The Index Investor
August 2019

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 (@31Jul19)

At the end of July, all but one of our indicators showed lower level of underlying market stress (the fifth, the liquidity spread, while unchanged on the month, is still significantly above its recent low in April 2018).

Macro Regime Forecast Probabilities (@31Jul19)

The Current State of Quantitative Regime Predictors

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 higher returns are associated with a higher underlying probability for the relevant macro regime.

This month’s Evidence File (see below) summarizes the high value indicators and surprises we observed this month in the areas of technology, the economy, national security, society, politics, and investor and financial market behavior. Here are the most important ones that affected how we updated our regime probabilities this month.

We have long noted that the impact of increasing uncertainty on the economy and financial markets only operates with a delay. This month saw reports of US retailers accelerating their exit from shopping mall locations as online shopping grows, and manufacturing confidence in Germany in “freefall.” But we also saw reports that were likely to further reduce uncertainty, including rising tensions between the US and Iran, continued clashes in Honk Kong and an increasing threat of military intervention, and Boris Johnson becoming Prime Minister in the UK, which raises the probability of a “No Deal” Brexit at the end of October.

At a slightly longer time frame, another report suggested that the probability of a Russian cut off of gas flows to European customers in January is increasing, because of the ongoing failure to resolve disputes over the Nord Stream II pipeline. While the impact of a Russian gas cutoff will be offset by higher LNG flows to Europe from the US and other suppliers, their impact remains uncertain.

Finally, at a time horizon that is longer still, a number of new reports, from the Federal Reserve Bank of San Francisco and McKinsey Global Institute, among others, strengthened our belief that, as was the case at the beginning of the Industrial Revolution, the accelerating deployment of automation and artificial intelligence technologies is likely to have a substantial negative impact on employment and competition (in part because of a growing shortage of workers with appropriate skills), with attendant social and political effects.

On the basis of this new data and analysis, I’ve increased the 36 month probability of being in the Persistent Deflation regime from 35% to 40%, decreased the probability of being in the Normal Times regime from 15% to 10%, decreased the probability of still being in the High Uncertainty regime from 40% to 25%, and increased the probability of being in the High Inflation regime from 20% to 25%, based on an underlying logic of a sharp economic downturn that triggers deflation coming sooner than previously expected (given rising uncertainty on multiple fronts), the rising chance of Donald Trump being reelected, and the attendant likelihood of policy paralysis in Washington, which has a better than even chance of leading to monetization of rapidly rising deficits and eventually a loss of confidence in the US Dollar.

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? Here are three possibilities:

• The leaders of the world’s three major powers – Xi Jinping, Donald Trump, and Vladimir Putin are all facing weakening 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 between Russia and one or more European countries, it would generate a sharp increase in uncertainty that would likely cause an equally sharp economic slowdown and, given high debt levels, speed the arrival of the Persistent Deflation Regime.

• As we have previously noted, while the probability is remote, a supply side shock of some type could produce a sudden increase in inflation – the most likely scenario being a reduction in oil supplies due to a kinetic conflict in the Middle East (e.g., escalation of the current US-Iran conflict) that produced a prolonged disruption in global oil supplies, or, less likely, an infectious disease pandemic or major crop failures (e.g., due to climate change and/or disease).

• While we believe it is very unlikely, we can envision a scenario in which for a range of possible reasons, both Xi Jinping and Donald Trump leave their current roles, and are replaced by leaders who are more committed to lessening conflicts both between China and the United States and in the international system as a whole. This would likely provide a strong boost to confidence (and thus lead to an equally strong reduction in uncertainty). Whether this would also create an opening for a reduction in domestic political conflict in the United States, and thus progress on policy reforms to address weak growth and rising inequality isn’t clear.

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.

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.

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.

High Value Information Observed In July 2019

In our methodology, we classify new information as significant and highly valuable if either it (1) is an “indicator”, which reduces or increases 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, and causes us to revaluate the structure of our mental model.

