The Index Investor
June 2020

Our 12-month regime forecast probabilities have not changed over the past month. The probability of the Persistent Deflation Regime remains 65%; the High Uncertainty Regime 15%; the High Inflation Regime 15%, and the Normal Regime 5%.

This month we have seen accumulating evidence that equity investors have substantially underestimated the underlying damage COVID19 has done to the demand side of the US economy (which will quickly become apparent if the US Congress fails to pass another stimulus bill to support middle and lower income households).

We have made no change to our 36-month regime forecasts. The probability the United States will be in the Persistent Deflation Regime remains 50%.

This forecast still rests on three assumptions. The first is that the increasing headwinds that were restraining aggregate demand growth before the devastating arrival of the COVID-19 pandemic will almost certainly worsen. These headwinds included population aging, weak productivity growth, declining labor share of GDP, high levels of both inequality and debt, and the growing threat of job displacement as increasingly capable automation and artificial intelligence technologies are deployed.

The second assumption is that COVID19’s unprecedented demand and supply shocks will worsen corporate and household debt servicing problems, and lead to rising defaults and liquidations that on balance will have a deflationary impact.

The third assumption is that the lagging recovery of demand, accelerating debt problems, continued increase in tensions with China, and increasing domestic tensions in the United States between now and the November election will keep uncertainty higher than most people currently expect, which will further depress aggregate demand relative to supply and cause price declines in many sectors.

Our 36-month forecast probability for being in the High Inflation Regime remains at 35%.

Whether the High Inflation Regime comes to pass will depend on the future evolution of, and interaction between, the demand and supply shocks, COVID19 had created. We know that the demand shock has been swift and severe, and has triggered falls in the US Consumer Price Index. A critical uncertainty is how long the demand shock will last.

In the absence of a vaccine, social distancing (and the possibility of second, third, and subsequent waves of seasonal coronavirus) will permanently depress demand in many service industries (and therefore reduce the stimulative impact of government deficits on consumer spending).

Increasing layoffs, high debt service burdens, and higher levels of precautionary savings in the face of continuing uncertainty will further depress domestic consumer spending, and thus private domestic investment spending as well.

However, the nature of the COVID19 shock to aggregate supply is also important; many past episodes of high inflation were caused by supply side shocks (e.g., oil supply cutbacks in 1973 and 1979). However, while we are seeing price rises on some food items where the supply chain has been disrupted, the overall CPI is still declining.

In the coming months, the combination of weak demand, high debt levels and a bankruptcy process that is much less favorable to small and medium sized businesses than to large ones will almost certainly lead to a supply reductions in other sectors (e.g., restaurants). Social distancing rules could have the same effect, even if a company stays in business. Think of a restaurant that can now seat only one third as many people as before, and must therefore triple its prices to stay in business (assuming that doesn’t destroy its demand).

COVID19’s disruption of global supply chains (reinforced by the intensifying cold war between China and the United States) will also almost certainly lead to reshoring of production, which could create new jobs and stimulate domestic demand. However, the impact of reshoring on supply side costs is uncertain; greater use of automation could result in lower supply costs at reshored facilities, while greater demand for labor with scarce skills could raise them.

Finally, we have to acknowledge two potential wildcards in this forecast. The first is a sharp increase in uncertainty related to the November elections in the United States (e.g., a Trump announcement that because of claimed interference he will not accept their result and leave office if he is defeated).

The second is the outbreak of open conflict between the China and the United States (e.g., a Chinese invasion of Taiwan or rapid escalation of a kinetic encounter in the South China Sea). The first might trigger an outflow from the US dollar, which would be inflationary if it was directed into another currency (e.g., the Euro or Yen), but less so if it primarily went into gold. The second would trigger a sharp increase in precautionary savings that would be deflationary (as well as increased demand for US Treasury securities and gold).

On balance, at this point it seems much more likely that the COVID19 shock’s net impact on demand and supply will lead to the Persistent Deflation rather than the High Inflation Regime.

Finally, at the 36 month time horizon, the forecast probabilities for the High Uncertainty (10%) and Normal Regimes (5%) also remain unchanged.

