With the ‘one and a half years’ we immediately touch upon a relevant notion. A popular way of analyzing crisis returns is by looking at peak-to-bottom moves in the S&P500. According to this definition, the Covid-19 crisis would have already ended on 23 March 2020, on the back of massive financial support from governments and central banks. However, we all know that many more people fell victim to the virus after that date. And as we write this, here in the Netherlands infection rates are rising to a level that brings us close to ‘code red’ again, while various other regions in the world are facing alarmingly high hospitalization levels and/or lockdowns again.

This ongoing crisis also continued to be the dominant factor impacting markets after 23 March 2020. Directly, as markets responded to increasing or decreasing numbers of patients, vaccine developments and lockdowns — the broad risk-off move a few days ago is a recent example — but also indirectly, as the monetary policies of most central banks cannot be viewed in isolation from the impact of the pandemic on our economies. We are effectively still in a crisis that consists of different episodes. Some investment styles were able to cope better with the conditions in the first episode, other styles fared better in later episodes. This also holds for CTA programs. Measured over the whole crisis period until now, DTP posted a decent positive return. The question is: does this qualify as ‘crisis alpha’?

In our three-piece series Why to avoid alpha, and how, we presented our alternative view on alpha and beta in investing:

According to this model, (positive) ‘crisis alpha’ does not exist. All potential investment return is attributable to beta offered by the market, but dragged down by (negative) alpha caused by the manager’s inefficiency in capturing this beta. Applied to CTA trend programs, in part 3 of the series we stated:

Let us now apply this to the Covid-19 crisis and the sources of DTP’s return during this period.

Crisis trend beta

‘Crisis alpha’ generally refers to returns during crisis periods accompanied by declining stock markets. Since trend strategies will typically be long stocks the moment stocks start to sell off from their peak, why would they perform well in such an environment? Or, viewed from our alternative alpha/beta perspective: how can ‘crisis beta’ be embedded in such a strategy?

This to a certain extent depends on the specific type of trend strategy. DTP is primarily built on the diversified portfolio principle, which is based on the idea that market-impacting developments typically start as local developments with only local market impact before they broaden into ‘a crisis’. For instance, the Russian crisis started with a long-running downtrend in the oil price dragging down Russian markets before it triggered a sharp sell-off in developed market equities in the summer of 1998. And the Credit Crisis had been driving up treasury bond prices since the summer of 2007 before it really hit global stock markets. DTP performed strongly in both of these crises, not so much by being short stocks, but above all by being sizably positioned in or near the sources of these crises before they escalated: short oil markets in 1998 and long treasuries in 2007.

As we wrote in Why to avoid alpha, and how — part 3:

The spreading of the coronavirus in February 2020 was a clear example of this. It did not spread from somewhere within the economy — it was an outside event. This explains the initial negative impact of the Covid-19 pandemic on DTP’s performance. The losses on among others long positions in equity markets were not compensated for by profits on positions in, or near the source of the crisis. DTP did not have long positions in coronaviruses. Which leads to the conclusion that there was no Covid-19 crisis beta embedded in DTP. At least not at the start of the crisis when it first hit global stock markets.

The situation changed when Covid-19 became a dominant factor driving markets. The way society at large responded to the pandemic initiated various new trends while amplifying a number of dormant longer term trends. Central banks abandoned their careful exit from quantitative easing and made a U-turn back into supporting their economies, now severely hit by lockdowns. In Europe, this was partly justified by reframing the difficult process of energy transition into a green recovery initiative. From November onward, vaccination optimism drove a dominant reflation trend. Earlier in the crisis, people locked up in their homes fueled a staying-at-home trend in technology, online shopping and home entertainment stocks. And keeping people locked up in their homes for an extended period of time also seemed to have fueled powerful flows into crypto currencies and meme stocks.

