Machine learning, deep learning and creating an investment edge

Learning to be smarter and run faster is a core part of the hedge fund business. Markets are fairly efficient, so there is always a desire to find an edge. And, if you have an edge, you have to accept that it may not last, so you should be looking to enhance the edge or look for a new one. There are several ways managers can create an edge:

1. Information advantage - get new and better information. This is a big new business with new alternative data sets.

2. Processing advantage - Look at information differently.

3. Operational advantage - Be faster at trading and operational efficiencies

Machine learning and deep learning are focused on generating an information processing edge, yet they approach the processing edge problem in very different ways. It is important to understand the difference between the two. 

Machine learning is directed by the analyst, so the process edge starts with the quality of the analysts who is picking the features being used in a model. Deep learning is not driven by the feature choice of the analyst but by the data processing of the information and the ability of the model to extract relationship. The expertise with deep learning is on raw processing as opposed to managing relationships of curated information. 

Is one approach better than another? My preference is with machine learning and using expertise to focus algorithm construction on key features. Deep learning can generate unique insights but will not have the important feature of explainability. Press on explainability and core features before resorting to deep learning classification.

source: Bismart.com

source: Artificial Intelligence Solutions | USM 

source: Anil Gupta

The problem of "should" and "is" with investing

"Market should do X, therefore I will do Y."

"Market is doing X, therefore, I will do Z."

One word separates the difference between the two statements above, yet that makes all the difference in the world. 

The "should" investor believes that he understands market dynamics and behavior better than the market itself. He has a view of value and believes that he has predictive behavior. "The market should reaction to the Fed." "The market should rebound." "This stock is overvalued and should decline."

The "is" investor believes that all key information is wrapped in the price. The trend-follower is the perfect "is" investor. "Prices are rising, there is a trend, and I should buy the trend." There is less analysis and more acceptance of what the weighted opinion of all market players is telling us. 

The believer in efficient market falls within this class. The investor who can express behavior in probabilities can be an "is" an investor. However, there is a difference between saying there is a likelihood and saying the market should behave in a certain manner.  

It is hard for many to always be an "is" investor. We would like to be a wise "should" investor; however, the should investor usually does not have a good track record. Hence, it is critical to understand and appreciate the difference between should and is. 

Equity investing - It is all about the timing of cash flows

 

Equity investing is all about cash flows - how large will they be, and when will they be coming.  If you can get the guess on cash flow right, you will be a good investor. Yet, forming future cash flows is not easy. Extrapolation usually does not work because there will be fluctuations with the business cycle. Some industries will react quickly to a macroeconomic shock while others will see slow reaction. There will be industry leaders based on cash flow reaction and laggards. Given this differential timing, laggard industries can learn from leading industries, which is the view of researchers who have written the paper, "The Leading Premium". 

The researchers find that those industries that are leaders receive a meaningful premium relative to lagging industries. This empirical result makes sense on two levels. One, industries that will reach strongly and immediately to macro growth shocks will price in a premium for the added risk. Two, leading industries tell us something about future risks for lagging industries. Leaders resolve uncertainty for laggards. 

The authors do some heavy lifting to show the lead-lag relationships between growth and industries, but the story makes sense on an intuitive level. The difference in earnings (dividends) and thus pricing between leading and lagging industries is a forward equity premium. Industries that will have a strong and immediate reaction to a macro shock will have more risk than those that lag a macro shock. Sensitive industries will generate a premium and provide insight on the less sensitive industries. 

Yes, there is opportunity for macro trading with equities

 

The macroeconomic regime matters. Regardless of style, if you tilt to industries that have historically had higher Sharpe ratios in different economic regimes, investors can add value to their portfolio. Call it industry business cycle rotation, but accounting for simple macro regimes to industry allocations will improve portfolio performance. 

Industries will perform differently across the business cycle, but many have viewed that predicting the macro regime is difficult. A careful research paper takes a relatively simple approach to address this problem and show macro timing value. (See "Does History Repeat Itself? Business Cycle and Industry Returns".) 

Industry Sharpe ratios can be categorized across business cycle regimes. Given this information, the researchers make a judgement of the macro regime based on the output gap at the end of the year for the next year. Sorting the industries between long and short portfolios, it is found that buying high Sharpe ratio industries conditional on regime will outperform the market portfolio and a short portfolio.