Portfolio Allocation Implications of Our Forecast

We take two approaches to deriving the tactical asset allocation implications from our analyses. The first takes a systematic approach, and is based on relative asset class valuations. Our starting point is our “neutral” model portfolio, which is equally weighted across nine broad asset classes, and also includes a 10% allocation to alpha strategies (equity market neutral and global macro) that are designed to have a low correlation to returns on broad asset classes. Based on asset class valuations, we systematically vary the asset class weights (but not the active strategy weight), increasing from 10% to 15% when an asset class is likely undervalued, and 15% when it is very likely undervalued. In the case of overvaluations, we go to 5% and then into cash, if there are no undervalued asset classes with room for an increase. In effect, this replicates the systematic rebalancing strategy we used for 15 years in our previous model portfolios.Based on subscriber requests, this month we are re-introducing a feature from the previous version of The Index Investor: Tactical Asset Allocation Implications from our analyses.

The second tactical approach is based on our subjective view not only of current asset class valuations, but also of the implications of the broader macro trends and uncertainties that we analyze each month. Importantly, this subjective view reflects our primary goal of avoiding large downside losses, rather than seeking large upside gains.

Two final notes: First, with respect to US fixed income, we include credit products (investment grade and high yield) in the same asset class as government debt, and will shift into the former when their valuations become attractive. Second, we regard gold not as a separate asset class to be held long-term, but rather as a complement to cash, into which we shift in periods of substantial overvaluation across multiple asset classes.

More information about our investment beliefs, including our core philosophy, approach to asset allocation (including our model portfolios and their long-term track record), and views on various approaches to active and passive management can all be found here.

Here is our latest asset allocation view:

Implications of the Most Recent Three Month Asset Class Returns

Our quantitative forecast methodology focuses on the level and change in three-month returns, over the most recent and previous three-month periods, for those asset classes, which should perform best under different regimes (in this sense, our regimes can be regarded as macro factors). We assume that relatively higher returns are associated with more widely held investor belief in the probability that a given macro regime will develop in the future.

Note: The language we use to describe our estimated likelihood of asset class over or undervaluation is based on US Intelligence Community Directive 203 on Analytic Standards, which includes the following table:

Market Stress Indicators (@29May20)

As always, this month’s Evidence File contains important new indicators and surprises observed over the past month.

Many of them indicate that a substantial increase in uncertainty very likely lies ahead that is not yet reflected either in dominant popular narratives or financial market prices. These include:

(1) China’s unilateral imposition of a new security law on Hong Kong that will sharply reduce personal liberties there. This will also trigger stronger opposition in Taiwan to closer ties with China. Along with the proposed US blockage of advanced microchip shipments to China (and possibly worsening domestic political conditions there), this raises the probability of some type of kinetic conflict between the US and China – e.g., rapid escalation of an incident in the South China Sea, or an outright invasion of Taiwan.

(2) Indications of worsening credit problems as COVID19 social distancing and other measures drag on.

(3) An increasing number of COVID19 infections in US states that were early to reopen their economies.

(4) Riots in many US cities in which property destruction and looting reached into affluent areas that had not been affected by riots in the 1960s. While ostensibly a reaction to the murder of George Floyd by a police officer in Minneapolis on May 25th, they also very likely represented an expression of many people’s rage and frustration over the increasingly unequal health and economic impacts of the COVID19 shutdown. These feelings will very likely further intensify as emergency government support payments to businesses and individuals expire, which will very likely lead to sharp increases in job losses, housing evictions, and pressures on an already overtaxed food bank system. In short, given building stresses on the economic, social, and political system, there is substantial potential for more demonstrations and riots in the run-up to the November election.

(5) President Trump’s authoritarian reaction to the George Floyd riots – particularly his threat to deploy military troops to cities to “dominate the rioters” – triggered stinging public rebukes from Admiral Mike Mullen and General Jim Mattis, two highly respect former Chairmen of the US Joint Chiefs of Staff. Ominously, as his approval ratings continued to decline, the president’s rhetoric about the potential illegitimacy of the November election has been increasing, causing some observers to wonder whether he will either attempt to cancel it, prevent the adoption of postal voting (which would be critical to voter turnout if the country is in the middle of a second wave of COVID19 infections), and/or refuse to accept his defeat if that is the final outcome.

