AnotherAspect: What Was Trendy in 2021?

Examining the liquid alternatives landscape in a year of transition

In a similar fashion to previous years, we would like to invite you to join us as we provide some colour on the investment styles that caught our attention within the liquid alternatives space in 2021.

To do this, we used our growing collection of computer-driven models harnessing globally investable effects across multiple asset classes (sectors) and timeframes. They trade commonly found liquid assets as well as alternative assets such as Chinese industrial futures. They also use a range of techniques such as econometric and machine learning approaches.

With persistent inflation becoming the talk of the town, investors are increasingly seeking to reduce reliance on traditional long-only investments in favour of alternative investment themes such as the ones listed below. Each theme houses a set of models with common premises which can help us explain what was trendy in 2021, alongside other groupings such as model type, speed, and sector focus.

Around 160 multi-asset derivatives models (almost all of which trade real client money as part of Aspect’s funds) were scaled to target 9% annualised volatility and their simulated 2021 returns (gross of transaction costs and fees) were analysed. With respect to model timeframes (“Speed”), “FAST”, “MEDIUM” and “SLOW” denote holding periods of <1 month, 1-6 months and >6 months, respectively. It is also important to remember that transaction costs and capacity become more of an issue as trading speeds increase, albeit our “fast” models sit in a speed range where such considerations do not invalidate the takeaways from this analysis. Overall, we still remain diversified across speeds.

Regarding model type (“Type”), there are three classifications which include directional, relative (or cross-sectional) and models which consist of a blend of both. Pertaining to asset classes or sectors (“Sectors”), where possible, we delineate sectors to the lowest level of granularity found within the model. For example, the commodities sector models may trade any combination of agriculturals, energies or metals whilst the metals sector models would strictly only trade metals.

From the 2021 summary graphic above, the synopsis is that slower to medium-term, directional, momentum-based, commodity-heavy models with a focus on more esoteric or Chinese markets tended to outperform in the year. Models in fixed income had the weakest average profitability and experienced multiple large gyrations, especially in themes such as Slope/Curve and Carry.

Slower speeds generally outdid faster ones with the final quarter being an eventful exception. In Q4, models trading risky assets with faster speeds and cross-sectional positioning pared earlier losses, including models in the Volatility theme which closed out profitable short VIX trades at the start of November (when the fear gauge hit its 2021 low) just in time for increased risk aversion surrounding the news of the Omicron variant.

Value’s prominence was notable, particularly in Q2, where it was led by long positioning in assets which received little love in 2020. These included various commodities, the US dollar and stock market segments which were all buffeted by the reflation trade earlier in the year. The following section will focus on the year’s trendiest area, momentum on industrials and commodities. Following that, we can delve into the year’s other major story, weakness in fixed income.

Casting the net as wide as possible when trading momentum seemed to be the best course of action in 2021 and the chart above shows that commodity and industrial focused momentum models on less mainstream markets significantly outperformed. Eye-catching alternative markets included European electricity contracts and Chinese glass futures. In the first half of the year, models applying directional signals to these sorts of commodities and industrial futures mostly pointed long, as the narrative shifted from an epic boom period to a potential commodity supercycle revival. Momentum in international commodity markets and Chinese onshore industrials and metals markets picked up on upwards price action linked to China's exceptionally strong Q1 recovery, the boost from the commodity-intensive green energy transition and global vaccine optimism.

The second half of the year began with bulky returns from the industrials sector models, which tend to have relatively longer holding periods, employ entirely directional momentum-based positioning and trade mostly Chinese onshore markets. Many of these markets witnessed intense supply shortages linked to power supply issues, transportation disruptions and soaring freight costs as well as ongoing Covid-19 labour disruptions. Surges in global energy prices during the quarter were driven by a number of factors including hurricane Ida, which forced oil production shutdowns off the Gulf of Mexico. Additionally, natural gas, coal and power markets rose on the back of increasing winter demand and fears of global supply shortages. Gains came from trend-following models’ ability to ride the rallies in international and Chinese energy markets. Towards the end of the year, China Momentum and the Industrials sector struggled due to rangebound movement and a weak growth outlook amid a Chinese property downturn.

Yield curve steepening in February and subsequent flattening in June and October proved to be events which explained variation within the challenging fixed income sector. Carry, Sentiment and Slope/Curve themes in fixed income struggled amid indecision from central banks. Whilst commodities and stock markets appreciated, bond (and oftentimes currency) markets were not always sure where to take cues from as hot inflation data steadily poured in. Chinese onshore markets remained strongly diversifying to the rest of the world and stood out for much of the year. This was particularly true in December, when China was one of few economies to cut interest rates whilst most of the world enacted or projected rate hikes to combat inflation. On average within fixed income, all speeds were unprofitable and only directional model types were profitable.

Machine learning bond models, which are largely cross-sectional in nature, struggled due to a breakdown in bond market correlation structures, despite being profitable in all other sectors. The Economic Fundamentals theme performed well in fixed income with its short positioning ahead of the flurry of hawkish policy decisions in December including the surprise rate hike from the Bank of England.

2021 was a positive year on average for the managed futures industry, represented here by the SG CTA Index, whose constituent managers are known to employ liquid alternative strategies. The box plot above shows the range of monthly returns, and the white dots show the average monthly return for the 20 constituents of the SG CTA Index last year. Whilst it is difficult to say what other managers in the index were doing, we can highlight February, September and November as months that could have created material differences to the year’s overall picture. February saw historic bond yield jumps and was the industry’s best month on average, but with a very diverse set of returns. The degree of exposure to short duration strategies may have had something to do with this. September, last year’s worst month for US equities, had the widest interquartile range and perhaps the extent of stock market beta explains this dispersion. The outbreak of Omicron in November was such a risk-off event that managers who had long commodity exposure probably took a hit.

2021 felt like a year of transition. Inflation transitioned from ‘transitory’ to ‘persistent’. Global central banks transitioned from dovish to hawkish. China, the world’s largest exporter of goods, transitioned from the globe’s foremost growth engine to an economy experiencing property, energy, and shipping crises. Against this backdrop, the trendiest corners of liquid alternatives consisted of directional medium and longer-term momentum on alternative markets which were primarily found within commodity and industrial sectors.

We noted that economic growth tended to surprise on the downside whilst inflation surprised on the upside and bond yields were often perceived to be more agitated when compared to stock market volatility. An environment like this makes the case for diversification across many dimensions within the liquid alternatives space especially compelling.

Please contact us for further insight on the analysis shown above.