The business cycle is measured through the sign of the output gap each year. The output gap is the deviation of growth from a linear and quadratic trend. This industry business cycle effect is robust to different measures of the business cycle.

According to the authors, the reason for this industry rotation edge is that investor do not seem to account for the impact of the business cycle on future cash flows. Albeit simple, investors are often surprised by the changes in firm cash flows across regimes and seem to under-react to these cash flow changes.

This industry effect is present even after accounting for the usual Fama-French factors and robust to industry momentum which has been found in the past to be significant.  

Limited upside for stock and bonds over the next 5 years

Equity markets are still overvalued, and yields are still at low levels. This combination does not look good for estimates of asset class return for the next five years. The simple approach of 60/40 stock/bond allocations will disappoint relative to the last five years.

From the AQR return estimates, which are similar to what is calculated from other firms, the expected real return for US equities is below 4% and real yields between credit and government bonds is at best zero. Surprise inflation or a recession will only make these estimates worse. 

These poor return forecasts have been made for the last few years and they have proved wrong. The better performance than expected is associated with exogenous factors like strong fiscal stimulus and loose monetary policy which provided an offset to what should be considered normal conditions. 

It cannot be assumed that governments can or will prop-up financial markets, so investors must look at other asset allocations to provide extra return. International diversification will help, but the only real alternative is to play style factor diversification. Even this diversification play will only 1-2% real return but that will add close to 50% extra return in the current unfavorable environment. 

Is this what a return to normalcy looks like in the stock market?

Bear markets and corrections are just numbers, respectively 20% and 10% declines. While we have not hit those levels for key benchmarks, there is a new market regime. It could be a classified as a risk-off environment. It can also be called a reaction to a more aggressive Fed. It is a return to normalcy after strong returns in 2021 lifted by both fiscal and monetary policy. Less fiscal stimulus and Fed QT with anticipated rate increases make an adjustment to more normal returns seems likely. Valuations will come down and longer-term returns will see annualized numbers consistent with less speculative excess.

The SPY ETF benchmark has peaked. The Russell 2000 broader market has moved to negative returns for the last year, and the froth in the IPO markets as measured by the IPO ETF has moved this benchmark back to levels consistent with the SPX index. You may not like it, but it is a return to market normalcy. However, there is still a threat to something much worse.

From just-in-time to just-in-case commodity markets

 

There has been a significant change in inventory management thinking around the globe. Businesses have moved from a just-in-time philosophy to a new just-in-case mentality. A just-in-case environment is rather simple, build and hold more inventory than previously viewed as optimal. Don't rely on the transportation system or better logistics to meet the threat of stocking-out. Across the board from raw commodities to finished goods, holding more inventory is the new normal. 

For commodities, the traditional wording is that there is a higher convenience yield from holding inventory. Markets should show more backwardation in forward prices. 2021 was difficult supply year with excess demand versus expectation yet backwardation is found across most markets in 2022. Looking at March to December 2022 contracts, the higher March values as of January 19th are significant:

Corn       5.46%

Wheat     0.64%

Soybeans 6.02%

WTI oil     9.35%

NY coffee  2.07%

This is great news for long commodity holders; nevertheless, a measure of lower supply congestion will be a decline in backwardation. The last decade was a period of lower backwardation relative to the 90's. Commodity index holders were disadvantaged especially in the energy complex that saw strong contango. The long period of high market contango may be over.  

Trend-following and crisis alpha - Style, timing, and market selection determines benefits

 

There has been significant discussion concerning "crisis alpha" and trend-following managers. The concept of crisis alpha is valuable in its simplicity. Look for those periods with a substantial decline in equities, a crisis, and measure the return from trend-following program during these drawdowns. The excess return over the equity return decline will be the crisis alpha. The rationale for holding a trend manager is receiving the crisis alpha when core equity returns are falling. 

Researchers, who have looked more closely at the crisis alpha story, have concluded that all crises are not the same, all trends are not the same, and all methods for exploiting trends are not the same. Crises can be a short life or last for extended time periods. The pandemic crisis of March 2020 was not the same as the GFC of 2008. Hence, one should not expect that the crisis alpha between these two events will be the same. 

