Wednesday, January 27, 2021

Factor returns and the economic cycle - Performance will flip on changes in macro expectations



Factor returns move with the economic cycle. These factor relationships with the economic cycle are well-documented, so if the economic cycle changes or expectations about economic growth change, there will be changes in the relative performance of factors. This can help explain some of the factor switching that was seen in the spring, summer, and fall of 2020. Great upheavals in the business cycle will not only lead to changes in the market risk premium but significant shocks to factors returns even though they are often expected to be less volatile that market returns. 

Recent research show shows these relationships are more complex yet potentially valuable for global macro timers. Factors are forward-looking, that is, a factor may anticipate changes in the economic cycle. They embed expectations about the cycle; however, these relationships are often unstable. (See "Do Factors Carry Information About the Economic Cycle" by Marlies van Boven at FTSE Russell)

When economic growth is divided into three regimes, there are strong differences in conditional factor returns. Clearly, market returns are significantly higher in high growth periods over low growth regimes. On the other hand, quality and momentum factors do very well during low growth periods.  


These investment factors also tell us something about future economic growth. Size returns have a positive prediction on growth while quality has the opposite sign. On the other hand, value and momentum have less predictive power concerning future GDP. Nonetheless, value has additional predictive information beyond market returns. These market signals have actually strengthened since the GFC as noted in the second conditional panel. 





This evidence can be further sliced with inflation to provide a four regime world of expansion, slowdown, contraction, and recovery. Investors want to avoid value and hold momentum factors during an expansion, and avoid size in a slowdown and contraction. In a recovery period, holding market risk and momentum will be rewarded. Still, these rules are not hard and fast especially when looking at the post GFC period. 


Markets, which include the performance of factors, are forward-looking and provide expectation signals. They can anticipate economic expansion and slowdown, so the global macro trader who wants to engage in factor rotation has a tough job. The macro manager has to form economic cycle expectations and determine whether those view have already been discounted in factor returns. The gains can be significant, but these relationships are volatile and require strong opinions no different than those needed to forecast the overall direction of the market. 


Thursday, January 21, 2021

Commodity super-cycle fever is here but shorter-term considerations should drive investment decisions




There were many reasons for strong performance across commodity markets in 2020, albeit equities showed better returns versus commodity indices. There are also good reasons for continued commodity support in 2021, but super-cycle fever may be premature. The chart below is just one example of how some analysts have posed the super-cycle argument.
 

Recoveries in commodity prices offer investment opportunities but these upward price trend may not be the same as a super-cycle. Commodity super-cycles are actually longer than the normal business cycle and are caused by a combination of events:
  • Weak investment in production especially for extraction commodities which cannot be easily reversed in the short-run;
  • Excesses in demand caused by long-term secular trends in growth that usually involve demographic shift such as the ascent of China as an economic engine or the overall growth in emerging markets;
  • Shocks that disrupt shorter-term global supply chains but also have impact across more than one growing season; 
  • Loose credit conditions for an extended period as signified by a declining dollar and the desire by investors to hold real assets as inflation protection. 
Commodity markets, at this time, look more like the strong price reversal after the GFC. The commodity index reversal after the crisis never reached the pre-GFC highs. In the GFC case, commodity markets fell significantly in 2008 only to reverse with strong gains in 2009 on lower rates and a rebound in growth. However, the market then fell into the long down cycle that may have only shown a recent bottom. 


The four conditions for a super-cycle are pointed higher, but business cycle considerations are still paramount. Credit conditions are easy and inflation expectations are growing albeit not showing excesses relative to old targets. Investment in extraction firms has fallen but production constraints have not tightened. The China - EM story is still strong however growth will be lower although on a larger base. The demand wild card is continued unprecedented fiscal expansion in developed markets. Finally, supply shocks were present which makes for some demand/supply deficits for selected commodities   

A super-cycle is good opportunity for the long-only passive commodity investor with a long investment horizon but mixed for the trend-follower who will often make trading decisions based on a few weeks of data. The trend-follower can ride a super-cycle wave but more importantly should focus on the business cycle supply/demand shock opportunities.  

Wednesday, January 20, 2021

Dispersion in CTA returns and small managers again lead with strong performance in 2020


Averages don't tell a good or complete story for CTA's in 2020. The average return for a set of managers reporting to SocGen Prime Services in their Nelson Report show positive performance but not what would expected given the size of market dislocations. We looked at large managers above $400 million in AUM and who also reported December numbers. This includes the generally favorable trading for the end to the year. The average return was 2.69 percent, but there were some strong winners with returns above 10% for the year. Dispersion was with a range of over 30%. 

When you slice the entire dataset based on performance for all CTA and quantitative macro managers, the numbers become more interesting. The average is 4.27 percent return which is almost 60% higher than the selected large managers. If we look at the top decile of performance, the 2020 average return is 30.96% or over 10 times higher than the returns for the large managers. Only 8 of 23 (35%) managers in the top decile are above $100 million in AUM and only 4 of 23 (17.5%) are in our large group category with AUM above $400 million. This top decile has a standard deviation that is about 40% higher than the large group. There is more risk-taking, but returns are significantly higher.

The small managers proved to be nimble in 2020 and again suggests that investors should not be focused on just the largest managers for return generation. Nevertheless, the investor has to be careful about taking on the added business risk from a small firm which may not have the same set of controls and infrastructure versus larger managers. Of course, the real question is whether these high performing managers will show persistence or just be one-hit wonders. 

Tuesday, January 19, 2021

Resilience engineering - Concepts that can be applicable to asset management

 


Some important concepts in other fields often provide useful insights for asset management. For example, there is a clear trade-off between optimality and brittleness associated with engineering. The best optimized solutions may not accommodate large shocks, frequent change, and uncertainty. A model may break after the first time market conditions change from the period used for testing and training. An over-optimized model may be fragile. For quants, this is the problem of overfitting. A system that is overfit to the past will provide a great result on past data but will deviate from expected performance or break once there is new information or there is a change in market sentiment not seen in the past. 

Investors need models or processes that is resilient. Taleb would call this anti-fragile, but his idea or term has not gained acceptance as a structural requirement from the viewpoint of the model builder. 

Erik Hollnagel, a Scandinavian expert in the fields of resilience engineering, system safety, and cognitive systems engineering, developed the key features or cornerstone concepts behind resilience which have relevance for all investment and hedge fund management. A resilient system or model should have the following four skills: 

  • the ability to react when new information is encountered;
  • the ability to effectively monitor the environment to identify the current regime and any shift to a new environment; 
  • the ability to anticipate change in regimes or in responses to market events;
  • the ability to learn from mistakes and learn from the environment faced as new information is acquired.
A resilient model or system for making decisions will have some common properties:

  • learning to live with uncertainty - uncertainty is not feared but addressed in a consistent manner;
  • maintaining internal diversity - there is not just one solution or approach but a combination of approaches that can be reweighed based on changes in the environment;
  • combining different types of knowledge - single paths or threads to problem solving will create brittle systems so alternative data and points of view are incorporated for flexibility;
  • create opportunities for self-organization - some form of information organization that weighs factors that impacts predictions.

Ask two simple questions. Do your models show resilience skill? Do your models have resilient properties?

The principles of resilience can be incorporated with how modeling is approached. New data can be tested. Models can be reviewed and trained against different environments. Models can be developed to identify regimes to support switching of information weights. Processes can be developed to prepare for failure as opposed to reaction to model failure after the fact. Perhaps holding to consistent model specifications is the right answer, but resilience engineering will always be testing this assumption in order to prepare for the worst.