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Summary­

  • Recent research suggests that equity factors exhibit positive autocorrelation, providing fertile ground for the application of trend-following strategies.
  • In this research note, we ask whether the same techniques can be applied to the active returns of long-only style portfolios.
  • We construct trend-following strategies on the active returns of popular MSCI style indices, including Value, Size, Momentum, Minimum Volatility, and Quality.
  • A naïve, equal-weight portfolio of style trend-following strategies generates an information ratio of 0.57.
  • The interpretation of this result is largely dependent upon an investor’s pre-conceived views of style investing, as the diversified trend-following approach generally under-performs a naïve, equal-weight portfolio of factors except during periods of significant and prolonged factor dislocation.

There have been a number of papers published in the last several years suggesting that positive autocorrelation in factor returns may be exploitable through time-series momentum / trend following.  For example,

  • Ehsani and Linnainmaa (2017; revised 2019) document that “most factors exhibit positive autocorrelation with the average factor earning a monthly return of 2 basis points following a year of losses but 52 basis points following a positive year.”
  • Renz (2018) demonstrates that “risk premiums are significantly larger (lower) following recent uptrends (downtrends) in the underlying risk factor.”
  • Gupta and Kelly (2018; revised 2019) find that, “in general, individual factors can be reliably timed based on their own recent performance.”
  • Babu, Levin, Ooi, Pedersen, and Stamelos (2019) find “strong evidence of time-series momentum” across the 16 long/short equity factors they study.

While this research focuses mostly only long/short equity factors, it suggests that there may be opportunity for long-only style investors to improve their realized results as well.  After all, long-only “smart beta” products can be thought of as simply a market-cap benchmark plus a dollar-neutral long/short portfolio of active bets.

Therefore, calculating the returns due to the active bets taken by the style is a rather trivial exercise: we can simply take the monthly returns of the long-only style index and subtract the returns of the long-only market-capitalization-weighted benchmark.  The difference in returns will necessarily be due to the active bets.1

Below we plot the cumulative active returns for five popular equity styles: Value (MSCI USA Enhanced Value), Size (MSCI USA SMID), Momentum (MSCI USA Momentum), Minimum Volatility (MSCI USA Minimum Volatility), and Quality (MSCI USA Quality).

The active returns of these indices certainly rhyme with, but do not perfectly replicate, their corresponding long/short factor implementations.  For example, while Momentum certainly exhibits strong, negative active returns from 6/2008 to 12/2009, the drawdown is nowhere near as severe as the “crash” that occurred in the pure long/short factor.

This is due to two facts:

  1. The implied short side of the active bets is constrained by how far it can take certain holdings to zero. Therefore, long-only implementations tend to over-allocate towards top-quintile exposures rather than provide a balanced long/short allocation to top- and bottom-quintile exposures.
  2. While the active bets form a long/short portfolio, the notional size of that portfolio is often substantially lower than the academic factor definitions (which, with the exception of betting-against-beta, more mostly assumed to have a notional exposure of 100% per leg). The active bets, on the other hand, have a notional size corresponding to the portfolio’s active share, which frequently hovers between 30-70% for most long-only style portfolios.
  3. The implementation details of the long-only style portfolios and the long/short factor definitions may not perfectly match one another. As we have demonstrated a number of times in past research commentaries, these specification details can often swamp style returns in the short run, leading to meaningful cross-sectional dispersion in same-style performance.

Source: MSCI.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

Source: MSCI; AQR.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

 

Nevertheless, “rhymes but does not replicate” may be sufficient for long-only investors to still benefit from trend-following techniques.

In our test, we will go long the style / short the benchmark (i.e. long active returns) when prior N-month returns are positive and short the style / long the benchmark (i.e. short active returns) when prior N-month returns are negative. Portfolios are formed monthly at the end of each month.  Performance results are reported in the table below for 1, 3, 6, 9, and 12-month lookback periods.