The fact that crisis alpha depends on the characteristics of crises and different portfolio features has led to confusion and distortion in the crisis alpha story. Trend-follower will not provide similar crisis alpha. Managers will define crises differently, and trend-following performance with varying crisis drawdowns and length lead to varying narratives for why perform was strong or weak. "Yes, my trend model has crisis alpha but not for this crisis; wait for the next one."

All assets and strategies may have a crisis alpha. It can be positive or negative based on its conditional behavior during these equity declines. For example, fixed income can have crisis alpha so trend managers should compare their performance against the simplest diversification option as well as other alternatives.    

The crisis alpha benefit can come in several forms based on the style, timing, and markets that represent the trend-following program. There is crisis alpha gain from holding a diversified portfolio of markets. A low beta will allow for excess return versus a long-only equity position, but the key benefit comes from being able to adjust from long to short positions using a trend model. 

Some crises, equity declines, can be quick with a drawdown in days while other can last months. Trend models may have short look-back periods or have a long-term trend identification. In general, a short or fast trend model will do better during short crises while longer crises will create better opportunities for longer-term models. The size of the crisis will impact potential crisis alpha. Deeper crises, a greater price decline, will allow the trend model to extract more return and will have more crisis alpha.

Managers may also have different weights on sectors. Hence, a trend manager that has a high equity risk exposure will have a different crisis alpha than a manager who has risk exposure more evenly distributed across equities, fixed income, currencies, and commodities.

Not often discussed is the issue of trend-following during the recovery phase after a crisis. The crisis alpha should be compared with the recovery alpha, the return between the max drawdown and recovery period. The key crisis alpha benefit is being able to have a smaller decline in wealth when funds may be needed for consumption during the crisis. The overall portfolio benefit is smoothing the growth path for wealth. The trend-following program with its high liquidity serves as a "piggy bank" for investors when a crisis makes it difficult to sell their declining and illiquid equity portfolio.

Machine learning provides significant value for asset pricing

Machine learning can add significant value for investors who employ these predictive tools as an alternative to traditional regression analysis. A comprehensive study of equity prediction using machine learning shows a significant improvement in out of sample R-squared. 

This added predictive value is seen across a variety of machine learning techniques. Using machine learning also allows investors to identify the most informative predictive variables. Just as important, the more expansive list of predictors with different specifications and functional forms employed with machine learning allows for an improved understanding of the drivers for stock prediction. (See "Empirical Asset Pricing via Machine Learning")

The authors provide a good three-part definition of machine learning:

• A diverse collection of high dimensional models for flexible statistical prediction. 
• Regularized methods for model selection and overfit minimization to ensure high out of sample performance.
• Efficient algorithms for searching among a large set of model specifications to control computation costs.

Trees, (boosted regression trees and random forests), and neural nets clearly outperform linear regressions and allow for a broader set of predictive features. Machine learning, which focuses on nonlinear methods, needs not be overly complex to add value. Along with predictive gains, machine learning also provides clear economic gains through higher portfolio Sharpe ratios. The study shows that price trends, liquidity, and volatility measure prove to be the most effective predictors.

Machine learning, coupled with advances in data science, allow investors to generate better predictive models and improved methods for finding the key variables that drive stock performance. The cost of entry for using machine learning may be high, yet the greater predictive value makes this a useful investment.  

Dispersion heralds the ascent of stock-pickers and active managers

 

Valuation dispersion heralds a new era for stock-pickers and active managers. If there are greater difference in valuation across firms, there will be a greater opportunity for stock-pickers to find cheap and rich equities. 

Clearly, some of these opportunities have already presented themselves during the transition from tight to wider dispersion. Tight stock dispersion suggests that there is a single factor that may be driving all stocks. Notice the tight dispersion during the GFC, when the focus was on the recession and financial crisis. Dispersion has increased during the pandemic because COVID has not had a single effect on the market but has had a differential impact across industries. 

Differentiation between sectors will reduce correlation across stocks and will allow those who can effectively identify stock differences to generate excess return in a way that cannot be found with passive indexing. Dispersion does not guarantee excess returns, but the set of opportunity will improve.   

