Gold extremism, fundamentals, and trends - When in doubt, follow the prices

I would like to believe the fundamental stories about the rise in gold prices. Inflation is coming. The dollar is being debased. Real rates are negative. A further economic decline is around the corner. There is excessive debt issuance. All are good stories. All could be true given a focused look at recent data and a strong desire for confirmation of the trend, yet I want to be careful with my judgment. The long-term relationship between economic fundamentals and gold have been tenuous, so care should be taken with extrapolating the gold rise to coincident news.

For example, the link between inflation expectations and gold is somewhat of a head-scratcher. Inflation expectations have fallen since the second half of 2018 albeit the last three months have seen a significant rise. Still, inflation expectations are below levels from 2018. During the same time, gold has moved from 1200 to above 1900.  

However, as a follower of trends as a base position, I have to look to the number one rule of trend-following - when in doubt follow the trend. It could be smart money knows something I don't know. It could be big money has a strong opinion. It could be that small investors are filled with fear.  Does it matter? Price is the information signal and price is the ultimate clarifier. 

Fighting the Fed's short volatility position - Hold some divergent trades and strategies for tail risk

I think we are actually at a point of encouraging risk-taking, and that should give us pause. Investors really do understand now that we will be there to prevent serious losses. It is not that it is easy for them to make money but that they have every incentive to take more risk, and they are doing so. Meanwhile, we look like we are blowing a fixed-income duration bubble right across the credit spectrum that will result in big losses when rates come up down the road. You can almost say that that is our strategy.

[W]hen it is time for us to sell, or even to stop buying, the response could be quite strong; ... it’s also unloading our short volatility position.

it just seems to me that we seem to be way too confident that exit can be managed smoothly. Markets can be much more dynamic than we appear to think.

Comments from yet to be named Fed Chair, Jerome Powell, during the October 23-24, 2012 FOMC meeting 

These insightful comments from Jay Powell eight years ago capture the core monetary policy and fixed problem we will be facing. It may not be an immediate problem, but markets are forward-looking. Although many moan about market short-sightedness, there already is discussion about the Fed end game, and most do not think it will be pretty. If you are a macro thinker, this can be described as an inflation issue. If you are a trader, it is a micro problem of a massive player being short fixed income volatility. Once the short volatility pressure is relieved, markets will adjust rapidly.

The Fed is the ultimate convergent trader; forcing convergence to their short-rate target and what looks to be a target yield curve. Being short volatility impacts more than just bonds. It reduces risk across all financial assets and markets will respond as predicted. Investors think about total risk, price times quantity. If price risk is capped, then investors will take a greater quantity of risk. This is especially true if the "risk" being discussed is the downside or left tail.

There may be little reason today to trade against the bond convergence to the Fed goals; however, there is every reason to hold some investments to take advantage of divergence or tail opportunities that may arise. Out of the money puts and strategies like long-short trend-following are perfectly suited as a long volatility Fed offset. When the market is dominated by convergence trading by the central banks, it still makes sense to prepare for an alternative divergent world. 

Market rushes, panics and frenzies - Associated with a desire to trade early

I have been partial to the market laboratory work used in economics and finance. These experiments are not perfect given the limitation associated with lab experiments, but it provides a controlled environment for testing specific economic hypotheses. Of course, these controlled experiments are not the same as the real world, but they provide useful information especially for events that are infrequent or have many competing influence factors. Unfortunately, digging deep into the  construction of these lab experiments takes time and effort.

Market efficiency and rationality has been often tested with laboratory work, but now we have some insightful experiments on "market rushes" which can exist as either market declines, panics, or market increases, frenzies or bubbles. The results for these experiments are presented in "Market Panics, Frenzies, and Informational Efficiency: Theory and Experiment" by Chad Kendall American Economic Journal: Microeconomics 2020 12(3). 

The author finds that if there is fear of an adverse price movement up or down it causes traders to trade before they are  better informed. They will rush forward with their trades to beat others to the market. That is, they will sell or buy before others relative to the future information flow and use the signals in price as an alternative to revealed future information. Given information can be noisy or of poor quality, it can make sense to use a heuristic like momentum to take action quicker. If there is good information quality and prices start to move, traders may rush ahead given the assumption that others know what is going on. However, if information quality is low and it makes sense to wait, traders may delay their actions and there is less likely to be a market rush. Unfortunately, information quality is hard to measure, so there is tendency to focus on price action or momentum as a signaling mechanism. There is a cost of waiting for further information just as there is a cost based on the quality of information. 

