The Index Investor
April 2020

This month we raised the probability of the Persistent Deflation regime over the next 12 months from 50% to 60%; increased the probability of the High Inflation regime from 10% to 15%; and reduced the probability of the High Uncertainty regime from 35% to 20%.

The key driver of the change to our forecast was the exponential and increasingly global impact of COVID19 on aggregate demand over the past month. The combination of an unprecedented collapse in demand and the very high levels of debt that prevailed before the coronavirus pandemic began is a recipe for deflation.

We also increased the probability of entering the high inflation regime over the next 12 months, from 10% to 15%. The logic here is driven by the unprecedented shock to global supply chains that COVID19 has wrought, along with central banks' monetization of equally unprecedented government deficits. If the economy takes longer to recover from the supply shock than from the demand shock (a big “if”), price increases could result, particularly if supplies of food and other goods in high demand are constrained. The danger is that these shortages and price rises (along with monetization of huge government deficits) could cause people to lose faith in the future value of a currency, and drive them to both further increase their demand for scarce goods and flee from their currency into other currencies or hard assets (e.g., gold, property, timber).

While at present this scenario seems very unlikely, it is critical to keep in mind that in periods of elevated uncertainty social learning and copying substantially increase, which makes the dominant narrative (i.e., the prevailing conventional wisdom) exponentially more fragile. As John Maynard Keynes noted in the middle of the Great Depression, “The state of confidence…is a matter to which practical men always pay the closest and most anxious attention.”

We did not make any changes to our 36-month forecast probabilities this month.

We continue to believe that the Persistent Deflation regime is most likely over this time horizon for three reasons.

The first is the increasing headwinds that were restraining aggregate demand growth before the devastating arrival of the COVID-19 pandemic. 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 reason is the unprecedented demand and supply shocks that are now hammering what was already a weakening economy that was struggling to service very high levels of debt, even at record low interest rates.

The third reason, which we explain in much greater detail in this month's feature article, is our conclusion that financial market valuations have not fully priced in the difficulties that lie ahead in the struggle to recover from COVID19, and how critical uncertainties are likely to evolve.

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 beliefs in the probability that a given macro regimes will develop in the future.

Over the last three months, there has been a relative strengthening of investors’ belief that the normal times regime lies ahead.

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 (@31Mar20)

Based on our qualitative analysis of accumulated and new evidence, we are more pessimistic about the future than those investors whose trades have driven recent returns on different asset classes. We continue to believe that three years from now the most likely outcome is that the global macro system will be in the persistent deflation regime.

This conclusion is based on three critical assumptions.

First, we don’t believe that investors fully appreciated the increasing strength and implications of the headwinds weakening global aggregate demand growth before the arrival of COVID19, and the difficulty in weakening and reversing them.

Second, we don’t believe that financial market prices fully reflect the medical obstacle that COVID19 still presents that must be overcome (and the length of time that could take) before the economy can be significantly restarted. Put differently, there are still substantial tail risks. The following chart maps them out:

Third, as described in much more detail in this month’s feature article, we don’t believe that investors have fully internalized the interacting impacts of COVID19 on the future outcomes of critical uncertainties in the areas of technology, the environment and health, the economy, national security, society, and politics.

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.

• 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 being a reduction in oil supplies due to a prolonged kinetic conflict between Iran and the US. A less likely cause could be major crop failures.

• 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: COVID's Impact in 2023: Trends, Uncertainties, Scenarios, and Forecast Probabilities

As a former student of Shell’s Pierre Wack, I have long appreciated the value of scenarios as a way of reasoning about a range of possible and plausible future outcomes driven by interacting trends and uncertainties. As a humble veteran of the Professor Philip Tetlock’s Good Judgment Project, I also know the power (and ego risks) of making forecasts that include specific probability estimates for verifiable outcomes. And above all, over 40 years I have seen time and again that complex adaptive systems generate a non-linear distribution of tail risks that are usually underestimated (in likelihood, speed, and impact), if they are recognized at all.

With those caveats in mind, this month I will build on a number of our previous Index Investor feature articles (e.g., “Global Macro Risk Dynamics in the 2020s and Beyond”, “The Next Downturn: How Different, How Deep? How Long?”, and “What Do We Know About Escaping the Persistent Deflation Regime?”) to provide some specific forecasts about the impact of the COVID19 pandemic on what the world will look like in 2023.

