Academic studies have shown that trends exist in markets over different time horizons, with some persisting for a few days or weeks, and others running for several months (Moskowitz et al., 2011). By ‘speed’ we mean trend-length sensitivity; ‘fast’ and ‘slow’ trend systems focus on capturing the short- and long- end of this spectrum respectively. (View Highlight)
While there are a variety of algorithms that can be used to identify trends, in this note we will investigate performance characteristics of a suite of double exponentially weighted moving-average crossover (‘MAC’) models, as per Table 1. (View Highlight)
These, or variations thereof, have been in use at Man AHL for around three decades and still represent the model with the greatest risk allocation in Man AHL’s trend-following strategies. (View Highlight)
Risk allocations are split equally across asset classes: equity (25%), fixed income (25%), FX (25%) and commodities (25%). Individual markets are volatility scaled such that each has equal risk weight within an asset class. (View Highlight)
As would be expected, turnover decreases with slower speeds which, as we will see, has implications in the real world via transaction costs. Reassuringly, Sharpe ratios are all significantly positive. Skewness is positive for almost all speeds, but is clearly more so for fast strategies. (View Highlight)
Perhaps the most interesting aspect of Table 2 is the apparent trade-off between Sharpe ratio and skewness; risk-adjusted returns increase with slower speed, but risk-management properties, via skewness here, deteriorate. (View Highlight)
Slower trend models also have modestly higher correlation to traditional asset classes, which might be expected since we expect traditional assets to increase in price over the long term because of their embedded risk premia, which may be captured by the slowest measures of trend. (View Highlight)
A systematic mindset says that this diversification should be captured by trading all the speeds, easily afforded by automation, thereby increasing risk-adjusted returns and, with the judicious use of leverage, returns themselves. (View Highlight)
But what weights should we allocate to each model speed? As we have found, faster trading potentially delivers greater risk-management properties, but at some cost to Sharpe ratio. (View Highlight)
At Man AHL, we find a persuasive argument for having proportionate weights to fast trend models through the analysis of ‘Crisis Alpha’. (View Highlight)
We have shown that there is no perfect trend speed; slow speeds can indeed deliver good long-term performance but lack the attractive risk-mitigating properties of faster speeds. However, to our knowledge, very few investors own solely trend-following strategies. Instead, they tend to be used as part of a portfolio. If the aim of the trend-following allocation is to boost the defensive properties of a portfolio, then perhaps a more responsive system, allocating more to fast trend models, may suit best in our view. (View Highlight)