Equity factor premia through time - Variable but persistent

 

Equity factor premiums have underperformed relative to the expectations of many investors, but the longer history suggests that holding a portfolio constructed around an equity risk factor would have outperformed the market risk premium. If you are an active trader, you care about recent performance. If you are a longer-term allocator, you care about playing the odds from large samples. 

This, of course, does not mean that holding equity risk premia are risk-less either in the short or long-run. It does mean that care should be taken in the selection across time and the business cycle. There is time risk - some factors will underperform for an entire decade. There is also business cycle risk. Equity factor premia will change with economic growth. 

We can draw some general long-term conclusions across equity factor premia. 

In 6 of 9 decades, all equity factor premiums beat the market portfolio.

Only once was market risk in the top two spots in a decade and never in the top spot.

Momentum has done better than the market risk premium in every decade.

In only one decade was momentum not in the top two spots.

Low volatility has generally been a lower return premium except for the recent period. 

The differential between the best and worse equity factor premium was never more than 10%.

Regardless of current factor premium dislocations, it is worth focusing on differences in core risk premia.  

 

Business cycles drive global asset return differences

All business cycles are not aligned. There is a global business cycle and there are times the regional business cycles are synchronous, but that is not the norm. The disconnect across business cycles us the core opportunity for global macro investing. 

In a world where business cycles are not perfectly correlated, there will be disconnect with global equity indices and bonds. International diversification is just business cycle diversification. Local business cycles are not destiny, but asset classes and risk premia will be linked with the behavior of local and global economic growth. Of course, globalization has increased linages across markets so speeds of adjustments are faster, but flows are compensated for relative risks. 

A major shock like COVID will cause business cycle co-movement increase, but now we are seeing dislocations and growth differences. China was an early leader. The US provided extra stimulus. Other parts of the world playing catch-up. All of these differences provide ways to change and improve allocation decisions.

 

Portfolio Management Intelligence - Analytics, Creativity, and Practical Action

Being a good portfolio manager is not about having a high IQ. Of course, you need a good base intelligence, but genius levels are not necessary. The intelligence needed for this job is different from what is measured with standard tests or found in the MBA classroom. It is also not about education from prestigious schools which have a strong signaling effect although appropriate education will help with providing foundational tools. 

Good portfolio management skills are about experience and practical knowledge applied repeatedly in order generate positive reinforced learning. There are three skills or knowledge sets needed for portfolio management: analytical, creative, and practical.

 

Analytical skill - The ability to find or measure relationships and linkages across markets and time. This could be through advanced data science but more likely through the assessment of large amounts of data that require signal extraction from noise.

Creative skill - The ability to find, manipulate, and assess new information and relationships between markets. Creativity is not about gathering information but assessing the meaning of information. 

Practical skill - The ability to convert information and learned relationships into action; make decisions or take risks. Some are very good at gathering and assessing information but cannot convert to action. A good trader will beat a great analyst who cannot take action. 

Some may say that these are the skills of any engaged businessperson or even a good artist. A businessman assesses, creates, and acts. An artist does the same. These skills are also necessary for the systematic manager who has to engage in repeated tasks. Data science skills are needed, but the true value-added for the quant manager is finding the right problem to attack with the right tools. The problem set-up is more important than the  sophistication of the tools. The features and data used in a model are more important than the techniques employed. 

Follow the thinking, not the intellect. Trust the practical narrative, not the technique. Action over elegance. Focus on the analytical decisions on not sophisticated elegance.  

Momentum crashes should be expected - Herds and information

One interesting feature of a momentum strategy across all asset classes is that it is subject to crashes or reversals. This should not be surprising given that momentum can be viewed as the manifestation of herding behavior by investors. The strong herding can be defined as crowded trades where flows are pushing prices to extremes. Herding when delinked from fundamentals is the basis for price bubbles; the more successful a momentum trend, the more likely there will be a reversal crash. However, the measurement of this reversal risk is difficult. Ideas for measuring oversold and overbought have been tested for decades with mixed results. Take away the crowd or provide a change information that adjusts herd expectations quickly, and the asset flowing into momentum moves in reverse.  

Some have argued that the potential for crashes is the reason for momentum compensation. There is a premium for taking on crash risk. It can also represent skew in the return distribution, or from the perspective of the trend-follower, a reversal of profits from the market high. 

Time series momentum investors should be ready for crashes. The simplest contingency for downside protection with time series momentum is a stop-loss. While the use of stop-losses may be suboptimal, it is the most direct method for stopping losses from a momentum crash. The stop-loss will limit downside regardless of momentum reversal size.  Similarly, cross-sectional momentum needs to be ready for shorts that rise and long that fall contrary to expectations; however, cross-sectional stop-losses deconstruct the momentum trade. 

Investors want to extract any momentum premium but also have a contingency for when momentum decides to reverse. Hence, there is significant value with measuring traded crowded, herd behavior, and information flow changes.