As a reminder, as we have often described in The Index Investor, our approach to global macro forecasting assumes that the changes we eventually observe in asset class valuations and returns are the end result of a roughly chronological process (albeit one with many feedback loops).

At the earliest stage of this process there are changes in technology and in the areas of energy and the environment (which also includes health related wildcards like disease and pandemics).

These fundamental drivers have a significant impact on subsequent changes in the economy and national security, which in turn affect social and political outcomes.

In taking this approach, our goal is to better capture the roles of time and speed, and to make second and third order consequences (and important forecasting questions) easier to discern.

At the end of December 2019, our recommended asset allocation was already highly defensive, based on our conclusion that rising stresses in many areas would eventually produce a sharp and very likely extended downturn in the global macro system.

Technology

• Poorly performing (but increasingly expensive) education systems, which slowed labor productivity growth and strengthened employers’ incentive to invest more in rapidly improving automation and artificial intelligence technologies. This limited wage growth and contributed to increasing inequality.

• In developed countries, the rising cost of health and social care systems that were struggling to effectively and efficiently serve populations that were both aging and suffering from a rising number of chronic ailments (e.g., diet and obesity related hypertension and type 2 diabetes).

Energy and the Environment

• Emission reductions in western nations were being more than offset by increases in China and other nations, resulting in accelerating changes in average global temperatures, rainfall, storm intensity, and other effects whose negative impact (in terms of both velocity and impact) was still highly uncertain.

• The marginal cost of producing traditional forms of fossil energy (e.g., the energy return on energy investment) was rising, while the full suite of technologies (e.g., grid control, utility scale battery storage, market design, etc.) needed for widespread deployment of cost effective solar electricity generation was not yet mature, and there was still considerable popular resistance to increased use of nuclear generation.

Economy

• A variety of forces were increasing downward pressure on aggregate demand growth, leading to a state of “secular stagnation”. These included (1) An aging population; (2) Declining labor share of GDP; (3) Worsening income and human capital inequalities; (4) Weak productivity growth; (5) Rising marginal cost of fossil fuel production; and (6) High debt levels, whose sustainability in an era of slow growth depended on the maintenance of historically low interest rates.

National Security

• Worsening relations between China and the United States, and, to varying degrees, between China and other nations.

• Slowing growth and very high levels of debt in China’s economy, which was very likely creating increasing pressures on the Xi Jinping regime, with very unpredictable consequences.

• Accelerating weakening of the European Union (e.g., Brexit).

Society

• Growing economic pressures on, and the shrinking size of the middle class in many developed countries.

• Even stronger pressures on the growing working class, as evidenced by a range of economic and social indicators, including disintegrating social networks, withering institutions, and rising “deaths of despair”.

• In many countries, growing alienation, falling confidence in traditional institutions (except the military), and intensifying popular anger at increasingly detached and ineffective elites.

Politics

• Rise of Left and Right Populism, growing polarization, and increasingly bitter political conflict in many developed countries, as traditionally stabilizing center left and right parties lost support.

• Growing evidence that governments were incapable of addressing critical issues, due to a combination of weakness in policy development, gridlock in policy debates due to interest group competition, and the increasing ineffectiveness of ossifying public sector bureaucracies in policy implementation.

Financial Markets

• Extremely high valuations across most asset classes, especially ten years into the post-2008 expansion, due to over-reliance on monetary policy and low interest rates to stimulate economies suffering from worsening structural problems.

• Very high government, personal, and corporate debt levels, with much of the latter provided outside the banking system, via high yield bonds, leveraged loans held in Collateralized Loan Obligation (CLO) structures, and credit hedge funds. Compounding the problem, a significant portion of this relatively illiquid corporate debt was held in vehicles (e.g., Exchange Traded Funds) that promised investors daily liquidity, which increased the risk of cascading market panic and debt deflation in a downturn.

At the end of December 2019, we here at The Index Investor were waiting for the arrival of the proverbial straw that would break the camel’s back. Instead, we got a boulder, in the form of a global coronavirus pandemic.

