Because of the adaptive nature of position sizing, trendfollowing strategies can generate the positive skewness of their returns, when infrequent large gains compensate overall for frequent small losses. Further, trendfollowers can produce the positive convexity of their returns with respect to stock market indices, when large gains are realized during either very bearish or very bullish markets. The positive convexity along with the overall positive performance make trendfollowing strategies viable diversifiers and alpha generators for both longonly portfolios and alternatives investments.
I provide a practical analysis of how the skewness and convexity profiles of trendfollowers depend on the trend smoothing parameter differentiating between slowpaced and fastpaced trendfollowers. I show how the returns measurement frequency affects the realized convexity of the trendfollowers. Finally, I discuss an interesting connection between trendfollowing and stock momentum strategies and illustrate the benefits of allocation to trendfollowers within alternatives portfolio.
Interested readers can download the pdf of my paper Trend following strategies for tailrisk hedging and alpha generation or access the paper through SSRN web
Key takeaway
1. Riskprofile of quant strategies
The skewness and the convexity of strategy returns with respect to the benchmark are the key metrics to assess the riskprofile of quant strategies. Strategies with the significant positive skewness and convexity are expected to generate large gains during market stress periods and, as a result, convex strategies can serve as robust diversifiers. Using benchmark Eurekahedge indices on major hedge fund strategies, I show the following.

 While long volatility hedge funds produce the positive skewness, they do not produce the positive convexity.
 Tail risk hedge funds can generate significant skewness and convexity, however at the expense of strongly negative overall performance.
 Trendfollowing CTAs can produce significant positive convexity similar to the tail risk funds and yet trendfollowers can produce positive overall performance delivering alpha over long horizons.
 On the other spectrum, short volatility funds exibit significant negative convexity in tail events.
2. The riskprofile of TrendFollowing CTAs as function of return measurement frequency
Trendfollowing strategies adapt to changing market condition with the speed of changes proportional to the trend smoothing parameter for the signal generation. As result, when we measure the realized performance of a trendfollowing strategy, the return measurement frequency should be low relative to the expected rebalancing period of the trendfollowing strategy. Using the data of SG Trendfollowing CTAs index, I show that trendfollowers are expected to produce both the positive skewness and convexity for monthly, quarterly and annual returns. As a result, trendfollowing strategies should not be seen as diversifiers for shortterm risks measured on the scales less than one month. Overall, I recommend applying quarterly returns for the evaluation of the riskprofile of a trendfollowing strategy.
3. Trendfollowing CTAs as hedge against the tail risk
By analyzing quarterly returns on the SG trendfollowing CTAs index conditional on the quantiles of quarterly returns on the S&P 500 index, I show that trendfollowing CTAs can serve as diversifiers of the tail risk. On one hand, the trendfollowers generate significant positive average returns with positive skewness conditional on negative returns on the S&P 500 index. On the other hand, the trendfollowers generate large positive returns, but with insignificant skewness conditional on large positive returns on the S&P 500 index. Conditional on index returns in the middle of the distribution during either rangebound or slow updrifting markets, the trendfollowers generate negative returns yet with significant positive skewness.
4. Autocorrelation as explanatory factor for trendfollowers returns
The nature of trendfollowers is to benefit from markets where prices and returns are autocorrelated, which implies the persistence of trends over longer time horizons. I present the evidence that the recent underperformance of trendfollowers since 2011 to 2018 could be explained because the lag1 autocorrelation of monthly and quarterly returns on the S&P 500 index become significantly negative in this sample period. The negative autocorrelation indicates the presence of the meanreverting regime, even though the overall drift is positive, in which trendfollowers are not expected to outperform. I introduce an alternative measure of the autocorrelation that can be applied to test for the presence of autocorrelation in short sample periods. I show that my autocorrelation measure has a strong explanatory power for returns on SG trendfollowing CTAs index.