Feature Article: The Next Downturn: How Different? How Deep? How Long?

"Each [failed estimate] involved historical discontinuity, and, in the early stages…unlikely outcomes. The basic problem was…situations in which trend continuity and precedent were of marginal, if not counterproductive value."

“Report on a Study of Intelligence Judgments Preceding Significant Historical Failures”, CIA, 1983 (Declassified 2006)

The global economy is approaching the end of a ten-year long expansion following the 2008 financial crisis. This analysis focuses on three questions that increasingly preoccupy investors: How different will the next downturn be from those we have experienced in the recent past? How deep will this downturn be? And how long will it last?

How Different?

At the highest level of aggregation, our model of global political-economic dynamics is driven by two factors: (1) the natural tendency of complex adaptive systems to vary between states of relative order and relative disorder, and (2) the natural tendency of social systems to vary between periods of cooperation and conflict.

Together, these give rise to four regimes that recur throughout history in a predictable sequence: order/cooperation; disorder/cooperation; disorder/conflict; and order/conflict. At the risk of appearing overly deterministic, a crude approximation is that each regime occurs at intervals of about 40 years. Today’s regime is characterized by growing disorder and conflict; by our reading of history, we previously saw such regimes in the vicinity of the 1970s, 1930s, and 1890s (again, we stress these decades are just approximations of both the timing and length of these regimes).

All of these periods of growing turmoil were followed by ones in which heightened conflict (WW1, WW2, and the Cold War in the 1980s) re-imposed a degree of order on the global system that, following the resolution of the conflict, later gave way to increased cooperation.

So in this sense, the next downturn will very likely take place in a global political-economic context that many people working in companies, financial markets, and government have never experienced before.

Beyond this context, there are many disruptive trends underway that are likely to make the next downturn very different from ones we have seen in recent years. Looking just at technology, economics, and finance, these include:

Technology

• Transition from industrial to digital/knowledge-based economy; the last time this happened (agriculture to industry) it took 40+ years and involved substantial economic disruption, social suffering, and political unrest.

• We now live in a world of global hyperconnectivity, where information, emotion, and behaviors diffuse with unprecedented speed.

• Persistent weakness in aggregate demand, driven by a complex mix of reinforcing causes that include population aging; record levels of debt/GDP; a declining labor share of national income, compounded by worsening income inequality and winner-take-all competitive dynamics in a growing number of markets; and financialization of the economy (which has driven increases in leverage at non-financial corporations, and a preference for passing on the benefits of productivity gains to investors rather than workers).

• Expansion of potential supply in many traded industries, due to globalization (particularly since China joined the World Trade Organization in 2001) and/or improved technology.

• Bifurcated productivity growth, with high rates in some companies and industries (which has put downward pressure on employment in them), and low to negative productivity gains in others (e.g., healthcare and education), which has led to substantial price increases relative to incomes, and increasing pressure on household and government budgets. In the case of households, further pressure has come from restrictions on housing supply, which in many markets has driven up prices faster than income.

• The increasing use of algorithmic decision-making has made many markets both efficient and potentially less liquid in a downturn.

• The growth of indexing, and particularly products like ETFs based on potentially illiquid assets (e.g., corporate bonds) that promise investors daily liquidity.

• Record levels of total credit/GDP, and the increasing use of credit growth to support current consumption (by the private and public sector), rather than investments that theoretically will increase both demand and supply. Put differently, weakly growing income streams are supporting ever-higher levels of debt. As Michael Pettis has noted, use of credit to support current consumption necessarily leads to reduced growth and consumption in the future (see, “Why a Savings Glut Does Not Increase Savings”).

• Historically low levels of interest rates, including a rapidly rising stock of sovereign debt that now pays negative rates of interest, even as ratios of government debt/GDP have risen to historically high levels. To put this as starkly as possible: interest rates (the cost of credit) have been falling, even as the stock of public and private sector credit (and arguably its riskiness) has rapidly grown.

