The Index Investor
November 2018

Key Takeaways

• Based on global developments in October and through the US midterm elections in November, we conclude that we have transitioned from the Normal Regime to the High Uncertainty Regime. This should lead to falls of 20% or more in all equity asset classes.

• How long the High Uncertainty Regime will last, and which regime will follow it are now our key forecasting questions. At this point, we believe that over the next 12 months there is a low probability of either a return to the Normal Regime or transition to the High Inflation Regime (issues surrounding the latter are the subject of this month’s feature article).

• The probabilities are much more closely divided between staying in the High Uncertainty Regime for at least the next 12 months, and slipping into the Persistent Deflation Regime.

Asset Class Valuation and Momentum Indicators (@31Oct18)

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 (@31Oct18)

Compared to September, all five of our indicators have risen, which implies an increased by not yet critical level of underlying stress in financial markets at the end of October 2018, and thus a higher risk of substantial changes in asset class valuations. BB rated bonds’ spread over the 10 Year US Treasury is still just 2.53%, which put it in just the 26th percentile since the series started in 1996. Such low credit spreads on speculative grade bonds are usually a sign of excessive credit growth, which underlies many sources of market stress.

Forecast Discussion

We view financial markets as a complex adaptive system. The size of changes generated by such a system follows a power law rather than a normal (Gaussian) distribution. The critical point is that large changes are much more common in complex adaptive systems than most people’s intuition leads them to believe.

While predicting the behavior of complex adaptive systems remains far more art than a science, various researchers have found that large changes in these systems are often preceded by subtle warning signs, as stress accumulates within them. While this research is not definitive, we believe that five warning signs are worth monitoring as potential indicators of growing stress within financial markets that could suddenly give rise to large changes in asset class valuations.

Our first indicator is the month-to-month autocorrelation of broad asset class returns (i.e., the relationship of this month’s returns to last month’s). A system under increasing stress loses resiliency, causing it to take longer to recover from perturbations; hence, autocorrelation increases as it approaches a critical transition (see, “Early Warning Signals for Critical Transitions” by Scheffer, et al).

The one-month autocorrelation of returns for the broad asset classes we monitor increased to (.62) in October (.05) in September. This indicates that financial markets are becoming more ordered and are closer from a critical transition point than they were last month.

The second market stress indicator we monitor is the Equity Market Related Economic Policy Uncertainty Index published by the Federal Reserve Bank of St. Louis (via its FRED economic database), which is based on research by Baker, Bloom, and Davis (see their paper, “Measuring Economic Policy Uncertainty”). The index is based on automated text analysis of leading newspapers and magazine publications, to identify the frequency with which words and phrases are used that indicate uncertainty.

In our evolutionary past, when uncertainty increased our probability of survival was enhanced by staying close to our group. We still have that instinct. Research has found that as uncertainty increases, we have an unconscious bias towards higher conformity of our own views with those of a larger group (i.e., reduction in cognitive diversity). Behaviorally, heightened uncertainty induces more “social copying” of others, likely due to both conformity bias and the rational belief that others may be acting on the basis of superior information. This increase in conformity and copying makes a social system more ordered as uncertainty increases, and also reduces its responsiveness to perturbations (i.e., increases autocorrelation) because of delays in the social copying process.

The key point is that increasing uncertainty induces more, not less order in social systems, and in so doing primes them for sudden non-linear change.

For the month of October, the average Economic Policy Uncertainty Index stood at the 63rd percentile of its values since the data series began in 1985 – to be clear, 37% of values were higher over that period. However, our measure of intra-month uncertainty (the number of daily changes in the top and bottom 20% of the historical distribution) was in the 82nd percentile at the end of the month-- in just 18% of rolling 30-day periods since 1985, has it been higher. This is a very significant increase since the end of September. To reiterate the point made above, high levels of uncertainty tend to cause people’s opinions to become more ordered, due to a higher tendency toward conformity and social copying. This primes a system for sudden, non-linear change.

Our third market stress indicator is the spread between the yield on AAA rated bonds and the 10-year US Treasury. We interpret this as a proxy for the level of investor concern about financial system liquidity. At the end of October 2018, this spread stood at 1.12%, which was the 33rd percentile of all observations since the index series began in 1983 – 67% of observations have been higher.