Completion portfolio investments - A fad or something worth considering

A relatively new area of investment research focuses on portfolio completion. Given investors have a target or reference portfolio based on asset classes and style exposures, the completion strategy or manager will find gaps between the reference and actual portfolio that can be filled with asset or strategy mixes by a completion manager to close the gaps. If this is done as an overlay, the adjustments can be made without disturbing the existing portfolio mix. This will reduce transaction costs and manager adjustments. 

Recent interest in completion strategies has increased with the rise of factor strategy risk identification. In an older world that only focuses on asset classes the completion manager would only have to look at actual allocations against the target and then use futures or index products to close the gap. 

When investors focus on style risk, the completion problem becomes more difficult. First, there are more degrees of freedom or choices that must be managed. Second, the instruments that can be used to close gaps and complete the portfolio are more complex. Third, investors must think through the appropriate target levels for style risks and then determine whether any style gaps should be closed.

Is this a fad? I don't think so. 

1. Investor should have a target or reference portfolio. No surprise here.

2. The target should consider risk or factor exposures. Again, this should be done regardless of any completion strategy although style exposure analysis is not easy process to manage in practice. This becomes more difficult when the investor has a high exposure to alternative investments.

3. Measuring and determining the deviations from target is the core asset allocation job of the investor.

4. Using completion specialists to manage this process and providing guidelines for how this job should be done is where there is room for discussion.

There is commonality between the overlay and completion manager. Both exist to cut specific risk exposures. Perhaps the fad is in the naming convention between overlay and completion. There is a place for the completion manager to get the actual portfolio back to target quickly and efficiently at low cost. The value for completion increases when there is a higher exposure to illiquid private and alternative investments. 

Endowments and equity factor - Just too much exposure?

Richard Ennis is an old school endowment consultant and pioneering quant who has been arounds for ages. He has been able to provide effective arguments with simple numbers. His sage advice should always be heard. You don't have to agree, but it is worth thinking through the reason for why he may be wrong. 

His latest paper "The Modern Endowment Story: A ubiquitous United States Equity Factor" should serve as a warning sign for many endowments. He analyzes some of the largest endowments in the United States and makes some strong conclusions:

• Endowments are well diversified across managers but their overall high stock allocations with limited alpha suggest that underperformance is driven by high fees. Reduce fees and returns will go up.
• US endowments have a high home bias / US dollar bias. If something goes wrong in the US, there is limited diversification benefit.
• Endowments have an extraordinary high risk exposure to equities, much more so today than in the past. Stocks and a small allocation to cash are their current version of the endowment model.
• Risk tolerance has increased significantly given this high stock allocation. An endowment may not even be fully aware of its more aggressive tendencies given the trustees have not likely changed. 

The following show the endowment problem. Performance has been below an empirical benchmark while at the same time exposure to equity risk has increased.

All you have to do is ask a simple question to see the problem, "What happens if we have a bear market?" Even with alternatives, the environment will not be pretty. This is the time to think about adjusting equity exposures. 

 

Machine learning and portfolio optimization

Machine learning can be used to enhance classic portfolio optimization through dividing the portfolio selection problem into two parts. By making this simple division, investors can use the best of both worlds to improve stock portfolio construction. Use machine learning data science techniques to improve predictions of expected return and optimization techniques to find the right diversified mix of stocks meet an objective like maximizing the Sharpe ratio.

The core problem of mean-variance optimization is not with employing an optimizer but choosing the expected return for the stocks to be selected. The optimizer will do a good job of selecting stocks given the expected returns, correlation, and volatility provided. However, if you get the expected return wrong, the optimizer has limited value. Using past returns does not work. Forming some expected value using linear regression may provide some improvement, but the variation explained through most of these models is low. These approaches may have theoretical validity, but their predictive power renders poor results. 

Instead of throwing out optimization completely, machine learning can be employed to improve the expected return predictions. Different machine learning (ML) techniques have been compared with linear regression predictions and it has been found that these ML tools will often beat the traditional finance techniques. By ranking expected return using ML tools, the optimizer can then form portfolios that have a better chance for success.

ML focuses on prediction and optimization techniques focus on building diversification with constraints. The division of tasks allow for specialized skills and allows for a clear breakdown of value-added between tasks. The success of the ML can be measured by accuracy predictions. The success of the optimizer can be measured through portfolio efficiency. The two can be used to build better portfolios.  