Covid-19 did not only drive trends; it also (temporarily) changed market dynamics. Profiting from these trends can surely be qualified as core CTA trend beta. Whether it also qualifies as ‘crisis beta’ is debatable. However, if it constitutes core trend beta, then what explains the significant dispersion in returns between different CTA trend programs after Covid-19 began dominating markets? A dispersion that is also observed within other systematic trading styles. It does not seem to be very different from the dispersion we have witnessed during previous crises. And just like then, we believe the current dispersion can be attributed to the various managers’ success in controlling negative crisis alpha. Dealing with changed market dynamics was probably the biggest challenge in this respect.

Negative crisis alpha

In general, there are different sources of negative crisis alpha. To start with, crisis environments typically offer more operational challenges to execute a trading strategy than normal market environments do. The Covid-19 pandemic for instance forced managers to quickly switch to work-from-home mode, which could have caused managers to (temporarily) cease part of their operation if they were not (immediately) able to do so. As we have not heard of any CTAs being affected by this, we do not believe that such operational challenges — while still challenging — are the primary factor explaining negative crisis alpha during this pandemic.

And yet, we cannot completely erase it from the equation. Especially in an operation like ours, with success being dependent on colleagues working together, exchanging observations and views, and together finding solutions to problems, it would be naive to claim that we have not incurred any negative crisis alpha due to our team being spread out. This suboptimal way of operating has likely had at least some impact on the speed of keeping DTP in-sync with the changes in market dynamics.

Which brings us to what we consider to have been the largest challenge for our program during this pandemic, and therefore also the largest source of potential negative alpha. The markets exhibited an abundance of trends, many of them Covid-19 related. Systematically picking up these trends with technical trend indicators in itself was relatively straightforward. However, ensuring that the program ultimately participated sufficiently in the different trends proved much more difficult.

Again from Why to avoid alpha, and how — part 3:

In this particular crisis the strength of the storm was not a new experience for our crew. But navigating this particular storm surely was, since we were sailing in uncharted territory (again). Which among others means: a territory that never shows up in any backtest of a strategy. Our crew had to sail on their fundamental understanding of the techniques used, relying on the ones that could reasonably be useful in this environment while adapting or replacing others.

Most adjustments had one thing in common: they were made to prevent DTP from overestimating market volatility and correlations. Otherwise, the program’s positions in potentially promising trends would have been too small. And although having smaller positions may sound pretty harmless, it can have large consequences. The market dynamics around the pandemic presented a common pitfall for (systematic) trading strategies, which we have also described in our article Speed skating — volatility versus drawdowns: ineffectively responding to market volatility, essentially avoiding market risk and paying a premium for that. In the past, we ourselves have experienced the likely but somewhat paradoxical consequence of this pitfall: the daily volatility of the program comes down at the expense of longer-running and deeper drawdowns.

It is no coincidence that we introduced the term negative alpha in the speed skating article. The series Why to avoid alpha, and how built on this initial concept. From a philosophical point of view it would be interesting to chart the volatility versus drawdown figures of highly correlated trading programs, not just for CTA trend but also for other trading styles. Periods with deep, long-running drawdowns may well indicate negative crisis alpha.

The term negative alpha and the deep, long-running drawdowns combined with low volatility may give the impression that we are dealing with a small return spill almost every day. For an active investment strategy, this would typically be the case when the risk premium paid is mostly a liquidity premium, which indeed is a likely source of negative crisis alpha, also during this Covid-19 crisis. However, fundamentally different is the prolonged underperformance resulting from an inability to swiftly adapt. Basing trading strategies predominantly on backtesting, as well as the fear for using discretion, in our view are notorious contributors to this type of alpha.

In our speed skating article we wrote about Tomas Gustafson, a former Olympic champion on the ice rink. His technique was superb. However, when we went ice skating together on a Swedish lake, he used a different technique as well as different skates. A different environment requires even an Olympic champion to adapt. That also holds for any investment strategy. The market environment before and after the Covid-19 outbreak are just as different as the Olympic oval in Calgary and a Swedish lake.