In the following sections, we will assess the potential impact of COVID19 on the key uncertainties we identified in our January 2020 feature, “Global Macro Risk Dynamics in the 2020s and Beyond.” This will include probability forecasts for specific questions related to the broad uncertainties we identified, and probability forecasts for the scenarios that could be produced by the interaction of these uncertainties.

As you will see, the scenarios that follow are consistently presented, so that the most benign outcome is in the lower left quadrant, and the most challenging is in the upper right quadrant.

Technology: The two uncertainties driving these scenarios are:

(1) The speed at which artificial intelligence capabilities (possibly enhanced by quantum computing technologies) develop and are deployed (e.g., their deployment will increase the speed at which many activities and processes are carried out, and may also, depending on the rate at which education systems improve, require fewer human workers).

a) Forecasting Question: What is the probability that, by the end of 2023, AI will have acquired a general capacity for causal and counterfactual reasoning that can easily be applied in evolving (non-stationary) environments? This will almost certainly lead to much more widespread deployment of AI. Outcome test: both Gary Marcus and Judea Pearl (two leading authorities in this area) will agree that these capabilities have been developed.

b) Impact of COVID19: Minimal on development. However, if there are still many people unemployed in 2023, if this goal is reached its actual deployment could be constrained.

c) Forecast: 33%. The achievement of this goal will very likely be based on significant departures from the currently dominant paradigm in AI, which is based on neural networks and deep learning. Instead it will require substantial development in symbolic representation and manipulation, which are not areas that today receive nearly as much funding as the dominant paradigm.


(2) The speed at which advanced energy technologies (e.g., batteries and other energy storage, grid control, solar, and carbon capture) are developed and reach a level of cost effectiveness that allows them to be effectively deployed at scale without sharply increasing consumer energy prices.

a) Forecasting Question: What is the probability that the capital cost per kilowatt-hour of utility scale battery storage will be less than or equal to $50 by the end of 2023. Based on recent research, this is the threshold required in order for combined solar/storage electric generation to begin to displace significant amounts of fossil fuel and nuclear generation capacity (see, “Storage Requirements and Costs of Shaping Renewable Energy Toward Grid Decarbonization”, by Ziegler et al).

b) Impact of COVID19: Minimal, unless subsequent rounds of government stimulus spending accelerates the deployment of alternative battery storage technologies.

c) Forecast: 20%. Cost projections for utility scale battery storage forecast best-case capital costs of about $170/kwh by 2025 (“Cost Projections for Utility-Scale Battery Storage” by NREL and “The New Rules of Energy Storage” by McKinsey). However, other, though less mature, storage technologies (e.g., flow and solid state batteries) have the potential to deliver substantially lower costs.

Environment: The two uncertainties driving these scenarios are:

(1) Whether food supplies are significantly affected by climate change.

a) Forecasting Question: What is the probability that maize (corn) production in the world’s four largest producing countries (US, China, Brazil, and Argentina) will experience a decline of 10% or more at any time between 2020 and 2023?

b) Impact of COVID19: Slow global recovery from the coronavirus pandemic should reduce the annual increase in emissions and, in the absence of increased solar activity, global warming.

c) Forecast: In “Future warming increases probability of globally
synchronized maize production shocks”, Tigchelaar et al find that if average global temperature increases by 2 degrees Celsius versus the 1850-1900 baseline, there will be a 7% chance each year that corn production in the world’s four larges producers will decline by more than 10%. Below 2 degrees, the probability is estimated to be minimal. In its most recent Decadal Forecast, the UK Met Office estimates that there is a 10% chance that the increase in global temperature versus the 1850-1900 baseline will exceed 1.6% by 2024.

Conservatively, assume that the annual probability of a 10% production decline equals 7% x 10% or about (rounding up) 1% per year. This leads to a probability of not experiencing a crop production decline is therefore 1-(.994), or about 4%.

However, NASA projects that the 2020 to 2025 solar cycle will be the weakest in the last 200 years, which could lead to lower than average temperatures that could also cause crop losses (e.g., see, “Do Sunspot Cycles Affect Crop Yields?”, by Vernon Harrison of the US Department of Agriculture, which found annual falls in Illinois and Nebraska corn yields of between 8% and 10% during years of low solar activity). Based on this limited solar data (which does not include the impact on maize output in China, Brazil, and Argentina), I increase the annual probability of a 10% decline in annual maize production between 2020 and 2025 to 10%. This increases the cumulative probability of a decline of 10% or more in annual maize production in any year between 2020 and 2023 to 35%.

