Using Time-Series Momentum to Intentionally Miss the Best Months.
Yes, Really.

The buy-and-hold crowd, including many mutual fund companies and a large cross-section of vocal pundits, like to talk about how missing the N best days/months in the market causes a serious impairment to long-term investor returns.

What they fail to mention is that, because stock market volatility clusters during periods of market crisis, the best daily and monthly stock market returns are directly adjacent in time to the worst monthly returns. In fact, investors would be considerably better off missing both the best and worst days or months in the market. These investors would realize the same gains as buy-and-hold investors, but with much less grief in the form of volatility and drawdowns.  Simple trend-following strategies work for precisely this reason.

Missing both the Best AND Worst Periods Improves Performance

Missing the best months is roughly equivalent in terms of long-term total returns to missing the worst months, just in the opposite direction. For example, investors drop about 2% per year when missing the best 10 months, and add about 2% per year when missing the 10 worst months. In fact, you can see from Figure 1 that investors that miss both the 10 best AND worst months end up with slightly better returns than the market, but with much less volatility and smaller drawdowns.

Figure 1.

Source: ReSolve Asset Management. Data from Robert Shiller.

In fact, per Figure 2 there is a progressive improvement in returns coupled with reductions in both volatility and maximum drawdowns as increasing numbers of best AND worst performance months are excluded. An investor who managed to miss the most extreme 50 monthly returns would have experienced slightly higher compound returns than a buy-and-hold investor since 1900, with 25% less volatility and less than half the maximum drawdown. Note that this investor would have been in the market almost 97% of the time.

Figure 2.

Source: ReSolve Asset Management. Data from Robert Shiller.

Momentum as a Method to Avoid Crisis Markets

If we can improve risk-adjusted performance by avoiding the best and worst months, and these months tend to cluster together during periods of market crisis, it prompts the question: is there a simple way to avoid these extreme periods?  It turns out there is. Simple trend-following/momentum rules are effective for exactly this reason. They work because extreme returns tend to occur when markets are in steep downtrends.

Consider Meb Faber’s simple 10-month moving average strategy (also discussed in Siegel’s “Stocks for the Long Run”). When we apply this strategy to the S&P 500, results are remarkably similar to what we observe when we miss the most extreme months: similar returns, with lower volatility and smaller drawdowns. Note that this example assumes an investor earns nothing when he is cash – returns exceed the returns of buy-and-hold when cash returns are included.

Figure 3.

Source: ReSolve Asset Management. Data from Robert Shiller.

From a risk-adjusted performance perspective, a simple 10-month moving average rule – a simple indicator of positive or negative momentum – produced results that are competitive with what we observe from a strategy that manages to avoid the 50 best and worst months since 1900.

Trend Following (aka Momentum) is Robust across Asset Classes

Were this phenomenon isolated to US stocks, it might be easy to dismiss.  But the behavior of diverse asset classes above and below their respective 10-month moving averages displays remarkably similar characteristics.  Specifically, when the market is experiencing negative moment, or even crisis  (as indicated by being below its 10-month moving average), asset classes yield significantly lower returns, and at higher risk.

Figure 4.

Source: ReSolve Asset Management. Data from Robert Shiller.

Using Momentum to Build a Simple Diversification Model

When we apply the simple moving average rule to the five assets in Figure 3, not only do we benefit from avoiding the most extreme months, but we also accrue substantial diversification benefits.  Note that this simple strategy – first described by Mebane Faber in 2006 – produced returns just slightly below those of the S&P 500, but with just one-third the volatility and maximum drawdown from 1973 through the end of 2016.

Figure 5.

 Source: ReSolve Asset Management. Data from AllocateSmartly and Global Financial Data

Importantly, the GTAA strategy is a simple, rules-based strategy that does not rely on gut instinct, macroeconomic analysis, narratives, political insight, or any other discretionary intuition. It is a rules-based approach that relies on one of the most widely recognized, persistent, pervasive, and economically significant risk premia in markets – time-series momentum. It may not be the best way to harness this effect, but it is remarkably effective for such a simple strategy.

The point is, when absorbing content on markets and investing it pays to be aware of the economic incentives behind the material. Clearly, the mutual fund industry and large issuers of passive index products are highly motivated to keep you invested through thick and thin. On the other hand (full disclosure), ReSolve offers active asset allocation solutions – similar to GTAA – based on time-series momentum and other proven strategies – with the goal of delivering strong, stable returns in good times and bad. So yes, we have a dog in this fight.

To learn about our dog, click here.

NOTE: The Sharpe ratios in this article are simple ratios of compound return to risk, and do not include a risk-free rate.


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These materials do not purport to be exhaustive and although the particulars contained herein were obtained from sources ReSolve believes are reliable, ReSolve does not guarantee their accuracy or completeness. The contents hereof does not constitute an offer to sell or a solicitation of interest to purchase any securities or investment advisory services in any jurisdiction in which such offer or solicitation is not authorized.

Forward-Looking Information. The contents hereof may contain “forward-looking information” within the meaning of the Securities Act (Ontario) and equivalent legislation in other provinces and territories. Because such forward-looking information involves risks and uncertainties, actual performance results may differ materially from any expectations, projections or predictions made or implicated in such forward-looking information. Prospective investors are therefore cautioned not to place undue reliance on such forward-looking statements. In addition, in considering any prior performance information contained herein, prospective investors should bear in mind that past results are not necessarily indicative of future results, and there can be no assurance that results comparable to those discussed herein will be achieved. The contents hereof speaks as of the date hereof and neither ReSolve nor any affiliate or representative thereof assumes any obligation to provide subsequent revisions or updates to any historical or forward-looking information contained herein to reflect the occurrence of events and/or changes in circumstances after the date hereof.

General information regarding returns. Performance data prior to August, 2015 reflects the performance of accounts managed by Dundee Securities Ltd., which used the same investment decision makers, processes, objectives and strategies as ReSolve has used since it became registered and commenced operations in August, 2015. Records that document and support this past performance are available upon request. Performance is expressed in CAD, net of applicable management fees. Indicated returns of one year or more are annualized. Past performance is not indicative of future performance.

General information regarding the use of benchmarks. The indices listed have been selected for purposes of comparing performance with widely-known, broad-based benchmarks. Performance may or may not correlate to any of these indices and should not be considered as a proxy for any of these indices. The S&P/TSX Composite Index (Net TR) (“S&P TSX TR”) is the headline index and the principal broad market measure for the Canadian equity markets. The Standard & Poor’s 500 Composite Stock Price Index (“S&P 500”) is a capitalization-weighted index of 500 stocks intended to be a representative sample of leading companies in leading industries within the U.S. economy.

General information regarding hypothetical performance and simulated results. These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account or fund managed by ReSolve will or is likely to achieve profits or losses similar to those being shown. The results do not include other costs of managing a portfolio (such as custodial fees, legal, auditing, administrative or other professional fees). The contents hereof has not been reviewed or audited by an independent accountant or other independent testing firm. More detailed information regarding the manner in which the charts were calculated is available on request. Any actual fund or account that ReSolve manages will invest in different economic conditions, during periods with different volatility and in different securities than those incorporated in the hypothetical performance charts shown. There is no representation that any fund or account will perform as the hypothetical or other performance charts indicate.

General information regarding the simulation process. The systematic model used historical price data from Exchange Traded Funds (“ETFs”) representing the underlying asset classes in which it trades. Where ETF data was not available in earlier years, direct market data was used to create the trading signals. The hypothetical results shown are based on extensive models and calculations that are available for any potential investor to review before making a decision to invest.