Pre-Mortem Analysis

One of the most important forecasting disciplines is to ask yourself why your forecast could be wrong. Dr. Gary Klein’s research has shown that a very powerful and insightful way to do this is via a “pre-mortem analysis.” This method asks you to assume that it is a point in the future, and your forecast has been proven wrong (or your strategy or company has failed). You are then asked to look backward from this imagined point in the future, to explain why you failed, what you missed, and what you could have done differently to avoid your fate.

The pre-mortem method takes advantage of the fact that humans reason much more concretely and in more detail when explaining the past than they do when trying to forecast the future.

So let us assume that it is one year from now, and our current forecast has turned out to be wrong.

How did this happen? What developments did we fail to anticipate?

• A number of world leaders – Xi Jinping and Donald Trump, and to a lesser extent Vladimir Putin and Ali Khamenei are all facing sharply weakened economies and declining political popularity. History teaches us that this can lead to increased “foreign adventurism” to distract the public from worsening domestic conditions, as a nation rallies around its leader in a period of heightened external conflict. Should a “kinetic” conflict develop between China and the United States, or Iran and Israel, Saudi Arabia, and/ or the US, or between Russia and one or more European countries (e.g., due to a Russian incursion into the Baltics), it would generate a very sharp increase in uncertainty that would likely accelerate the already sharp COVID-19 economic slowdown. Given given high debt levels, this would very likely accelerate the arrival of the Persistent Deflation Regime.

• On the other hand, the removal from office (by one means or another) of Xi Jinping or Donald Trump * could * lead to a reduction in the dangerously growing conflict between the two nations, and increase cooperation in both the fight against COVID19 and the recovery of the global economy. This would somewhat reduce the probability of both the Persistent Deflation and High Inflation Regimes, and raise the relative probabilities of the High Uncertainty and Normal Times Regimes.

• A supply side shock of some type – beyond the disruption of global supply chains caused by COVID-19 -- could produce a sudden increase in inflation. The most likely scenario is a reduction in oil supplies due to a prolonged kinetic conflict between Iran and the US. An unlikely scenario could be major crop failures associated with the next solar cycle, which NASA forecasts will be the weakest in 200 years.

• Another route to the high inflation regime (repeatedly noted by Bridgewater’s Ray Dalio) would be a sudden loss of confidence in the US dollar relative to other currencies (driving up import prices), perhaps because of deficit monetization and policy paralysis if a severe downturn continues without meaningful policy reform to increase productivity and growth and reduce inequality. However, this would also require that there was relatively more confidence in another currency, with the Euro being the most likely candidate. This currently seems unlikely, given both the Eurozone’s economic and political situation, and the prospect of an intensifying conflict between the West and China. However, if confidence collapsed in all major currencies, a sharp increase in inflation would lead to a dramatic rise in the price of gold.

Note: Combining Our Forecasts with Others From Other Sources and Extremizing the Result Should Increase Your Predictive Accuracy

Research has found that three steps can improve forecast accuracy. The first is seeking forecasts based on different forecasting methodologies, or prepared by forecasters with significantly different backgrounds (as a proxy for different mental models and information). The second is combining those forecasts (using a simple average if few are included, or the median if many are). The final step, which significantly improved the performance of the Good Judgment Project team in the IARPA forecasting tournament, is to “extremize” the average (mean) or median forecast by moving it closer to 0% or 100%.

Forecasts for binary events (e.g., the probability an event will or will not happen within a given time frame) are most useful to decision makers when they are closer to 0% or 100% than the uninformative “coin toss” 50%. As described by Baron et al in “Two Reasons to Make Aggregated Probability Forecasts More Extreme”, forecasters will often shrink their probability estimates towards 50% to take into account their subjective belief about the extent of potentially useful information that they are missing.