(2) Whether infectious disease prevalence and severity are significantly affected by climate change.

a) Forecasting Question: Will the world experience a substantial increase in global disease prevalence and severity between 2020 and 2023?

b) Impact of COVID19: It has increased the probability to 100%.

c) Forecast: 100%

Economy: The two uncertainties driving these scenarios are:

(1) Whether and to what extent the average rate of productivity growth increases.

a) Forecasting Question: Given an aging population and (in the absence of immigration) shrinking workforce, increasing productivity growth is critical to raising living standards. Between 2009 and 2018, US total factor productivity growth averaged 0.84% per year, compared to 1.19% in the previous decade (based on OECD data). What is the probability that between 2020 and 2023, average US total factor productivity growth will equal or exceed 1.20% per year?

b) Impact of COVID19: Thus far, the arrival of COVID19 has been a massive negative shock to productivity, due to quarantining and the implosion of global demand. Between 2020 and 2023, its impact on productivity will depend on how soon productivity reducing factors like quarantines and social distancing are reduced, how fast both aggregate supply and demand recover, and the changes that are made to economic processes (e.g., home vs. office working, government support for innovation, industry structure, regulations, etc.).

c) Forecast: 10%. There is considerable tail risk associated with the time required to develop and deploy an effective coronavirus vaccine, which could result in intermittent social distancing. The slow recovery of the economic demand and employment could also result in constraints on the deployment of productivity increasing automation and artificial intelligence technologies if they were likely, on a net basis, to destroy jobs. To a substantial degree, this depends on the extent to which the US education system improves its results, since more capable workers speed the absorption across the economy of productivity increasing innovations. Unfortunately, there are substantial institutional barriers to improving US education results, and base rate data suggests they will not be significantly overcome before 2023, even in the wake of a dramatic increase in remote learning after COVID198 arrived.

(2) How the global debt problem (including governments’ off balance sheet liabilities for future pension and healthcare costs) is resolved.

a) Forecasting Question: Before COVID19 arrived, there were many signs that excessive debt had become a significant driver of weakening aggregate demand growth, which, (along with central bank monetary policy) had in the short term led to extremely low interest rates that made the growing debt burden sustainable. Based on Bank for International Settlements (BIS) data (the IMF has another debt dataset), at the end of September 2019, Emerging markets nations owed USD $57 trillion in debt (15 government, 29 non-financial corporate, and 14 household), while advanced countries owed $124 trillion (47, 43, 34). In total, this amounted to $181 trillion in debt, or 2.1x global GDP of USD $86 trillion.

Assume the average nominal interest rate on this debt was around 6.5% (say, 3.0% real) annual interest payments alone on this debt mountain required about 12% of global nominal GDP. And since, before COVID19, real global GDP was only growing at about 3% or so per year, the world was already very close to the threshold of the “debt trap” (where the real interest rate is greater than the real growth rate) in which outstanding debt would grow uncontrollably. Commentators like William White (formerly of the BIS and now at OECD) believed that these global debt levels had reached a level where prospective economic growth could not sustain them, and that massive restructuring (e.g., via write-offs and/or debt/equity conversions) would be necessary to return to the global economy to a health and sustainable growth path.

In light of this, here is our forecast question: By the end of 2023, at the global level, will at least USD $20 trillion dollars in individual, corporate, and emerging market debt (of all kinds) have been restructured (including write-offs, extensions, and debt/equity conversions)? Note that this amounts to about 15% of emerging markets plus advanced non-financial corporate and household sector debt. In comparison, in the case of the 1980s Latin American debt crisis, private lenders wrote off or converted to equity about 33% of the debt they were owed.

b) Impact of COVID19: The pandemic has dramatically increased government issuance of new debt (and to a lesser extent corporate issuance). Much of this newly issued new government debt has been monetized by central banks and held on their books. The sharp reduction in global demand has almost certainly made it extremely difficult or impossible for borrowers to service a substantial – but as yet unknown – portion of the global debt load.