The result of traders acting rushed will be a reduction information efficiency for the market. What may seem to be good for the individual may not be good for the market as a whole. Momentum trading which can lead to rushes creates negative externalities. Running for the exits is rational for one but negative for the information flow embedded in prices. 

When playing the game repeatedly, there is a tendency for traders to use a momentum heuristic to get ahead of trading competitors who may have useful information. If there is anticipation of new high quality information, traders may want to rush before the information becomes available. When information quality is poor and there should be a reason to wait with trading, there is still a tendency for following prices and act based on some price threshold. 

The author focuses on potential inefficiencies and the downside of following prices, market rushes. I am biased toward thinking of what makes sense from the perspective of a trader. There is a threat of panic and frenzies but in a competitive uncertain world, it may make sense to get ahead of information and use price behavior to direct decisions. If you cannot predict, then doing the next best thing of acting in response to price action is rational. Heuristics like following the trend may be both rational and prudent under uncertainty.   

Overlapping momentum strategies - Gains from diversification of timeframe models

Momentum trading is relatively simple to implement. Look at past performance for a set timeframe, rank the returns from highest to lowest, buy the high decile and sell the low decile. Of course, controlling for other risks is more difficult. Still there is a simple way of improving returns for a momentum portfolio by using more than one timeframe. Call this a simple method of diversification for momentum trading. Testing of this momentum work using two timeframes shows improvement in excess return and  with conditional risk characteristics. This was not the core intent of recent research, but it provides some useful evidence for timeframe diversification. See "Overlapping Momentum Portfolios" by Blanco, De Jesus, and Remesal.)

For an overlapping momentum (OMOM) strategy, the researchers create long (short) portfolios that belong to the top  (bottom) decile of winners using both 6-month and 12-month prior returns. This generates a stronger filter for an equity momentum strategy and reduces the number of stocks by 50%. There are stronger excess returns, alpha, Fama-French 3 and 5 factor alphas over almost holding period periods. The OMOM risk characteristics are less compelling, but the value of OMOM for different sub-periods is also strong.

The rationale for this improvement is based on higher selectivity and differences in the diffusion of information across equities. There is a slow speed of adjustment of prices that likely allows different timeframes to provide useful return contribution. The OMOM does better for stocks that are less closely followed and have slower information diffusion. Additionally, the diversity of timing periods exploits the fact that momentum investors use different timing strategies. Hence, there is different price momentum to be exploited in models. 

The researchers provide theoretical justification for their results and are careful with the construction of their portfolios, but the overall theme is fairly simple. Diversification of momentum timeframes improves a strategy because the investment behavior of momentum traders varies. All traders do not look at momentum the same way. This is no different than the improvement that often occurs with trend followers that use different time frames to improve their signal return performance.

The pandemic - Multiple components define the problem and solutions

The pandemic uncertainty is high. The right set of responses for the health crisis spills over to what will happen to the economic recovery. The health care responses will also feedback onto new fiscal and monetary policies. Each issue has to be looked at both individually and how it may feedback on other policies.

A second pandemic wave will slow or reverse the lockdown across states. Any slowdown of the lockdown reversal will slow any economic recovery. There will be no V-shaped recovery, rather there will be a square root or W-shaped recovery that will be pushed further into the future. To offset the economic slowdown, fiscal and monetary policies have to be employed to mute the pandemic policies.

The Fed reversed the liquidity crisis of March through strong intervention in what were one-way markets. However, all of the liquidity available does not now need to be used. There may actually be an issue with excess liquidity that has led to financial asset inflation. 

Fiscal policy was actively employed in the second quarter, but now there needs to be further stimulus to offset the continued pandemic lockdown effect. Pandemic policies, which curtail growth, have to offset with protective fiscal policy. The funding for fiscal policy will have to be made through further monetary policy bond purchases. 

Stress and response for one part of the crisis leads to policy responses elsewhere. The macro economy is a complex system that is trying to be engineered by a number of policy players with different goals and timing responses. If there is not effective coordination, the system may veer out of control.