Narrative drives return - even if stories do not generate measurable risk

Narrative drives return. We define narrative as the description and representation of unscheduled unique information that usually does not take the form of numbers that can be easily reflected in quantitative analysis. Narrative both creates and resolves uncertainty. Narrative attempts to provide meaning to unscheduled unique information that cannot be easily converted into some measure of risk. Unscheduled unique information is by definition not countable, so it cannot be given a probability measure. It is novel and thus given value through stories or interpretation as an attempt to reduce uncertainty.

Narratives both represents and addresses the Knightian uncertainty found in information that cannot be converted into something countable. Nevertheless, just because information cannot be converted into a measure of risk does not mean that it cannot drive returns. Narratives impact sentiment and creates memes and focuses the attention of investors. A positive narrative may generate popularity which will impact demand and drive prices. 

 

Professor Nicolas Mangee, through the series Studies in New Economic Thinking, begins his new book How Novelty and Narratives Drives the Stock Market with a description that encompasses the problem and the compelling investor interest in this work. His work attempts to provide some structure around the narrative problem.

“Where there is novelty, there is instability. Where there is instability there is uncertainty. Where there is uncertainty there are narratives – narratives are the currency of uncertainty.” 

While I am a quant, I accept that narratives and stories associated with unscheduled information may be a key driver of the daily back and forth that drives prices. Unfortunately, we are only at the infancy of being able to extract systematic narrative drivers. 

Thoughts on narrative:

Narrative and investing - Follow the facts

Narrative and price - Know the line of causality

Narrative economics - food for thought for quants

Narrative and numbers - You need both for effective forecasting

Is investment management a science - Use the "narrativeness" test

Narrative Economics by Robert Shiller - Storytelling but no predictions

 

Trend-followers - A multiple naming convention problem

Not all trend-following strategies are the same. This is well known, but there is a naming convention problem for this style. A trend-follower in name is not always the same as being a trend-follower. Truth in advertising - those who call themselves trend-followers many not solely use trend strategies. There is no standard to say how much percentage of risk capital needs to be associated with trends to be called a trend-follower. 

Trend-followers can trade futures, but do not have to use these instruments. There can be non-futures trend-followers. Most trend-followers are CTAs, but all CTAs are not trend-followers. Most trend-followers are involved in managed futures, but all managed futures managers are not trend-follower.

The largest CTAs are trend-followers. The oldest CTAs are trend-followers. The largest and oldest managed futures managers are generally trend-followers. Many interchangeably use the words trend-followers, managed futures, and CTAs. Some say trend-followers are part of the momentum style which creates another area of confusion. 

I don't think there is another alternative investment style that has this naming convention problem. Poor classification and definitions lead to confusion and misperception. There are still many differences in trend-followers between the markets used, the timing horizon for trend identification and the style of risk-taking and trend measurement. This style does not need further complexity through mixing non-trend investment decisions.  

Systematic investing - Reducing the noise from discretion

 

What is one the important reasons for employing systematic investment strategies that is often not discussed - the ability to reduce noise from discretionary decisions. There is noise or variability in investment choices based on the person making the decision. Judges, given the same information, will not rule the same way. Traders, given the same situation, may not make the same choices. There is variability in answers that can lead to wider dispersion in performance. Most decision error focus is on biases, yet the dispersion in decisions will impact long-term returns.

A simple example that describes the problem was published in the Journal of Behavioral Decision Making "Clouds make nerds look good: field evidence of the impact of incidental factors on decision making". The researchers looked at admissions to a selective college for a sample of 682 students and found that when it was cloudy outside, there was a higher weighting on academic attributes over non-academic attributes on sunny days. Odd, but true.

This may not be the best example of noise, and I will accept that the title caught my eye. I am subject to biases. Vivid stories or information stick in my brain. Of course, this result could be a flagrant example of p-hacking and atheoretical analysis looking for causation when there is seeming correlation, but it is suggestive of a behavioral problem and decision noise.

There always is noise when we have individuals make decisions. They may not make the same decision twice given the same information. There are mood swings. There are attention problems. We are only human. The solution is setting rules in place to eliminate the noise. We can work on forecast biases, finding better rules, but we can take comfort in the value from noise reduction when we are systematic with our decision making.