• Aggregate demand growth has been weak, with consumption supported by increasing expansion of credit. The recovery of aggregated demand is constrained by factors that cannot be changed quickly. These include demographic forces, depressed labor share of national income, income inequality, weak productivity growth (e.g., in healthcare and education), and record levels of credit/GDP. And with interest rates approaching, at, or, in real terms, below the Zero Lower Bound (ZLB), the effectiveness of monetary policy has been greatly reduced.

• Globalization substantially increased the productive capacity of the world economy. In a world of weak demand, this supply shock was fundamentally deflationary. In a world of low debt/GDP ratios, this deflation could have been beneficial. However, in an increasingly leveraged global economy, its impact is much more likely to be negative. While trade conflicts and disruption of international supply chains may either temporarily or permanently reduce effective global capacity, they will not reduce the growth in potential supply, and thus deflationary pressures, being created by the increasing use of automation technologies.

• The current level of uncertainty in the world is already high (and thus weighing down aggregate demand growth), due to a combination of technological, economic, environmental, national security, social, and political trends and events (e.g., see the Cumulative Evidence File on our website).

• Liquidity problems and high-speed algorithmic decision making will accelerate any significant downturn in financial markets.

• Social media and other forms of hyperconnectivity will rapidly transmit shocks that further increase already high levels of uncertainty, and trigger reductions in consumer and business spending.

• Businesses will initially cut workers to stave off debt problems. This will further reduce spending.

• Accelerating declines in spending will be translated into accelerating defaults on household and non-financial corporate debt.

• Spending declines and rising defaults will cause declines in equity market values, which will feed back into an accelerating vicious cycle.

• Current political conflicts, both domestic and international, will prevent the development of a coherent narrative that might otherwise stem the accelerating rise in uncertainty and fall in confidence. Instead, these conflicts are likely to increase under the pressure of a global economic crisis.

• While the chances remain very unlikely, global economic decline may, either because of the preoccupation of the United States and other nations with domestic crises, and/or because of their urgent need to deflect attention from accelerating domestic crises, tempt China, Russia, Iran, and/or Korea to undertake a sudden strike intended to substantially improve their geostrategic advantage. These could include China invading Taiwan, a Russian move into the Baltic nations, an Iranian attack on or attempt to seize Saudi oil fields, and/or an attack by North Korea on South Korea. Any of these, particularly if accompanied by attacks on US space based systems, and/or cyber attacks on US infrastructure (e.g., power, etc.) would almost certainly trigger a global conflict.

• Coordinated fiscal stimulus across countries that is focused on investment that will support higher growth, such as infrastructure, research & development, and improved education outcomes (although the US experience after 2008 with allegedly “shovel ready” infrastructure projects that were repeatedly blocked by various parties’ litigation makes one skeptical about the future effectiveness of fiscal stimulus);

• Agreement among countries to restrict the use of damaging monetary, structural, and trade policies;

• Initiatives to reduce income inequality, which may need to include initiatives to increase competition/reduce concentration in key sectors of the economy;

• Initiatives to increase productivity growth, especially in those sectors where it has been chronically low (e.g., healthcare and education). Note, however, that achieving this will require accelerated deployment of productivity improving technologies like automation and artificial intelligence, as well as an increase in the number of employees who can apply them;

• Initiatives to increase the supply of housing and reduce its price;

• New initiatives (including regulatory initiatives) designed to minimize the employment impact from the deployment of productivity enhancing automation and artificial intelligence technologies (note that some of these will tie back to improved productivity in the education sector, e.g., around lifetime learning). For an excellent summary of what these initiatives might include, see Oren Cass’ excellent new book, “The Working Hypothesis” and the summary of it in an article with the same title in The American Interest.

• Extended austerity provokes political resistance, particularly in an environment of substantial income inequality, and easy social comparisons between the majority’s reduced consumption and the elite minority’s conspicuous consumption. Except over short periods, it is not sustainable.