Different components of rationality - Deeper thinking beyond behavioral biases

There is significant talk of investor rationality and biases but little work which focuses on the details of what it means to be rational. Most of the behavioral finance revolution has been about what investors do wrong versus the "rational man". The rational man, homo economicus, is an optimizer. Usually nothing more is said about the topic. You either have biases or follow the axioms of choice and optimize. This framework is too simplistic.

Finance people have been weak on the details of what it means to be rational. Thankfully, others, especially in psychology, have done some of the heavy lifting concerning defining rationality. They separate rationality from intelligence. At a high level, rationality can be separated into two types, instrumental and epistemic. Instrumental rationality is associated with our ability to optimize in order to obtain our goals. Individuals, if rational, should act in a manner consistent with goals. Epistemic rationality is our ability to have beliefs that are consistent with the structure of the world. For example, it is not rational to believe the world is flat. This form of rationality is often tested with behavioral biases which can be distortion from reality. For example,  overconfidence can be viewed as a belief that is disconnect with reality.

A taxonomy of rational thinking can be divided into two main parts, fluid and crystallized. See the work on the development of rationality in "Assessing the Development of Rationality" by Toplak, West, and Stanovich. Fluid rationality refers to our ability to process information and situations. This rationality represents our behavior associated with thought and action. Irrationality will be the behavioral biases connected with fluid thought. There is a second component referred to as crystallized rationality. This is associated with our reasoning mind-ware and malware. We may have a framework for rational decisions but not the tools to solve the problem. Tools serve as rational thought facilitators while our belief mechanisms may be inhibitors.

To understand how better investment decision can be made, there needs to be first a framework for the components of rationality. This framework has to move beyond a list of faults and serve as a way to classify our rationality with well-defined situations. 

Go with trend-following over put protection to hedge tail risk

Should I hold a trend-following program or hedge with out-of-the-money put options? It is an old question but a critical one for any investor currently worried about downside risk in today's markets. Over the last ten years, trend-follower have used the term "crisis alpha" as their catch phrase for tail risk protections, yet many investors have been disappointed with the concept. Nevertheless, when compared with using puts, trend-following has shown to be a better way of gaining tail risk protection. 

Many have preached the theoretical benefit of trend-following over puts; however, this potential advantage is an empirical issue. When the numbers are compared over a long period, the value of holding a trend-following portfolio based on return surpasses anything constructed as a put strategy. First, it is a better long-term investment. Second, it still does well during equity drawdowns and the worst equity return months. (See the new paper, "Tail Risk Hedging: Contrasting Put and Trend Strategies" from the researchers at AQR)

There may still be skeptics about the benefit of trend-following over a put strategy so it is valuable to review the reasons for why trend-following will work, and puts will not. It may seem natural that holding puts on an equity index would be the perfect tail hedge, but that view misses the point that any strategy has both benefits and costs. 

There is a strong difference between the base strategy between buying puts and buying a trend program. A put buying program is explicit insurance and solely concerned with return protection. Trend-following is implicit insurance that is still foremost focused with return generation. Buying puts generates a negative risk premium associated with insurance. Trend-following may a negative risk premium given it will do well during down markets, but also has a positive premium associated with risk-taking from exploiting trends. Puts are expected to do well only when there are adverse market conditions when the insurance is needed. 

A put buying strategy is not like normal insurance whereby you pay a premium for downside protection against a rising equity index value. Investors buy a put that has a strike, an expiration date, and is valued based on expected volatility. There is protection at a certain strike that will last until expiration if it is not rolled and is priced based on market volatility. At expiration, the option will have no value at prices above the strike. All the premium will be paid except against an adverse move, and insurance costs will increase with higher volatility, all else equal.

A put strategy has to be managed and the put only offers protection based on market conditions. It may do better if there is a sharp surprise decline but will offer only limited protection for a slow trend lower. Managing a strategy of buying puts with different maturities and strikes out-of-the-money will create different pay-offs but will still produce a negative risk premium. Gains are only achieved under special tail conditions. 

A trend-following strategy is a correlation hedge. The amount of protection will be related to the type of down trend faced. Hence, it is not clear the level of protection received. Past history can tell investors the likelihood of gains from a hedge but there are no guarantees.

There is a major benefit with trend-following that is not available from a put strategy - positive convexity. A trend-following program may do well if there is a market downturn, but it will also do well if there is a long-term trend higher with equity indices, albeit the correlation with equity indices will be related to the amount of risk held in the equity index and other markets. 