Cheers for plain English in investment management

It’s too much to expect the people who run big Wall Street firms to speak in plain English since so much of their livelihood depends on people believing that what they do cannot be translated into plain English.  - Michael Lewis The Big Short 

Quantitative analysis has generated major improvements in our ability to trade and make better investment decisions, but quantitative work that cannot be easily explained is detrimental to good investing and can lead to greater market failure. Models will fail, but for investors there is a second level of failure from surprise. The model you thought would work doesn't at critical times.

All quantitative analysis (in fact all investment work) should explain in plain English:

• How it will potentially add to return or reduce risk
• How it works at a level that can be understood by an average investor not just CFAs, MBAs, or PhDs. To say it is understood means that the average investor can explain it to another investor and field questions.
• Why it is needed - the investment problem that need to be solved, what is being counted or measured in the case of a quant model, and why a level of complexity or specific technique is needed over a simpler approach.
• When it will work and when it will not; no surprises for a model breakdown.

Einstein said, "if you can't explain it to a size year old, you don't understand it yourself." That may be an extreme, but it should apply to any model or investment idea. Cheers to plain English. 

 

No drag from commodities - now bonds are the drag for risk parity portfolios

One of the great advantages and problems with risk parity portfolios has been the cyclical differences between commodities, credits, equities, and bonds. The different cycles for major asset classes are one of the key drivers for diversification. The problem is that an equal risk-weighted long-only portfolio will suffer from the switching in cycles. 

The last two years have turned risk parity performance on its head. Instead of seeing commodity drag which has been a problem for a decade, commodities have been a clear winner. However, the volatility equalization means that a similar portion of the portfolio will still be in fixed income and credit which have had underperformance. The 15% volatility institutional risk parity (HFR 15% institutional index) portfolio showed a return of 8.22% through November in 2021. 

The return difference between the aggregate bond and benchmark commodity indices was 6% in favor of commodities. A commodity allocation improved any equity and bond asset allocation over the last two years especially if the allocation came from the bond component. While some commodity market gains on supply shocks may revert in 2022, the higher inflation environment still favors commodities.  

Asymmetric globalization - at the country level closet mercantilism

A current systemic risk is the continued asymmetric globalization between the US and China. Remember the "Chimerica" meme that was the rage in the post-WTO period. That is over. Asymmetric globalization may not be shock threat, but it is a risk that drive markets lower. 

China would like to disconnect with the West but still accept capital, western institutions, and technology on its own terms. China would like sell goods to the West and only import capital goods that will further improve their terms of trade or technology. China would like to control commodity imports to ensure that they are not dependent on other countries. Trade will have to be strategic. The US would like to be more strategic with China trade, but the particulars are less clear.

A key tension between countries is the move to globalization versus a form of modern mercantilism. In the case of the US and China, there is often globalization rhetoric and mercantilist behavior. The producer country wants to have open markets but also control its internal positioning against multinational control. The importing country would like more control over imports but realize the cost of import limits in the short run are high. 

The asymmetric globalization becomes more obvious when culture and political systems clash, but it exists in many countries - a desire to be export globalists but import mercantilists. Asymmetric globalization is also played-out at the class level. Rich countries or rich educated individuals are clearly more pro globalization than poor countries and less educated classes where the benefits may not seem as clear.  

This asymmetry affects all global and EM equity and fixed income benchmarking. It also affects all companies that have China revenue and capital exposure or China companies that have listed in the US. How should you allocate capital when this country threat exists? If you have not addressed this question, you are behind the curve in your asset allocation.

The negative bond/positive stock return of 2021 - will it continue?

 

There is a trade-off between stocks and bond returns. For the entire post-GFC period, the return correlation between these two has been negative, yet generally, both assets generate positive returns in any given year. The stock-bond asset allocation mix provides diversification with limited cost - the pain from holding bonds is limited.

While negative returns for both asset classes have been the exception (2015, 2018), there have been a few years when stock returns have been positive and bonds negative. The poor bond performance in 2013, 2015, 2018, and 2021 has a lot to do with the low yield for bonds in a zero-rate environment; however, except for 2021, these years were also low inflation periods.  

The question is what quadrant the stock bond mix will fall in 2022. A lot has to do with the expected macro environment. Make a call on the environment and you will likely know return performance. Similarly, make a call on return performance and you are implicitly making a call on the global economic environment. Given these quadrant probabilities and you have a reasonable and measurable framework for predictions. 