When you average multiple forecasters’ estimates, you are including more information, which should increase forecast confidence and push the mean estimate closer to 0% or 100%. However, this doesn’t happen when you use simple averaging. For this reason, forecast accuracy is increased when you employ a structured “extremizing” technique to move the mean estimate closer to 0% or 100%.

You can download an extremizing model from our website to use when combining the forecasts you use in your decision process.

The extremizing factors in our model are those that the Good Judgment Project found maximized the accuracy of combined forecasts. Note that the extremizing factor is lower when average forecaster expertise is higher. This is based on the assumption that a group of expert forecasters will incorporate more of the full amount of potentially useful information than will novice forecasters.

Feature Article: Making Good Decisions in Highly Uncertain Situations

Introduction

At the end of December 2019, global leaders faced an environment that was becoming increasingly uncertain, due to the interaction of accelerating changes in multiple areas (e.g., technology, energy and the environment, the global economy, national security, society, and politics).

And then COVID-19 arrived, creating a global health and economic crisis, and plunging the world into what is, for many people, a situation of unprecedented uncertainty.

One of our core beliefs is that decisions should be judged not on their results (which will always be affected by both unforeseen factors and normal randomness), but on the quality of the process used to make them.

But in the fact of the complex, rapidly evolving uncertainty we face today, what constitutes a high quality decision process?

This month’s feature article provides our answer to that question.

There are many criteria that can be used to categorize different kinds of decision challenges. In our experience, the three most salient are time, the nature of goals and constraints, and the degree of uncertainty.

In situations involving high time pressure, Naturalistic Decision Making (NDM) researchers like Gary Klein and Marvin Cohen have determined that high quality decisions follow a common process:

• Using cues to recognize the situation and matching it to previous experience;
• Using that experience to quickly recall from memory a preliminary course of action (COA) that worked in the past;
• Mentally simulating whether it will achieve the minimally required goals in the current situation (i.e., satisficing);
• Implementing the COA if it does, and either modifying the initial COA or repeating the process if it does not.

For decisions in evolving situations involving multiple (and often competing) goals and complex constraints, researchers like RAND’s Robert Lempert have developed methodologies like ensemble modeling and Robust Decision Making (RDM). Other researchers continued to develop scenario planning as a decision support methodology under these same conditions.

In comparison to classic multi-attribute decision theory (which focuses on identifying the optimal COA in a relatively static situation), approaches like RDM and scenarios focus on making decisions that are “robust”. Such decisions maximize the estimated likelihood of achieving a given set of goals under a wide range of possible futures.

The nature and degree of uncertainty in a situation is another critical consideration in decision processes. Such uncertainty broadly falls into three increasingly severe categories:

Risk

• The underlying causal process generating key outcomes is understood theoretically and/or can be reliably modeled (e.g., using either classical econometric and statistical methods or via machine or deep learning);
• The distributions of possible values for the model’s parameters are well known (e.g., based on historical frequencies);
• The relationships between model outcomes and target goals is clear.

Epistemic Uncertainty

• The underlying causal process generating key outcomes is understood theoretically and can be reliably modeled;
• The distribution of parameter values is not well understood, but through more research it is possible to eventually understand it;
• The relationships between model outcomes and target goals is not clear, but through more research it is possible to eventually understand them.

Ontological Uncertainty

• The underlying process generating key outcomes is not fully understood theoretically and/or cannot be reliably modeled.
• This includes situations in which machine and deep learning are used to produce forecasts instead of theorizing about and modeling the causal processes that produce the outcomes of interest.
• It also includes data generating processes (like the political economy and financial markets) in which causation is complex (e.g., characterized by time delays and non-linearities) and constantly evolving.

Since the arrival of COVID-19, we have largely been operating in the Ontological Uncertainty zone.

At first, the health challenge in the face of disease was met by Naturalistic Decision Making methods, focused on COAs that satisficed versus a simple goal: Stop people from becoming infected and dying.

As the economic, social, and political costs of measures to stop people from dying increased, the goals and constraints facing many decision makers have become more complex, causing a shift from satisficing to a search for new courses of action that are robust across a range of possible future scenarios for the evolution of COVID-19 (e.g., the effect of social distancing and masks, and the timing and effectiveness of a vaccine). At the same time, we are gradually developing a better understanding of the underlying processes generating key health and economic outcomes in the novel situation we face. But, as recent events have painfully demonstrated, we still face considerable ignorance about the drivers of medium-term national security, social, political, and financial market outcomes.