Forget language like crisis alpha and just focus on the key feature of convexity from trend-following. By definition, a trend-following program will hold long (short) positions in up (down) equity indices which will support tail risk hedges. The correlation between a trend-following program and equity downside is variable, which is its main tail hedge risk. However, the odds suggest that hedge protection is highly likely from trend-following with the added benefit of potential return upside instead of a guaranteed return drag from put insurance. 

Why are top performing hedge funds successful? Some non-parametric evidence

Top performing hedge funds have skill, but how do they show it? This is not an easy answer when looking at the set of all hedge funds across a wide variety of styles. However, so some researchers have focused on nonparametric analysis through looking at concepts of stochastic dominance. (See Hedge Fund Strategies: A Non-parametric Analysis.) 

Stochastic dominance looks at choice under uncertainty through whether the expected utility received from one hedge will be preferred to another. A non-parametric analysis often focuses on first order (for every expected utility maximizer) or second order (for risk averse utility maximizers) stochastic dominance. Comparison for dominance can be conducted for a large set of hedge fund strategies. Researchers find that top firms have persistence out of sample for months after their value is identified. 

The researchers also find that top performing hedge funds are systematically different from mediocre hedge funds. The researchers find that top performing managers have more market and momentum risk. They also find that top performers accept fewer risk factors which suggest that they may be harder to describe and take more idiosyncratic risks. I am surprised by these results. 

Additionally, top managers seem to anticipate troubling economic conditions and avoid those risk factors tied to negative market conditions. Top managers will avoid illiquid investments and accept market risk when appropriate. They will exploit momentum trading but have the ability to avoid momentum reversal. 

Many of these results are subtle and subject to interpretation, but it seems that the top hedge funds can avoid key risks during troubling times, do a good job of exploiting market risk and the herd through momentum trades, but also know when to get out and not be the last man at the party. More work needs to look at what makes a successful hedge fund. This would should also be more strategy specific.

In the context of today's markets, it seems that top managers will measure extreme sentiment and start to adapt before markets may again turn down. I would not be surprised that the best hedge funds will be reducing exposure to market leaders and starting to reduce risk exposure after a successful second quarter ride. 

Idiosyncratic risk increases internal uncertainty - A requirement for more investor knowledge

As the factor risk for holding an asset class goes down, the idiosyncratic risk increases. If there is less factor risk, there is more unique or unexplained, although diversifiable, risk associated with a security. 

An investor who switches to investments that have lower factor risk is not reducing total risk. There is a switching to risks that cannot be generalized or easily measured. There is a move from risk factors you know and can measure to risks that are not well-known; however, total risk is still important. 

Rebalancing across asset classes will change factor risks. It will change correlations, but it will also increase idiosyncratic risks.    

There is a problem with the switching from factor to idiosyncratic risk. These unique risks, to be managed effectively and not just diversified away, require more security-specific knowledge. There is more internal uncertainty with picking those securities that have more idiosyncratic risk versus core risk factors like equity, credit and rates. 

We have discussed the difference between internal and external uncertainty. Internal uncertainty is associated with our knowledge, skill, or ignorance about a specific situation. Investors may not have all of the information necessary on the specific company or asset class. It cannot be linked to specific risk factors. External uncertainty is associated with the disposition of an event.

In the case of an asset class that has a high degree of credit risk, we know the risks, so we have limited internal uncertainty; however, we have the external uncertainty with not knowing the direction of the credit risks. If there is high idiosyncratic risk, there may be little external risks associated with known factors, but a significant amount of security specific risk.

Those who have research issues associated with idiosyncratic volatility find that it is important with pricing cross-sectional returns. There is a positive relationship between risk and return as measured by total risk. Know your factor risks but realize that reducing factor risks for "unexplained" risks is not a solution. It just changes risks and changes the work requirements for any investor. 

(See Internal versus external uncertainty - Making distinctions for decision-makers)

  

A comparison between ARP, hedge funds, and real assets

The choice set in the alternative investment space has grown so investors need to think through the widening set of opportunities. The opportunities are not just based on strategies or risk factors but on the broad mechanism for delivery of strategies. 