Our fourth market stress indicator is the yield spread between speculative BB rated bonds and the ten-year US Treasury. Throughout history, excessive credit growth has been a root cause of many financial crises. An indicator of such growth is falling credit spreads, particularly in the case of riskier borrowers. At the end of October, this spread was only 2.53%, which put it in the 26th percentile of spreads recorded since this data series began in 1996 – 74% the previous observations were higher.

Our fifth market stress indicator is what we term the implicit “political risk premium” that is implicit in the price of gold. Our starting point for estimating this premium is the three different roles that gold plays. First, gold is a store of value in a world of fiat currencies. When the rate of money supply growth exceeds the growth of nominal GDP, gold’s price should increase to maintain its purchasing power.

Between 2007 and 2017, the US money supply (M2) grew by about 86%, while nominal US GDP grew by 35%. The stock of gold grew by 18%, based on mine production over this period. We therefore infer that 33% of the increase in the price of gold represented the maximum potential gold price change that could be attributed to a desire to hedge inflation risk (86% less 35% less 18%).

Second, gold is a unit of account. We take this to mean that the annual change in GDP expressed in terms of physical gold (i.e., nominal GDP divided by the price of gold) should equal the change in real GDP calculated using the GDP price deflator to account for actual inflation over the period. A key challenge is the point at which to start this calculation.

We chose the price of gold in 1995/1996. In that period, the change in real global GDP measured using the IMF’s price deflator just about equaled the change in GDP measured in terms of physical gold. We interpret that coincidence as indicating that at that point in time, concerns about future inflation and political risk were minimal, and the change in the price of gold was mostly driven by its role as a unit of account. We calculated a subsequent series of gold prices that would produce the same change in “gold GDP” as the actual real GDP as calculated by the IMF. Between 2007 and 2017, “gold as a unit of account” warranted a 21% increase in its price.

Gold’s third role is as a hedge against inflation and what we term “political disaster” risk. We subtract the 21% estimated compensation for actual inflation from the 33% “gross” inflation risk hedge to derive an apparent 12% increase in the gold price that reflected the true risk premium to hedge against possible future inflation.

However, between 2007 and 2017 the price of gold actually increased by 81%. This implies that 48% of this (81% less 21% less 12%) represented a premium for some other type of uncertainty at the end of 2017.

The interesting question is the nature of the uncertainty for which gold is believed by some investors to be a superior hedge than traditional ports in a storm like short-term US government securities, or similar securities issued by other developed countries.

The logical inference is that the uncertainty in question must reflect a situation in which short term US Treasuries would be a less effective hedge than gold. This could be a world of widespread hyperinflation, capital controls, and/or radical changes in nations’ governments (of course, this would also imply a preference for investing in gold coins rather than bullion, as while the latter may be a store of value, it is far less convenient as a means of paying for transactions).

To put this in further perspective, this gold price “disaster risk” premium sharply increased from 2008 to 2012, then declined before sharply increasing again after 2016. Arguably, a significant part of the former increase reflects concerns about the potential inflationary consequences of dramatic quantitative easing by central banks. But this is not likely to be the case after 2016.

Over the last month, the price of gold rose by almost 3%. Through October 2018, the price of gold has fallen by about 6% since the end of 2017, so, on a rough approximation, the political uncertainty premium now stands at about 42%, compared to 39% at the end of August.

Macro Regime Forecast and Implications for Asset Class Values

Based on new information collected and analyzed this month, and the result of the November mid-term elections in the United States (for which we delayed publication this month), we have concluded that we are entering the High Uncertainty Regime, which will eventually produce a fall of 20% or more in the value of all equity asset classes. Our forecasting question has thus become, what is the probability we will either remain in this regime or enter a different one over the next twelve months?

At this point, we estimate there is a 40% chance of remaining in the High Uncertainty regime over the next 12 months, which will see the commencement of what promises to be a tumultuous US presidential campaign, as well as resolution of the Brexit saga (or at least the “end of the beginning”, and possibly the “beginning of the end”). Elsewhere in Europe French president Macron’s reforms are running into stiffening resistance, Germany continues to search for a successor to Angela Merkel, and there is much uncertainty about whether Italy will trigger another Eurozone crisis (which, because of its size, will be much more dangerous than the ones we have seen thus far). And in Asia, China will continue to struggle with an intensifying trade conflict with the United States and rising domestic stress as the economy slows, and the consequences of its unprecedented debt growth become more painfully apparent.