Nine Steps to Better Decisions

So how do you make good decisions in the face of these conditions?

Step #1: Identify the goal(s) you’re trying to achieve, and the constraints on the possible COAs you could pursue.

Step #2: Regain your “Situation Awareness” (SA).

SA exists on three progressively more complete levels, which correspond to your ability to accurately answer three questions:

(1) What are the most important factors/variables/forces in the situation I face? In any uncertain situation produced by a complex adaptive system, there are always many. Focus on the ones that are most important, which will likely have the biggest impact on the way the situation evolves in the future.

(2) How are these factors related to each other? In complex adaptive systems, there are usually many relationships between key variables. The key to accurate forecasts is to focus on those relationships that are most important, which tend to be the ones that are changing the fastest, and/or have non-linear and/or time-delayed effects. These interactions are often the ones that generate emergent threats (and opportunities).

(3) How could the system evolve in the future, depending on how key uncertainties turn out, and/or the effects produced by possible actions you could take?


Step #3: More deeply examine your answers to the first two questions.

For each of them, start by listing the key facts (what you know to be true) that contributed to your answers about each important element and relationship.

Then list the key assumptions (what you believe to be true) that led to your answers. Where did those assumptions come from? Possible answers:

(1) Derived from theory. How reliable is the theory, and how accurately does it describe the current situation?

(2) Derived from history (e.g., a base rate). In an evolving system, history may not always be a good guide to the future.

(3) Derived from an analogy. How different is the analogous situation from the one you face today?

(4) Inferred from your previous personal experiences. How different were they from the situation you are facing?

(5) Social learning (e.g., what other people are saying/doing; the conventional wisdom or most popular narratives). To what extent are these people relying on the same set of common, but widely shared information inputs? To what extent do these common inputs conflict with your own information?

Step #4: More deeply explore the assumptions you have listed.

What is your 95% confidence interval around the “most likely” value you have estimated for an assumption?

What is the critical uncertainty or uncertainties that drive that confidence interval?

What could you do to reduce that uncertainty (e.g., collect key information, run an experiment, etc.), and narrow the width of your confidence interval?


Step #5: Use the facts and assumptions you have provided for Questions (1) and (2) to update your answer to question (3):

How could the system evolve in the future, depending on how key uncertainties turn out, and/or the effects produced by possible actions you could take? Remember that in complex adaptive systems, the distribution of possible outcomes usually follows a power law, and not the familiar “bell curve”. Put differently, they have a lot more “tail risk” than most people appreciate.

If possible, improve your predictive accuracy by combining your forecasts with those made by others using different methodologies and/or information.


Step #6: Identify possible courses of action (COAs), and forecast their outcomes versus your goals, based on your facts and assumptions about key situation factors and relationships.


Step #7: Test the robustness of each possible COA.

How much would each assumption have to change from its “most likely” value before you would choose a different COA?

The most robust COA is the one that will achieve your minimally acceptable outcomes versus your goals under the widest range of possible value for your assumptions.

Step #8: Always do a Pre-Mortem analysis.

As human beings, we have natural tendencies towards over-optimism and overconfidence that we need to offset.

Assume it is some point in the future and your preferred COA has failed. Ask why this happened. What indicators did you miss? What could you have done differently?

Based on your answers, either modify your COA or reconsider others.


Step #9: Implement your COA, while collecting key information and using it to constantly update your Situation Awareness.

Be especially alert to information that surprises you, which is a sign that your SA is incomplete. Write the information down, because you will soon forget it as your mind subconsciously tries to incorporate it into your existing mental model(s). Then take the time to consider what the surprising information means.

For more information on how to weigh new information to update your beliefs and Situation Awareness, see this post by our affiliate, Britten Coyne Partners.