The alternative space can be divided into real assets, hedge funds and alternative risk premia. There are other ways to categorize; however, this may be one helpful approach for discussion purposes. Real assets will represent direct investments in non-traditional assets such as private equity. Hedge funds represent managed liquid alternative, discretionary or systematic, strategies. Alternative risk premia (ARP) can be accessed total return index swaps or investments tied to specific risk premia or factor strategies.  

The stars in our table below represent which alternative, real, hedge fund, or ARP has an advantage for investors across a number of criteria. There can be wide dispersion within each category; however, we are providing some general observations on relative merits.

The correlation with equity market risk suggests the level of diversification that can be achieved through each category. Given the strategy focus of an alternative risk premium, there can be greater diversification benefit. The investor can choose a specific level of diversification that may not be achieved with a real asset or a hedge fund investment where the managers chooses the level of diversification.

The liquidity terms for an ARP swap may be superior to hedge funds and real assets. Hedge funds may not provide daily liquidity, and real assets such as private equity may be subject to less liquidity.  

ARP swaps based on indices of risk premia strategies or factors provide full transparency; albeit index terms will not change. Hedge funds may have limited transparency based on reporting requirements. Private equity may have known investments, although they are subject limited details.

Pricing is superior for ARP given values are usually posted every day. Real assets may not have daily pricing available and prices at the end of year for private equity may be subject to interpretation. Hedge fund pricing is sensitive to the liquidity of the underlying assets held in the portfolio.

The return and volatility rating for each category is complex. ARP have daily pricing which may make calculating return and risk straight forward, but these measures will be subject to significant noise based on daily variation. Return and risk will be highly variable based on the risk premia held. Return and risk are usually smoothed for real assets. Hedge funds will have less smoothing but will be subject to the fluctuations in net asset value.   

The choice of where investors will obtain their diversification faces a number of generalized trade-offs. ARP swaps will allow for more flexibility, transparency, and liquidity but places more of the management responsibility on the manager.

"I am uncertain" vs. "It is uncertain" - Internal Versus External Uncertainty

Many throw around the word uncertainty without regard to its subtle meaning. There are different types of uncertainty. There can be internal uncertainty associated with your knowledge of facts. For example, "I am certain Mount Shasta is over 14,000 feet." You are not sure of its height, but you can look up the information and get the right number. This is uncertainty about a past event. This uncertainty can be resolved or eliminated through gaining knowledge. There is also external uncertainty which cannot be resolved with knowledge. This is uncertainty about the future that cannot be solved until the event described occurs. For example, "The odds of "Sea Biscuit" winning the next race is 4:1." 

How a speaker phrases uncertainty has an impact on the amount of uncertainty faced and the perception of the listener. For example, "I am uncertain about the growth of the money supply," has a different meaning than saying, "The growth of the money supply is uncertain." The subject of a probability defines a "who" or a "what". The phrasing may refer to the past, a piece of knowledge, or refer to a prediction of the future. A person has internal uncertainty while a topic is externally uncertain. 

Researchers have actually tested the phrasing of uncertainty and gained some interesting insights. They find, for example, that phrasing use the pronoun "I" by a speaker who is an expert conveys more confidence. Speakers use pronouns to mark subjectivity of source of uncertainty and may use "I" for more knowable outcomes. These are subtle issues but have impact on the message sent from the speaker and the message received by the listener. 

So much of investment decision-making is done by committee, so the use of language is highly relevant. The "I" versus "It" uncertainty issue is real and must be addressed. Committee members must probe on the type of uncertainty referred to in discussions by speakers. When risk and uncertainty is conveyed in language there is opportunity for misunderstanding.

See, “I am uncertain” vs “It is uncertain”. How linguistic markers of the uncertainty source affect uncertainty communication. Judgment and Decision Making, Vol. 12, No. 5, September 2017, pp. 445-465.

For more on internal and external uncertainty see, Internal versus external uncertainty - Making distinctions for decision-makers

Sharpe indifference curves and the power of diversification - Fundamental to the strategy approval process

"I want  high Sharpe ratios. If a manager does not have a high Sharpe, forget about it, I'm not interested. He is cut from my list. We only invest in high Sharpe managers."  

You have all heard or may have used those phrases. This is based on the simple view, "If I invest in a set of high Sharpe ratios, I will have a portfolio that has the highest Sharpe ratio possible". Not exactly. I can actually increase portfolio Sharpe by adding uncorrelated investments even if these Sharpe ratios are lower than the existing portfolio. These second order effects of diversification are meaningful.

