Trend-following and improving portfolio construction - Know your correlation matrix

 

Trend-following is more than just finding trends. Portfolio construction matters. Research has shown that positioning based on volatility will help with portfolio construction (see "Trend-following with and without volatility scaling - Two different worlds"); however, accounting for correlation will provide even more value especially if markets move to correlation extremes like what we have seen in the last decade. Accounting for correlation adapts allocations processes to incorporate the marginal contribution to risk and allows for full implementation of risk parity principles. 

Many investors discuss risk parity without understanding the dynamics or benefits when these techniques are used. I am not stating that all risk should be equalized nor am I saying that investors should not include their trend views. Using all of the information in the covariance matrix will allow investors to incorporate changes in volatility but also the changes in the relationship across markets.  In a good paper, "Trend-following. risk parity, and the influence of correlations", Nick Baltas  explains the nuances of risk parity when applied with trend-following futures markets and shows how correlation extremes harmed those who just focused solely on volatility positioning. 

The paper focuses on seemingly minor but critical change in portfolio structuring and position sizing. A normal approach to position sizing is to use volatility parity which positions solely on relative risk. Positions can be set to equal risk through using an inverse to market volatility; however, this improvement does not account for correlation between markets. Volatility parity is suboptimal especially as markets become more correlated. Including correlation centers the problem on the contribution to risk. This slicing of contributions to total risk is the essence of risk parity and will more efficiently allocate exposures through using covariance terms. 

We will dispense with the difficulties with measuring correlation which is a much more complex topic. Baltas looks at the impact of employing volatility parity, naive risk parity, versus full risk parity which is equal risk contribution. This research finds that the difference between volatility and risk parity seems small when correlations between markets are low and do not move to extremes as seen in a crisis or if there is a single market driver, but there is a world of difference in the post-GFC period when correlations have increased and moved to higher levels. 

In the post-GFC period there is a significant difference between the choice of position sizing. Notice the significant differences in Sharpe ratio between correlation regimes. The Sharpe ratio can increase by a factor of 3 when in an extreme correlation environment. Not accounting for correlation will place a drag on performance and change the Sharpe ratio even if the same signals are employed. By this time, most firms have tried to account for risk contributions; however, it is important for investors to appreciate the value of these portfolio construction improvements.

Working on better signal extraction is always critical but improvement in portfolio craftsmanship is almost as important. A good set of signals can be masked when there is poor portfolio construction. A good model can be turned into a better model through accounting for the correlation and cross-market linkages. 

Hat tip to Darren @reformedtTrader for noting this paper.

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. 

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.  

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.