Large, medium to long-term, trend-following managers dominate the CTA industry, and this leading position is increasingly resulting in institutional investors using the two terms interchangeably. Despite this dominance in assets under management, trend following represents just a small part of the industry by the number of firms. This snapshot will take a closer look at the CTA landscape, strategies, and performance characteristics of the 75% of the industry that is not represented by trend-following managers.
CTAs and trend following
The association of CTAs with trend following is not unreasonable; the Newedge CTA Index, which represents the largest 20 CTAs by assets, has a 0.97 correlation to the Newedge Trend Index (which represents the largest 10 trend followers) since inception. Trend following now accounts for almost 80% of the assets in the Newedge CTA Index as shown in Fig.1, which details the breakdown of the index by year into trend and non-trend strategies.
Trend-following programmes (shown in purple) have become the dominant strategy, increasing from just under 50% of assets in the early 2000s to almost 80% at the end of 2014.
When we consider the breakdown by the number of programmes, however, we get a very different picture. Fig.2 reveals that non-trend programmes (shown in blue) have fairly consistently accounted for half of the index by number since inception. Given that non-trend programmes represent so many CTAs eligible for the Newedge CTA Index, this snapshot will take a closer look at the wider CTA landscape.
The term Commodity Trading Advisor (CTA) is a US regulatory designation covering investment programmes that trade futures, currencies or swaps. This is a fairly broad description, and Table 1 details the qualitative framework we use to classify all of the different managed-futures strategies. Each level of this framework is treated independently of one another, and each individual CTA can be classified using any possible combinations of these elements. Managers can differ in data that they use, what asset classes and time frame they look at, and whether they make discretionary investment decisions or have built everything into a system. This is all identified prior to looking at the specific methodology for generating positions or trades.
The entire managed-futures space is made up of all the possible permutations of this framework, which can therefore also include global macro. This means discretionary macro could easily be categorised as a CTA within this framework. However, we typically classify this strategy separately from managed futures and therefore we exclude it. We do, however, include other discretionary-based strategies.
An example of how this framework might be used would be to take trend following, which is typically characterised as utilising technical, price-based data, in a systematic fashion, over medium to long-term time horizons, trading a diversified portfolio of assets, to capture directional market trends. However, trend following represents just one possible way of combining these elements.
Current CTA landscape
Our CTA database includes more than 800 unique CTA programmes, representing $358 billion in assets. We have removed multiple share classes (e.g., currency, leverage) to avoid double counting and duplication. This total assets figure includes Bridgewater, which, it is important to note, represents a significant proportion of assets. Although Bridgewater is potentially eligible, investors are split whether or not to include the programmes in a CTA dataset, and because of the distortion to the data we have chosen to exclude Bridgewater from this analysis.
When we apply the classification framework detailed above to our managed-futures database, we find the following six strategy groups to be the most common, and we will use these groupings in the analysis that follows:
Fig.3 highlights the breakdown of the CTA database into these six strategy groups: assets on the left-hand side and number of programmes on the right-hand side. As we saw in Fig.1, the majority of assets are employed using trend-following strategies, representing 56% of CTA assets across our wider dataset. The two other strategy groups of note by assets are quantitative macro and short-term, which when combined with trend following account form more than 80% of CTA assets. As we saw in the Newedge CTA Index, there is a meaningful difference between the breakdown by assets versus by number of programmes, with the same three strategy groups representing less than half of all CTA programmes.
The average CTA programme size in our dataset is approximately $260 million, but there are significant differences between strategy groups. Comparing the strategy breakdown by assets and number we can infer that strategies with an asset breakdown larger than their relative number of programmes are larger than average, in particular trend-following and quantitative macro programmes. Conversely short-term, commodity, currency, and technical programmes tend to be smaller than average.
Fig.4 shows how the assets in each of these strategy groupings are distributed between managers of different sizes. To do this we have separated programmes into six asset groups and then stacked each group relative to the total strategy assets. As expected, large trend-following and quantitative macro programmes with assets greater than $1 billion represent more than 80% of respective strategy assets. More than 65% of short-term and currency strategy assets are managed by programmes greater than $1 billion, increasing to 80% with the addition of established managers in the $500 million to $1 billion group.
Conversely, commodity and technical strategy assets are more evenly distributed, with smaller and emerging managers up to $500 million representing approximately 50–60% of strategy assets.