• I have more confidence than White in the ability of higher growth to reduce the burden of debt, having seen this happen, for example, in Mexico and Chile. However, triggering that growth requires both fiscal stimulus and structural reform, including some level of debt relief.

• Inflating away debt only works under two circumstances: (a) when most of a nation’s debt has been issued in its own currency, and (b) when the average maturity (or duration) of the debt is sufficiently long to enable a rise in inflation to produce a substantial reduction in its real value and burden. For example, many members of the Greatest Generation in the United States saw the real value of their 30-year, 5% mortgages dramatically cut during the high inflation of the late 1970s. Today, however, the median maturity of developed countries’ central government debt is about seven years, so the potential benefit from high inflation would be much lower. Of course, this does not take into account the possibility of hyperinflation, which could be triggered if the monetization of large government fiscal deficits (along, one hopes, with other structural policies) fails to produce a substantial and sustained improvement in real aggregate growth, causing a collapse in confidence in the currency and a flight into real assets like gold, property, and timber.

• On balance, it is hard to escape the conclusion that at some point, significant debt reduction will be necessary if the United States and other nations are to escape secular stagnation and minimize the risk of future hyperinflation.

• The key question is whether such a reduction is possible in today’s environment. Recent private and municipal bankruptcy experiences and sovereign debt restructurings do not fill one with hope. Too many have been marked by extended and contentious litigation by various classes of creditors that has drawn out the debt reduction process for extended periods of time. Put differently, it does not appear that private sector processes are equipped to handle the scale of the debt reduction that may be required in the future. Government action will be necessary. Unfortunately, whether today’s polarized politics will allow that also remains doubtful.

• That said, there are some steps that would likely have a large positive impact. One would be the conversion of $1.5 trillion in American student loan debt into equity. Such equity would require the payment to the government (collected through the tax system) of a fixed percentage of Adjusted Gross Income for a fixed period of time (not just until the loan was repaid). Any loan amount still outstanding after this period would be written off. This would likely have a large positive impact on spending by the 44 million borrowers who have outstanding student loans.

• With respect to the restructuring of private sector debt, the government could also change regulations to discourage the seizure and liquidation of collateral (which forces many companies to close) and make conversion of debt to equity and partial writedowns easier than they are today (an LDC debt crisis analogy to the latter was the creation of deeply discounted zero coupon Brady bonds which were used to guarantee principal repayment for restructured sovereign debts).

• Coordinated fiscal stimulus across key countries focused on investments that increase potential growth (___%)

• Structural changes that increase productivity growth (___%)

• Policy changes that mitigate the employment (and therefore consumption demand) impact of increased use of productivity enhancing automation and artificial intelligence technologies (___%)

• Significant reduction in income inequality to increase consumption spending (___%)

• Significant debt reduction (___%)

• International conflicts limit the extent and effectiveness of fiscal stimulus (___%)

• US housing prices (which have a 33% weight in the Consumer Price Index) decline for a prolonged period (___%)

• Minimal reduction in income inequality (___%)

• No significant increase in productivity growth (___%)

• No significant debt reduction (___%)

• Substantial increase in federal deficit payments focused not transfer payments to limit reduction in consumption spending (___%)

• No significant increase in productivity growth (___%)

• No significant debt reduction; e.g., as in Japan, "zombie companies" are kept alive (___%)

• Minimal reduction in income inequality (___%)

• Supply shock (e.g. food crisis, oil price shock, worsening trade wars) triggers sharp increase in inflation, which compounds as it feeds through rising wage/transfer payment demands (___%)

• Substantial increase in federal deficit payments focused not transfer payments to limit reduction in consumption spending (___%)

• Rising international use of "beggar-thy-neighbor" policies (___%)

• No significant increase in productivity growth (___%)

• No significant debt reduction; e.g., as in Japan, "zombie companies" are kept alive (___%)

• Leadership pursues external aggression to deflect attention from worsening economic conditions in China, Russia, Iran, and/or North Korea (___%)