We conclude that over the next 12 months, the probability of returning to the Normal Regime is slight, at 10%. For reasons we cover in more depth in this month’s feature article, we also believe that the probability of entering the High Inflation regime over the next 12 months is also slight, at only 5%.

We estimate that the probability of entering the Persistent Deflation Regime over the next 12 months is 45%. With the waning in the US of the stimulative effect of the Trump tax cuts, the negative impact of prolonged uncertainty will have an increasingly negative effect on aggregate demand. This will be magnified by the substantial amount of debt that has been taken on by many companies, which more of them will likely struggle to service, which in turn will put more pressure on their cost structures, and potentially lead to increased unemployment, which will accelerate the downward spiral.

Forecast Methodology

The focus of our monthly macro forecast is twofold. First, the probability of a change in financial market regime that causes changes of 20% or more in asset class valuations over the next year. Second, contingent on such a change taking place, the probability of a subsequent transition to other regimes.

Our analysis focuses on four possible macro regimes: (1) Normal Times, where equity asset classes perform well; (2) a High Uncertainty regime that is usually short and transitory, where asset classes like short-term government bonds perform best; (3) High Inflation, where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation, which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains most uncertain.

Our forecasting methodology is derived from our experience on the Good Judgment Project, as described in the book, “Superforecasting” by Gardner and Tetlock, as well as a range of other sources, from the intelligence community to systems dynamics and complex adaptive systems to statistics and political economy.

We start with base rate/reference case data about the historical probability of large changes in equity and bond valuations. We then analyze the current situation from both a quantitative and qualitative perspective. In the latter, we focus on the key endogenous drivers of macro regime change, including technological, economic, national security, social, and political trends and uncertainties. We also focus on three potential sources of exogenous shocks that could also produce a macro regime change, caused by environmental, disease, and cyber related events.

While most of our attention typically focuses on various flows (e.g., economic growth, change in the price level, sales, earnings, job creation, etc.), endogenously caused regime changes result when those flows push key stocks beyond a critical threshold or tipping point, often setting off non-linear reactions across multiple areas. As noted by Hyman Minsky and others, a classic example is the steady accumulation of outstanding debt until it reaches the point where it can no longer be serviced and triggers a crisis.

Base Rate Data

Since the end of World War Two, there have been fifteen months where a downturn in the US equity market began that eventually reduced asset class value by 20% of more. That is a hazard rate of about 1.75% per month. Put differently, in any given month there is a 98.25% probability that a 20%+ downturn won’t occur, or, in a given year, an 81% probability.

However, as the time without a 20%+ downturn extends, the compound probability that one will not occur shrinks. At the end of August 2018, it is more than nine years since the last equity market decline of 20% or more. The probability of that happening is only 15%.

To estimate the base rate for a 20% fall in bond prices (which historically has been caused by a sharp increase in inflation, as we saw in the late 1970s and early 1980s), we analyzed monthly historical AAA bond yields since 1919. For consistency, we used them to calculate the price of a ten-year zero coupon bond. We then calculated the probability of a price decline of 20% or more over three different holding periods: 12, 18, and 24 months. In any month, the annualized probability of a decline of 20% or more over the subsequent 12 months is 12%; over 18 months, 20%, and over 24 months, 25%.

The Current State of Quantitative Regime Predictors

Our quantitative 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.

As you can see in the following table, at the end of October 2018, this analysis indicates that, over the next 12 months, the balance of expectations appears to favor a shift to the High Uncertainty Regime, with a subsequent change to the Persistent Deflation Regime.

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.

In our methodology, new information is valuable insofar as it provides an updated indicator of how close we are to a critical threshold, or it is surprising and causes us to question the structure of our overall system model (e.g., the existence of another critical threshold we should monitor, or the range of possible outcomes for a key uncertainty).

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 estimate of the time remaining before different critical thresholds will be reached.

As described in our August 2018 issue, at the highest level, we believe the 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. We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict.

As we said at the beginning of this month’s forecast, based on new information collected and analyzed this month, and the result of the November mid-term elections in the United States (for which we delayed publication this month), we have concluded that we are entering the High Uncertainty Regime, which will eventually produce a fall of 20% or more in the values of all equity asset classes.