c) Forecast: 67%. Fundamentally, there are only four ways to solve the problem of excess debt: (1) Grow your way out of it. Given the headwinds already facing global aggregate demand before COVID19, and its negative impact, this approach is very unlikely to succeed. (2) Impose prolonged austerity to facilitate debt repayment. In today’s political environment, this would very likely be suicidal. (3) Inflate it away. This only works when debt has a relatively long average maturity, and is denominated in the currency of the country trying to inflate away its debt. For a substantial portion of outstanding global debt, these conditions do not hold. In addition, imposing high inflation on an economy after the COVID19 shock would almost certainly have negative social and political consequences. That leaves (4) writing off, converting to equity, or otherwise restructuring a portion of outstanding debt to reduce the burden it imposes on the economy, and free up funds to stimulate spending and renewed economic growth.

On the other hand, it is undeniable that creditors have a substantial amount of political power, and will likely try to use it to minimize debt write-downs. However, as we have seen in Japan, avoiding debt restructuring keeps alive many so-called “zombie” companies whose continued existence tends to depress productivity growth.

National Security: The two uncertainties driving these scenarios are:

(1) Whether the China-US conflict intensifies.

a) Forecasting Question: While there are many dimensions of the US-China conflict, one of the easiest to measure is trade in goods. Over the five years ended in 2019, the US spent an average of $488 billion per year on goods imported from China. If the conflict between the two nations intensifies, these flows should shrink. Hence, our forecasting question is this: In 2023, will US imports from China (expressed in 2019 dollars) be less than $244 billion (i.e., 50% lower than the five year average)?

b) Impact of COVID19: The pandemic has increased distrust between the US and China, and should also convince many companies to shorten their supply chains and produce more previously imported goods in the United States. Worsening relations between the two nations, and increasingly onerous Chinese regulatory requirements (e.g., sharing of intellectual property) had already spurred the movement of production out of China and into other Asian countries (e.g., Vietnam). Worsening US-China relations after COVID19 should accelerate this trend.

c) Forecast: 67%. If this forecast turns out to be wrong, the most likely reason will be a change in both nations’ leadership. However, this would also require a substantial change in many policies China has put in place in recent years (which may not be politically possible, even for a new leader). It would also require reversal of US attitudes towards China, which in recent years have considerably hardened across the political spectrum.


(2) Whether cross-border migration flows substantially increase.

a) Forecasting Question: Between 2010 and 2019, international migration flows (as estimated by the UN, in their “International Migrant Stock” database) grew at a compound rate of 2.92% per year, with the number of migrants reaching 272 million in 2019. Will the number of international migrants in 2023 exceed 343 million (compound growth of 6% per year between 2019 and 2023)?

b) Impact of COVID19: If the pandemic has a substantial impact on countries in Africa, the Middle East, Latin America, and South Asia, it will very likely increase motivations to migrate.

c) Forecast: 15%. COVID19’s effects will almost certainly increase resistance to inward migration in nations many migrants seek to reach (e.g., because of fears of importing disease, and/or continuing high unemployment in a still weak economy). Border conflicts will very likely increase, which will discourage large-scale migrant flows.

Society: The two uncertainties driving these scenarios are:

(1) Whether income, wealth, generational, and regional inequalities decrease or increase.

a) Forecasting Question: Based on Census data, in the United States the top 20% of Households earned 44% of Household Income in 1980. The top 5% earned 17%, and the next 15% earned 27%. By 2018, the top 20% share was 52%, with the top 5% share 23% and the next 15% share 29%. It is interesting to note that almost all (five out of six percent) of the increase in the top 5%’s share came between 1980 and 2000, with just another 1% increase coming between 2000 and 2018. This suggests that the financialization of the economy may have had a bigger impact on the top 5%’s income share than did globalization and the growth of “winner take all” technology-driven markets since 2000.

Our forecasting question is this: In 2023, will the top 5%’s share of US income still be equal to or greater than 23%?

b) Impact of COVID19: Extended weak economic growth would very likely cause declines in asset values, reducing capital gains, interest, and dividend income of the top 5%, and in some cases, their labor income as well. Lower earners will also suffer income losses. Tax rates on top earners will almost certainly increase if the Democrats win the White House and both houses of Congress. Even if this doesn’t occur, a tax increase is likely, given the amount of debt being incurred by the US government to fund its response to COVID19.

c) Forecast: 80%. First, because of their political donations, top earners will be able to effectively resist calls for much higher taxes. Second, COVID19 will very likely cause a great reduction in incomes in the middle and lower quintiles of the US income distribution than it will at the top. This may even result in an increase in the top 20% and 5%’s share, despite absolute declines in their income.

