Introduction

The turbulence of 2018 made it a difficult year for most systematic investment products. To the surprise of several investors, many of these quant products had been sold as market neutral. In particular, the new breed of alternative risk-premia (ARP) products – that had flooded the market a few years prior to 2018 – performed exceptionally badly. For example, the composite HFR Bank Systematic Risk-premia Multi-Asset Index lost -18%, in comparison with a loss of -4% on the S&P 500 total return index. However, traditional alternative asset classes also underperformed, with the flagship HFRX Global Hedge Fund Index losing -7% and the SG Trend Index losing -8%.

In the face of such losses, both investors and managers are asking how and why so many quant strategies underperformed? Still more importantly, what are the implications for the diversification of traditional equity-bond portfolios and alternative investments? In particular, since trend-following CTAs belong to a handful of tried-and-tested diversifiers, why did trend-followers not diversify in 2018?

To address such questions, we first intend to look at how trend-following programs are expected to perform when crises last for extended periods of at least two months, because trend-followers need to adjust to profit from sustained crises in equity markets. Second, we shall focus on the way in which the risk profile of ARP products, hedge funds, and trend-following CTAs can change in bear and bull market regimes because of their potential exposures to tail-risk. We analyse the risk-premia alpha in these products by taking into account regime-conditional risk.

For this analysis, we are proposing a new quantitative model to explain the risk of investment strategies by accounting for extreme market conditions and for their exposure to tail risk, such as selling volatility and credit protection. We apply this model to the cross-sectional risk attribution of about 200 composite indices of hedge funds and ARP products. We show that there is a strong linear relationship between risk-premia alpha and the tail risk of systematic ARP strategies. We can demonstrate that our model explains nearly 90% of the risk-premia for volatility strategies and about 35% of the risk-premia for hedge fund and ARP products. In this way, most ARP and hedge fund type products can be seen as risk-seeking strategies. Importantly, our model predicts that ARP products offer smaller risk-premia compensation compared to hedge funds.

We are able to illustrate that, interestingly, trend-following CTAs are exceptions since they belong to defensive strategies with negative market betas in bear regimes, yet risk-premia alphas for CTAs are insignificant. CTAs cannot be seen either as ARP products with positive risk-premia alpha from exposures to tail risk, or as defensive products with negative risk-premia designed to reduce tail risk, such as long volatility strategies. Instead, trend-following CTAs should be viewed as an actively managed defensive strategy with the goal to deliver protective negative market betas in strongly downside markets along with risk-seeking positive market betas in strongly upside markets. Overall, after adjusting for the downside and upside betas, the risk-premia alpha of CTAs is insignificant. Yet, because of the negative protective betas in bear markets, trend-followers well deserve their place as diversifiers in alternative portfolios to improve risk-adjusted performance and capture risk-premia alpha on a portfolio level, as we will show in the last section.

Finally, since our risk-attribution model assumes conditional equity betas in specific market regimes, we are able to illustrate the misunderstanding behind strategies claiming to be “zero-correlated” and “market-neutral”. Given a specific market regime, most typically in the bear regime, many risk-premia strategies tend to produce a strong exposure to equity markets because of their hidden tail exposures. For example, a strategy selling delta-hedged put options would have a small market beta during normal regime; yet the strategy would exhibit a significant market beta during crisis periods because of its negative gamma and vega exposures. When we analyse systematic strategies unconditional to market regimes, the performance may appear to be smooth and uncorrelated because of the aggregation across different regimes.

We will conclude the introductory section and our article by answering the above questions in the following way. Firstly, ARP strategies are expected to perform well during normal regimes. However, since the excess performance of these strategies is derived from a hidden tail risk, these strategies are expected to underperform during turbulent markets, as in 2018. To earn risk-adjusted alpha from these products, investors need to look at long time horizons that include both bull and bear markets. Second, while the performance of trend-following CTAs is not derived from risk-premia alpha as compensation for hidden tail risks, the performance of trend-followers is conditional on trends lasting for sustained periods. Since trends reversed rapidly multiple times during 2018, trend-followers underperformed. As a result, in what proved to be an extraordinary year, both ARP products and trend-followers underperformed, but for different reasons.

Going forward, investors and allocators need to understand how different strategies are expected to perform during bear and normal markets and how to diversify their portfolios accordingly. Our results provide a valuable aid in quantifying the hidden tail behaviour of systematic strategies as well as suggesting an approach for the risk attribution and diversification of alternative portfolios.

How trend-following works

Trend-following is an active strategy that seeks to leverage both sustained downside and upside trends in the markets. Nevertheless, a common question from the investment community is why trend-following programs can be too slow to benefit from quick and steep reversals in broad markets. How, for instance, can we explain the poor performance of the SG Trend index in 2018?

This section focuses on illustrating how trend-followers rely both on the duration of trends and on how they can deliver protection during extended equity market crises. For our illustrations, we turn to the actual performance of the SG Trend Index, which serves as a benchmark in the trend-following industry, and the S&P 500 Total Return Index as the proxy for the equity index.

Typical trend-following CTAs trade in a variety of liquid futures markets covering major global stock indices, fixed-income instruments, short rates contracts, FX and commodities. Trend-followers can take both long and short positions with leverage controlled through targeting of portfolio risk or volatility. The objective of trend-following CTAs is to identify medium to long-term trends in a systematic way. The implementation of a trend-following strategy on an instrument level includes two key elements: signal generation and sizing of exposure. Finally, implementation at a portfolio level involves sophisticated risk-based allocation and efficient trade execution.

Simplified trend-following strategy for illustration

To create a hypothetical performance of a trend-follower in action, we applied a simplified trend-following strategy to the S&P 500 index during the first three months of the global financial crisis, from September 2008 to November 2008. This simplified strategy became representative of a trend-following approach in the real world.

We began with the assumption that we had launched our strategy on the last day of August 2008 with zero exposure to the equity index. Then, at the end of each trading day, we computed the cumulative return of the equity index and the trailing minimum of the cumulative return. The equity exposure of our simplified trend-following strategy at the end of each day was set to the trailing minimum observed the day before. The rationale behind sizing our exposure was straightforward. We had established that the equity index was on the downside trend (signal part), but we were not certain about the potential extent of the correction (exposure part). As a result, we increased our short position gradually and set the exposure to the running minimum of the cumulative return from the launch. Since the running minimum changes slower than the cumulative return, we avoided over-trading in case the equity index experienced a bear market rally.

The bottom of Figure 1 illustrates the cumulative return of the equity index and the equity exposure of our simplified trend-following strategy. In the top part, we show the P&L of the strategy and its trend-line which grows in a quadratic way. In Figure 2, we plot trailing weekly returns of the trend-following strategy against trailing weekly returns of the equity index during each of the three months.