Quadrant I is the beautiful policy mix of liquidity and fiscal stimulus with and end of COVID and increases in global productivity. It is what we would like, but seems unlikely from current macro data. Quadrant II is the mild world status quo. COVID peaks in the winter and then retreats. Fed monetary policy follows a careful glide path and global fiscal policy provides downside protection albeit at lower levels. Stocks will do well, but bonds have headwinds from current high inflation levels and excessively low real rates. Quadrant III suggests policy mistakes from central banks - either tightening too fast or not fast enough. Quadrant IV is slower growth from continued COVID, limited fiscal response but continued dovish monetary policy and elevated inflation albeit without the extreme disruptions. 

These are simple sketches on possible environment but using the quadrants as a map for return performance is a simple way to address the return environment potential.

Gold, oh so old school - no interest in the precious metal

Gold has the interesting property of projection - it projects the hopes and desires of investors at a specific time. It can be an inflation hedge; then again it may not. It can be a hedge against uncertainty and geopolitical risk; then again it may not. It can respond to negative real rates, but now it is not the case. Gold should have been one of the great hedge investments for a pandemic and for a period it was until the end of summer 2020. Since then, it has been sinkhole for those fearing inflation and uncertainty. 

Despite higher inflation, the dollar has still been attractive. Despite volatility, crypto has been an asset class of inflation hedge interest. Despite low real rates, fixed income has still seen capital flows. For equities, the allure of technology has outshined gold. Gold does not have a compelling fundamental story. Hence, the current rangebound behavior. 

Can the gold story change? Of course, investors will project again their views on this metal, but there is no clarity on when or how this will occur.

The big systemic risk - the need for stability and order

Government institutions fail when they cannot clearly communicate the realities that surround them. These institutions fail when they do not realize that in a real politic environment - there is always a play for power and a focus on self-interest. Government institutions fail when they don't provide stability and order both for those being governed and across institutions. These institutional failures are the real geopolitical systemic risk which should be the concern for investors. Tail risk events are often the result of institutional failure not the cause.

Systemic risk usually focuses on connectivity, technical details, and specific market failures and not broad failure of governing institutions.  Yet, the stability of the global order provides an environment for other more micro issues to take center stage for investor concerns - which is a good thing. When the global order is disrupted, there will be less investment and.  a flight to safety because the animal spirits of optimism are curtailed. 

Perhaps not so hidden in the pandemic is a turning away from experts and government institutions. Whether on the local, national, or international level, the lack of confidence in the current order will have an impact on financial investments. 

How can rising systemic risk be the case when we just had equity returns (SPY) of close to 27 percent for the year? Fiscal and monetary largess with pent-up demand was able to mask declining confidence, but globally there is less confidence in governments being able to articulate and address crises. The institutional glue that will bind behavior in a crisis has weakened which increases systemic risk. The failure of order at the local level will then play-out across countries. 

Unfortunately, this cannot be easily measured or isolated through some quant model. This is the essence concerning animal spirits and investment confidence. There is no easy measurement of optimism in the face of uncertainty. For Keynes in the 1930's, it was the role of government to prod aggregate demand and get confidence rising. However, if there is less confidence in potential solution providers, the threat of systemic failure increases. Yes, while this is pessimistic, discussing the systemic risk framework is critical for any 2022 predictions. 

Long-run commodity prices - it is all about cycles

Inflation is high relative to past few decades, but there will be mean reversion and many commodities which have risen significantly will revert to the mean. While many think of commodities as an inflation hedge, it is important to review the long-term cyclical nature of commodity prices. For many commodities, the increases over the last two years are market specific.

We can focus our attention on three commodities, crude oil, copper, and wheat. The three charts are in log scale. The speed of the oil increase was unprecedented, prices are well below the extremes even after the GFC, but these increases are related to returning demand and some production constraints. Copper prices are near all-time highs, but these levels are associated with the current demand for copper for the green revolution and the great build in China since 2000. Wheat prices are near highs from a decade ago based on weather shocks for key growing areas. 

Yes, inflation has caused prices to move higher, but much of the gains are market-specific. Making commodities an inflation trade will disappoint some investors.