Ultimately, the use of a good process to make decisions in situations of high uncertainty has two benefits. First, it raises the odd that you will make good decisions, and that over time this will result in cumulatively better outcomes for you than for people who use weaker decision processes.

Perhaps more important, good decision processes reduce your future exposure to self-destructive regret. Using a good process makes it far less likely you will torment yourself by endlessly revisiting past decisions and playing “woulda-coulda-shoulda” in your head.

High Value Information Observed In May 2020

In our model of the complex global macro system, change drivers are arrayed across a roughly chronological process (albeit one with many feedback loops), in which technological and environmental changes precede changes in the economy and national security, which in turn lead to changes in society and politics, all of which produce the effects we observe in investor behavior and financial market valuations and returns.


In our methodology, we classify new information as significant and highly valuable if either it (1) is an “indicator”, which reduces our uncertainty about the value of a parameter in our mental model for making sense of the dynamic macro system, or (2) it is a “surprise” which increases our uncertainty about either the range of potential values for a parameter or the structure of our model. With respect to indicators, the higher our priori probability is for a regime, the more we look for indicators that it will not occur, and the lower our prior probability for a regime, the more we look for indicators that it will occur. Put differently, we tend to look for high value indicators that disconfirm our prior views.

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System Tipping Points/Critical Threshold Analysis

Like Professors Andrew Lo, Doyne Farmer and others, we regard financial markets as a complex adaptive system (CAS), that exist as part of a larger macro system comprised of other CAS between which there are multiple feedback loops. These other systems include those that produce technology innovations, and economic, environmental, national security (including cyber), social, demographic, and political outcomes.

We also find that these systems tend to operate and generate effects in a rough chronological sequence, albeit with many feedback loops between them. The following chart highlights that the changes we observe in different areas at any point in time are actually part of a much more complex evolutionary process.

While most media coverage of these systems focused on flows (e.g., the size of the government deficit), rapid non-linear change in complex adaptive systems is often caused by a key stock (e.g., the amount of outstanding government debt) exceeding a critical threshold.

The next table highlights the key macro system stocks that we monitor.

In the next section, we will discuss information received over the past month that is related to these stocks, and which we believe is significant to our assessment of the probabilities that a critical threshold will be reached and a regime change will occur. We will conclude with our estimate, at the end of this month, of how close the macro system is to these critical thresholds, and the implications for financial market regime change probabilities.

How Close is the Macro System to One or More Critical Thresholds?

As we have noted, the macro drivers of financial market regime changes typically follow a rough chronological sequence, from technology to economic, security, social, and political causes and effects. Yet there are many feedbacks loops between them, creating complex root causes for many of the critical thresholds we have identified.

Understanding the time dynamics in this complex system is critical to avoiding substantial downside investment risk.

We use the UK Met Office Warning Model to communicate our assessment of these time dynamics. We estimate the time remaining before a critical macro system threshold is reached that could trigger a regime change, which is usually accompanied by substantial changes in asset class valuations.

The model uses three increasingly serious levels of warning, from “Be Aware” (condition yellow), to “Be Prepared” (condition orange), to “Take Action” (condition red).

For our purposes, we denote as “Be Aware” (yellow) critical thresholds that we assess to be three or more years away. We estimate that “Be Prepared” (orange) thresholds could be reached within 1 to 3 years. “Take Action” thresholds are very likely to be reached within one year.

Given their nature, we also note that in our three “wildcard” areas (Environment and Energy related; Disease and Human Caused Bioevents; and Cyber and Electromagnetic Events), our forecasts have higher levels of uncertainty.

The following charts summarize our current estimate of the time remaining before different critical thresholds will be reached.

Conclusion

At the highest level, we believe the complex adaptive global macro system can be in one of four states, based on its degree of order versus disorder, and degree of social cooperation versus conflict. A very coarse-grained reading of history suggests that these states evolve in a predictable cycle, from ordered/cooperative, to disordered/cooperative, to disordered/conflicted, to ordered/conflicted.

We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict, both domestically and internationally. Beyond some point, intensifying conflict eventually increases the degree of order in the system. That appears to be happening now, via the increasing conflict between China, Russia, and Iran and the United States and other Western nations.