5. Construction of Trendfollowing strategy for the S&P 500 index
To quantify the relationship between the trend smoothing parameter, which defines fastpaced and slowpaced trendfollowers, and the risk profile of fastpaced and slowpaced trendfollowers, I create a quantitative model for a trendfollowing system parametrized by a parameter of the trend smoothing and by the frequency of portfolio rebalancing. The backtested performance from my model has a significant correlation with both BTOP50 and SG trendfollowing CTAs indices from 2000s using the halflife of 4 months for the trend smoothing.
6. Risk profile of S&P 500 Trendfollowing strategy
Using the trend system parametrized by the halflife of the trend smoothing, I analyze at which frequency of returns measurement the trendfollowing strategy can generate the positive convexity. The key finding is that the trendfollowing system can generate the positive convexity when the return measurement period exceeds the halflife of the trend smoothing and the period of portfolio rebalancing. I recommend the following.
 If a trendfollowing strategy is sought as a tail risk hedge with a shorttime horizon of about a quarter, allocators should seek for trendfollowers with relatively fast smoothing of signals with the average halflife less than a quarter.
 If a trendfollowing strategy is sought as an alpha strategy with a longertime horizon, allocators should seek for trendfollowers with medium to low smoothing of signals with the average halflife between a quarter and a year.
An alternative way to interpret the speed of the trend smoothing is to analyze the trendfollowing strategy beta to the underlying asset. For the slowmoving smoothing, the strategy maintains the long exposure to the uptrending asset with infrequent rebalancing. As a result, the higher is the halflife of the trend smoothing, the higher is the beta exposure to the index. Thus, while fastpaced trendfollowers can provide better protection during sharp shortlived reversals, they suffer in periods of choppy markets. There is an interesting article on Bloomberg that some of fastpaced trendfollowing CTAs fared much better than slowerpaced CTAs during the reversal in February 2018.
7. Trendfollowing vs Stock Momentum
I examine the dependence between returns on the trendfollowing CTAs and on the marketneutral stock momentum. I show that the trendfollowers have a stronger exposure to the autocorrelation factor and a smaller exposure of higherorder eigen portfolios. As a result, the trendfollowing CTAs produce the positive convexity while stock momentum strategies generate the negative convexity of their returns.
8. Benefits of Trendfollowing CTAs for Allocations in Alternatives
The allocation to trendfollowing CTAs within a portfolio of alternatives can significantly improve the riskprofile of the portfolio. In the example using HFR Riskparity funds and SG trendfollowing CTAs index, the 50/50 portfolio equally allocated to Riskparity funds and trendfollowing CTAs produces the drawdown twice smaller than the portfolio fully allocated to Riskparity funds. The 50% reduction in the tail risk is possible because the occurrence of the drawdowns of Riskparity HFs and Trendfollowing CTAs are independent. While trendfollowers tend to have lower Sharpe ratios than Riskparity funds, trendfollowers serve as robust diversifiers with 50/50 portfolio producing the same Sharpe ratio but with twice smaller drawdown risk.
References
 Artur Sepp (2018) “TrendFollowing Strategies for TailRisk Hedging and Alpha Generation”, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3167787
 Artur Sepp (2018) “Machine Learning for Volatility Trading”, https://youtu.be/CwiSvzyEyMY
 Artur Sepp (2017) “Diversifying Cyclicality Risk of Quantitative Investment Strategies”, https://artursepp.com/2017/12/01/diversifyingcyclicalityriskofquantitativeinvestmentstrategiespresentationslidesandwebinarqa/
 Artur Sepp (2017) “Allocation to systematic volatility strategies using VIX futures, S&P 500 index puts, and deltahedged longshort strategies”, https://artursepp.com/2017/09/20/allocationtosystematicvolatilitystrategiesusingvixfuturessp500indexputsanddeltahedgedlongshortstrategies/
 Artur Sepp (2016) “Volatility Modelling and Trading” https://ssrn.com/abstract=2810768
 TungLam Dao, TrungTu Nguyen, Cyril Deremble, Yves Lemperiere, JeanPhilippe Bouchaud and Marc Potters (2017) “Tail protection for long investors: trend convexity at work”, Journal of Investment Strategies 7(1), pages 6184, https://arxiv.org/pdf/1607.02410.pdf