Our forecasting question has thus become, what is the probability we will either remain in this regime or enter a different one over the next twelve months?

At this point, we estimate there is a 40% chance of remaining in the High Uncertainty regime over the next 12 months, which will see the commencement of what promises to be a tumultuous US presidential campaign, as well as resolution of the Brexit saga (or at least the “end of the beginning”, and possibly the “beginning of the end”). Elsewhere in Europe French president Macron’s reforms are running into stiffening resistance, Germany continues to search for a successor to Angela Merkel, and there is much uncertainty about whether Italy will trigger another Eurozone crisis (which, because of its size, will be much more dangerous than the ones we have seen thus far). And in Asia, China will continue to struggle with an intensifying trade conflict with the United States and rising domestic stress as the economy slows, and the consequences of its unprecedented debt growth become more painfully apparent.

We conclude that over the next 12 months, the probability of returning to the Normal Regime is slight, at 10%. For reasons we cover in more depth in this month’s feature article, we also believe that the probability of entering the High Inflation regime over the next 12 months is also slight, at only 5%.

We estimate that the probability of entering the Persistent Deflation Regime over the next 12 months is 45%. With the waning in the US of the stimulative effect of the Trump tax cuts, the negative impact of prolonged uncertainty will have an increasingly negative effect on aggregate demand. This will be magnified by the substantial amount of debt that has been taken on by many companies, which more of them will likely struggle to service, which in turn will put more pressure on their cost structures, and potentially lead to increased unemployment, which will accelerate the downward spiral.

It is unlikely that any other asset class will experience a gain of 20% or more. There is a roughly even chance that gold could be the exception, and see a 20% or higher price increase. While the apparent inflation and political uncertainty premia in the gold price today are high relative to the last 25 years, they are still below the peak reached in 2012, and it is possible that herding in the face of higher uncertainty could produce 20% price gains.

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.

So let us assume that it is one year from now, and the high uncertainty regime has given way to a return to the Normal Regime, rather than to the Persistent Deflation regime.

How did this happen? What didn’t we anticipate happening?

(1) Perhaps because of an intensifying domestic debt crisis (and its own fear of Japanese-style deflation), or a belief that it had not yet achieved sufficient advantages to pursue more intense conflict with the United States, China reached a new trade agreement with the US and EU to support continued economic growth. This reverses (at least in the short-term) the growing tension in the US/China relationship, providing a strong confidence boost to the world economy and financial markets.

(2) Donald Trump’s replacement by Mike Pence, as well as divided party control of the US Congress after the 2018 mid-term elections led to new bipartisan initiatives to improve the productivity of the US healthcare and education systems, address stagnant middle class incomes, and reduce high levels of concentration in many industries. These and other structural changes contributed to higher expected growth rates, employment, and wage gains which, along with a more predictable and internationally focused United States, increased global confidence and produced a return to the Normal Regime.

Note: Combining this Forecast with Others and Extremizing the Result Should Increase 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: Examining the Arguments For and Against a Transition to the High Inflation Regime

There are two ways the High Uncertainty regime could transition to High Inflation. The first scenario would be a substantial negative supply shock, such as a sharp decrease in the supply of oil (e.g., due to Iran making good on its longstanding threat to mine the strait of Hormuz), or massive crop failures (e.g., due to faster and larger than expected changes in global temperatures). To put this risk into perspective, just three grains -- wheat, rice, and corn -- account for 40% of all global dietary energy consumption.

Inflation caused by higher oil prices would likely quickly self-correct, as higher prices would reduce aggregate demand, which in turn would eventually lead to lower oil prices and inflation. On the other hand, a supply shock to the global food supply would very likely take longer to correct.

In the second scenario, the US Government would increase spending to fight a potentially deep downturn, finance this spending with debt, and then monetize that debt by having it purchased by their central banks. A slightly different version of this scenario has been offered by Bridgewater’s Ray Dalio. He posits that entitlement payments will rise faster than tax collections, and widen the US Government deficit at the same time as investors (both domestic and foreign) are increasingly unwilling to purchase it unless yields significantly increase. Fearing that this will choke off economic growth, the US Government successfully pressures the Federal Reserve to purchase more government debt, which holds down yields but at the cost of a sharp increase in the money supply, which eventually precipitates a run on the dollar and a rise in domestic US inflation.