(2) Whether the level of individual alienation and fragmentation of social bonds and cohesion decreases or increases.

a) Forecasting Question: The Social Capital Project of the Joint Economy Committee of the US Congress (JEC) has created a data set measuring annual “Deaths of Despair” per 100,000 population. The theory advanced by Anne Case and Angus Deaton (in their book, “Deaths of Despair and the Future of Capitalism”) is that these deaths are closely related to the deterioration of prevailing social and psychological conditions. The JEC report on “Long Term Trends in Deaths of Despair” found that, “In 2000, there were 22.7 deaths of despair per 100,000 Americans — not that different from the 1970 rate of 21.5. By 2017, the rate had doubled to 45.8 per 100,000.” In 2018 it dipped slightly to 45.3. Between 2009 (the first year after the Great Financial Crisis) and 2018, Deaths of Despair per 100,000 at a compound annual rate of 4.5%.

Our forecasting question is this: In 2023, will the rate of Deaths of Despair, as measured by the JEC, be equal to or greater than 60 per 100,000? This number assumes that the rate grew by 4.6% per year through 2019, and then increased by 50%, to 6.75% per year, between 2019 and 2023.

b) Impact of COVID19: The social distancing and quarantining required to combat the coronavirus pandemic, and its likely economic aftermath, will probably further weaken many people’s social bonds and worsen their psychological stress and mental health.

c) Forecast: 80%.

Politics: The two uncertainties driving these scenarios are:

(1) Whether political polarization increases or decreases.

a) Forecasting Question: There is evidence that worsening political polarization (what some have called “the cold civil war”) has had a significant negative impact on investment, output, and employment in the United States (see, “The Political Polarization Index” by Marina Azzimonti of the Federal Reserve Bank of Philadelphia). For example, the Philadelphia Fed’s monthly “Partisan Conflict Index” had an average value of 94 between its start in January 1981 and December 2007. However, between January 2008 and December 2015, its average increased to 130, indicating rising partisan conflict. Azzimonti’s research has shown that this increased uncertainty, which in turn contributed to the slow recovery from the Great Recession). Most recently, between January 2016 (the start of the last presidential campaign) and February 2020, the index average rose even higher, to 167. At the end of February 2020, it stood at 151.

Our forecasting question is this: Between February 2020 and the end of December 2023, will the average value of Partisan Conflict Index be greater than 151?

b) Impact of COVID19: It is not obvious how COVID19 will affect partisan conflict in the United States. We have already seen such conflict over the way the pandemic has been handled thus far. A key question is whether conflict will intensify during and after the November election, as it did in the lead up to and years since the 2016 election. It seems likely that, regardless of who wins in November, much will depend on the effectiveness of America’s evolving response to COVID19 between now and December 2023.

c) Forecast: 67%. For a growing number of people, in recent years their political orientation has evolved from one of many identifies to a meta-identity that is more closely correlated with other identities (e.g., class, race, gender, geographic location, the sector in which one works, religion, consumer preferences, etc.) than ever before. This suggests that a higher degree of partisan conflict is now “hardwired” into the American political system, and indeed into the culture itself. It is possible that, as was the case in America after 9/11 or New Orleans after Katrina, a very effective national response to COVID19 could reduce political polarization, at least for a time. But it seems unlikely.


(2) Whether national capacity for taking collective action to address critical issues increases or decreases.

a) Forecasting Question: National capacity for taking effective national action in the face of potentially existential threats is not only a critical element of national power, but also a key driver of public respect for and belief in the institutions of government and society more broadly. COVID19 is currently testing this capacity in many nations.

Each year, Gallup polls Americans’ confidence in various institutions. In 2019, the percentage expressing “a great deal” or “quite a lot” of confidence in different institutions was a follows: (1) The Presidency 38%; (2) Congress 11%; (3) Medical System 36%; (4) Banks 30%; (5) Big Business 23%; (6) Public Schools 29%; (7) TV News 18%; and (8) Organized Religion 36%.