An approved set of strategies can be indifferent between a new investment's Sharpe ratio and its average correlation to a portfolio. The impact of adding a high Sharpe ratio  with a high average correlation can be the same as a lower Sharpe and lower correlation investment. This can be a hard concept to accept. It is an even harder concept to implement. How easy is it to go to an investment committee and say, "I know this manager is not as good as others, but he is a great diversifier"? The answer will likely be, "That is great, but I don't eat diversification". Nevertheless, the simple math in the paper, "The strategy approval decision: A Sharpe ratio indifference curve approach" 

The blend, not the individual Sharpe ratio, is the critical issue with adding a new strategy investment. The paper provides some useful graphs which help to describe the dynamics between Sharpe ratios and correlation. The improvement of the portfolio Sharpe ratio is non-linear with respect to falling correlation. More strategies will improve the portfolio Sharpe for any given correlation, but greater improvement will come when correlations are low. This is consistent with the fundamental law of active management, add trades that are uncorrelated.

There is a trade-off between Sharpe and correlation that is measurable. Investors can find a lower Sharpe strategy that will have the same value as a higher Sharpe ratio based on its correlation. This why finding different strategies to include in a portfolio mix is so important.

Every hedge fund investor should have the idea of a Sharpe ratio versus correlation indifference curve emblazed in his mind. It is not a hard concept but is not at the forefront of the strategy approval process.

Keeping it simple with downside protection

Rule #1 Don't Lose Money 
Rule #2 Don't Forget Rule #1 
-Warren Buffet 

I was recently reviewing a presentation and having an investment discussion with a hedge fund manager. The strategy was thoughtful and well researched, but the language used to describe it was fairly complex. This complex language cluttered the manager's objective and did not clarify his strategy. The average pension trustee would have a hard time understanding repeating to someone else what the manager would be doing to add value. Simplicity and directness would cut through all of the excess verbiage. At least for an opener, the Buffet Rules serves that purpose.

The Buffet Rules prioritize manager goals and are a good start to expressing simplicity; however, there needs to be some structure on how to implement the "don't lose money" rule. A deeper discussion can be broken into four questions:
1. What is the strategy for preserving money? 
2. What will be the tactics employed for protection?
3. How is downside risk measured?
4. How do you measure the cost of protection?

Any manager should be able to answer these short questions. There can be added complexities with how this is done but first the four questions have to be answered.

Regression, "Oh so 20th century"; Machine learning, "Oh so 21st century"

The movement from regression or attribution statistics to prediction algorithms is not just a fad but a significant change in focus associated with how we use information and the purpose of data analysis. Appreciating the change in perspective is important for all investors looking at systematic managers and quantitative analysis.

Regression is the framework of choice for most older quants who were trained in the 20th century. This world focuses on the formulation of what could be called "surface plus noise" with the surface describing the model or scientific truths we wish to learn, and the noise representing what obscures the truth hidden in the model. The emphasis is on model estimation and less on prediction. Develop a good estimated model, find factors, and the prediction will take care of itself.

Pure prediction algorithms are 21st century analysis and  include neural nets, deep learning,  and random forests. These algorithms have moved to the center analysis attention given the increase in computing power and the explosion of large data sets. These are important advancements on existing statistical analysis, but it also is a change in orientation. The pure prediction algorithms focus on prediction with less emphasis on estimation and attribution. Don't worry about the model estimation. There is no focus on significance. It is all about accuracy and error reduction. The connection between prediction and attribution is not relevant.

The table and a deeper discussion are available in "Prediction, Estimation, and Attribution" by Bradley Efron in the 2020 Journal of American Statistical Association. 

Data analysis cultures are changing. A challenge is for the old guard to learn new tricks and the new culture to appreciate the power of traditional estimation. Right now, the link between these two cultures is stilted and needs to be bridged. Estimation may not work for all data, and the pure prediction culture may need to temper their use of complex algorithms. However, the old guard is going to have to accept the prediction algorithms to be part of the 21st century. 

I want my CTA diversified but not too diversified

"I want my trend-follower diversified to smooth returns, but not too diversified..."

Diversification for trend-followers works at smoothing returns and increasing the Sharpe ratio. Diversification, through increasing the number of markets, increases the set of potential opportunities; however, diversification causes a drag on performance when only a limited number of markets trend. 