Our assessment of this second high inflation scenario will begin with this familiar macroeconomic equation: MV=PQ. On the left side, M represents the money supply (i.e., a stock) and V (for velocity) represents the rate at which a given stock of money is spent (i.e., a flow). On the right side, P represents the price level (i.e., an increase representing inflation and a decrease representing deflation), while Q represents the real output of goods and services in an economy over a give period of time (i.e., real GDP). Hence, P x Q equals nominal Gross Domestic Product.

In theory, if monetary velocity (V) and real output (Q) remain constant, an increase in the money supply (M) should produce an increase in the price level (P). Yet despite a very substantial increase in the money supply after the 2008 financial crisis, we did not see a sharp increase in inflation. Why was that?

Let’s look at how the individual components of the MV = PQ equation behaved between December 2008 and December 2017. The cumulative increase in the US price level (as measured by CPI) was 17%. The cumulative increase in real output was 19%. The cumulative increase in nominal GDP was 39% (since the PQ relationship is multiplicative, not additive).

But the M1 money supply increased by 124% over this period. The reason the US did not experience a sharp increase in inflation despite an unprecedented increase in the money supply (via the Fed’s Quantitative Easing policy) was due to an equally unprecedented fall in monetary velocity.

The obvious question is why this occurred. Why did the private sector choose to save money rather than spend it? In a 2014 blog post (“What does Money Velocity Tell Us About Low Inflation in the U.S.?”) economists at the St. Louis Federal Reserve Bank offered to possible explanations: “A gloomy economic outlook after the financial crisis” and “the dramatic decrease in interest rates [to zero or negative real yields] caused a portfolio shift away from interest bearing assets and into cash.” In essence, the latter describes a liquidity trap (see our May 2001 article, “What’s a “Liquidity Trap” and Why Should I Worry About It?”).

Let’s now consider how these results could differ if in the future the federal government increases deficit spending and finances it via the issuance of bonds that are bought by the Federal Reserve, because of a lack of market buyers at what the government perceives as an acceptable yield. Let’s further assume that this produces an increase in both real output (Q) and the money supply. Once again, the question of whether this results in a sharp increase in inflation algebraically turns on the likely behavior of velocity.

Our view is that if the increase in government deficit spending takes place in the context of weakening private sector demand, with the current level of private sector debt overhang, and real rates still low or negative, it is hard to see why velocity would increase from its present low level. But that said, at the current level, there is much less room for it to further decline, so arguably that could produce some increase in inflation – but probably not that much unless the monetary expansion and deficit spending were truly massive. But in the current political environment, with the Democrats poised to control the US House of Representatives, it is hard to see how that could come to pass.

Moreover, there are also three other important factors to consider. The first is the assumption that central bank monetization of part of the US deficit will be necessitated by a lack of other buyers for US government debt at a yield deemed acceptable by the Treasury. In a weak economy, that may also be facing a Eurozone crisis and a more aggressive China, this assumption may not hold true. Even if the price of gold skyrockets, at some point it cannot absorb the likely savings flows seeking a low risk haven in the storm. And while not as attractive an investment as in the past, US Treasuries may still be the least ugly of the alternatives on offer (e.g., because of the superior breadth and liquidity of the US Treasury market).

Second, it seems unlikely that the US Federal Reserve will give up its independence (and the anchor on inflation expectations it provides) without a political fight. Any assumption that an administration can easily win that fight and win approval for a large increase in deficit spending is likely to be wrong.

Finally, given the amount of supply capacity that has built up in the world economy relative to what has been weak demand in recent years (a powerful if latent deflationary force, as we have noted), it may also be the case that the potential increase in prices caused by rise in demand driven by government spending will be offset by a sharp increase in supply (unless, of course, that supply increase is blocked by much higher trade barriers or other political actions).

To be sure, history – and the present – warn us that hyperinflations can occur. But in the case of the United States, there are also a series of countervailing factors that seem likely to offset most (but not all) of the potential causal pathways to the High Inflation Regime.

If you have any questions about anything we have written in this issue, please don’t hesitate to get in touch, at contact@indexinvestor.com