 

Annualized ReturnAnnualized VolatilityInformation RatioMaximum DrawdownSample Size (Months)
1Value1.7%6.1%0.28-15.1%261
Size-0.8%8.2%-0.10-44.4%303
Momentum-0.2%7.5%-0.03-21.3%302
Minimum Volatility-0.1%5.7%-0.01-25.0%375
Quality1.3%3.8%0.35-8.9%302
3Value3.3%6.0%0.55-15.5%261
Size1.1%8.2%0.13-34.5%303
Momentum-0.8%7.5%-0.11-38.0%302
Minimum Volatility0.7%5.7%0.13-19.4%375
Quality0.9%3.8%0.24-10.1%302
6Value2.9%6.0%0.48-21.0%261
Size1.7%8.2%0.20-20.8%303
Momentum0.7%7.5%0.09-28.8%302
Minimum Volatility0.5%5.7%0.09-27.8%375
Quality0.6%3.9%0.16-14.6%302
9Value3.4%6.0%0.57-14.8%261
Size2.0%8.2%0.24-27.1%303
Momentum1.2%7.5%0.16-23.4%302
Minimum Volatility0.9%5.7%0.15-20.8%375
Quality0.3%3.9%0.07-14.7%302
12Value3.2%6.0%0.54-11.2%261
Size1.8%8.2%0.22-29.9%303
Momentum1.9%7.5%0.25-20.0%302
Minimum Volatility1.4%5.7%0.24-17.3%375
Quality1.3%3.8%0.34-11.0%302

Source: MSCI.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

Below we plot the equity curves of the 12-month time-series momentum strategy. We also plot a portfolio that takes a naïve equal-weight position across all five trend-following strategies.  The naïve blend has an annualized return of 2.3%, an annualized volatility of 4.0%, and an information ratio of 0.57.

Source: MSCI.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

This analysis at least appears to provide a glimmer of hope for this idea.  Of course, the analysis comes with several caveats:

  1. We assume that investors can simultaneously generate signals and trade at month end, which may not be feasible for most.
  2. We are analyzing index data, which may be different than the realized results of index-tracking ETFs.
  3. We do not factor in trading costs such as impact, slippage, or commissions.

It is also important to point out that the per-style results vary dramatically.  For example, trend-following on the size style has been in a material drawdown since 2006.  Therefore, attempting to apply time-series momentum onto of a single style to manage style risk may only invite further strategy risk; this approach may be best applied with an ensemble of factors (and, likely, trend signals).

What this commentary has conveniently ignored, however, is that the appropriate benchmark for this approach is not zero.  Rather, a more appropriate benchmark would be the long-only active returns of the styles themselves, as our default starting point is simply holding the styles long-only.

The results, when adjusted for our default of buy-and-hold, is much less convincing.

Source: MSCI.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

What is clear is that the strategy can now only out-perform when the style is under­-performing the benchmark.  When the portfolio invests in the style, our relative return versus the style is flat.

When a diversified trend-following portfolio is compared against a diversified long-only factor portfolio, we see the general hallmarks of a trend-following approach: value-add during periods of sustained drawdowns with decay thereafter.   Trend-following on styles, then, may be more appropriate as a hedge against prolonged style under-performance; but we should expect a cost to that hedge.

Source: MSCI.  Calculations by Newfound Research.  Results are hypothetical.  Results assume the reinvestment of all distributions. Results are gross of all fees, including, but not limited to, manager fees, transaction costs, and taxes. Past performance is not an indicator of future results.  You cannot invest in an index.

For some styles, like Minimum Volatility, this appears to have helped relative performance drawdowns in periods like the dot-com bubble without too much subsequent give-up.  Size, on the other hand, also benefited during the dot-com era, but subsequently suffered from significant trend-following whipsaw.

Conclusion

Recent research has suggested that equity style premia exhibit positive autocorrelation that can be exploited by trend followers.  In this piece, we sought to explore whether this empirical evidence could be exploited by long-only investors by isolating the active returns of long-only style indices.

We found that a naïve 12-month time-series momentum strategy proved moderately effective at generating a timing strategy for switching between factor and benchmark exposure.  Per-style results were fairly dramatic, and trend-following added substantial style risk of its own.  However, diversification proved effective and an equal-weight portfolio of style trend-following strategies offered an information ratio of 0.57.

However, if we are already style proponents, a more relevant benchmark may be a long-only style portfolio.  When our trend-following returns are taken in excess of this benchmark, results deflate dramatically, as the trend-following strategy can now only exploit periods when the style under-performs a market-capitalization-weighted index.  Thus, for investors who already implement long-only styles in their portfolio, a trend-following overlay may serve to hedge periods of prolonged style drawdowns but will likely come with whipsaw cost which may drag down realized factor results.

 


 

  1. For indices, at least.  For actively managed portfolios, implementation effects and costs will also come into play.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients. Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn. Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University. You can connect with Corey on LinkedIn or Twitter. Or schedule a time to connect.