Some of the wide dispersion across trend-followers in 2020 is related to the market selection skill of the manager. Managers who traded too many markets, some of which did not see strong trends, may not have performed as well as those that had more concentrated risk. Similarly, focused managers may have missed the best opportunities of the year.

One secret to effective trend-following is the strong benefit from market sectors being correlated.  Global bonds and rates moving together, equity indices trending together, currencies tied to a dollar move, and the energy sector facing a common shock. The full value from trend-following comes when there is a common trend within a sector (allocation breath) or strong trend amplitude. A strong market trend is profitable but there is a limit based on the size of market exposure.

Following the fundamental law of active management, the information ratio = skill * sqrt(breath). A manager has to see an increase the number of bets or an increase is skill. For  any trend-follower, there are three trade buckets: the winners, scratch trades, and losers which are capped by stop-loss. The size and number of winners have to offset the scratch and loser trades.

The winners are associated with identifying the trend and the amplitude of the price move which may be out of their control. Skill or the information coefficient is identified as the correlation between forecasted and actual returns, but this can be restated at the win to loss ratio for trades times the probability of success. This success ratio can be multiplied by the number of trades to obtain the winners. 

Trading more markets allows the trend-followers to find more trends, yet if there the number of trends decline, there will a larger performance drag. Adding markets has to be based on the assumption that the marginal addition will increase the potential number of trends. For example, adding another commodity market with a risk allocation taken from other markets may not add to the return potential of the program. It will add to diversification. 

Investor to ask some simple question of their trend-followers. How do you determine the number of markets traded? Why would you add another market to the program? Would you ever drop markets from the program? Do you rank order and select trades from a broader universe of markets? 

Just as the trend model is critical, the choice and mix of markets for any trend-follower is an important part of the manager skill. Selecting trends and markets and sizing exposures are complex issues that serve to differentiate managers. Finding the right amount of diversification is an important portfolio selection issue.



  

Biggest long-term threat in 2020 - Deglobalization

Everyone talks about the economic wall of worry, and the wall seems to be getting taller. Pandemic. Recession. Credit issues. Lockdowns. Nevertheless, these worries will pass. They are cycles, shocks, and bumps. They do not define our global system of trade and benefits. Policy-makers can see the immediate harm and are using their set of policy tools to dampen and reverse the negative effects.

Unfortunately, the longer-term worry that will stick with all economies is our trend to cut connectedness, globalization. This worry does not have a set of voices or policy prescriptions to offer as solutions. There are no current advocates against deglobalization, yet trade and the broader term of globalization has been the key generator for world economic growth. 


The free movement of labor, capital, ideas, goods, and services across time and space has created enhanced economics opportunities for most and explosion of a middle class in emerging markets. These flows have driven global growth in spite of the financial excesses and shocks over the last few decades. 

If the current globalization era ends, all economies will suffer, and no localized or national policy will solve the problem. There is a need for worry because globalization is not something that just happens. It is not inevitable. It takes work. Mercantilism is often viewed as a natural policy when countries think trade is a zero-sum game.

History has shown surges in world economic growth when there has been more freedom of global trade and financial flows. The great globalization surges include the pre-WWI period, the Bretton Woods period, and the China-WTO period. All were associated with national and global structures open to international flows, technology that allowed for low flow costs, and organizations that could coordinate economic activity. 

Globalization has been under assault for some time. Some of the arguments against globalization are relevant, should be heard, and require adaptation, but the fundamental premise of globalization is being questioned. This generalized globalization assault is misplaced. Globalization is disruptive and will thus create both winners and losers. The wins will often be dispersed while loses will be focused. Hence, reaffirming the benefits of globalization requires constant reinforcement, yet also requires an appreciation that competitive trade may need localized support for those most disrupted 


Barriers to trade are being erected with tariff growing as a normal tool. Pandemic has restricted travel and the flow of labor. Regulations impeded the flow of capital. The global institutions of cooperation have failed at their mission. New and longer-term geopolitical tensions further erode the desire for cooperation and increased national competition. 

None of these trends toward deglobalization will be reversed without a conscience choice for global integration and cooperation. There needs to be champions for globalization that provide advocacy with candor; however, there first needs to be a recognition that deglobalization is a problem that needs to be solving.