Our forecasting question is this: In 2023, will at least six of these institutions see an decrease of 10% or more in the percent of people expressing a great deal or quite a lot of confidence in them?

b) Impact of COVID19: Institutions’ response to COVID19 will have a very substantial, and likely lasting impact on the public’s confidence in them, and thus on national capacity for collective action in the face of future threats.

c) Forecast: 80%. Popular frustration with various institutions has been increasing since the number of COVID19 cases began to exponentially increase. If this forecast turns out to be wrong, it will be due to (a) the election in the United States of a broadly acceptable centrist government, (b) that placed in positions of leadership practical leaders of acknowledged competence, integrity, and empathy, who collectively worked to (c) enact and implement policies that addressed the deep structural problems that, before COVID19, led to a recognized increase in “secular stagnation.”

Conclusion

There is still a very high degree of uncertainty about how the COVID19 crisis will evolve, and the impact it will have on the global macro system, broadly defined.

Through the use of trend and scenario analysis, as well as specific forecasting questions, this article has provided a structured framework for evaluating the uncertainties that underlie the high level of anxiety about the future that most of us feel today.

The conclusions that emerge from this analysis are neither as bad nor as good as they could be.

Between 2020 and 2023, it seems unlikely that we will see the deployment and scaling of substantially improved artificial intelligence and efficient utility scale battery storage technologies, both of which would have disruptive impacts on employment.

In the area of the environment and health, the global pandemic that we previously cited as a critical uncertainty has arrived. The other major uncertainty is a possible disruptive reduction in food supplies due to environmental changes. This could increase food prices and trigger social and political unrest. The specific forecasting question focused on a fall of 10% or more between 2020 and 2023 in the production of maize/corn, the world’s largest grain crop. We believe this is more probable than most people realize, in part because of the impact of the impending low solar cycle on global temperatures.

In the critical area of the economy, we conclude that the probability of a significant increase in average productivity growth between now and 2023 is, unfortunately, low. If the focus of governments’ response to COVID19 remains returning the economy to the December 2019 status quo ante, any victory won will be Pyrrhic, as that economy was facing multiple headwinds that were intensifying a “secular stagnation” that eventually would have triggered a global crisis. If the economy is to recover (with attendant social and political benefits), government interventions must address the deeper root causes at work. But there is, as yet, no sign that this is going to happen.

Compared to our bleak outlook for productivity growth, we are more confident that we will see substantial debt restructurings, equity conversions, and write-offs. However, due to the political power of creditors, we forecast that, as a percentage of total debt, the amount restructured will likely fall well short of the 33 percent of debt that was restructured to resolve the Latin American debt crisis and restore healthy growth. As a result of this policy failure, high debt levels will likely remain a structural drag on economic growth and slow or block recovery from the COVID19 shock.

In the area of national security, we conclude that conflict between the US and China will continue to increase. The good news is that the lingering effects of COVID19 in China are likely to restrain its external ambitions, and perhaps eventually trigger a change of leadership.

We also conclude that stricter (and more conflict laden) control of developed nations’ borders will ultimately limit the international migration flows (and domestic disruption in destination countries) that the devastating impact of COVID19 would otherwise likely trigger.

With respect to social uncertainties, we do not expect a reduction in current levels of inequality in the United States. We also expect that COVID19 will accelerate the fraying of the social fabric, and result in an increase in deaths of despair. Social pressures will likely intensify.

Politically, the net result of all these developments is likely to be increased polarization and partisan conflict (supercharged by what promises to be a uniquely bitter presidential campaign), as well as further declines in public confidence in many institutions. Together, these developments will further sap national capacity for collective action.

The bottom line is that we believe that investors (and the valuations to which their beliefs give rise) are currently over-optimistic, and have not given sufficient weight to a scenario in which recovery from the COVID19 shock will be far more difficult, and take much longer than many now believe.

Finally, two caveats are in order.

The first is that these forecasts must be updated in light of new high value information, including high likelihood indicators and surprises. We will do that, and update these forecasts in future issues.

The second is that the accuracy of a forecast is usually increased when it is combined with other forecasts that are based either on a different methodology and/or different information inputs. As always, we strongly recommend that forecasts made by The Index Investor be combined with those obtained from other sources.

High Value Information Observed In March 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.