CTA strategy performance
Table 2 compares the average performance statistics of the six CTA strategy groups from January 2000 to March 2015. The average annualized programme returns have been positive for all the CTA strategy groups, ranging from 6.46% for quantitative macro to 13.26% for the diversified technical group. We have also included the average annualized volatility, which is concentrated in the 10–15% range.
2011 to 2013 saw one of the most protracted drawdowns for CTAs, particularly for trend followers, but this period also affected the returns of the other strategies. Certain market environments are more conducive to different strategies, and within the return data there are periods of significant divergence between the different strategies.
Most recently trend following endured a difficult period, with negative average programme returns in both 2011 and 2012, before recovering and outperforming all strategies in 2014. It is often commented that other CTA strategies are complementary to trend following, and to highlight the differences between the various strategies, Fig.5 compares the average compound CTA strategy returns when trend following underperforms.
We identify three distinct periods when trend-following 12-month returns are below -5%: March 2004 to March 2005, February 2009 to February 2010, and May 2011 to April 2014. Interestingly, during these periods average returns for all other CTA strategies are significantly different and consistently positive. The outperformance by CTA strategies is variable, and we should point out that commodity-strategy returns are inflated due to higher volatility up to the end of 2010.
CTA strategy correlations
Rather than just looking at extreme periods of returns, Fig.6 details the 12-month rolling correlation of CTA strategies to the Newedge Trend Index (which we use as a proxy for trend following). Clearly trend is correlated to trend, displaying a high and stable average correlation of approximately 0.95.
The other CTA strategies display more dynamic relationships; in particular, diversified technical and quantitative macro correlations range up to 0.95 but also fall as low as 0.31 and -0.30 respectively.
Other CTA strategy correlations are consistently low, with an average of 0.43, and periods of negative correlation to trend following. During 2014, correlations to trend following were at the lower end of the historical range, a period when trend following outperformed other CTAs.
The variable correlations to trend following suggests that CTA returns are significantly different, and Table 3 details the cross-strategy correlation relationships. CTA strategies are broadly non-correlated to each other, with an average cross-correlation of 0.46. Diversified-technical and trend-following strategies display the highest cross-strategy correlation. The remaining cross-strategy correlations range from 0.66 to 0.11, confirming the CTA strategy distinctions we made in our classification framework.
Taking the correlation study a level deeper, Fig.7 visualises the distributions of the intra-strategy pairwise correlations, which is the relationship between a programme and every other programme within a strategy group. The six CTA strategies groups are plotted side by side, the “fatness” (x-axis) indicates relative frequency, and the height equates to the correlation (y-axis). Trend-following programmes tend to be relatively highly correlated to each other. The intra-strategy correlation plot is top heavy, with a mode of 0.65. Almost 50% of trend programmes have a pairwise correlation of greater than 0.6. There is, however, diversity within trend following, a function of various parameters including allocation of risk, asset-class selection, trading speed, etc. As we have shown in our biannual CTA performance reviews, these parameter differences can add up to meaningful differences in returns.
In comparison, other CTA intra-strategy pairwise correlations are lower, centred round 0.1. The distributions indicate that non-trend strategies are varied, with no one style representing a strategy’s returns. Not only are CTA returns different to trend following and other CTA strategies, but also non-correlated within strategy groups.
We have observed the Newedge CTA Index is increasingly dominated by large, medium to long-term trend followers, resulting in institutional investors using the two terms interchangeably. The association of CTAs and trend following is not unreasonable given the correlation relationship, and trend-following assets currently represent almost 80% of the Newedge CTA Index and more than half of our CTA database. However, diversified medium to long-term trend following only accounts for half of the index by number and only 25% of our CTA database.
The entire managed-futures space is made up of all the possible permutations of our classification framework, in which trend following represents just one possible combination. Six common strategy groupings fall out of our CTA database, which we use for the rest of the analysis. The average returns of all CTA strategy groups have been positive since 2000, at similar levels of volatility. 2011 to 2013 saw one of the most protracted drawdowns for CTAs, and we notice periods of under and outperformance between strategies.
We have shown that CTA returns can be different and non-correlated to trend following, both in extreme periods and on a rolling basis. Additionally, we find that CTA strategies are relatively non-correlated to each other, comparing all strategies in a correlation matrix. Trend-following programmes display the highest level of intra-strategy correlation but still maintain diversity. Other non-trend CTA strategies have very low intra-strategy programme correlations; with no one style representing a strategy’s returns. These correlation relationships, combined with the ability to generate positive returns, mean CTAs may well have an interesting role in institutional investor portfolios.
Article courtesy of Alternative Investments Consulting team, Societe Generale Prime Services
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