a smart-beta strategy that employs the liquidity of the bonds would help to build liquid indices for all kinds of bond sectors, which could then easily be replicated by funds. In addition, a smart-beta approach could help investors overcome the major struggle of market-weighted bond indices: these indices give the highest weightings to issuers (companies, countries, etc.) with the highest outstanding debt in the respective investment universe. This approach can lead to high single-issuer risk within the portfolio, which is normally not the intention of an investor who buys into a broad market index. A smart-beta approach could limit the issuer risk by introducing a cap within the index methodology. From my point of view smart-beta ETFs could be the answer to the questions and concerns raised by investors around bond indices. Since investors tend to buy only products they understand, the index construction must be quite smart. At the same time it must be as simple as possible, so investors can easily understand the investment objective and the risk/return profile of the index and therefore of the ETF. That said, in my opinion it is time for smart beta to enter the bond markets. The views expressed are the views of the author, not necessarily those of Thomson Reuters.